Towards Equilibrium 2024

Towards Equilibrium 2024 | Page 1

OUR SINCERE THANKS TO

Mrs. Poonam Kalra, Staff Advisor,

The Economics Society

Mr. Sanjeev Grewal, Head of Department,

Department of Economics

EDITORS-IN-CHIEF

Rachel Elsa Jude

Rachel Batra

THE EDITORIAL TEAM

Bhavya Agrawal

Rudraksh Chawla

Tarun Sunil Malayil

Yakov George Pulimood

COVER DESIGN

Tarun Sunil Malayil

Yakov George Pulimood


FROM THE EDITORS

Dear Reader,

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

Equilibrium, our annual journal. This esteemed publication serves as a platform for the brightest

young minds in India to present their research and discoveries in the field of economics. Through

a diverse collection of articles and research papers, the journal encompasses a wide range of

perspectives, methodologies, and recommendations, providing invaluable insights for aspiring

scholars.

Our first research paper examines the impact of the COVID-19 pandemic on sustainable

development in India, and examines how the pandemic has disrupted investment in education

and exacerbated the wage gap. The second entry analyses the sustainability of the Russian

economy and its response to the US-imposed sanctions by assessing the extent to which the

Russian economy has diversified, and the implications of such diversification. The next paper

focuses on Military Keynesianism by showing how in India, a positive relationship exists

between government defence spending and output growth during the post-war economic era.

This year, the journal also contains two research articles that bring into light new concepts and

under-appreciated issues of society. The first article highlights the gendered economy in

Kumartuli and its detrimental impacts on the idol making industry. The next article delves into

degrowth, and advocates for reduced consumerism and resource usage as a response to

unsustainable growth models. The fourth research paper studies the inequality that often comes

hand in hand with economic growth, by showing the impact that gender and spatial inequality

have on economic development.

The following entry addresses the ‘Marriage market’ and explores the concept of arranged

marriage by examining the preferences of both the sexes towards entering the marriage market,

and draw implications about the gender wage gap in the marriage market. Our final paper studies

the spill-over effects of the skill upgradation schemes referred to in the New Education Policy by

analysing the various provisions for skill upgradation at Higher Education Institutes (HEIs) and

projects their future impact on the economy.

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 commend our contributors for their relentless pursuit of knowledge, and for constantly

expanding the body of economic literature.

Lastly, we extend our sincere gratitude to you, the reader, for exploring this publication, a

product of our pursuit of knowledge and collaborative enquiry in the field of economics.


CONTENTS

Assessing the Impact of COVID-19 Pandemic on Sustainable Development

through Education and Labour Market in India

Srinjoy Majumder, Spriha Mondal

Jadavpur University, Kolkata 4

Assessing the Sustainability of the Russian Economy through Diversification

of its National Currency, Employment and International Trade Amidst Current

War and Sanctions

Pranav Pande, Chyanika Duhan

P.G.D.A.V College (M), Delhi 29

A Causality Analysis of the Military Keynesian theory in India

Anshuman Das

Shaheed Sukhdev College of Business Studies, Delhi 45

The Goddess in the Lanes

Sahil Pradhan

Atma Ram Sanatan Dharm College, Delhi 58

The Enigma of Degrowth: What, Why and How?

Amrit Thakur

Banaras Hindu University, Varanasi 67

Analyses of Inequality and its Impact on Economic Development: Gender and Spatial

Nistha Shrestha

St. Joseph’s University, Bangalore 71

Marriage Market Stability, Inequality and Growth: A Micro-theoretic Analysis

Vibha Nath, Meghna Menezes, Mayukh Dutta, Aditya Raj Chatter

St. Xavier’s College (Autonomous), Kolkata 94

Economic Potential for Skill Upgradation in Higher Education Institutes as Envisaged

in NEP 2020: A Case Study with respect to University of Delhi

Anuruth R, Madhav Jay, Pavithra T Babu, Gaurav Shankla, Vipul Bansal

Hansraj College, Delhi 104


Assessing the Impact of COVID-19 Pandemic on Sustainable Development

through Education and Labour Market in India

- Srinjoy Majumder 1 , Spriha Mondal 2

Abstract: The outbreak of the coronavirus was officially declared a ‘public emergency of

international concern’ by the WHO on 30 January 2020, and a ‘pandemic’ on 11 March 2020.

The explosion in cases and fatalities makes it one of the most lethal pandemics in human history.

The suddenness with which it engulfed the world, as well as the novelty of the virus, caught the

world off guard, and no country had any measures to deal with the onslaught of the virus. Just

three years later, the scope of the effect of the pandemic has still not been truly discovered. A

health crisis such as this is intrinsically intertwined with human development .The paper explores

the impact of the pandemic on human capital through educational investment and wage

differentials. Therefore, we divide the paper into two sections: (i) a macroeconomic forecasting

exercise by considering quarterly data on Private Final Consumption Expenditure (which we use

as a proxy for Aggregate Household Expenditure on Education) in India to estimate the expected

expenditure on education from 2020, using ARIMA modelling technique (ii) estimating the wage

differences in the Indian labour market in two categories: male-female wage differential and

formal-informal wage differential, using the Oaxaca-Blinder decomposition technique. This

exercise has been carried out using two sets of data (using nationally representative PLFS data)

from pre and post-pandemic to derive the impacts of human capital (skill/endowment effect) and

discrimination (unexplained effect) in both the segments and whether the structural break of a

pandemic impacted wage differential patterns. Only time will tell how the pandemic has truly

harmed those whose education was impacted detrimentally by it. The paper includes a

commentary on the existence of imperfect competition in the Indian labour market, which may

be a cause of wage differentials. On a concluding note, we assess and critique the performances

of the Sustainable Development Goals proposed by the United Nations (SDG 4: Quality

Education; SDG 5: Gender Equality; SDG 8: Decent Work and Economic Growth; SDG 10:

Reduced Inequalities).

1. Introduction

“The world has seen many crises over the past 30 years, including the Global Financial Crisis of

2007-09. Each has hit human development hard but, overall, development gains accrued

globally year-on-year. COVID-19, with its triple hit to health, education, and income, may

change this trend.” ~ UNDP administrator Achim Steiner

In a world of have and have-nots, enter Covid-19. The virus was an equaliser, it did not

discriminate between the wealthy and the needy when it spread like wildfire from person to

person, country to country, and eventually captured the world in a vice-like grip for nearly two

years. Overpowering both people’s bodies and economies, the pandemic was the cause of misery,

1,2 Jadavpur University


suffering and unforgettable trauma all across the world. The novelty of the coronavirus led to

there being a huge gap in the amount of information that was possessed regarding the virus.

Three years in, there is still more that can be discovered about the short and long term effects of

the virus. A drastic change in lifestyle and their lives as people knew it, as brought on by the

pandemic and associated lockdowns have resulted in a spike in reported psychological stress.

Infection from the virus was hence not the lone reason people’s health, both physical and mental,

was negatively affected, perhaps permanently. Global economies too, experienced massive

setbacks and were left struggling to cope with nationwide lockdowns and closures, overburdened

healthcare systems, and a crisis they didn't have an idea of how to begin to abate.

Now, in the wake of the pandemic, the bequest it leaves behind is harsh to contend with. It was

the cause of the greatest economic crisis in more than a century, widening both intra and

inter-country inequalities. Stoppages to the income flow for such a prolonged period as was seen

in the case of many workplaces and industries, due to the mandated social distancing and

lockdown decree, exposed and exacerbated weaknesses in economies. The World Development

Report, 2022 says that according to studies based on pre-crisis data, “more than 50 percent of

households in emerging and advanced economies were not able to sustain basic consumption for

more than three months in the event of income losses.” It also revealed that the average business

could cover fewer than 55 days of expenses with cash reserves. The halted income flow would

only have increased the burden on developing and emerging economies. As with most crises, the

effects are felt most harshly by those who were already in a position of disadvantage beforehand,

such as the poor, women, children, and elderly citizens. The key findings of a UN report show

just how grave the impact of the worst economic and humanitarian crisis seen in a lifetime was.

2020 witnessed the first rise in global poverty levels since 1998. Millions will be pushed into

extreme poverty due to a combination of halted income flows, rising prices, and rising healthcare

costs. Unemployment due to the pandemic would affect a staggering number of workers in the

informal sector globally, with them experiencing an abrupt stoppage in their income earnings.

The most vulnerable sections of society, such as roadside and slum dwellers, already battling

poor living conditions and limited, if any, access to healthcare and education, would be most

affected by the effects of COVID-19. Global school closures meant that most students were kept

out of schools, and millions of children who were dependent on meals provided at school, had to

forgo them. A switch to online and/or hybrid modes of teaching only aggravated the digital

divide, causing great harm to those without access to proper technology and network

connectivity.

Focusing particularly on education, one of the spheres of life most impacted by the pandemic,

the fallouts of the necessary school closures are numerous. As mentioned earlier, schools shifted

to online modes of learning which worsened the digital divide. Adapting to a new form of

learning was not easy, with plenty of hiccups in the beginning stages. Teachers were as unused to

this method of teaching as the students, and initially were unprepared for the same, but usually

adapted quickly. India in particular saw exams at the secondary and higher secondary levels


cancelled for the first time, due to concern for students’ safety in an environment of burgeoning

cases. A major aspect of education in schools is the interaction with peers, and isolation at home

only leads to stunted emotional and social development for many children. A lot of the load of

proper education now fell on the shoulders of parents. These parents were already overburdened,

with having to manage their careers from home, and usually in the case of the mother, were

expected to take care of the children and the household. Unemployment and lack of income due

to the pandemic meant that many parents found themselves unable to pay for their child’s

education. This also meant that many students across the country were forced to drop out of their

schools and colleges to look for jobs to help support their families. The contribution of education

to human capital development is invaluable, and education, especially at the tertiary level, is

what helps an individual develop their skills, become employable, and contribute to the

economy. The level of education prevailing in a country is indicative of a country’s future

economic progress, and the pandemic was a major setback.

“What is good for gender equality is good for the economy and society as well. The COVID-19

pandemic puts that truth into stark relief and raises critically important choices.” says a 2020

article by Mckinsey and Company. The same article says that women’s jobs are 18% more

vulnerable to the virus crisis than men’s (based on calculations by the authors of the article). A

major reason behind this disproportionate burden on women is that the stay-at-home mandate has

increased the burden of unpaid care work on women, who are usually designated the de facto

caregivers in the household.

Since the adoption of the 2030 Agenda for Sustainable Development in 2015, the progress of the

nations of the world in this regard was certainly sub-optimal. Even before the onset of Covid-19,

the Sustainable Development Goals (SDGs) were off track. Perhaps if the proposal of the 2030

agenda had been more strictly adhered to since 2015, the world might have had less on its plate

to handle, and been better equipped to handle this crisis. Progress had been made in some areas,

while others were stagnant, but not as much was required, and the pandemic would have more

than negated this progress. There is a huge emergency concerning education, wherein children

have fallen below the minimum reading proficiency level at the primary and secondary levels,

eliminating two decades of gains made in the areas of education and schooling. School

completion rates have dropped off sharply. Many children are now entirely dependent on people

from within the home for education and learning, rather than without, even though enrollment in

organised primary learning had increased up until 2019. Recovering from the aftermath of the

pandemic requires a certain level of infrastructure. Improving basic infrastructure would go a

long way towards helping schools reopen and function as normal. Improvements in technology

and internet access have never been more important than it is now, in a world that is dependent

on connectivity and integration. The economic repercussions of the pandemic have impacted

funding and budgets that are liable to make it harder for countries to work towards these ends.


2. Literature Review

There exist many previous studies on gender and wage differentials in an economy. Shaban et

al. (1993) and Assaad (1997) estimated joint models of sector choice and wage determination in

the public and private sectors using 1987 and 1988 household-level labour force sample survey

data. Marwa Biltagy (2014) attempted to estimate the gender wage gap in Egypt using Egypt

Labour Market Panel Survey (2006) data and understand their decomposition to control for the

variations. For this paper, we have also looked at the decomposition of unadjusted gender pay

gaps in the European labour market (2014) using the Structure Earnings Survey (2014) by

Eurostat.

This paper also takes into account the Oaxaca-Blinder decomposition technique (1973) and

derives the impact of discriminatory hiring practices when 'equal pay for equal work and value'

conditions were applicable.

Blau and Kahn (2016) use a new empirical strategy including psychological attributes and

non-cognitive skills as a newer explanation for gender differences in outcome. They suggest that

these attributes account for a small to moderate portion of gender wage gaps present, however

insignificant to occupation and industry effects. Boris Hirsch (2019) suggests that there exists

‘monopolistic’ wage discrimination in the labour market, whereby employers exploit their

wage-setting monopolistic power over women, owing to pre-existing prejudice.

Ravi Kanbur, in his paper titled, ‘Sustainable Development Goals and the Study of Economic

Inequality’, has shown that the study of economic inequality has much to contribute to the global

policy discourse which is underpinned by the SDGs. Research on inequality from an economic

perspective is well-placed to speak to the concerns of the SDGs. Not only is SDG-10 specifically

about inequality, but other SDGs also relate intimately to inequality in general and to income

distribution in particular. Even when SDG concerns go beyond narrowly defined income

inequality, to health and education for example, or to inequalities across broad groups defined by

gender, ethnicity, or region of origin, there are vibrant components of the study of economic

inequality that intersect with these concerns. The perspectives and methods of economic

research, with emphasis on clear conceptual foundations and rigorous statistical methods, can

help in the task of quantifying some of the targets in the SDGs, and in the task of identifying and

assessing policy interventions to achieve these goals.

Andreoli in his 2018 paper, “Public education provision, private schooling, and income

redistribution”, takes up the theme of SDG-4, “Quality Education”. The goal intersects with an

old debate in economics on the distributional consequences of public education provision. Using

data from Italy, he tested if universal availability of public education acts as a transfer in kind

that redistributes income (i.e. inequality-reducing) across households with school-age children.

One of the issues that arise is that if higher-income households are more likely to take up

educational services, especially at the upper-secondary level, then universal provision could have


regressive consequences. Further complications arise when there is also private schooling. In this

case, if wealthier parents opt into private schooling, the regressive redistributive effect of public

provision needs to be recalibrated.

Klazoglou and Dritsakis (2018) used an ARIMA model to forecast US Health expenditures, for

the period 1970-2015. They proved that using an ARIMA model constructed by the Box-Jenkins

Method gave the best result for forecasting US Health Expenditures. The trend forecasting values

closely followed the actual values. They also used the Theil Inequality Coefficient as an indicator

to evaluate the model's forecasting performance, which was in the optimal range.

Sarbajit Chaudhuri, in his paper titled ‘International Migration of Skilled and Unskilled

Labour, Welfare and Skilled-unskilled Wage Inequality: a Simple Model’ provides an alternative

explanation for the increasing wage inequality in many less developed countries in the regime of

liberalised trade and investment in terms of higher international mobility of skilled and unskilled

labour during this period. He uses a Harris-Todaro (1970) framework where the central principle

of the Stolper-Samuelson theorem holds. He shows that in a reasonable production structure for a

developing economy, a brain drain of skilled labour may raise the welfare of the economy. On

the other hand, the emigration of unskilled labour is welfare-reducing. Also, the emigration of

skilled/unskilled labour lowers the urban unemployment of unskilled labour and widens the

skilled-unskilled wage gap. The rural sector produces the export commodity using capital and

unskilled labour while the urban sector is tariff-protected import-competing sector that produces

its output using unskilled labour, capital and the sector-specific input, skilled labour. In this

production structure, we find that an emigration of unskilled labour is likely to be

welfare-reducing while the international migration of skilled labour under a reasonable sufficient

condition is welfare improving. An emigration of labour (skilled or unskilled) raises the wage

rates of both types of labour. However, the effect on total wage income is ambiguous since factor

endowment decreases. The international migration of labour (skilled as well as unskilled) lowers

the urban unemployment of unskilled labour and more interestingly leads to an increase in the

skilled-unskilled wage gap.

Hamid Beladi, Avik Chakrabarti and Sugata Marjit, in their paper titled ‘Skilled-Unskilled

Wage Gap and Urban Unemployment’, offers a simple general equilibrium framework for

understanding the effect of trade liberalisation and fragmentation on employment and the

skilled-unskilled wage differential in the third world where rural urban migration is a pervasive

phenomenon in the presence of urban unemployment. The impact of trade liberalisation on the

labour market in the North (America) has drawn tremendous attention in the face of the growing

skilled-unskilled wage gap. But in the South (Latin America) it has been somewhat neglected.

One of the key structural differences between the North and the South is that the South

experiences a pronounced rural-urban migration in the presence of urban unemployment. This is

the phenomenon they explore. They show that while fragmentation necessarily improves the

unskilled wage and the skilled wage, more lucrative global opportunities for the skilled final


product, in the absence of fragmentation, can reduce the rural wage and increase urban

unemployment. The effect of fragmentation, ceteris paribus, on the skilled-unskilled wage gap is

sensitive to the degree of substitutability between land and unskilled labour. As such,

fragmentation can magnify the increase in the skilled-unskilled wage gap resulting from an

improvement in the terms of trade. It is also shown that technological progress in the

intermediate goods sector increases the skilled-unskilled wage gap and raises urban

unemployment.

3. Education

3.1. Theoretical Background

The process of Auto Regressive Integrated Moving Average (ARIMA) modelling as performed

here is used as a forecasting tool for economic variables, as proposed by the Box-Jenkins Method

(1976).

The ARIMA Model has three parameters:

● p (AR): the number of autoregressive terms

● d (I): the order of differencing required to remove stationarity from the time series data

● q (MA): the number of lagged forecast errors in the prediction

Under the Box-Jenkins method, stationarity in the time series data is first determined using a unit

root test, such as the Augmented Dickey-Fuller (ADF) test which is used in this paper. The null

hypothesis for the ADF test is that the data is stationary. Stationarity can also be rudimentarily

gauged visually from a time series plot. If the data shows a constant trend. The data is non

stationary. However, if the data fluctuates around the 0 mark, the data has a constant mean and

variance, and hence is stationary.

Differencing the data is a solution to the non-stationarity problem. Usually no more than two

differences are required. The number of times that differencing needs to be done to remove

stationarity determines the d parameter of the model.

The determination of the p (AR) and q (MA) values is done using partial autocorrelation and

autocorrelation functions respectively. A few values of p and q are chosen, and some initial

potential models are identified.

Next, each of these models are tested and the log-likelihood values are noted. Additionally the

models are evaluated using the Akaike Information Criterion (AIC) and Bayesian Information

Criterion (BIC). The model with the lowest AIC and highest log-likelihood value is chosen.


The final model is then used to dynamically forecast the required values, for the time period in

question. The actual and forecasted values for the concerned time period are then plotted on the

same graph to provide a visual representation of the divergence, if any.

Finally, the accuracy of the forecasted values, and hence the model is evaluated using Theil’s U

(1996), as compared to the naïve forecasting method (wherein the value of the immediate past

period is taken as the forecast for the current period), using the simplified form of the statistic

given by Makridakis et al. (1998).

3.2. Method

The entire process is carried out on Stata. First the data on Private Final Consumption

Expenditure is loaded into the software. The data is obtained from the FRED, which has sourced

the data from OECD. This exercise is carried out using data till the fourth quarter of 2019, right

before the onset of the pandemic. Values are forecasted till the fourth quarter of 2022, i.e. the last

period for which actual data is available, and hence can be compared with forecasted data.

First, we plot the time series line for pfce, and the result is as follows:

Figure 1.a: Time series line for PFCE

We notice an upward trend in the values, which indicates non-stationarity in the data. PFCE has

been increasing with minor fluctuations over the time period in question. Stationarity is a basic

assumption of time series analysis, which is violated here.


To confirm this, we perform the Augmented Dickey Fuller test. The result is as follows:

Dickey Fuller Critical Value - pfce

Test Statistic 1% 5% 10%

1.270 -4.053 -3.456 -3.154

Table 1.a : Dickey-Fuller Test for Unit Root Variable - pfce

At the 5% level of significance, we fail to reject the null hypothesis that the time series data is

stationary, as the test statistic is in the acceptance region.

To solve the problem of non-stationarity, we take the first difference of the pfce variable, and

denote it as dpfce. Then, we repeat the same process as above, by first plotting a time series line

and then confirming our observation with the ADF test.

Figure 1.b: Time Series Line - dpfce

There still appears to be an upward trend in the data. However, it is much less than before the

data was differenced. The mean and variance of the data seem likely to be around 0.

Performing the ADF test:


Dickey Fuller Critical Value - dpfce


-13.309 -4.055 -3.457 -3.154

Table 1.b: Dickey- Fuller Test for Unit Root Variable - dpfce

On the basis of the test statistics and the critical value, we reject the null hypothesis at 5% level

of significance. This indicates that the data is stationary.

But again, we consider second differencing the variable further, and denote the new variable as

d2pfce. Then we repeat the process above again. The time series line result is:

Figure 1.c: Time Series Line - d2pfce

The data no longer shows an upward trend, and resembles a graph characteristic of white noise.

It appears to be a better result than was obtained only by first differencing the series.

Performing the ADF test:


Dickey Fuller Critical Value - d2pfce


-17.768 -4.058 -3.458 -3.155

Table 1.c : Dickey-Fuller Test for Unit Root Variable - d2pfce

Once again, we reject the null hypothesis at the 5% level of significance, on the basis of the

corresponding critical value and test statistic, meaning that the data is stationary. So far we have

determined that the order of differencing, d, can take values 1, or 2. We can now proceed with

determining the p and q parameters with partial correlograms and correlograms respectively.

The correlogram (AC) for the first difference variable, dpfce, is obtained as follows:

On examination, we see that only the third lag is significant out of the 95% confidence limits.

But we also consider the second and sixth lags as well. Hence for d = 1, q may take values 2, 3

and 6.

Figure 1.d: Correlogram - dpfce


The partial correlogram (PAC) for dpfce is:

Figure 1.e: Partial Correlogram - dpfce

Only the third lag is significantly out of the 95% confidence limits, so for d = 1, p = 3. We repeat

the process of determining the possible values of p and q for the variable d2pfce, i.e. the second

differenced variable at d = 2, as above.

Figure 1.f: Correlogram - d2pfce

Only the first lag is significantly outside the confidence limits. But, we consider the third lag as

well. Therefore, for d = 2, q = 1, 3 is considered 1

1

Determining the orders of p and q from PAC and AC are somewhat experimental in nature, as it is up to one’s visual judgement

in many cases to decide which lags to consider. However, when all the possible ARIMA models are evaluated, only the best fitting

one is kept as the final result. So, considering all the possible values does not affect the final result.


The partial correlogram (PAC) for d2pfce is:

Figure 1.g: Partial Correlogram - d2pfce

The first and second lags are significantly outside the confidence limits. Therefore, for d = 2,

p = 1, 2 is considered. Finally, we have determined all possible values of p, d and q.

Subsequently all possible ARIMA models were determined.

For first differenced PFCE, dpfce:

AR (p) I (d) MA (q) ARIMA (p,d,q)

3 1 2 (3,1,2)

3 1 3 (3,1,3)

3 1 6 (3,1,6)

For second differenced PFCE, d2pfce:

Table 1.d : ARIMA data on dpfce


AR (p) I (d) MA (q) ARIMA (p,d,q)

1 2 1 (1,2,1)

1 2 3 (1,2,3)

2 2 1 (2,2,1)

2 2 3 (2,2,3)

Table 1.e : ARIMA data on d2pfce

All models are evaluated using Akaike’s Information Criterion (AIC). The lowest value of AIC

and highest value of log-likelihood is obtained for ARIMA (1, 2, 3).

N ll (model) df AIC BIC

93 14.354 6 -16.709 -1.513

Table 1.f : Akaike’s Information Criterion and Bayesian Information Criterion

This model is hence used to predict and forecast the future values of PFCE.

3.3. Result

The actual and forecasted values of PFCE are plotted on the same graph, and analysed post the

onset of the Covid-19 pandemic in the country. The graph is obtained as:

Figure 1.h: Real vs Forecasted Data on PFCE


Simple observation shows that the actual PFCE (i.e expenditure on education) diverges from the

forecasted values in the second quarter of 2020, and the second quarter of 2021. The first wave

of the Covid pandemic officially began in India in April 2020, and the second wave began in

March 2021. This implies that an explosion in coronavirus cases in the country and subsequent

lockdowns in the first two years of the pandemic, caused investment in education to fall sharply

and unexpectedly. Education is one of the primary contributors to development of human capital,

and SDG 4 which is to “Ensure inclusive and equitable quality education and promote lifelong

learning opportunities for all” cannot be achieved without the proper infrastructure and required

investment in education. Achieving the 2030 Agenda in time was already a tall task to complete

in time, seeing that the level of progress was already sub-optimal pre-pandemic. The pandemic is

definitely a setback in this regard, a setback the country could ill afford.

3.4. Theil’s U

Theil’s U is a statistic that measures and compares the relative accuracy between the values

forecasted using the model and the values forecasted using naïve forecasting. In naïve

forecasting, minimal historical data is used. This means that the forecast value for the current

period is taken to be the actual value of the previous period. In this paper, we compute Theil’s U

using the simplified statistic given by Makridakis et al. (1998). Therefore,

U =

n−1

∑ ( F t+1 − Y t+1

t=1

n−1

t=1

Y t

) 2

∑ ( Y t+1 − Y t

Y t

) 2

(1)

Where,

U = Theil’s U statistic,

F = forecasted value

Y = actual value

n = number of observations

For our model, we obtain U = 0.7941289888.

For U < 1, the model provides more accurate results than the naïve forecasting method.

For U = 1, the model is as good as the naïve forecasting method.


For U > 1, the model gives less accurate results than the naïve forecasting method.

The lower bound for U is 0. Hence it is worthwhile to consider the model only when 0 < U < 1.

For the obtained value of U, we conclude that the proposed model can be accepted, as it

provides better results.

4. Gender

4.1. Theoretical Background

The Blinder-Oaxaca decomposition (1973) is often used to study labour market differentials to

decompose mean differentials in log wages based on linear regression models in a counterfactual

manner. This method of decomposition divided the wage differentials into two groups, part that

can be explained by human capital differences entailing from education, skill or even bequest,

and an 'unexplained' part that cannot be attributed to the aforementioned class. This part is used

as an indicator and measure of discrimination, but also subsumes the effect of group differences

in unobserved predictors.

4.2. The Empirical Model

We state that the economy is divided into two groups of workers: male and female. The wage

function, or the earnings function in the economy is defined as follows:

ln (W) i

= a 1

+ b 1

(MS i

) + b 2

(exp i

)

+ b 3

(exp2 i

) + b 4

(YS i

) + u i

(2)

Here, W represents the monthly wage earned by the i th individual. YS is defined as the years

spent in formal education. exp is the experience of the i th individual, calculated as follows:

exp i

= Age i

− YS i

(3)

exp2 is the square of experience as it has been empirically observ ed that wages of a worker in

an economy initially has increasing returns, then follows a diminishing trend.

MS is a dummy variable for marital status and is taken to be 1 for married and 0 for unmarried

(at the current point of estimation).

The earnings/wage function for male workers is as follows:

ln W mi

= a 1

+ B 1

(MS mi

) + B 2

(exp mi

)

+ B 3

(exp2 mi

) + B 4

(YS mi

) + u mi

(4)

The earnings/wage function for female workers is as follows:


ln W fi

= a 1

+ b 1

(MS fi

) + b 2

(exp fi

)

+ b 3

(exp2 fi

) + b 4

(YS fi

) + u fi

(5)

The total difference in the wages earned by both the genders can be expressed as follows:

ln W = ln W m

− ln W f

(6)

Hence, we can express the Oaxaca-Blinder decomposition equation as follows:

n W m

− ln W f

= (H m

− H f

) B M

− (B M

− B f

) H f

(7)

Here,

(H m

− H f

) B M

accounts for the difference in skills or simply human capital differences,

while (B M

− B f

) H f

captures differences which are unexplained, treated as discrimination.

4.3. Data Used

This paper uses repeated cross-sectional surveys of industries in India from two years 2018-19

and 2020-21. The data has been obtained from the Annual PLFS report, which is conducted

every year. The survey report used is an authentic source of information that allows us to conduct

the research on the impact of gender inequality and human capital and a nationwide lockdown

due to COVID-19 pandemic on female wage inequality force participation in the Indian

economy.

Unit level has been considered in the study as published in the PLFS report . The dataset has

been cleaned by replacing missing values with zero, and population data unavailable for the two

particular time periods concerned have been omitted. For the purpose of decomposition of male

and female wage difference, a Oaxaca-Blinder (1973) estimation strategy has been utilised.

In total 420757 unit level data points have been considered from the 2018-19 PLFS survey and

428525 unit level data points have been considered from the 2020-21 PLFS survey.

4.4. Estimation and Results (2018 – 19 PLFS):

The results for the male and female wage functions have been summarised in the following table:


Variable Male Female

Age 30.353

(19.455)

Marital Status 0.499

(0.500)

Years of Formal Education 7.466

(5.112)

Wages 2778.399

(8886.292)

31.161

(19.368)

0.610

(0.487)

6.133

(5.225)

668.081

(5234.079)

Table 1.g; Note: Standard Deviation in parentheses; Source: Author’s Calculations

Estimation results for Wage function (female sample): 2

Log Monthly Wage Coefficient Standard Error t

Intercept -0.531 0.011 -48.05

Marital Status -0.241 0.0164 -14.71

Experience 0.043 0.001 40.95

Experience_Square 0.000 0.000 -37.11

Years of Formal Education 0.084 0.000 94.08

Table 1.h; Note: T tests are done at 1% significance level; Source: Author’s calculations

Estimation results for Wage function (male sample):


Intercept -0.834 0.017 -46.81

Marital Status 1.181 0.026 44.37

Experience 0.072 0.001 39.59

Experience_Square -0.001 0.000 -54.36


2 All co-efficients are significant at a 1% level of significance


Table 1.i ; Note: T tests are done at 1% significance level; Source: Author’s calculations

4.5. Oaxaca-Blinder Gender Decomposition Results (2018-19):

Wage Decomposition

Components

Male

Female

Mean of Monthly Wages 2778.399 668.0816

Overall Wage Gap 2110.3174

Endowment (Skill) Effect -0.1150

Discrimination Effect -0.9255

Table 1.j; Source: Author’s calculations

The above table represents Oaxaca-Blinder wage decomposition results. Data shows that male

workers in the Indian economy have higher years of formal education and higher experience in

the labour market. It is estimated that the wage gap between male and female workers in the

economy attributed to differences is approximately 104% and the mean wage difference is

approximately 75%. From our decomposition, we yield the result that female workers earned less

than their male counterparts in 2018 by 104% approximately, which can be divided into 2

categories. The lack of skill acquisition or human capital generation for the female workers led to

them earning about 11.5% less than their male counterparts, while 92.55% of the lower wage

was accounted for due to the existence of the unexplained effect, treated as discrimination here.

4.6. Estimation of Results (2020-21 PLFS)

Variable Male Female

Age 30.882

(19.730)

Marital Status 1.538

(0.564)

Years of formal education 7.469

(5.101)

Wages 4848.045

(100877.6)

31.675

(19.767)

1.711

(0.649)

6.226

(5.212)

2003.465

(94191.16)

Table 1.k; Note: Standard Deviation in parentheses; Source: Author’s Calculations


The results for the male and female wage functions have been summarised in the following

table: 3

Estimation results for Wage function (male sample):


Intercept 7.064 0.011 636.98

Marital Status 1.502 0.014 101.93

Experience 0.196 0.001 175.23



Table 1.l; Note: T tests are done at 1% significance level; Source: Author’s calculations

Estimation results for Wage function (female sample)


Intercept 7.360 0.009 742.28

Marital Status 0.041 0.011 3.73

Experience 0.090 0.000 102.91


Years of Formal Education -0.019 0.000 -24.46

Table 1.m; Note: T tests are done at 1% significance level; Source: Author’s calculations

3

All co-efficients are significant at a 1% level of significance


4.7. Oaxaca-Blinder Wage Gap Decomposition Results (2020-21 PLFS)

Wage Decomposition

Components

Male

Female

Mean of Monthly Wages 4848.045 2003.465

Overall Wage Gap 2844.58

Endowment (Skill) Effect 0.052

Discrimination Effect -1.576

Wage without discrimination

effect

2005.041

Table 1.n ; Source: Author’s calculations

The above table represents Oaxaca-Blinder wage decomposition results. Data shows that male

workers in the Indian economy have higher years of formal education and higher experience in

the labour market. It is estimated that the wage gap between male and female workers in the

economy attributed to differences is approximately 152% and the mean wage difference is

approximately %. From our decomposition, we yield the result that female workers earned less

than their male counterparts in 2018 by 152% approximately, which can be divided into 2

categories. Post pandemic scenario estimates that female workers have acquired more skill and

generated more human capital leading to them earning about 5.2% more than their male

counterparts, while 157.67% of the lesser wage was accounted for due to the existence of the

unexplained effect, treated as discrimination here.

4.8. Inference

From the Oaxaca-Blinder decomposition for male and female wages in 2018-19 and 2020-21, we

find that female workers earn considerably less than their male counterparts. While in 2018-19,

female workers were earning 104% less than male workers, they earned 152% less than male

workers in 2020-21. The data from PLFS (Periodic Labour Force Survey) suggests that in

2018-19, skill/human capital acquisition of female workers was lower compared to male workers

(contributing 11.5%), which was coupled with discriminatory workforce characteristics

(contributing 92.5%). In 2021, post the pandemic, we see a different scenario from the estimation

results. The results yielded tell us that female workers acquired more skill/human capital during

the pandemic, resulting in a 5% high due to skill effect. But the pandemic actually heightened the

level of gendered discriminatory practices in the economy, as female wages suffered a decrease

by 157% due to discrimination, resulting in a net 152% lower wages than their male

counterparts.


4.9. Note on Female Human Capital in Urban and Rural India During Pandemic:

It has been observed that due to Covid-19-induced job extremity rural women abandoned their

stickiness towards agricultural jobs and took up low- paid, informal work to condense family

inflows during a period of extremity. So, the nature of the work in which men and women are

engaged in the pre- and post-pandemic period was worth exploring. Most men and women in the

working age - group are self- employed and a deeper study of the employment order shows a

sharp increase in the proportion of self employed women compared to men and further in

agricultural areas relative to civic. This was accompanied by the decline in the regular

employment of women during the epidemic andpost-pandemic ages. In the case of men, change

in the order of employment over the times has not been veritably significant. Most of these toneemployed

men are ownaccount workers (i.e. running enterprises without any hired workers)

while self - employed women in civic areas are largely ownaccount workers. Still, in pastoral

areas within the toneemployed order, women are substantially engaged as overdue family

aides( working in ménage business without getting any payment). The engagement as ownaccount

workers reflects the nature of work isn't opportunity driven, but necessity- driven and the

engagement of women in pastoral areas as overdue family aides indicates the precarious

employment condition. The change has been noticed in industry-wise employment as well during

this period. Mainly employment has increased in the trade and hostel assistance, and in

construction with a borderline decline in husbandry, manufacturing, and other services sectors. In

2020- 21, men were substantially engaged either as ownaccount workers or regular workers in

the trade sector, but nearly 80 percent of them were casual labourers union-public work. On the

negative, in the recent period, women’s employment has increased by 8 per cent in the agrarian

sector from 2017- 18 to 2020- 21 where substantially they're working as overdue family aides.

It's associated with a decline of 14 per cent in manufacturing and 18 per cent in other services

sectors, which can be explained by the rear migration from civic to pastoral areas due to the

epidemic and posterior lockdown. A near look at the average diurnal pay envelope rate

indicates that for the first time since the PLFS was launched, there was a significant decline in

absolute and real stipend for both men and women during the epidemic( 2019- 20). Still, the

decline in real pay envelope was sharper in rural areas compared to urban and for men relative to

women in 2019- 20.

5. A Critical Assessment of the Sustainable Development Goals

The progress of India in some of the SDGs and their respective indicators is analysed through the

Sustainable Development Report (2022):

● SDG 4 (Quality Education for all): India ranks 121st out of 163 countries considered

in the report. Compared to its progress in most of the goals, the progress here is

better, but that does not necessarily mean that is in any way an indication that we

should become complacent. Moderate progress has been made, but there is stagnation


in the indicators. Measured in 2020, participation rate in pre-primary organised

learning (% of children aged 4 to 6) is at 85.2%, net primary enrollment rate (%) is at

94.6%, and lower secondary enrollment rate is at 84.6%, and the final indicator shows

stagnation. The literacy rate ((% of population aged 15 to 24), measured in 2018, is

91.7%. There is still an urgent need to improve these indicators if there is any hope of

achieving this goal by 2030.

● SDG 5 (Gender Equality): Demand for family planning satisfied by modern methods

(% of females aged 15 to 49) is 72.8%, measured in 2016. Challenges remain, but

there are signs of moderate improvement. Ratio of female-to-male mean years of

education received is 62.1%, measured in 2019. Here there are major challenges, and

concerningly, there is stagnation in progress. Ratio of female to male labour force

participation rate is 26.8%, measured in 2020. This indicator shows the most

worrying signs. Not only do there exist major challenges, in addition to a very poor

value, the trend is actually decreasing. Seats held by women in the national

parliament is 14.4%, measured in 2020. Here too, major challenges exist and

stagnation is observed. India has a history of deeply ingrained gender inequality and

so these numbers, though worrisome, are unsurprising.

● SDG 8 (Decent Work and Economic Growth): Adjusted GDP growth is -3.6%,

measured in 2020. Clearly there are huge issues here that must be addressed, and

quickly. Victims of modern slavery (per 1,000 population) is 6.1, measured in 2018.

There has been some progress, but there are still changes that must be made.

Unemployment rate (% of total labour force) is 5.4% measured in 2022. This can be

improved, but progress has slowed down. Adults with an account at a bank or other

financial institution or with a mobile-money-service provider (% of population aged

15 or over) is 79.9%, measured in 2017. This indicator, though thus far still reflects

problems, has an upward trend in the progress. Fundamental labour rights are

effectively guaranteed (worst 0–1 best) is 0.5 measured in 2020. Work is being done

in this regard as this indicator shows signs of improving. Fatal work-related accidents

embodied in imports (per 100,000 population) is 0.1 as measured in 2015. This last

indicator is not only quite good, but it still shows signs of improvement.

● SDG 10 (Reduced Inequalities): The Gini Coefficient, measured in 2011, is 35.7.

The Gini Index or Coefficient is the measure of income inequalities across the

population in a country. It can take a value between 0 and 1, 0 indicating perfect

equality, and 1 indicating that net incomes are concentrated in the hands of one

person. Hence the lower the value of the Gini Coefficient, the better. This does have

its drawbacks, but India’s progress in the fight against income inequality is real and

faces serious challenges. The Palma Ratio, also measured in 2011, is 3.1. The Palma

Ratio is a more recently developed indicator of inequality. It divides the income share


6. Conclusion

of gross national income of the top 10% of the population by that of the bottom 40%.

It is therefore more sensitive to values and variations at the extremes, as compared to

the Gini Coefficient which focuses more on the mean areas of income, where

variations are actually less likely. Therefore, sometimes the Palma Ratio is preferred

over the Gini Coefficient. A higher Palma Ratio indicates higher inequality. The value

in India means that the top 10% of the population hold more than 3 times the income

of the bottom 40%, which is certainly a matter of great concern.

Development of human capital and achievement of the Sustainable Development Goals has

always been closely related. The power of a nation is its people. The coronavirus pandemic was a

humanitarian and economic crisis. To get just a glimpse of the effects of the pandemic we have

considered education and labour data. The effects which might have been theorised, have been

substantiated with data here.

The forecasting model shows that after the first boom in cases in the first wave in 2020, when a

nationwide lockdown was announced, expenditure in education fell sharply. The same is

observed after the boom in cases in the second wave of cases in 2021. This fall in educational

expenditure would no doubt have caused great impedance to the progress in SDG 4. Curiously

enough, the very SDGs that have been deferred even further due to the pandemic, have helped

cushion the blow from the pandemic to some extent. For example, since there was emphasis on

no contact, and hygiene and cleanliness, SDG 6 (access to clean water) helped people wash their

hands, clothes, etc. in an attempt to stay safe. So, working towards the SDGs now more than

ever, is very important. The more obvious result will obviously be progress towards the 2030

Agenda, but it will also help people and countries to get back on their feet, as every one of these

goals will in some way go towards mitigating the effects of the pandemic.

The main areas that countries must focus on is ensuring that pre-existing progress in the goals is

not pushed back too far, work towards provision of basic provisions and living requirements,

including infrastructure, and protect and conserve the environment as well. The final item is very

important. The only thing that the pandemic did not slow down was climate change and global

warming. It is unrealistic to expect nations to achieve such lofty goals acting in singularity.

Therefore there must be a greater emphasis on cooperation and coordination between nations as

well.

For education and India in particular, the rural-urban divide and digital divide was exposed

harshly by the pandemic. Internet access must be improved, as well as access to devices that can

make use of said internet access.

Instructors and teachers should also be properly trained on the proper approach to hybrid and

online modes of learning, because adjustment took time, and there were many issues that were


unaccounted for. Support should also be increased for students who have returned to school,

especially at the primary level. It is difficult for young children to readjust to schools and social

environments after spending two years learning from home.

Since many children have slipped below the required reading and arithmetic skills for their age

groups, regular assessments should be carried out to assess the progress of these children,

identify the ones who are in need of extra attention and help, and provide them with all the

resources they need.

The responsibility of change does not lie squarely on the shoulders of the government, but also

of educational institutions, who should step up in the hour of need, before there are thousands of

students who have received sub-par education, as today’s youth is the future of tomorrow. There

is no better way to enhance the capabilities and skills of an individual than education.

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Assessing the Sustainability of the Russian Economy through Diversification

of its National Currency, employment, and International Trade Amidst

Current War and Sanctions

- Pranav Pande 1 , Chyanika Duhan 2

Abstract: This research project aims to analyse the sustainability of the Russian economy

despite ongoing challenges such as war and sanctions. The study focuses on diversifying the

country's National Currency, employment situation, and International Trade as a potential

pathway toward economic resilience. The project uses secondary data to assess the extent to

which the Russian economy has diversified and the implications of these efforts for its

sustainability. The study concludes with recommendations from policymakers, suggestions for

future research, and a Literature review of many geopolitical and economic experts.

1. Introduction

On February 24, 2022, the Russian Federation officially launched a special military operation on

the border of Ukraine. More than 2 years have passed, yet the conflict shows no signs of

stopping. Despite efforts to negotiate a resolution, the situation remains tense and the future of

the region remains uncertain. The ongoing conflict has caused significant human suffering and

has disrupted the lives of countless individuals and communities. Since the outbreak of the war, a

series of critical economic battles have ensued between Russia and various Western nations,

particularly the United States, the European Union, Britain, and NATO. These economic

conflicts have added a layer of complexity to the already tense political and military situation,

and have had significant impacts on the global economy. War is a destructive endeavour with

far-reaching effects on economies, cultures, and the environment that are not fully understood.

Despite the severe economic sanctions imposed by Western nations, the EU, and Britain because

of Russia's military operation, Russia has demonstrated an impressive level of resilience and

economic stability. Despite the significant financial burden of the ongoing war and the economic

pressure of the sanctions, Russia has been able to maintain a strong economy and even partially

overcome the negative impacts of the sanctions. While the long-term effects of the conflict and

the sanctions are still uncertain, Russia's ability to weather these challenges suggests a degree of

economic strength and adaptability that is impressive in light of the circumstances.

In this research project, we aim to analyse the effectiveness of Russian government policies in

maintaining economic stability, despite the financial burden of the ongoing war and the severe

economic sanctions imposed. Additionally, we will explore how Russia has been able to thrive

while much of Europe is on the brink of another potential recession. Also, it analyses the

1,2. P.G.D.A.V College , Delhi


sustainability of the Russian economy during times of war and sanctions through diversification

and explores the effectiveness of this strategy.

This analysis will include a literature review of several studies on the topic of diversification and

economic sustainability in times of conflict. The review will highlight the various strategies and

techniques that have been used in similar situations and examine their effectiveness. In addition,

this research will focus on the Russian ruble, which has been managed effectively to mitigate the

impact of the economic sanctions. Furthermore, the project will examine how Russia has

diversified its international trade and found alternatives for its natural resource exports to

countries such as China, Japan and India. This has allowed the Russian economy to thrive

despite the challenges posed by the ongoing conflict and sanctions.

Overall, this research project will provide a comprehensive analysis of the strategies used by

Russia to sustain its economy during times of war and sanctions, with a focus on the

diversification of international trade. The findings of this study will be valuable to policymakers

and scholars interested in understanding the economic sustainability of nations facing similar

challenges.

2. Research Questions

What are the current challenges facing the Russian economy in the context of war and sanctions,

and how can diversification help to address these challenges?

1. What are the different forms of diversification that the Russian economy can undertake,

and what are the potential benefits and risks of each option?

2. How does the Russian government's economic policy affect the success of diversification

efforts, and what policy changes could be implemented to promote economic

sustainability?

3. What is the role of international trade in the diversification of the Russian economy, and

what are the challenges and opportunities presented by the global marketplace?

4. How can employment policies and strategies support the diversification of the Russian

economy,

5. How did the Russian Banking system prepare for the long sustenance of the National

Currency Ruble?


Several research papers have analysed the effectiveness of Russia's proactive economic

management strategies in light of the Ukraine conflict, which includes boosting oil and gas

revenues, exploring alternative markets, and addressing unemployment during times of sanctions

and war to maintain a functioning workforce.


1. An article by Paddy Hirsch discussed how Russia was able to rescue the ruble despite the

sanctions imposed on the country.

a. According to the Article, the recovery of the Russian ruble has several contributing

factors. One significant factor is the exemption of natural gas from the sanctions

imposed by the coalition of countries allied with the U.S. These sanctions were

intended to limit Russia's access to foreign currency, particularly U.S. dollars, and

euros. However, several European countries continue to purchase Russian gas due

to their dependence on it and the lack of alternative suppliers to meet their

demands

b. Furthermore, the research indicates that the increase in oil and natural gas prices,

along with the stability of Russia's trading relationships with major economies

like China and India, have also contributed to the recovery of the Russian ruble.

As a result, the foreign currency continues to flow steadily into Russia, alleviating

concerns about insolvency and providing support for the ruble.

2. Todd Prince's article titled "Despite Sweeping Sanctions over the Ukraine War, Russian

Unemployment Touches Post-Soviet Low" explores how the Russian government has

managed to maintain low levels of unemployment despite various economic shocks. The

article highlights that in May 2022, Russian unemployment fell to a post-Soviet low of

3.9%, which is below the average rate in the European Union and only slightly above the

US rate of 3.6%. This low rate persisted through June of that year.

3. Several articles have highlighted how Russia has been able to diversify its trade partners

and maintain its economic stability despite heavy sanctions imposed after the 2022

invasion of Ukraine.

a. For instance, in his article titled "In the Months Since Russia’s Brutal Invasion of

Ukraine, Its Trade with China Has Surged," Ambassador Mark A. Green discusses

how Russia has successfully turned to China as an alternative partner to sustain its

crude oil industry. Russia exports oil and other hydrocarbons to China while

importing electronics such as broadcasting equipment and computers, which

account for about 14% of Chinese imports to Russia.

b. This trade diversification has helped Russia mitigate the economic impact of

Western sanctions, as countries such as China and India have stepped in to fill the

void left by Western countries. In addition, Russia has also taken steps to reduce

its reliance on the West by developing its domestic industries, such as agriculture,

manufacturing, and high-tech sectors, and forming strategic partnerships with

other countries. These efforts have helped Russia maintain its economic stability

despite external pressures and sanctions.


4. Aim and Objectives

This research project has examined the extent to which the Russian government has been

successful in assessing and diversifying its economy in response to ongoing challenges such as

war and sanctions. The findings indicate that efforts to diversify the country's National Currency,

employment situation, and International Trade have been crucial in building economic resilience.

The recommendations put forth by policymakers and experts emphasise the importance of

continued efforts toward diversification and expanding trade relations with non-Western partners.

Furthermore, this project highlights the need for further research in this area to better understand

the impact of ongoing geopolitical events on the Russian economy.

5. Data Collection (Secondary Data)

To gather relevant data for the research project, we utilised various resources such as Statista to

obtain up-to-date information on Unemployment rates, and the XE site to monitor the value of

the Russian Ruble with precision. Additionally, we also consulted multiple government reports to

draw conclusive facts and conclusions for the research paper.

While there are certain limitations to the data available in the public domain, specifically about

the accuracy of the reported Unemployment levels, it has been suggested by experts that the low

rate is not solely due to dubious statistics. Rather, many attribute it to a combination of factors,

including demographic trends, government policies that prioritise reducing labour wages over

job cuts (to mitigate politically sensitive unemployment rates), and extensive informal lab

practices.

6. Methodology

To illustrate the relationships between the variables in the research, we utilised graphs as the

research methodology. The graphs included both bar graphs and line graphs. Bar graphs were

used to display the relationships between different aspects of the research, while line graphs were

used to show time trends.

In addition to creating the analysis, we also analysed various graphs from reputable sources that

were available on the internet. This allowed us to gather the necessary secondary data for the

research. By utilising graphs as the methodology, we were able to present the relationships

between the variables in the research in a visual and easily understandable manner.

To illustrate the relationships between the variables in the research, we utilised various graphing

techniques to draw a statistical analysis. Specifically, we incorporated both bar graphs and line

graphs. The line graph depicts the relationship between the values of the Russian Ruble over

time (before the Ukraine Invasion, during Russian banks' new strategies being implemented etc.).

Meanwhile, we used a separate line graph to show the relationship between Unemployment rates


and different times, based on the available data. We studied various literature readings mentioned

above and analysed how the Russian Government policies were aligned with what the data and

statistics showed/ depicted, this way we were able to do the content analysis aspect of the

research.

7. Analysis and Discussions

Figure 2.a: Value (USD to Rouble); Source: XE currency converter

1. The graph illustrates the Russian government's efforts to diversify the Russian ruble to

stabilise the national currency against the USD have been quite successful. It also shows

how the value of the Russian ruble increased significantly after the Ukraine conflict,

indicating the effectiveness of this strategy.

2. Despite facing international sanctions and a major military conflict, Russia's currency, the

ruble, has not experienced the typical depreciation that would be expected in such

circumstances.

3. The graph presented above illustrates the significant increase in the value of the ruble

against the dollar. Since January, the ruble has experienced a 45% jump, with the

exchange rate of one dollar equaling 53.45 rubles in June of 2022. As of March 2023, the

ruble appears to have stabilised at around 75-76 rubles per dollar.

4. The analysis shows key factors for such an unusual trend for the ruble. The ruble's value

has been pushed up by a combination of Russia's aggressive measures to prevent capital


outflow and the significant increase in fossil-fuel prices. This has led to a rise in demand

for the ruble, which in return adds to boost its value.

Figure 2.b: Dollar-Rouble Exchange Rate; Source: CBS News

5. The graph above displays the fluctuations in the Dollar-ruble exchange rate since the

military operation in Ukraine. One factor contributing to the recovery of the Russian

ruble has been the Central Bank of Russia's decision to increase interest rates to 20% on

February 28. When a country's central bank raises interest rates, it makes it more

attractive for people to hold onto the country's currency, as they can earn more money by

keeping it rather than exchanging it for another currency. By increasing interest rates, the

Central Bank of Russia is ensuring that people hold onto their rubles, which is supporting

the currency's value and aids in its recovery. In addition, external factors, such as the

increase in oil and natural gas prices, are also contributing to the currency's recovery,

while internal factors, such as the strict capital controls and requirements for Russian

exporters to convert their excess revenues into rubles, are making it harder for money to

leave the country and creating demand for the ruble.

6. The Russian government has mandated that 80% of any money that Russian businesses

earn overseas must be converted into rubles. To illustrate this concept, let us assume a

Russian steelmaker sells steel to a company in France for 100 million euros. Under the

government's mandate, the steelmaker would be required to convert 80 million euros (or

80% of the total amount earned) into rubles, regardless of the current exchange rate. This

means that the steelmaker would need to purchase rubles on the foreign exchange market

to meet this requirement.


DATE

EVENT

VALUE

(USD to

RUB)

24th

February

2022 Russia invades Ukraine 81.3166

28th

February

2022

Putin imposes capital controls, barring Russians from transferring

hard currency abroad, including for servicing foreign loans. Key

Rate increased to 20% from 9.5%. 83.9496

1st March

2022 Capital controls introduced by the Russian Central Bank. 105.184

8th March

2022

Putin issues a decree to ban Russian exports of certain commodities

and raw materials. 128.302

10th March

2023 Canada bans imports of steel and aluminium from Russia 75.8502

11th March

2022

Russia bans exports of wheat, meslin, rye, barley, and corn to its

Eurasian Economic Union partners until August 31, 2022. 132.908

31st March

2022 Russia demands "unfriendly" nations pay for gas in rubles 83.7785

4th April

2023 Finland formally joins NATO. 78.6489

6th April

2022

26th April

2022

27th April

2022

18th May

2023

21st May

2022

Post G7 and EU announcement, Biden issues EO 14071, blocking

new US investment in Russia and services exports. US Treasury

sanctions Sberbank, Alfa-Bank, and family members of Putin,

Lavrov, and Russian Security Council members. 84.0203

China eliminates applied MFN tariffs on coal imports from 3-6

percent, a controversial move expected to particularly favor

displaced Russian exports. 75.8895

Russian energy company Gazprom cuts off natural gas exports to

Poland and Bulgaria over their refusal to pay in rubles. 75.7637

The United Kingdom bans imports of Russian diamonds and

Russian-origin copper, aluminium, and nickel. 79.8534

Russian energy company Gazprom cuts off natural gas exports to

Finland for failing to pay in rubles. 62.1206

24th May Russia loosens capital controls 58.6084


2022

26th May

2022

3rd June

2022

5th

September

2022

14th

September

2023

12th October

2023

29th October

2022

8th

November

2023

Key Rate decreased to 11% (from 14%) citing a slowing in

inflation and the recovery of the ruble 60.9329

6th sanction package by EU banning imports of Russian crude oil

and petroleum products with limited exceptions, bans SWIFT for

three Russian banks and one Belarusian bank, suspends

broadcasting in the EU for three Russian media outlets. 63.5216

Russia states it will not fully resume natural gas shipments to

Europe until the "collective west" lifts its sanctions. (Russia had

indefinitely suspended shipments through the pipeline on

September 2 after the G7 price cap announcement.) 60.2617

The US Treasury imposes nearly 100 sanctions on Russian elites

and Russia's industrial base, financial institutions, and technology

suppliers. 96.0153

The US Treasury sanctions two ship owners for violating the price

cap set by the Price Cap Coalition 99.8722

Russia suspends its participation in the UN-brokered grain deal

with Ukraine for an "indefinite term" indicating it 61.3576

The UK government announces sanctions targeting individuals and

entities operating in and supporting Russia's gold, oil, and strategic

sectors. 91.974

18th

December

2023 12th Package of sanctions adopted by EU. 90.0958

20th

December

2023 The US Treasury tightens the price cap for Russian oil 90.4913

Table 2.a: General Timeline; Source: XE, Global News


Figure 2.c and 2.d: Ruble Chartline and Gold Reserves; Source: XE Currency Converter

Many Russian companies are doing a lot of business with foreign companies and earning a lot of

foreign currency, such as euros, dollars, and yen. By requiring these companies to convert 80%

of their overseas earnings into rubles, the Russian government is creating significant demand for

the Russian currency. This demand helps to support the value of the ruble by increasing its

buying power relative to other currencies. In short, the government's requirement that Russian


businesses convert 80% of their overseas earnings into rubles is designed to create demand for

the currency and support its value on the foreign exchange market. The Russian Government was

able to manage its low Unemployment rates. One of the key elements of the Russian economy

being kept intact was the declining levels of unemployment over the years and it seemed as if the

Russians were always ready for sanctions.

Despite facing significant economic challenges, Russia has managed to achieve a post-Soviet

record low unemployment rate of only 3.9% in May, which is considerably lower than the

average rate in the European Union. In June, the rate remained unchanged, still well above the

United States figure of 3.6%. The graph presented below indicates a decline in unemployment

rates in Russia, an increase in real income growth by 4%, and 15% in salaries which suggests

that the government's robust policies to combat unemployment have been effective. Russia has

faced many economic challenges, including the global financial crisis of 2008-2009, the first

round of Western sanctions in 2014, the COVID-19 pandemic in 2020, and the recent invasion of

Ukraine. Despite these challenges, the Russian government has gained experience in handling

the potential political repercussions of unemployment, particularly in smaller towns dominated

by a single employer or a few enterprises. The government has taken steps to protect jobs,

including subsidising employers to hire vulnerable workers, facilitating worker relocation from

struggling to healthier companies, and increasing training programs.

To retain skilled workers President Putin introduced several economic measures, including a

three-year tax holiday and subsidised loans for IT companies. He has also approved subsidised

mortgages for IT workers and granted a waiver from army service to all tech experts of draft age.

In addition, federal and regional working groups meet weekly to discuss the situation in the

country.


Figure 2.e: Unemployment Rate in Russia; Source: Labor Statistics of Russia

As Western countries have imposed sanctions and limited purchases of Russian oil, India and

China have emerged as the biggest buyers of Russian oil. However, the G7 economies, with the

support of the EU and Australia, have imposed a price cap to limit the price at which Russian oil

can be bought, causing uncertainty in global markets. In response, Russia has offered its oil at a

discounted price to interested buyers in Asia, while major oil-producing nations seek to control

output to maintain global prices. After invading Ukraine, Russia experienced a reduction in the

demand for its Ural crude oil, as several foreign entities opted to avoid its energy exports,

resulting in a decline in its price. At a certain point earlier this year, the price of Russian Urals

crude was over $30 less per barrel than the global benchmark, Brent crude. Although the

discount decreased to $20 per barrel in September, it increased again in November, reaching a

discount of $33 per barrel in comparison to Brent crude.

The recent purchases of Russian oil by India and China have been instrumental in maintaining

the supply of Russian crude oil, which would have otherwise faced pressure from European

powers to sell at discounted prices. The chart above shows how the Oil and Gas revenue in

Russia’s Budget has significantly increased from the year 2021 to 2022 (Ukraine Conflict), the

Russian Government has been successful in not just managing its Oil and gas revenues but has

rather reversed the bleeding of the sanctions and restrictions.

Figure 2.f: Russian Oil Imports by India and China; Source: BBC News


Figure 2.g: Share of the Oil and Gas Industry to Russian GDP; Source: Statista

Figure 2.h: Oil and Gas Revenue in Russia’s budget; Source: TRT World


Figure 2.i: Descriptive Chart of the Russian Fleet; Source: Kpler

Russia's shadow fleet replaced G7 shipping services, allowing it to charge higher fees and rates

for oil. Between February 2022 and October 2023, the shadow fleet increased by 40%, while G7

shipping services decreased by 43%.

Figure 2.j: Russian Raw Diamonds Export Value; Source: Observatory of Economic Complexity


The nation with the highest diamond deposits in the world generated a revenue of $4.7 billion in

2021 (which accounts for about 1% of the Total Russian Exports in 2021), from diamond

exports, in addition to its oil reserves. Following a 2023 sales surge by Russia's state-owned

diamond firm Alrosa, the European Union prohibited the purchase of diamonds from Russia.

8. Conclusions

Russia's ability to maintain resilient trade relationships with major economies such as China and

India, coupled with the increase in oil and natural gas prices and the sovereign debt carve-out,

has enabled the country to continue making interest payments on its debt. This highlights the

importance of diversifying international trade, regardless of a country's abundant natural

resources.

The internal factors, on the other hand, were somewhat less tangible and were also used very

smartly:

1. Considering how the Russian central banks were able to not just stop the bleeding of the

Russian Ruble in the forex and Domestic market but also able to Reverse the effects of

sanctions by policies that increased interest rates,

2. Therefore, if fewer people are selling their rubles, there is less downward pressure on the

currency's value. This can help to stabilise the currency and prevent it from losing value.

3. Additionally, The Russian government requires businesses to convert 80% of their

overseas earnings into rubles, creating demand for the currency and supporting its value.

The government also banned Russian brokers from selling securities owned by

foreigners, protecting the stock and bond markets, and keeping money in the country.

Additionally, Russian citizens are restricted from transferring money abroad.

Russia has been focused on reducing its unemployment rates since the beginning and has taken

targeted measures to ensure the resilience of its labour force against foreign sanctions.

1. One of the strategies employed by the government is to subsidise employers who hire

vulnerable workers and to support the relocation of workers to areas with better job

prospects. President Putin has also introduced several economic measures to retain skilled

workers, such as a three-year tax holiday and subsidised loans for IT companies. In

addition, the government has granted subsidised mortgages to IT workers and exempted

all tech experts of draft age from army service.

2. To address the situation in the country, federal and regional working groups hold weekly

meetings. These efforts are crucial, especially as some foreign companies have left the

Russian economy following the conflict in Ukraine. The country has responded by

developing its domestic alternatives, as seen in the case of McDonald's. When the

fast-food giant closed its outlets in Russia, the Russians opened their burger chain called

"Vkusno I Tochka" (Tasty and That's It). Such steps have proven important in keeping the


economy running smoothly and preventing the labour force from becoming a liability to

the nation.

The Western Alliance's imposition of price caps, aimed at restricting revenue streams to Moscow,

proved unsuccessful. This is evident from the Russian government's decision to independently

sustain its crude oil trade. They achieved this by utilising shadow fleets, thereby reducing costs

associated with insuring trade shipments and cargo vessels. Consequently, this approach led to an

overall increase in revenue.

9. References

1. SAPIR, J. Diversifying the Russian economy. In Aims, Constraints and Investment Policies,

French-Russian Seminar on Russian Economic Development, Financial Issues, Paris, June.

2. Gavrilenkov, E. (2004). Growth in Russia and Economic Diversification. Slavic Eurasia’s

Integration into the World Economy and Community. Sapporo: Slavic Research Center,

Hokkaido University, 93-121.

3."How Russia Rescued the Ruble," NPR, April 5, 2022,

https://www.npr.org/sections/money/2022/04/05/1090920442/how-russia-rescued-the-ruble.

4. "Monthly unemployment rate in Russia from February 2021 to February 2022," Statista,

accessed April 17, 2024,

https://www.statista.com/statistics/277043/monthly-unemployment-rate-in-russia/

5. XE - The World's Trusted Currency Authority, accessed April 17, 2024, www.xe.com.

6. Andrew Finkel, "Why Russia's Unemployment Rate Remains Low Despite the Sanctions and

War in Ukraine," Radio Free Europe/Radio Liberty, accessed April 17, 2024,

https://www.rferl.org/a/russia-low-unemployment-sanctions-ukraine-war/31999174.html.

7. "Months into Russia's Brutal Invasion of Ukraine, Its Trade with China Has Surged," Wilson

Center, accessed April 17, 2024,

https://www.wilsoncenter.org/blog-post/months-russias-brutal-invasion-ukraine-its-trade-china-h

as-surged.

8. "Ukraine crisis: Russia's Putin 'highly likely' to recognise Luhansk and Donetsk," BBC News,

accessed April 17, 2024, https://www.bbc.com/news/world-europe-60125659.

9. “Russia's making more money from oil and gas in Ukraine war than US: Report," South China

Morning Post, accessed April 17, 2024,

https://www.scmp.com/news/world/russia-central-asia/article/3181110/russias-making-more-mo

ney-oil-and-gas-ukraine-war-us.

10."Russia Unemployment Rate," Trading Economics, accessed April 17, 2024,

https://tradingeconomics.com/russia/unemployment-rate.

11."Russia Sanctions Database," Atlantic Council, accessed April 17, 2024,

https://www.atlanticcouncil.org/blogs/econographics/russia-sanctions-database/


12. "Bilateral Product Trade Between Russia and All Countries," Observatory of Economic

Complexity, accessed April 17, 2024,

https://oec.world/en/profile/bilateral-product/diamonds/reporter/rus.

13. Chad P. Bown, "Russia's War in Ukraine: A Timeline of Western Sanctions," Peterson

Institute for International Economics, accessed April 17, 2024,

https://www.piie.com/blogs/realtime-economics/russias-war-ukraine-sanctions-timeli


A Causality Analysis of the Military Keynesian Theory in India

- Anshuman Das 1

Abstract: John Maynard Keynes, the institute of Keynesian Macroeconomics, established the

colloquially mooted and argumentative theory of Military Keynesianism in 1936 which spells

out the existence of a positive relation between defence expenditure incurred by a country and its

economic prosperity. This symbiosis between defence expenditure and augmenting size of

economy is well grounded for developing countries as government spendings in the defence

sector ensures peace in the nation which would entice local as well overseas investors to boost

manufacturing and investments in various sectors of the economy. The conclusions derived from

the research advocate that for a developing country like India, a positive relationship exists

between government spending in the defence sector and real output growth during the post-war

economic era and this theory is inconclusive of the period of military truce between India and its

neighbours. The research also suggests there should not be a severe deflection of public

resources and funds away from civil sectors in India as it could also instigate a negative

relationship to come into sight.

1. Introduction

1.1. Theory and Criticisms of Military Keynesianism

The fundamental basis of the military Keynesian model came into being from Keynes’

explanation of the effect of government spending on the demand/multiplier mechanism of the

economy. During the peak of the ‘’Great Depression’’ in the 1930s, J.M. Keynes studied the

stimulus of defence spending upon the economy as an expansionary push to the aggregate output

and employment into the economy.

He noted that wars could act as a medium to increase employment levels in the country and this

theory was further supported by Michal Kalecki’s study of the German economy and its military

spending in 1943 which reflected the effectiveness of a tremendous military expenditure over

other classes of government expenditures. His interpretation of military stimulus on the economy

was also a mouthpiece of nationalistic ideals, instigating which among the people would create a

desperate need for defence according to him. There have also been several criticisms against this

theory; particularly that it crowds out necessary investment, and it creates a jingoistic national

policy that believes in an aggressive military strategy that can entice neighbouring countries to

engage in war. Furthermore, externalities caused by war and casualties have also been pointed

out as major criticisms of this theory. Concerning this, critics of the military Keynesians speak of

the “Parable of the broken window theory” by French economist Frederic Basteriat which

propounds that an expenditure incurred by the government to heal the society from destruction

1. Shaheed Sukhdev College of Business Studies


does not incur a ‘net benefit’ to the society. It explains that the window broken by a shopkeeper’s

careless son and the cost incurred to repair it does not account for a benefit to the economy since

fered, cannot be recovered. It has been observed that many developed countries have relied upon

the usage of military Keynesian policy in the post-war era to bring stabilisation into the

economy.

Figure 3.a: Crowding Out effect due to

expansionary Government expenditures in

the IS-LM Model; Source:

economicsdiscussion.net

1.2. An Overview of India’s Military Expenditure Policy

In the Union Budget (2022-23) the Ministry of defence was allocated a total outlay of Rs. 5.94

Lakh Crore in the Union Budget 2023-24. India ranks after the USA and China in terms of the

highest military spending across the world. Still, its share of the defence budget as a proportion

of total budgetary outlay has been falling consecutively for the last five to seven years. While the

share of defence spending was 17.8% of the total budget, this figure has fallen to 13.2% and the

amount assigned to the armed forces was 28% less than the required needs.


Figure 3.b: A trend analysis of the military budget allocation as the proportion of total budgetary outlay (Y axis as percentage);

Source: prsindia.org

There has been a steeper shortfall in the capital outlay as compared to the outlay assigned for

salaries and pensions. The total capital outlay of the defence forces consists of construction,

military machinery and equipment like tanks, naval vessels, air crafts, and research and

development expenditures. The Standing Committee on defence (2021) recommended drafting a

60:40 budgetary outlay of revenue to capital spending. Salaries and pensions constitute over 70%

of the defence outlay.

Forces Revenue Capital Ratio

Army 3,03,748 37,342 89:11

Navy 42,722 56,341 43:57

Air Force 56,454 58,269 49:51

Table 3.a: Capital to revenue expenditure breakdown in the military outlay (Revenue And capital expenditure in crores); Source:

psrindia.org

Another point of concern in the defence outlay is the reduction in expenses for modernization.

Modernization involves acquiring advanced technologies and weapon mechanisms to facilitate

operational preparedness for defence. This composition of the defence budget reflects upon the

dependence on growth models of the economy which are stimulated by government spending in

civilian sectors over the ones relying on military outlays.


Head Amount Allocated % of service budget

Salaries 1,18,889 35%

Pension 1,19,300 35%

Modernization 30,163 9%

Maintenance 35,475 10%

Other forces 14,036 4%

Agnipath 3800 1%

Miscellaneous 19,426 6%

Total 3,41,090 100%

Table 3.b: Amount allocated to various heads under the military outlay; Source: psrindia.org

Figure 3.c: Spendings on capital outlays; Source: PRS India


1.3. Research Question

Figure 3.d: Expenses on Modernization of (Rs. Crore); Source: prsindia.org

After addressing the underlying gaps in the existing literature, the research aims to study the

causal relationship between military expenditures and Aggregate Gross Domestic product

concerning post-war and military truce economic eras to interpret the results and study the role

of defence spending as a post-war economic healer.


Surplus literature exists on the relationship between defence expenditures and its stimulus on

economic growth in the country. However, the diversity of this literature and the countries it

examines is still unable to explain its inconclusiveness and dis-uniformed applicability across

various economies and this indecisiveness is apparent in the existence of a dichotomic trench

between the defence economists to analyse this relationship. The colloquially accepted view of

military expenditure propounds that it helps to sustain peaceful conditions for the economy to

attract foreign investments, ensure investors’ trust, and generate employment for the nation.

As a necessary component of the Fiscal Policy, an expansionary defence Budget is expected to

shift the IS schedule to the Right, leading to a stimulus in aggregate demand in the basic

Keynesian model through the demand/ multiplier effects. The converse view believes that an

expansionary Military expenditure policy would lead to crowding out of public investment and

deviate resources from allocation at productive sectors. In the context of the history of military

spending in the USA, we find that there has been the sharpest rise in defence expenditure and it

stimulated the all-time low poverty rate of 11.1% during the early seventies in the ending years

of the Vietnam war. However, in the 1980s during the revolutionary Reagan military buildup,

there was no such significant causal impact on the fall of poverty rates, which could be

profoundly attributed to heavy cuts in social welfare expenditures. In the further studies


reviewed, a similar asymmetry was sustained. According to Szymanski's (1973) study of the 18

OECD countries, there exists partial soundness in this relationship while Griffin, Devine, and

Wellsace (1982) argue that military expenditures do not necessarily cause a recovery from

economic stagnation, but benefit the interests of monopoly capital and organised labour, which

constitute most of the politically empowered classes of USA.

Marxist political activist Rosa Luxembourg developed the theory of underconsumption which

addressed an expansionary military expenditure policy in a post-war economy as a medium to

invest the surplus without expanding production activities such that realisation crises are avoided

without an upsurge in wage or capital. Alesina, A., Spolaore, E. and Wacziarg, (2008) made a

noteworthy emphasis on the importance of safety as a public good and claimed that per capita

defence spending is a measure of safety as a public good which ensures a holistic investment

environment in the country. Smaller countries will have to spend comparatively more in this

public good than larger countries due to economies of scale. Furthermore, this theory has also

been backed by a simple linear regression test of the relationship between defence spending and

GDP growth in 15 of the largest and smallest economies of the world through the measure of R

square which explains the influence of the independent variable (here military expenditures) on

the variation in dependent variable (GDP at current prices) in percentages. The difference in the

value of R square between the two sets of countries explained the applicability of the military

Keynesianism theory based on the country's economic profile. While military expenditure

stimulated nearly 87% of the change in GDP change in the richest 15 countries, the same figure

is as low as 22% in the poorest countries. Henceforth this disparity perfectly explains the

economics of scale that richer and much more developed countries enjoy in the case of military

spending.

The Keynesian model, being the widely accepted one, dictates that the level of income inequality

is scaled down during the expansion of the economy and its size increases during the contraction

of the economic output and thus the poor gain relative to the rich during peaks of the business

cycles, taken that ensuring efficient resource allocation. However, a major literature gap in many

types of research by the Keynesians on this model is the lack of emphasis on the study of India to

test this theory and no major analysis on the non-uniformity of the theory’s applicability and the

results in various types of countries with diverse economic profiles. Tiwari, Aviral, and Shahbaz,

Muhammad (2011) extensively studied this relationship in the Indian context and concluded that

an expansionary defence expenditure policy certainly stimulates economic growth; however,

huge deviation of allocation of public resources from social welfare to the defence sector may

even cause an inverse effect after reaching a certain threshold point.

3. Data and Methodology

Military expenditure as an indicator of economic prosperity is barely used in research outside the

domain of defence economics. Henceforth the research also studied popular macroeconomic


indicators of economic well-being like exports, Gross domestic product, and manufactural output

along with military expenditure. The data for the aforementioned variables for India from the

period between 2001 and 2021 has been sourced from databases published by SIPRI (Stockholm

International Peace Research Institute) and MacroTrends.org while official statistical projections

about the government policies in this domain have been sourced from Annual reports published

by the Ministry of defence, Government of India and defence budget analysis by Manohar

Parrikar Institute for defence Studies and Analyses.

3.1. Preliminary Analysis

The research also focuses on the preliminary analysis of the dataset’s descriptive statistics and

regression tests which shall further help to evaluate the nature of the very data being considered

for the research.

Variables Mean Standard Deviation

GDP 1688.9447 835.22803

Military 43.5209 20.21523

Export 349.5323 178.19667

Manufacture 259.3078 116.83847

Table 3.c: Descriptive statistics analysis of the given data; four variables i.e. Gross Domestic Product, Military expenditure,

Export, Manufacture; Source: Author’s analysis


3.2. Relationship Map

Table 3.d: Correlation analysis between the variables; Source: Author’s analysis

A relationship map helps us to analyse how the given sets of variables move in the data set, or in

what aspects they are interlinked with one another. Through the relationship diagram drawn from

the author’s analysis, we can interpret that military expenditure and GDP are two such variables

that have progressed parallelly in the given historical data. A major inference from this

observation could be the reiteration of the ‘Safety as a public good’ theory mentioned in the

literature review that with the growing size of the population and economy, military expenditure

is a variable that would have to augment with time to ensure a sufficient quantum of per capita

safety (through a rise in defence expenses) available to every individual and the assets he/she

possesses.

Multicollinearity, in statistical terms, refers to a situation when two or more independent

variables considered in the project are correlated to each other. Such a condition creates several

problems in smoothly running a regression model and can cause manipulation in the final results.

It can be interpreted through indicators like VIF (Variance inflation factor) and collinearity

tolerance. A VIF factor above 10 and collinearity tolerance below 0.1 signifies high levels of

multicollinearity.

s

by1,2

=

2

s y,12

2 2

∑x (1−r12

1 )

(1)


Under the aforementioned relation, we find the standard error of the b weight which is equal to

the mean square residuals over the sum of squares x1 times one minus the square correlation

between two variables.

Figure 3.e: Relationship map between GDP and Military Expenditure; Source: Author’s Analysis

2

s

by1,2

=

2

s y,12

= s y,12

2

∑x 2 (1−r12 ) 2

1

∑x 1

2

⎡

⎢

⎣

1

2

1−r 12

⎤

⎥

⎦

(2)

s

by1,2 k =

2

s y,12…k

2 2

∑x (1−R1,2…k

1

)

(3)

VIF 1

=

1

2

1−R 1,2…k

(4)

The value of Collinearity Tolerance happens to be the reciprocation of VIF factor. Three of the

four variables the research focuses on, i.e. Exports, manufactural output, and Gross domestic

product exhibit high levels of multicollinearity among themselves. Thus, such a case presents a

dilemma in the research methodology to judge which variable should be taken as dependent or

independent. To solve multicollinearity, we can choose any one of the independent variables, i.e.

military expenditures (since the aim of the research remains to assess the relationship between

defence outlay and Gross domestic product) and eliminate the rest.


Variable Collinearity Tolerance Statistics VIF

Military expenditure 0.029 34.699

Output 0.018 56.691

Exports 0.041 24.357

Table 3.e: Multicollinearity analysis, Variance inflation factor and Collinearity Tolerance

3.3. Granger Causality Tests

The Granger Causality test is a statistical hypothesis test which helps us to assess the causality of

one variable upon the either, with both of the variables tested being given a chance of acting as

independent and dependent variable. It was developed in 1969 and emerged as a significant test

to study the causal relationship between two time series variables more accurately instead of

merely assessing the relationship through the coefficient of relation. Granger Causality test

assesses a predictive causality, or precedence of one variable upon the other.

y( t) =

∞

∑ α i

y( t − i) + c 1

+ v 1

( t)

i=1

(5)

y( t) =

∞

∑ α i

y( t − i) +

i=1

∞

∑ β j

x( t − j) + c 2

+ v 2

(t)

j=1

To conduct a granger causality test, firstly lags (i) and (j) are selected by running a model order

selection method such that the result is sensitive to the lags chosen. Further, the F value is

calculated using the aforementioned formula.

x( t) =

∞

∑ α i

x( t − i) + c 1

+ u 1

( t)

i=1

(6)

x( t) =

∞

∑ α i

x( t − i) +

i=1

∞

∑ β j

y( t − j) + c 2

+ u 2

(t)

j=1

F =

(ESS R

−ESS UR

)

q

ESS UR

(n−k)

(7)


Based on the value of the F statistic (through the last mathematical formula mentioned) we

accept or reject the null hypothesis. For an analysis of the Granger Causality Test, data from two

different timelines have been taken for India and its neighbouring country Pakistan to examine

the causal relationship between defence spending and GDP during the post-war economic era

and times of overall military truce.

4. Empirical Results

4.1. Granger-Causality Test

Case- I: Economic era during wars (1960-1975)

During 15 years between 1960 and 1975, India engaged in the Sino-Indian war of 1962, and

1965, and 1971 Liberation War with Pakistan. Hence this period is certainly suitable enough to

study the stimulus in the Indian economy due to abnormal military expenditures due to wars.

Equation Excluded Chi2 df Prob > Chi2

Ln GDP

Ln GDP

Ln Military

Ln Military

Ln Military

ALL

Ln GDP

ALL

0.70928 2 0.701

0.70928 2 0.701

14.218 2 0.001

14.218 2 0.001

Table 3.f: Granger Causality Wald tests for the era of Wars; Source: Author’s analysis

From the given Granger Causality Wald tests, with a significance value of 0.001, we reject the

null hypothesis that GDP doesn’t Granger cause military expenditures. This result brings a new

dimension into the study of military Keynesian because so far the research focused on military

expenditures as an independent variable in itself which had influenced GDP size. However, here,

we see that during times when the country is surrounded by wars, the aggregate GDP size in fact

granger causes the military expenses incurred by the government.

Case II: During Military Truce (2001-2021)

Equation Excluded Chi2 df Prob > Chi2

Ln GDP

Ln GDP

Ln Military

Ln Military

Ln Military

ALL

Ln GDP

ALL

1.3485 2 0.510

1.3485 2 0.510

4.5473 2 0.103

4.5473 2 0.103

Table 3.g: Granger Causality Wald tests for the era of Military Truce; Source: Author’s analysis


Apart from cross-border skirmishes, and military standoffs, India has not witnessed any major

war after 1999 and the reflection of this fact can be seen in the significance levels of the

aforementioned Causality Wald tests where the significance level lies above 0.05. Thus, we

accept the null hypothesis that military spending does not stimulate Aggregate GDP growth

during the era of Military truce.

Henceforth, from the Granger causality tests we can derive the following conclusions:

1. Military expenditures can stimulate economic growth majorly during the post-war

economic healing phase. During times of Military Truce, it is unable to create a stimulus

on change in aggregate GDP.

2. Military expenditures, especially during wars, can also act as variables dependent on the

size of the aggregate GDP to ensure better safety for the nation’s economic activities and

assets.

5. Conclusions and Limitations

From the research it can be concluded that in the Context of India, Military Keynesianism theory

is not sustainable for every economic phase the country would go through. In other words,

Military Keynesianism appears to be successful in Post-war economies where abnormal rise in

military spending are economic healing. While in conditions of Military truce between India and

its neighbours, an abnormal rise in defence expenditure is unable to induce an overall rise in

aggregate GDP.

However, it is important to note that the government’s budgetary policy profoundly makes

fluctuations in the budgetary outlays of modernization and other forms of capital expenditures

that are not recurring and occasional, i.e. acquisition of advanced military equipment beyond the

level of sustenance of the country’s defence mechanisms. Thus the very essence of military

Keynesianism theory, which propounds that budgetary outlays on defence nurture the growth of

aggregate GDP can be explained through the Indian government's budgetary scepticism in

‘moderating’ up the defence outlay by cuts in modernization and non-recurring capital

expenditures; since the government aims to maintain an optimal level of defence budget

allocation in times of military truce such that it doesn’t lead to excessive deviation of public

resources from being allocated to the public sector.

A central policy suggestion for the budgetary framework for defence is the aggregation in

research and development expenses which can help to sustain long-term cost-effectivity and

modernization in the Indian army. As per the Global Innovation Index 2022, India ranks 53rd

globally in terms of spending in defence R&D, spending as low as just 0.7% of its GDP.

A limitation of the research pertains to the non-stationarity of time series variables used, which

could be explained by the disparity in results of the Granger causality tests between the two


different periods considered. In case of non-stationarity of the time series, difference variables

can be used to conduct the granger causality test.

6. References

1. Aviral Tiwari, Shahbaz, and Muhammad, "Does Defence Spending Stimulate Economic

Growth in India?," 2011, https://issuu.com/ecosoc.ssc/docs/te_2022-merged_1.

2. Amir Aijaz Syed, "The Asymmetric Relationship Between Military Expenditure, Economic

Growth and Industrial Productivity: An Empirical Analysis of India, China and Pakistan Via the

NARDL Approach," 2021,

https://www.researchgate.net/publication/350950316_The_Asymmetric_Relationship_Between_

Military_Expenditure_Economic_Growth_and_Industrial_Productivity_An_Empirical_Analysis

_of_India_China_and_Pakistan_Via_the_NARDL_Approach.

3. J. Paul Dunne, "Military Keynesianism: An Assessment," 2011,

https://www2.uwe.ac.uk/faculties/BBS/BUS/Research/economics/Military%20Keynesianism%2

0An%20Assessment.pdf.

4. Errol Anthony Henderson, "Military Spending and poverty," 1998,

https://www.jstor.org/stable/2647920?read-now=1&seq=5.

5. H. Sonmez Atesoglu, "Defence Spending Promotes Aggregate Output in the United States--

Evidence from Cointegration Analysis," 2002,

https://www.tandfonline.com/doi/pdf/10.1080/10242690210963.

6. Rajiv and Singh, "Defence Budget 2023–24: Trend Analysis," 2023,

https://idsa.in/system/files/issuebrief/ib-smuelcrajiv-abhayksingh_170223.pdf.

7. Prs Legislative Research India, "Demand for Grants 2023-24 Analysis," 2023,

https://prsindia.org/files/budget/budget_parliament/2023/DfG_2023-24_Analysis-Defence.pdf


The Goddess in the Lanes: Gender Discrimination and Economic Struggles

of Women Idol Makers in Kumartuli

- Sahil Pradhan 1

Abstract: In the serpentine lanes of the Kumartuli area, often filled with mud and foul odours,

the goddess takes her shape under the skilled hands of idol makers. Though pandal hoppers

across Kolkata worship over 4000 idols in pomp and grandeur during the festive season, they

likely do not know that almost all of them originated from just 400-odd workshops in this artisan

hub. For a few months every year, Kumartuli employs over 300,000 workers directly or

indirectly for the festive idol-making rush. But only 20-25 of them are women artisans, even as

the 'Maa' they create is worshipped across the city. This stark gender disparity leads to

discrimination that seeps into the craft and curtails employment opportunities for women due to

societal customs and beliefs. The women idol makers of Kumartuli know well that battling these

living, breathing demons will be an uphill struggle. In-depth analysis of 10-year employment

trends, compensation and orders data along with personal interviews with women artisans,

especially the Pal quartet, highlights the worsening gendered economy in Kumartuli and its

detrimental impact on the idol-making industry. Uncertain climatic conditions, acute labour

shortages, reduced compensation and societal stigma continue to throttle economic progress for

these talented women. This article elucidates the reasons for such discrimination and highlights

the resulting hidden economic crisis, even amid festive plenty.

1. Introduction

In the alleys of Kumartuli, where the goddess manifests amidst mud and odours, skilled artisans

craft thousands of idols for Kolkata's grand festivities. Despite employing over 300,000 workers

annually, only 20-25 are women, perpetuating gender discrimination rooted in societal norms.

Interviews with artisans, including the Pal quartet, reveal worsening economic prospects due to

climate uncertainties, labour disputes, and meagre compensation. This article exposes the hidden

economic crisis fuelled by gender disparity, hindering progress amidst the abundance of the

festive season.

2. The Festive Boom and its Artisans

During Kolkata's vibrant Durga Puja festivities, over 400 registered workshops in Kumartuli

employ 3000 artisans, with an estimated total of 5000. They craft over 4000 idols, celebrated

nationwide. The frenzied season engaged around 300,000 workers in allied services. Amidst

challenging conditions, artisans innovate with sustainable materials, preserving tradition while

1. Atma Ram Sanatan Dharma College, Delhi


embracing contemporary themes. Durga Puja epitomises their passion and devotion, igniting

joyful celebrations citywide.

Fig 4.a: Gender Divide in Artisans of Kumartuli

Surprisingly though as per the Samity's membership records, only 22-25 of the over 3000

registered artisans are women. Kumartuli thus thrives on a heavily gendered workforce centred

around men as primary earners and women only in supporting capacities like painting assistants

or construction helpers. Even for the 'Mother Goddess', societal discrimination severely curtails

economic and skilling opportunities for women artisans.

3. Understanding the Pal Quartet:

In the heart of Kumartuli, Mala Pal, buoyed by familial support, established herself as an

acclaimed artist, striving to uplift other women in the craft. Despite initial familial pressures, she

pursued recognition, dreaming of a school exclusively for women sculptors, while juggling

multiple responsibilities, including caring for her ailing mother. Specialising in miniature idols,

her craftsmanship garnered international acclaim, reflecting her determination amidst financial

hardships and scepticism.

In contrast, Kakoli Pal's journey into idol-making stemmed from necessity after her husband's

demise. With no formal training, she overcame challenges, expanding her business beyond

Kolkata despite ongoing struggles, including safety concerns and worker poaching. Despite

sculpting idols in narrow lanes without a studio, Kakoli perseveres, aiming to broaden her

clientele beyond Bengal.

Similarly, China Pal's story epitomises resilience, as she inherited her father's studio and

mastered the craft despite initial scepticism. Managing a team of 12, China's dedication to her

profession outweighs personal challenges, emphasising the demanding nature of idol-making in


Kumartuli. In contrast, Kanchi Pal represents a new generation of artists, seamlessly managing

her workshop and challenging gender norms in the industry, inspired by her mother's legacy.

While Mala and Kakoli envision growth and expansion, China and Kanchi navigate modern

challenges, from social media attention to bureaucratic neglect. Despite financial struggles and

the lack of institutional support, these women artists embody hope for the future, inspiring others

to join the craft through their creativity and dedication.

Amidst a changing landscape, Kumartuli's women artists continue to carve their place in history,

symbolising resilience, creativity, and determination against all odds. Their stories illuminate the

gender-specific challenges faced by women in the industry, yet their unwavering spirit

underscores the transformative power of art in shaping communities and inspiring generations to

come.

4. The Insidious Creep of Gender Discrimination

In recent years, Kumartuli’s community has suffered tremendously from climate vagaries, labour

shortages, and worrying compensation trends. Their experiences reveal a creaking economy

centred around inherent gender bias.

Increasingly erratic monsoonal rainfall over the past decade has severely disrupted idol

production schedules, confirming climate researchers’ predictions. Work usually starts by

June-July once rainfall reduces but has often extended through September now. The artisans are

forced to cover structures with temporary shades while chasing any sunny interval for drying the

clay. However such adjustments hike infrastructure costs and do not fully prevent damage from

persistent humidity. As per organisers’ estimates, over 20% of orders now face weather-related

delays or last-minute fixes.

Fig 4.b: Expenses and Losses of the Artisans in Percentage


Labour availability has also dipped drastically due to poor compensation and a lack of contracts.

Per capita idol artisan incomes as per Union surveys have remained stagnant around Rs

6000-8000 monthly despite inflationary pressures. But the strenuous work requires being on

one's feet for 14-16 hours daily amid extreme heat, humidity, and dust. Workers now prefer

flexible construction jobs under rural employment schemes that assure minimum daily wages

without intensive labour or health impacts.

With expanding production, organisers also negotiate pricing very aggressively to control

budgets. Competition from cheaper fibreglass idol makers has strained workshops’ shaky

finances further. Material and labour now account for 60-70% of the artisans’ expenses as they

refuse to compromise on quality. Reducing profit margins leaves a minimal buffer for external

uncertainties.

While all artisans face such hardships now, the issues get particularly exacerbated for women

due to prevalent gender bias. They constantly battle the notion that idol-making is a 'male'

domain with doubts about their design and sculpting prowess. Organisers, even labourers resist

taking orders from women supervisors. Contract negotiations focused primarily on the lowest

cost completely dismisses their extensive expertise.

As single earners, they cannot match workshops with more artisans churning out cheaper

fibreglass idols. Abandonment by their husbands denies them both spousal income and

legitimacy within the local community. Constant barbs like "women lack creative visions" or

"can't handle production responsibilities" hurt more than their calloused hands, even after years

of proving their excellence. China articulates this discrimination candidly,"We carry the same

sacks of clay, sculpt similar figures. But our hourly wages or schedule flexibility remain

secondary considerations, if at all.”

The societal stigma and erosion of compensation directly throttles women's economic progress

and career growth. There exists no platform recognizing their artistic talents or unique feminine

perspectives on idol conceptualization. For them, the frenzied festive season does not represent

increasing prosperity but rather deepening uncertainty and despair. As the quotes suggest, all

artisans face such disruptive trends impairing Kumartuli’s economy currently. However, the

issues get particularly exacerbated for women due to prevalent gender bias bordering on

discrimination.

Societal mindsets doubting women artisans’ competencies remain rampant, dismissing their

extensive expertise as trivial or inferior. As per the Union’s member survey, over 40% of new

client outreach ends unsuccessfully due to inherent scepticism from organisers on assigning

major projects to women-led teams. Tech-savvy young women artisans looking to modernise the


craft using digital sculpting or online branding also report facing prejudice while pitching their

ideas to traditional community stakeholders.

Fig 4.c: New Client Outreach Orders Cancelled for the Pal Quartet

Contract negotiations focused primarily on the lowest production cost completely ignoring the

women's teams’ finesse, quality assurance, and rich experience. Their status as single earners

supporting families denies them the flexibility of workshops with multiple income streams that

can underbid rates. The Pals highlight how even after proving expertise over the years, they still

fight notions that idol-making remains a ‘male’ domain.

The erosion of earnings and respect directly throttles their career advancement. There exists

negligible formal skilling support focused on harnessing women’s unique perspectives. The

limited funding or incubation avenues lead bright young talents like their daughters or others in

the community to consider abandoning their ancestry. For most women then, the frenzied festive

season represents deepening uncertainty rather than increasing prosperity.

5. Reasons for Persistent Discrimination

India's current labour statistics portray a worsening gender disparity in workforce participation

and job vulnerability over the past decade, partly attributed to persisting stigma. As per 2022

surveys by CMIE, India's overall unemployment rate hovered around 7-8%. But while male

joblessness fluctuated between 5-7% with cyclical variations, women faced double-digit

unemployment throughout, peaking at 47% during COVID lockdowns.5 Currently, only around

20% of working-age urban women actively engage in the workforce.

Moreover, existing gender gaps in entrepreneurship ecosystems greatly disadvantage women-led

enterprises, as evident among Kumartuli's artisans. A survey report studying Indian women

entrepreneurs revealed only 2% of all venture funding over 2014-19 got allocated to all-female


founding teams while mixed-gender leadership startups received 79% of the funding. Inadequate

access to capital, incubation, networks, and role models denies women entrepreneurs the support

systems allowing sustainable scaling.

5.1. Structural Challenges Around Women's Work

Goldin's analyses help highlight how such stark gender divides often get shaped by 'structural'

barriers within professions imposing greater trade-offs for women between careers and family

duties.

Her research traced the emergence of the US gender wage gap during the 20th century to

differences in male and female labour force participation. Women tended to exit from inflexible

yet highest-paying occupations the most as their domestic responsibilities increased after

marriage. But male incomes kept rising as they gained experience without career breaks. Goldin

thus differentiates between labour divided by gender versus divided merely along skill lines.

Eliminating the former even within skilled sectors can promote merit-based equitable

participation. However cultural definitions and evaluation of 'high skill' act as man-made

barriers preventing women's opportunities.

Kumartuli similarly demonstrates a highly skilled domain with stringent demands on labour time

and mobility that disadvantages women disproportionately due to societal stigma. Rampant

scepticism over female artisan capabilities often limits their access to large patronage contracts,

training, and mentor networks perpetuating unequal success divides. Constantly battling doubts

around professionalism and overcoming family obligations adds mental labour draining

motivation levels.

5.2. Structural Support - The Sustainable Solution

Goldin optimistically argues that such 'structural' divides limiting women's workforce

participation signal focus areas for policy reforms. Solutions promoting labour flexibility, remote

work, access to capital, skill training, and leadership representation can transform gender gaps

across domains.

Kumartuli similarly needs a structural revamp in its patronage, training, and financing models

focused on merit and craft sustainability. Innovative institutional support through digital

platforming, co-working spaces, skill incubators, and microfinance access exclusively for its

women artisans can promote enterprise stability. Sensitising all ecosystem stakeholders and

bringing successful women role models to the forefront would also help tackle discrimination.


Goldin views the rapid narrowing of medical career gaps through innovative flexibility policies

as a template for structured transformations achievable in a generation. Kumartuli likewise can

transition into an equitable globally renowned crafts hub if its structural flows receive prompt

policy redressal. Else resisting forces of stigma risk draining the hub of all its abundant feminine

talent and energy.

5.3. The Road Ahead

Kumartuli's present turmoil thus mirrors broader Indian and global challenges around expanding

women's workforce equity. The trends demand urgent public-private collaborations delivering

structured changes promoting sustainable women's livelihoods.

Skill-building programs, access to finance, formal contracts and social security must target

women specifically to offset existing disparities. Progressive labour regulations, incubation

support, and results-based financing can incentivize their productive participation. Platforms for

collective voice and representation would help shape policy dialogues per their priorities.

However such external structural shifts need aligning with internal mindset changes to sustain

progress. Sensitization drives and measurable progress tracking must engage entire communities

towards equitable opportunity unshackling women's potential. Lasting prosperity shall only

emerge once feminine creativity and leadership get accorded equal prestige as male counterparts.

The Pal quartet's example demonstrates that when provided adequate nurturing support despite

societal barriers, women artisans can reach glorious heights even in traditionally masculine

spaces. They continue chipping away at glass ceilings, quietly transforming karmic cycles of

discrimination. The final idol to be dismantled now is that of regressive public attitudes doubting

women's capabilities. For India's goddesses shall bloom fully only when the feminine spirit flies

free of man-made shackles!

Academic researchers have studied the Kumartuli gender dynamics extensively to highlight key

reasons societal stigma and discrimination continue thriving despite rapid urbanisation and legal

safeguards.

The religious outlook viewing the goddess Durga and other female deities as the universal

mother and protector promotes conceptions of women primarily as homemakers rather than

career professionals. Dr. Sayantani Banerjee, a gender studies professor notes, "Resistance

persists against women entering non-traditional roles like hands-on idol making which gets

labelled masculine work. Women artisans thus constantly battle doubts about their serious

vocational abilities. As Hindi cinema showed through Pink, our society still accords women's

conditional legitimacy."


Moreover, women depend greatly on familial support to balance long artisan hours with

domestic duties. However, the intergenerational craft traditionally got passed down among male

family members only. Reema Das, PhD Scholar researching artisan women highlights,

"Daughters or wives assisting actively in workshops is a recent trend over 2-3 decades only. The

current elder generation remains unable to reconcile.”

6. The Future Beckons with Hope and Despair

The Pal quartet symbolises women artisans overcoming male-dominated barriers, yet facing

uncertainty. Government and private efforts train more women, combat discrimination, and

promote sustainability. Challenges include declining patrons,attracting youth, and fibreglass

dominance. Compensation and stability initiatives aim to improve conditions. Mala's optimism

fuels hope, envisioning a future where more women shape the Goddess. Despite ongoing

struggles, the resilience of Kumartuli's artisans shines through. However, without swift action

against discrimination and economic challenges, darkness looms over these historic lanes,

particularly for women artisans, making their battle urgent.

7. References

1. Mukherjee, U. (2023, October 15). In thy likeness, their unlikeness, darlings: Trials of

Kumartuli’s women artisans. Kumartuli | In thy likeness, their unlikeness, darlings: Challenges of

Kumartuli’s women workers - Telegraph India.

https://www.telegraphindia.com/feeds/culture/in-thy-likeness-their-unlikeness-darlings-challenge

s-of-kumartulis-women-workers/cid/1973466

2. Global Climate Care. (n.d.). Climate change damages road of Durga’s home coming.

https://globalclimatecare.in/climate-north-america/f/climate-change-damages-road-of-durgas-ho

me-coming

3. Kolkata: Rain spells trouble for Kumartuli Idol-makers ahead of festive season - times of

India. The Times of India. (n.d.)

https://timesofindia.indiatimes.com/city/kolkata/rain-spells-trouble-for-kumartuli-id%20ol-make

rs-ahead-of-festive-season/amp_articleshow/103096959.cms

4. Board, T. E. (2023, August 5). Letters to the editor: Climate change hurdle for Kumartuli

artisans ahead of Durga Puja. op-ed | Letters to the Editor: Climate change hurdle forKumartuli

artisans ahead of Durga Puja - Telegraph India.

https://www.telegraphindia.com/amp/opinion/letters-to-the-editor-climate-change-hurdle-for-ku

martuli-artis%20ans-ahead-of-durga-puja/cid/1956894

5. Board, T. E. (2023, August 5). Letters to the editor: Climate change hurdle for Kumartuli

artisans ahead of Durga Puja. op-ed | Letters to the Editor: Climate change hurdle for Kumartuli

artisans ahead of Durga Puja - Telegraph India.


https://www.telegraphindia.com/amp/opinion/letters-to-the-editor-climate-change-hurdle-for-ku

martuli-artis%20ans-ahead-of-durga-puja/cid/1956894

6. Rathore, M. (2024, March 5). India: Unemployment rate by gender. Statista.

https://www.statista.com/statistics/1306898/india-unemployment-rate-by-gender/

7. “Why women won.” “Why Women Won.” (n.d.).

https://scholar.harvard.edu/goldin/publications/why-women-won

8. Chowdhury, S. (2023, September 18). At Kumartuli, handful of women idol-makers break

tradition, taboo as they leave an imprint. The Indian Express.

https://indianexpress.com/article/cities/kolkata/at-kumartuli-handful-of-women-idolmakers-break-tradition-taboo-as-they-leave-an-imprint-8944559/


The Enigma of Degrowth: What, Why and How?

- Amrit Thakur 1

Degrowth, as the name suggests, is a radical economic theory that advocates decrease of

consumerism and intelligent use of resources in the society. It widely opposes the ‘capitalist’

idea of endless pursuit of increasing output and instead supports shrinking the economy. André

Gorz, an Austrian-French social philosopher is said to have coined the term ‘degrowth’ in

the1970s. The primary motive behind the growth of this idea was the philosophy of shift of

anthropocentrism to ecocentrism. Degrowth began to become popular as a movement in the early

2000s, as climate change concerns started to gain traction. The advocates of degrowth want to

stop viewing Gross domestic product (GDP) as a measure of economic development. Instead,

they want the world economy to put the well-being of the society ahead of output.

1. What goes behind the idea of degrowth?

It is important to note that degrowth doesn't imply decreasing the Gross domestic product. It

simply means decreasing the amount of resources and energy used. Modern degrowth promoters

like the French economist Serge Latouche, claim that the current model of world economy is

unsustainable and highly hazardous for the planet in the long run. They argue that by pursuing

degrowth policies, economies can help themselves, their citizens and the planet by becoming

more sustainable.

Figure. 5.a : A ‘degrowth’ strategy could cut CO2 emissions by 2050 more deeply than alternative economic growth strategies;

Source: Nature Communications

1. Banaras Hindu University


The world economy has been centred around growth, i.e, expansion of production leads to

growth of firms, which leads to growth in consumer spending and results in growth of the overall

economic pie. However, this model has shown signs of failure over the years. The world has

witnessed several economic crises which testify the inherent flaws present in the model of

growth. The groundbreaking 1972 book, “Limits to Growth,” spotlighted our planet's sustainable

boundaries. This work evaluated how population, living standards, and resource utilisation

converge and affect sustainability. Almost four decades later, Professor Jorgen Randers, one of

the authors of the book, published an update titled "2052." Here, he highlighted a critical turning

point: our economic model becomes flawed when equity becomes central, and justice prevails. It

is historically seen that the growth of developed nations has come at the cost of dilapidation of

developing countries. It is also seen that the major contributors of greenhouse gases emissions

today are the upcoming middle-income countries like India, China and Brazil. This creates a

political dichotomy between the development policies of the advanced and the third-world

countries. It is at this juncture that the idea of degrowth comes into picture

Boston, USA, infrastructure of a state of the art harbour

Chennai, India; infrastructure incapable of containing floods


2. Why is the idea of degrowth so important today?

Western countries often equate a happy life with high resource consumption and wealth.

However, Bhutan offers a contrasting model. It introduced the "happiness economy," where the

nation prioritises citizens' happiness over economic growth, suggesting that happiness can be

decoupled from resource-intensive activities. The idea of growth, however, continues to

dominate global strategies, evident in the UN's Sustainable Development Goals (SDGs). SDG

number 8, for instance, emphasises "decent work and economic growth." Recent Holberg Prize

awardee, Professor Joan Martinez-Alier, has openly criticised this, arguing that such a goal might

be incompatible with other SDGs. The goal of degrowth is to achieve better well-being and

improved ecological conditions, reducing the size of the global economy to fit within the planet's

biophysical limits.

3. How does degrowth fit in the world today?

There are several key principles to degrowth, including sustainability, social well-being, equity,

direct democracy, and localised economies. Understanding degrowth also requires us to examine

the concept of demand reduction. This can be categorised into three intertwined yet distinct

components:

Efficiency: Maximising output while minimising resource use. It's about doing more with less.

Sufficiency: Re-evaluating the amount of production and consumption truly necessary for

human well-being.

Behavioural Change: Shifting societal habits towards sustainability, wherein society

collectively and willingly opts for less consumption. Degrowth favours a strategy of hegemony

through a number of cultural drivers.It is a concept-platform with multiple meanings, shaped by

five sources of thought:

1. The ecological source, which affirms the primacy of nature

2. The bio economical source, which accepts the limits of economic growth

3. The anthropological source, which calls into question the uniformization of the world

4. The democratic source, which re-legitimizes public debate

5. The spiritual source, which responds to the crisis of meaning in modern societies.

Balancing the national economy will require new macroeconomic models that combine

economic, financial, social and ecological variables. Models such as LowGrow SFC (developed

by T.J. and P.A.V.), EUROGREEN and MEDEAS are already being used to project the impacts

of degrowth policies, including redistributive taxes, universal public services and reductions in

working time. But these models typically focus on a single country and fail to take into account

cross-border dynamics, such as movements of capital and currency. New forms of financing will


be needed to fund public services without growth. Governments must stop subsidies for

fossil-fuel extraction. They should tax ecologically damaging industries such as air travel and

meat production. Wealth taxes can also be used to increase public resources and reduce

inequality.

Government action is at the core of implementation of degrowth. Most developed countries have

neoclassical ideology at the core of their government. It is therefore a challenge to persuade

those in power to make and implement new policies. Strong social movements are necessary.

Addressing the question of how to prosper without growth will require a massive mobilisation of

researchers in all disciplines, including open-minded economists, social and political scientists

and statisticians. Research on degrowth and ecological economics needs more funding, to

increase capacity to address necessary questions. Also, the agenda needs attention and debate in

major economic, environmental and climate forums to create a better climate of political

epistemology and action.

4. References

1. Giorgos Kallis, Federico Demaria, Giacomo D'Alisa. Degrowth , International Encyclopedia

of the Social and Behavioural Sciences (Second edition), Elsevier, pg 24-30, 2015.

2. Jason Hickel, Giorgos Kallis, Tim Jackson, Daniel W. O’Neill, Juliet B. Schor, Julia K.

Steinberger, Peter A. Victor & Diana Ürge-VorsatzÜrge-Vorsatz. Degrowth can work - Here's

how science can help, Nature magazine (www.nature.com), 2022.

3. Victoria Masterson. Degrowth - What's behind the economic theory and why does it matter

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Analyses of Inequality and Its Impact on Economic Development

Gender and Spatial

- Nistha Shrestha 1

Abstract: As the world progresses, the primary goal of all nations is to attain economic growth.

In that competitive process, the basic principle of stability might not be of top priority. When so

happens, even the inequality concerns could take a back seat. Hence, in this paper, we shall

attempt to establish the impact that inequality, specifically gender and spatial, would have on

economic development. Furthermore, we will analyse the state-level scenario of these

inequalities. Finally, we will attempt to fit a model to determine the possible relationship

between economic development and various socio-economic factors. This paper was able to

identify some socioeconomic factors and the impact they had on economic development all the

while analysing inequality in the five states that were studied.

1. Introduction

Inequality often defined through income inequality is mostly interpreted as disparities in the

amount of income or per capita income that different people earn. It could be disparities between

individuals belonging to different nations or within a nation as well. In a more general term, we

could refer to how David Ricardo defined it. In his On the Principles of Political Economy and

Taxation (1817), Ricardo says, “[t]o determine the laws which regulate this distribution [between

rent, profit, and wages], is the principal problem in Political Economy.”

The oldest evidence of inequality as stated by Scheidel can be traced back to a Pleistocene burial

site near Moscow. In that, the graves of those primordial hunter-gatherers were not uniform.

Some of the graves had more prestigious items and larger ivory beads. Although there isn’t much

on the inequality of that time, it became prominent with the rise of agricultural societies that

brought with them social hierarchies. It so happened as these classes allowed for the existence of

a “ruling class” which began taking away surpluses from agriculture for their benefit. Inequality

began rising through the times of ancient Mesopotamia (1500-500 BCE). It can be evidenced

from the records of inheritance and dowries at that time. 2 Inequality has since then had its

periods of decline and primarily rise.

Inequality isn’t just a product of income disparities. The categorization of inequality into

“Inequality of Opportunity/Access” and “Inequality of Outcome” will help us in understanding

other factors that lead to inequality. With the former factors such as wealth, religion, gender,

residence and more come to play. The extent of the impact these will eventually have on

inequality will yet again depend on the nation and how its socio-economic situation. The latter is

concerned with the inequality that comes about as a result of inequality due to lack of access.

That is why in his book INEQUALITY: What Can Be Done? Atkinson, 2015 states that the

Inequality of Outcome will affect the inequality of opportunities for the next generation. When

1,St. Joseph’s University, Bangalore


we look at Inequality of Opportunity, it is affected not just by income but also by gender, age,

disability, sexual orientation, class, religion, and so on. These result in inequalities between and

within countries which pose serious concerns to today’s world. Most of these factors are

continuously contributing towards widening the gap and making inequality even more

prominent. Attributable to the impressive economic growth in the past 25 years, income

inequality between nations has improved. It has however worsened within nations with about

71% of the world living in nations with elevated inequality. This is the result of the majority of

wealth and income being concentrated only at the top. This only just results in worsened

conditions of the bottom percentile. It is due to these concerns that the United Nations has treated

confronting and reducing inequality with utmost priority. It has included it as an agenda of SGDs

"Leave no one behind".

There are many demographic difficulties coupled with gender-based discrimination.

Environmental degradation resulting in loss of land and livelihoods are all pushing people further

into the shackles of inequality But this is more complex as to help the ones ‘“left behind”, it

would be necessary to address the wealth, income, and the powers that comes with it with the

top. If so is not done then the needs of the marginalised communities will never be heard or

acknowledged. As long as people of power and affluence have decision-making -power, any

change that is perceived as a threat to them will be contradicted and the marginalised will yet

again be excluded. This is quite similar to how the mechanics of Inequality of Outcomes work.

In the many factors that result in Inequality, we will be taking a closer look at “Gender

Inequality” and “Spatial Inequality”. Gender inequality is experienced when an individual is

treated differently, mostly discriminated against, based on sex or gender where one gender is

prioritised over the other. It tends to start from their childhood itself. These happen due to the

gender roles determined and imposed by society and it is bound to differ from place to place.

[Save the Children]. Spatial inequality is a crucial part of the economic development of any

nation. It is important because development in most cases tends to be extremely uneven and

mostly centralised. This too like income and gender inequality will differ from country to

country and within the country comparison. When it comes to countries, it would be developed,

developing, and least developed nations. Within countries, we would normally see regional

disparities. This form of inequality is an important feature of developing economies wherein it

has been discovered to rise with economic growth and development.


2. Motivation of the Paper

India is referred to as a low-inequality country in discussions on a global scale. But if we were to

take a deeper look at it that is not the full picture. Globally, nations tend to use income inequality

to make comparisons on inequality whereas India uses consumption expenditure as its proxy for

inequality. That tends to cause differences and makes a proper comparative analysis of inequality

nearly impossible. And because consumption inequality tends to be lower than the income

inequality measures, India’s inequality is understated.

If we begin evaluating the income inequality of India instead, then the data from the Indian

Human Development Survey (IHDS) in 2005-06 places India among high inequality countries

with a Gini coefficient of 0.532. However, the Gini consumption expenditure coefficient of India

for 2004-05 according to the NSSO Consumption Survey was only 0.347, which indicated how

understated inequality is. This survey also highlighted the disparity in the consumption spending

of the top 10% and 20% with that of the bottom 20% and 40%. The most astonishing data was

the consumption spending of the top 10% was 29.90% whereas that of the bottom 20% was only

8.10%. [Table 1]

Table 6(a): Estimates of Income Inequality from NSSO Consumption Survey;

Source: Prepared by Himanshu, from NSSO unit level data.


The inequality in India doesn’t just end here in that it is stretched into different dimensions and

situations. India, a developing nation, is affected by inequalities of ownership of assets, primarily

land ownership, and inequality of opportunities that are found in access to education, nutrition,

healthcare, and so on. There is an added form of inequality here which entails social status that

covers caste, religion, and gender. The inequality of assets can be highlighted by looking at the

Gini coefficient of distribution of adult schooling years in the population which was 0.56 in

1999-2000. These inequalities can further be highlighted by the access of people belonging to

different states to services such as electricity and basic sanitation. There were evident disparities

among these states as well with less than 50% of rural households in states such as Bihar,

Jharkhand, Chhattisgarh, and Odisha having access to toilets whereas more than 70% of rural

households in states like Sikkim, Punjab, Mizoram, and more had access to toilets according to

data from 2011 census.

With all of these factors hinting at a high level of inequality, it is concerning that inequality in

India has been underestimated for such a long time and continues to be the same way. It has been

so because compared to poverty, inequality has received less attention in discourses as the nation

has been occupied with the extreme poverty that exists here. But it is even more concerning as

there has been heightened inequality with the increase in income since 1991. As inequality is

intertwined with development, it will affect the efforts of the government to tackle poverty and

attain sustainable development.

That is why this paper is attempting to take a look at the lesser looked into a dimension of

inequality to try to arrive at a conclusion that could be different from that drawn from the

consumption expenditure. The next section will include a review of similar literature in the fields

of Inequalities in the two dimensions under consideration and how it applies to India. Chapter I

of this paper attempts to establish the current scenario of Gender Inequality and Spatial

inequality around the world. Chapter II will delve deeper into the state-wise scenario of both of

these forms of inequality. Chapter III will analyse inequality in India as a whole and will include

some empirical analyses as well. The final section, chapter IV, will include a basic analysis of the

policies formulated to address these forms of inequalities and how effective they have been so

far.

Both gender and spatial inequalities are prominent all around the world and are furthermore

evident in India due to the heightened amount of gender-based discrimination and mostly

centralised development. One of the most common examples of these forms of inequality is the

female and male literacy rate which has further been divided into rural and urban literacy rates

for spatial comparison.

There has been a rise in literacy rate as of 2017 in comparison to 1951, but there is still about

15% literacy gap between the female and male literacy rate. This is one of the classic examples

of how belonging to a certain gender leads to them being discriminated against. This is only the


surface of how deep-rooted gender inequality is. We will look into more parameters of Gender

Inequality in the coming chapters.

When it comes to spatial inequality in India, we can observe this parameter itself. There is quite

a bit of difference between the literacy rates of rural and urban India. When we observe the total

literacy wages of the two are different by nearly 14%. There are also differences in the literacy

percentage of men and women in rural and urban areas. This goes on to show the disparities in

how many individuals tend to be educated in rural and urban areas. Some possible reasons for

these disparities could be that there aren't enough educational institutions or that low per capita

income made it compulsory for children to also go to work and contribute to expenses.

As India's development hasn't been equally divided and it has been attempting to tackle poverty

and social inequality across regions, we need to analyse spatial inequality to understand how it

plays into inequality as a whole.

3. Selected States

The states selected for this research are Bihar, Chhattisgarh, Haryana, Karnataka, and Kerala.

(Referred to Table 3)

Bihar, a state home to 100 million people, lies on the upper end of poverty with 36 million of its

inhabitants being poor. It also has concerning statistics with only 9% of its female citizens being

a part of its labour force. Gender inequality is prominent there and is now on the rise. The

facilities that its citizens have access to also raise concern with only 31% of households having

access to electricity. Although 73% of its people have access to drinking water, social inequality

deems many of its Scheduled Castes falling behind. [India States Brief-Bihar, World Bank.

(2016)]

Chhattisgarh has a fairly small number of people, 26 million, residing there with 10 million

being poor. The poverty rate is alarmingly high for a smaller state like it at 40%. The state being

mostly dependent on farming has still not been able to create enough job opportunities for

women. Along with this, maternal mortality is also very high with 221 deaths per 100.000 live

births. There too social inequality is prominent and it creates uneven distribution of and access to

resources like electricity and drinking water which is already less at 27%.[India States

Brief-Chhattisgarh, World Bank. (2016)]

Haryana is fairly similar in terms of the size of the population of Chhattisgarh but the poverty

rate there is much less at only about 11%. This is attributable to a faster decline in poverty in the

state. There has also been a consistent shift towards sectors other than farming which has led to

increasing jobs in the service sector but the job opportunities for women are still less which is

why only 19% were in the labour force as of 2012. Although poverty has been declining, caste

prejudices have left behind some communities but it hasn't affected the access that these


communities have to electricity or drinking water. [India States Brief Haryana, World Bank.

(2016)]

Karnataka has been seeing a decline in poverty in recent years with only 21% of its 61 million

population living under the poverty line. However, there are parts of the state that have very high

levels of consumption inequality. There has also been a fast shift from farm jobs to service

sectors but the jobs available so far have not been able to compensate for the large amounts of

job losses in the farming sector. This has also led to a sharp decline in female participation in the

labour force. Probing gender disparities, there is a lot of gender inequality in terms of schooling

and labour force participation, but maternal mortality has been showing a declining trend. In

terms of access to resources, most of Karnataka has access to electricity but the access to

drinking water is less and even less for people belonging to SC/ST communities. [India States

Brief-Karnataka, World Bank. (2016)]

Kerala, a pioneer in health and education, has 33 million inhabitants out of which only 8% of

them are living below the poverty line. As most of the people in Kerala work in the farming

industries, all the new job opportunities created were mostly in construction. There weren't

enough opportunities for women due to which only 32% of women were a part of the labour

force. However, when compared to most other parameters, gender inequality is not as prominent

there with improved sex ratios and low maternal mortality. Social inequality also isn’t as severe

there with most people having access to electricity and drinking water. [India States

Brief-Kerala, World Bank. (2016)]

Concerning the presented scenario of the above-mentioned states in the period 2011-13, we can

see that many of them had high levels of gender inequality. Many had managed to tackle income

inequality to a certain extent by reducing poverty but when it came to gender there was still a

long way to go except for Kerala. Even in a richer state like Haryana, gender disparities were

concerning. The same applies to the first four states when it comes to spatial inequality. With

India being highly influenced by factors like religion and caste, it further contributed to

heightening spatial inequality. Hence, this paper attempts to study and evaluate how gender

inequality and spatial inequality are in these states by looking at more parameters and how they

contribute to it and finally how it affects the overall inequality in those states by comparing their

gini coefficients and socioeconomic factors. These very states were chosen to evaluate different

levels of inequality in terms of rich, poor, densely, and sparsely populated characteristics and

possibly identify patterns.

4. Methodology

All the data used in this paper is secondary data obtained from reliable sources mostly

Comprising NFHS reports, Swachh Bharat Mission Reports, World Bank Reports, and similar

works of literature. These data sets were obtained to analyse the current scenario of gender and

spatial inequality in a select few states and then obtain these statistics for India as a whole. That


will be followed by a look into how all of it affects India’s inequality as a whole and a brief

comparison with similar nations. To analyse how much impact both gender inequality and spatial

inequality make on inequality as a whole in India, a multiple regression model will be used.

All the data for this analysis has been compiled from The World Bank Database. The data

analysis has been done with the help of R and the descriptive statistics have been visualised

using MS Excel.

5. Objectives

1. To analyse the impact of different forms of inequality on economic development on a

global level.

2. To understand the impact of gender and spatial inequality with a detailed review.

3. To do a qualitative analysis of the state of inequality in selected states: Bihar,

Chattisgarh, Bengaluru, Kerala, and Haryana.

4. To identify proxies for socioeconomic factors to quantify gender and spatial inequality.

5. To fit a model that establishes a relationship between the selected proxies and GDP (a

proxy for economic development).

6. Review of Literature

Inequality often used as an umbrella term to describe disparities of any type at all can be

segregated into different types of inequality. So accordingly, several works of research have

studied a plethora of inequalities and their impact on inequality as a whole and eventually

economic development as a whole. We will be reviewing a few of these works of research

relevant to our research with a focus on developing nations.

6.1. Income Inequality and Economic Development

Income inequality is often at the crux of the development of a nation. It gravely affects how

accessible development has been and what the focus of the governments of respective nations

has been. In the case of India, income inequality tends to be measured differently than in other

nations. Many calculate income inequality based on the per capita income whereas India

observes it based on the consumption expenditure of the household. But at the core of both these

approaches, lies the inequality of opportunity; the opportunity of earning a livelihood. It is so

because if someone does not get an opportunity to make money, they will not be able to consume

either. According to the Kuznets hypothesis, with a hike in development, income inequality will

also increase. Soon enough, it will result in the emergence of a middle-class income group that

will encounter a decline in inequality. Hence there would be an inverted U pattern which is also

called the Kuznets curve. It is normally measured using the Gini coefficients. India’s Gini

coefficient increased from 0.364 to 0.357 from 2018 to 2019 which hints that there was an

increase in income inequality.


Figure 6.a: The Kuznets Curve; Source: Northeastern University Economics Society

6.2. Gender Inequality and Economic Development (including education as well)

With economies striving towards development and their policies being modelled around growth

and development, gender inequality is gaining a lot of interest, and academicians and politicians

are also focusing on it. It exists more evidently in developing countries like India with the

significant gap that exists between men and women in the labour market, representation, and

decision-making power. For example, in 2007, women in India received only $0.64 for every $1

that a man earned. Some attribute the disparities to be a result of differences in physical abilities

and others put forth an explanation that the gender gaps exist as women spend comparatively

more time in unpaid household work. There have been many theories that have attempted to

investigate the impact of economic development on gender inequality.

Greenwood, J., Seshadri, A., and Yorukoglu, M. (2005) argued that one explanation behind any

possible effect is that technological progress led to more capital-intensive production. That

resulted in women having more time to get involved in the labour market and that increases their

involvement in the labour market. Hence, improvement in the economy resulted in a reduction of

the gender gap. Similarly, Lagerlof, N.P. (2003) postulated that education equality in terms of

gender would result in a positive impact on economic growth. He formulated it based on a

coordination game that would result in the Nash equilibrium condition being gender

discrimination. He said economies would eventually move towards a more gender-equal

equilibrium, which would result in higher stock of capital (human) and faster growth.

Although there have been many theoretical attempts to try and explain the gender gap, there have

also been attempts to empirically reason. Dollar, D., and Gatti, R. (1999) researched the impact


that economic growth had on the education gender gap and strongly stated that a rise in per

capita income led to a reduction in gender inequality in education. But the relationship of the

impact was different. For developed nations, this rise in per capita income did reduce the

education inequality but there was barely any difference for developing or least developed

nations. There is also a reverse impact of gender inequality on economic growth. Bloom, D. E.,

& Williamson, J. G. (1998) stated that fertility was crucial in explaining East Asia’s success.

They said that an increase in gender equality in education resulted in lower fertility and

eventually led to increased availability of younger workers. These are the general works of

literature in the field of gender inequality.

There has also been extensive research on gender inequality in India. In India, gender inequality

stems from gender stratification. In 2021, according to the United Nations Development

Program’s Gender Inequality Index, India had a GII of 0.490 and it ranked 122 out of 187

countries. According to Sen, A. (2001) there are seven types of gender inequality in India:

mortality, employment, basic-facility, natality, ownership, special opportunity, and household

inequality. Traces of these can be found back when technology was just advancing. Female

feticides became highly practised due to wanting a son or not wanting to invest in a girl child.

That led to there being stark differences in the sex ratios of different states in the 2011 census.

All of this continues in the workplace, inequality in the inheritance given to women, lack of

decision-making power, and so on. As these issues are so intertwined with the daily lives of

Indians, they affect the economy’s growth as well and these are now becoming a challenge to all

social scientists and policymakers.

6.3. Spatial Inequality and Economic Development

Spatial inequality has been gaining a lot of traction in recent years. It goes hand-in-hand with the

overall inequality of individuals which has brought this topic into mainstream discourse. Political

instability and ethnic differences have also contributed to this. Kuznets (1955) also pointed out

that the inverted U relation applies to income inequality. Williamson (1965) later applied this

case to spatial inequality as well. He related it to access and utilisation of resources wherein

these resources are not equally divided. Hence, economic growth will not be equally distributed

resulting in spatial inequality.

However, there hasn’t been much research that has been done in this field due to a lack of data.

There have been attempts at data collection and even the use of cross-sectional data to measure

regional inequality. There are however many theories that have found some reasoning or

explanation behind the relationship between spatial inequality and economic development.

Williamson (1965) postulated that regional availability of resources would lead to spatial

inequality in earlier stages which would then be followed by a more equal dispersion. Soon

enough, there would be an increase in the availability of resources that will again result in


uneven development. That would result in spatial inequality. There have been works done by

Barrios and Strobl (2009), and Amos (1988) who have contributed to the hypothesis that the

inverted-U-shaped relationship of increase, peak, and decrease in inequality applies to spatial

inequality as well. It has also often been attributed to being a result of ethnic discrimination as

well.

Developing nations such as India which are making a shift from the agricultural sector to the

industrial sector, will encounter a rise in spatial/regional inequality. So happens as after the

attainment of a certain level of development, the relation will be reversed till the development

attained is high. While this process is continuing, spatial inequality will be a hindrance to the

achievement of economic development.

A recent study on the spatial inequality in human development in India With a focus on the state

of Karnataka, evaluated regional disparities through Human Development India, HDI. In his

paper, Ranjana. N (2020) pointed out that India had attained an astounding level of economic

growth nationally but at the same time there was hiking economic inequality at different levels;

urban versus rural; across different regions; and within different classes. These differences have

increased in the severity of spatial inequalities in the nation and state. Along with gaps in the

growth of respective states, there were disparities when it came to access to basic healthcare,

sanitation, safe drinking water, and education. All of these result in differences in the lives of

individuals. Spatial econometric tools such as Moran’s I had been used in this study. It concluded

that there influence or spatial correlation between the cities inside Karnataka. A higher spatial

correlation indicates that there is spatial inequality when it comes to Human Development India

(HDI).

6.4. Health Inequality and Economic Development

Economic development has always been linked to a healthier population or citizens of good

health. Through various works of literature, we can observe that people are healthier and live

longer in well-off countries. All of this also ties back to income equality. In societies that tend to

be more equal, there is more social unity and less stress. So happens because these nations are

found to be providing public goods, social support, and employing fairness among all of its

citizens. Hence Richard Wilkinson (2000) argued that people are healthiest in societies that

actively try to be and are equal. The same has been found in the works of Ichiro Kawachi, Bruce

Kennedy, and Wilkinson (1999).

It is well-accepted that there is an interrelation between income inequality, health, and poverty.

Although the application of this might vary among nations of differing incomes, it is still a

generally acceptable pattern. So can be observed from the Preston curve which plots the life

expectancy against the GDP per capita of various countries.


From the graph, we can observe that in poorer nations, an increase in the GDP per capita results

in a high increase in life expectancy. However, the same relationship is not replicated in richer

nations. Such a change is almost absent in the richest nations such as the USA. As Preston said,

due to the negative correlation between income inequality and life expectancy, a redistribution of

income would benefit the poor more than it would hurt the rich. Hence, income inequality leads

to health inequality and that eventually affects economic development.

Figure 6.b: The Preston Curve: Life Expectancy against GDP per capita; Source: Deaton (2003, pp.116), Journal of Economic

Literature, Vol. XL

In the context of India as well, rising levels of income have been found to result in escalated

health inequalities. So happens because the burden of bad health isn’t shared proportionately by

different socio-economic groups. In 2008, India’s first female president, Pratibha Patel also

said,” The sunshine of the country’s growth and development is not yet experienced by the

underprivileged and disadvantaged sections.”

Although most research in the field of health inequality has been done for developed economies,

an article titled,” Health Inequality in India: Evidence from NFHS 3” gives us some insights into

health inequality in India. This paper has taken the health of children as the proxy for health

inequality and investigated child health disparities. They have compared under-five mortality

rates across all the states in India and the data was obtained from NFHS 3. They employed

concentration curves and concentration indices.

The theory behind this curve and index is that the cumulative of bad health should be equal to the

cumulative population shares. If there is a difference, then it indicates inequity. The

concentration curve plot’s x-axis of cumulative population ranges from ones facing the most

inequality to the least amount of inequality.

According to the data from the National Statistical Survey (61st round), India was assigned an

overall concentration index of -0.1582. The highest concentration index was that of Uttaranchal

at -0.4107 and the lowest was that of West Bengal at -0.0388. All of these values hint that there


was inequality and that the poor were vulnerable. A concentration plot was also obtained with

some of the major states with high concentration indexes.

Figure 6.c: Under-five mortality concentration curve; Source: Joe, W., Mishra, U. S., & Navaneetham, K. (2008). Health

inequality in India: evidence from NFHS 3

From the graph, we can observe that all the concentration curves above the egalitarian line which

means that ill health is concentrated in the low-income groups. The farther the graph is, the

higher the inequality. Hence, income has gravely affected health inequality and if the citizens

themselves are not in good health, then economic development would also be at risk.

6.5. Social Inequality and Economic Development

Any form of exclusion from society based on age, gender, ethnicity, race, and religion is social

inequality. All of these have often been used as a measure of stratification against disadvantaged

groups. It has also been defined as disparate opportunities and recognitions for people of

different social strata within a society by Moffitt (2017).

Social inequality has always been a significant contributor to social inequality in India.

Whenever inequality is studied, it becomes necessary for us to also consider at least caste,

religion, and gender. Caste-based discrimination or caste disparities are more concerning as it

has become an important factor when it comes to who has power, money, and a good livelihood.

Many studies like Deshpande. A (2001), and Mohanty (2006), have all concluded that the


so-called “lower caste” groups are worse off than their counterparts all across India. Similar

results apply to people belonging to the minority religion, gender, and so on.

A lot of these disparities are attributable to inequalities in land ownership, access to schooling,

and wealth ownership. These disparities predispose some demographics to face inequality of

access and outcome. The decomposition analysis done in the paper titled, “Caste Stratification

and Wealth Inequality in India” by Zacharias and Vakulabharanam (2011) concluded that cased

based inequality accounted for about 8% to 13% of the total wealth inequality in India in 2002.

All these different forms of inequality are in one way or the other related to each other. A

culmination of all of these tends to impact income inequality and that eventually impacts

economic development furthering inequalities.

7. State-wise Gender and Spatial Inequality and their impact on Economic Development

We will now be looking at the state of gender and spatial inequality in the selected five states:

Bihar, Chhattisgarh, Haryana, Karnataka, and Kerala through specific indicators: sex ratio, mean

age of marriage of women, maternal mortality rate, labour force participation rate, literacy rate,

and Gender Parity Index to evaluate gender inequality. As for spatial inequality, we will be

looking at infant mortality rate, net state domestic product, access to toilet facilities, poverty

headcount ratio, number of people not using government health facilities, and residence parity

index.

7.1. Bihar

According to the 2011 census, Bihar had 104 million people residing there where the sex ratio

was 918 women for every 1000 men. This number was not an improvement but a decline of

about 8.21 percent when compared to the 60 years. Their child sex ratio of 935 girls for every

1000 boys was however better than the national average at that time. The mean age of marriage

was pretty low in Bihar as of 2020 at 22.2 years of age. With women getting married around the

age of 22.2 years in 2020. The maternal mortality rate of Bihar as of the 2018-20 data was 118

deaths per 100,000 live births which is very high but it is still an improvement compared to the

past. With the most recent data of 2021-22, the labour force participation of 46.3 % of women in

rural areas and 48.7% in urban areas was much higher than the male labour force participation.

However, the overall labour force participation was very low at 29.3%. With a Gender Parity

Index of 0.91 for primary education in Bihar, we can see that the disparity is in favour of men,

which means more men are enrolled in this level of education. However, the Gender Parity Index

of all other levels of education was over 1, hence it was favourable to women.


When we focus on the location-based factors, the net state domestic products come up first.

Bihar’s NDP for 2020 was 533583 crore which was a significant improvement from past years’

NDP although the rate of increase in recent years was affected by the pandemic. If we also look

at the infant mortality of Bihar, it was 36% which has declined from the past but is still high

suggesting some improvement in health disparities. Although 77.1% of households had access to

toilet facilities, there are still problems of open defecation there. Even with the government

health facilities, 80.2% of the households did not use them due to reservations due to distance,

waiting time, and poor quality of the service. According to data for 2011-12 from the Economic

Survey 2022- 23, the Poverty Headcount Ratio of 33.7% hints at high poverty in Bihar.

7.2. Chhattisgarh

According to the 2011 census, there were 26 million people residing in Chhattisgarh and the sex

ratio was 991 women for every 1000 men. This number was not an improvement but a decline of

about 3.26 percent when compared to 60 years ago. Their child sex ratio however of 969 girls for

every 1000 boys was much better than the national average at that time. These women were

getting married at the mean age of 21.6 years old as of the 2012 data. Although the maternal

mortality rate has seen a significant improvement in Chhattisgarh as compared to 2001, a

statistic of 137 deaths per 100,000 live births is still concerning and the highest among the states

being studied. There was high labour force participation of women here in 2021-22 with the rural

and urban female labour force participation rates being 60.4% and 61.3% respectively.

Chhattisgarh’s GPI for secondary education was exactly 1 hinting at gender parity and for the

rest, it was above one and was favourable to women. Chhattisgarh too has made mixed progress

in reducing gender inequality. They have improved labour participation rate but at the same time,

they have a high maternal mortality rate.

For Chhattisgarh’s locational scenario, it had a NDP of 312532 crore in 2020 wherein it has seen

consistent growth in its NDP. They also had an infant mortality rate of 38% which is high but has

declined in comparison to the past. There were 98.8% of households with access to toilets but

there still is the problem of open defecation. About 30.2% of households do not use government

health facilities due to waiting time, poor quality, and distance. Even with the Poverty Headcount

Ratio, about 30.9% of households were living in poverty as of 2011-12.

7.3. Haryana

There were 25 million people residing in Haryana according to the 2011 census where the sex

ratio was 879 women for every 1000 men which was an improvement by 0.87 when compared to

the past 60 years. It was the highest recorded sex ratio till 2011 Their child sex ratio of 834 girls

for every 1000 boys was much lower than the national average. They probably had the lowest


child-sex ratio in the nation. The mean age of marriage for women was 23.3 years which was

higher than the other five states being studied. The maternal mortality ratio had declined quite a

bit but in recent years has been hiking yet again with the current data being 110 deaths per

100,000 live births.

Although the total labour force participation in both urban and rural areas was low for Haryana

as of 2021-22, their female labour force participation rate for rural and urban Haryana was pretty

good at 53.5% and 54.7% respectively. The GPI of Haryana was favourable to women at 1.16

and 1.01 for primary and secondary levels of education but there was a disparity in favour of

women for higher secondary and higher education at 0.99 and 0.97 respectively. Haryana’s

gender statistics all hint that there are high levels of gender inequality.

Haryana had a NDP of 683810 crore in 2020 which was lower than that of 2019. This could be

attributable to the COVID-19 pandemic. Along with that, they have an infant mortality rate of

18% which is lower compared to other states. Almost every household in Haryana had access to

toilets and they recorded the lowest rate of open defecation in India. There too, about 48.1% of

households did not use government health facilities due to the waiting time and poor quality of

healthcare. Their Poverty Headcount Ratio was also much lower at 11.2% as of 2011-12.

7.4. Karnataka

As stated in the 2011 census, the population of Karnataka was 61 million. Their sex ratio of 973

women for every 1000 men was an improvement. With a child sex ratio of 948 girls for every

1000 boys, it was placed at the higher end of the national average. The mean age of marriage of

women in Karnataka was 22.8 years of age which was on the higher end. Karnataka has made

impressive progress with its maternal mortality rate in the past 20 years with it being at a lower

ratio of 69 deaths per 100,000 live births. Karnataka has a pretty good labour force participation

as a whole and its female labour force participation rate is also high at 61.9% 28.2% and 59.8%

for urban and rural areas respectively. Karnataka has parity in both secondary and higher

education.

However, there was a disparity in favour of women and men in primary (1.07) and higher

secondary (0.99) education respectively. Karnataka has made good progress in reducing gender

inequality but there is still disparity/ inequality in the lives of women in rural Karnataka.

Karnataka has seen a consistent increase in its NDP since 2013 with the latest NDP for 2020

being 1575400 crore. This is the highest NDP among the states being studied at the moment.

They had an infant mortality rate of 175 which was also the lowest among states being studied.


Around 91.7% of households had access to toilets but there was still high open defecation in

low-income groups. Even when it came to the government health facilities, 44.8% of households

did not use these facilities as the waiting time was too long and there was poor quality of

healthcare. The Poverty Headcount Ratio for Karnataka was 20.9% as of 2011-12.

7.5. Kerala

Kerala had a population of 33 million according to the 2011 census wherein their sex ratio was

1084 women for every 1000 men which was the highest among these five states being studied.

This was an improvement of 5.48% when compared with the past 60 years. They had a high

child-sex ratio of 964 girls for every 1000 boys. We can observe that the mean age of marriage

for women in Kerala in 2012 was 23.6 years similar to that of Haryana. Kerala possibly has one

of the lowest maternal mortality rates of 19 deaths per 100,000 live births which is a lot of

progress as compared to 110 in 2001-03. With the highest labour force participation among the

five states, its female labour participation rate was also consistently high with the urban

participation rate being 57.1%. Kerala almost had parity in all three secondary, higher secondary,

and higher education levels of education at 0.99, 1, and 0.99 respectively. There was however a

disparity in favour of women in primary education at 1.52. Kerala has the most impressive

gender equality achievement. However, there has been a reduction in labour participation and

there are disparities in gender equality in rural and urban women.

Kerala had mostly experienced improvement in its NDP from 2013 to 2019. However, there was

a drop of about 24000 crore from 2019 to 2020, its NDP for 2020 being 718034 crore.

Surprisingly, their infant mortality rate was 24% which is pretty high compared to the average of

India. Although 100% of households had access to toilets, there was still very low open

defecation. Even with government healthcare, only 23.9% reported not using them due to

reservations about the quality of care. They also had a recorded low Poverty Headcount Ratio of

7.1% among the states being studied.

As can be understood, the sex ratio throughout the states is improving and overall the labour

force participation rates have also improved but that of men is decreasing. Hence, as a whole

gender inequality is still prevalent in these states but it is on a path of recovery. As for spatial

inequality, the data sets in the tables in the annexures make it clear that there are varying degrees

of spatial inequalities and a reduction in these has improved the economic development in

respective states as reduced inequalities saw reduced gini coefficients and vice versa.

8. Relationship between GDP and socio-economic indicators


For this paper, all the data has been sourced from the World Bank Databank. The dependent

variable here is the GDP and all other variables are all independent variables. With this study, we

want to establish and test the relationship between the proxies to understand the impact of

socioeconomic factors on development.

For that, the hypothesis for the model is as follows:

Ho: b0= b 1 =……..= b 8 =0

H1: At least one b value is not equal to 0

After doing a regression analysis on R, we obtained the following model after removing a few

variables that do not have a linear relationship with GDP:

GDP = b 0

+ b 1

GII + b 2

Electricity Consumption + b 3

Female + b 4

life exp + b 5

HDI + b 6

Access to electricity

+ b 7

Mortality rate + b 8

Fertility rate + ε

Where GDP is the Gross Domestic Product of India with 2015 US$ as the base year, GII is the

Gender Inequality Index, Electricity Consumptions is the Electricity consumption by households

(Kilowatt - hours per million), LF Female is the female labour force out of a population in

percentage terms, life exp is the life expectancy at birth in years, HDI is the Human

Development Index, Access to electricity is the percentage of households that has access to

electricity, Mortality rate is the maternal mortality rate per 100,000 live births and Fertility_rate

is the number of children per women. All of these variables are continuous. Out of the fourteen

variables that had been taken into consideration, two variables Sex Ratio and Net Migration were

excluded from our final model as they did not present a linear relationship with GDP. So can be

observed from the following correlation matrix.


Figure 6.d: Correlation Matrix

So accordingly, LFP ratio, LFP Female, and Adolescent. fertility rates have been removed from

the model as they have severe multicollinearity. Following is the output that was obtained from R

software:


Model coefficients post removal of some variables:

Coefficients

Value (in ‘000000s)

Intercept 2048000

Access to electricity 1192

Mortality rate 9913

GII -4944000

Electricity Consumption 3.153

LF Female -47080

Fertility Rate -448900

Life exp 93950

HDI -47480

Table 6.b; Source: Author’s calculations

Significance level and final coefficient values:

Coefficients

Estimate (in

‘000000s)

Std. error (in

‘000000s)

T value

Pr(>ItI)

Intercept 20480 345000 0.593 0.554961

Access to

electricity

1192 1555.8 0.766 0.452047

Mortality rate 9913 7191 1.252 0.223795

GII -4944000*** 1291000*** -3.829*** 0.000915***

Electricity

Consumption

3.153*** .7394*** 4.264*** 0.000316***

LF Female -47080* 17330* -2.717* 0.012589*

Fertility Rate -448900 339200 -1.324 0.19924

Life Exp 93950 49820 1.886 0.072608

HDI -47480 2732000 -1.738 0.096203

*p<0.01,**p<0.001,***p<0

Table 6.c; Source: Author’s calculations


Residual Standard Error

37160000000 on 22 degrees of freedom

Multiple R-squared 0.998

Adjusted R-squared 0.9972

F-Statistic

1358 on 8 degrees of freedom

Table 6.d; Source: Author’s calculations

As the R 2 value is 0.9974, we can infer that the model is a good fit. Similarly, as the

p-value is < 2.2e-16, we reject Ho and conclude that the proxies for the independent

variables are significant here. Through our research, we aim to establish the notion that

gender inequality does contribute to income inequality and negatively impacts economic

development. In the statistics that we observed, India's states have been making attempts

to improve gender gaps and empower women but most of their performances so far are

mixed and it is not definitive as well. We can see that the sex ratio has slowly improved in

many states but at the same time either their maternal mortality is high or their female

labour force participation is depleting. One interesting finding of our research would be

that the Gender Parity Index in the states studied had a disparity in favour of women in

most levels of education, that is there were more women enrolled in these institutions than

men. Such findings did to a certain extent explain how gender inequality could impact

income inequality when we compared their Gini coefficients. With spatial inequality,

many other factors and forms of inequality are tied into it. For example, one person's

residence would determine their access to educational institutions but their gender would

also play a big role. When studying spatial inequality, we came across a few statistics that

would lead to us confirming our initial hypothesis of spatial inequality affecting income

inequality. When people had easier and more access to toilet facilities, their health

improved and that led to an improvement in their productivity as well.

9. Conclusion

From the above model and the analyses of data and other works of literature, we can conclude

that these relationships from the multiple regression model were applicable theoretically and

empirically. With the analyses of yearly data, we can prove the impacts of gender and spatial

inequality using socioeconomic factors around gender and location on economic development.

We are also left with a clearer understanding of where the selected states stand in terms of gender

and spatial inequality.


Table 6.e; Source: Author’s calculations

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Marriage Market Stability, Inequality and Growth: A Micro-theoretic

Analysis

- Vibha Nath 1 , Meghna Menezes 2 , Mayukh Dutta 3 , Aditya Raj Chatter 4

Abstract: ‘Marriage market’ is an inquisitive facet of Indian society, characterised by the

popular concept of ‘arranged marriage’ and the orthodox gender roles assigned by the society,

which portrays Indian society in a level of obduracy that is rather appalling. We have attempted

to model the preferences of both the sexes (and their families) towards entering the marriage

market, considering the rather patriarchal mindset of society. While men are motivated to get

married with a higher income (as most literature suggests), women view increased income as a

means of forestalling fettering themselves to a nebulous future upon getting married (or what

marriage is generally representative of in India). Against this backdrop, we attempt to theorise

the effect of economic growth in the marriage market with implications drawn related to the

male-female wage gap.

1. Introduction

Marriage, in Indian society, is not just a coalition of individuals but an alliance of families

leading to the popularity of the concept of ‘arranged marriage’. Religion, community, caste,

language, familial pressures, and traditions play a significant role in marriages in India when

compared to any Western country. At the same time, materialistic considerations play an equally

important role. In a simplified sense, we can consider marriage to be an economic contract

between two individuals (and their families) that is regulated by laws and customs. Despite

copious social reform movements and laws passed by the Government, India reveals a level of

obduracy that is rather appalling. The average age at which a woman in India gets married is

19.2 years (National Family Health Survey 2021), which implies a large portion of the female

population does not pursue higher education. This trend is apparent in rural regions and among

economically weaker sections of society where families would rather spend their limited income

on their son’s higher education than their daughter’s. Gender roles assigned by society play a

pivotal role in this biassed allocation of income. As men are traditionally considered to be

‘breadwinners’ of the family, they need to be able to earn a living, and hence, pursuing higher

education is perceived to be more important to them to be able to engage in socioeconomic

activities, including marriage. Women, on the other hand, are often deemed to be ‘homemakers’,

which gives families the choice to refrain from sending their daughters for higher education. In

urban areas, only 32% of married women, between ages 15 to 49, are a part of the workforce,

whereas for men in the same age group, the percentage is 98% (NFHS 2021). Banerjee et al

(2013) hold how even in a sample of highly educated males and females, fewer than 25% of the

matched brides were working after marriage.

1,2,3,4 St. Xavier’s College Mumbai


Another factor that exerts a major influence is the system of dowry which, though illegal in India

since 1961, continues to play a significant role in marital choice. Dowry refers to the payment, in

cash or kind (including property), that the bride’s family pays to the groom at the time of

marriage. It is possible that households that are not financially affluent would choose to save

their earnings for the payment of dowry rather than their daughter’s education (Mahata et al

2023). When considering the male perspective in the marriage market, it is perspicuous to see

that they are ‘better off’ in materialistic terms if their prospective bride is well educated or

employed, whether it be because of the extra income that his partner brings, the elevation of

social status (if we assume that there is a positive correlation between higher education of a girl

and wealth of the family) or expectancy of dowry. However, a patriarchal side to him would

prefer a partner who would be willing to be a homemaker and keeping in mind the rather

obstinate picture of India and the prevalent joint family structure, this is not a far-off assumption.

When taking the female point of view about marriage, we see that an educated and employed

woman would choose to delay her marriage, as she can earn a living and is self-sufficient, not

having to depend on her partner for sustenance. Her willingness to marry will increase only if the

prospective groom is as educated as her, if not more, and falls into a similar (or higher) income

bracket (Anderson 1995). There is also an omnipresent societal pressure on women to get

married, discouraging higher education and inducing lower female participation in the

workforce. Against this backdrop, we attempt to develop a micro-theoretic model capturing the

preferences of individuals into entering this ‘contract’ and try to derive the implications of

economic growth on marriage market stability. While many researchers have published papers

on the marriage market, the particular area about male psychology in a conservative and

patriarchal society has often been overlooked when building theoretical frameworks. The novelty

in our paper is that we consider both sides of the argument towards marrying an educated woman

which is involved in preference mating for males. The economic significance of the marriage

market lies in the fact that it is a potential panacea to the problem of inequality in a country, also

keeping in mind that family planning plays an integral role in child outcome and the formation of

human capital. 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 construct the payoff functions of males

and females opposite to marital choice, following which we establish the equilibrium conditions

in Section 4. The comparative statics analysis is performed in Section 5. Finally, Section 6

concludes the paper.



Anderson (1995) focuses its research on whether favourable markets influence marital choices.

When considering a preference-based matching between suitors, marriage market equilibrium is

assumed, i.e., the demand for entering into a marriage is balanced by the supply of the potential

mates, ignoring market constraints. There is a high degree of positive assortative mating (women

end up seeking partners whose qualifications match their own). It was found that when

constraints such as a demographic shortage of suitable marital partners are introduced, the search

process is affected. When there is a shortage of men, personal traits such as beauty come into

play and can influence decisions. Individual covariates such as employment in the case of

women or occupation in the case of men are also accentuated further in a more competitive

market.

However, if we are presented with a favourable marriage market for women, with plenty of

suitable partners, the odds of marrying a man with a higher status (in terms of education and

occupation) is increased. Ray Chaudhuri (1955) draws a general assignment model and after a

rather elaborate analysis has given proof for the existence of a unique solution. Without going

into much detail, we shall hold this to be true for our framework as well. Becker (1973)

compares costs and gains to marriage, with special emphasis on the set of hours that the couple

contributes towards work before and post-marriage. While the preferences of the agents are

vastly different, the inference concerning change in income has a few similarities to our analysis,

depending on some of the intrinsic conditions of our model.

Banerjee et al (2013) analyse how noneconomic characteristics such as caste, affect marital

preferences and their theoretical model backed by an empirical study suggests that in India,

however, economic factors play a rather diminutive role and the perceptive marital trends are a

result of changes in these noneconomic parameters themselves. This analysis brings upon a stark

contrast among the quintessential research performed with regards to the marriage market and

the Indian example of marital preferences. Our paper is an attempt at bringing the two

contrasting studies in congruence, i.e., engineering these economic factors such that they

resemble the so-called “noneconomic parameters” which are some of the crucial determinants of

marital choices in Indian society. Addition of these noneconomic parameters in a theoretical

setting could possibly reflect a closer resemblance to the machinations of the marriage market.

3. Willingness of Eligible Men and Women to Enter the Marriage Market: Construction of

“Payoff” Functions

For our analysis, we take the number of males and females who are eligible to marry (while the

law states the age to be 21 or above, it is not uncommon to see individuals marrying in their

teenage years) as M and F respectively. From the eligible candidates, let there be ‘m’ number of


males and ‘f’ number of females willing to enter the marriage market. Various factors determine

the willingness to marry, and we express the same with the help of “payoff” functions. Let Y m

and Y f be parameters denoting the incomes of males and females respectively. and are constants

demonstrating the positive relation between the income of males and the willingness of both

sexes to enter the marriage market. When analysing the male perspective, we assume that their

willingness to marry would increase if the prospective bride is educated and employed, based on

the premise that highly educated and working women would be from financially affluent families

and could also serve to elevate their status in society and even raise their expectations for a

higher amount in dowry from the bride’s family. This positive relation is represented by α.

Keeping the largely conservative and patriarchal nature of society in mind, we also need to take

into account the opposing ideology that males would also be unwilling to marry a partner who is

earning a substantially high income, and they also show a preference to have their wives become

homemakers post-marriage. We denote this negative relation using β. The utility function of

males is given by:

u = γY m

+ α − βY t

− A m

+ f + ω

(1)

where A is a constant.

Employed women earning a high income would have a lower incentive to enter the marriage

market as they are economically independent, and face less marital pressure from society. This

negative relation is shown by λ. On the other hand, an increased female income can be correlated

with age, as it is often the more experienced worker who will gain an increment in her income. A

delay in marriage will, thus, also observe an accretion in the marital societal pressure that women

face, and we represent this positive relation using σ.

The payoff function of females is given by:

v = δY m

+ σ − λY f

+ B m

− f + ε

(2)

where B is a constant.

External pressures due to emotional disturbances are denoted by the constants ω and ε.

4. Equilibrium Conditions: Optimal Number of Males and Females entering the Marriage

Market

To understand the operation of the payoff functions that we have defined in the previous section,

let us assume u > 0, i.e., men will have an incentive to enter the marriage market, so m increases.

From the payoff functions we see that m is negatively related to u, so an increase in m is


accompanied by a fall in u and this process will continue till u=0. A similar adjustment

mechanism occurs when v > 0.

Therefore, at equilibrium:

u= v= 0

The optimal number of men and women in the marriage market:

given the constraint m*≤ M, f*≤ F,

γY m

+α+σ−β−λY f

+ω+ε

m * = (3)

A−B

Aδ+BγY m

+Aα+Bσ−BβY f

−Aλ+Bω+Aε

f * = (4)

where, A,B>0 ; α,β,γ,δ,σ,λ>0 and A≠B (for the existence of a solution).

To ensure unique positive equilibrium values of m*and f*, the following conditions need to hold:

Case I: if A>B,

A−B

(γ + δ)Y m

+ (α + σ)Y f

+ ω + ε > (β + λ)Y f

(5)

(Aδ + Bγ)Y m

+ (Aσ + Bα)Y f

+ Aε + Bω > (Aλ + Bβ)Y f

Case II: if A<B,

(γ + δ)Y m

+ (α + σ)Y f

+ ω + ε < (β + λ)Y f

(6)

(Aδ + Bγ)Y m

+ (Aσ + Bα)Y f

+ Aε + Bω < Aλ + BβY f

The number of matches in the marriage market is given by:

N = min{m *, f *}

(7)

If m* > f*, the number of matches would equal the number of women in the marriage market (f*)

and unmarried males = − m *+ (m *− f *)

Here, the first component -m* represents the number of men eligible for marriage but unwilling.

This could be for reasons such as employability or financial instability, for which he would not

want to enter the marriage market. The second component (m*-f*) represents the men who are

willing to get married but are unsuccessful in doing so because of insufficient partners.


Unmarried females = -f*

representing the number of women who are eligible for marriage but are unwilling to enter the

marriage.

If m* < f*, the number of matches would equal the number of males in the marriage market (m*)

and unmarried males = -m*

Unmarried females=(− f *) + (f *− m *)

Here, (f *− m *) is a representation of women who are willing to get married but are

unsuccessful in doing so.

The expression below shows us the gender disparity in the pool of willing participants,

demonstrating either a surplus or deficit in men (or women) in the marriage market:

m *− f *= {(1 − A)δ + (1 − B)}Y m

+ {(1 − A )(α − β) + (1 − B)(σ − λ)Y f

+ (1 − A)ε + 1 − B

For the purpose of making more concrete inferences from the model which we have drawn, we

manipulate some of the conditions to illustrate a closer representation of the Indian society:

Assumption 1:

α < β

The implication behind this assumption is that men in the marriage market hold a preference for

homemakers compared to women who participate in the workforce. As women in the workforce

would have less time for domestic chores and raising children, there is still an orthodox

preference in the marriage market for women who would be able to dedicate their hours to

household affairs.

Assumption 2:

σ < λ

This relation holds that women show a greater tendency towards delaying their marriage than

starting a married life when their income rises. Financially independent, marriage is no longer

the only viable option for a woman to be able to support herself.

If we have α<β and σ<λ, and suppressing the effects of emotional factors ε and ω,

Lemma 1: If 0<A<B<1, then from condition (vi)

f* < m*

Lemma 2: If 0<B<A<1, following from condition (v)


m*< f*

Lemma 3: If A>B>1, it follows from condition (v) that:

f* > m*

Lemma 4: If B>A>1, it follows from condition (vi) that:

m*> f*

To make sense of the above discernments, we need to provide an economic intuition behind the

constants A and B which we have used while drawing the model. We can say that when A,B>1,

males are more eager (or perceived to be more eager) to marry than women, whereas they appear

relatively less keen when A,B<1. Males could be thought of as more eager to enter the marriage

market as their lifestyles would not be subject to major changes post-marriage, whereas women

would have to adjust to shifts in the dynamics depending on the preferences and lifestyles of

their husband or their families. If there are a scant number of women who are educated and

employed, women’s tendency to marry would be greater, seeing as the more financially

dependent they are, the more societal pressure they must face. So, in a sense, our choice of

constants represents the inherent characteristics of the society in which we are functioning.

When A>B, males are more responsive and sensitive towards changes in the marriage market

whereas when A<B, women are the ones who show greater responsiveness to marriage market

fluctuations.

5. Comparative Statics

To theorise how marital choices are affected by respective changes in the incomes of the

economic agents, we take the partial derivatives of m* and f* concerning Y m and Y f :

∂m

∂Y m

∂f*

∂Y m

γ+δ

A−B

= (8)

Aδ+Bγ

A−B

= (9)

∂m*

∂Y f

α+σ−β−λ

A−B

= (10)

∂f*

∂Y f

Aα+Bσ−Bβ−Aλ

A−B

= (11)

Again, assuming α<β and σ<λ (minimising emotional factors ε and ω), we make the following

inferences:


Proposition 1: If we increase Y m , then it will lead to an increase in m* and f*.

This may be because men, now earning a higher income, are financially more capable to be able

to start a family. Thus, men who were previously not earning sufficient income could now enter

the marriage market. The number of women willing to marry would also increase as they would

prefer their partner to earn a high income.

Proposition 2: If we increase Y f , it will lead to a fall in m* and f*

As women gain financial independence, they would be unwilling to enter a marriage that might

limit their independence or take away their choice to work. As for men, considering the gender

norms and social expectations, we theorise that they may be less willing to enter the marriage

market if their partner earns a greater income than them, as they would prefer their wives to

become homemakers post-marriage.

Proposition 3: Increasing Y f and decreasing Y m would lead to a fall in both m* and f*.

The number of women willing to enter the marriage market will fall due to greater financial

independence and increased self-sufficiency and the number of men in the market might fall

(a)

Figures 7 (a) and (b): Comparative Static Analysis

(b)

The figures above depict two possible comparative static scenarios as indicated by our model.

Initially, we have assumed the equilibrium number of males and females participating in the

marriage market to be equal. Both cases show an increase in the number of marital partners. In

panel (a) of the diagram, the number of males willing to participate in the marriage market


exceeds the number of females and therefore at equilibrium, we observe the presence of

unmatched males willing to marry. The case illustrated in panel (b) shows the existence of

unmatched females who are willing to participate in the marriage market due to insufficient

income to provide for their family. Men would prefer to focus more on their career before

entering the marriage market.

Proposition 4: Increasing Y m and decreasing Y f will lead to an increase in m* and f*.

The increment in male income creates a marital incentive for women, while men who were

abstaining from entering the marriage market in favour of their career now have the preference to

enter matrimony. Hence, there is an increasing willingness to marry for both men and women.

Proposition 5: When there is an equal increase in Y m and Y f :

when the income gap is small i.e., Y m ≅Y f then there is an increase in m* and f*,

when the income gap is sufficiently larger i.e., Y m > Y f then there is a decrease in m* and f*.

However, we must keep in mind that analysis of the former case, where the male-female wage

gap is small, might not provide us with an accurate picture seeing as we drew this model against

the backdrop of a patriarchal society characterised by inequality. It is, nevertheless, an interesting

observation.

Our analysis suggests that merely a reduction in income inequality discourages participation in

the marriage market. Whereas economic growth that exacerbates inequality in the economy leads

to greater participation in the marriage market by both agents. What this analysis tries to reflect

is the crucial impact changes in income cause for the participation of both agents in the marriage

market.

6. Conclusion

We have created this model against the backdrop of a patriarchal society, which illustrates the

marital preferences of men and women. The crux of our analysis has been that merely a

reduction in income inequality discourages participation in the marriage market. Whether less

participation in the marriage market is a desirable change depends on the perspective of people.

However, having observed some of the marital trends, such as the premature age at which

women often get married and the fact that marriage has often played a detrimental role in

women’s economic participation, we do not see a discouragement in participation in the marriage

market as an undesirable change. Again, this issue is likely to create much debate among

differing factions, but we stand to express our opinions.

We have overlooked some of the intertemporal implications of our model about societal pressure

or progression. We have also limited our case to heterosexual spouses. The fact that homosexual

marriages have still not been legalised in India plays a contributing factor in keeping people


away from the marriage market, especially in some of the metropolitan cities, which are getting

adopted of more progressive ideas lately.

Arranged marriages and marital trends in India are a rather complex affair and analysing some of

the factors involves blatantly ignoring the others—but this is perhaps how the ever-popular

concept of ceteris paribus works in economics. We have attempted to model the complications of

a rather circuitous society, and perhaps from the limitations of our model, more research could

progress in the future.

7. References

1. Banerjee, Abhijit, Esther Duflo, Maitreesh Ghatak, and Jeanne Lafortune. "Marry for what?

Caste and mate selection in modern India." American Economic Journal: Microeconomics 5,

no. 2 (2013): 33-72.

2. Rosenzweig, Mark R., and Oded Stark. "Consumption smoothing, migration, and marriage:

Evidence from rural India." Journal of Political Economy 97, no. 4 (1989): 905-926.

3. Foster, Andrew D., and Mark R. Rosenzweig. "Imperfect commitment, altruism, and the

family: Evidence from transfer behavior in low-income rural areas." Review of Economics

and Statistics 83, no. 3 (2001): 389-407.

4. Lichter, Daniel T., Robert N. Anderson, and Melinda D. Hayward. "Marriage markets and

marital choice." Journal of Family Issues 16, no. 4 (1995): 412-431.

5. Chaudhuri, Prasanta R. "Generalized assignment models: with an application to technology

transfer." Economic Theory 10, no. 2 (1997): 335-360.

6. Becker, Gary S. "A theory of marriage: Part I." Journal of Political Economy 81, no. 4

(1973): 813-846.

7. Mahata, S., Khan, R. K., & Mandal, S. "Dowry and Female Education: A Theoretical

Evaluation." The Pakistan Development Review, no. 41-59 (2023).

8. Government of India | Ministry of Statistics and Programme Implementation | MOSPI.

"www.mospi.gov.in." n.d. Accessed April 17, 2024. https://www.mospi.gov.in.

9. Home | Ministry of Health and Family Welfare | GOI. "main.mohfw.gov.in." n.d. Accessed

April 17, 2024. https://main.mohfw.gov.in.

10. India Code: Home. "www.indiacode.nic.in." n.d. Accessed April 17, 2024.

https://www.indiacode.nic.in.


Economic Potential for Skill Upgradation in Higher Education Institutes

as Envisaged in NEP 2020: a Case Study with Respect to University of

Delhi

-Anuruth R. 1 , Madhav Jay 2 , Pavithra Babu 3 , Gaurav Shankla 4 , Vipul Bansal 5

Abstract: “Our Mission is Skill Development. There can be no development with a satiated

system”. These words by the Prime Minister of India, Mr Narendra Modi underlines the very

thought which powered the pivotal step of projecting skill education and upgradation as a

necessity for economic growth in India. With this in mind, the rationale behind the study

was to Economically analyse the various provisions for skill upgradation among students of

Higher Education Institutes(HEIs) in India’s National Education Policy:2020, and to project

their future impact on economy by highlighting how this can solve the mismatch of skills

between those desired by industries from employees and what is gained by them from

educational institutions, elucidating the data obtained from campus placements among

Undergraduate students in University of Delhi, in context with industrial reports published

by leading recruiters and facilitators. Moreover, the enlarged scope for expenditure and

income generation through such provisions were also to be forecasted on a supplementary

basis. The study turned out to be noteworthy as it predicted an increase in quantity of

campus placements and a favourable impact on the sector wise composition of such

recruitments (with an increase in advanced and high income jobs) if the provisions of current

FYUP programme were meticulously implemented in University of Delhi, with a focus on

new generation skills and with minimal structural friction. It was also able to match the

desired abilities of an employee with the new set of courses and teaching methods

implemented by University of Delhi, something which can pave the way for large scale

vocationalisation of Higher Education in India. It also highlighted the benefits and spill out

effects from enabling foreign universities and institutions to collaborate with Indian

Universities, an integral feature of NEP provisions.

1. Introduction

"A winner is someone who recognizes his God-given talents, works his tail off to develop

them into skills, and uses these skills to accomplish his goals''. This quote byLarry Bird is

the best example to illustrate the practical financial aspects of the modern world, where even

minute and resource-less societies are making full utilisation of their potential to further

power their societies to higher levels of growth and economic development. Consequently

India, currently the 5th largest economy in the world in nominal terms, dreams of projecting

greater opportunities of momentary growth by poising to become what it terms as

1, 2, 3, 4, 5 Hansraj College, Delhi


'Vishwaguru', a knowledge-based economy, thriving in all manners. With this in mind,

various stakeholders and international agencies have foretold India's position as 3rd largest

economy in nominal terms by 2030. By 2030, with a projected GDP of $7.3 trillion by then,

the crucial test for India would be to train and employ it's demographic dividend, in terms of

a humongous youth population, that too with 24% of the total population expected to be in

the productive age group of 15-29 by 2030, as per India's National Youth Policy.

The task for Indian policy makers is further aggravated by the fact that many of these

youngsters are unable to attend colleges, with even those who do often fail in critical arenas

of skill application and upgradation in the fast-paced world. At this crucial juncture for

providing skill education, India often lags behind, which has hugely affected its economic

competencies and schemes for mass employment and growth. For instance, India finds itself

at a rank of 104 out of 134 countries in attracting and improving talents as specified in

INSEAD GTCI index of 2023 and at 68th position in Coursera’s Global Skills Report

lacking performance in many areas of skill proficiency. The large number of students

migrating out of India to various countries, for skill upgradation and employment, itself is a

testimony to the lasting impact of the lack of a prominent skill development system within

India. This can be traced to the very origins of higher education, where firms from large

scale MNCs to MSMEs complain of the mismatch in skills desired by them from an

employee, whom they have recruited and the actual skills that he/she acquires from the

Higher Education Institutes (henceforth HEIs) inIndia. The reasons for this pathetic

condition were, lack of an effective and concrete policy to inculcate future oriented

education and a skill based vocational education stream with inputs from the industry. With

over 476.67 million employees, this lacklustre in skill impartation began to impact the

Indian economy at length and whicheventually led the government to introduce a policy that

could revolutionise skill education in India.

So the National Education Policy of 2020 (henceforth NEP 2020) was a step which was

crucial, if not necessary in India. The very objective of this policy, as stated by the

Government is to place India as a prominent knowledge power in the world, by focusing on

technology, research and digital based holistic education. With this in mind, the primary

focus of this work is to predict the economic impact that NEP 2020 is possibly going to

create in the Indian economy, with analysis of various variables that are aimed to be

included in the original policy. Consequently,employability of students trained in India was

the foremost concern (with the latest employability ratio of India being 50.3%), which was

ought to be studied through the quantity, quality and value of campus placements made in

Universities. Due to paucity of time and lack of adequate data availability, University of

Delhi (henceforthUoD), the premier institute in India was chosen for the analysis, focusing

on wide aspects of collegiate education, observing the impact of NEP sponsored


UnderGraduate Curriculum Framework (henceforth UGCF 2022) in various indices to

assess the quality and quantity of skill impartation. Moreover, the large volume of campus

placements made in UoD and its sensitivity to industrial developments, made the analysis

get a generalised picture of events happening among all HEIs in India.


Many scholars have pointed out the fairly proportional relation between human capital and

economic growth, through defining works. Moreover, this is an integral part of policy

formulation in emergency economies, where mass scale manufacturing outlets, sustained

through market-oriented skill formation and huge FDI inflows are required. This particular

aspect of skill investment having large returns in emerging countries has captured publicity

worldwide throughSingapore. Numerous studies have been published regarding the skill

upgradation and the 'on the job training' schemes in Singapore and whether they hold a

model for other developing countries. Consequently, a large number of studies regarding the

future potential of the Indian economy have essentially focused on the human capital

development and skill formation arenas. Of particular interest is the India Skills Report

published annually, which enumerates the employability ratio of Indian youngsters through a

widely accepted method. Studies have also focused on how the gap in skill formation in

India can be bridged, regarding the impact of "Skill India Mission"and how gender

inequality has a say in it, how the particular challenge of absence of required infrastructure

in rural India can be overcome and the role of IT in revolutionising skill education. Of

particular interest is the work, which was concentrated on observing the skill formation in

entry level jobs (like campus placement) and how they are crucial for the economy. It

concluded that any policy aimed to encourage the recruitments in this sector should also

consider the long-term conversions and migrations within and outside the industry, as an

acute assessment of the data here, say for example - the number of campus placements may

not be an excellent indicator of long-term economic prospects, given other associated

factors.This study whole handedly summarises the defects or limitations of the present one,

assists primary aspect was impact on undergraduate course end placements. So this aspect

was supplemented by analysing the impact of NEP on various other indices that assess

industry readiness as well as quality of higher education. For this, the All India Survey On

Higher Education (henceforth AISHE) was used, which also throws light upon the various

aspects of higher education in India and also describes the infrastructure limitations faced by

policy makers while implementing the provisions of NEP. This was also analysed by

qualitative assessments of NEP provisions, which contributed much to form the theoretical

framework for the present study. Prominent government agencies in India also tried to


analyse the impact of NEP 2020, through various reports, giving crucial data inputs, which

were otherwise not feasible to obtain.

3. Skill Upgradation as Envisaged in NEP 2020

"For the purpose of developing holistic individuals, competent for the modern society,it is

essential that an identified set of skills and values be incorporated in them at each level of

learning, accumulating and manifesting at the highest order in higher education". These lines

found in the policy document of NEP is a befitting testimony to the fact that the concept of a

skill-based economy powering Indian growth dynamics is the soul and source of the

suggestions made in it. Consequently, the various policy inputs from vide arena of

stakeholders were considered to fundamentally alter Indian Education (especially higher

education on following lines;

1. NEP 2020 aims to provide high quality education, integrated with effective VET

(Vocational Education and Training) and skilling henceforth. The previous system of strict

distinction between general and vocational education would be dropped (by establishing

academic equivalence and mobility within and between them), to ensure that vocational

components are integrated with mainstream curriculum to make students skilled and

employable in a wide range of modern and advanced sectors. These components to be made

a foundational part at every phase of collegiate education.

2. Altering the very fabric of the higher education system through Multiple

Entry-MultipleExit (henceforth ME- ME) pathways in general & vocational education and

between them.

This would enable a switch on/off model of skill impartation in which, the students can join

a sector as employee/intern at any phase of academic pathway and once the objectives

therein are met, turn back to the academic institution for further skill upgradation. The

mobility of academic qualifications, which is a necessary condition for this step, would be

achieved by designing a national database of verified academic qualifications, which could

be accessed by various stakeholders to gather information about the various skills acquired

by the students at various levels. This would also power end scale promotion of education as

minute gaps in the learning journey will not affect the overall performance or skill formation

in the students.

3. Comprehensive credit-based academic & skill qualification frameworks, with assignment,

accumulation, storage, and transfer of credits in Academic Bank of Credits. (henceforth

ABC). This would create a database depicting the quality and quantity of skill acquisition in


India, which will benefit employers and other stakeholders to get reliable information about

the students, without any prior interaction.

4. Internationalisation of education which will provide a growth impetus for the aspirations

of the Indian economy through collaboration with various foreign universities and industrial

players. Even here, shared skill impartation to be the primary objective of all the initiatives.

5. Skill Formation Clusters to become the premier higher education institutes in India,with a

huge registry of skill development and consequently forming a resource pool to ensure

optimum utilisation of resources. Moreover, these cluster institutes, to be focused majorly on

multidisciplinary cum modern age skills, will abandon the rigid classroom model' followed

in India and replace it with 'partial association upgradation of academic skills model'.

Encouraged by these principles, UoD formed its own Undergraduate Curriculum Framework

(henceforth UGCF 2022) with the following features;

1. UGCF 2022 combines the prestigious honours degree program of UoD with the landmark

ME - ME model as prescribed in NEP to form a comprehensive new model for

undergraduate courses, namely Four Year Undergraduate Programme (henceforth FYUP).

This FYUP system replaced the erstwhile Choice Based Curriculum Scheme (CBCS). Under

FYUP, Undergraduate courses are of 4 years, with each year acquiring a different level of

degree, namely Certificate - 1st year, Diploma- 2nd year, honours degree - 3rd year and

honours degree with research- 4th year. This system would further enhance the flexibility

and academic standards of the courses offered by UoD.

2. As UoD is the premier institute in India having the single the greatest number of

placements as well as Gross Value of Recruitments, the employability factor of

undergraduate students in UoD to be amplified through the introduction of a separate and

compulsory component called Skill Enhancement Courses (henceforth SECs). These SECs

include those skills that are vital to the employees in the upcoming '4th age of industrial

revolution' as well as those which are necessary for holistic development of personality. To

further ensure increased achievement of this objectives, these SEC courses are designed

through inputs from industries, leading firms in the world and from government agencies, to

ensure that the gap between the skills desired by employers and those acquired by the

employees in India is reduced to a considerable extent. Central to the analysis of the present

study, this component single handedly provides an incentive for industrial players to increase

their volume of mid - course as well as end course campus placements in UoD.

Further, these SEC courses along with another component called Value Added Courses have

limited methods of conventional assessment and will focus more on practical and innovative


methods of talent proficiency assessment. Both of them will constitute an integral part of

academia.

3. To further improve the internationalisation of education, UoD will meticulously follow

the credit structure as prescribed by NEP 2020. Also, large scale participation with foreign

powers through 'pupil swap' as well as 'method swap 'techniques. This to increase the brand

value of UoD in leading industrial circles in the globe, to further increase MNC investment

and recruitments from the university.

Also, through innovative methods, various indices like student - teacher ratio, number of

undertaken research projects and number of students progressing to higher education to be

improved.

4. All academic structures including syllabus to be constantly updated, with this industrial

factor in mind and focused on improving the phase wise skill formation in undergraduate

courses of UoD. International agencies suggest initiatives for this streamlining of academic

quality, by hiring in more faculty, investing more in ICTenabled infrastructure, enhancing

the availability of e-resources and decentralising learning to a student-based approach. For

this, a practical component in the form of weekly tutorials to be an integral part of academic

planning.

5. UoD to sign Memorandum of Understanding (henceforth MoU) with all leading firms in

the world, to gain insights from their experience and to inculcate those lessons in the

academic curricula of UoD. This will also familiarise these firms regarding the availability

of skilled manpower in UoD which will certainly boost the amount and value of internships,

apprenticeships and recruitments offered by foreign firms towards undergraduate students of

UoD.

This provides the theoretical framework for the model to be created for the analysis of the

present study.

4. Methodology

As the present study was meant to forecast the impact of NEP provisions in India on the

higher education sector, especially through course end campus placements made in UoD,

lack of any passed out NEP batches or any reliable sources of data to that extent was the

single most limitation of the study. This could be covered by the use of advanced technology

available in the form of artificial intelligence based 'predictive modelling' created using the

software in Microsoft Excel. For this, an artificial model was created for a situation in the


future, where the NEP provisions were meticulously entered and forecasted to be

implemented.

This model was used to quantify the impact of NEP provisions on the Gross Value of

Campus placements, separately for with NEP and without NEP situations. For this, precise

and accurate data was necessary from reliable sources. The lack of a centralised system for

collection of data on the number, median salary package and total compensation of course

end campus placements in UoD poised a problem for the study. So an intensive data

collection, entailing all affiliated and constituent colleges of UoD was initiated along with

the data available with the university regarding the centrally offered courses like the

certificate course in foreign languages.

The quantity and vastness of the data required volunteers apart from the authors to

participate in the data collection, according to pre-set data collection guidelines. Here too,

private sources or claims made by the colleges could not be incorporated, due to lack of

authenticity. Instead of them, two sets of government reports namely NAAC reports and

NIRF reports were utilised for the same.

NAAC report: National Assessment and Accreditation Council is a statutory body

constituted by the Ministry of Education, India with the aim of accrediting HEIs in India

regarding their quality of deliverance of education. NAAC reports are prepared by colleges

through Self Study Reports (SSR) and Annual Quality Assurance Reports (AQAR) which

were verified by a NAAC peer team which visited the college in a routine manner. As these

reports were verified through a widely acclaimed and rigorous process, the data available

here was highly reliable and assumed to be nearly accurate. This dataset was collected

through SSRs and AQARs for three academic years, respectively 2019-20, 2020-21 and

2021-22 which were then feeded into the model created in MS Excel. All data variables,

except gross value of placements were sourced from this database.

NIRF report: National Institutional Ranking Framework is another part of the Ministry of

Education, India working to rank HEIs in India according to a pre - set and expert

determined criterion, with the data being provided by institutes with preliminary proofs. As

the number of colleges taking part in NIRF ranking was limited and changing with time, the

data variable was set to be the gross variable of course end campus placements in top 10

colleges of UoD, appearing in NIRF ranking. As these top colleges account for a large value

of the gross placements, this would give a general indication of the trends in campus

placements. This dataset was collected for 7 academic years, namely 2015-16, 2016-17,

2017-18, 2018-19, 2019-20, 2020-21 and 2021-22 and fed into the same data set for

comparison, for both 'with NEP - UGCF' and 'without NEP- UGCF' situations. Due to lack


of an institutional method for verification, this dataset, though official, was not considered to

be part of the primary data source for the study, which was essentially the NAAC reports.

The lack of a centralised and efficient data source was a factor which could have impacted

the quality of the forecast. So, the above two different reports were used for the analysis, but

even then variations could be observed within them. So, their correlations for three common

years were found out (2019-20,2020-21 and 2021-22), which was also made a part of the

model. This correlation came out to be 0.9697842, which shows minor variations amidst

general convergence as expected. Only then, both the datasets, adjusted to their variations,

were used for the data analysis. As specified, only course end campus placement for 3-year

undergraduate programme UoD was collected with various other indices indicating the

appropriateness of the current infrastructure and system towards the policy suggestions made

by NEP - UGCF. These indices were total number of faculty recruited, total number of

faculty posts sanctioned, ratio of recruitment of faculty to official sanction, total capital

expenditure for the 3 respective academic years in NAAC data set, Number of students

progressing to other HEIs in 3 respective years of NAAC data set, Number of students

undertaking projects, research work or other experimental learning techniques, number of

physical classrooms, number of ICT enabled classrooms and number of students enrolled in

UGCF suggested subject related certificate courses for the latest years. As the provisions of

UGCF were not implemented in medical or technical institutes under UoD, they were

removed from the dataset to get a precise and quality analysis regarding impact of FYUP

programme. Also recently opened Institutes, which lacked any concrete policy to attract or

necessitate campus placements were also removed from the dataset for convenience. So, the

data collection from primary sources was completed and thus fed onto the system.

For the prediction of gross value of placements, a parameter of particular importance in MS

Excel was the confidence interval (henceforth CI). So, determining CI turned out to be of

utmost importance, as it could change the precise value of the forecast. This was to be done

as per a formula as described below,

1. The placement ratio of UoD (number of students placed to number of students passing out

for 3-year undergraduate program) was allowed to progress naturally in the created model,

with a confidence interval entered as 68.9%. This is because, India Skills Report of 2023, the

prime source of industrial skill expectations for the present study found the employability of

youngsters in the state of Delhi to be the same.

So, any increase in the number of employment in UoD should reasonably be based on this

proportion, where CI specifies the deviations from the current value in a future model.

The upper bound of this forecast for 2026-27 would provide us the CI for the forecast of

gross value of placements in the model, for the situation without NEP, UGCF.


2. The gross value of placements, as stated earlier was found separately in two different

datasets- one with NAAC report and one with NIRF report. But, to avoid the deviations

caused by extreme causes of outliers (which are not based on general trends, but on

exceptional individual talents) and the discrepancy caused by undeclared salary packages,

elements of robust statistics were used, which required gross value to be found not from

mean, but by multiplying median salary package in a particular Institute with the number of

placements, respectively for seven academic years (NIRF) and 3 academic years (NAAC).

This was how the without NEP situation was predicted for reference. Now the expected

value of gross placements and only the positive region of growth was considered for the

model, assuming no disruption would affect the Indian industry throughout this time

duration.

After feeding each and every feature of this policy (NEP-UGCF) into the software, the AI

powered forecast, with its own derivation of CI was used for the forecast, which denoted the

possible value of course end campus placements in UoD for the first passed out NEP batch

of 2026-27, leaving beside intermediary exits (whose campus placements is difficult to be

quantified). The data used for the analysis is attached in the appendix as proof and for

further sector wise analysis. The discrepancy in the upper bound of the prediction being high

with the lower bound being too low, only positive region of growth was considered from the

projected number as the theoretical framework sets the condition for an increase in value of

campus placements with other variables assumed to be constant in this model, like

increasing number of hybrid(online-offline) employment and increasing participation of

women in workforce, evident from higher productivity for women as evident in India Skills

Report . A similar analysis through the model was also done to total capital expenditure in

the colleges of UoD, with data collected from NAAC database. But, as the criterion for

increase in capital expenditure were mostly qualitative, an alternative formula was devised

for this aspect also.

1. The total capital expenditure for all 51 colleges in the NAAC database was found and this

was divided by the number of students to find per student investment on infrastructure.

2. Now, as to account for inflation accumulated in purchasing materials, this per student

investment on infrastructure was adjusted with Wholesale Price Index prediction provided

by the Chief Economic Advisor to the Government of India.

3. Now according to UGCF, UoD expects a 20% increase in number of students by the time

of first fully functional FYUP batches by 2025-26. So, the value obtained in step 2 of this

process was multiplied by (total number of students multiplied by 1.2) to find out the


expected total capital expenditure by 2025-26. Though NEP may lead to an increase in

capital expenditure, this could be offset by the increase in number of online students and

resource pooling through skill enhancement clusters. So, the estimate will approximately

hold. Due to inadequacy of data, this model was generalised for all HEIs in India, with the

same characteristics of the dataset as specified in the present model, to find out the projected

Gross Enrollment Ratio for 2026. This will also fairly hold as UoD provides data impetus

for the Government of India regarding the impact of educational reforms in HEIs, which

could be adopted for the present study also.

5. Projected Impact of NEP - UGCF on Gross Placements Value

As evident, a large segment of UGCF 2022 was to focus on increasing the quantity and

quality of course end campus placements and subsequent upgradations made by industries

with Undergraduate students of UoD. So, any analysis of the impact of the same should

essentially focus on quantifying it, which can be done only with proper collection,

classification and analysis of data. Subsequently, the data collected from two different

databases, namely NAAC and NIRF, were categorised for 54 and 10 colleges respectively.

Table 1 shows chronological collection of Gross Value of Placements in UoD as per NAAC

data (excluding medical colleges) and Table 2 shows chronological collection of Gross

Value of Placements as per NIRF report (for top 10 colleges in respective years). Now the

forecast was done by steps as mentioned in methodology namely;

1. The undergraduate placement ratio in UoD was allowed to progress in the model created

(as described in methodology) which gave an upper bound of XYZ in. 2026-27 with the

confidence interval of 68.9%.

2. The value obtained in Step 1 was made to be the confidence interval for projecting Gross

Value of Placements for course end campus placements for Undergraduate courses in UoD

through NAAC as well as NIRF database.

Figure 1 illustrates the Gross Value of Placements projection graph created in the model

through NAAC data in the artificial situation without NEP. Figure 2 illustrates the Gross

Value of Placements projection graph created in the model through NIRF data in the

artificial situation without NEP. Table 3 (NAAC) and Table 4(NIRF) quantify the data

through the linear forecast and upper bound (to only consider the positive growth).

Now, in the same model, the features of NEP were fed with minor quantitative changes. The

software, with the help of artificial intelligence, derived its own confidence interval, which

was used to project the Gross Value of Placements with other parameters being constant.


Years

Total value of gross placement

2019 2893676557

2020 3339001361

2021 4164947199

Table 8.a: Gross Value of Campus placement with NAAC database, where 2019 stands for 2019-20, 2020 for 2020-21 and

2021 for 2021-22 respectively; Source: NAAC database

Years

Total value of gross placement

2015 341843000

2016 681754298

2017 1162667400

2018 1255190000

2019 1530700000

2020 2122455000

2021 3396989734

Table 8.b: Gross Value of Campus placement with NIRF database, where 2015 stands for 2015-16, 2016 for 2016-27, 2017 for

2017-18, 2018 for 2018-19, 2019 for 2019-20, 2020 for 2020-21 and 2021 for 2021-22 respectively; Source: NIRF database


Years

Placement

Ratio

Forecast (Placement

Ratio)

Lower Confidence Bound

(Placement Ratio)

Upper Confidence Bound

(Placement Ratio)

2015 0

2016 0.0917

2017 0.109

2018 0.057

2019 0.131

2020 0.170

2021 0.136 0.136 0.14 0.14

2022 0.177 0.14 0.22

2023 0.198 0.16 0.24

2024 0.219 0.18 0.26

2025 0.240 0.19 0.28

Table 8.c: collection and forecast of ratio of students having course end campus placement in undergraduate courses of

UoD ( each academic year denoted by its first year); Source: NAAC Database

Figure 8.a: Forecast of ratio of students having course end campus placements in undergraduate courses of

UoD(confidence interval - 68.9%)


Years

Total value of

gross

placement

Forecast (Total

value of gross

placement)

Lower Confidence

Bound (Total value

of gross placement)

Upper Confidence

Bound (Total value

of gross placement)

2019 2893676557

2020 3339001361

2021 4164947199 4164947199 4.16E+09 4.16E+09

2022 4751406473 4.71E+09 4.79E+09

2023 5371950360 5.33E+09 5.41E+09

2024 5992494247 5.95E+09 6.04E+09

2025 6613038135 6.57E+09 6.66E+09

Table 8.d: Forecast data for gross value of placements without NEP, for NAAC database. With each academic year denoted

by its first year

Figure 8.b: Forecast graph for gross value of placements without NEP, for NAAC database. With each academic year

denoted by its first year


Years

Total value

of gross

placement

Forecast (Total

value of gross

placement)

Lower Confidence

Bound (Total value

of gross placement)

Upper Confidence

Bound (Total value

of gross placement)

2015 341843000

2016 681754298

2017 1162667400

2018 1255190000

2019 1530700000

2020 2122455000

2021 3396989734 3396989734 3.40E+09 3.40E+09

2022 4598371039 4.48E+09 4.72E+09

2023 5801185685 5.54E+09 6.06E+09

2024 7004000332 6.57E+09 7.43E+09

2025 8206814979 7.58E+09 8.83E+09

Table 8.e: Forecast data for gross value of placements without NEP, for NIRF database. With each academic year denoted

by its first year

Figure 8.c: Forecast graph for gross value of placements without NEP, for NIRF database. With each academic year

denoted by its first year


Years

Total value of

gross

placement

Forecast(Total

value of gross

placement )

Lower

Confidence

Bound(Total

value of gross

placement )

Upper

Confidence

Bound(Total

value of gross

placement )

2019 2893676557

2020 3339001361

2021 4164947199 4164947199 4.16E+09 4.16E+09

2022 4751406473 4.53E+09 4.97E+09

2023 5371950360 5.15E+09 5.60E+09

2024 5992494247 5.76E+09 6.23E+09

2025 6613038135 6.36E+09 6.86E+09

Table 8.f: Forecast data for gross value of placements with NEP, for NAAC database. With each academic year denoted by

its first year

Figure 8.d : Forecast graph for gross value of placements with NEP, for NAAC database. With each academic year denoted by its

first year


Years

Total value

of gross

placement

Forecast(Tota

l value of

gross

placement)

Lower Confidence

Bound(Total value

of gross

placement)

Upper Confidence

Bound(Total value

of gross

placement)

2015 341843000

2016 681754298

2017 1162667400

2018 1255190000

2019 1530700000

2020 2122455000

2021 3396989734 3396989734 3.40E+09 3.40E+09

2022 4598371039 3.93E+09 5.27E+09

2023 5801185685 4.37E+09 7.23E+09

2024 7004000332 4.65E+09 9.36E+09

2025 8206814979 4.80E+09 1.16165E+10

Table 8.g: Forecast data for gross value of placements with NEP, for NIRF database. With each academic year denoted by

its first year


Figure 8.e : Forecast graph for gross value of placements with NEP, for NIRF database. With each academic year denoted by its

first year

So Figure 3 illustrates the Gross Value of Placements projection graph created in the model

through NAAC data in the artificial situation with NEP and Figure 4 illustrates the Gross

Value of Placements projection graph created in the model through NIRF data in the

artificial situation with NEP. The tables above quantify the data through the linear forecast

and upper bound (to consider only the positive growth). An analysis of the data shows a

considerable increase in the upper bound value of Gross Value of Placements From six

hundred sixty-six crores to six hundred eighty-six crores (NAAC), with an increase of

200000000- twenty crores, which acts as the moderator or the limit to which the gross value

of campus placements can increase, given all provisions of NEP being systematically and

purposefully implemented. So this amount is projected to be added to the Indian economy by

NEP - UGCF in UoD, just through course end campus placements in the undergraduate

Programmes.

6. Projected Impact of NEP - UGCF on Structural Composition of Course End

Campus Placements

Unlike the case of gross value, a quantitative analysis was not feasible in this situation due to

the vibrant and dynamic nature of the Indian economy as well as its structural prospects.

Nonetheless, the analysis from India Skills Report(5), industrial reports and government

reports states that there would be considerable changes in the structural composition of

recruitments from undergraduate courses, if provisions of NEP and UGCF are implemented

by 2026. Consequently, consumer oriented and data-oriented sectors like retail sector and IT

will demand a large number of placements from UoD by 2026. A promising statistic in this

regard was the turnout of employability among the age group of 22-25 in Skill India Report,

which also includes an increasing appetite for skilled, but entry level employees among

manufacturing, healthcare, biotech, renewable energy, FMCG and connectivity & mobility

sectors. Also, with the increase in foreign collaboration with various universities, the talent

pool present there can also be sourced from UoD( with UGCF placing much emphasis on

foreign collaboration, both curricular and co - curricular). But the biggest increase would be

in AI, data analysis and Fintech sectors, as evident from; number of internships and

apprenticeships associated with those sectors. The hybrid work model will oversee a large

and exponential growth with these provisions, as necessitated by contemporary situations.

UoD undergraduates may also see a reduction in the number of foreign employments, as a

large number of them were employed in emerging unicorns (a startup company valued at

over US$ 1 billion) with India holding the base for a large number of unicorns. (111, as of

3-10-2023).


7. Projected Impact of NEP - UGCF on Research and Infrastructure Investment

The number of students undertaking research projects or such experimental learning

techniques in UoD was 64,455 (for 51 colleges observed through NAAC database). This is

naturally expected to increase even without the application of NEP. But NEP has set high

standards for the research activities in universities through the establishment of a National

Research Foundation. According to NEP, "Knowledge creation and research are critical in

growing and sustaining a large and vibrant economy, uplifting society and continuously

inspiring a nation to achieve even greater heights". NEP conceptualises research as critical

today, more than ever before, by focusing on issues like climate change, population

dynamics and management, expanding digital marketplace, biotechnology and the rise of

marketplace and artificial intelligence. The research incentives from the government would

be streamlined, but not limited to these sectors. As quality research on the above-mentioned

sectors is a necessity for tomorrow and the demand for them is increasing worldwide,

students of UoD will certainly benefit from it. Despite the importance of the research sector,

the research and innovation investment in India is just 0.69% of the GDP as opposed to

2.8% in the USA, 4.3% in Israel and 4.2% in South Korea.

So, NEP aims to address this gap by large scale government expenditure and fostering

equally vital partnership from the private sector by encouraging industrial internships and

apprenticeships among students, primarily with research-oriented themes. A large number of

MoUs (Memorandum of Understanding) signed by UoD with foreign research and

development institutes illustrate this fact.

Further to channelise and catalyse funds made for innovative research projects, a separate

National Research Foundation (NRF) will be formed, equivalent to such authorities in

competent economies like China (National Natural Science Foundation of China) and South

Korea. Though the present agencies will continue funding projects under their aegis, NRF

will coordinate between them. The UoD branch of NRF will materialise soon and further

amplify the research works in UoD, especially with schemes like RSNA (Research Scheme

of NITI Aayog). But an exact or foretold projection of its value or quantity will not be

possible, given the time taken by quality related aspects of education to react to a new

initiative.

Investment in infrastructure was a critical sector, where Indian HEIs were said to be lacking

by AISHE reports. Though NEP and UGCF focus on optimum utilisation of available

resources in terms of physical capability as well as manpower, the increased number of

students and Institutional funding will eventually lead to an increased expenditure on capital

and infrastructure availability in HEIs. In the particular case of UoD, data was collected


from the NAAC database regarding Total expenditure on infrastructure by the referred 51

colleges for 3 academic years and their projection was done through 2 methods namely.

Method 1: The total expenditure on infrastructure was feeded onto the model created for

'With NEP situation' in MS Excel, with confidence interval being derived from the steps as

taken during the projection of Gross Value of Placements. Now, with this confidence

interval, data was projected with the data for 2026-27(with bills made in 2025-26, the first

fully NEP based undergraduate programmes progressing in the institute, being submitted for

financing and consequently being accounted for in the financial year 2026-27). This found

the total value of investments for year 2026-27 to be ₹9626.08 lakhs. (with an upper bound

of ₹10194.30 lakhs and lower bound of ₹9057.87 lakhs.)

Method 2: Formula derived for projecting infrastructure investment in Methodology was

used with current per student expenditure being ₹3881.06, WPI forecast for given duration

being 4.8(2022-23), 0.9(2023-24), 5.5(2024-25) and 4(2025-26). So the inflation adjusted

value was ₹4502.87. Consequently, total expenditure expected in UoD by 2026-27 was

₹9012.13 lakhs.

The need for two different methods as well as differentiation between them is explained by

the fact that while NEP and UGCF may ignite an increase in the value of capital

expenditure, it may still be limited due to the optimum utilisation of infrastructure through

clustering and emphasise on online courses as well as skill based distance learning. Also,

during the post - pandemic era, there was a massive uptick in infrastructure creation in UoD,

which was reflected in the current data. So the present study took a realistic approach on this

issue by taking the natural progression of current infrastructure investment as the forecast for

2026-27 also. While, the software used in MS excel proceeded with the data input in earlier

analysis and projected the total expenditure, for complete utilisation of NEP objectives.

8. Impact of NEP - UGCF on Vocationalisation of Higher Education

Vocational education and training (VET) plays an important role in developing skilled

manpower in a country. The integration of vocational education into mainstream academic

education is evident from the inclusion of SEC courses and certificate courses as an

important component of academic curriculum by UGC. This milestone event will motivate

more and more students to pursue skill based vocational courses that may offer them with

better employment opportunities.

In the particular case of UoD, which has got a premier stand amongst the HEIs in India, this

paradigm shift from mainstream academia to vocational and employment based educational


opportunities will have reflections all across India, as the change will begin from the creamy

layer of HEI students.

Moreover, this will also impact their subsequent performance and earnings in the industry.

The study done on this particular aspect of higher education, namely employability of

students through provisions of vocational education similar to the one suggested in UGCF,

found out that wages of workers employed in all sectors of contemporary relevance

increased with those provisions.

Years

Total expenditure of

infrastructure

Forecast

Lower

Confidence

Bound

Upper

Confidence

Bound

2019-20 5303.431

2020-21 6286.122

2021-22 6473.040 6473.040 6473.040 6473.040

2022-23 7160.659 6701.588 7619.729

2023-24 7777.015 7308.943 8245.088

2024-25 8393.372 7905.717 8881.027

2025-26 9009.729 8489.130 9530.327

Table 8.h: Projection of Total Investment in Infrastructure, using the method 1 of predictive modelling (Figures in lakhs of

rupees), with each year being represented by its first year; Source: NAAC database

Figure 8.f : Projection graph of Total Investment in Infrastructure, using the method 1 of predictive modelling (Figures in

lakhs of rupees)


Also, VET allows students to further streamline their careers by initially opting for

specialised sectors, then making painful transitions later. However, the same study states that

impact on the probability of participation of entry level employees (like those recruited

through campus placements) in various sectors differed, due to varying levels of skill

development. It also highlighted the reduction in class, caste and gender wise discrimination

faced by recruited employees in initial days of employment, due to upgrading talents in their

resume, which will bolster their chance of remaining in the same field.

9. Skill Formation Clusters: The Way Forward

HEI clusters focused on skill formation, as envisaged in NEP are knowledge hubs, each of

which will have a large number of students, either in full time/regular manner or through

partial involvement, which will turn into large and vibrant communities of scholars and

peers, with focus on artistic, creative and analytic subjects as well as sports and research

activities, to increase cross - disciplinary research, resource efficiency and institutional

uniformity. This was the primary aspect behind all initiatives regarding higher education in

India, which the policy itself has mentioned many times. NEP also aims to create a

large-scale academic database of students through such an initiative, by removing the

pressure on students to obtain skill upgradation and academic qualification as two separate

entities. Instead, it will pave the way for large scale skill clusters, within universities or

colleges, which will specialise in one or more competent skills, that will be of particular use

to students in increasing their chance of employment as well as progression to better career

opportunity. Such a large-scale database of students will also help industries in recruiting

specialists in certain fields or tasks, say Business Process Outsourcing (BPO) which will

also increase outsourcing initiatives and consequently Foreign Direct Investment from

industries abroad. This will bring out a fundamental change in Indian HEIs from being

single discipline academic centres to multidisciplinary skill initiation and upgradation

centres. This policy suggestion in NEP, central to all its initiatives, has been absent in UGCF

2022. Though UoD has begun experimentation with cluster education at minute level

through courses like Ability Enhancement Courses, the large-scale implementation of this

for industrial purposes can bring out momentary changes.Consequently, a large amount of

the impact that the present study for NEP- UGCF can materialise only if the fundamental

rigidities within collegiate education in India is removed by adopting Skill Formation

Clusters as an integral part of UGCF. Along with this, as visible from various government

reports, it would be highly effective for the concept of Skill Formation Clusters in NEP as

database of student skills to be replaced by fostering it as a concept of student data registry

(a collection of information or databases whose organisers receive information from multiple

sources, maintain the information over time, keep it updated and control access to the

information). This system, integrated with 4th generation electronic features will certainly


act as an asset as well as enabler for the higher education sector in India. This is certainly the

way to the future for the skill economy in India.

10. Generalised Analysis of Results

The study found out that almost all objectives mentioned in UGCF 2022 are sourced from

NEP 2020, which was formulated from analysis of data variables in the past and also those

which could be proven through the methods used in the study, reported to have little or high

improvement through implementation of FYUP. As UoD is the premier institute in India in

terms of quality, quantity and brand value, the general trends observed in the delivery of

quality education here will have an impact all across India. But it would be highly unlikely

to have a similar impact pan-India on criteria like gross value of campus placements and

total expenditure on infrastructure as they are dependent on various factors and tend to vary

from institution to institution. But the findings with respect to vocationalisation, research

quality and skill formation can be generalised for the whole of India, if supplemented with

adequate infrastructure and industry support.

The FYUP system and the option for multiple entry or exit within it (ME - ME), can

transform the very structure of Indian education and make it more competent to address the

skill gap observed in the formal education sector. Various studies confirm the same.


Years

GER(%)

Forecast

(GER(%))

Lower Confidence

Bound(GER(%))

Upper Confidence

Bound(GER(%))

2015 24.3

2016 24.5

2017 25.2

2018 25.8

2019 26.3

2020 27.1

2021 27.3 27.3 27.30 27.30

2022 28.181 27.88 28.48

2023 28.393 28.09 28.70

2024 29.274 28.84 29.70

2025 29.486 29.05 29.92

2026 30.367 29.84 30.89

2027 30.578 30.05 31.11

Table 8.i: Collection and projection of Gross Enrollment Ratio, using the same Confidence interval as in with NEP model;

Source: AISHE report

Figure 8.g : Projection graph of Gross Enrollment Ratio, using the same Confidence interval as in with NEP model


11. Conclusion

The primary concern associated with the present study was to assess the economic impact of

policy measures for skill upgradation in National Education Policy 2020. By analysing

various indices available for undergraduate courses in University of Delhi, it found out some

possible and interesting aspects of the same. While the qualitative measures for this arena

have been established, it focused majorly on quantitative aspects. Thereupon, it projected a

possible ₹20 crore increase in course end campus placements for undergraduate courses in

UoD, for the first batch of UGCF, provided all schemes as formulated in NEP - UGCF,

including the historic Skill Formation Clusters programme are implemented. Additionally,

the study also gave possible statistics for total infrastructure investment to be expected with

implementation of NEP - UGCF as well as some possible parameters to monitor the impact

in certain other critical areas like vocational education and skill upgradation. So, the present

study hereby projects a positive and highly efficient impact for NEP - UGCF in all required

aspects in the case of UoD. The unique position of UoD among the HEIs in India and with

the help of some highly sophisticated data analytic reports, it also could generalise these

trends as some possible pathways for the whole of India. But any theoretical analysis in the

case of Economics is based on the fundamental principle of 'ceteris paribus' and

consequently the fundamental assumption in the case of present study, that only the positive

side of forecast ought to be considered, needs specification and the analysis may not hold

good for if the situation turns to the economic point of view, through events like recession or

Covid pandemic. The analysis is made, in view of an economy having the same constructive

growth momentum, as the Indian economy holds now. Any alteration to the same will also

alter the results. Any further appropriation on this result ought to be based on this fact.

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