the case of iganga district - Economic Policy Research Centre

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3. Methods and dataThis section presents the methodological approaches employed followed by the data sourceand some of their limitations.3.1 Methods3.1.1 Measuring efficiencyIn economic analysis, efficiency is generally defined in a number of related ways including:the use of resources in such a way as to maximize the production of goods and services; orcomparison of what is actually produced or performed with what can be achieved with thesame level of resources (land, capital, labour, time, etc.). Farrell (1957) pioneered themethodology to measure technical, allocative and economic efficiency. According to Farrelland other subsequent literature, a producer is efficient if the producers’ behaviouralobjectives are met; and inefficient if they are not (cited in Fare et al. 1985). Hence efficiencyof the producer can be measured by comparing any given situation with (or the) situationthat satisfies the producers’ behavioural goal (Fare et al. 1985). This kind of analysis, oftenregarded as the data envelope analysis (DEA) compares producer efficiency to some idealbenchmark.Other related literature, however, simply define efficiency as the relationship between a setof inputs and output(s). Comparison of producer efficiency is conducted in terms ofquantities (inputs and outputs) or values (costs, revenue and profit). As such, in agriculture,yield, which is output per land area under cultivation, is widely used as a measure of howefficiently land is used in production. In value terms, profit (gross or net) or revenue to costratio is used to measure efficiency. In most cases however, cost‐effectiveness analysis (CEA)–which relates the resources to results and/or impact (e.g. yield) is applied (Eureval‐C3E2006).In this paper, production/technical efficiency is represented by yield while economicefficiency by gross profit (Gross profit). The cost effectiveness of NAADS intervention iscompared with no government intervention using the cost‐effectiveness (CE) ratio as:(1)CostCE Ratio Gross profitNAADS farmersNAADS farmers CostNonNAADS farmers Gross profitNonNAADS farmersHowever, since the numerator in Eq. (1) is simply equal to the cost of NAADS inputs subsidyprovided by government to selected farmers, then Eq. (1) can be expressed as in Eq. (2).(2)CE RatioGross profitCostNAADS farmersNAADS subsidy Gross profitNonNAADSfarmersThe CE ratio in Eq. (2) is calculated at two levels by comparing the ratios between the NAADSand non‐NAADS farmers. At the first level, the ratio compares the average value of6
government input subsidy per crop per acre with the value of marginal yield generated;whereas at the second level, the ratio compares the value of government input subsidy percrop with the value of marginal gross profit realised. The possible results are presented inTable 2. The interpretation of the information in this table is as follows: if the CE ratio is lessthan 1, when both the cost of NAADS subsidy and the marginal yield or gross profit valuesare positive, it implies that NAADS subsidy is cost‐effective.Table 2: Possible results of cost effectiveness analysisNAADS SubsidyMarginalyield/gross profit CE ratio interpretation+ + < 1 NAADS subsidy cost‐effective+ + > 1 NAADS subsidy not cost‐effective+ ‐ < 1 NAADS subsidy not cost‐effectiveSource: Author’s formulation based on Schleiniger (1999).3.1.2 Analysis of efficiency determinantsIn this section, the paper endeavour to employ a multivariate approach to measuringproduction efficiency of farmers in Iganga district. The magnitude and significance of theproduction inputs including the influence of farm/farmer characteristics on farmers’ outputand gross profit were examined (Bravo‐Ureta and Pinheiro 1997). The paper assumed anormalised Cobb‐Douglas production function as expressed in Eq. (3) ‐ a functional form thathas been widely used in farm‐level analysis.(3) , ; ; i = 1, . ., NWhere Yiis normalised output or gross profit of farmer i; X ji is the normalised cost of input jused in production by farmer i, R ki is farmer/farm characteristic k of farmer i. Variablenormalisation involves the division of the variable with output price (Ali and Flinn 1989;Hyuha et al. 2007). β is a vector of coefficients to be estimated. In this paper, maximumlikelihood method was employed to estimate Eq. (3).3.2 DataData used in this study was obtained from both primary and secondary sources. Details ofthe study area, sampling design, data sources and collection process are explained below.3.2.1 Study area and sampling designThis study was undertaken in Iganga district. The choice of Iganga as the primary site for thestudy was motivated by the fact that there were conflicting accounts of NAADS success onone hand (Benin et al. 2007) and mismanagement on the other, in the district (for example,see Sunday Vision 17 May 2009). Besides, the district is one of the earliest beneficiaries of7
Page 3 and 4: Table of contentsAbstract .........
Page 6 and 7: In the course of NAADS implementati
Page 8 and 9: 2. Overview of NAADS operationsNAAD
Page 12 and 13: NAADS funding -for which one would
Page 14 and 15: particular, results for maize showe
Page 16 and 17: Table 7: Stochastic frontier estima
Page 18 and 19: Furthermore, FGD participants state
Page 22 and 23: own interests. When funds reach the
Page 25 and 26: Table 14: Iganga district: Comparis
Page 27 and 28: Thus, the very concept and importan
Page 29 and 30: ReferencesAli M. and J. Flinn (1989
Page 31 and 32: Table A 1: Summary of government fu
Page 33 and 34: Table A 4: Monitoring and evaluatio

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