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Towards Equilibrium 2023

Annual Journal of The Economics Society of St. Stephen’s College, Delhi, "Towards Equilibrium 2023"

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TOWARDS

EQUILIBRIUM

THE ECONOMICS SOCIETY

ST. STEPHEN'S COLLEGE


Towards Equilibrium 2023

OUR SINCERE THANKS TO

Mrs. Poonam Kalra, Staff Advisor, The Economics Society

Mr. Sanjeev Grewal, Head of Department, Department of Economics

EDITORS-IN-CHIEF

Anushree Jain

Suprabath Reddy

THE EDITORIAL TEAM

Akshaya Rathour

Elena Mathew

Rachel Elsa Jude

Radhika Gupta

COVER DESIGN

Rachel Elsa Jude


Towards Equilibrium 2023 | Page 2

FROM THE EDITORS

Dear Reader,

The Economics Society of St. Stephen's College, Delhi, is delighted to present Towards

Equilibrium, our annual journal. It is an intellectual repository that offers a platform for India's

brightest young minds to showcase their research and findings in the field of economics.

Comprising an array of articles and research papers, the publication includes a great variety of

viewpoints, strategies, research paradigms, and recommendations for aspiring academicians. It

serves as a testament to these young economists' promise and their tenacious desire to conduct

research that improves our understanding of the world we live in.

Our first paper implements an ARDL model to identify the major determinants of India’s current

account deficit post-liberalisation and adds substantive India-specific insights to the existing

literature. The second paper exploits a natural experiment created by a policy of mandated

reservation of certain leadership posts on educational and social outcomes at the district level. The

next paper again utilizes an ARDL model to assess the efficacy of Quantitative easing in India, an

often talked about unconventional monetary policy.

Moving forward we have an article detailing the history of India’s growth and development (or

the lack of it), articulately highlighting pain points and strengths in the country’s growth story. In

the same spirit, the next article sheds light on the problem of malnutrition in India, discussing

extant national policies to tackle this important indicator of development and provides policy

recommendations to build on.

The journal also houses a subset of micro-theoretic papers. The first one- models the platform

economy, and the unique nature of it’s wage contracts to show how the platform economy serves

as a fallback substitute option for workers during global economic shocks. The following paper

explores the concept of Financial De-Dollarization using a variant of the Tobin model and its

impact on the real and financial sectors of developing countries like India. The final paper models

instances of domestic discrimination using a Beckerian family optimisation technique. It brings

forth several interesting insights including how the initial distribution of orthodox and unorthodox

families in the economy and the relative gender wage rates affects foreign capital inflow and

female labour participation.

The Editorial Team of Towards Equilibrium would like to convey our sincere gratitude to the

Department of Economics at St. Stephen's College, Delhi, for its constant support and guidance.

We also want to acknowledge the contributors' continued efforts to add to the corpus of the

literature in their respective fields. We also want to express our gratitude to everyone who helped

create this journal.

Finally, we would like to express our gratitude to you, the reader, for picking up this humble result

of our collaborative efforts and curiosity about the world of Economics.


Towards Equilibrium 2023 | Page 3

CONTENTS

Determinants of Current Account Deficit in India Post Liberalisation

Deepali Desai, Ojasvi Ghai, Christ University, Banglore 4

Do Political Women Help Working Women? Evidence from a

Randomized policy in India.

Soumil Agarwal, Yasashvi Paarakh, Ashoka University, Delhi 15

Estimating the Efficacy of QE in India: An ARDL Approach

Aishwarya Venkat and Twinkle Adhikari,Christ University, Banglore 40

Policies on Nutrition and Economic Development in India

Ishita Deb, Lady Shri Ram College for Women, Delhi University 56

The Great Indian Growth Exposé

Arz Taneja, Sakshi Singh, St. Stephen’s College 63

The Platform Economy in the face of a Global Economic Crisis:

A Micro-Theoretic Analysis

Kashika Iyer, Mayukh Dutta, Meghna Ghosh, Saniya Ilyas, 72

St. Xavier’s College, Kolkata

The Real Aspect of Financial De-Dollarization in a new

Tobin-Walrus-Jones Model

Arshia Goswami, Aritra Mazumdar, St. Xavier’s College, Kolkata 85

The Rise and Fall of Gender Discrimination in a Heterogenous Family

Model and the Role of Globalization: A micro theoretic analysis

Manjari Agrawal, Rohini Datta, Torsha Sen, St. Xavier’s College, Kolkata 103


Towards Equilibrium 2023 | Page 4

Determinants of Current Account Deficit in India Post Liberalization

- Deepali Desai, Ojasvi Ghai 1

Abstract: This paper attempts to explore whether a long-term relationship exists between Current

Account deficit in India post-liberalization and institutional factors and trade restrictions as key

variables. Significant literature on the determinants of Current Account Deficit have not focussed

on these key factors. Our paper, using an ARDL model, finds these variables to be statistically

significant in the context of India and statistically significant in the long run. The Institutional

variable is created using an aggregate of the world development indicators such as voice and

accountability, political stability, government effectiveness, regulatory quality, rule of law and

control of corruption. Trade restrictions are measured through the Measurement of Aggregate

Trade Restrictions and their Economic Effects, which aims to measure how restrictive a

government is towards the flow of goods and services. Secondary data is used for research and

analysis.

1. Determinants of current account deficit in India post liberalization

The value of the current account is an important determinant in order to make predictions about

the economy in the future. Post liberalization, India has maintained a high current account deficit

in the country which has depended on India’s structural and temporary factors. Concomitantly,

developed countries in the world, like the United States also exhibit high current account deficits.

While there has been debates on how much control should countries exhibit on reducing their

current account deficit, Milesi Ferreti & Razin (1996) stated that sustainability of the country’s

current account deficit should not be determined solely by the level of its current account deficit

as a percentage of its GDP but also by imbalances in current account, openness of trade and

financial system of savings and investments (as cited in Behera & Yadav, 2019). Therefore, there

are numerous variables that affect the current account deficit of a country, which, in turn,

determines the behaviour of economic agents and the stance of macroeconomic policies that the

country takes.

Major literature on India points to the importance of increasing investment and focusing on Indian

exports. While increasing exports is important in the short run, increased exports in the long run

can also lead to expansion of the current account deficit (CAD) as the countries get the ability to

repay their debt (Calderon et al. 1999). India has been moving towards a neo-mercantilism policy

with recent IMF reports imploring policymakers in the country to reduce its tariffs on imports.

Against this backdrop and the volatility of other macroeconomic variables, our study seeks to

understand the major determinants of the current account deficits in India, by, not only focusing

on major macroeconomic variables, but, also on institutional factors and variables like capital

controls in order to present a holistic picture of the reasons behind high current account deficit in

India in order to provide policy recommendations for the same.

2. Theoretical Literature

The Ramsey model demonstrates via a small open economy that transitory disturbances affect

domestic saving rather than consumption, and therefore country specific shocks affect the current

1.Christ University, Bangalore


Towards Equilibrium 2023 | Page 5

account. Hence, in response to a productivity or trade shock, a country would prefer running a

current account deficit to counter the adverse effects of the shock, since these shocks are likely to

affect domestic savings. The intertemporal approach states that the current account acts as a

temporary shock absorber of the fluctuations in national cash flow to smoothen consumption and

maximize welfare (as cited in Debelle and Faruqee, 1996). Modern theories of current account

determination also posit the stage of development hypothesis where the stage of economic growth

of the country indicates the amount of current account deficit (as cited in Debelle and Faruqee,

1996). A country with poor economic growth having access to international capital and markets

will run a current account deficit for a long time to build its capital stock and improve its deficit.

Therefore, an initial capital poor country will attract large amounts of foreign capital on account

of high productivity of capital. This will increase its external debt, and as the capital stock of the

country improves, the current account balance will approach zero.

An extension of the intertemporal approach is the life cycle hypothesis which states that the age of

the population of the country determines a current account deficit. As per the life cycle hypothesis,

consumption and saving are directly tied to the stage of life cycle which in turn determines saving

and investment. If a larger percentage of the population is non-working, then it is dissaving and

therefore, the country is more likely to run a deficit. This has ramifications on the current account

via the fiscal balance channel as well. If private savings are less than public debt to offset it, then

government spending will also affect the current account. This is known as the twin deficit

hypothesis stipulated by Feldstein (1985, 1987). According to the Keynesian School of thought,

the budget deficit has an impact on the current account deficit (Chawdhury & Saleh, 2007),

whereby a fiscal account deficit and a current account deficit move along simultaneously.

Moreover, intertemporal account models that incorporate Ricardian agents consisting of

households who spend, and households who save according to the permanent income model and

smooth their consumption, current account deficit depends on the percentage of these non-

Ricardian households (Bussiere et al, 2010).

The HLM Effect (Harberger, 1950; Laursen and Metzler, 1950) identifies another determinant of

trade account, that is terms of trade. If in a small open economy trade increases, raising national

income and consumption propensity is less than one, then increased trade will increase saving and

improve current account balance. If domestic savings increase then the need to depend on external

finance reduces. The same permanent model can incorporate risk aversion and uncertainty to

demonstrate that a change in any of these factors due to trade, variability in national income will

affect the savings and investment level and thereby the current account balance. All these theories

stem from the basic determinants of savings and investment which form the core of current account

determination.

3. Empirical Literature

Conducting a cross section regression analysis on a sample of developed economies, Debelle and

Faruqee (1996) found fiscal policy, real exchange rate and GDP to be significant determinants of

a current account. The authors also conducted a study on a sample of developing countries, with

the stage of development (measured by capital per worker and relative income) being the only

variable that was significant. While the study was conducted on a large number of countries, the

cross sectionality of the data limited the understanding of current account deficit determinants over

time. In Methodology for CGER Exchange Rate Assessments (2006), a regression was conducted


Towards Equilibrium 2023 | Page 6

on industrialized countries with all variables depicting significance. But the presence of a large

sample again results in a specification bias with certain country specific variables not being

included. Calderon et al. (2000) is a more comprehensive study on determinants of current account

accounting for within country and cross-country effects and utilizes the GMM model to control

joint endogeneity.

Kahn and Knight (1983) looked at a sample of developing non-oil developing countries and found

terms of trade, fiscal balance, interest rates, exchange rates to be significant indicators of CAD. In

addition, through a correlation exercise Kahn and Knight are able to showcase that not accounting

for country specific factors does not produce vastly different results. An improvement in trade, led

to an improvement in Current account balance. Chinn and Ito (2007) expand on Chin and Prasad

(2003) work on current account determinants taking into account institutional and legal factors.

These factors play an important role in determining the returns to savings and investment and

therefore are vital control variables. In their study concentrating on East Asian economies and the

US, they find institutional development to be a significant determinant of CAD. Moreover, in

emerging economies higher financial development leads to higher financial savings.

Calderon et al. (1999), find the determinants of current account deficit in developing countries

because most developing countries are credit constrained and their structural factors differ from

the structural factors of developed countries. The paper confirms the stages in development

hypothesis and considers the transitory and permanent impact of the variables on the current

account deficit. The paper provides a lead into understanding that the impact of variables on current

account deficit differs in the short run and in the long run. However, different structural backdrops

of developing countries create specific institutions due to which factors affecting their respective

current account deficits differ, depending on the country under consideration.

In the context of India, Behera & Yadav (2019), arguably, show that though current account

deficits in India are high due to capital inflows that are used to finance its CAD as well as high

fiscal deficit in the country, in the long run, India will have sustained Current Account Deficit.

Mohd & Bhatia (2016) calculate the first difference of the variables; Net Foreign Assets, Trade

Openness, Real Effective Exchange Rate and Wholesale Price Index so that they become

stationary in nature. The study stresses that it is essential for the government to increase investment

in the development of the transportation industry, tourism industry and miscellaneous services in

order to increase competitiveness and stresses on the harmful impacts of increasing imports on the

current account deficit of the country. The paper by Haidery (2021) argues that due to high demand

in the country, instead of focusing on reducing imports, it is important to focus on increasing

competitiveness since a certain number of imports is required by the country to increase its external

competitiveness.

Table 1: Summary of Empirical Findings

Name of Paper Methodology Variable

What determines a current

account? A panel and cross

section approach

Determinants of Current

Account Balances of Non-

Oil Developing Countries in

Panel

Regression

Ordinary Least

Squares

Regression

Stage of development, ratio of capital stock to

GDP (marginal productivity of capital),

dependency ratio, inflation, export-import of oil,

real interest rate, inflation, net foreign assets

financial liberalization index (Milesi- Feretti)

Fiscal deficit, real effective exchange rate,

trade, real interest rates, GDP


Towards Equilibrium 2023 | Page 7

the 1970s: An Empirical

Analysis

Methodology for CGER

Exchange Rate Assessments

Determinants of Current

Account Deficits in

Developing Countries (July

2000)

Regression

Regression

Fiscal Balance, NFA, Dependency Ratio, Oil

prices, economic growth, financial crises,

financial centers

GNI, Interest Rates, exchange rate, financial

liberalization, (Milesi- Feretti, 1995 Index),

savings, investment, output growth rate,

Current Account Deficits in

Africa: Stylized Facts and

Basic Determinants

Journal Article

Current Account Deficits in

Africa: Stylized Facts and

Basic Determinants.

Medium Term Determinants

of Current Account in

Industrial and Developing

Countries

Generalized

Method of

Moments

(GMM)

Regression

Savings, private and public savings, real

exchange rate, trades, exports, Exchange

controls (Capital account restrictions), financial

aid, external debt, macroeconomic uncertainty

Fiscal income, dependency ratio, average GDP

growth, terms of trade, capital control,

openness, net foreign assets,

Current account balances,

financial development and

institutions: Assaying the

world “saving glut”

Panel

regression

Fiscal balance, NFA, interest rates, exchange

rate, oil prices, dependency ratio, financial

development, legal development, trade

openness, terms of trade, GDP growth,

financial openness

The Persistence and

Determinants of Current

Account Deficit of FYROM:

An Empirical Analysis

ARDL Model

Fiscal balance, exchange rate, oil prices, GDP

growth, NFA, financial development, Terms of

Trade, FDI, financial openness


Towards Equilibrium 2023 | Page 8

Long-run determinants of

current accounts in OECD

countries: Lessons for intra-

European imbalances

3 step panel

econometric

methodology

Dependency ratio, interest rate, real exchange

rate, fiscal balance, terms of trade, productivity

oil prices, financial deepness

Determinants of current

account deficits in

developing countries

(November 1999)

Explaining India’s Current

Account Deficit: A time

series perspective

Trends, Patterns and

Determinants of Indian

Current Account Deficit

Assessing Determinants of

the Current Account Deficit:

A Case Study of India

Generalized

Method of

Moments

(GMM)

Granger noncausality

test,

unit roots test,

Johansen

cointegration

test

Johansen

Cointegration

test, Vector

Error

Correction

Model

Error

correction

model

Income, Saving, Exchange Rate, Balance of

Payment controls and impact of black-market

premium on foreign exchange

Fiscal Deficit, terms of trade, inflation, real

deposit rate and age dependency factor

Net Foreign Assets, Trade Openness, Real

Effective Exchange Rate and Wholesale Price

Index

Current Account Balance, Gross Domestic

Product, Real Effective Exchange Rate: Trade

Based, Trade Openness (sum of exports and

imports), Fiscal Deficit, Foreign Exchange

Reserves

4. Research Gap

From the literature it is evident in the case of developing economies, capital controls, trade,

exchange rate play important roles in determining CAD. Empirical Literature in India focuses on

key variables like Trade Openness, Net Foreign Assets, Exchange Rates, Savings and Investment.

However, there is a lack of focus on institutional factors like current policy trends in the country,

rules, norms and the level of corruption and their impact on the current account deficit. Moreover,

trade openness is estimated through the sum of imports and exports divided by the GDP. However,

it is not considered as a comprehensive measure. Through our analysis, we include variables that

have not yet been factored in while determining the current account deficit in the country.

Moreover, we estimate trade openness using a compressive index (Aggregate of rule of law,

regulatory quality and corruption provided by world governance indicators) that will be useful in

determining the actual impact of trade protectionism and export policies on the current account

deficit in the country. While there exists a consensus amongst a few, that trade protection could


Towards Equilibrium 2023 | Page 9

lead to lower trade deficits, and while tariffs and other protection measures do not necessarily

influence trade balance (Furceri et al., 2009), there could still be transmission channels via

productivity shocks and exchange rate. Moreover, in the current account determinants in case of

India, capital controls have been overlooked which are important indicators as per previous studies

considering developing countries.

5. Research Objectives

● To Identify the determinants of Current Account Deficit.

● To examine the long run relationship among the determinants of Current Account Deficit

while analyzing the post liberalization policy scenario of India and the openness of trade.

6. Research Methodology

We will be doing quantitative and qualitative analysis in the research paper. As a part of

quantitative analysis, to be consistent with previous literature which have employed either

regression models or ARDL models in the case of analyzing a country specific current account

determinant. The methodology adopted in this paper will also involve an ARDL model to ascertain

the determinants of the current account. By using an ARDL approach we aim to estimate the long

run and short run determinants of CAD. The variables identified in case of the Indian context are,

real exchange rate, capital controls (Milesi- Feretti) index, GDP growth, fiscal balance, trade

openness, institutional development (aggregate of rule of law, corruption and regulatory quality),

oil prices and trade protection measure (A Measurement of Aggregate Trade Restrictions and their

Economic Effects). Using the ARDL model, the impact of the variables mentioned in Table 2 on

the current account deficit in India will be estimated. Moreover, it would provide insight into

whether the current account deficit is unsustainable and using critical analysis, we would implore

the reasons behind the large current account deficits in the country, despite the Liberalization,

Privatization and Globalization policy measures implemented in 1991. We use the time period,

1991 – 2022 in order to understand the impact of liberalization as well as the changes in the current

account deficit created by the pandemic.

7. Results and Discussion

The significant long run factors that affect current account deficit are 1 year lagged value of current

account balance, fiscal balance lagged value, institutional development and Measurement of

Aggregate Trade Restricts and oil prices play a vital role in determining current account balance

(Table 1).This also indicates that current account deficit is heavily influenced by institutional

factors and trade restrictions which demonstrates the role of political stability and trade openness.

Rather than taking the sum of exports and imports as trade openness, a trade restrictions index was

used to ascertain the role of trade restrictions on current account deficit. The robustness of the

model is indicated by the high R-square of 87% and adjusted R-squared was 67%. The long run

bounds test depicts the F-value as greater than I(0) and I(1) value indicating cointegration between

variables (Table 2). Thus, there exists a long-run relationship between the variables.

A rise in institutional development, fiscal balance, MATR and oil prices tends to generate larger

current account deficits. Therefore, in order to maintain a stable current account balance, it is

necessary for governments to focus on the essential factors mentioned above and make policy

decisions regarding import restrictions and export promotions keeping the potential consequences

on the current account deficit in mind.


Towards Equilibrium 2023 | Page 10

9. Conclusion

As per the gaps in the literature identified, a dearth of research of institutional factors, and trade

restrictions on India’s current account deficit necessitates the evaluation of these factors as

determinants of the current account. The ARDL model suggests a statistically significant

relationship between institutional factors, fiscal balance, trade restrictions and current account

deficits. This has several policy implications, for apart from real and financial variables, political

stability, trade openness have a vital role to play in generating current account deficits. The long

run bounds tests also indicate significant long run cointegration between the variables. Therefore,

this paper attempted to add to the study of current account deficit literature in the context of India

by looking at these significant factors affecting Current Account Determinants.

10. References

Behera H.K. & Yadav I.S. 2019. Explaining India’s current account deficit: a time series

perspective. Journal of Asian Business and Economic Studies.

https://www.emerald.com/insight/content/doi/10.1108/JABES-11-2018-0089/full/html

Bhatia S.K. & Mohd F. 2016. Trends, Patterns and Determinants of Indian Current Account

Deficit. Applied Econometrics and International Development.

https://www.usc.es/economet/reviews/aeid16113.pdf

Bussière, M., Fratzscher, M., & Müller, G. J. 2010. Productivity shocks, budget deficits and the

current account. Journal of International Money and Finance, 29(8), 1562–1579.

https://doi.org/10.1016/j.jimonfin.2010.05.012

Calderon C., Chong A. & Loayza N. 1999. Determinants of current account deficits in

developing countries. Central Bank of Chile Working Papers.

https://si2.bcentral.cl/public/pdf/documentos-trabajo/pdf/dtbc51.pdf

Calderon, C., A. Chong, and L. Zanforlin, 2007. Current Account Deficits in Africa: Stylized

Facts and Basic Determinants. Economic Development and Cultural Change, 56 (1).

Calderon, C., Chong A. and Loayza, N. 2002: ‘Determinants of Current Account Deficits in

Developing Countries’ (Policy Research Working Paper 2398). The World Bank.

https://openknowledge.worldbank.org/bitstream/handle/10986/19825/multi_page.pdf?sequence=

1&isAllowed=y

Chinn, M. D., & Ito, H. 2007. Current account balances, financial development and institutions:

Assaying the world “saving glut.” Journal of International Money and Finance, 26(4), 546–569.

https://doi.org/10.1016/j.jimonfin.2007.03.006

Chinn, M., D. and E. S. Prasad .2003. Medium-term determinants of current accounts in

industrial and developing countries: an empirical exploration. Journal of International

Economics,59(1),47-76.

Chowdhury K. & Saleh A.S. 2007. Testing the Keynesian Proposition of Twin Deficits in the

Presence of Trade Liberalisation: Evidence from Sri Lanka. Department of Economics,

University of Wollongong. https://core.ac.uk/download/pdf/36990443.pdf


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Debelle, G. and Faruqee, H. 1996. What determines the current account? A Cross-Section and

Panel Aproach. (Working Paper WP/96/58). IMF.

https://www.imf.org/en/Publications/WP/Issues/2016/12/30/What-Determines-the-Current-

Account-a-Cross-Sectional-and-Panel-Approach-2039

Feldstein, M. 1985. American Economic Policy and the World Economy. Foreign Affairs, 63(5),

995–1008. https://doi.org/10.2307/20042365

Feldstein, M. 1987. Correcting the Trade Deficit. Foreign Affairs, 65(4), 795–806.

https://doi.org/10.2307/20043094

Furceri, D., Ahmed Hannan, S., Ostry, J., & Rose, A. 2019. Macroeconomic Consequences of

Tariffs. IMF Working Papers, 19(9), 1. https://doi.org/10.5089/9781484390061.001

Furceri, D., Hannan, S., Estefania-Flores, J., Ostry, J., & Rose, A. 2022. A Measurement of

Aggregate Trade Restrictions and their Economic Effects. IMF Working Papers, 2022(001), 1.

https://doi.org/10.5089/9781616359645.001

Gossé, J. B., & Serranito, F. 2014. Long-run determinants of current accounts in OECD

countries: Lessons for intra-European imbalances. Economic Modelling, 38, 451–462.

https://doi.org/10.1016/j.econmod.2014.01.008

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Haidery J. 2021. Assessing Determinants of the Current Account Deficit: A Case Study of India.

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Developing Countries in the 1970s: An Empirical Analysis. Staff Papers (International

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Towards Equilibrium 2023 | Page 12

12. Appendix

Table 1: Long run Bounds Test

Variable Coefficient t-Statistic Prob.

EX1 -0.001686 -0.028986 0.9775

FISCAL1 -4.04E-06 -1.949904 0.0830

GDP1 -0.238798 -3.947188 0.0034

INSTIUTIONAL1 6.785580 2.358153 0.0427

MATR1 0.194667 0.286476 0.7810

OIL1 -0.000996 -5.299161 0.0005

c 0.135065 0.921209 0.3810

EC = CAB1- (-0.0017'EX1-0.0000'FISCAL1 -0.2388'GDP1 + 6.7856

'INSTIUTIONAL1+ 0.1947'MATR1 -0.0010'01L1 + 0.1351)

F-Bounds Test

levels relationship

Null Hypothesis: No

Test

Statistic

Value Significance I(0) I(1)

Asympt

otic n =

1000

F-statistic 13.7412

3

10% 1.99 2.94


Towards Equilibrium 2023 | Page 13

k 6 5% 2.77 3.28

2.50% 2.55 3.61

1% 2.88 3.99

Actual Sample Size 20 Finite

Sample:

n=30

10% 2.334 3.515

5% 2.794 4.148

1% 3.976 5.691

Table 2: ARDL Model

Variable Coefficient t-Statistic Prob.

CAB1(-1) -0.689119 -3.182703 0.0129

GDP_GROWTH -0.283051 -1.693136 0.1289

EX1 -0.053463 -0.628251 0.5474

EX1(-1) 0.168660 2.291899 0.0511

FISCAL1 -4.54E-06 -1.141096 0.2868

FISCAL1(-1) -7.55E-06 -2.354340 0.0464

INSTIUTIONAL1 8.450890 1.882688 0.0965


Towards Equilibrium 2023 | Page 14

INSTIUTIONAL1

(-1)

-5.209616 -1.440377 0.1877

MATR1 -2.242251 -2.374635 0.0449

MATR1(-1) 1.457478 1.596313 0.1491

OIL1 -0.001132 -3.732024 0.0058

c 2.067855 1.676280 0.1322


Towards Equilibrium 2023 | Page 15

Do Political Women Help Working Women? Evidence from a

Randomized Policy In India

1.Introduction

-Soumil Agarwal, Yasashvi Paarakh 1

Women are grossly under-represented in politics at all levels across the world. In India itself, the

17th Lok Sabha has the highest percentage of women members, and yet it was just 14.39% of the

entire strength of the Lower House. Over the years, the world has witnessed increasing equity in

economic, social and legal rights for women, however, the gap between men and women’s political

representation has only marginally narrowed (Inglehart and Norris (2000)). Even in regions where

there is increased women representation, we are unable to ascertain if they have an impact on

policy decisions. As per the standard median voter model (Downs (1957)), even though candidates

can commit to particular policy motives and objectives, the overall political decision will only

reflect the preferences of the electorate. In such a set up, it may be difficult to identify an impact

on women’s representation in policy outcomes.

Moreover, if a country, or particular region, has higher representation of women in politics, that

may simply be the preference of the electorate. Besley and Case (2000) show that worker

compensation and child support enforcement policies are more likely to be introduced in states

where there are more women in parliament, after controlling for state and year fixed effects. But

they explicitly recognize that the fraction of women in parliament may be a proxy for women’s

involvement in politics, more generally. Regions where women are more active participants in

politics are also more likely to be developed and progressive in nature. Hence, even by interpreting

the results of such cross-sectional studies is not sufficient; the correlation between policy outcomes

and participation then may not imply a causal effect from women’s participation.

However, we can overcome this by studying the impact of mandated women’s representation

which has been introduced through the forms of reservations and quotas. Through introducing such

mechanisms, the demographic of the pool of political candidates is changing (a number of male

candidates are eliminated while new female entrants are introduced). Reservations and quotas have

been seen as an effective tool in policy-making to bridge the political gap between men and

women. Reservations have created a space for women to enter the field of politics in which they

have been traditionally disadvantaged; women’s representation fell significantly in Eastern Europe

when gender quotas were eliminated during the transition of Communism (King and Mason

(2001)). Moreover, the existence of representations further encourages the discourse of more

inclusive topics; the surge in women Labour MPs in the United Kingdom increased pressures on

the Conservative Party to respond to obvious gender disparities in the Parliament (Norris (2001)).

Across the world, and across levels of development, reservations have been effective in

introducing women into the political sphere.

This paper wishes to study the policy consequences of one such mandated representation of women

on female labour force participation trends and income patterns in India, through a

1.Ashoka University, Delhi


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unique and randomized experiment implemented in 1993, the 73rd Amendment in the

Constitution. The 73rd Amendment mandated the need for female and SC/ST representation in

multiple levels of local government. The reservation was allocated randomly to 1/3rd of the

districts within every state. Due to the randomized nature of this reservation allocation, we are able

to minimize endogeneity that may otherwise exist in districts with women leadership and high

political participation.

We will be looking at two groups of women, (i) those who are already of working age and can be

a part of the workforce, and (ii) those that are currently engaged in education and can potentially

join the workforce in the future. We would like to understand what drives the impact, i.e. whether

it is optics-related reasons or are women political leaders actively investing in women-centric

policies which encourage more women to partake in productive activities.

We will be using the data collected by Iyer et al. (2012) which provides information regarding the

reservation allocation for the years 2004-07. We will be using National Sample Surveys (NSS)

Schedule 10, Rounds 62 (2005-06) and 64 (2007-08) to obtain information regarding working

habits and income patterns. We will also be using Indian Human Development Survey (IHDS)

Wave I which provides information about educational practices and learning outcomes. Through

these data sets we will be able to have a holistic understanding of how the 73rd Amendment has

impacted women in education and working spaces.

We see that there is a negative and significant impact of female political representation on female

labour force participation, which is more pronounced for urban areas as compared to rural.

Similarly, we also see a decline in rural women’s wages as a result of more female political

representation. In the case of education, we see that female political representation leads to a

decrease in the number of days of absence of students, and a fall in hours spent in private tuitions.

We also see that female students are spending more time in schools than male students in reserved

districts.

The rest of the paper is organised as follows: Background and Related Literature provides an

extensive and detailed literature review on the 73rd Amendment and its impact on outcomes for

women and other marginalised communities; Data and Methodology examines the data and

sample construction and the empirical strategy employed; Results presents and analyses the

results; Limitations looks at a few limitations of the paper; Conclusion concludes the paper and

explores potential policy suggestions. A detailed Appendix is given at the end for the

convenience of the reader, which contains all tables and summary statistics.

2.Background and related literature

2.1 The 73rd Amendment

In April 1993, the 73rd Amendment to the Indian constitution came into force. This required:

● Each state to set up a three-tier governing system: village, intermediate, and districtlevel

bodies which were collectively to be known as the Panchayati Raj Institution

(PRI).

● At least 1/3rd seats (at the village, intermediate or district levels) were required to be

filled by women.


Towards Equilibrium 2023 | Page 17

● Further reservations for SC and ST candidates were also made (in proportion to their

population).

The Amendment also created a further reservation for the District Chairperson position; 1/3rd of

the seats had to be reserved for women in every state. All members of these local bodies were to

be directly elected by the people every five years, and the Act provided for the establishment of

State Election Commissions to conduct such elections. The reservation allocation was conducted

on a completely randomised basis.

Twenty-nine areas of administration, including decisions over health and education services,

roads, sanitation and other local services were to be devolved to these local government bodies.

State Finance Commissions were set up to provide recommendations on revenue-sharing and

making grants to these local government institutions. The Act thus provided for a considerable

degree of political, administrative and fiscal decentralization to the local bodies. Almost all states

passed or amended their existing laws to be compliant with the Act within one year.

Even though the Amendment was passed in 1993, there was considerable variation in the

implementation of the mandated reservation amongst multiple states. As detailed by Iyer et al.

(2012) there are three main reasons for this variation. First, several states had a system of local

governance and mandated representation of marginalised communities since before the passing of

the 73rd Amendment; states that had a system of local self-governance implemented waited for the

term of the incumbent governing bodies and then implemented the system put forth by the

Amendment. States that already had mandated representation had either done it as they were preempting

the 73rd Amendment or were those states which could be considered as forward and

progressive.

The second reason of timing variation that was predominantly noticed was due to the legal

complications and lawsuits that were filed challenging certain features of the Panchayati Raj

Institutions. There were states that proposed reservations of other marginalised communities that

were predominant in their individual state, such as Bihar. The third reason was that some states

delayed the elections due to budgetary constraints or other unspecified reasons that were never

clarified. These reasons may be caused by exogenous or endogenous factors, the nature may be

difficult to ascertain.

We acknowledge that this state-wise variation may increase endogeneity, however, we are able to

tackle this by conducting a district-level analysis. By introducing demographic controls for

individual districts and using district-level fixed effects, we are able to minimise the endogeneity.

2.2 Related Literature

Scholars have studied the impact of female political representation, through the 73rd Amendment,

on the crime rates and likelihood of women (and other socially disadvantaged groups) reporting

crimes (Iyer et al. (2012)). This paper finds that having female political representation at the local

government level induces strong positive and significant effects on reporting of crimes by women.

It also induces greater responsiveness of law enforcement officials to crimes against women, as

measured by the number of arrests as well as the quality of women’s interactions with police.


Towards Equilibrium 2023 | Page 18

This paper takes an additional step to analyse at which level of reservation is a woman the most

effective; whether having a particular leadership position reserved for a woman or having a higher

proportion of women in local representative bodies is more beneficial for women. The paper

discovers that having more women in local bodies has a more significant impact on the rate and

incidence of crime highlighting that the proximity of political representatives is key to solving

problems for women.

Moreover, it has also been found that women representatives, even at lower levels, invest in public

goods that are more closely linked to women’s concerns such as drinking water and roads as

compared to needs of men such as education and roads (Chattopadhyay and Duflo (2004)). This

paper studies 265 Village Councils across West Bengal and Rajasthan and is able to prove why

reservations are important and must be implemented at various levels of government. Moreover,

these results remained statistically significant even when controlled for individual Pradhan

characteristics such as age, education, and other demographic characteristics.

Women leaders have also been able to significantly raise the quality of children’s education

(Burchi and Singh (2020)); female political representation improves primary school enrolment as

well as increases the likelihood of the completion of a primary school cycle. A further

disaggregated analysis reveals that women’s political representation raises the educational

achievements of girls significantly more than that of boys. This is predominantly driven by an

increased focus on implementation of policies that also improve the access to education as opposed

to just the learning outcomes. Previous literature is able to show that women leaders have had a

positive and significant impact on policy outcomes for all, but especially women.

Our study contributes to the growing literature in this field by focusing on the impact on the current

labour force as well as the future labour force participation. Further, it disaggregates female labour

force participation (FLFP) and looks at principal and subsidiary employment, seeing differential

degrees of impact on them. Moreover, our paper also looks at a more holistic definition of

education and is able to incorporate more robust educational outcomes such as absenteeism,

reading, writing and language ability, as well as hours spent in private tuitions. This detailed

analysis allows us to dissect different channels through which female representatives impact FLFP.

3.Data and Methodology

3.1 Data

For this study, we are going to be utilising 3 data sets. The first is a data set that we obtain from

Iyer et al. (2012) that assigns a dummy to a district d for whether that district had women’s

reservation for year (t − 1) for 2004-2007. This further provides us information on how many

years in the past has the district received reservations.

Next, we are using National Sample Surveys (NSS) Schedule 10, 62nd Round (2005-06) and 64th

Round (2007-08). In this survey, a nation-wide enquiry was conducted in a moderately large

sample of households to provide estimates on various characteristics pertaining to employment

and unemployment in India and some characteristics associated with them at the national and state

levels.


Towards Equilibrium 2023 | Page 19

Information on various facets of employment and unemployment in India were collected through

a schedule of enquiry (Schedule 10). The survey covered the whole of the Indian Union except (i)

Leh & Ladakh and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated

beyond 5 kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which

remain inaccessible throughout the year. Through a stratified sampling method employed in

Rounds 62 and 64, the NSS is able to survey both rural and urban areas. Lastly, for the purpose of

collection of information on industry of activity, National Industrial Classification (NIC), 2004

was used in this survey. This data set gives us our outcome variables (the principal and subsidiary

activity a woman engages in), other socioeconomic and demographic controls along with district

and time fixed effects.

We restrict the data collected from NSS to only women between the ages of 15-65 years (as they

form the working age group in India). We drop the observations where women are engaged in

education as their principal activity or cannot work due to a disability or illness.

We then merge the dataset from Iyer et al. (2012) and the NSS dataset for two years (2005- 06,

2007-08) on the district and year keys. We get a dataset that consists of 172 districts across 10

states, with 129335 observations consisting of only women in the age range described above.

Lastly we use the Indian Human Development Survey (IHDS) Wave I (2004-05); The IHDS is a

nationally representative survey, covering over 1500 villages and close to 1000 urban

neighbourhoods across all states and union territories (except the Andaman and Nicobar and

Lakshadweep Islands). Each household took part in a two-hour interview covering employment,

health, education, gender, wage levels among other topics. The 2004-05 sample of 14,820 urban

households and 26,734 rural households was drawn using stratified random sampling with the help

of the 1993-94 survey by the National Council of Applied Economic Research and an additional

28000 households. This data set provides us with detailed information regarding the access to

education and learning outcomes of children. It also provides us with other socioeconomic and

demographic controls which may impact one's education attainment.

We restrict the data collected from IHDS to the ages 6-14 years (as they form the group that must

be compulsorily engaged in education in India). We look at both male and female students, and

run further heterogeneity tests to see if there is any differential impact on female students.

We then merge the dataset from Iyer et al. (2012) and the IHDS dataset for one year (2005- 06)

the district key. We get a dataset that consists of 122 districts across 10 states with 14526

observations consisting of both boys and girls in the age range described above.

3.2 Outcome Variables

National Sample Survey: Employment and Unemployment

We are looking at

(i) whether the woman is employed or not (as her principal activity) and

(ii) whether the woman is engaged in subsidiary employment. When an individual is pursuing

more than one activity, the economic activity in which they spend a greater number of hours is

their “principal activity” and the next economic activity will be referred to as their “subsidiary


Towards Equilibrium 2023 | Page 20

activity”. Domestic work and the employing of domestic help (or any other unproductive labour)

does not count as subsidiary activity. The list of activities included in subsidiary activity is

employer, employee or own account work/self-employed. We define “unemployed” as those

who are only engaged in domestic activities or sourcing goods for household consumption and

rentiers.

We also utilise the weekly wage and earnings of those who are employed. These wages are

divided into cash earnings and those received in kind.

Indian Human Development Survey: Educational Access and Outcomes

There exists no singular, all-encompassing variable to measure a child’s literacy in the IHDS

dataset; we took this opportunity to assess multiple parameters of education to provide a more

holistic understanding of how education has been impacted. We use the following variables:

Set I: Time Spent Engaged in Educational Activities

• Whether the child is enrolled in school

• The number of hours spent in school

• Number of days absent in a month

• Hours spent doing homework

• Hours spent in private tuitions

This set of variables tell us whether the child is engaging in educational activities and detailed

information about the time that a child is spending engaged in educational activities inside and

outside of school. The time engaged in educational activities may have an impact on the learning

outcomes of the child.

Set II: Learning Outcomes

• Has the child repeated a grade or not

• Significant Reading Level (SRL): The IHDS dataset provides us with scores to measure

children’s reading ability. We create a dummy variable called Significant Reading Level,

which takes the value 1 if the child is able to at least read words and 0 otherwise.

• Significant Mathematical Level (SML): The IHDS dataset provides us with scores to

measure a child’s mathematical ability. We create a dummy variable called Significant

Mathematical Level, which takes the value 1 if the child is able to at-least perform subtraction

and 0 otherwise.


Towards Equilibrium 2023 | Page 21

• Significant Writing Level (SWL): The IHDS dataset provides us with scores to measure

a child’s writing ability. We create a dummy variable called Significant Writing Level,

which takes the value 1 if the child is able to write at least words and 0 otherwise.

This set of variables tell us about the quality of education received by the children and their

subsequent learning outcomes.

3.3 Empirical Strategy

As the allocation of the reservation (the policy treatment) is randomised, we have minimised

endogeneity that may have otherwise existed in districts with women leadership.

Our main estimating equation will be:

Yihdt = α + β1reservationd(t−1) + β2reservation_yearsd(t−1) + δt + φd + γz Xz + εihdt

• Our outcome variable is Yihdt which takes on the multiple outcome variables discussed

above.

• Our main independent variables are reservationd(t−1), a dummy for whether District Chairperson

post is reserved for woman in district d at time (t − 1), and reservation_yearsd(t−1),

a variable that measures the number of years for which a particular district d in state has

had a woman district chairperson at time (t − 1). These two variables will allow us to

identify the immediate optics-related benefits of having a woman as a political

representative, as well as long-term policy changes that women leaders may implement

to improve working conditions for women.

• Xz is the vector of socioeconomic and demographic controls. We include household

controls such as household type, number of members, wealth and asset holdings, religion

and caste group. We also include individual demographic controls such as age and marital

status.

• δt is for time (year) fixed effects and φd is for district fixed effects. We further introduce

district fixed effects to control for time invariant differences between each district as well

as time (year) fixed effects to control for time varying differences between each district.

• We have introduced a one period (year) lag here because the impact of female political

representation may have delayed impacts on female labour force participation. Even if

more women are encouraged to join the workforce after seeing women leaders assume

positions of leadership, there is a cost associated with women joining the labour force

which may delay their decision. Women may have to make arrangements at home to

ensure childcare and elder-care responsibilities are taken care of before they join the

workforce.


Towards Equilibrium 2023 | Page 22

Since we have lagged the results by one period, we hope to capture this impact as well.

• We will be clustering the standard errors at the district level since the reservation impacts

a district as a whole.

It is important to note that district and time fixed effects will not apply to our analysis of

educational outcomes because we only have data for one year (2004-05). The estimating equation

will thus be:

Yihdt = α + β1reservationd(t−1) + β2reservation_yearsd(t−1) + γz Xz +

εihdt

We will also be creating interaction terms to conduct heterogeneity tests to understand the

differential impacts on various groups of people. The first heterogeneity test we run is on the basis

of region, how are people living in rural areas impacted differently than those living in urban areas.

The District Chairperson is the head of the Zilla Parishad, and a Zilla Parishad is only given to

districts with a rural population. For example, Maharashtra has 36 districts and only 34 Zilla

Parishads as two of its districts (Mumbai city and Mumbai suburban) have no rural populations.

Due to this feature of the Zilla Parishad system, districts which have rural populations are the only

ones that are under the district chairperson’s jurisdiction. Even in districts with a mixed urbanrural

population, we believe that due to the existence of other urban local bodies, the impact that

district chairperson can have in urban areas is limited. Due to this differential allocation, we wish

to understand if there is a differential impact.

The next heterogeneity test we are running is to understand the differential impact on caste. The

73rd Amendment also created further mandated reservations for SC/STs (in proportion to their

population in every district) and specifically for OBCs in some states. However, this reservation

is beyond the ambit of our paper. Regardless, we do wish to analyse if women are better

representatives for any group that has traditionally been marginalised, including those from lower

castes.

Lastly, as we are looking at all students when analysing the impact on education, we run an

additional heterogeneity test for the education sample based on gender. We wish to understand

whether the impact of the reservation is higher for female students or not.

4. Result

4.1 Employment and Wages

We first look at the impact of having the District Chairperson seat reserved for a woman in the

previous time period on the decision of women to work or not as their principal activity. The

coefficient on "reservation" in Table 1 is 0.01 (column 3) and statistically insignificant showing

that there was no impact. This suggests that even if there was a woman District Chairperson,

women were not encouraged to join engage in a principal activity. This highlights that simply

observing women get leadership positions and assume roles of political representation is not

sufficient to encourage more women to work. The coefficient on "reservation_years" is 0.006

(column 3) and also statistically insignificant. Even if the District Chairperson seat has been


Towards Equilibrium 2023 | Page 23

reserved for a greater number of years, women are not more likely to enter the labour force or

engage in productive activity as part of their principal activity. This highlights that even when

women are elected as District Chairpersons and are given enough time in the position they are not

implementing policies to make workplaces more conducive to women joining it.

We now wish to analyse the impact of the policy on whether a woman chooses to engage in

subsidiary activity or not. Our results in Table 2 show that there is a negative and gradual impact

of female political representation; the coefficient of "reservation" is .025 whereas the coefficient

on "reservation_years" is −.0219. Both these values are statistically significant at the 95% level,

however are moving in opposite directions. This would essentially indicate that for the first year,

there is 0 effect of district chairperson reservation for females (.25 − .0219 = .0031), which is

insignificant) and the effect becomes negative and statistically significant when more years pass

for which there is reservation for women at the district chairperson level. For example, if a

particular district has received reservation for 5 years until (t − 1) year, then a woman in that

district is approximately 8.45% less likely to engage in a productive subsidiary activity compared

to a woman in a district where no reservation has been implemented.

We also look at women’s weekly wages as our outcome variable. We see in Table 5 that there is

no impact of reservation on women’s weekly wages (in cash, in kind, and total).

Heterogeneity Test

Table 3 shows region-wise heterogeneity in employment, whether it be principal or subsidiary

activity. We introduce interaction terms "rural_reservation" and "rural_reservation_years", which

are basically indicating the difference in the outcome variable between rural and urban regions

caused by female District Chairperson reservation with urban being the base case. By "urban" and

"rural" we mean districts that have been classified as urban and rural as per the NSS dataset. Let

us look at Column 1 of Table 3 which is the same regression as Table 1, column 3, but with

interaction terms described above. We see that for urban areas, there is a negative and gradual

impact of female political representation on the likelihood of a woman engaging in productive

principal activity; the coefficient on "reservation" is insignificant and that on "reservation_years"

is -.0281, which is negative and significant. However, when we look at our interaction terms, we

see that the coefficients are both positive and statistically significant. For urban areas the impact

of reservation for 5 years is close to -14% (0.0281 ∗ 5) whereas for rural areas the impact is close

to 0% ( 5 ∗ [−.0281 + .0280], which is insignificant and the coefficient on "rural_reservation" is

significant only at the 90% level). Similar trends are shown in column 2 as well which has results

for subsidiary activities, however, there is a net negative impact on female labour force

participation in subsidiary activities even in the rural areas. In urban areas this negative impact is

very high and much more than in rural areas. This shows that our negative results are largely driven

by women residing in urban areas, although rural areas are still being negatively impacted.

Similarly, we run a caste heterogeneity test for female employment, with both principal and

subsidiary activity in Table 4 and find that there seems to be almost no variation in the impact of

mandated political participation on the outcome variable for upper and lower caste women as our


Towards Equilibrium 2023 | Page 24

interaction terms are either economically insignificant (the coefficient on "reservation_years_uc"

is significant and negative but it is very small) or statistically insignificant at the 95% or in both

columns and the coefficient on our regressors remain largely the same as in Table 1 column 3

and Table 2 column 3.

Looking at weekly wages, from Table 7 we also do not observe any such heterogeneity based on

caste. However, from Table 6 showing region-wise heterogeneity we can see that the coefficient

of "wages_cash" and "total_wages" on "rural_reservation_years" is negative and statistically

significant. This points to a negative impact on rural women’s weekly wages of mandated female

political representation. The explanation for this seems unclear because we also see women’s

employment falling in rural areas, although it might point to an overall fall in demand for female

labour which is not being offset by a fall in supply, and hence leading to a fall in weekly wages.

Our estimates may also be driven by some other factor leading to a fall in rural women’s weekly

wages.

This raises questions of de facto and de jure leadership–are the women who are being elected

under the reservation policy actually the ones making the decisions or not? If women were the

ones taking decisions, they may have introduced policies that would have made it easier for

women to join the labour force, however, we are not observing that trend. We believe that this

may suggest that even though a woman candidate is the de jure political representative, she is not

the one making decisions. Instead, she is a puppet in the hands of the male members of her family

(father, brother or son) who are the ones making decisions behind-the-scenes. Turnbull (2022)

finds that even women who are appointed through reservations face resistance by a society not

entirely willing to accept them in a public and independent position; and how men, technically

blocked by the gender quota from holding office themselves, continue to exert control and

influence over women officeholders, even side-lining them in many cases as proxies. Moreover,

he also documents the role of men from the families of the appointed women, colloquially known

as parshad-patis, who have uniquely subverted the gender quota without violating any of the

formal quota rules. Due to this dissonance that exists between the de jure and de facto leader,

women political representatives cannot always make effective decisions Chary (2012).

Moreover, even if it is the woman Chairperson making the decisions, she may still feel the need to

appeal to the "median voter" instead of just women voters in the district. To appeal to the median

voter, women cannot implement policies that improve outcomes for women specifically, but need

to focus on policies that improve outcomes for all. Women may feel a greater pressure to justify

their position of leadership and may overcompensate in such a manner that their policies only

appeal to the demands of the men in the district. Even if women wish to implement women- centric

policies, they may meet economic, political and social impediments that constraint their ability to

showcase their true preferences.

Women also have been traditionally disadvantaged as they have not been given equal access to

education and social opportunities. Due to this, they may lack the experience and skills to govern

a district efficiently. However, as women acquire more experience via the reservations system, and

as the system continues to mature, women will become more effective leaders.


Towards Equilibrium 2023 | Page 25

4.2 Education

We first analyse the impact of having the district chairperson seat reserved for a woman on the

quantity of education and educational access of students as in Table 8. Most of the results are

insignificant. However, the coefficient on the number of absent days per month is statistically

significant at 95% level for "reservation" and at the 99% level for "reservation_years". If the

district chairperson seat has been reserved for a woman in year (t − 1), then students are going to

be absent by 0.572 lesser days. This impact is further increased when there are increasing years of

the seat being reserved. In the first year itself, students will be absent for 0.752 (-0.572 - 0.180)

lesser days per month. While this may seem economically insignificant, if these days are scaled

up to an academic year or the entire academic career of the student, it may lead to having increased

academic participation in school. Moreover, this effect only increases as the district chairperson

seat continues to get reserved for more years; for example, if a particular district has received

reservation for 5 years until (t − 1) year, the impact increases to 1.472 lesser absent days per

month. Students may be more willing to go to school as there is improved infrastructure and

teaching facilities that improves their learning experience. This may be driven by educational

access being an important aim of female political representatives, rather than just focusing on

improving learning outcomes.

The coefficient on hours spent in private tuition hours per week for "reservation_years" is also

statistically significant; students are likely to spend 0.199 hours less in private tuition. Private

tuitions are defined as classes that are organised outside of school hours, often when children

require more help. As students are spending lesser hours in private tuitions, we understand that it

is because they no longer need additional help outside of school. This may be driven because of

better quality of education received or better schooling infrastructure, allowing them to improve

their understanding and comprehension. This impact again only continues to increase as the

district chairperson seat continues to get reserved for women for more years; for example, if a

particular district has received reservation for 5 years until (t − 1) year, the impact increases to

0.995 (0.199 ∗ 5) lesser hours spent in private tuitions. Moreover, private tuitions is an

additional cost that households may have to bear, however as students are spending lesser hours

in private tuition, it may free up some amount of money that can be invested for other purposes.

We now move on to the impact that the reservation has on the quality of education as seen in

Table 9 There seems to be no statistically significant impact on education quality and learning

outcomes, there is a minor negative impact on the Significant Writing Level, however the impact

is 0.31% less likely to be able to write words which is economically insignificant, and as it is

only significant at the 90th percent level, we choose to reject it. Hence, the reservation does not

seem to have any impact on the quality of education, however, we believe that is only as it takes

longer to impact learning outcomes. Improved access to education will take longer to reflect in

the learning outcomes; however, if students keep attending school more often, it is bound to

impact their outcomes a few years down the line.

Heterogeneity Tests

We now move onto analysing the differential impact on multiple groups of people. We first look

at whether female students were impacted differently than male students. We are able to do so by

creating interaction terms "reservation_female" and "reservation_years_female" which capture the

difference in the outcome variable between male and female students caused by female


Towards Equilibrium 2023 | Page 26

District Chairperson reservation with male being the base case. We observe that most results are

statistically insignificant on time spent engaged in educational activities as seen in Table 10

suggesting that male and female students are impacted by the reservation policy similarly.

However, the coefficient for hours in school per week for "reservation_years_female" is significant

and suggests that female students are spending 0.155 hours more in school than male students in

reserved districts. The coefficient of "reservation_female" is negative, however, only significant at

the 90% level, hence we choose to reject it. This shows us that with every year a district chairperson

seat is reserved for women, female students spend 0.155 hours more in school than male students.

There is no statistically significant impact on the quality of education and learning outcomes.

These results may again be driven by what women representatives choose to focus on while trying

to improve education for children. As a lot of female students may be adversely impacted by lack

of educational facilities closer to their homes, or the lack of female toilets in schools, women

representatives may focus on such issues to improve access to education which allow female

students to spend more time engaged in school and educational activities. As female students are

spending more time in school, if scaled up to their entire primary and secondary schooling, their

returns from school are likely to be greater. This may increase their chances of joining the

workforce in the future.

Now we conduct a region heterogeneity test which looks at students from urban areas and rural

areas as observed in. We create the interaction terms "reservation_rural" and

"reservation_years_rural" which capture the difference in the outcome variable between rural and

urban regions caused by female District Chairperson reservation with urban being the base case.

Table 12 and Table 13. Here it can be seen that again the only statistically significant coefficients

are those of number of days absent per month and hours spent in private tuitions. There is no

immediate impact on those living in rural areas or even in (t − 1) time period, however as the seat

is reserved for more and more years, the impact begins to increase and become statistically

significant. Students in rural areas are likely to be absent for 0.141 lesser days per month as

compared to rural students for ever year a seat is reserved for a woman. This impact would increase

if the reservation was received for 5 years until (t − 1) period to 0.705 lesser absent days.

Similarly, students in rural areas are likely to spend 0.261 lesser hours in private tuitions as

compared to urban students. Again, the impact would increase if the reservation was received for

5 years until (t − 1) period to 1.305 (0.261 ∗ 5) lesser hours. There is no statistically significant

differential impact on rural students’ quality of education. This is in line with the overall impact

on the quality of education we observed above; it is likely to take time for increased time spent in

education to reflect in learning outcomes.

These results are in alignment with our initial hypothesis where we believe that the impact of

having a district chairperson seat reserved for woman is more pronounced in rural areas as

compared to urban areas. This may be driven by the level of authority and power a district

chairperson has in rural areas as compared to urban areas. District Chairpersons are likely to have

more direct control in rural areas due to lesser red tape and bureaucracy and potentially increased

proximity to local power structures. Moreover, urban areas have other municipality bodies and

administrative councils which may have to be consulted before making any change, this makes the

process slower. Due to this, women district chairpersons have a more significant impact in rural

areas.


Towards Equilibrium 2023 | Page 27

We now look at the last heterogeneity test that was conducted on the basis of caste. We create

interaction terms "reservation_lowercaste" and "reservation_years_lowercaste" which measure the

difference in the outcome variable between lower and upper castes caused by female District

Chairperson reservation with upper caste being the base case As it can be seen in Table 14, for

every year of reservation, lower caste students are likely to be absent by 0.111 lesser days per

month as compared to higher caste students. Moreover, there is also a small impact on the learning

outcomes of lower caste students as well, as seen in Table 15. Lower caste students are 0.69%

more likely to be able to identify numbers and perform basic mathematical operations. While this

may seem economically insignificant, with every year of a seat being reserved for women this will

increase and may lead to a larger impact in the future. These results also show us that women are

better representatives for those who have been traditionally marginalised.

5. Limitations

Before we begin with the discussion of employment and education results observed above, we

would like to address a few potential limitations of our analysis. First, while the position of the

District Chairperson and the jurisdiction is well-defined, there is still lack of clarity on what

constitutes the roles and responsibilities of a district chairperson, especially in relation to the other

local authorities and councils. As we are unable to ascertain the degree of power that a district

chairperson can exert, there may be a possibility that the Chairperson has minimal or no impact at

all in enacting policies and public goods for the betterment of the public.

Second, our paper only analyses the results from 10 states (due to informational constraints) of

the country. These states may not be completely representative of the trends and patterns noticed

around the country which may be biasing our results. Moreover, we are only taking few years

into consideration while doing our analysis (2005-06 and 2007-08). If we were to use more years,

there might have been minor impacts on the significance of our results.

Third, while there is no explicit reason to believe, the reservation allocation may not be completely

randomised. The method of reservation allocation is described as "random" however, there is no

further information about the logistics of this allocation. This lack of clarity accompanied by the

growing significance of the political economy may question the random nature of the allocation.

This introduces a level of endogeneity in our data, which we believed to have eliminated due to

the random nature of the policy.

Last, we would also like to acknowledge that this analysis is being conducted in a relatively early

period of the policy’s implementation. If we were to conduct a similar analysis for more recent

years, we would be more likely to see more significant impacts of female district chairpersons on

outcomes as they would have been introduced to the political space for quite some time now.

6. Conclusion

In this section we aim to understand the implications of both sets of results in conjunction with

each other. Through our paper we have primarily observed the following impacts of the district

chairperson seat being reserved for women: (i) A fall in employment as the primary or subsidiary

activity, (ii) The fall being driven primarily by urban areas, however, rural areas being impacted

negatively as well, (iii) Increased time spent engaged in educational activities (lower absences for

all students and increased school hours for female students), (iv) This increase being driven by


Towards Equilibrium 2023 | Page 28

rural areas more, and (v) Lesser hours spent in private tuitions by all (driven more by rural) leading

to lower expenses on non-school educational expenses.

These results seem to be indicating that women District Chairpersons have a larger impact on

education instead of employment. This could be driven by the median voter theorem. As

mentioned earlier, all political representatives have the need to cater not only to the part of the

electorate that they identify with, but the "median" voter instead. Due to this political obligation,

women focus on issues that impact everyone rather than just women; education is seemingly one

issue. Women representatives may believe that by increasing access and quality of education they

can drive a social change in norms which in turn may lead to the social upliftment of women.

Moreover, we see a significant impact not on learning outcomes, but instead on the time spent

engaged in educational outcomes (increased school hours for female students and lesser absences

for all). This shows that women representatives are not focused on the hyper-visible learning

outcomes that have been popularised by the political economy 1, instead they wish to improve

access to education. This can be observed as there is a greater impact on female and lower caste

students who are likely to spend more hours in school than male and upper caste students

respectively. This shows that if the seat is reserved for longer periods of time, women are able to

implement policies that make schools a more conducive place for marginalised students.

This may also be pointing to the fact that seeing women in power for more and more years creates

a change in social attitudes and norms; as woman leadership continues to get normalised in society,

marginalised communities (especially women) may find it easier to integrate into public spaces

such as those of education. The differential results observed on women are also in line with the

findings of Iyer et al. (2012) and Chattopadhyay and Duflo (2004). District Chairperson seats that

are reserved for women also have lower incidence of crime against women as well as increased

construction of public goods that are demanded by women. As reserved districts have lower crime

rates, households may be more comfortable in letting their female children out for increased hours

and hence spending more time in school. Moreover, public infrastructure demanded by women

may include more schools that are closer to houses (a school in every village), more wells (leading

to lesser time engaged in household chores for female students), female toilets (increased comfort

in attending school) which again allows females to spend more time in schools.

As there is such a positive impact on education, we believe that that is what is driving the fall in

workforce participation. First, as children are engaging in education for longer periods of time, the

expected future income of the household increases. Moreover, as students are spending lower on

private tuition (as returns to schooling are increasing), the educational expenses of the household

also fall. Due to this the need for another working member in the household falls. Second, female

students spend increased hours in school, she may be unable to contribute as much in household

chores, due to which adult women in the household may have to contribute increased hours. Due

to this, women engage in productive primary and subsidiary activity lesser in reserved districts.

1.As seen in Mbiti (2016), the growing significance of the political economy has forced politicians to focus on educational outcomes

that are hyper-visible and better for publicity. They want to invest in visible infrastructure or focus on improving scores, rather than

developing accountability structures or improving access.


Towards Equilibrium 2023 | Page 29

There is also a negative impact on weekly wages which may show that woman district chairpersons

are unable to create jobs. Even with falling supply of female workers, there is no upward pressure

on the weekly wages. This may suggest that there are even fewer jobs available in reserved districts.

However, we do not focus on these results too much as we believe that they lack economic

significance and have a minimal impact on one’s weekly earnings. However, we do acknowledge

that women district chairpersons are not working towards increasing female labour force

participation, and instead focusing on education which in turn causes an additional downward

pressure on female labour force participation.

The impact on employment and weekly wages are primarily being driven by urban areas, though

there is a significant negative impact on rural areas as well. The impact on education is primarily

being driven by rural areas, with no significant impact on urban areas. These impacts could be

potentially due to the degree of influence a district chairperson has in urban and rural areas, as well

as, the proximity and accountability to voters. First, in urban areas, the existence of multiple other

municipal and local bodies may dilute the authority a District Chairperson has over policy issues,

and further reduced proximity to the voters may also reduce their accountability and answerability

to them. Due to these reasons, women may be unlikely to focus on issues demanded by the

electorate, and even if they wish to, are unable to deliver the outcomes due to bureaucratic hassle.

To conclude, our paper has been able to critically dissect two important aspects of labour force

participation: (i) those of working age, and (ii) those engaged in education and will be a part of the

future labour force. We show that District Chairpersons have a larger impact on rural areas, as

compared to urban areas. Further, we do notice some trends of the median voter theorem and the

need to appeal to the larger electorate as they focus on education for all, as compared to increasing

female labour force participation by making workplaces more conducive for female workers.

However, even within overall improvements we do see a few outcomes being driven by (i) female

and (ii) lower caste students solely. This highlights that even within broader policy goals, women

focus on changes that they believe will uplift marginalised communities. Lastly, we do believe that

optics-related reasons may also be a huge driving factor in our results. As women leadership begins

to get normalised in society, women (and other marginalised communities) may find it easier to

assert themselves in public spaces and also believe that they too can achieve success (increasing

the incentive of spending time engaged in education). Lastly, due to this focus on increased access

to education, there is a downward push on female labour force participation due to increase in

expected future income and increased household responsibilities as more female students spend

more time in schools.

7. References

Besley, T. and A. Case (2000). Unnatural experiments? estimating the incidence of endogenous

policies. The Economic Journal 110(467), 672–694.

Burchi, F. and K. Singh (2020). Women’s political representation and educational attainments:

A district-level analysis in India. Journal of South Asian Development 15(1), 7–33.

Chary, M. R. (2012). Women and political participation in India: A historical perspective. The

Indian Journal of Political Science, 119–132.

Chattopadhyay, R. and E. Duflo (2004). Women as policy makers: Evidence from a randomized

policy experiment in India. Econometrica 72(5), 1409–1443.


Towards Equilibrium 2023 | Page 30

Downs, A. (1957). An economic theory of political action in a democracy. Journal of political

economy 65(2), 135–150.

Inglehart, R. and P. Norris (2000). The developmental theory of the gender gap: Women’s and

men’s voting behaviour in global perspective. International Political Science Review 21(4), 441–

463.

Iyer, L., A. Mani, P. Mishra, and P. Topalova (2012). The power of political voice: women’s

political representation and crime in India. American Economic Journal: Applied Economics

4(4), 165–93.

King, E. and A. Mason (2001). Engendering development: Through gender equality in rights,

resources, and voice. The World Bank.

Mbiti, I. M. (2016). The need for accountability in education in developing countries. Journal of

Economic Perspectives 30(3), 109–32.

Norris, P. (2001). Breaking the barriers: Positive discrimination policies for women. In Has

liberalism failed women?, pp. 89–110. Springer.

Turnbull, B. (2022). Women Who Only Serve Chai: Gender Quotas, Reservations and Proxies

in India. Taylor & Francis.


Towards Equilibrium 2023 | Page 31

8. Appendix

8.1 Results

Employment and Wages


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Education

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8.2 Summary Statistics

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Towards Equilibrium 2023 | Page 40

Estimating the Efficacy of QE in India: An ARDL Approach

-Aishwarya Venkat, Twinkle Adhikari 1

Abstract: Unconventional Monetary Policy Tools (UMPTs) have been in the spotlight during the

COVID-19 era, with Quantitative Easing (QE) being one of the most popular tools used by Central

Banks across the world to combat the pandemic’s recession. Most academic research on the

impact of UMPTs in emerging market economies (EMEs) focus on the angle of spillover effects of

advanced economies’ QE and the response to combat volatility resulting from the employment of

QE. Therefore, this paper attempts to assess the nexus of fiscal and monetary policy measures with

economic growth in India, given the internal dynamics of the Indian economy. This paper uses an

Autoregressive Distributed Lag (ARDL) model to estimate the efficacy of QE against other policy

alternatives for the period of 2004-2018. The study determines the short-run and the long-run

dynamics of Money Supply (as a proxy for QE), Weighted Average Call Rate (WACR as a proxy

for conventional monetary policy) and Fiscal Deficit (as a proxy for fiscal policy) on GDP Growth.

Keywords: Quantitative Easing, Emerging market economy, spillover effects, Unconventional

Monetary Policy, ARDL Modelling, Optimal QE.

1. Introduction

The monetary policy framework in India has evolved over time. India like most other advanced

and developing countries has now adopted targeting inflation as its primary goal with short term

interest rates as the main policy response instrument used. However, the limitations of the efficacy

of short-term interest rates as a policy response was most strongly felt during the 2008 financial

crisis. When short term interest rates hit their effective lower bounds, most of the advanced

economies turned to Unconventional Monetary Policy Tools (UMPTs) to bring their economies

out of a recession. UMPTs are in the spotlight again with the economic crisis of the pandemic

hitting the global economy.

Conventional monetary policy entails using the policy interest rate to signal the monetary stance

of the central bank, influence the market interest rates and target the ultimate goals of price stability

and economic growth (RBI Bulletin 2014). Monetary policy takes on an unconventional stance

when this policy rate loses its effectiveness and the central bank resorts to quantity instruments

that can take various forms including QE (Quantitative easing) and CE (Credit easing). QE

involves an expansion of the balance sheet of the central bank through purchase of long-term

assets, while CE involves the central bank intervening in a specific segment of the credit market

to ease liquidity (RBI Bulletin 2014). UMPTs are usually accompanied by forward guidance which

the central bank uses to communicate its policy goals to the public to use the expectations channel.

Overall, unconventional policies aim to ease monetary conditions, lower the long-term bond rates

and signal a monetary stance of lowered interest rates for a prolonged period. Advanced economies

have had diverse experiences with QE including Japan, US, UK and the EU. These large-scale QE

programs of the advanced economies have had a significant

1.Christ University, Bangalore


Towards Equilibrium 2023 | Page 41

impact on the global financial markets, especially the spillover effect that developing countries

were left to deal with. The Indian experience with UMPTs so far have largely been as a policy

response to

US policies or in reaction to them. There have been three main phases of unconventional measures

in India before the pandemic- the first phase was when RBI took a slew of conventional and

unconventional measures during the 2007-08 crisis to insulate the economy from the global shock,

the second phase was after the Fed’s announcement of early QE tapering in May 2013, and the

third phase which is happening at present to counter the recession caused by the pandemic (RBI

2021a).

2. Indian Experience with UMPTs

During the 2008 crisis India remained insulated initially. Before the crisis India’s growth was

relatively high and inflation was low and stable. But as the global crisis began to loom larger, India

was no longer immune to its effects. Financial markets, supply chains and investor confidence

took a hit. Table 1 very clearly presents the impact of the unconventional monetary policies

pursued on the major macroeconomic variables in India.

Table 1: Select Macroeconomic indicators - India.

2003-04 to

2007-08

(average)

2008- 09 2009-

10

2010-

11

2011-

12

2012 -

13

2013-

14

2014-15*

(latest)

Real GDP Growth

(%) 8.7 6.7 8.6 8.9 6.7 4.5 4.7 5.5

WPI Inflation Rate

(average) (%) 5.5 8.1 3.8 9.6 8.9 7.4 6.0 5.7

CPI Inflation Rate

(average) (%) 4.9 9.2 10.6 9.5 9.5 10.2 9.5 8.1

Centre's Fiscal

Deficit (% of GDP) 3.6 6.0 6.5 4.8 5.7 4.9 4.5 4.1

Overnight Call Rate

(%) 5.6 7.1 3.2 5.8 8.2 8.1 8.3 8.0

10-year G-Sec Yield

(%)

6.9

7.6 7.3 7.9 8.4 8.2 8.5 8.8

Exchange Rate (₹/$) 43.1 45.9 47.4 45.6 47.9 54.4 60.5 59.8

Current Account

Deficit (% GDP) 0.3 2.3 2.8 2.8 4.2 4.7 1.7 --

Source: Mohanty (2013). https://www.bis.org/review/r140728a.pdf


Towards Equilibrium 2023 | Page 42

GDP growth initially took a hit due to capital outflows and market volatility in the aftermath of

the recession, showing a temporary recovery to dip again. This was because dipping inflation

levels were not favourable for sustained growth. The Indian rupee experienced continuous

depreciation against the dollar. Rising oil prices contributed to an expanding CAD. Thus by 2013

when the Fed announced its intentions of an early tapering, the Indian economy was more

vulnerable to external shocks with its fundamental macroeconomic indicators in a weak position.

As a result, investor confidence took a significant hit. Policy instruments deployed by RBI to

tighten monetary policy included forex market interventions, forex swap windows for oil PSUs,

raising the MSF (Marginal Standing Facility) window by 200 points accompanied by OMOs and

OTs (RBI 2021a; Mohanty 2012).

Coming to the COVID-19 pandemic at present - so far there have been two phases of QE, the G-

sec Acquisition Programme (GSAP) which was announced in two phases - GSAP 1.0 and 2.0. The

Operation Twist (OT) conducted involved purchase of long-term government securities coupled

with simultaneous selling of short-term government securities through Open Market Operations

(OMO) (S. Gupta, Malpani, and Subramanian 2021). The GSAP programs were halted in October

2021 with the RBI declaring that adequate liquidity had been restored in the markets. Now in

January 2022, Indian financial markets are once again in a slump with speculation amidst the

possibility of Fed rising interest rates. India is again at a juncture where it is facing a trade-off

between accommodative monetary policy to assist economic recovery and raising rates to prevent

capital outflows (The Hindu Business Line 2021).

3. Literature Review

A majority of the papers that research the usage of UMPTs in EMEs focus on the angle of spillover

effects of advanced economies’ QE, and usage of UMPTs by EMEs as a response to combat

resulting volatility. This has been examined from different viewpoints including timing and phases

of advanced economy QE (Fratzscher, lo Duca, and Straub 2013; Krishnamurthy and Vissing-

Jorgensen 2011), unanticipated announcements (P. Gupta, Masetti, and Rosenblatt 2017) and

differing strategies to tackle entry and exit from QE (Mohanty, 2014; Rajan, 2013). Common

concerns have been expressed for EMEs about exit from QE being trickier as compared to entry,

with exit posing potential threat of significant collateral damage with asset prices going in a

downward spiral with capital outflows from the country.

Tackling with volatility arising from sequencing, timing and announcements of advanced economy

QE has been established as a primary concern for EMEs. But this finding would only state the

obvious until we address the specific questions of the exact transmission mechanisms that trigger

said volatility in EME markets. General investor confidence is the answer to this question

(Mohanty 2014). Net private financial inflows are much larger for EMEs as compared to advanced

economies. Expectations formed are a very powerful channel to affect all classes of assets broadly

by influencing general investor confidence in EMEs. The other question that now arises is why do

EMEs have such a large difference in the impact of spillovers on their economies?

Literature has identified specific characteristics that help us understand why the transmission

mechanism of the spillover effects are qualitatively different among EMEs. Fundamentals of

economy and credibility of the central bank (Mishra et al., 2014), monetary policy autonomy (Paul

and Reddy 2021), greater macroeconomic imbalances, i.e., larger debt-to-GDP ratios, fiscal deficit


Towards Equilibrium 2023 | Page 43

and current account deficits (CHAKRABARTI 2021) are few characteristics that determine the

resilience of an EME in withstanding spillover effects. A point of interest here is, India was dubbed

as one of the countries known as the ‘Fragile Five’ economies (Brazil, India, Indonesia, Turkey,

South Africa). Compared to the other EMEs, these economies showed a four times larger peak

response in long term bond yields and exchange rates.

Thus, the difference of why EMEs differ in their resilience to volatility is fairly well answered in

existing literature. However, stability (in price, financial markets and exchange rate) is only one

of the goals of monetary policy, the other key goal that monetary policy aims to influence is

economic growth; and this is the key research gap that remains to be explored for India. To

elaborate, most literature looking at UMPTs in the context of India look at it from a perspective of

insulating our economy from external volatility and measuring how successful monetary policy

has been through the spillover insulation perspective. Evaluating the domestic effectiveness of

UMPTs in general is little explored. RBI (2021) working paper evaluating the effectiveness of

UMPTs in times of COVID-19 is a core paper in this direction as it looks at the impact of UMPT

measures - LTRO and TLTRO on bond and money markets and testing for their differential

impacts. We wish to add a fresh angle to UMPTs and QE possibilities in India by bringing in

economic growth, specifically – to what extent have historical UMPT measures been successful

in stimulating domestic economic growth? We believe that this question is important to be

answered to determine the extent to which UMPTs could be an effective tool for India in the future

to steer the economy on the path of recovery after a global crisis.

Lastly, when evaluating the effectiveness of UMPTs on economic growth, we cannot consider it

in isolation without bringing in the other two major tools to usher domestic economic recovery

during a crisis – conventional monetary policy and fiscal policy. A comparative analysis of the

relative effectiveness of these three alternatives would yield a more meaningful analysis to decide

which of them has been most effective historically. This would go on to the body of literature on

the fiscal-monetary nexus in India so far (Arora 2018; 2017; Raj, Khundrakpam, and Das 2011;

Nandi 2019). Primary concerns that these papers address include comparative effectiveness of two

policy responses on output and inflation, their opposing effects and need for better coordination

between them.

4. Research Gap

All literature on UMPTs in the Indian context primarily looks at the spillover effects of advanced

economies’ QE on India. The question of how the movements of Indian policy variables (fiscal

and monetary) have affected its economic growth trajectory is still unanswered. This is an

important question to be answered, as during the present COVID-19 pandemic, RBI’s usage of

UMPTs is unlike previous UMPTs experience in India. Previously, it has been largely shaped by

spillover effects, but at present it is by the internal macroeconomic dynamics within the country.

Thus, our research gap is to identify how crucial fiscal and monetary policy variables have affected

economic growth whenever India has used UMPTs previously. This will help us evaluate the

effectiveness of future independent decisions that India will make to use unconventional monetary

policy.


Towards Equilibrium 2023 | Page 44

5. Research Objective

To examine the impact of three key macroeconomic variables - M3 Growth, Weighted Average

Call Rate (WACR) and Fiscal Deficit on economic growth in the periods during which India

adopted QE (rationale for choice of variables provided in the next section).

6. Research Methodology

This study uses secondary data collected from the Database of Indian Economy collated by the

Reserve Bank of India. The data is a quarterly time series and spans over 15 years from 2004Q1

to 2018Q4. This period was chosen due to the structural break in the data during the 2007-08 crisis.

This structural break marks the first ever usage of UMPTs by the RBI and provides sufficient data

to understand QE’s short-run and long-run efficacy. Since some variables were not available

quarterly, we used mean as the aggregation method and calculated quarterly data.

The variables chosen for the final analysis are Real GDP Growth (constant 2015 prices), M3

Growth, Weighted Average Call Rate and Fiscal Deficit. We adjusted the Real GDP data collected

from the RBI for seasonality. Later, we calculated the growth rates of GDP, CPI and M3 to arrive

at the growth rates we used in our analysis. We also include a dummy variable for the period when

the RBI engaged in QE (2007Q4 – 2009Q4, 2013Q1 – 2013Q4). The econometric method used to

estimate the long-run relationship between QE instruments and GDP Growth is the Auto

Regressive Distributive Lag Model. The study uses two software for statistical and econometric

analysis – EViews and Microsoft Excel. The rationale for selecting the variables and the

methodology is given in the following two subsections.

7. Rationale for Selecting Variables

We choose GDP Growth Rate as the key macroeconomic variable that is affected due to the use

of monetary policy. The ultimate aim of using UMPTs is to expand a country’s GDP in a recession

when conventional tools are deemed inadequate. Therefore, we try to estimate the long-run

efficacy of the QE programs on GDP Growth.

The study uses M3 and Weighted Average Call Rate as the proxies for UMPTs. While the

definition of QE necessitates us to use the data on the Net Assets of the RBI as the variable

representing QE, there exists a lack of data availability for Net Assets during the 2007-08 financial

crisis. Therefore, we had to resort to using M3 as a proxy of QE since the ultimate aim of QE is

for the central bank to expand its assets and inject money into the economy, which is represented

by M3.

RBI’s Operating Procedure of Monetary Policy documents state that WACR is the chosen

operating target of monetary policy in India (RBI Publications 2021). Additionally, Charmal and

Goyal (2021) found in their work - “we found WACR to be more responsive than short-term

government securities to the repo rate…. The response of WACR is found to increase and sustain

over a longer period compared to 91-day g-secs. Our conclusion, therefore, is that WACR is a

better operating target.”.


Towards Equilibrium 2023 | Page 45

WACR better captures the short-term fluctuations in the overnight money market and thus suits

our model best. Therefore, we use WACR as a proxy for Conventional Monetary Policy.

Fiscal deficit is an important variable in the decision to QE for EMEs. In the long run the cost of

servicing debt goes up significantly as central banks raise interest rates to tame inflation. Thus,

debt sustainability will be a key factor in determining the success of an EMEs QE program in the

long run. Further it’s one of the most important factors looked at by investors to gain the confidence

that the fundamentals of the country will remain strong and stable. Fiscal expansion is usually

accompanied by a QE program in times of recession. Thus, the fiscal deficit is taken as a proxy for

Fiscal Policy in our paper (Drakopoulos et al. 2020; BOSSONE 2015; Cukierman 2021).

We aim to conduct a comparative analysis of how effective these three types of policy measures

are on expanding GDP Growth.

8. Rationale for Selecting the Method of Analysis

Before we selected the method of analysis, we ran unit root tests to understand the nature of our

time-series data 2 . We ran the Augmented Dickey-Fuller test to determine the existence of unit

roots in our data. The null hypothesis of the test is that there is a unit root in the distribution, while

the alternate hypothesis of the test is that there exists no unit root – i.e., the distribution is stationary

(Pesaran, Shin, and Smith 2001). The following table shows the t-statistics of the ADF unit root

tests.

Table 2: Augmented Dickey-Fuller Tests.

Variable Case of Test Level First Difference Integration Order

Equation

GDP Growth Intercept and Trend -7.939163* - I(0)

(0.0000)

M3 Growth Intercept and Trend -6.168620* - I(0)

(0.0000)

WACR Intercept -3.818093* - I(0)

(0.0046)

Fiscal Deficit Intercept -0.997955

(0.7482)

-17.50126*

(0.0000)

I(1)

Source: Author’s Calculations. * Statistically significant, () p-values.

2.Time series data can be of two types – stationary and non-stationary. Stationary variables do not vary with time,

while non-stationary variables have a significant relationship with time. Non-stationary variables are commonly found

in time-series data as the data changes with time. Unit root tests determine the stationarity of the variables.


Towards Equilibrium 2023 | Page 46

The above table shows that the data contains variables with both I(0) and I(1) integration orders.

The method we would choose needed to account for the lagged effects of QE on GDP because the

impact of these variables can be systematically felt for more than one quarter. Further, it had to

account for variables of mixed integration orders. Therefore, we chose the Autoregressive

Distributed Lag model to understand QE's short-run and long-run effects on GDP Growth.

9. Autoregressive Distributed Lag Model of QE and GDP Growth

The ARDL model was first introduced by Pesaran and Shin (1998) in their seminal work – An

Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis. The model

accommodates the analysis of both stationary and difference-stationary variables, which is not

possible in either the Vector Autoregression or the Johansen Cointegration Test, which require for

the variables to be of the same integration order. In our model specification, we take GDP growth

as the dependent variable and M3 growth, WACR and Fiscal Deficit as dynamic regressors. We

further take the dummy variable of QE as a fixed regressor. The following equations shows the

mathematical representation of our model:

GDP Growth ! = α " + α # M3 Growth + α $ WACR + α % Fiscal Deficit + α & dumQE + ε !

In the above equation, α " is the constant while α # to α & are the coefficients of the variables M3

Growth, WACR and Fiscal Deficit respectively. t is the time period while ε ! is the error term that

represents the white noise in our model. In the ARDL form, the equation looks like the following:

To determine the maximum lag length specification in our model, we ran a Vector Autoregression

with all the variables and arrived at the following lag length criteria.

Table 3: Lag length criteria

Lag LogL LR FPE AIC SC HQ

0 -524.0648 NA 1822.5 18.85946 19.00413 18.91554

1 -410.4081 207.0177 55.81904 15.37172 16.09506* 15.65215

2 -384.2541 43.90134 39.20522 15.00907 16.31109 15.51386

3 -358.1835 40.03687 28.0051 14.64941 16.5301 15.37855

4 -314.022 61.51077* 10.70973* 13.64364* 16.103 14.59713*

From the above table we understand that except for the Schwarz Information Criteria, all the other

criteria show that the maximum lag length of 4 is suitable for our analysis. Therefore, we specify

4 as the maximum lag length of our model. We chose the Akaike Information Criterion to

determine the best model for our analysis and arrived at the model (3,4,3,2).


Towards Equilibrium 2023 | Page 47

To understand the reliability of this model, we then test the model’s residuals for serial collinearity

and heteroskedasticity. Both the Breusch-Pagan-Godfrey Tests do not yield any significant p-

values, indicating that our residuals are not serially correlated and are homoscedastic. We further

determine the stability of our model using the CUSUM of Squares Test which shows that our

model is stable. All these tests indicate that any results generated by this model are reliable.

We move forward with our analysis and examine the results of the F-Bounds test. The F-Bounds

test determines if there exists a long-run cointegrating relationship between the dependent variable

and the dynamic regressors. The null hypothesis of the test is that there exists no cointegrating

relationship between the variables. To reject the null hypothesis and establish a relationship

between the variables, the value of the F-statistic has to be greater than the upper bounds of the

critical values at 1%, 5% and 10% confidence levels. From the following table we understand that

the absolute value of the F-statistic is greater than the critical values of the I(0) and I(1) bounds at

all confidence levels. From this, we infer that QE dynamics have a significant influence on GDP

Growth in the long run.

Table 4: F-Bounds test

F Bounds test

Test Statistic Value Significance I(0) I(1)

F-statistic 7.551811 10% 3.47 4.45

k 3 5% 4.01 5.07

2.50% 4.52 5.62

1% 5.17 6.36

Null hypothesis: no levels relationship

Source: Author’s calculations.

Before we examine the magnitude and the direction of the impact of UMPTs on GDP Growth, we

establish the error correction form of our ARDL model. The error correction of an ARDL equation

is the speed at which the long run form of the model adjusts for short run fluctuations. The error

correction coefficient of an ARDL model is significant if the ECM passes the F-Bounds test and

the t-bounds test and if the coefficient lies between 0 and -1. In the case of our model, the error

correction coefficient is -0.7584, which is significant.

Hence, we can conclude that the long run form of our ARDL equation adjusts for 75.84% of the

short run fluctuations.


Towards Equilibrium 2023 | Page 48

10. Analysis of the Model Estimates

The following tables gives the estimates of the coefficients of the ARDL equation in the short-run

and long-run:

Table 5: Estimates of the Short-Run Coefficients

Variable Coefficient Std. Error t-Statistic Prob.

C 0.022557 0.004341 5.195735 0.0000

TREND -0.000436 9.23E-05 -4.724965 0.0000

D(GDP_GROWTH(-1)) -0.545428 0.133376 -4.089409 0.0002

D(GDP_GROWTH(-2)) -0.222848 0.106069 -2.100975 0.0423

D(M3_GROWTH) 0.105302 0.065879 1.598416 0.1182

D(M3_GROWTH(-1)) 0.474522 0.099895 4.750201 0.0000

D(M3_GROWTH(-2)) 0.513243 0.103835 4.942849 0.0000

D(M3_GROWTH(-3)) 0.241849 0.067195 3.599189 0.0009

D(WACR) -0.001050 0.000857 -1.224758 0.2282

D(WACR(-1)) -0.004307 0.000736 -5.854847 0.0000

D(WACR(-2)) -0.004010 0.001023 -3.921006 0.0004

D(FISCAL_DEFICIT) 1.35E-08 4.20E-08 0.322495 0.7488

D(FISCAL_DEFICIT(-1)) -1.97E-07 5.06E-08 -3.898786 0.0004

DUMQE -0.004036 0.002348 -1.718950 0.0938

CointEq(-1)* -0.758448 0.132853 -5.708943 0.0000

R-squared 0.832780 Adjusted R-squared 0.775681


Towards Equilibrium 2023 | Page 49

Table 6: Estimates of the Long-Run Coefficients

Variable Coefficient t-Statistic p-value

GDP_GROWTH(-1) -0.303876* -2.182448 0.0353

GDP_GROWTH(-2) 0.32258* 2.558054 0.0146

GDP_GROWTH(-3) 0.222848 1.719476 0.0937

M3_GROWTH 0.105302 1.230304 0.2261

M3_GROWTH(-1) 0.269749* 2.926026 0.0058

M3_GROWTH(-2) 0.038722 0.461742 0.6469

M3_GROWTH(-3) -0.271394* -3.234914 0.0025

M3_GROWTH(-4) -0.241849* -3.251226 0.0024

WACR -0.00105 -1.117574 0.2708

WACR(-1) -0.003868* -3.57414 0.001

WACR(-2) 0.000297 0.24248 0.8097

WACR(-3) 0.00401* 3.571954 0.001

FISCAL_DEFICIT 1.35E-08 0.253492 0.8013

FISCAL_DEFICIT(-1) 1.90E-07* 2.894791 0.0063

FISCAL_DEFICIT(-2) 1.97E-07* 3.102872 0.0036

DUMQE -0.004036 -1.314802 0.1965

C 0.022557 1.666224 0.1039

TREND -0.000436* -2.698027 0.0103

R-squared 0.661664 Adjusted R 2 0.510303

The above tables give us the lagged effects of QE instruments on GDP Growth in the short-run

and the long-run. From the results, we understand that M3 Growth has a positive effect on GDP

Growth in the short-run model and in the long-run model, it has a positive effect up until a lag of

2 quarters and has a negative impact on the same in lag 3 and 4. Since the lagged coefficients of

the short run model are significant, we can conclude that in the short-run, M3 Growth will have a

positive effect on GDP Growth after a lag of 1 quarter. However, in the long-run model, only one

positive coefficient is statistically significant, while both the negative coefficients are statistically

significant. This suggests that in the long-run, the positive impact M3 growth has on GDP is

dubious, but it surely will have a negative impact on GDP Growth after a lag of 3 quarters.

Therefore, to conclude, M3 Growth has a positive impact in the short-run and a negative impact

in the long-run on GDP Growth.


Towards Equilibrium 2023 | Page 50

We try to explain these dynamics with the Mundell-Fleming model as our base. According to the

Mundell-Fleming Model, in a flexible exchange rate regime, an increase in the money supply leads

to a subsequent rise in GDP. However, the exchange rate goes down, leading the currency to

depreciate. The results we observed could be a reflection of these dynamics, which are also

represented in Table 1, where the exchange rate depreciated as India engaged in QE. In the

beginning an expansion in money supply would lead to a subsequent expansion in GDP, and when

the currency depreciates, there is scope for expansion of exports. However, for a country with a

BOP deficit like India, this means that imports become costlier, affecting the current account

balance of our country. Further, we explain these dynamics through the monetarist point of view.

Initially, when there is an expansion of money supply, there is an increase in the nominal income

of the consumers. But inflation does not adjust to any change in money supply, since prices are

sticky in the short-run. So, the initial increase in nominal income is perceived as an increase in

purchasing power, which leads to higher aggregate demand in the short-run. This expands the GDP

of the country. However, inflation adjusts to the corresponding increase in money supply in the

long-run, and there will be a higher price level with respect to the high money supply. High

inflation, especially a demand push inflation, is not always conducive to the economy since there

can be a shortage of supply, leading to GDP contraction.

Before we understand the coefficients derived from the model, we try to explain how WACR

works as a policy instrument. Whenever the country finds itself in a recession, the central bank

reduces interest rates and thereby reduces the cost of borrowing for firms and households. This

induces the consumers to invest and demand more, and this expansion in investment and demand

leads to a corresponding expansion in the GDP. Essentially, when interest rates go down, GDP

rises. Therefore, negative coefficients are desirable in this context to reflect the inverse relationship

between these variables. We do see that WACR has negative coefficients in the short-run model

and in the long-run model, there are negative coefficients, followed by positive ones. The

coefficients in the short-run model are statistically significant, however, the long-run model has

varying significance levels. Therefore, we infer from these results that reducing WACR is effective

in increasing GDP Growth in the short-run, but not in the long-run.

We also see that Fiscal Deficit has a continuous positive impact on GDP in the long-run model,

however, in the short-run, there is inconclusive evidence. This signifies that fiscal policy

instruments have a longer impact on increasing the GDP than the monetary policy instruments.

This can be attributed to the incompetency of local governments to effectively implement fiscal

programs and the large-scale corruption and red-tapism seen in the country.

Finally, when we compare the absolute values of all the coefficients, we see that the coefficients

of M3 Growth were greater than the coefficients of WACR and they were followed by the

coefficients of Fiscal Deficit.

M3 Growth > WACR > Fiscal Deficit

From this, we can infer that the magnitude or the impact of Quantitative Easing is greater than the

magnitude of Conventional Monetary Policy. Further, the magnitude of Monetary Policy was

greater than the magnitude of Fiscal Policy.


Towards Equilibrium 2023 | Page 51

11. Conclusion

Our study has identified the effect of three specific variables (WACR, Money Supply growth and

fiscal deficit) to look at how QE policies affect economic growth and better understand the fiscalmonetary

nexus when UMPTs are used. The ARDL results demonstrate that there exists a longrun

cointegrating relationship between QE instruments and GDP growth. We conclude from the

results of the coefficients that monetary policy instruments are much more effective in the short

run and fiscal policy is effective in the long-run. Therefore, monetary policy better helps

economies combat recessions. However, there is a negative consequence to using QE in the longer

run and therefore, this must be kept in mind whenever the central bank resorts to UMPTs.

Due to our specific objectives, we have tried to capture the effects of only these select variables,

while the dynamics in real time QE are far more complex and extend a range of other variables

including the ones that works in the literature review have estimated. As we have seen throughout

our analysis, being able to instil confidence in the investors and public and setting stable

expectations is the key to carrying out a successful QE program in an EME. Further, the

effectiveness of the transmission mechanisms also plays a major role in determining the efficacy

of monetary policy. Future research can look into modelling other important factors like sovereign

ratings, central bank credibility and transmission mechanisms.

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13. Appendix

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Towards Equilibrium 2023 | Page 55


Towards Equilibrium 2023 | Page 56

Policies on Nutrition and Economic Development in India

-Ishita Deb 1

A critical issue posing a roadblock to the development of the Indian Economy is malnutrition.

According to the World Health Organization malnutrition is technically defined as, deficiencies,

excesses or imbalances in a person’s intake of energy and/or nutrients. Even after several attempts

to abate the rates of malnutrition, the issue has been marginally addressed with a lack of adequate

investment in the blueprint of the government plan. Stunting and Wasting which comes under the

category of undernutrition together constitute the problem of dual disease burden. The data from

Observer Research Foundation (March 2020) in India states that around 46.6 million people were

stunted and 25.5 million people were wasted. The data reveals that as the income and standards

of living of the households ameliorated, the rates of stunting decreased. Thus, stunting is more

prevalent in rural areas than in urban areas. An inter-state difference can also be observed as states

like Bihar, UP and Jharkhand have 48%, 46% and 45% stunted people among the total population

respectively. Whereas, states like Kerala and Goa have 20% of stunted people among the

population. Furthermore, according to a study by Galasso and Walstaff in 2015, nearly 2/3rd of

the workforce was stunted in their childhood.

The major threat to the health of women is anaemia which has a negative impact on future

pregnancies and has a high chance of transferring it to their children, thus forming a vicious cycle.

The first 1000 days of an infant are extremely pivotal and provide a window of opportunity to

eradicate malnutrition. However, according to the National Family Health Survey (2015-2016),

only 41.6% of infants are breastfed within the first hour of their birth, 54.6% for a duration of 6

months and 9.6% of children above the age of 2 are given an adequate diet. The average age at

which a child comes into contact with any primary health centre in India is 3 years, thus the child

is devoid of even rudimentary healthcare. Studies have shown that 80% of brain development

happens by the age of 3 making it even more vital to enhance healthcare in this vulnerable group.

Malnutrition is not only pernicious to human health but also degrades the quality of human capital

leading to lower economic growth. It tends to push people into the vicious cycle of poverty

especially those who belong to the Scheduled Castes and Scheduled Tribe (27.4% of them

stunted), Other Backward Classes (23.5% of them stunted) and the ones working in the informal

sector with no or negligible savings to deal with the unseen economic shocks. It increases the

rates of mortality, and morbidity, attacks immunity and increases the risk of being affected by

communicable diseases. Since productivity is reduced the education potential is also reduced as

many young students choose to drop out of school. According to a study by Maluccio et al. (2005)

in rural Guatemala, there is a positive and significant effect of early-childhood nutritional

intervention on education. The results displayed an increase in grade attainment by women,

increased speed of grade progression by women and higher scores on cognitive tests and

educational achievements.

1.Lady Shri Ram College for Women, Delhi University


Towards Equilibrium 2023 | Page 57

This produces low-skilled and unhealthy labourers which may lead to unemployment due to

layoffs in future. Struck with the current high rates of inflation and increase in prices of fuel and

basic essential commodities, this increases the gap between the haves and the have-nots. Research

studies have found that an increase in a healthy population is due to an increase in productivity.

Leiebenstein’s (1957) renowned concept of efficiency wages was also based on this. Studies show

that a 1% loss in adult height due to stunting leads to a 1.4% loss in human productivity.

Policy Choices and India’s Past Policies on Nutrition

To find a policy framework which best fits this situation, we need to focus on empirical micro

studies, studying the behavioural model of individuals, households and communities and taking

into consideration any unobserved characteristics. For impact estimation and comparing different

policy alternatives, there are several methodologies which can be undertaken. Firstly, a doubleblind

experiment with random allocation to treatment and control groups to evaluate the effects of

interest. A well-planned and executed study may exhibit the underlying causality directly.

However, the scope of a double-blind experiment is limited when it comes to health and nutrition

policies as of the randomised structure of the experiment and the long duration of time required

to observe the effects since the effects of malnutrition in infants are seen much later in life.

Other methods to compare policies are ‘Cost and Impact Analysis’ (CAI) or using econometric

methods using an analytical framework along with taking into consideration the measurement of

errors. Moreover, ‘Cost Effectiveness Analysis’ (CEA) can also be undertaken to rank and

analyse alternative investment policies where we estimate the cost per unit of effectiveness. The

drawback of using CEA is that it requires a single effectiveness measure and the motives of

policies cannot be assessed comprehensively. In addition to this, there is the widely used ‘Cost

and Benefit Analysis’ where benefits are expressed in monetary terms. Studies show that $1

invested in reducing malnutrition gives a return of approximately $16-$18 in future. However,

since this method uses partial equilibrium analysis, it is bound to miss out on several crucial

factors.

It was recognized way back in the 1990s by the National Nutritional Policy in India that,

“nutrition affects development as much as development affects nutrition”. (DWCD,1993). The

inception of Integrated Child Development Services (ICDS) along with the mid-meals

programme has been central to embarking on a path of improving the nutrition intake of the

nation. By converging health, education and nutrition the government launched a flagship

programme of Anganwadi Centres under the ICDS in 2006. Still, the issue of malnutrition has

been marginally addressed and there is a lack of adequate investment in social sectors like health

and education which hampers the rate of overall economic growth.

One reason why this problem of myopic public investment by the government persists is that the

effects of malnutrition are hidden and come into play only in the long run.


Towards Equilibrium 2023 | Page 58

In an academic paper by Lancet, it was found that the cause of 68% of deaths among children

below the age of 5 was due to malnutrition. India was even ranked 107 out of 121 in the Global

Hunger Index, 2022. The annual reduction of 1% is very less if we compare it with the reduction

rates of countries like China, Peru and Brazil (Keefe, 2016; Marini & Rox, 2017; Implementation

of MDGs, 2015).

The neighbouring countries with lower economic growth than India such as Pakistan, Nepal,

Bangladesh and Sri Lanka have performed better. An interesting fact is that Bangladesh did not

have a nationwide nutrition policy but focused majorly on micro-level community efforts to

ensure increased standards of living and improved social indicators like sanitation and economic

growth. This was evident from the increased wealth and rates of education, especially among

young girls.

POSHAN Abhiyaan

Malnutrition in India has been a neglected area showing sub-optimal progress for many decades.

To achieve the ambitious targets of Zero Hunger (Goal 2) of Sustainable Development Goals by

2030 and nutrition targets of the World Health Organisation by 2025, political involvement

increased in this area where micro-level implementation became the pivot. Policies to ensure food

security, poverty alleviation, access to sanitation and clean drinking water were strengthened.

The Prime Minister's Overarching Scheme for Holistic Nourishment (POSHAN) Abhiyaan, was

launched as a flagship programme on March 8, 2018, in the Jhunjhunu district of Rajasthan. This

programme focuses on enhancing the nutritional status among vulnerable groups such as children

below the age of 6, pregnant women and lactating mothers. In developing human capital formation,

this has been a huge step in prioritising, targeting, showing commitment and increasing political

involvement. POSHAN seeps down in the hierarchy to the ground-level institutions such as

Panchayati Raj, Village Organisations and Self Help groups with an aim of convergence and

employing innovative technology. The use of ICDS- Common Application Software (ICDS-CAS)

to digitise data management, educate pregnant women through interactive media content, and give

updates on upcoming immunisation dates. The issue of minimal contact with infants and irregular

health check-ups to help with the early detection of diseases has also been resolved as the

Accredited Social Health Activists or the ASHA workers will now conduct home visits for a

duration of 15 months educating and counselling mothers on

healthy feeding practices, the importance of energy-rich diets and the management of the disease.

They conduct drives, outreach programmes, camps and regional fairs for children and adolescents.

POSHAN Abhiyaan is themed annually, for instance, "Mahila aur Swasthya" (Woman and Health)

and "Bacha aur Shiksha" (Child and Education) for 2022.

To capitalise on the demographic dividend, and improve implementation and quality, the

government launched POSHAN 2.0 in February 2021, merging the programme with the

Supplementary Nutrition Policy. It focused on solving the pragmatic issues in service delivery

and resolving problems faced in POSHAN.


Towards Equilibrium 2023 | Page 59

With the involvement of the Ministry of Women and Child Development and the National

Nutrition Mission and integration with the Department of Public Health and Family Welfare, the

Department of Human Resource Development and the Department of Rural Development, the

rate of reducing malnutrition accelerated. The focus was given to dietary diversity to have a

balanced diet which includes macros and micros. The supply side is emphasised by ensuring

continuous supply from domestic sustainable agricultural food systems and the demand side is

aimed to build up by generating public demand for locally sourced nutrition-rich food. To abridge

the gap between access, quality and availability, POSHAN vatikas (nutritional gardens) were

created for optimal utilisation of land to grow food, generate employment and provide food at a

low cost as due to the COVID-19 crisis, the budgets shrunk and inflation increased especially for

the people from the lower strata of the society started supplementing vegetable and fruits for other

food grains as they were costlier.

Furthermore, there have been plans to extend the idea of POSHAN vatikas and create food

forestry for achieving long-term nutritional targets.

Other Policies and Individual Efforts by States in India

To attain a sustained rate of reduction in malnutrition, under the National Food Security Act

(NFSA), 2013, the government launched the Pradhan Mantri Matru Vandana Yojana (PMMVY)

which is a direct benefit transfer (DBT) scheme in 2016. It started with being implemented in 52

districts and was further extended to 650 districts. This scheme was aimed at providing partial

wage compensation of INR 5000 to pregnant women and lactating mothers for the provision of

energy-dense diets and healthcare to their children and themselves. The shortcoming of this

scheme is that it excludes teenage mothers with more than one child and in a developing country

like India problem of child marriage (23.3% of total marriages) and teenage pregnancies (8% in

rural areas and 4% in urban areas) persists.

The Aspirational District Programme takes into account 117 states in need of exigent assistance

and improvement of the Human Development Index. It is based on the principles of collaboration,

convergence and competition. The monitoring surveys are conducted by a 3rd party where 60%

of weightage is given to health, nutrition and education (Niti Ayog, 2018).

In Chandigarh, there is a POSHAN helpline to book home visits of the Anganwadi workers and

accredited social health activists (ASHAs). In September marked as POSHAN Maah (POSHAN

month), they conduct cultural celebrations like Annaprasan, Godh Bharai, and Fulwaris along

with Bal Sandarbh Yojana. The Rajpusht programme in Udaipur not only provides cash

incentives to women but also reaches out to the male members of the household, family members

and community members to sensitise and educate them about malnutrition and other related

health issues. Moreover, the state of Odisha focuses on fighting the problem of malnutrition by

providing essentials to the people.

\


Towards Equilibrium 2023 | Page 60

Recommendations

Though the policies on paper seem to be driven, there is much than can be done to scale up the

impact on the reduction of malnutrition. The biggest solutions to this priority problem are the

simple ones arising from the loopholes and shortcomings in the execution of policies.

First of all, the supplemental nutrition that is given such as iron and folic acid and calcium tablets

but the accessibility to these is very less. The ‘Public Distribution System’ can also be extended to

those who do not have ration cards due to inefficiencies. Moreover, there needs to be increased

public sector spending by the government in building human resources. From the allocated funds

of INR 31.4 billion, only INR 5.69 billion has been spent till now. Around 4.6 million cases of

stunting can be prevented with higher monetary investments in the provision of sanitation,

continuous supply of electricity to remote areas, appropriate equipment in Primary Health Care

Centres (PHCs) and clean drinking water facilities. A switch to prioritising nutrition and health in

negotiations with other nations and talks with the UN will bring about the change needed.

The government can call for international funding from UN Children Fund CARE, strengthen

inter-level collaboration and have a general body, operational body and executive body for

increasing the efficacy of the implementation. Integration, building synergies and collaboration

on policies with other departments such as the Department of Tribal Welfare and the Ministry of

Education should be considered. A factor to be kept in mind while formulating and implementing

policies is that not just the quality and quantity of nutrition intake is crucial but also its frequency.

People won’t be able to absorb the nutrition if affected by disease/ill-health. (Nisbett, et al, 2014).

The problem faced in reality is that with the meal packages and mid-meal programme, a child gets

one balanced meal in a day but for the rest of the day he/she either lacks an adequate diet or is

devoid of a nutritionally enriched diet. Dissemination of knowledge regarding nutrition and

cultural factors in dietary practices, education talks and hands-on training to ground-level ASHA

workers will help in capacity building for the programme. Furthermore, providing counselling to

parents regarding healthy breastfeeding practices and complementary

feeding and addressing the issue of intra-household food discrimination can amplify the efficacy

of impacts as this is something that is usually overlooked.

In addition to this, there should be a strengthening of coordination among the departments to

facilitate mutual learning. If the contribution is well defined at all levels of leadership and among

different stakeholders with an effective conflict resolution body, India can aim to achieve the

desired annual rate of a 3% reduction in malnutrition.

To abridge the gap in implementation, we need to address the variation in services provided due

to geographical diversity and inter-state differences.

A unique approach keeping in mind the varied circumstances and conditions, adjusting and adding

new initiatives according to local demands. For a sustained impact, we need to do away with a

standard system, address intra-family dynamics and have a strong mechanism for monitoring and

feedback systems. The need to include further technological innovation for surveillance and webbased

tracking will prove to be valuable.


Towards Equilibrium 2023 | Page 61

Conclusion

Malnutrition in India is a persisting problem which has implications for the economy as well as

its future workforce. To achieve higher rates of reduction in rates of malnutrition, the policies need

to be compared and ranked appropriately to utilise the limited resources available to the country

optimally. This programme will only come into full force through community efforts and turning

it into a Janandolan (people’s movement) where different stakeholders such as both public and

private medical colleges, state and centre leaders, health activists and students are involved. By

integrating traditional knowledge, scientific evidence and political involvement India will be able

to capitalise on its demographic dividend.

References

Paul, Vinod K., Anamika Singh, and Sneha Palit. "POSHAN Abhiyaan: Making nutrition a jan

andolan." Proceedings of the Indian National Science Academy 84, no. 4 (2018): 835-841.

Strauss, John, and Duncan Thomas. "Health, nutrition, and economic development." Journal of

economic literature 36, no. 2 (1998): 766-817.

Das, Pragnya, Mahendra Dwivedi, Sanjay Sharma, Naresh Ramnani, and Ranu Arora. Toward

Improved Nutrition: The Atal Bal Arogya Evam Poshan Mission. POSHAN Implementation

Note 7. New Delhi, India: International Food Policy Research Institute (IFPRI) http://ebrary.

ifpri.org/utils/getfile/collection/p15738coll2/id/128465/filename/128676. pdf, 2014.

Dasgupta, Rajib, Susrita Roy, and Monica Lakhanpaul. "An uphill task for POSHAN

Abhiyan: examining the missing link of ‘convergence’." Indian Pediatrics 57, no. 2 (2020):

109-113.

Suri, Shoba, Kriti Kapur, and P. O. S. H. A. N. Abhiyan. "POSHAN Abhiyaan: Fighting

Malnutrition in the Time of a Pandemic." (2020).

Khan, A. M., and P. Chhabra. "Strategies to Improve Maternal, Infant and Young Child

Nutrition under POSHAN Abhiyaan by Involvement of Community Medicine Departments of

Medical Colleges in India."

Kapur, Kriti, and Shoba Suri. "Towards a malnutrition-free India: Best practices and

innovations from POSHAN Abhiyaan." ORF Special Report 2020 (2020). 8. Puri, Seema, and

Urvashi Mehlawat. "Infant and Young Child Feeding Practices and Poshan Abhiyaan: A Case

Study from India." In Narratives and New Voices from India, pp. 231-249. Springer,

Singapore, 2022.


Towards Equilibrium 2023 | Page 62

Awasthi, Ananya. "Dr. Ananya Awasthi discusses dietary diversity and how it could be a game

changer in fighting India’s malnutrition problem."

Kumar, Laxmi Mohan. “Poshan Abhiyaan's 5th Edition Ropes in Panchayat and Local Bodies

to Spread Awareness on Nutrition.” The Logical Indian. The Logical Indian, September 3,

2022. https://thelogicalindian.com/health/poshan-maah-5th-edition-37258.

Perappadan, Bindu Shajan. “Government Worried about Teen Pregnancies.” The Hindu, July

29, 2022.

https://www.thehindu.com/news/national/high-teenage-fertility-in-some-areas-a-cause-of

concern-says-health-ministry/article65694204.ece.


Towards Equilibrium 2023 | Page 63

THE GREAT INDIAN GROWTH EXPOSÉ

- Arz Taneja, Sakshi Singh 1

Global growth is expected to slow down from 6.0% in 2021 to 3.2% in 2022 and 2.7% in 2023 as

forecasted by the International Monetary Fund in a recent report. It is no surprise that global

economic activity is experiencing a sharp slowdown, possibly turning into a global recession.

Global conflicts, supply constraints leading to inflationary pressures and tightening financial

conditions in most regions have heightened despair further after the difficulties brought upon by

the pandemic. This reflects the state of the world, including the three largest economies – the

United States, China and the Euro region, which make up roughly half of the global economy.

Gloominess and uncertainty are still lingering in the air even as we enter the new year.

But as the world appears to be abating, India is growing. Despite the fallbacks due to the pandemic,

India has bounced back with real GDP growth of 8.7% in FY21-22, bringing total output above

the pre-pandemic levels. IMF projections show that this growth will moderate but still remain

above the global average in the coming year, with a forecast of 6.1% for FY23- 24, larger than any

other developing country. Optimism around India also seems to be high. This can be due to

multiple reasons. Firstly, India surpassed China to become the world’s most populous country with

a population count of 1.417B people as opposed to China’s 1.412B as estimated by the World

Population Review. This leaves India with a large demographic dividend to capitalise on.

Secondly, India surpassed the UK in 2022 to become the 5 th largest economy. Thirdly, the recent

G20 Summit hosted by India and noteworthy success in digitization efforts, has made the country

stand out on the global front. This optimism has translated into an increased inflow of foreign

direct investments, with India registering an all-time high inflow in FY21-22. Private consumption

and gross fixed capital investment also showed growth in the last year and projections for further

increases are positive.

Undoubtedly, big projections and high growth rates sound promising for our country at first

glance, especially when compared with the dismal numbers reported by the rest of the economy.

But at times like these it is important to take a step back and evaluate. To take stock of where we

are at and where we want to go. Are these numbers a true representation of India’s growth? If they

are, is this growth equitable and sustainable? Is this growth translating into better standards of

living for the average Indian citizen? These are some of the questions we aim to shed light on.

GDP growth: Is it really enough?

It is common knowledge amongst economists that GDP may not be the best indicator for the

welfare of the country’s citizens. But it is by far the most cited due to easy measurability and large

applicability. In its essence, GDP is the measure of the size and health of an economy. This raises

the age-old question of Quality versus Quantity. Just because the proverbial economic pie is big

doesn’t mean that everyone gets an equally large share. And it most definitely doesn’t mean that

the pie tastes good. So how do we judge the quality of our economy?

1.St. Stephen’s College, Delhi University


Towards Equilibrium 2023 | Page 64

Here we shall bring forward some common indicators to measure different dimensions of the

economy, and to analyse where we as a country may have further scope to grow. It is not a surprise

that a comprehensive and comparative assessment of the world's 15 largest economies across

several aspects of development diminishes the shine of India topping the list with the highest GDP

growth rate. The rising Indian economy, which has emerged from the ashes like a phoenix after a

year of negative growth brought on by the pandemic-led lockdown, is without a doubt the lone

bright light amid the gloomy setting globally. However, this does not mask how India is still

lagging behind its contemporaries, most of whom once shared the same history as India, in other

essential facets of growth and development.

For our comparative analysis, we have used World Bank data of the top 15 largest economies in

the world in 2022. Of the 15 countries considered, 8 were classified as developed, 7 as developing

and 1 as in transition. We further went on to rank them in ascending order of their place according

to the measured indicator. It is important to mention here that it is not uncommon to see developing

economies growing at higher rates than developed and comparatively richer economies, as

explained by convergence theory. Here we do not wish to compare the growths of different

economies, but rather bring to light other developmental aspects which may often be overlooked,

especially in developing economies.

*Rank 1 in Gini means the most unequal. Rank 8 would mean the 8th most unequal.

As seen in figure 1, India had the highest growth rate in 2022 amongst the top 15 economies

globally. But its poor performance in other areas cannot go unnoticed. India ranked last in terms

of life expectancy, poverty level, and literacy rate and second last in terms of female labour force

participation. It is also the 8 th most unequal amongst the 15 largest economies. These numbers do

not paint a good picture of the economy and for the lives of its citizens.


Towards Equilibrium 2023 | Page 65

A country is made by its people. Hence, the purpose of economic growth remains unfulfilled if

the lives of its citizens aren’t improved.

So, what are the key areas where India can improve? What are the current trends and future

expectations of the different arenas of growth? Below we analyse the past and the present of

various aspects of growth and development with special emphasis on India.

Labour Force: The soldiers behind our growth

Firstly, let’s dissect the situation of the labour force participation rate (LFPR) in India. LFPR is

defined as the percentage of the total working population who are either employed or actively

looking for a job; it gives a better picture of the labour market when compared with the

unemployment rate. According to the World Bank data, India’s LFPR in 2021 was 45.57%, lower

than even the pre-covid era, earning India a rank of 159 out of 180 countries.

The effects of decreasing LFPR are potentially severe: slower economic growth and a rising

dependency ratio. This may look contradicting to India’s current “high GDP growth” of 6.8% but

it is actually not. If the LFPR was higher than the status quo, this 6.8% number could have been

even larger. The natural question here would be, what is pulling back Indians from participating

in the economy? Generally, there can be many reasons for a low LFPR, for instance, recessionary

periods. However, in the Indian context, the low LFPR stems from an abysmally low level of

female labour force participation. According to CMIE data, as of December 2021, while male

LFPR was 67.4%, the female LFPR was as low as 9.4%.

Even if one sources data from the World Bank India’s female labour force participation rate is

around 20% when the global average is as high as 47% as seen in figure 2. The reason for such a

woeful scenario boils down to the poor working conditions such as insufficient laws and

regulations, inaccessibility of safe public transportation, hierarchical gender inequality at the

workplace, societal norms among many other factors. However, one would still assume to see a

slow but distinct increase in the female LFPR as seen in other countries.


Towards Equilibrium 2023 | Page 66

Rather, an alarming trend seems to appear in India. Despite rapid economic growth, urbanisation

and an increase in education levels among women, their economic activity has seen a gradual but

long-term decrease. In the last 15 years, the proportion of 25-59-year-old women as a portion of

the labour force has declined by over 15% from 42.07% in 2004-05 to 27.4% in 2015-16, while

the average female labour participation rate for the top 15 largest economies has remained about

constant (barring 2020 and 2021 which saw pandemic-related attrition). This can be because of

many reasons. Firstly, the nature of economic growth may be such that there has been insufficient

job creation leading to inadequate absorption of women into the workforce. A rise in income may

also have raised standards of living, disincentivising women from participating in the labour force.

This however seems less likely. Regardless, prosperous long-term growth without tapping into

over 70% of the women of India seems impossibly tough. India needs gender-responsive policies

that encourage more women to be a part of the workforce.

Education: Our key to a better world

Literacy rate is an important measure for a country as an enhanced literacy rate leads to the

enhancement of the country’s human capital. Differences in the level of education and training of

citizens elucidate why some economies flourish while others falter. In India, the literacy rate has

grown at a sluggish pace of 1.5 per cent per year (NSSO report, 2008) with more than 33% of the

children dropping out of school before reaching the fifth grade and 90% dropping out before they

reach high school. Compared to whence India began in 1947 with a literacy rate of 12%, it has

unequivocally improved to 77.77% literacy.

However, surprisingly, surveys measuring student learning outcomes have found that the rise in

literacy levels has not translated into improved quality of education. There is a globally acclaimed

Programme for International Student Assessment (PISA) under which the learning level of 15-

year-olds is tested. The last PISA test that India took was in 2009 where it ranked 72 out of 73

countries. After that, it was only in 2021 that India decided to resume but unfortunately, the

government cried it off citing pandemic-induced learning losses. So, we don’t have much reliable

data to assess India’s performance vis-à-vis other countries. Within the country, however, the

Annual Status of Education Report (ASER) 2019 revealed that nearly half of all students in Grades

III-VI are at least two years behind their grade level. The National Achievement Survey (NAS)

also cautions that average scores across several grades and subjects are as low as 40-50%. Making

matters worse, now that children have returned to school after the pandemic-induced closure, with

almost two years of no formal education, the learning impairments are substantially deeper. The

state of government schools and the quality of education provided by them fail to give any respite.

For instance, in August 2022, the Assam government ordered the shutdown of 34 state-run schools

because they saw a zero-pass percentage in class X boards. One of the major problems that have

come to light in government schools is teacher absenteeism.

A survey by Karthik Murlidharan, an Indian economist, found that 23.6% of the teachers were

absent during unannounced visits to public schools. Even when teachers were present the

instructional time was rendered low due to large amounts of administrative work16. All of these

highlight that public education needs to be improved and supposedly that necessitates more

expenditure from the government on education, which currently stands at about 3.5% of the GDP.


Towards Equilibrium 2023 | Page 67

However, according to a research paper by the World Bank, just the expansion in resources does

not lead to substantial increases in children’s competencies and learning achievement. The paper

finds that it is incredibly important to get the institutional structure right.

Poor Employment Quality: I work, therefore I am. Or am I?

The poor quality of employment is one of the gravest socio-economic problems faced by the

majority of citizens but is left in a dark corner while the high GDP growth gets all the limelight.

Poor quality of employment often manifests due to insufficient and uncertain incomes, poor

working conditions, lack of labour market security and overqualified employment. The major

reason often cited for this is a lack of awareness about job opportunities and the large share of the

informal sector in the Indian economy. Despite implementing formalisation measures, the

informal sector continues to employ about 80% of India's labour force and produces 50% of its

GDP. Clearly, the great bulk of jobs in India are informal but policymakers spend a greater share

of their time and efforts focusing on policies needed for jobs in the formal sector.

For increasing the incomes of the workers, a well-planned and well-developed education and

training system, which will increase labour productivity and diversify the pool of job opportunities

is necessary. In contrast to developed countries, where more than 95% of the workforce is termed

as skilled by a UNDP report of 2021, only 22% of Indian workers were recognised as skilled.

Thinking of skill as a measure of productivity is not farfetched, and since incomes are highly

positively related to productivity, it can be said that Indian workers are not getting incomes on par

with workers of the top economies.

Poverty: Living on the edges of life

India’s efforts to reduce poverty have fared comparatively well, leading to the upliftment of 271

million people facing multidimensional poverty in the last decade, a 2019 report by the UN stated.

We did push back poverty but still, India is home to the largest poor population in the world. About

16.4% of India’s population still remains below the poverty line, whereas a further 18.7% are

highly vulnerable to being pushed into poverty. Among the poor, 90% belong to rural households.

India, to everyone’s dismay, ranked 107 out of 121 countries in Global Hunger Index 2022.


Towards Equilibrium 2023 | Page 68

In Asia, with Pakistan, Bangladesh, and Nepal getting a rank below 100, only Afghanistan is

behind us. India also has the largest number of poor children, with one in every three children

living in poverty. A lack of resources at such an initial stage would lead to generational

deficiencies in health and education as these children grow to form the youth of the country. The

huge number of poor children, to a major extent, can be attributed to the propensity of poor families

to have more children. Quite popularly development and economic growth are said to be the best

contraceptive but in India’s case, economic growth doesn’t seem to extend to the poorer strata. It

must be remembered that the extent to which economic growth reduces poverty depends on the

degree to which the poor get the chance to be a part of the growth process and share in its proceeds.

Thus, India should objectively aspire to not only grow fast but also to ensure that the entailed

benefits reach the weaker sections.

Living Standards: Averages speak louder than extremes

Next comes the subject of living standards, which have undoubtedly improved post the 1991

reforms. In economics, GDP per capita is often taken as a more or less comprehensive measure of

an individual’s standard of living and quality of life. Following the convention, we would also

employ the same for our analysis.

Since 1991, India’s GDP per capita has increased almost 7.5 times, which is surely a remarkable

feat. However, when compared to its neighbours Bangladesh and China, India stands behind them

in this aspect. Even amongst developing nations and emerging markets, India’s GDP per capita

growth has been slower than the average as can be seen in figure 4.

In terms of per capita income, India’s rank according to the IMF is 142 out of 197 countries. If

GDP per capita is taken as a measure of an individual’s standard of living, much change is required

to pull up the graph. Certainly, a higher GDP growth rate can be a harbinger of prosperity, but not

if all the citizens are not prospering.

The Human Development Index (HDI) published by the United Nations Development Programme

(UNDP) is also a much-cited measure of the average well-being of a country’s citizens.

Incorporating the three basic aspects of human development: health, knowledge and standard of

living, it acts as a comprehensive measure which provides a richer insight into the life of the


Towards Equilibrium 2023 | Page 69

average citizen. It asks for a shift of policy attention from economic statistics to a focus on human

and social outcomes. As of 2022, India saw a fall in its HDI measure, ranking it 132 out of 191

countries. However, even though much improvement is required, the UNDP report acknowledges

that the drop in rank can be related to the global pandemic. They also go on to say that India would

benefit greatly by focusing on the three I’s, that is, Investment, Innovation and Insurance.

Manufacturing Sector: India’s golden ticket?

Lastly, let us have a look at the underdog of the Indian economy: the manufacturing sector, which

is expected to grow by 12.5% next period. According to the latest Economic Survey, it currently

accounts for 15% of India’s total GDP and employs 12% of the total workforce while the

agriculture sector still contributes about 23% to the GDP.

As a benchmark for comparison, let us consider China’s manufacturing sector. Currently, more

than 27% of China’s GDP comes from its manufacturing sector, reaching as high as 33% during

peak growth periods. So, what is responsible for the relatively small size of India’s manufacturing

sector as compared to other components of the GDP? Many reasons can be attributed to the same:

minuscule investment in R&D, low labour productivity, lack of quality in products to compete in

international markets and a bias towards capital-intensive manufacturing would be the

frontrunners.

At present India’s manufacturing sector is facing headwinds due to the global downturns, which

has even forced employers to lay off workers due to lack of profits. Nonetheless, India managed

to achieve a high GDP growth rate. This tells an Indian growth story, which has largely been

consumption-driven over the last three decades, since the economic liberalisation, marking the

emergence of a new Indian economy that transitioned from a supply-constrained to a demandconstrained

one. While for the rest of the emerging economies, the growth is coming from

investment and production, which is sustainable, India’s growth is consumption-led, which lacks

sustainability in the long run and may plateau very soon. In the long run, consumption-driven

growth doesn’t build the country’s wealth like industrial growth does. This means that even

though India’s GDP is growing at a high rate right now, this may not translate into future growth

unless there is a shift.

Conclusion

Despite India’s lacklustre performance in certain areas, it can’t be denied that India is undoubtedly

growing and developing as a country. As mentioned before, India has recently surpassed the

population of China, and currently has the largest youth population in the world.

MNCs worldwide are seeing India’s young population as an irreplaceable and valuable asset. They

can attract robust investments if utilised productively. India certainly has the capability to reap

substantially from its demographic dividend provided that it has strong and inclusive policies

targeting for better lives of its citizens.


Towards Equilibrium 2023 | Page 70

This young India has the potential to transport and transform the country from a ‘developing’ to

a ‘developed’ nation in the next two decades. For this, it is essential we adopt a multifaceted

outlook on development. In order to reap the potential benefits of a growing population, not only

do we need significant investments in education and health but also appropriate macroeconomic

and financial reforms to encourage savings. This will shift India’s growth trajectory from

consumption driven to investment led, which is imperative for long-term growth.

But most importantly, we need to give due thought to how we can improve the life of the average

citizen. Because when the community grows, the growth of the economy is not far behind. As

rightly said by Dr Amartya Sen, the renowned Indian economist, “Economic growth without

investment in human development is unsustainable – and unethical.” In the race for growth, we

as a country need to ensure we are not leaving anyone behind.

References

World Economic Outlook, October 2022

Blog, WEO update, July 2022

IMF Executive Board Concludes 2022 Article IV Consultation with India, IMF Press release

2023: A Love Story… of India and economic growth, ORF

This chart shows the growth of India's economy, WEF

India gets the highest annual FDI inflow of USD 83.57 billion in FY21-22, Press Release, PIB

Growth Rates and Composition of Real Gross Domestic Product, Annual Report, RBI

Labour Force Participation Rate, modelled ILO Estimate, The World Bank

Over the next one year, do you expect these to get better or worse in your country?

TheGlobalEconomy.com

Retreat of female labour participation, CMIE

Labour Force Participation Rate, Female, modelled ILO Estimate, The World Bank

Why is female labour force participation declining so sharply in India? ILO

PISA 2009 at a Glance, OECD

Report, ASER Centre

National Achievement Survey (NAS)-2021 Report Card

Reforming the Indian School Education System, Karthik Muralidharan

The Role of Education Quality for Economic Growth, Hanushek, Eric A. & Woessmann, Ludger


Towards Equilibrium 2023 | Page 71

Importance of literacy in India’s economic growth, Vaman S Desai

Union Budget 2022: Why the informal sector needs govt support, Business Today

Report: India Lifted 271 Million People Out Of Poverty In A Decade, GDC, UNICEF

India, Global Hunger Index

Human Development Report 2021/22, UNDP

Child Labour in India

‘India can become developed nation in...’: Ex-RBI chief explains, Live Mint

India ranks 132 on the Human Development Index as global development stalls, UNDP

Global Multidimensional Poverty Index 2022, UNDP

Economic Survey 2021-2022


Towards Equilibrium 2023 | Page 72

The Platform Economy in the face of a Global Economic Crisis: A

Micro-Theoretic Analysis

- Kashika Iyer, Mayukh Dutta, Meghna Ghosh, Saniya Ilyas 1

Abstract: Our paper attempts to study the platform economy, with our interest being in the

peculiarity of the platforms’ hiring of workers as “sub-contractors,” instead of having them work

directly under them. For our analysis, we construct a micro-theoretic multi-sector multi-factor

market optimisation model besides laying down its foundation with a partial equilibrium

household modelling. We demonstrate that the platform economy serves as a modern technologydriven

fallback option or a substitute for the traditional informal sector and the formal sector in

times of collapse of all the other sectors of the economy. Sub-contracting however makes the

platform workers vulnerable to economic shocks. Next, we extend the model to analyse whether

regulating the platform economy in terms of recognising platform partners as workers with formal

wage contracts can counteract the global economic shocks.

Keywords: Household Economics, Multi-sector general equilibrium, Wage Gap

JEL Codes: D13, D50, F20, J31

1. Introduction

The recent phenomenon in sectoral trends highlights how platformisation has been a

transformative phenomenon across sectors, reorganising value chains and restructuring the labour

market. The basic understanding of the business model of any platform economy, or the gig

economy (we will be using both the terms interchangeably in this paper), is that the platform, using

software applications (apps), acts as a digital mediator between service providers and the service

requesters (Stewart and Stanford 2017; Prassl 2018). “Gig work” is central to the platform

economy in general. It is basically the temporary and short-term work that is often available on

websites that link employers and employees. The aggregators refer to the people who supply their

services as “platform partners,” highlighting the fact that their relationship with them is only dayto-day.

This untypical form of employment is referred to as “sub-contracting”. The tactic of

considering their services as entrepreneurial, places the risk and labour costs on the workers. This

raises the question whether platformisation of the workforce is a modern variant of the traditional

informal sector.

Against this backdrop, we attempt to model explicitly the platform sector, the subcontracting

between the platform owner and the platform partner, and the decision-making of workers in

choosing to engage in the platform sector. In what follows, we examine the consequences of global

economic crises on the dynamic rise and fall of the platform economy. In so doing, we build a

micro-theoretic multi-sector multi-factor market optimisation model besides laying down its

foundation with a partial equilibrium household modelling.

Any nation’s Banking Financial Services and Investment sector is impacted by domestic

macroeconomic issues and the world's political economic environment. Even if they are

uncommon, globally disruptive events, like the COVID-19 pandemic, force the economy

1.St. Xavier’s College, Kolkata


Towards Equilibrium 2023 | Page 73

towards change, as observed in the patterns of economic activity across the globe for the better

part of the years 2020 and 2021.

Another such incident, which is now expected to have a significant impact on the Indian economy

and the financial industry, is the Russian invasion of Ukraine. While India maintains a neutral

political stance and only depends on Russia and Ukraine for a small portion of its imports (2.1%)

and exports (1%), their conflict is slowing down India’s GDP development. In February 2022,

when the military action took place, global oil prices shot up to $92 per barrel, shooting up again

to $124 per barrel and then further up to $130 per barrel. Oil prices are considered a mark-up of

the prices of all goods and services produced in an economy. So, the increase in oil prices increases

the price level for any given level of output, causing the aggregate supply curve of an economy to

shift upwards to the left. The sharp surge in oil prices has increased the cost of necessities, with

the annual inflation rate having risen to 7.8% in April 2022, the highest level since May 2014 2 .

Economists believe that the oil shock will mean prolonged inflation if the Reserve Bank of

India (RBI) fails to take adequate measures to anchor inflationary expectations. “Global

uncertainties emanating from the Western sanctions on Russia and supply chain disruptions of oil

can exacerbate fuel price conundrum to long term,” Lekha Chakraborty of the National Institute

of Public Finance and Policy told DW. 1 Morgan Stanley has revised down the GDP prediction for

India to 7.9% for 2023. Again, when Wall Street in the US and the stock markets in Europe fell as

a result of the financial crisis of 2008, its effects spread to India, and our stock market (Dalai

Street) was severely affected.

An abundance of papers have been written on economic recessions and quite a few on the platform

or the gig economy, but research performed on the platform economy during a global crisis has

been very little. In this paper, we have tried to discuss the impact of various global crises such as

the COVID-19 pandemic and the widening tensions in Russia-Ukraine on a section of the Indian

economy, namely, the platform economy. The platform economy got some importance for the first

time when COVID-19 hit, and now in the wake of another recession, the question remains: how

will the platform economy fare—and most importantly, how will the presence of the platform

sector affect the economy at large?

Although it may seem that there exists job autonomy among the platform workers, in practice there

is none. For instance, there is no interaction between management and workers regarding issues

like pay, incentives, or the nature of the labour, and employees provide no input in the price

charged for delivery to clients. During the COVID-19 pandemic, the workers were seen as

independent individuals having difficulties as a result of the pandemic (Parwez Ranjan 2021).

They were experiencing a scenario in which aggregators were absolved of any need to give

socioeconomic help to the workers. Since more than 92% of the Indian economy is unorganised,

this was largely acceptable. (National Sample Survey Office, 2012)

According to the Online Labour Index (OLI), demand for services offered by the platform and

digital sector initially declined in March 2020 but increased at the end of April 2020. Since the

onset of the pandemic, gig workers have been considered “emergency workers” and there has been

an increase in the number of gig workers during that time (Rani and Dhir 2020). Due to the

1. https://timesofindia.indiatimes.com/business/india-business/impact-of-russia-and-ukraine-war-on-indian-economy-by-ajay-amarassociates/articleshow/92273933.cms

2. https://www.dw.com/en/india-is-the-war-in-ukraine-behind-rising-fuel-prices/a-6140707


Towards Equilibrium 2023 | Page 74

concurred with this assessment and quoted the rise in the gig sector as “exponential”. This paper

states that the positive impact observed in the platform or the gig sector is because of a change

induced in the labour market itself, followed by the coronavirus-induced lockdown, showing an

increasing trend towards short-term and temporary gig jobs. During the lockdown, when people

had to stay indoors, it was the consumers’ reliance on the gig workers for the delivery of their daily

necessities, that gave these workers promising employment opportunities. It is estimated that there

were 68 lakh (6.8 million) platform workers in 2019-20, using both principal and subsidiary status,

forming 1.3% of the total workers in India. Following the pandemic, in 2020-21, 77 lakh (7.7

million) workers were estimated to be engaged in the platform economy. They constituted 2.6%

of the non-agricultural workforce or 1.5% of the total workforce in India. The gig workforce is

expected to expand to 2.35 crore (23.5 million) workers by 2029-30 forming 4.1% of the total

livelihood in India by 2029-30. (Niti Aayog, 2022)

The expansion of the gig economy in the pandemic could be seen as workers transferring to “jobs”

that offer them no social security or healthcare benefits and leave them vulnerable and exposed to

the crisis at large. The gig economy does not ensure them any economic stability and makes them

undertake risks that they have no incentive to manage. The expansion of the platform sector during

the pandemic essentially meant wastage of intellect, talent, and skills. Later in this paper, we also

study how a contraction in the platform economy because of a tightening in the flow of foreign

investment (or capital, in general, because of a global recession) will leave a huge number of

skilled workers exposed to economic and social crises.

We have organised our paper as follows. In section 2, we have written a brief literature review

relating to the academic papers and articles we reviewed before writing our paper. In Section 3,

we have constructed our model with scrutiny on the gig sector, following which we have described

the partial equilibrium of the households in Section 4. Section 5 discusses the general equilibrium

analysis, followed by a brief analysis of the model's comparative statics in Section 6. In Section 7,

we recommend strategies for our model to sustain through fluctuations in the global economy so

that workers gain at least a modicum of economic and social stability when the economy is faced

with a global crisis. Finally, section 8 concludes the paper.


Towards Equilibrium 2023 | Page 75

2. Literature Review and Interventions

Although many papers have discussed the ways in which platform and gig workers fared during

the COVID-19 pandemic when the lockdown was imposed, the results of our analysis when

considering another recession would tend to differ. This is because the COVID-19 pandemic,

despite being rightly classified as an “economic crisis”, is structurally unlike any other recession

(like the economic tension developed due to the Russia-Ukraine conflict), particularly by the

nature in which it hit the Indian economy. However, some distinguishing features of the digital

platform sector are prevalent in those papers, which are instrumental to our analysis.

Rani and Dhir (2020) observed that in most countries, including India, workers who were engaged

in online platforms were declared part of the “emergency services” during the COVID-19

lockdown. India had observed a rise in online work demand, mainly in software development and

technological activities. There was an increase in the number of registered workers in India as

online work demand increased. The platform or gig sector has been positively impacted because

of the pandemic, contradicting many earlier research which had predicted only devastating effects

of COVID-19 on the traditional economy. This sector stood out among many others that had

suffered, mainly because of the situational advantages it was given because of the queer nature of

the “economic crisis”. Joo and Shawl (2021) concurred with this assessment and quoted the rise

in the gig sector as “exponential”. This paper states that the positive impact observed in the

platform, or the gig sector is because of a change induced in the labour market itself, followed by

the coronavirus-induced lockdown, showing an increasing trend towards short-term and temporary

gig jobs. During the lockdown, when people had to stay indoors, it was the consumers’ reliance

on the gig workers for the delivery of their daily necessities, that gave these workers promising

employment opportunities.

Parwez (2022), however, does not paint the picture in such an optimistic light and points out some

of the glaring fallacies which the platform sectors faced. It has described the digital platform sector

as a sector “without employment rights, social security measures, protection and compensation.”

This paper has drawn special attention to food delivery platforms. These platform aggregators do

not recognize their delivery workers as employees and instead refer to them as “delivery partners,”

which exempts them from complying to traditional employer-employee laws (Parwez and Ranjan,

2021). Moreover, the arrangement excludes workers from social protection. In the absence of

labour rights, the workers remain highly exposed and vulnerable to market forces. This paper also

highlights the poor implementation of the COVID-19 guidelines. Even though many of the

platform workers’ services were labelled as “emergency services,” their movements were severely

restricted although they were supposedly “available.”

These idiosyncrasies paint a very dismal portrait of the digital platform sector, underlining the

problem of lack of recognition that the platform workers face despite the media’s popularity in the

urban sector, and perhaps these shortcomings are more realistic and relevant when we discuss the

platform sector in our model, where the economy is approaching an impending economic

recession, which is not likely to possess the “situational advantages” that the COVID-19 pandemic

did.


Towards Equilibrium 2023 | Page 76

3. Description of the Economy

For our analysis, we take into consideration a small, open, developing economy with four sectors,

all of which are defined by a perfectly competitive market. Our analysis is performed in the

medium run.

Sector 1, or the traditional informal sector, with unskilled workers L, earning wages W and using

mostly traditional capital K ' at a rental rate of r. The unskilled workers sell their services

to the market at a rate of P 1 . Domestic workers, street vendors, street food stalls, etc., comprise

this sector. This part of the economy is neither taxed nor monitored by any form of government,

and most are daily-wage earners. It makes up a significant portion of a developing country’s

economy (83% of the work force in India consists of the informal sector).

Sector 2 represents the gig workforce, comprised of skilled workers S earning wages W ( for their

labour, where W ( > W. This assumption ensures that unskilled workers have enough incentive to

undergo training for specialisation to join the gig workforce. The gig workforce requires at least

some degree of specialisation to operate within the platform economy. It mainly includes the skills

necessary to use advanced capital. Suppose a worker decides to join the Uber platform, then he

needs formal driving training (which includes the issuance of a driver’s licence) and needs to be

able to surf through the Uber app on the internet. The gig workers need to employ advanced capital

K ) , which can also be considered as an entry cost for the workers in the gig economy, with a much

higher rental rate of R. Going by the example of the Uber driver, he needs to purchase a car to be

able to participate in the platform. They earn an income of P 2 by offering their services to the

platforms (sector 3). 3 These platforms set the wage rate P 2 (which is also the income of the gig

sector workers) by establishing long-term contracts with the workers. They charge a price P 3

*

from

the consumers for the services they provide. This price depends on the demand function of the

services offered by the platforms and is explained in more detail later in this paper. Sector 2 and

Sector 3 together form the gig economy, the main highlight of our study. 4

Sector 4 is a high-skilled, capital-intensive sector producing commodities by means of skilled

labour S (at a wage rate W ( ), unskilled labour L (at a moderated wage rate W) and advanced capital

K ) (at the rental R). 1 If the gig workers decide to leave the gig economy, it is likely they will join

this sector as skilled workers and vice versa. Similarly, unskilled workers may also choose to work

in this sector. Sector 4 participates in world trade and is thus vulnerable to external shocks.

In the conventional labour market literature, the informal sector is viewed as a shock- absorbing

sector which can absorb as many workers retrenched from the formal sector. This is due to the

flexibility of wages in this sector.

1. We are assuming that the wage rate for the skilled workers is remaining the same in both the gig sector and the high-skilled sector. Another

important assumption which we make is that the rental rate for hiring advanced capital KA is the same for sector 2, sector 3 and sector 4.


Towards Equilibrium 2023 | Page 77

4. A Worker’s Choice to become Skilled: Household Optimisation

In our framework, let us consider L homogenous households who can either choose to work as

unskilled workers or as skilled workers. For simplicity of calculation, let the number of working

members in a household be normalised to 1. We take l ( as the fraction of the members of a

household who choose to become skilled by undergoing some training and attaining some degree

of specialisation.

We consider a Cobb-Douglas utility function for the households, where the satisfaction a

household derives is a function of the goods it consumes C and the satisfaction it derives from

acquiring some degree of skill or specialisation, rather than remaining unskilled. The utility

function is given as:

U = lnC + lnl ( (1)

Let P be the price of a typical consumption basket. The income earned by the households from

joining the market by contributing skilled labour is W ( l ( , while kW ( l ( represents the part of

income which the members of the household have to forgo for specialisation training or purchase

of advanced capital to become a part of the skilled workforce (k < 1, a trivial assumption which

suggests that the worker would not spend more than the wage he earns on training). Those

members who choose to remain unskilled (1 − l ( ) earn an income of W(1 − l ( ). The budget

constraint of the household is, thus, given by:

PC = W(1 − l ( ) + [W ( l ( − kW ( l ( ] (2)

To maximise the utility of the household given the budget constraint, we substitute equation (2) in

equation (1) and solve the equation by the maximisation condition:

U = ln W(1 − l () + l ( W ( (1 − k)

+ lnl (

The first order condition of utility maximisation:

dU

P

= 1 dl ( C (−W + W ((1 − k)

) + 1 = 0

P l (

The second order condition for utility maximisation:

d 2 U

2

dl = − 1 (

C 2 (−W + W ((1 − k)

P

) 2 − 1 l (

2 < 0

Solving for the first order condition (as the second order condition is almost definitely satisfied)

the fraction of members in a household, who choose to enter the skilled labour force is:

l ( =

+,

-.(1.0)- !

(3)

Lemma 1: Strictly positive optimal value of l ( (W, W ( ) is implied by (1 − k)W ( < W.

An intuitive explanation for this condition could be that despite higher wages, the net income of

the skilled workers after specialisation training must be less than the wages earned by unskilled

workers, otherwise the informal sector would cease to exist. Again, it can be established that, l ( is

a positive function of W ( and a negative function of W.

Proof: Taking log on both sides of equation (3), we get:

lnl ( = lnP + lnC − ln[W − W ( (1 − k)] (4)

Differentiating both sides of equation (4) with respect to W:

·

1 ∂l (

= 1 l ( ∂W C (1 − l 1

()· −

W − W ( (1 − k)


Towards Equilibrium 2023 | Page 78

⇒ ∂l S

∂W = l S[ 1 C (1 − l 1

S)· −

W − W S (1 − k) ]

Lemma 2: ∂4 !

< 0, iff 1 (1 − l 1

5- ,

()· < .

-.- ! (1.0)

Again, differentiating both sides of equation (4) with respect to W ( :

1 ∂l (

=

l ( ∂W (·

1 C l (1 − k)

((1 − k)· +

W − W ( (1 − k)

Which means, )l S

> 0, given k < 1.

∂W S

Intuitively, l ( is positively dependent on W ( basically translates to, when the wage gap between

the skilled labour and the unskilled labour increases, more workers would be interested in forgoing

some degree of specialisation, despite the entry costs, and become a part of the skilled sectors. l (

is also, quite trivially a negative function of the wage rate for unskilled workers W. This is because

if the wage rate for unskilled labour rises, the unskilled workers are relatively better off than before,

which means less workers would have an incentive to join the skilled workforce.

Therefore, the total number of workers who enter the skilled labour force in the economy:

S = Ll ( (W ( 9 , W .) (5)

5. Equilibrating the Sectors in our Model

The general equilibrium system consists of four sectors, and the competitive industry equilibrium

of the sectors is given as follows:

Wa :1 + ra ;1 = P 1 (6)

W ( a (2 + Ra ;2 = P 2 (7)

N

P 2 a 23 + Ra K3 = P 3 (8)

Wa :4 + W ( a (4 + Ra 04 = P 4 (9)

In the above equations, a >? represents the input-output ratios. For example, sector 1 needs to

employ a :1 units of unskilled labour and a ;1 units of traditional capital, at the wage rate of W and

rental rate of r respectively, to produce one unit of output. So, (Wa :1 + ra ;1 ) is the cost incurred

by sector 1 to produce one unit of output. The price rate P 1 is the revenue from selling one unit of

output, and by the zero economic profit condition in a perfectly competitive firm in the medium

run, we get equation (6). Equations (7), (8) and (9) are derived in a similar fashion.

The full employment conditions for the unskilled workers (in the traditional informal sector and

sector 4), skilled workers (in the platform sector and sector 4) and capital, are explained by the

equations as follows:

a :1 X 1 + a :4 X 4 = L − S(W ( 9 , W .) (10)

a (2 X 2 + a (4 X 4 = S(W ( 9 , W .) (11)

a ;1 X 1 = K ' (12)

a ;2 X 2 + a ;3 X 3 + a ;4 X 4 = K ) + K @ (13)

In equation (13), K F represents the foreign direct investment. An influx of investment from abroad

naturally increases output in the gig sector and sector 4.

The demand function for the services offered by the gig sector is given by: 1

X 3 = X 3 B ( - !C !4 D 4

+ 3

# , γ 9 ) (14)


Towards Equilibrium 2023 | Page 79

Here, we are assuming that the skilled workers from sector 4 are the ones who demand the services

offered by the platform sector. The nature of the demand function suggests that the consumers

have a Cobb-Douglas type utility function, dependent solely on their income and the price the

platforms charge for their services. W ( a E4 X 4 is the income earned by the skilled workers in sector

4, and naturally, if their income increases, the demand for the platform services would increase. If

the price of the services offered by the platform workers P 3

*

increases, the demand for their

services will fall. γ is an exogenous parameter which will determine willingness of a consumer to

purchase their services. To elaborate on the γ parameter further, we consider the situation when

the COVID-19 lockdown was imposed. People were forced to stay at home and so the demand for

transportation services offered by Uber or Ola had fallen massively, with almost negligible

demand for their services in the urban sector. However, people wanted the daily necessities to be

delivered at their doorstep, so services offered by Bigbasket and Grofers (online grocery selling

platforms) received a spike in demand, so much so that people were booking slots one week or ten

days in advance. Even food delivery platforms like Swiggy, Zomato, Amazon and fast-food chains

like Domino’s started providing groceries through their delivery services, when previously they

had limited their services to only delivering food from restaurants.

The equations have the endogenous variables W, W ( , r, R, P 3 * , X 1 , X 2 , X 3 and X 4 .

Solving equations (7) and (9), we get:

W ( = W ( (P 4 . )

R = R(P 4 9 )

From equation (8), we find P 3

*

in terms of R as:

P 3 * = P 3 (P 4 9 )

From equation (12), we get: X # = ; $

C %&

, which is a constant.

From equation (10), we find X & in terms of S, which comes down to:

X & = X & (W 9 )

From equation (11):

X $ = X $ (W . )

And equation (13) gives us:

X % = X % (W)

From equation (14), we equate: X % = X % B , to get the value of W.

Substituting the value of W in equation (6), gives us r.

6. Comparative Statics: Entry points of a Recession

In this section of the paper, we theorise how an impending world recession would affect the gig

economy, given our framework. The effect on the gig economy would vary depending on how the

recession enters the country and there might be varying outcomes observed, as a result. We have

already discussed how the platform sector was affected by the COVID-19 pandemic induced

lockdowns and now we try drawing the situation of the pandemic in our model to see if the model

supports what had been observed. Then we try predicting how a recession (say, because of the

Russia-Ukraine conflict) will enter the economy, and how dissimilar the effects turn out to be

compared to the pandemic.

1. X3 is assumed to be demand determined and the platforms set the prices for their services keeping the inverse demand function in mind.


Towards Equilibrium 2023 | Page 80

6.1. A Positive Parametric Shift in the Demand for Platform Services (Increase in γ)

Agreeably, the COVID-19 pandemic did not follow the most quintessential routes through which

one might expect a recession to enter an economy. As previously discussed in this paper, the

government had imposed a lockdown to control the spread of the coronavirus, which essentially

prevented people from coming out of their homes unless they were a part of the “emergency

services”. In such a situation, people started hoarding their daily necessities and turned to online

delivery platforms to fulfil their demand. Many of these delivery services had been exempted from

the restrictions imposed by the lockdowns, which suited people’s needs. We take the following

series of events as a positive change in the parameter γ.

From the demand function represented in equation (14), there is an increase in demand for services

offered by the platform sectors X 3 B , and naturally, the quantity X 3 increases. While the variables

W ( and R suffer no change, the increase in quantity of X 3 produced, consumes much of the capital

and therefore capital availability for sector 2 and sector 4 decreases. Because sector 4 is a capitalintensive

sector, quantity of output produced X 4 by this sector decreases. However, because sector

2, where platform workers are hired, is a labour-intensive sector, the demand for labour (i.e.,

demand for services offered by the platform sector by the platform) increases, as a result of which

quantity produced in sector 2, X 2 increases. From equation (10), the wage rate for the unskilled

workers in the traditional informal sector W increases, and rental rate r for traditional capital falls

(from equation (6)).

The increase in wage rate in the traditional informal sector means workers have less incentive to

become skilled, as the wage gap reduces. So, the number of skilled workers in the economy S

contracts.

Proposition 1: A favourable demand shock owing to the pandemic crisis, results in the expansion

of the gig economy. This falls in line with the observation which was made concerning the gig

sector during the COVID-19 pandemic. However, the amount of skilled labour in the economy

falls.

6.2. A cessation in the flow of Foreign Direct Investment (Fall in K @ )

A recession has alternative routes through which it can enter the Indian economy. It has often been

observed that obstruction in the flow of foreign capital is one of the most immediate consequences

of a recession, so in our analysis we consider how a restriction in foreign direct investment affects

our model.

If the flow of foreign direct investment ceases, from equation (13), it is apparent that quantity

produced by sector 4 will get affected. Sector 4 being a capital-intensive sector, X 4 will fall. Seeing

as sector 2 is labour intensive, the workers’ willingness to work in the platform sector X 2 will

increase. Output produced by sector 1, X 1 , the wage rate of the skilled workers W ( , rental rate R

of advanced capital K A and price the platforms charge for their services P 3 * will remain unchanged.

The fall in X 4 will however bring the income of the skilled workers in sector 4 down and given

equation (14), the demand for platform services will shrink.

Moreover, the decrease in X 4 and increase in X 2 , will together bring the wage rate in the informal

sector down, which induces people to undergo specialisation training to join the skilled labour

workforce S (from equations (10) and (11)). Although the skilled workforce in the economy

expands, X 3 being demand determined leaves much of the skilled workforce, who were willing to

work in the platform sector, unemployed.


Towards Equilibrium 2023 | Page 81

Proposition 2: Disruption in the flow of foreign investment will create a contraction in the platform

economy. Although the specialised labour in the economy increases, they remain underutilised and

subjugated to unemployment because of lack of contracts offered by the platform economy.

Because X 3 is also decreasing, it implies that the platforms may choose to even layoff some of the

workers who were previously working in the sector.

6.3. A Fall in Exports (P 4 falls)

Another alternative route by which a recession may crawl into our economy is through a fall in

demand for the economy’s exports. A fall in world demand for the goods produced by sector 4

will decrease the price P 4 which it charges. From having solved equations (7) and (9), a fall in P 4

will will reduce the rental rate R for advanced capital K ) and increase the wage W ( for skilled

workers (sector 4 being capital intensive, if their revenue decreases then they would employ less

capital and more labour).

From equation (8), the price for services offered by the platforms P 3 * will fall and given the demand

function in equation (13), the demand for platform services X 3

B

will increase.

Expansion in sector 3 leaves less capital for sector 2 and sector 4. Sector 4 being capital intensive,

means there is a fall in output X 4 . The contraction in sector 4 implies that the skilled labour moves

from sector 4 to the platform sector (equation (11)) as a result of which, X 2 increases. We had

already established that: X 2 = X 2 (W . ), the wages in the traditional informal sector would thus fall

and the rental rate for traditional capital r would increase. More workers would hence be attracted

to the prospect of becoming skilled, as the wage-gap between the two professions increase. The

wage gap between the skilled and unskilled workers would increase (as W falls and W ( increases)

and give incentive to the workers to become skilled and so the number of skilled workers S in the

economy would increase. However, since there is a contraction in sector 4, the skilled workers

would choose to work in the platform sector.

Proposition 3: Terms of trade shock in terms of fall in volume of export leads to an expansion of

the gig economy. The number of skilled workers in the economy expands because of the increase

in the wage gap between the skilled and the unskilled workers.

The crux of the comparative statics analysis is to exemplify the role of the platforms: that the

platform economy serves as a modern technology-driven fallback option or a substitute for the

traditional informal sector and the formal sector in times of collapse of all the other sectors of the

economy.

7. An Alternative Model: Can Regularising the Gig Economy Combat Recession?

The gig economy does not highlight a proper employer-employee trade-off relationship. In the gig

economy, the platforms shift a portion of their burden of risk-taking to their “workers.” This

striking feature of the platforms sub-contracting workers and calling them “working-partners,”

might be a clever tactic from the perspective of the platforms, but bluntly speaking, it is simply

exploitation of the skilled workers. An expansion of the gig economy with such characteristic

features is undesirable. In a developing country, characterised by high population, unemployment

and underemployment, and with the employed population mostly being in the informal sector, if

remotely skilled workers also must endure the crudeness of economic and social instability, then

it portrays a rather unflattering image of “developing.” The emergence of the platform sector, on

the other hand, has also had the positive effect of creating more employment (both directly and

indirectly), increasing the flexibility of urban life and boosting the economy as a whole.


Towards Equilibrium 2023 | Page 82

We, therefore, suggest a model that removes the inconsistencies and shortcomings of the gig

economy. The changes are elementary—removing sub-contracting and establishing a proper

employer-employee relationship between the platform and the workers. We dissolve sector 2

entirely and leave the platform (sector 3) to employ workers at a regulated wage W ( . Sector 1 and

Sector 4 remain unaffected. Even mathematically, this simplifies our model to a huge extent, being

scaled down to three sectors, the equations being represented by:

Wa :1 + ra ;1 = P 1 (15)

W S a S3 + Ra K3 = P 3

N

(16)

Wa :4 + W ( a (4 + Ra 04 = P 4 (17)

(Note: Although we have removed sector 2, we still call the platform as sector 3 and the highskilled

sector as sector 4 for relatability with the previous picture drawn)

The full employment conditions for the skilled and unskilled workers and capital will now be given

by:

a :1 X 1 + a :4 X 4 = L − S(W ( 9 , W .) (18)

a (3 X 3 + a (4 X 4 = S(W ( 9 , W .) (19)

a ;1 X 1 = K ' (20)

a ;3 X 3 + a ;4 X 4 = K ) + K @ (21)

The demand function for the platform services remains unchanged:

X 3 = X 3 B ( - !C !4 D 4

+ 3

# , γ 9 ) (22)

(Note: Equation (18) is equivalent to equation (10), equation (20) to equation (12) and equation

(14) to equation (22) respectively from the original model)

Having established this new model, we consider the effect of a contraction in the flow of foreign

direct investment on the platform economy and the other sectors. In our previous model, we had

seen that although the number of skilled workers in the economy increases, some of them remain

unemployed as production falls both in sector 4 and the gig economy. In the current model,

however we see an altered picture:

The disruption in the flow of investment from abroad will bring X 4 down (from equation (21)),

sector 4 being a capital-intensive sector and increase X 3 , seeing as sector 3 is a labour-intensive

sector. To sustain the equality of equation (18), the number of skilled workers in the economy S

will have to increase, which can be written off as an increase in W ( and a fall in W (in other

words, a rise in the wage-gap between the skilled and the unskilled workers provides more

incentive for the workers to become skilled and thus the skilled workforce in the economy

expands). Since W ( increases, from equation (17), we can say the rental rate for advanced capital

R will fall as a result of which the price the platforms charge for their services P 3 * falls (equation

(16)). The demand for the platform services would rise (the increase in demand due to fall in P 3

*

overrides the effect of a fall in income of the skilled workers in sector 4 as X 4 decreases. Moreover,

W ( is also increasing which makes the fall in income relatively less, that is if income falls). 1

1.A simple assumption of price elasticity of demand being greater than income elasticity of demand for platform services will assure that X3D

would increase.


Towards Equilibrium 2023 | Page 83

There is thus an expansion of the gig economy accompanying the contraction of sector 4. The

number of skilled workers in the economy S is also increasing, so we can say that as more workers

become skilled, they join the platform sector, and there is also a flow of workers from the highskilled

sector to the gig sector. This shift in employment, unlike the previous cases discussed, is

not necessarily a harmful effect because in this paradigm, the workers are directly employed under

the platforms instead of having been “sub-contracted.” Here, the gig workers enjoy the same

benefits of social and economic security that the sector 4 workers do. In a way, it can be said that

the economy is benefitting because of the presence of the gig economy because this sector is there

to absorb the skilled workers when the other high-skilled sectors contract, and account for the

expansion in the skilled workers in the economy.

We can observe similar adjustments when the economy is faced with external shocks on amending

the given model as we have discussed. The economy benefits from a platform economy expansion

if the government makes it mandatory for the platforms to hire the workers, adhering to proper

employer-employee relationship.

8. Conclusion

The paper primarily focuses on the gig economy, analysing how sub-contracting, a practice which,

despite being one of the main reasons behind the platform economy’s almost exponential growth

in India in the last decade, exploits workers to a huge degree by making them undertake risks with

deceitful promises of incentives, and has a detrimental effect on the overall health of the economy.

Since most platform workers are only engaged temporarily as needed for a project, they do not

often receive the benefits that a full-time employee would, which leaves them economically and

socially vulnerable, especially in the face of a crisis. When developing our model, the subcontracting

of workers has been one of our main highlights, and we constructed two sectors to

describe the platform economy as a whole, while the other two sectors describe the other two facets

of the economy.

Having described our model, we tried to analyse the various channels through which an impending

recession could impact the platform economy. As discussed in the comparative statics section of

the paper, we have opined three propositions by which the same could happen. A positive

parametric shift in the demand for services offered by the platform sector (mainly because of its

convenience when facing a crisis such as the lockdowns during the pandemic) would cause an

expansion of the gig sector despite a contraction in the number of skilled workers in the economy.

We also tried analysing more quintessential routes through which a recession might enter our

model, a cessation in the flow of investment from abroad being one of them. From our analysis,

we theorised that a fall in foreign direct investment would lead to a contraction in both the highskilled

sector and the gig sector. The number of skilled workers in the economy would increase,

but they would remain unemployed because of a shortage of contracts offered by the platform

sector. There may also be a fall in exports due to a global recession, which would lead to a

contraction in the high-skilled sector and an expansion in the platform sector. The number of

skilled workers in the economy would increase, so it signifies that skilled workers would shift to

the platform sector.

To remove the shortcomings of the platform sector, we alter our model such that platform partners

are now recognised as workers with formal wage contracts. The gig workers are required to be

employed directly under the platforms instead of being “sub-contracted.”


Towards Equilibrium 2023 | Page 84

In our new paradigm, we see the effect of a restriction on foreign investment on the economy, and

where previously there had been a contraction, we see an expansion of the gig economy.

However, it has more of a positive impact this time around because workers receive proper

healthcare and social security benefits, which puts them in an economically more stable position

even during a time of crisis. The platform economy’s expansion, in this case, boosts the economy,

giving it some semblance despite the fall in economic activity in the other sectors.

9. References

Behera, S. M., “Gig Work and Platforms during the COVID-19 Pandemic in India”, Economic

and Political Weekly, 55, no. 45 (2020): paras. 1-7. Accessed Sept 1, 2022.

https://www.epw.in/engage/article/gig-work-and-platforms-during-covid-19-pandemic

Henderson, R., “How COVID-19 Has Transformed The Gig Economy”, Forbes, Dec 10, 2020,

accessed August 31, 2022. https://www.forbes.com/sites/rebeccahenderson/2020/12/10/howcovid-19-has-transformed-the-gig-economy/?sh=7898a5516c99

Joo, Bashir Ahmad, and Sana Shawl. "COVID-19 pandemic and the rising gig economy: An

emerging perspective." Global Economics Science (2021): 16-23. Accessed Sept 1, 2022.

Mangla, S., “Impact of Covid-19 on Indian economy”, Times of India, July 11, 2021, accessed on

Sept 1, 2022. https://timesofindia.indiatimes.com/readersblog/shreyansh-mangla/impact-ofcovid-19-on-indian-economy-2-35042/

Mediawire, “Impact of Russia and Ukraine war on Indian economy by Ajay Amar Associates”,

Times of India, Jun 17, 2022. Accessed on Aug 30, 2022.

https://timesofindia.indiatimes.com/business/india-business/impact-of-russia-and-ukraine-waron-indian-economy-by-ajay-amar-associates/articleshow/92273933.cms

Panda, S. “Crude prices risk can be treated as supply shock: RBI deputy governor Patra”, Business

Standard, Mar 12, 2022. Accessed on Sept 1, 2022. https://www.businessstandard.com/article/economy-policy/room-to-adjust-excise-can-delay-fuel-price-hikepassthrough-rbi-dy-guv-122031100946_1.html

Parwez, S. “COVID-19 pandemic and work precarity at digital food platforms: A delivery worker's

perspective”, Social Sciences and Humanities Open, 5 no. 1 (2022): paras. 1-31. Accessed Sept 1,

2022. https://doi.org/10.1016/j.ssaho.2022.100259

Parwez, Ranjan, “The platform economy and the precarisation of food delivery work in the

COVID-19 pandemic: Evidence from India Work Organisation, Labour & Globalisation”, 11-30,

Jan 1, 2021, Accessed Sept 2, 2022. https://www.scienceopen.com/hosteddocument?doi=10.13169/workorgalaboglob.15.1.0011

Sharma, S. “100 days of Russia-Ukraine war: How has India been affected”, India Today, Jun 1,

2022. Accessed Aug 31, 2022. https://www.indiatoday.in/diu/story/india-affected-by-russiaukraine-war-day-100-of-war-1957134-2022-06-01

Stewart, S. “Regulating work in the gig economy: What are the options?”, Sage Journals, Aug 7,

2017. Accessed Aug 31, 2022. https://doi.org/10.1177/1035304617722461

Rani, R. K. “Platform Work and the COVID-19 Pandemic”, The Indian Journal of Labour

Economics, 163–171, (2020). Accessed Sept 1, 2022. https://doi.org/10.1007/s41027-020-00273-

y


Towards Equilibrium 2023 | Page 85

The Real Aspects of Financial De-Dollarization

in a new Tobin-Walras-Jones (TWJ) model

- Arshia Goswami, Aritra Mazumdar 1

Abstract: There is a significant amount of foreign currency in the monetary systems of several

economies. The presence of foreign currencies signifies a certain degree of dollarization. The

initial stages of dollarization bring economic stability, but institutional factors underpin growth

and stability in the long run. Thereby, amidst striking heterogeneity of patterns across regions, we

locate a broad global trend toward financial sector de-dollarization from the early 2000s to the eve

of the 2008-09 global financial crisis. De-dollarization has primarily stalled or even reversed in

many economies since then. Nonetheless, a few of them have continued to de-dollarize. This paper

seeks to explore the concept of “Financial De-Dollarization”, its impact, and its implications on

the “real sector” for a developing country like India. Using a variant of Tobin's (1956, 1958) model,

we derive the optimal holding of foreign vis-à-vis local currency. A multi-sector multi-factor

model Jones (1965) has been constructed to demonstrate the real side of the economy.

Interestingly, our analysis suggests that in the short run, de-dollarization in the financial sector

would increase the level of unemployment in the economy which is facilitated by falling prices

and an accentuation of wage gaps.

JEL Classification Codes: G11, G32, F41

Keywords: De-Dollarization, Financial Crisis, Empirical Analysis, Developing Country, Russia-

Ukraine, Currency Swap, Financial & Real sector interlinkage

1. Introduction

Many developing nations, as well as transitional economies that have just recently adopted market

procedures, have already experienced a limited, unofficial version of dollarization. Their

inhabitants already have foreign currency and foreign currency-denominated deposits at domestic

banks, to varying degrees. Dollars or other hard money may be widely used in daily transactions

alongside the local currency in nations with substantial inflation. Berg and Borensztein (2000) in

their paper “Full Dollarization the Pros and Cons” defined Dollarization as one country officially

adopting the currency of another for all financial transactions. A new global monetary system was

formed by the 1944 Bretton Woods Accord. The U.S. dollar took over as the primary world

currency, replacing the gold standard. By doing this, it made America the leading economic force

in the world. America was the only country with the ability to issue dollars once the agreement

was signed. The accord established two U.S.-backed institutions that would oversee the new

system: The World Bank and the International Monetary Fund (IMF). Bordo (1993) analyses that

when the dollar's value on the foreign exchange market is lower than the value of other currencies,

the U.S. dollar depreciates. The dollar index has fallen as a result of this. This typically indicates

that a foreign currency, like the euro, may purchase an increasing number of dollars.

1.St. Xavier’s College, Kolkata


Towards Equilibrium 2023 | Page 86

The value of U.S. Treasuries may decline along with the value of the dollar, which raises Treasury

yields and interest rates. Treasury note yields are the key driver of mortgage rates. It might also

imply that sovereign wealth funds and central banks abroad are holding less US currency. As a

result, there is less demand for dollars. Following the outbreak of the war between Russia and

Ukraine, the United States and Europe imposed significant financial sanctions on Russia. The

prevalent use of financial sanctions has adversely affected emerging market nation’s distrust on

the dollar system and has shaken the rationality of the dollar and the Society for Worldwide

Interbank Financial Telecommunications (SWIFT) as an international monetary system. However,

the US dollar's and SWIFT's positions remain difficult to change. Xu and Xiong (2022) claim that

this implies that the international monetary system will not remain unchanged indefinitely. As the

willingness to hold foreign exchange reserves falls, emerging markets and developing countries

will either increase their tolerance for exchange rate volatility or implement policies to mitigate it.

This global reliance on dollars by banks, corporate entities and institutions is a source of strength

for the United States. Nevertheless, recent developments have raised concerns that this may soon

be lost. Looking back, one can gaze upon the impacts of the Great Recession which has included

high unemployment, record federal deficits and financial distress. Furthermore, challenger

currencies such as the euro and China's renminbi are on the rise. Some believe that the dollar will

soon lose its status as the world's reserve currency. In the present global order, with the growth of

China, India, Brazil, and other emerging economies, the United States no longer looms large over

the global economy. Only if the United States regurgitates the mistakes that led to the financial

crisis and refuses to bring its fiscal and financial house in order, will the dollar lose its international

currency status.

De-dollarization, on the other hand, refers to a shift from this international order to one in which

nations sell US Treasuries to keep reserves in other currencies or gold and seek to utilize their

currencies for dealings with their most significant trading partners. The Reserve Bank of India has

implemented a new system to allow for the settlement of foreign trade in the Indian Rupee (INR).

Announced on July 11, 2022, RBI (2022) claims “The rupee’s use in trade settlement would help

the RBI in conserving foreign exchange and would help save about 16.38% of foreign currency

use in Indian trade.”. It was announced that this move by India's central bank follows the increased

pressure on the Indian rupee as a result of the Russia-Ukraine war and sanctions imposed on

specific countries by the United States and the European Union (EU). Businesses are looking for

alternative methods of payment for imports. Trade transactions under this arrangement, according

to the RBI announcement, must be settled in Indian rupees in the manner outlined below.

i. The authorized dealer bank in India must open Special Rupee Vostro Accounts of the

correspondent bank of the partner trading country to settle trade transactions with any

country.

ii. Indian importers using this mechanism must pay INR, which must be credited to the

Special Vostro Account of the partner country's correspondent bank, against invoices

for the supply of goods or services from the overseas seller or supplier.

iii. Indian exporters who use this mechanism to export goods and services will be paid

INR from the balance in the partner country's correspondent bank's designated Special

Vostro Account.


Towards Equilibrium 2023 | Page 87

All financial market exports and imports will be denominated and invoiced in Indian Rupees under

this new international trading framework. The RBI has stated that the exchange rate between the

two countries' domestic currencies will be determined by the market. While this appears to be a

positive initiative, we believe it will be a lengthy process that will take decades to complete.

Implementing this process will necessitate extensive consultation with trading partners.

Figure 1: USD-INR Exchange Rate Fluctuations [over five years]. Source: Business Insider

This paper documents financial de-dollarization, its impact, and its implication on a developing

country like India. We aim to understand the implications of such a measure in the Indian real

sector and financial sector with a special focus on how this will be affected and will affect

contemporary geopolitical relations and internal issues for India and if it is beneficial for India to

adopt such a measure shortly. The first model is accomplished using a variant of the Tobin (1956,

58) Model in which we are concerned with two mediums of exchange: Dollars (representative of

foreign currency) and Rupees (representative of local currency). On the real aspect of financial dedollarization,

we are deriving the government's optimized choice of holding foreign currency and

domestic currency. The national income of the country will comprise local currency holding and

foreign currency holding. Assumptions of mean and variance (fluctuations) of local and foreign

currency have been made which has helped us find a relationship between our local currency,

rupees, and the variance (fluctuations) of the foreign currency, dollars. The methodology has been

discussed in detail while explaining the model. In the second model, we have discussed the real

sector taking a version of Jones (1965) to structure a multi-sector model. We have taken Labour

(wages) and Capital (rents) into account and seen how their returns have been affected by the

dollarization and de-dollarization of the Indian economy. Here, we have included both skilled and

unskilled labour as a result of which we can explicitly analyse the aspect of unemployment.

The remainder of the paper is divided into 6 sections. The next section (Section 2) presents an

analysis of previous literature. Section 3 portrays a model for the Financial Sector and the impacts

on currency holding. Section 4, 5 and 6 present the Real Sector for the country’s economy by

taking two variations of production relation. Section 5 and 6 further deducts the variations of the


Towards Equilibrium 2023 | Page 88

solutions derived in Section 3.1 and the impacts they have on the real sector. Finally, section 7

draws a conclusion.

2. Literature Review and Interventions

Pami Dua (2019) uses the VAR (1)- multivariate GARCH (1, 1)- BEKK model to analyse the

returns, shocks, and volatility spillovers between a developing country like India and major world

currencies namely USD-INR, EUR-INR, GBD-INR and JPY-INR exchange rates and the

aftermath of RBI intervention of the returns, volatility, and covariance of these exchange rates.

Here, the RBI intervenes because of vulnerabilities of global shocks mainly the emergence of the

global financial crisis (GFC) and the Eurozone Debt Crisis (EZDC). However, in our analysis, we

have used a model to gauge the trade-off between the returns and volatility of using local currencies

and foreign currencies to examine and interpret the returns and volatility between only USD

(foreign currency instance)- INR (local currency instance) and got a similar result i.e., an inverse

relationship between INR and variance of USD. Moreover, the above literature reveals that RBI

intervention significantly affects the volatility of INR concerning USD, EUR, and GBD and points

out a notable amount of covariance between USD-INR and the other three exchange rates thereby

supporting our analysis of de-dollarization.

Agenor and Khan (1992) has talked about the corresponding demand for domestic and foreign

currency deposits by residents of ten developing countries. Here, they have mentioned that

currency swapping is observed in countries where financial development and exchange rate

mechanism and practices differ widely from the rest of the world. Countries with continuous

fluctuations in inflation rates and domestic policies, use foreign currencies for domestic sales and

contracts. However, in our model, we talk about the fluctuations of foreign currency in the foreign

exchange market because of various factors thereby making the domestic currency more stable to

act as a medium of exchange for imports and exports. Moreover, we are considering a multi-sector,

multi-factor model besides the financial sector to gauge the undulations caused by such measures.

We have also taken into account labour market imperfections which allows us to analyse the

impact of de-dollarization on unemployment which has not been taken into account in the referred

paper. We have also explicitly shown de-dollarization from an asset market approach using a

variant of Tobin’s Model.

Caldararo (2022) has deducted how Putin’s plans to Invade Europe have huge economic and

geopolitical implications. While blaming the neo-liberal global political system in place and its

inherent fallacies at large, Caldararo shows how Russia is emerging as a crucial player in global

trade and commerce, thereby politics. Enumerating Putin’s planned, yet gradual steps at making

the most of the economic order and the Ukraine war, Caldararo points out at the complacency in

the European and American views of the situation. Referring to Buckley (2022) he focuses on how

there is considerable discussion and evident displeasure in the media over India and China’s lack

of support for the sanctions on Russia while also mentioning how Russia’s dominance in natural

gas and oil production has an important role to play in India’s further decisions and policies. Thus,

he deems extensive De-dollarization, for most South Asian countries (and some of Africa also) to


Towards Equilibrium 2023 | Page 89

be imminent in the face of the flawed neo-liberal world order. However, we shall try to understand

how such drastic de-dollarization steps will implicate the Indian Economic sector.

Yelery (2016) has analysed the attempts at Bilateral Currency Swap agreements with various

nation states by China over the years (2000-2015). The study shows that just as the amount of such

BCS agreements of the PBOC has increased over the years, such attempts to stabilize the Chinese

RMB itself have been changing over the years. The first attempts by China, back in 2000-2002 to

counter forex volatilities had limited eventualities and functional disabilities in place of economic

security. Yelery shows how China followed the western banks in learning about the snow-chain

economy (although the basic model differed.) Yelery, in his analysis of the prospects of BCS

agreements with South Asian countries also mentions India, in regards to which Yelery (2016)

claims that India would want to avoid Currency exchange complexities in fears of hampering its

growth and therefore refrain from such agreements with China. However, the rising number of

trade and investment opportunities, and the need to bring down the trade deficit further, strengthen

India’s need for a currency swap agreement with China.

Duffy et. al. (2006) having analysed “Dollarization Traps” consider dollarization in the sense of

asset substitution, where a foreign currency competes with local assets, especially domestic capital,

as a store of value, and the impact of dollarization on capital accumulation and output, and why

economies remain dollarized long after a successful inflation stabilization. Dollarization has been

related to a financial inter-mediation failure that happens during high inflation. They have shown

that in dollarized countries, inflation stabilization policies may not have any effect on domestic

capital accumulation, thus preventing such policies from stimulating growth- i.e., dollarized

economies are vulnerable to “dollarization traps.” In this paper, they have analysed that many

times high inflation leads to dollarization. However, as inflation is controlled –asset accumulation

continues in the dollar and thereby leading to capital accumulation in the dollar. However, in our

paper, we have analysed the relative risks involved in dollarization vis-a-vis dollarization and its

impact on the real sectors of our economy.

Liu and Papa (2022) have systematically examined BRICS (Brazil, Russia, India, China, and South

Africa)’s initiative to develop multiple de-dollarization initiatives to reduce currency risk and

bypass US sanctions. They have attempted to develop a “Pathways to De-dollarization” framework

and applied it to analyse the institutional and market mechanisms that BRICS countries have

created. This paper has identified the leaders and followers of the BRICS de-dollarization

coalition, assessed its robustness, and discerned how BRICS mobilizes other stakeholders. They

have analysed that BRICS’ coalitional de-dollarization initiatives have established critical

infrastructure for a prospective alternative nondollar global financial system. However, there is a

need to analyse the impact of de-dollarization on the real economy so that the transaction costs

involved in the efforts should not outweigh the impact of de-dollarization on the real economy and

the same has been analysed in our paper.


Towards Equilibrium 2023 | Page 90

Ize and Yeyati (2005) emphasized on the concept of financial de-dollarization or de facto

dollarization. A reliable model that introduces the credit risk and price risk hypotheses support the

de facto dollarization theory. The paper discusses the de-dollarization topic in depth, including

whether it should be a coherent policy target and, if so, how to go about achieving it. Dedollarization

can no longer be viewed as an inevitable and mostly voluntary phenomenon. For this

reason, a policy on the type and degree of dollarization must be implemented. Considering the

expanding financial environment, the small economies in an increasingly globalized world, and

market and legislative inefficiencies, the study argues that dollarization is not only not harmful but

even, to some extent, desirable. The degree to which MVP (Minimum Viable Product) can explain

de-dollarization in terms of de-dollarization before moving past MVP to account for monetary

policy endogeneity. Prudential rules and practices must make sure that they effectively absorb the

credit risk of dollar loans in nations with limited dollarization. A proactive de-dollarization

approach should be taken into consideration by nations. Promoting local currency markets and

increasing monetary credibility through reforms and capacity building should be the main

objectives of this strategy. A regime that targets inflation and tightens prudential requirements to

make the financial sector more resilient to volatility is the preferable course of action. To assist

overcome the fear of floating, more forceful actions that directly restrict dollarization may also be

necessary. The promotion of price-indexed instruments, among other less intrusive market-based

strategies, can hasten the shift. Such actions are especially advised for nations where establishing

monetary credibility is anticipated to be a lengthy process. A case could be made for the option of

accepting de facto dollarization in heavily dollarized nations. This includes, among other things,

creating enough prudential buffers to guarantee that the exchange rate flexibility can be used

without putting too much strain on the economy. Small, open nations that are a part of the best

U.S. dollar currency zone may fare better than others.

3. Optimal Functionalities of the Currencies in the Global Financial Market and Derivation

of Optimal Currency Holding

Suppose the Government wants to trade in the local currency (INR), which is almost risk-free, and

some foreign currency (represented here by the USD). The government must decide how much to

trade in each. For instance, it might trade only in the local currency, the foreign currency, or some

combination of the two. As we will see this problem is analogous to the purchase choices by the

government given a budget. We further use a variation of Tobin (1956) to understand the process

in the financial sector. Tobin's mean-variance analysis of money demand is just an application of

the basic notions of portfolio choice theory. Based on the model we argue that the government's

utility from the respective currency form used is positively linked to the expected return on their

portfolio and negatively related to the uncertainty of this portfolio as depicted by the variance (or

standard deviation) of its returns. According to this concept, an individual government possesses

indifference curves that may be drawn. These indifference curves slope upward because a

government is prepared to tolerate more risk in exchange for a higher projected benefit.

Furthermore, as we move up the indifference curve, utility increases since the expected return for

the same degree of floatation increases. The easiest way to introduce risk is through the uncertainty


Towards Equilibrium 2023 | Page 91

of real returns. This is the main starting assumption of the portfolio paradigm, which views

dollarization or de-dollarization as the result of an optimal portfolio choice by risk-averse lenders

(and borrowers) responding to the probability distribution of real returns in each currency in an

economy with price risk but no credit risk. Here we define the National Income (Y). The national

income is the sum of the trade benefits from Local Currency (M) and Foreign Currency (F) use.

Thus,

Y = M + F

(1)

Dividing Y on both sides of (1), we get the allocation of trade in local and foreign currency per

unit of national income,

1 = M +

F

Y Y

M

F

Where, m = is the fraction of national income in local currency, and f = is the fraction of

Y

Y

national income in foreign currency. Thus, we derive

1 = m+

f

(2)

We further denote the return on the Foreign Currency as R and that of the Local Currency as R

. In addition, the transaction (adjustment) cost for the Local currency ( ) and foreign currency (

t F

) are taken implicitly. Guidotti and Rodriguez (1992) claim that transaction costs incurred in

shifting from one currency to another are justified by the assumption of economies of scale in the

use of a single currency which is further suggestive of the existence of a range of inflation rates

within which the degree of dollarization will remain unchanged.

An explanation of the hysteresis effect in dollarization is proposed by Duffy et. al. (2006). There

are two manufacturing technologies in their concept, and the more efficient one has a fixed cost of

operation. Arbitrage was used to compare the returns on productive capital and foreign money,

both of which may be employed to hold value. High inflation impairs financial intermediation,

leading to the adoption of less efficient industrial equipment, which allows the 'Dollarization Trap'

to occur in the first place. Thereafter, hysteresis lets us assume

F

t M

M

t

M

> t

F

(3)

A fluctuating currency provides a monetary flow that is at least in part random. In other words, the

monetary flow is not known with certainty in advance. For example, if a country (economy) holds

50,000 USD in its reserves, it would not know if the value of the currency falls or rises in the forex

market. Thus, we take the variable (g) accounting for the returns to the foreign currency. In

contrast, a return less currency (the local currency M) does not fluctuate in the domestic economy

and hence returns equals zero.


Towards Equilibrium 2023 | Page 92

So, the net returns to money are given by,

R

M

= 0 -t

M

M

Þ R =-t

M

(4)

And the net returns to foreign currency is given by the following

R = g-t

F

F

(5)

Here, as a measure of the impact of the fluctuation of the foreign currency on trade, we take the

2

s g

standard deviation (or variance) of its return ( g ) denoted by .

The value of the return on foreign currency often changes during the course of the year i.e., it may

have a large number of positive or negative values. Hence, its expected value is 0.

Thus, the distribution of

From (2), we get the fraction of the national income in local and foreign currency. Thus, the net

return (represented by R) is given by

R= mR + fR

M

F

(6)

The expected return on the entire trade portfolio of the government (represented by µ ) is a

weighted average of the returns on the two currency forms. Hence, we get

µ = ER ( ) = mER ( ) + fER ( )

After some algebraic manipulation of the above equation, we find

M

F

µ = mt ( -t )-t

F M F

(7)

We denote the standard deviation of the returns to the portfolio as s . With some algebra, we can

show that the standard deviation of the portfolio is the fraction of the portfolio invested in the

foreign currency ( f ) times the standard deviation of the returns to the currency ( s ). Further eq.

(2) gives us f = 1-m. Thus,

g

s = (1- m)

× s

s

Þ m = (1-

)

s

g

g

(8)

Substituting the value of (m) from (8) in (7), we get


Towards Equilibrium 2023 | Page 93

s

µ = (1- )( t -t ) -t

s

g

F M M

(9)

This equation gets us a budget line because it describes the trade-off between floating currency

rates and expected return. This equation depicts a straight line because t , t , s are constants. The

equation says that the expected return on the portfolio increases as the standard deviation of that

return increases.

Differentiating equation (9) we get ( tF

tM)

.

ds =- -

M F g

Since

1)

t

M

> t

F

from (3), we get GH

> 0, indicating at an upward rising budget line. (Refer to figure

GI

We further derive an indifference curve for the government while deducing the National Utility of

the country as a function of currency volatility and Portfolio returns U = U( s , µ ). The point of

tangency of the budget line and indifference curve gets us the optimal amount of dollarization or

de-dollarization given the constraints on minimizing the effects of floating currency rates and

maximizing returns. Further plotting the graph as shown in Figure 2, the levels of currency forms

m

in local to foreign trade ( ) is given. Thus, the partial solution is derived as a function of s

g

,

f

&

t F

. Thus, the optimal currency holding is obtained as follows:

t M

m

=Fs (

g, tM, tF)

f

(10)


Towards Equilibrium 2023 | Page 94

3.1. The Effect of Global Economic Uncertainty on Currency Holdings

We further discuss how changes in these factors affect the optimal currency holding ratio. As the

volatility Xσ J Z of the forex market rises, the country will wish to keep more local currency since it

is less sensitive to financial market undulations. This causes an inherent increase in the ( K L ) ratio.

Furthermore, when the transaction cost of switching to local currency (t M ) rises, the country will

wish to keep more foreign currency thereby lowering the optimal currency holding ratio. Similarly,

if the cost of adjusting to foreign currency (t @ ) rises, the government will wish to keep more local

currency, raising the de-dollarization ratio ( K ). The partial solution accounts for the economy’s

L

trade-off between dollarization and de-dollarization. As the ratio increases, the country’s holding

of local currency is comparatively more and therefore it can avoid global fluctuations, thereby

making the local currency more stable in the forex market. On the other hand, because of dedollarization,

the adjustment cost of holding money increases and we get a low return on the

portfolio. We deduce that the stabilization of money dominates the increase in adjustment cost and

low return thereby making de-dollarization advantageous.

4. The Real Sector

This model is an attempt to show how de-dollarization will affect the real sector of the economy.

We have a two-sector economy [Jones (1965)]. The skilled sector produces X with competitive

skilled labour and capital whereas Y is produced in the formal segment with unionized, unskilled

labour, hired at a fixed wage rate, and capital. Both the produces X & Y are traded. Capital is

segment specific (i.e., it cannot move between the sectors). We assume X is protected by tariff and

Y is the export good. Markets are competitive, technology is neo-classical and resources are fully

employed. Note that there is a wage differential between the skilled and unskilled sectors i.e.,

skilled wage is assumed to be more than unskilled wage. Additionally, the unskilled sector causes

a certain level of unemployment (enumerated here by U) caused by fixed wages.

The following is the list of symbols used for the formal description of the model.

P i

Price of the i th good

W

Fixed wage rate in the unskilled sector

W S

The wage rate for skilled labour

a fi

Fraction of factor f [ f = skilled labour(S),

unskilled labour (L) or capital(K)] employed

in i th sector

t i

Transaction costs for individual firms


Towards Equilibrium 2023 | Page 95

U

Unemployment

m

The fraction of local currency in the financial

market (refer to section 3)

f

The fraction of foreign currency in the

financial market (refer to section 3)

R, R* Returns to Domestic and Foreign investment

K

D,

K

F

Domestic and Foreign Capital Investment

Therefore, assuming the price of X as numeraire, the wage rate in the skilled sector automatically

becomes the real wage rate and R becomes the real return to capital 1 . The competitive price

conditions are given by:

Wa

m

+ Ra = 1 -t ( )

f

S S1 K1 1

( m

Wa )

L

+ Ra

2 K

= P

2 2

-t

2

f

(11)

(12)

Here, t t denotes the company’s adjustment cost. It is the transaction cost that the individual

1,

2

firms face in the short run 2 due to dollarization or de-dollarization.

Both foreign and domestic investments in the capital are considered. Speculation at the prospects

of de-dollarisation (or any kind of regime change in foreign trade) fluctuates the interest rate the

foreign investors will be willing to pay (they might withdraw their investments or reduce investing

till the economy stabilizes 3 ). Thus, foreign capital investment is a function of real return for capital

differential ( R-

R* ) and local to foreign currency in trade (de-dollarisation) ratio (m/f). For an

increase in the ratio, foreign investment goes down and vice versa.

For equilibrium conditions, the demand for factors will be equal to the supply of factors.

Thereby, giving us the following equations.

aS 1

X

= S

(13)

1.https://www.investopedia.com/ask/answers/020415/what-difference-between-cost-capital-and-required-return.asp

2.Freitas and Veiga (2006)

3.For further study, refer to “Dollarization and Financial Development” by Geoffrey Bannister, Malin Gardberg and Jarkko Turunen, September

2018


Towards Equilibrium 2023 | Page 96

aL 2

Y = L-U

( *, m

)

K K D F

a X + a Y = K + K R-R 1 2

f

(14)

(15)

For an economy, suppose we have some constant supply of skilled labour. For full employment

conditions at flexible wage rates, we get the value of the produce X from eq. (13). This would

imply that the skilled wage rates and the productivity of labour variates over time to accommodate

any transaction costs that the firms bear, in order to keep the produce X constant. Additionally,

foreign capital investment ( ) considers the effects of dollarization (or de-dollarization) in the

K F

financial sector. Some algebraic manipulation on eq. (15) gives us the value of Y. This in turn tells

us how much labour should be employed in the unskilled sector and gives us the level of

unemployment (U). For a contraction in the second sector, caused by a fall in Y, the level of

unemployment rises.

5. Global Economic Uncertainty, Destabilization of Foreign Currency and its Effects on the

Real Sector

The present crisis caused by the Russian invasion of Ukraine has impacted the entire global

economy at large. The United States, in an attempt to de-escalate the mounting pressure on NATO,

put several sanctions on Russia. The sanctions are so strong that they were described as an “Allout

economic and financial war” by Bruno Le Maire, the French economic minister. On the other

hand, Russia, being a huge exporter of natural gas and oil, is crucial to global trade. Attempts at

de-dollarization by Russia have been making an impact on the world economy for several years

now. This is further aggravated by contemporary political tensions. The value of the dollar

fluctuates due to these geo-political disturbances. These sanctions by the United States added to

the causal instability of the value of the INR are thereby prompting India to consider de-dollarizing

its economy. As we saw in the analysis of the financial sector, a partial solution (m/f) is derived.

This ratio considers the effective amount of de-dollarization of the economy. Here, we try to

deduce factors affecting the value of the ratio and therefore the impacts caused by the inherent

modulations of these factors. A fluctuation in the value of the USD in the forex market causes

to rise in turn causing the ratio of local to foreign currency (m/f) to increase in trade (i.e., dedollarization)

[refer to section 3.1].

Using equations (11)-(15), we obtain the following proposition:

Proposition 1 We deduce that in the two-sector model discussed above, de-dollarization in the

financial sector would increase the level of unemployment in the economy, the returns to capital

would fall inevitably and skilled-unskilled wage gap accentuates.

s g


Towards Equilibrium 2023 | Page 97

Proof. In what follows, we attempt to offer an intuitive proof of the above stated proposition. In

the two-sector model described in Section 4, foreign capital investment is a negative function of

the de-dollarization ratio. This can be intuitively realized as the level of de-dollarization increases,

speculation among foreign investors about the stability of the economy causes foreign investment

to fall. We can also see in eq. (15) that an increase in (m/f) and a (resulting) increase in the real

returns to capital (R) causes the foreign capital investment ( ) to go back. In the sector

employing skilled labour, the supply of skilled labour in the economy is fixed and therefore flexible

wage rates in that sector cause the output to be independent of the externalities of de-dollarization.

However, the price-taking firms must incur the transaction cost, being internationally traded. For

m

an initial increase in the de-dollarization level ( ), the second sector contracts due to increased

f

individual transaction cost that the firms must bear causing the real returns to capital (R) to fall.

This fall in returns to capital impacts sector one increasing the real skilled wage rate ( ). [Refer

to equations (11) and (12)] Furthermore, algebraic manipulations on Eq. (15) yields the value of

Y. An exemplar increase in the ratio (m/f) gets us a decreased foreign capital investment, therefore,

deducing the increasing levels of unemployment from eq. (14). One can draw similar conclusions

from the fact that a contracting unskilled sector results in increasing unemployment levels. For

increasing de-dollarization, the transaction costs incurred in the first sector ( ) increase as well.

Eq. (11) shows that this causes an increase in the real wage of skilled laborers. It is to be noted

here that an increase in the transaction cost incurred by the firms in the first sector employing

skilled labour does not affect the real returns to capital. For the transaction costs incurred in the

two sectors, we get contradicting results regarding the impact of de-dollarization on the flexible

wage rates. This can be explained by the fact that the severity of the effects of de-dollarization on

the skilled and unskilled sectors play out differently. An increased severity on the second sector

would make it contract more therefore the causal appreciation of the skilled sector wage rate would

W S

be more and vice versa. The ratio considers the comparative severity on the second and first

W

sectors due to such financial sector fluctuations. Foreign capital investment rolls back due to

increasing de-dollarization in this model.

6. The Model with a Non-traded Informal Sector and De-Dollarization

We add another non-traded informal sector to our model with free labour mobility. When there

exists unemployment in the second sector because of fixed wage rates, the unemployed labourers

can move to the third sector, and therefore unemployment vanishes because of the existence of

flexible wage rates in the informal sector. The price in this sector depends on the demand and

supply of the local commodities (Z) denoted by . Furthermore, the production of this sector

would not directly be impacted by global financial market fluctuations.

P 3 N

K F

t 1

W S


Towards Equilibrium 2023 | Page 98

Thus, we get the following equations accounting for full employment in this model.

Wa

m

+ Ra = 1 -t ( )

f

S S1 K1 1

( m

Wa )

L

+ Ra

2 K

= P

2 2

-t

2

f

Wa + Ra = P

L3 K3 3 N

(16)

(17)

(18)

Equating the demand and supply for goods produced, we get

aS 1

X

= S

(19)

a Y + a Z = L

L2 L3

( *, m

)

K K K D F

a X + a Y + a Z = K + K R-R 1 2 3

f

(20)

(21)

Z

= Z

WS+

WL

S

2

D

( )

P N

3

(22)

For some produce X in the first sector, deduced from eq. (19) and the real returns to capital (R)

from eq. (17), we get a new output for the second sector (Y) and some produce (Z) for the third

sector solving equations (21) and (20) simultaneously. Furthermore, substituting the value of (Z)

in eq. (22) gives us the price of the local commodities produced in the third sector ( ). Eq. (18)

gives us the wage level in the third sector (W).

After the addition of a non-traded, informal sector to the model, the unemployment caused by the

formal sector is accommodated. For full-employment conditions, we analyse the impacts of dedollarization

on the wage rates, real return to capital, and price levels of the economy. As seen

before in the discussion for the two-sector economy model, global uncertainties impact the value

of the dollar therefore impacting ( ). This effect is produced in the real sector through

s g

fluctuations in the local to foreign currency in trade ratio (m/f). This leads to the following

proposition:

Proposition 2 In presence of an informal sector, de-dollarization would lead to an increase in

skilled-unskilled wage gap, accentuates the informalization of labour, price of the non-traded

commodity falls and an outflow of foreign capital.

P 3 N


Towards Equilibrium 2023 | Page 99

Proof. From eq. (17), for an increase in the de-dollarization ratio, we see that the transaction cost

( ) increases and the returns to capital (R) fall. Since X is produced by skilled labour, as shown

t 2

above, the level of production won’t be impacted. However, the rollback of foreign capital

triggered by increased de-dollarization would cause the capital-intensive unskilled, formal sector

to contract, thereby the output Y decreases. Contraction of the formal sector causes

unemployment. Labour, therefore, moves to the third sector, and the production of the locally

productive sector, Z increases. Eq. (22) shows that the price of the local commodities ( ) would

decrease thereby suggesting an inevitable deflation in the economy. Eq. (18) further exhibits a

decrease in the wages for the informal non-traded sector. Thus, the ratio depicting the wage

W S

inequality ( ) increases due to increasing de-dollarization. The foreign capital outflow and

W

reduced returns to capital are simultaneous impacts on the economy. It is important to note here

that since the production of the informal non-traded sector increases, it employs more labour.

Although this helps in accommodating unemployment, it also causes increased informalization of

labour. On the other hand, decreased price levels of local commodities indicate at deflation.

7. Conclusion

The US's weaponization of trade, imposition of sanctions, and exclusion from SWIFT (Society for

Worldwide Interbank Financial Telecommunication) could hasten de-dollarization, as countries

demonstrating diplomatic and economic autonomy are wary of exploiting US-dominated global

banking systems. The US dollar, the world's reserve currency, may continue to fall in value in the

current atmosphere as major central banks seek to diversify their reserves away from it and into

alternative assets or currencies. De-dollarization fits well into the thought experiment of a

multipolar world in which each country strives for economic autonomy in the domain of fiscal

policy. Towards understanding this phenomenon of de-dollarization, we first modelled a country’s

choice of currency diversification using a variant of Tobin’s (1956, 1958) model. To link this with

the real sector, we constructed two multi-sector multi-factor [Jones (1965)] models with and

without the informal sector. We obtained that among other factor, de-dollarization is a result of

fluctuations of foreign currency. In the short run, this would lead to an increase in transaction cost

for the production firms which are internationally traded. In the presence of labour market

distortions, this would lead to an increase in unemployment of unskilled labour and widen skilledunskilled

wage gap. On the other hand, in presence of an informal sector, informalization of labour

increase along with a rise in wage gap. This is primarily because de-dollarization leads to

contraction of the traded sectors due to high transaction costs. Thus, resources move away from

the contracting traded sectors to the informal sector causing it to expand. Furthermore, the price

of non-traded commodities decreases indicating at a recession in the economy.

The result explains that why de-dollarization may be costly and have several worsening

consequences without proper Government intervention. Moreover, the US dollar is still the

favoured currency for trade because no other currency is liquid enough. Even if a currency does,

there would be apprehensions in nations about that currency becoming a mirror of the US dollar.

Thus, the

P 3 N


Towards Equilibrium 2023 | Page 100

dollar continues to occupy a robust position within the market. In conclusion, as major economic

powers such as China and India rise, the dollar's value will inevitably fall. The rise of Asia as an

economic powerhouse will boost the value of currencies such as the Chinese Yuan and the Indian

rupee. The frequent use of the US dollar as a potential weapon for achieving foreign policy goals

will undoubtedly hasten the process of de-dollarization. The decision to de-dollarize is a step

towards regaining autonomy, but the amount injected into the economy should be carefully

monitored. To ensure business support, this is a currency reform that should be accompanied by

institutional reforms. If adequate reserves are not available, the volume of the money supply should

not be changed for any reason other than to stimulate the economy.

8. References

Agenor, Pierre Richard, and Mohsin S. Khan. 1992. "Foreign Currency Deposits & the demand

for money in developing countries." IMF Working Papers, January 1.

Baumol, W. 1952. "The transactions demand cash: an inventory theoretic approach." Journal of

Econometrics, 66 545-56.

Berg, Andrew, and Eduardo Borensztein. 2000. "Full Dollarization, The Pros & Cons." IMF

Economic Issues No. 24, December.

Bordo, Michael D. 1993. "The Bretton Woods International Monetary system: A Historical

Overview." A retrospective on the Bretton Woods System: Lessons from International Monetary

Reform, January.

Buckley, Chris. 2022, April 4. Beijing campaign casts Russia as the West's longtime victim.

Newspaper, Newyork: The New York Times, A8.

Caldararo, Niccolo Leo. 2022. "Putin, China, Invasions in Europe and De-Dollarization: Fighting

a Longer War."

Freitas, Miguel Lebre de, and Francisco Jose Veiga. 2006. "Currency Substitution, Portfolio

Diversification and Money Demand."

Goldfield, S. 1976. "The Case of the Missing Money." Brookings Papers on Economic Activity,

Vol. 3 683-730.

Guidotti, Pablo, and Carlos Alfredo Rodriguez. 1992. "Dollarization in Latin America: Gresham's

Law in Reverse." Staff Papers (IMF), September.

Ize, Alain, and Levy Yeyati. 2005. "Financial De-Dollarization: Is it for real?" IMF Working

Papers, September.

Jones, Ronald W. 1965. "The structure of Simple General Equilibrium Models." The Journal of

Political Economy, Vol 73, No. 6., December: 557-572.


Towards Equilibrium 2023 | Page 101

Liu, Zongyuan Zoe, and Mihaela Papa. 2022. "Can BRICS De-Dollarize the Global Financial

System." Elements in the Economics of Emerging Markets, March.

Naina Bhardwaj. 2022. "RBI Notifies New Framework to Enable International Trade Settlement

in Indian Rupee." India Briefing (India Briefing). https://www.india-briefing.com/news/rbinotifies-new-framework-to-enable-international-trade-settlement-in-indian-rupee-details-inbrief-25484.html/.

Nikitin, Maxim, John Duffy, and Todd Smith. 2006. "Dollarization Traps." Journal of Money,

Credit, and Banking, Vol. 38, No. 8 36.

Pami Dua, Ritu Suri. 2019. "Interlinkages Between USD–INR, EUR–INR, GBP–INR and JPY–

INR Exchange Rate Markets and the Impact of RBI Intervention." Journal of Emerging Market

Finance Vol 18. Issue 1.

RBI. 2022. "International Trade Settlement in Indian Rupees (INR)." RBI Notification, Reserve

Bank Of India. https://www.rbi.org.in/Scripts/NotificationUser.aspx?Id=12358&Mode=0.

Singh, Dr Ram. 2017. International Trade Operations. Excel Books.

Tobin, James. 1958. "Liquidity preference as behaviour towards risk." Review of Economic

Studies, 25(1), 65-86.

Tobin, James. 1956. "The interest elasticity of transactions demand for cash." The Review of

Economics and Statistics 38(3) 241-47.

2022. VOX. https://www.vox.com/22968949/russia-sanctions-swift-economy-mcdonalds.

Xu, Qiyuan, and Aizong Xiong. 2022. "The impact of financial sanctions on the international

monetary system." China Economic Journal 15(22), August.

Yelery, Aravind. 2016. "China’s Bilateral Currency Swap Agreements: Recent Trends." China

Report Volume 52 Issue 2.


Towards Equilibrium 2023 | Page 102

9. Mathematical Appendix:

Eq 7:

µ = ER ( )

= mE( R ) + fE( R )

M

= m(0 - t ) + f(0 -t

)

M

M

=-mt

- ft

=-mt

-(1-m)

t

M

Þ µ =-mt

-(1-m)

t

F

M

F

F

F

F

Eq 8:

2

Var( R)

= s

= ER ( -µ)

2

= E( mR + fR + mt + (1-m) t )

Þ s = E( fg)

2 2

2 2 2 2 2

g

Þ s = (1-m)

s

2 2 2

g

Þ s = (1-m)

s

s

Þ m = 1-

s

M F M F

Þ s = f E( g ) = f s

g

g

2


Towards Equilibrium 2023 | Page 103

The Rise and Fall of Gender Discrimination in a Heterogenous Family

Model and the Role of Globalization: A Micro-Theoretic Analysis

- Manjari Agrawal, Rohini Datta, Torsha Sen 1

Abstract: Gender discrimination has been a contentious social issue for centuries worldwide. But

instances of “domestic discrimination” are more difficult to detect as people tend to hide such

facts from the public eye. Prejudice against women is primarily observed in the form of

discrimination in poor households and maintenance of tangible levels of reputation in society in

the case of rich households. Against this backdrop, this paper attempts to model household

discrimination endogenously using a Beckerian family optimization method. We demonstrate how

the “nature of discrimination” against women is different for the poor and the rich families. For

the poor families, it is the established social norms, while for the rich families it is the family

reputation. Thus, we extend the Akerlof (1980) model to capture family reputation in the context

of gender discrimination. Finally, using the Jonesian multi-sector model, we demonstrate how FDI

can lead to an endogenous transformation of the society in terms of variation in social norms,

discrimination and female labour force participation. We found that foreign capital inflow can

either increase or decrease female labour participation, as it is dependent on other factors like the

initial distribution of orthodox and unorthodox families in the economy and the relative rise in

male and female wage rates.

Keywords: Discrimination; Rich and poor households; Female labour supply; Male female wage

gap.

JEL Codes: D11, D91, J16, J22, J71

1. Introduction

The efficiency and quality of the policies adopted by the government both for socio-political and

economic development affect gender inequality and discrimination in households as well as

professional fields which is an issue of critical importance, in large developing countries. Gender

bias mostly revolves around issues like access to education, maternal health care, etc., and is a

predominant feature of most developing countries like India. Gender discrimination in the labour

and financial markets acts as a hindrance to the economic progress of a country and affects the

nation’s standing at the international level. While the manifestations of gender discrimination are

multi-faceted and correlated, a long persisting phenomenon is the gender-based wage inequality.

It is this inequality faced by the female work force that gives us an important basis of our model

i.e., discrimination faced by females on the basis of difference in salary income with their male

counterparts in a household. Against this backdrop, we attempt to model the role of “social norms”

in the context of “missing women” in a gendered society. By missing women, we mean the lack

of women participating in the workforce compared to the men in the same families and the

difference in the percentage of female workforce rather than their percentage share of the

population. Here social norms imply the biases and prejudices in the mind of people. According

to the latest World Economic Forum’s (WEF) Global Gender Gap Report 2022, India ranked 135th

out of 146 countries on the Gender Gap Index.

1.St. Xavier’s College, Kolkata


Towards Equilibrium 2023 | Page 104

According to Braunstein (2012), deflationary bias - combination of policies designed to keep

inflation low and global capital the assumption that the reproductive sector is linked with the

productive sector through a full-time breadwinner without significant family responsibilities

(Elson and Cagatay 2000). As a result, assumption that the reproductive sector is linked with the

productive sector through a full-time breadwinner without significant family responsibilities

(Elson and Cagatay 2000). As a result, policy makers try to ensure that males in a household are

provided with decent jobs and side-line women because financial support brought in by them is

seen as being supplemental to the family (Elson 2007). Society in general is driven by similar ideas

regarding the importance of financial independence of both genders. Households prefer the

females to spend more working hours at home rather than being employed elsewhere. This is very

prominent especially in the lower income sections of our society. As cited in Basu (2006), the

evidence that the same income can lead to different household decisions, depending on whether

the earner is a man or a woman, exhibits that a household & its decisions depend on the power

balance between the husband and the wife. As specified in Tashiro et al. there is an inverse

relationship between adherence to social norms and labour force participation among women, and

the effect of obedience to social norms on wages varies by birth cohort and the degree of obedience

varies on the basis of birth and also is influenced by educational quality and standards.

Against this backdrop, we attempt to analyse how globalisation in terms of injection of foreign

capital affects “household discrimination” endogenously and thus female labour force

participation. In so doing, we derive the rationality of persisting household discrimination.

Additionally, we attempt to analyse how household discrimination varies based on income level

and neighbourhood effect.

In our proposed model, the economy is divided into two types of households: poor households

characterised by minimal education or absence of education among the family members where

women face discrimination; and rich households characterised by a high level of education

inculcated in family members. Households consume two types of goods: private goods and

services purchased from the market and a household public goods whose benefits are consumed

by all the members of the household; like the advantage of living in a clean house. We assume that

each house has one earning male member and one female member. In the poor households, the

male member spends all his time in income-earning activities outside the house and the female

member allocates her time between income-earning activities outside her house and unpaid

domestic labour within the house. Discrimination against women within households depends upon

the amount of time she allocates for household chores for which she does not receive any

remuneration. Discrimination is thus included in the utility function as a factor affecting the

consumption of public goods. The households intend to maximise utility given the expenditure

constraint giving us the optimal amount of time allocated by the woman in and outside the

household.

However, the wage rate received by both males and females is the same in the case of rich

households. Orthodox families are not in favour of women working outside while unorthodox

households support this activity. For rich families, such social norms are guided by the “relative

reputation” of the family in the neighbourhood. Reputation is a major factor causing many women

in rich households to limit their income earning activities. Utility of rich households is maximised

keeping in mind these constraints. The market side focuses on the contribution of poor and rich

households to income earning activities, both labour and capital inputs. Supply of foreign capital

is included in the supply side in the model and hence impacts female labour force participation in

both rich and poor households. By globalisation we infer the removal of trade barriers and

liberalisation of the economy. Since FDI (foreign direct investment) is a major consequence of

globalisation, we analyse the impact of FDI on female labour force participation and thus study

the role of globalisation.


Towards Equilibrium 2023 | Page 105

The remainder of the paper is organised as follows. We survey a few extant literatures below. We

have described the poor and rich households of our model and defined their utility functions in

Section 1. In Section 2, we describe the properties of the poor household female labour supply

function. In Section 3, we present the market side analysis of our model. In section 4, we analyse

the effects of FDI inflow. Finally, Section 5 concludes our paper.

2. Literature Review and Interventions

Households are often considered as a singular microeconomic unit but a vast body of literature

ranging from Lundberg et al. (1997), Duflo (2003) to Luke and Munshi (2011) provide evidence

that they do not always act as singular units. The household members differ in bargaining power

according to their income which shapes household consumption. While household power balances

impact the decision making in households, Basu (2006) models the often disregarded opposite

relation between the two, that is, the impact of household decisions on power balances. This twoway

relation was modelled and used to derive the implications of it on female labour supply and

other aspects of household behaviour. In the present article, we aim to model the impact of trade

policy on the female labour supply, female wage rate as well as on gender discrimination and

subsequent changes in power balance in households. Heath and Tan (2019) have also proposed

and empirically supported an alternative noncooperative household model where women’s

increased unearned income through exposure to the Hindu Succession Act (HSA) leads to rise in

woman’s labour supply in India. The HSA improved Hindu women’s ability to inherit family

property, thereby increasing their unearned income and bargaining power. In contrast, McElroy

and Horney (1981) have discussed a negative correlation between women’s unearned income and

female labour supply. As pioneered by Becker (1957) and Arrow (1973), the forces of competition

ultimately reduce subjective discrimination in the labour market. Increased industrial competition

due to trade liberalisation should reduce the profits of competitive firms that serve as the source of

payment of different wages to men and women for similar skill sets. If they continue with the wage

gap, skilled female labour may be paid below their marginal cost, forcing them to leave the labour

market, making the market less productive, as claimed by Becker (1971). Therefore,

discrimination set in by employer or employee’s prejudices may be forced out due to it becoming

an additional cost for the firm. Becker (1971) and Ashenfelter and Hannan (1986) claim that if the

competitive firm continues with the discrimination even at zero profit, the firm would be running

at a loss and go out of business. hand, in non-competitive markets, firms will use the excess profit

to continue with their prejudice of paying the male labour more than the female. Braunstein (2012)

found that deflationary bias, i.e., a combination of policies designed to keep inflation low and

global capital flows stable, that also results in slow growth—may cost women more than it costs

men. Society in general is driven by similar ideas regarding the importance of financial

independence for both genders. Households prefer that females spend more working hours at home

rather than being employed elsewhere. This is very prominent, especially in the lower-income

sections of our society. Artecona and Cunningham (2002) studied the Mexican competitive market

and found that trade policies may be beneficial to women by decreasing wage gap but the relative

female wage can be improved greatly by upgrading their skill sets sufficiently enough to compete

in the newly competitive markets. Marjit et al. (2022) have developed a theoretical model which

shows that a competitive market may not help at all in reducing discrimination, similar to Jones

(1965) where discrimination was claimed to perpetuate even in perfectly competitive markets. In

fact, they show that if a sector is capital intensive and female dominated, gender discrimination

may depress female wage, widen male-female wage gap and also increase returns to capital.


Towards Equilibrium 2023 | Page 106

This has implications on international trade policy too. If any fluctuations in policy reduce returns

to capital, gender discrimination may be used as a substitute for free trade to compensate the

capital. Rodgers and Zveglich (2004) provide an alternative theory through an empirical model

using data from Taiwan and Korea in the 80s and 90s; that increase in international trade and

import competition appears to widen the female wage gap, affect their employment prospects and

reduce their bargaining power. As defined by Akerlof (1980), a social custom is an act whose

utility to the agent performing it in some way depends on the beliefs or actions of other members

of the community. Tashiro and Lo (2019) examine how social norms influence the decision to

work and wages of Japanese women using the Akerlof (1980) model. Akerlof’s model is simplified

to form a utility function aggregating both individual consumption as well as the individual’s

reputation.

The extant part of the residual literature has also tried to test several factors leading to the genderbased

wage gap and female labour force participation rate (FLFPR). Blau and Kahn (2017) in their

empirical analysis showed that rational factors could not fully explain gender-related

discrimination, thus highlighting the “unexplained” part of it. Using Oaxaca-Blinder

decomposition, Sengupta and Puri (2022) using an Indian dataset found that when personal and

job characteristics are taken as controlled variables, in that case, 46.6 % of the gender wage gap

can be explained, while 54% remain unexplained. Kingdon and Unni (2001) in their study of the

Indian states of Tamil Nadu and Madhya Pradesh found that education contributes little to the

gender pay gap. Their paper also reveals that 77.9 per cent and 78.5 per cent of the wage gap in

Madhya Pradesh and Tamil Nadu, respectively, cannot be explained by men and women’s

differing characteristics. Mahajan and Ramaswamy (2017) examined the effect of cultural barriers

on changes in the female labour supply and the wage gap. Their analysis obtained that 55 per cent

of the gender wage gap among northern and southern states was explained, while 45 per cent

remains unexplained.

Our paper is an intervention to explore the empirically “unexplained” part of gender

discrimination. In our paper, we show that household discrimination which remains hidden within

the boundaries of family could explain this part of unexplained gender-based discrimination. Our

contribution also relates to the discussion on how globalisation has impacted society's attitude

toward female participation in the labour market.

3. Household Discrimination and Choice of Female Labour Supply

In our model, we consider a small open developing economy where the households are divided

into two categories based on their income: rich and poor households. The male and female wage

rates (denoted by w K and w L , respectively) are different in case of poor households, while it is

uniform in case of skilled labour (w E ) in rich households for both male and female labour.

3. 1. Poor Households

The utility function of the unskilled, poor household is described as a function of Hicksian

composite goods and services (C N ) and household public goods (H) where public goods are the

goods which are consumed or utilised by all the members like a clean household, food, domestic

services, etc.

U = fXC N , HZ= logC p + logH (1)

We take l LN as the amount of time allocated by the unskilled female labour in income-earning

activities in the market. If 1 is the total working time of labour, then 1 − l LN represents the time

allocated by the female labour in domestic work. In a poor Indian household, a woman is valued


Towards Equilibrium 2023 | Page 107

more if she allocates more time to domestic labour. Discrimination (D) thus represents the

productivity of females in unpaid household chores as perceived by the family.

H = DX1 − l LN Z where D > 1 (2)

Substituting equation (2) in (1), the utility function of the household can be rewritten as

U = f aC N , DX1 − l LN Zb (3)

Here, discrimination becomes a part of the utility function.

Discrimination function consists of the autonomous and the induced component. The autonomous

component ( D) is the exogenous societal norms that influence the discrimination factor. ∅ is the

endogenous discrimination which varies inversely with the relative male- female wage rate. The

endogenous component represents the balance of power between male and female. The induced

component, which is a function of the ratio of the male wage rate (w K ) and female wage rates

(w L ), is positively related to D.

D = D + φ d P '

P ( 4 ()

e ; φ > 0 ; φ(1) = 0, φ > 0; φ ′′ = 0 (4)

If the female wage rate is less than the male wage rate, the ratio will be greater, implying more

household discrimination against females. Similarly, a higher female wage rate implies lesser

discrimination against them. Given these factors, the households seek to maximise their utility

subject to their budget constraint. If P is the weighted price level of the Hicksian composite goods

and services (C N ), then the budget equation is given by

PC = w K + w L l LN (5)

Substituting (2), (4) and (5) in (1) and taking their derivative with respect to l LN , we get the

following first-order necessary equation:

DX1 − l LN Zw L − Xw K + l LN w L Z hD + X1 − l LN Z. φ Q . d P '

P ( 4 ()

*e + φj = 0 (6)

Therefore, we obtain the lemma stated below.

Lemma 1: The strictly positive interior solution of l L ( w K , w L , D), is implied by

Hw L − PC N mD + X1 − l LN Z. φ ′ . n w K

w L l LN

2 o + φp = 0

Now, taking the second derivative of the first derivative equation with respect to l LN , we get our

second order necessary condition as

h P $

(

j > h R+ P '

j

+, ) P ( 4 , ( S

Therefore, we obtain the second lemma as,

Lemma 2: The second order necessary condition h P 2

(

j > h R′ P '

3

+, ) P ( 4 ( S

j guarantees that the value of

l L ( w K , w L , D) maximises family utility.


Towards Equilibrium 2023 | Page 108

3. 2. Rich Households

The utility function of rich households is determined by the consumption of composite goods and

services and “social reputation (denoted by R)” of the family. γ is the weight associated with the

level of reputation relative to the consumption level.

V = g(C, γR) = logC + γlogR (7)

γ is determined by two components: exogenous & induced components. γ is the inherent sense of

reputation instilled in the minds of people through social norms and upbringing. I is the average

income level of the society. w E is the wage rate of both working male and females. l LT is the amount

of time allocated by the females in income-earning activities outside her house. Since the male

spends all his time (1 unit) carrying out income-earning activities and the female spends l LT time

working outside the household realm, the total income of rich households is W E + l LT W E . Higher

the income of the family is with respect to the average income level & W s+l fr W s

', more concerned

I

the family will be about its social reputation.

γ = γ + h a - 094 (1 - 0

b ; h ′ > 0; γ > 0; h(1) = 0 (8)

W

Let "s 1 " be the number of unorthodox households who are willing to let their female member

participate on income-earning activities and "s 2 " be the number of orthodox households who are

unwilling to let their female members participate in economic activities. Thus, R is determined by

the amount of time allocated to economic activities by the female in unorthodox households and

the amount of time allocated to domestic work by the female in orthodox households.

R = l LT s 1 + (1 − l LT )s 2 (9)

Or, R = s 2 + (s 1 − s 2 )l LT (10)

Taking derivative of R with respect to (l LT ), that is the amount of time allocated to household work

by the females, we get the following result:

If the number of unorthodox households exceed the number of orthodox households, GX

G4 (1

will be

greater than 0, then female labour participation l LT will have a positive relationship with reputation.

On the other hand, if the derivative is negative, then l LT will have a negative relationship with

reputation.

The budget equation of rich households can thus be defined as:

(11)

PC = W E + l LT W E (12)

Substituting (7), (9) and (10) in (6) and differentiate with respect to l @X , we get

GY

= 1

t - 0

u + Z (s G4 23 , 3 + X

1 − s 2 ) + (logR)h′ - 0

W

The sign of GY

G4 23

i.e. positive or negative, depends on the value of s 1 − s 2


Towards Equilibrium 2023 | Page 109

Case-1 : If s 1 ≥ s 2 then GY

G4 23

> 0 ⩝ l @X > 0

⇨ l @X *=1

Case-2 : If s 1 < s 2 then GY

G4 23

≤ 0 accordingly as

⇨ Case-2.1: If the above inequality is strict then l @X *=0

⇨ Case-2.2: If the above equality holds then l @X *>0

1

t - 0

u + (logR)h′ - 0

≤ Z (s , 3 + W X

1 − s 2 )

We now derive the Second Order Condition by differentiating GY

G4 23

. We get,

G

a GY

b = .1

2 ( - 0

G4 23 G4 23 , 3 + )2 + 1 (s X

1 − s 2 )

For case 1, optimisation requires 1 X (s 1 − s 2 )< 1

, 3

2 ( - 0

+ )2

For case 2, optimisation requires

Thus, l @([ l @( (W E , I, γ, s 1 , s 2 )

G

a GY

b < 0 ⩝ l

G4 23 G4 @X ≥ 0

23

On the basis of the cases explained above we deduce our third lemma as follows:

Lemma 3: Based on the values of s 1 and s 2 , the following are obtained:

a) If the initial distribution of population is such that number of liberal families have outnumbered

or is equal to number of conservative families then l @X is perfectly inelastic and equal to unity.

b) If the initial distribution of households is such that the number of conservative families

outnumbered the number of liberal families then l @( (W E , I, γ, s 1 , s 2 ) ∈ (0,1) provided that

1

h W E

C X P j + W (logR)h′ E

I = γ R (s 1 − s 2 )

c) If the initial distribution is such that number of conservative families is more than the number

of liberal families then l @( = 0 provided that

1

C X

h W E

P j + (logR)h′ W E

I < γ R (s 1 − s 2 )

In what follows, we attempt to provide an intuitive explanation to the above lemmas.

If s 1 > s 2 that means majority households in the society prefer a higher value of l @( , then higher

female participation in the labour market earns a good reputation (higher R) for the family. Along

with this, higher l @( leads to higher family income thus allowing for higher consumption. So we

have l @( = 1 (upper-bound solution). This proves Lemma 3a.

If s 1 < s 2 that means majority households in the society prefer a higher value of (1-l @( ), then

higher female participation in the labour market earns a bad reputation (lower R) for the family. It

signifies disobeying social norms and the ‘social norm effect’ thus reduces utility of the household.

However as l @( increases, the gross family income of the household increases (C X increases) and

their utility increases. We find an interior optimal solution for the utility function. This proves

Lemma 3b.


Towards Equilibrium 2023 | Page 110

Finally, if the ‘social norms effect’ dominate the income effect then l @( decreases to 0 (lowerbound

value). This proves Lemma 3c.

4. Properties of Poor Household Female Labour Supply Function

We now try to deduce the impact on l LN due to societal norms regarding female labour participation

becoming more discriminatory, that is, increasing D.

Substituting D with the help of equation (3) and taking derivative of equation (12) with respect to

D (assuming all variables except w L and w K constant), we get :

∂l !"

∂ D = &1 − l !" (w ! − &w # + l !" w ! (

Dw ! + &1 − l !" (w ! φ ′ , w #

2

w ! l - + w ! .D + &1 − l ! (φ ′ , w #

2

!"

w ! l -/ + 2&w # + l !" w ! (φ ′ , w #

3

!"

w ! l - + w #φ ′ , w #

2

!" w ! l - + l !w ! φ ′ , w #

2

!"

w ! l - + w !∅

!"

The denominator is found to be positive as all terms are positive.

Therefore, ∂4 ()

5 B ≷ 0 accordingly as X1 − l LNZw L ≷ (w K + l LN w L )

As X1 − l LN Z is the time allocated in household work and w L is the female wage rate, then

X1 − l LN Zw L is the opportunity cost of the female labour of remaining at home. The equation

(w K + l LN w L )= PC N which was defined as the budget constraint of the poor household is also the

family income. Therefore, we observe that if the opportunity cost of remaining at home is higher

than the family income, then ∂4 ()

> 0, that is, with increase in inherent discrimination, the time

5 B

allocated to paid labour ( l LN ) increases or the time spent in unpaid domestic chores

(1 − l LN ) declines. This leads to the following proposition.

Proposition 1: With higher exogenous level of inherent discrimination, the female’s time in unpaid

domestic chores will rise if the family income is higher than her opportunity cost of remaining at

home and vice versa.

We now turn to discuss the impact of changes on male and female wage rate on the time allocated

to income earning activities (l LN ).

If the male wage rate in poor households is high, the family income rises. The family then deems

it unnecessary for the female counterpart to engage in income earning activities and thus allows it

to ‘afford’ higher discrimination. This means the time allocated to household work by female

labour in poor households rises. Also, the rise in male wage rate, increases the male-female wage

rate ratio, thereby increasing endogenous discrimination φ, thus increasing D. The importance

attached by the households to the time allocated in household chores rises, that is, X1 − l LN Z rises

or l LN falls.

On the other hand, rise in female wage rate also leads to increase in family income. The families

perceive that they can afford females to spend more time at domestic work as the stipulated portion

of income can now be earned by working less hours outside, that is, X1 − l LN Z rises or l LN falls

(negative income effect). However, in the second scenario, a rise in female wage rate, incentivises

the female labour to participate more in income-earning activities (l LN increases). Alternatively, a

rise in w L , reduces the ratio male-female wage ratio, thereby decreasing φ. Once again,

l LN increases. Thus, it can be deduced that if the above two effects dominate the former negative

(13)


Towards Equilibrium 2023 | Page 111

income effect, then there will be an overall increase in l LN . Thus, the following proposition is

immediate.

Proposition 2: We deduce that l LN varies negatively with male wage rate w K and varies positively

with female wage rate w L .

5. Production Structure

Considering the market contribution of rich and poor households in a perfectly competitive

framework, we obtain the following equations to achieve the criteria of zero economic profit:

w K a K1 + w L a L1 + ra 01 = P 1 (14)

w E a E2 + a 02 = P 2 (15)

w K a K1 and w L a L1 are the net wage revenues earned by the male and female members of all the

poor households. ra 01 is the total capital cost incurred by poor households where r is the rate of

interest on capital. P 1 is the price level of poor households.

w E a E2 is the net wage revenue of rich households, including both male and female labour. In rich

households, it is assumed that there is no difference between male and female wages. P 2 is the

price level of rich households.

The male and female labour equilibrium conditions in poor households is given by:

(16)

a K1 X 1 = L M

a L1 X 1 = L LN (D, w K , w L ) (17)

X 1 is the quantity demanded for goods produced by poor households. As discussed in the

description of poor household section, the labour demanded by females is dependent upon

discrimination (D), which is determined by three factors: inherent discrimination (D), male wage

rate (w K ) and female wage rate (w L ). The labour demanded by males is only dependent upon the

market supply of labour.

In the case of rich households, labour demand depends on two components: exogenous supply and

induced component.

a E2 X 2 = S + l LT (w E , γ, s 1 , s 2 ) (18)

X 2 is the quantity demanded for goods produced by rich households, or skilled labour. As discussed

in the description of rich household section, the latter is influenced reputation (γ), wage rate for

skilled labour (w E ), number of orthodox households (s 1 ) and the number of unorthodox households

(s 2 ).


Towards Equilibrium 2023 | Page 112

The capital market equilibrium is given by the following equation:

a 01 X 1 + a 02 X 2 = K B + K @ (19)

a 01 X 1 and a 02 X 2 are the amounts of capital demanded by poor and rich households respectively.

K B is the total domestic capital supply and K @ is the net capital supplied from abroad through

various means, most significantly foreign direct investment.

From the above-mentioned equations, w K , w L , γ, X 1 , X 2 and w E are the unknown constants.

From eq (4.3), we obtain the value of X 1 . Putting X 1 in (4.4), we obtain an equation with the

unknown constants w K and w L . From eq. (4.5) X 2 can be derived in terms of w E . Putting X 1 and X 2

in (4.6), we get the final value of w E . Finally, γ is determined from eq (4.2). Putting γ in eq. (4.1),

we get w K , w L . Finally, solving (4.1) and (4.4) we obtain the final values of w K , w L .

6. Effect of Foreign Capital Inflow on Household Discrimination and FLFP

Using equations (14)-(19), we obtain the effect of foreign capital inflow which is summarised in

the following proposition:

Proposition 3: An increase in foreign capital inflow may not increase female labour force

participation from the rich families. If the number of unorthodox families is increasing at a higher

rate due to social reforms and change in mindsets of people, the increase in FDI can have a greater

impact on female labour force participation from rich households.

Proof: The intuitive proof can be explained as follows. When K @ rises, the RHS of eq. (4.6) rises.

To compensate for this rise, X 2 rises. X 1 does not rise as capital is not one of the factors affecting

it in accordance with eq. (4.3). This rise in X 2 in turn causes the LHS of eq (4.5) to rise, causing

the female labour demand of rich households to rise. This induces an increase in wage rate for

skilled labour, female and male wage rates of unskilled labour. In eq. (4.2), the rise in wage rate

of skilled labour implies a fall in rate of interest to satisfy the equation. In equation (4.1), the fall

in rate of interest will induce an increase in both male and female wage rates. But again, the rate

of increase of these two factors will depend upon eq. (4.5), which needs to be satisfied in all cases.

In equation (4.5), for s 1 > s 2 , labour supply will become perfectly elastic with L E = 1. If s 1 < s 2 ,

labour supply will become perfectly inelastic with L E = 0. The impact of female labour

participation from rich households will depend upon the number of orthodox and unorthodox

households and the increase or decrease in their numbers over time. So, if the number of orthodox

families is increasing at a sufficiently high rate due to political and social factors, then the female

labour force from rich households may not increase with increase in FDI. If the number of

unorthodox families is increasing at a higher rate due to social reforms and change in mindsets of

people, the increase in FDI can have a greater impact on female labour force participation from

rich households. The impact of increase in FDI on female labour force participation from poor

households is dependent upon the relative rate at which the male and female wages rise. From eq

(4.4), if the male wage rate rises more than the female wage rate, discrimination may not change

or may even rise causing female labour demand from these households to fall. If the female wage

rate rises at par with male wage rate, then discrimination will tend to decrease causing an increase

in female labour force participation.


Towards Equilibrium 2023 | Page 113

7. Conclusion

The female labour force participation from both rich and poor households depends upon a

multitude of factors. Discrimination faced by women from poor households is inversely

proportional to female wage rate and directly proportional to male wage rate. When endogenous

discrimination rises the female labour force participation may rise or fall depending upon

opportunity cost of discrimination in relation to family income. Reputation faced by women from

rich households is directly proportional to the excess amount of income the household earns in

relation to the average income level of the society. Unorthodox households, which value working

women earn more reputation when they let their women participate more in income earning

activities. Meanwhile orthodox households are reluctant to let women devote more time for income

earning activities, and their reputation in the society increases if their women devote more time to

domestic labour. If the number of unorthodox families outnumber the number of orthodox families,

the labour demand becomes inelastic and equal to unity. If the reverse case happens, if the social

norms effect dominates the income effect, then utility will decrease and if income effect dominates

social norms effect, then the utility of households will increase. The final impact of increase in

foreign capital investment on female labour force participation is ultimately determined by the

inherent composition of orthodox and unorthodox families in the economy.

8. References

David Collard, G.S. Becker. The Economics of Discrimination, (The Economic Journal, Volume

82, Issue 326, 1 June 1972), Pages 788–790

Diane Elson. Gender Equality and Economic Growth in the World Bank World Development

Report 2006, (Feminist Economics, 2009) 15:3, 35-59

Elissa Braunstein. Neoliberal Macroeconomics. A Consideration of its Gendered Employment

Effects. (United Nations Research Institute for Social Development, 2012).

Elson, Diane & Cagatay, Nilufer. (2000). The Social Content of Macroeconomic Policies. World

Development. 28. 1347-1364. 10.1016/S0305-750X(00)00021-8.

Francine D. Blau, and Lawrence M. Kahn. "The Gender Wage Gap: Extent, Trends, and

Explanations." (Journal of Economic Literature, 2017) 55 (3): 789-865.

G. A. Akerlof. A Theory of Social Custom, of Which Unemployment May be One Consequence.

(The Quarterly Journal of Economic, 1980) 749-775.

G. Berik, Rodgers, Y. v., & J. Joseph E. Zveglich,. International Trade and Wage Discrimination:

Evidence from East Asia.(World Bank Policy Research Working Paper 3111, 2003, April).

G. S. Becker. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to

Education (3rd ed.). (Chicago: University of Chicago Press, 1993).

Geeta Gandhi Kingdon & Jeemol Unni. Education and Women's Labour Market Outcomes in

India, (Education Economics, 2001) 9:2, 173-195.

Global Gender Gap Report 2022. (n.d.). World Economic Forum.

K. Basu. GENDER AND SAY: A MODEL OF HOUSEHOLD BEHAVIOUR WITH

ENDOGENOUSLY DETERMINED BALANCE OF POWER. (The Economic Journal, 2006)

558-580.


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K. Mahajan, & B. Ramaswami. Caste, female labour supply, and the gender wage gap in India:

Boserup revisited. (Economic Development and Cultural Change, 2017) 65(2), 339–378.

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LABOR SUPPLY: THEORY AND EVIDENCE FROM INDIA. (Journal of the European

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9. Mathematical Appendix

Substituting (2), (4) and (5) in (1), we get,

U = fXC N , HZ =log log a P '94 () P (

b +log log {D(1 − l

+

LN ) }

U = log log & w m+l fp w f

' +log log [{D + φ , w m

-}(1 − l

P

w f l fp ) ]

fp

Differentiating U with respect to l LN , we get

Ga

= 1

t P (

u + 1 [−D + X1 − l G4 () , ) + S

LZ. φ ′ . d .P '

P ( 4 ()

2e − φ]

Equating

Ga

G4 ()

to 0, we get

Which on simplifying gives us equation (12)

1

, )

t P (

+ u + 1 S [−D + X1 − l LZ. φ ′ . d .P '

P ( 4 ()

2e − φ] = 0

Substituting (4) in (12) and taking derivative with respect to D, we get,


Towards Equilibrium 2023 | Page 115

Taking - 67 !"

6 9

common from the RHS apart from 4 terms we get,

∂l !"

∂ D

=

11 − l !" 3w ! − 1w # + l !" w ! 3

Dw ! + 11 − l !" 3w ! φ $ 7 w #

% 8 + w

w ! l ! 9D + 11 − l ! 3φ $ 7 w #

% 8: + 21w

!"

w ! l # + l !" w ! 3φ $ 7 w #

& 8 + w

!"

w ! l # φ $ 7 w #

% 8 + l

!" w ! l ! w ! φ $ 7 w #

% 8 + w

!"

w ! l ! ∅

!"


Towards Equilibrium 2023 | Page 116


THE ECONOMICS SOCIETY

ST. STEPHEN'S COLLEGE

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