metadata: authors: 0: author: Ravallion, Martin
1: author: Datt, Gaurav
2: author: Chaudhuri, Shubha
docna: 0: docna: Higher wages for relief work can make many
            of the poor worse off : recent evidence from
            Maharashtra's "Employment Guarantee Scheme"
docty: Policy Research Working Paper
subtopic: Labor Markets,ICT Policy and Strategies,Food Security,Inflation,Inequality,Rural Labor Markets
teratopic: Poverty Reduction
abstracts: cdata!: Among developing countries, the
            "Employment Guarantee Scheme" (EGS) in the state
            of Maharashtra in India is probably the most famous and most
            successful direct governmental effort at reducing absolute
            poverty in rural areas.  Since the mid-1970s, EGS has aimed
            to offer unskilled rural employment on demand.  The work
            creates or maintains rural infrastructure, through small
            scale irrigation and soil conservation projects,
            re-forestation, and rural road building.  EGS projects are
            designed to be highly intensive in their use of unskilled
            labor, which typically accounts for over two-thirds of
            variable costs.  Wages are set in the form of piece rates,
            stipulating rates of pay for a large number of specific
            tasks, such as digging, breaking rocks, shifting earth, and
            transplanting.  This paper investigates the effects on the
            scheme of the dramatic change in the EGS wage schedule in
            mid-1988.  Three issues are addressed:  (a) EGS employment,
            wage rates, and the cost of the scheme to the government
            after the increase in the statutory minimum wage rate; (b)
            the determinants of EGS employment and changes incurred by
            the wage increase; and (c) the availability of local
            employment at the going EGS wages.
repnme: repnme: Higher wages for relief work can make many
            of the poor worse off : recent evidence from
            Maharashtra's "Employment Guarantee Scheme"
display_title: Higher wages for relief work can
            make many of the poor worse off : recent evidence from
            Maharashtra's "Employment Guarantee Scheme"
listing_relative_url: /research/1991/01/700097/higher-wages-relief-work-can-make-many-poor-worse-off-recent-evidence-maharashtras-employment-guarantee-scheme
topicv3: Agriculture and Food Security m1326205 435,Labor Markets m1326488 855,Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Monsoon m1503657 3193
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Policy, Research & Ect Affairs
no_of_pages: 41
model_extractions: **Polic7, Research, and External Affalfs**

<!-- Start of picture text --> Agricultural Policies<br>WPS 568<br><!-- End of picture text --> This paper -a product of the Agricultural Policies Division, Agriculture and Rural Development Department -is part of a larger effort in PRE to research the pcrformance of poverty alleviation schemes and the implications for policy design. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cicely Spooner, room N8-039, extension 30464 (36 pages). Relief work schemes provide well-targeted relief increase in wage rates. This partly reflected to poor people, and valuable insurance against good monsoons but, controlling for monsoons, poverty. But their success may depend on the tlhere are signs that falling employment reflected - scheme's design particularly the wage rate rationing; some poor people who wanted relief and coverage offered. work could not get it.

increase in wage rates. This partly reflected good monsoons but, controlling for monsoons, tlhere are signs that falling employment reflected rationing; some poor people who wanted relief work could not get it.

The most famous and one of the most successful of these programs is the Employment Guarantee Scheme (EGS) that has been in operation since the mid-1970s in the Indian state of Maharashtra. In a typical year it provides about 100 million person-days of unskilled employment on rural infrastructure projects, at an average cost of about one dollar a day in the late 1980s. The demand for EGS work fluctuates enormously from year to year (depen(iinig on the vagaries of the monsoon) and across seasons in a given year.

In mid-1988 the piece rates paid to workers on EGS doubled, in line with new statutory minimum wage rates for agricultural labor. Ravallion, Datt, and Chaudhuri investigated the effects of this sudden increase on the scheme's cost, the workers' wages, and their ability to find work when needed.

They found that the impact of tlhe wage increase on real cost was dampened by inflation, adjustments in the composition of work, and, most important, by falling employment. The aggregate real cost per month fell after the Ravallion, Datt, and Chaudhuri found that EGS met less than half of the demand for work after the wage increase and that almost all of the fall in EGS employment was from rationing. The effects of the initial wage increase on the poor are ambiguous: some could get higher wages but others went without desired relief work.

The concept ol assured employment, albeit at a low wage, can be attractive in terms of poverty alleviation: it generally allows scarce resources to go to the poorest first (at least those able to work), it maximizes the insurance benefits to the poor, and it helps undermine some of the possibilities for corruption on such schemes - and for exploitation in labor markets and tenancy contract.s.

But acaieving these beneflts with limited budgetary resources requires a low enough wage rate. The recent experience in Maharashtra suggcsLs that imposing a higher wage rate when you don't have the budget to pay for it is not in the interests of all of the poor.

Thc PRE Working Paper Scries disseminates the findings of work under way in Lhc Bank's Policy, Rcscarch, and Extemal Affairs Complex. An objective oflhc scrics is to getthesef indings out quickly, ecen if presentations arc less than fully polished. The findings, interpretations, and concltisions in these papcrs (do not necessarily represent official Bank policy.

Produced **by** the PRE Disscmination Ccnter
Higher Wages for Relief Work Can Make Many Of the Poor Worse Off Recent Evidence from Maharashtra's "Employment Guarantee Scheme" by Martin Ravallion Gaurav Datt and Shubham Chaudhuri*

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Maharashtra's<br>"Employment Guarancee Scheme" Before and<br>After May 1988|3|
|3.|Did the Process of Employment Determination<br>Change<br>Significantly?|6|
||3.1<br>A Model of Monthly Attendances<br>on the EGS, 1975-88<br>3.2<br>Tests for a Structural Break in the Model After<br>Wage Increase|6<br>10|
||3.3<br>An Estimate of the Extent of Rationing After the<br>the Wage Increase|11|
|4.|Does the EGS Now Guarantee Employment?|13|
||4.1<br>The Effect of the Number of Works in Progress<br>on Employment|13|
||4.2<br>The Effect on Agricultural Wages|19|
|5.|Conclusions|21|
|Note|s|25|
|Ref|erences|29|
|Tab|les|31|
|Fig|ures|36|

* The authors are grateful to the Government of Maharashtra for providing generous access to their records on the "Employment Guarantee Scheme" and related data, and to Seema Arora for able research assistance. For their comments on this paper, our thanks go to Roger Slade and Dominique van de Walle. This research has been supported by the World Bank's Research Committee under RPO 675-96.
# 1. **IntroductIon**

Amongst developing countries, the "Employment Guarantee Scheme" (ECS) in the state of Maharashtra in India is probably the most famous and, by many accounts, the most successful direct governmental effort at reducing absolute poverty in rural areas.<sup>1</sup> Since the mid-197Us, EGS has aimed to offer unskilled rural employment on demand, as embodied in its slogan, "magel tyala kam" ("whoever desires work will get it"). The work creates or maintains rural infrastructure, through small scale irrigation and soil conservation projects, re-forestation, and rural road building. EGS projects are designed to be highly incensive in their use of un-skilled labor, which typically accounts for over two-thirds of variable costs. Wages are set in the form of piece rates, stipulating rates of pay for a large number of specific tasks, such as digging, breaking rocks, shifting earth, and transplanting.

The scale of the scheme is impressive; in a typical year, it provides about 100 million person-days of employment. (The state's aggregate rural workforce - including cultivators - was about 20 million persons in the mid1980s.) Given this scale, it is surprising how little we seem to know about the scheme's performance in alleviating income poverty. But the little we do know suggests that the scheme is well targeted, in that the non-poor are rarely attracted, and that the net transfer and income stabilization benefits to the poor are likely to be quite sizable.2 To many observers, the "employment guarantee" built into the Maharashtra scheme has been an important factor in realizing those benefits. With adequate (th-ugh still limited) budgetary resources, the combination of an employment guarante. and a wage rate low enough to balance the budget in an average year is likely to be the policy which will have the greatest impact on
2 measures of poverty which give highest weight to the poorest (Basu, 1981; Ravallion, 1990a).3 Until recently, the piece rates have been such that a typical **EGS** worker could earn wages roughly on par with prevailing agricultural wages. However, in May 1988 the piece rates paid by the EGS doubled, following a doubling in statutory minimum wage rates. Serious concerns have been expressed about the budgetary consequences of this substantial increase in the EGS wage (Subbarao, 1989; World Bank, 1989). The potential impact on cost would naturally create pressure to relax the employment "guarantee". Providing higher wages without the guarantee will mean that, although the actual participants are made better off, fewer of the poor will be able to become participants. High wages rates (relative to prevailing rates in agriculture) throw into doubt both the fiscal sustainability and the social desirability of an employment guarantee (Ravallion, 1990b).

The existing evidence on effects of the wage increase is inconclusive. Some observers have suggested that an unfulfilled demand for EGS work by the poor emerged. There were reports of un-seasonal migration into urban areas of Maharashtra in 1988/89, attributed to problems in finding EGS work in rural areas.4 Average attendances on EGS did fall after mid-1988. However, the crop-years following the wage increase were amongst the best in recent times, and this may equally well explain the falling attendances. During May 1990, the first two authors raised the issue in a few informal interviews with administrators and workers on EGS, and other interested parties. There was no obvious sign of a consensus on how the wage increase affected the scheme; "good monsoons since then" was easily as popular an explanation as "EGS work unavailable".
3 This paper investigates the effects on the scheme of the dramatic change in the EGS wage schedule in mid-1988. Three questions are addressed: i) What happened to EGS employment, wage rates, and the cost of the scheme to the government after the increase in the statutory minimum wage rate? Section 2 describes how the main variables of interest have evolved.

ii) What determines EGS employment, and did this change as a result of the wage increase? Section 3 examines this question using an econometric model of the monthly actendance count on EGS from 1975 to 1988. An estimatc is then made of the extent of work rationing on EGS during the 12 months after the wage increase.

ii) Does the EGS now "guarantee" employmant, in the sense that workars can find local employment when they want it, at the going EGS wages? We address this question using new data on the scheme for 1987/90 in Section 4.

2. Maharashtra's "Employment Guarantee 3cheme" Before and After May 1988.

We shall first look closely at the period April 1987 to February 1990, for which we have monthly data on a number of key variables.5 This will motivate our interest in the more analytical questions addressed in sections 3 and 4.

One can usefully divide the period into three sub-periods: Sub-period 1: Pre-May 1988, during which the average wage rate on EGS was approximately eaual to the agricultural wage rate. Sub-Deriod 2: June i988 to November 1988, during which there were some significant adjustments in the scheme, as will be discussed below. Sub-period 3: The period since December 1988 in which, we shall argue, the scheme appears to have settled into a new "equilibrium". The three sub-periods are identified in Figure 1, which plots
4 the averagt. real wage rate for EGS employees over the period April 1987 to February 1990, as well as our estimate of the average wage rate for agriculture, which we will discuss further in section 4.

The adjustment period saw the EGS wage roughly double initially, in line with the increase in the statutory minimum wiage rate. The wage rate then fell almost as dramatically, to end up at about one third higher (in real terms) than it had been prior to May 1988 (Figure 1).

Inflation contributed to this decline, but there are other factors to consider. Wage rates on the EGS are fixed according to the type of w?ork done and its output; for example, rock breaking is paid at a different rate per cubic foot to loading soil on trucks. The whole piece rate schedule doubled after May 1988. The allocation of the available work across these activities - - which we will refer to as the composition of EGS work is determined by the EGS authorities, through their project choices. Under such conditions it may be quite sensible for the authorities to adjust the latter in favor of activities with lower piece rates, in response to the increase in the wage schedule.6 Given that such adjustments are not instantaneous, a better indication of how the scheme responded to the change in wage schedule can be obtained by comparing outcomes across periods 1 and 3. Table 1 gives some data of interest for all three sub-periods, calculated from unpublished monthly EGS records.

By the third sub-period, the impact of doubling the piece rate schedule amounted to about a one-third increase in the average real EGS wage rate; roughly two-thirds of the initial wage increase was absorbed through inflation and changes in work composition. But the more striking observation is that,
5 despite the net teal wage increase, empluyitcnt actually fell, and by a similar proportion to the increase in the average wage.7 Comparing sub-periods 1 and 3, unit non-wage cost remained fairly constant in real terms. On balance, the average real monthly cost of running the scheme fell by about one-fifth after the doubling of piece rates in May 1988.

A decomposition formula can be used to throw further light on the - relative importance of these various factors wage rates, non-wage costs, and - employment in determining the scheme's operating cost. Let Ct denote the total operating cost of EGS in month t, which can be written as Ct

- VtLt where Vt is unit real cost per worker and Lt is the number of workers employed in month t. Unit cost can be written as Vt - Wt + Xt where Wt denotes average real wage cost per worker and Xt denotes average real nonwage cost. The value of Wt depends on the current purchasing power of the stipulated nominal piece rate schedule, and on the allocation of available w3rk across the various piece rate categories at date t. The pi"--e rate schedule is tied to the statutory minimum wage rate for agricultural labor, mt (in nominal units), for some pre-determined allocation of work across piece - rate categcries. If that is also the allocation at date t then Wt mt/pt - where Pt denotes the price deflator for date t. More generally Wt ktmt/pt, and a change in k will be referred as a change in "work composition" (relative to the date when the piece rate schedule was fixed). We consider two dates t - 1 (sub-period 1) and t - 3 (sub-period 3), and we know that m3 - 2ml. The change in monthly operating cost between sub-period 1 and 3 and its decomposition is then given in Table 2.

Both inflation and (more importantly) the change in work composition were important in holding down real costs after the increase in the minimum
6 wage. However, the overwhelming term in the decomposition is the cost saving due to the decrease in employment; if nothing else had changed, the saving Clue to the drop in employment would have eliminated about 60% of the initial impact of the doubling of the minimum wage rate (Table 2). Following sections will try to better understand this fall in employment.

# 3. Did the Process of Employment Determination Change Significantly?

We shall now look as far back into the history of the scheme as data permit, to better understand the determinants of employment. Section 3.1 presents a model of EGS attendances prior to mid-1988, which section 3.2 will use to perform statistical tests for drift in EGS employment after the wage increase. Section 3.3 will use the model to estimate the extent of employment rationing in the 12 months following the wage increase.

# 3.1 A Model of Monthly Attendances on the EGS. 1975-1988

The data on monthly person-days of employment on EGS used in the previous section are only available since April 1987. We need to go much further back in time to convincingly test for a structural change in the model determining employment after mid-1988. Fortunately, while a long time series of employment in person-days is not available, the Government of Maharashtra has kept a regular attendance count at EGS project sites since July 1975. And this is probably a good proxy for employment.<sup>8</sup> We assume that monthly EGS attendances are determined by the current EGS wage rate, foodgrain output in the current year, the level and pattern of rainfall, seasonal dummy variables, and a time trend, in_luded to allow for any time dependent omitted variables. We shall also allow the possitility
7 thdt EGS attendances in a given month do not adjusW instantaneously to the current values of these var.ables. Serial correlation in attendances can arise in a number of ways. For example, changes in asicultural output may6 have lagged effects on income of the poor and, hence, their demand for relief work. Under certain conditions (notably that the lags can be smoothed exponentially), simply adding lagged attendances to the model will adequately capture such lagged effects of the explanatory variables. The fortnightly payment period used by the EGS may also generate some "stickiness" in the adjustment of employment to changing conditions. It can also be conjectured that the attendance count will tend to be more serially correlated than person-days of employment.

Labor attendance data are available on a monthly basis for the 14-year period spanning July 1975 through June 1989.9 However, only annual data on the EGS time wage are available for this period; the time wage is measured as the average wage per person day for the EGS financial year April through March. Thus we have had to use the same value of the wage variable for the months April through March. lhe nominal wcnge rates were deflated by the Consumer Price Irlex for Agricultural Laborers ' r Maharashtra to obtain real EGS wages at 1975/76 prices.

Data on output of foodgrains pertain to the agriculturai year July to June,10 and (as for the EGS real wage) the foodgrain output variable takes on the same value over these months. Foodgrain output is of course observed only at the end of the agricultural year, and it may appear odd to use it to explain EGS attendances for the earlier months (of the agricultural year). However, it seems plausible that, insofar as EGS attendances in the earlier months depend on the level of agricultural activity in those months, the
8 conditions determining the latter (e.g. adequate and timely rainfall in the monsoon period) would also be reflected<sup>in the agricultural output for the</sup> entire year.<sup>11</sup> Monthly data on rainfall for four meteorological<sup>sub-divisionsin</sup> Maharashtra were collated from various issues<sup>of the Statistical Abstract of</sup> India and the Agricultural Situation in IndiA.<sup>Rainfall for Maharashtra is</sup> derived as a weighted average of the<sup>rainfall in the four sub-divisions, the</sup> weights being proportional to the average<sup>net sown areas in the sub-divisions</sup> during 1978/79 to 1980/81 (COM, 1984). The<sup>effect of rainfall on monthly EGS</sup> labor attendance, although potentially significant,<sup>need not be straight-</sup> forward. The rainfall effect would presumably<sup>be seasonally differentiated,</sup> depending on whether there is deficit<sup>or excess rainfall, and on the amount of</sup> deficit or excess. We thus use a fairly flexible<sup>approach in introducing the</sup> rainfall variables, by defining the following<sup>"rainfall excess" (RE) and</sup> "rainfali deficit" (RD) variables: where Mj is a dummy variable for month<sup>j, DE is a</sup><sup>_dummy_variable for excess</sup> rainfall (equals one if there is excess<sup>rain, zero otherwise), R and NR ar-</sup> the actual and normal rainfall respectively.<sup>The normal rainfall for any</sup> month is defined as the average rainfall<sup>for that month over the entire 13-</sup> year period 1975/76 to 1988/89.

The wage rate in agriculture is probably<sup>the most important omitted</sup> variable in our model. We considered including<sup>the annual agricultural</sup> wage
9 series available from Agricultural Wazs in India (AWI). However, these are currently only available up to 1986/7, so if we used that data as an independent variable in the model we would have missing values to deal with for the last two years, and we would have to forecast the series for 1988/89, prior to forecasting attendances. This creates complications, and it is probably better to give our model a reduced form interpretation, in which an equation for the agricultural wage rate has been "solved out". Monthly agricultural wage data for Maharashtra over the last few years ara available from a different source (though their co.nparability with AWI data is unclear). These are reported in Figure 1 and are used in section 4. They do no suggest that the drop in EGS attendances after mid-1988 could piausibly be attributed to an increase in the agricultural wage rate, which remained fairly constant. Our later inferences based on the post-sample forecasts are likely to be robust to the omission of agricultural wages from our cmdel.

Our model of EGS labor attendances can be written as where Lt is the recorded labor attendance on EGS for month t, Wt is the real EGS wage rate for the year including month t, Qt is the output of foodgrains for that crop year, Mjs are the monthly dummy variables fcr j-l,...11 (j-l represents July), t is a time trend, REj and RDj are the month-specific excess and deficit rainfall variables (as defined above) respectively for j-1,.. .12, and Et is an i.i.d. error term, Lt, Wt. and Qt are measured in natural logarithms. The above model was estimated by ordinary least squares over the period July 1975 to June 1988.12 13 After pruning the model to eliminate
10 parameters with absolute t-ratios less than unity (one variable at a time), the final estimates presented in Table 3 were obtained. The within sample predictiva performance of the model is good; the standard error of estimate is less than 1% of the mean log attendance, and fitted ;alues track actual values well (Figure 2). The model passed all diagnostic tests performed (Table 3).

The parameter estimates indicate significant short-run elasticities of **EGS** attendances with respect to foodgrain production and the real EGS wage rate of -0.28 and 0.20 respectively. The dynami: process of adjustment in attendances to changing conditions is quite slow, as indicated by the coefficient on lagged attendances of 0.8. Thus, the long-run elasticities for output and the real wage are considerably higher than for the short-run; the long-run elasticities are -1.4 and 1.0 respectively. A negative time trend in attendances is indicated, although it is quantitatively small, implying a monthly rate of decline of (.07 percent (the latter cumulated over 13 years would result in a fall by 10 percent). Attendances also follow a highly seasonal pattern, induced in part by the. observed seasonali v of raintall (Table 3).

# 3.2 Tests for a Structural Break in the Model After .he V- Increase

The results of Table 3 suggest that, ceteris paribus, 'abor attendances on EGS would have increased in response to the higher wage afLer mid-l988. However, attendances actually declined sharply during the sagricult.lralu year 1988/89 (Figure 2). The average monthly attendance during 1988/89 'was barely half of what it was during the previous year, which itself was not, by a long shot, a peak EGS year. An explanation may be sought in the higher than normal rainfall during 1988/89 (also reflected in the foodgrains production for that
11 year), resulting in greater availability of agricultural employment. The critical issue here is whether this explanation is sufficient to account for the observed levels of attendance on EGS for this period. Does the above model adequately explain the decline in attendances after the wage increase, taking account of the prevailing conditions?

We performed two statistical tests of the accuracy **of** the model's predictions in the post-sample period, July 1988 to June 1989.14 **15** The first involves estimating a new model for the entire (sample and post-sample) period, where the original model is augmented with a set of dummy variables, one for each observation in the post-sample period, and **then1** testing for the joint significance of the dummy variables (Pesaran and Pesaran, 1987). The second test uses the ratio of the sum of squares of the one-step ahead forecast errors to the variance of the regression error of the model as the test sta-istic, which is distributed as Chi-square with degrees of freedom given bv the !enit-h of the forecast period (Granger and Newbold, 1986).

The resul.s of the two tests are reported at the bottom of Table 3. Thev, both **indl' c i** iat the null hypothesis of parameter stability is strongly re,ec ted for :he pos -sample period July 1988 to June 1989. The attendance count for th c l-f' ueriod p, cannot plausibly have been generated by the model prevailing prior to the wage increase.16 3.3 An Estin.atke of the Extent of Rationing After the Wage Increase The drift ir. the attendance model after the wage increase is statisticaliv significant. We shall now offer an assessment of its quanti.ative importance. We shall assume that the model we have estimated for 1975-88 satisfactorily represents the notional demand function for EGS work.
12 This could be a strong assumption, for there may well have been some rationing in the earlier period as well.<sup>17</sup> To the extent that this assumption does not hold, we will under-estimate the extent of rationing in recent years. However, the results we have obtained for 1975-88 are at least consistent with what we would expect to find for a notional demand schedule (and quite unlike those we have obtained for recent years, as we shall discuss later).

Under this assumption, we can use the 1975-88 model to estimaCe the latent notional demand for EGS work in the 12 months after the wage increase. This can then be compared to actual employment, to estimate the extent of rationing after the wage increase. Figure 2 gives the forecasted attendance counts for the post-sample period. Note that these are dynamic forecasts using the lagged forecasts (rather than lagged actual attendances).18 That is appropriate, since we want to know what attendances we would have expected if there had been no rationing in the post-sample period.

The forecasts indicate a sharp fall in attendances immediately after June 1988, but not as large as that actually observed. Substantially higher attendances are predicted by the model, and the deviation between forecast and actual attendance continues to increase well into the crop year. Table 4 gives the estimated employment rationing by month.

The mean predicted attendance for the post-sample period is 491,269 per month. The mean actual attendance was 212,840. We thus estimate that almost 280,000 persons per month - about 3.3 million for the whole year - desired EGS employment in 1988/89 but could not get it. The amount of rationing increased steadily after the wage increase, peaking in March 1989 (Table 4).

How much of the observed decline in attendances after the wage increase is attributable to this rationing, rather than other factors, such as the good
13 monsoon of that year? The mean monthly attendance at EGS in the sample period, 1975-88, was 534,974 persons. The decrease in 1988/89 was thus 322,134, of which 86% is attributable to rationing.

# 4. Does the EGS Nov Guarantee Emvlovment?

The above results suggest that the higher minimum wage was associated with a significant change in the way employment on EGS is determined. We shall now return to the 1987/90 period, to see if the recent data are consistent with the existence of rationing. Two tests will be performed: the first uses information on works in progress, while the second uses information on agricultural wage rates. For both, we have a data set of a little over 33 months, though 14 months are in sub-period 1, prior to the wage increase. Rather than drop this period, we have retained the full 33 observations. Since rationing is less likely in sub-period 1, its inclusion will probably bias our tests toward accepting the null hypothesis of no rationing.

# 4.1 The Effect of the Number of Works in Progress on Employment

Demand for EGS work will generally depend on the wage schedule, the type of work available, work-leisure preferences, other wages and prices, and alternative earning opportunities. However, it will also depend on the accessibility of the available work. The demand for work at a long distance from home is presumably small, and it would be unsurprising to find that this demand is easily met by the scheme. Here we shall ask whether employment is available to accommodate the notional demand for local employment, for which travel costs (pecuniary or non-pecuniary) are negligible. This is consistent with the sRirit of EGS; since its inception, the scheme has aimed to provide
14 work within reasonable walking distance of home. However, it may still be a somewhat stronger form of "guarantee" than is intended by the govertument, as t"ie legislation does not assure that travel costs will be negligible.

The motivation for our test can best be understood by noting that the number of EGS projects should not affect the notional demand for local EGS work, ceteris paribus. If the scheme is truly successful in providing guaranteed local employment, then the number of projects, and attendances at the given projects, will adjust flexibly to accommodate the notional demand for work. An extra project, for example, would simply displace employment elsewhere; the aggregate would remain unchanged, unless other conditions facing workers have changed.

The null. hypothesis of guaranteed local employment can thus be interpreted as the hypothesis that the observed monthly employment on EGS equals the unobserved notional demand for EGS employment at zero travel cost. It then follows that, under the null, L should not be influenced by the number of EGS works in progress during the same month (denoted P).

The simplest testing procedure is as follows: Regress Lt on Pt and on other variables which, a priori, might be expected to influence Lt. A significant estimate of the coefficient on Pt implies rejection of the null, subject to the usual caveat that the estimate be consistent.

We thus estimate the following test equation: where Lt is observed person-days of employment on EGS for month t, Wt is the average daily wage paid to laborers on EGS sites in that month, WAt is the
15 reported daily wage in agriculture for month t, St is a set of monthly dummies, **Pt** is the number of works in progress in month t, and ut is an i.i.d. disturbance term.<sup>19</sup> The lagged dependent variable was included to preclude a spurious estimate of bs as a result of serial correlation in both the L and P series. One would generally expect that the notional demand for EGS employment depends positively on W. Since agricultural employment is an alternative to EGS employment, we would expect L to depend negatively on WA under the null. And the monthly dummies are included to capture the seasonal nature of agricultural activity.

Since April 1987, EGS authorities have been keeping more detailed monthly records of employment, wages paid, and works in progress. Our employment data after April 1987 will be estimates of actual person-days of employment, rather than attendances, as were used in the previous section. At the time this study began, these data were available for the period from April, 1987 to February, 1990. For the period up to December 1989 monthly data on agricultural wage rates were also available from the Director of Economics and Statistics, Government of Maharashtra.20 OLS estimates of the parameters of interest are given in Column 1 of Table 5. The estimated coefficient on P is positive and significant. The estimate of the coefficient on W is significant, but does not have the sign one would expect of the notional demand function for EGS work. The elasticity w.r.t. the agricultural wage rate is not significantly different from zero.

While these results are suggestive of rationing, they do not warrant an immediate rejection of the null. There may be sources of bias in the OLS estimates which would invalidate any inference based on them.
16 A necessary condition for the consistency of OLS estimates is that all the regressors be exogenous. This includes P. Under the null, exogeneity<sup>of</sup> P would necessarily imply that the costs of access to EGS projects are negligible. Only then would a full accommodation of **the** demand for employment in all periods be consistent with an exogenously given number of projects. This seems unlikely. A more plausible assumption is that P is responsive to the notional demand for work in the current period and hence, under the null, to L. Thus, there is reason to suspect the presence of simultaneity bias in the OLS estimates.

We correct for the possible bias by instrumenting for Pt. The natural instrument is Pt-,. Possible additional instruments are the lagged values of L, W, and WA.<sup>21</sup> With these instruments for Pt, we obtained the IV estimates given in Column 2 of Table 5. Column 3 gives the slightly more precise estimates obtainable by deleting strongly insignificant variables from equation (2). The estimates of b5 are again highly significant, and the aforementioned comments on the two wage elasticities, b2 and b3, continue to hold. Alternative assumptions about the partial adjustment process governing P might suggest somewhat different instruments. While the use of those instruments would affect the efficiency of the estimates, it should not affect their consistency.

The assumptions we make about the processes generating W and WA will also be important. They determine the status of the regressors in (2), which in turn dictates the appropriate estimation procedure. The null hypothesis does not, a prior'., imply any restrictions on these processes. So far we have implicitly assumed that W and WA are exogenous. Relaxing this assumption, we estimated (2) instrumenting for Wt and WAt as well as Pt i.e., these variables
17 were dropped from the set of instruments; a dummy variable to capture the doubling of the EGS wage was added. Again, the IV estimate of the coefficient on Pt was highly significant (with similar results to Table 5), and the wage responses remained inconsistent with what one would expect of a notional demand function.

The other potential source of bias is the possibility of omitted variables. The problem in this instance is somewhat more serious than in most, for the following reason. Even if the omitted variables are uncorrelated with the remaining included variables, given the endogeneity of Pt, they will be correlated with this variable. A significant estimate of the coefficient on Pt may therefore simply reflect the influence of these omitted variables. We have no way of distinguishing this effect from that which would arise from rationing.

Very little can be done about this problem. We attempted to mitigate its effects by adding to equation (2) various potential proximate influences on the notional demand for EGS employment. Among the variables we included were: year dummies, the deviations in rainfall from the mean, monsoon dummies, and various interaction terms. In each case, the instrumental variables estimate of the coefficient on Pt was highly significant.

We cannot eliminate the possibility of omitted variable bias and so we may bc incorrectly rejecting the null. Nevertheless, the repeated estimation, under different procedures, of different variants of the test equation failed to yield an insignificant estimate of b5, or anything that might accord with our intuition about the determinants of the notional demand for EGS employment.
18 Thus, it is hard to believe that we are observing the notional demand function for local EGS employment. The opening and closing of EGS project sites gives the authorities an effective instrument for influencing EGS employment and, hence, budgetary outlays, independently of the need for EGS employment by the poor.

Unfortunately, we cannot repeat this test for any reasonable length of time prior to May 1988, as the necessary data do not exist. However,<sup>given</sup> that we found strong evidence in section 3 of a structural break in the model determining EGS attendances after that date, our combined results are consistent with the view that rationing was introduced into EGS in response to the increase in the minimum wage rate. The revealed negative wage elasticity of employment in the above results, in contrast to the expected positive response found in the previous section, is also consistent with this interpretation.

Note that workers need not have been turned away from existing and ongoing sites to achieve employment rationing on EGS. The process of opening and closing works allows the scheme to influence employment, irrespective of notional demand. For example, by closing an existing site, the authorities will generally raise the (pecuniary and non-pecuniary) cost the displaced workers incur in participating in the EGS. The way in which employment is rationed across workers may thus be quite complex, involving the various factors which influence effective access to EGS sites, including, for example, the demands on a worker's time at home.
19

# 4.2 The Effect on Agricultural Wazes.

A second testable implication of -he existence of an effective employment guarantee is that one would expect to see a relationship between the EGS wage rate and the agricultural wage rate. The strength of that relationship will depend on specific labor market conditions. Many EGS workers are also regular participants in the agricultural labor market, and vice versa. Furthermore, the work involved appears to be quite similar, as are the hours worked. In these circumstances, with EGS and agricultural wages roughly equal initially, higher EGS wages with guaranteed employment would have to result in higher agricultural wages.

A visual inspection of Figure 1 does not suggest that the agricultural wage rate responded much to changes in the EGS wage rate during this period. Granted the simple correlation coefficient between the two wage rates is high (r-.74). But this is la gely a spurious correlation, reflecting the serial correlation of both seri.;;22 the correlation coefficient is only .32 between the innovations in each series around its first order autoregression. The following dynamic regression performs fairly well in reproducing the agricultural wage series in Figure 1: Within the current month, the impact on the agricultural wage rate of a (say) RplO increase in the EGS wage rate is Rpl.3. This is substantially off-set by
20 the negative lagged response; by the next month tne **total im.pdct falls to** Rpl.2.

While the short-run agricultural **wage** response is **small, one might still** conjecture thal there is some sort of long-run equilibrium relationship between the two wage rates. Neither Figure I nor the **above regression offers** much support for that conjecture. The long-run **response** of the **agricultural** wage rate to the EGS wage rate implied by the above regression is very much less than unity; a RplO increase in the EGS wage rate would **result** in **less** than Rpl increase in the agricultural wage in the long-run. The existence of a long-run equilibrium wage differential can also be **assessed** by **testing for** cointegration, using the methodology proposed by Engle and Granger (1987). The wage ser,es in Figure 1 fail the cointegration **test;** the null **hypothesis** of no cointegration cannot be convincingly rejected with these data.23 Possibly the number of available observations **is** inadequate for detecting the true long-run response; the power of these tests is questionable in small samples. Nonetheless, the lack of an appreciable short-run or longrun impact on agricultural wage rates casts doubt on any presumption that the "guarantee" has provided a secure fall-back position for agricultural workers. Indirect transfer benefits to the poor from the second-round effects of public employment on agricultural labor markets are potentially large (Ravallion, 1990a,c). But these data do not suggest that the potential has been realized by the EGS, at least over recent years.
21

# 5. Concluslion

Increasing the statutory minimum wage rate can seem an attractive policy for politicians keen to enhance their popularity amongst the poor. Political pressure for an increase in the minimum agricultural wage rate in Maharashtra led to its doubling in mid-1988. Subsequently, there has been concern in government circles and the development community about the consequences for the state's famous "Employment Guarantee Scheme". The view one takes of the Maharashtra experience also has implications for the design of other schemes of this sort, such as the proposed national EGS.

Historically, statutory minimum wage rates in rural areas have been virtually impossible to enforce, and the agricultural wage rate has often been belcw the minimum wage rate. In theory at least, EGS gives the government a policy instrument for enforcing a minimum wage rate, as the guarantee should be a credible threat in the labor market. However, unless the politicians are also willing to foot the bill, the need for budgetary rastraint will create pressure for a relaxation of the "guarantee". If so, the poverty alleviation impact of an increase in the minimum wage ea.e may be quickly dissipated; some will get higher wages, but others will have to go without. Under these - - circumstances, it is quite possible for poverty by any reasonable measure to increase after the imposition of a higher wage rate for relief work (Ravallion, 1990a).

Our comparison of the budgetary aggregates and related statistics on EGS reveals that average monthly expenditures actually fell after the increase in the statutory minimum wage rate. This is attributable to two main factors: i) After an initial adjustment period of a few months, we find that the real average EGS wage rate increased by only one-third, despite the initial
22 doubling of all nominal piece rates. Inflation helped (and it will continue to do so). However, the more important factor was that the available work on EGS shifted toward activities which are paid at lower piece rates. This allowed considerable "buffering" of the average EGS wage rate from the doubling in the piece rate schedule.

ii) There was a sharp fall in EGS employment after the increase in wage rates. Employment fell by about one-third. However, this alone does not imply that the authorities introduced rationing. There are other variables to consider. The two crop-years after the wage increase were good for agriculture, thanks to excellent and timely monsoons, particularly in 1988/89.

The comparison of aggregate statistics on EGS before and after mid-1988 is thus consistent with rationing, but it is not convincing evidence. Only by careful modelling of the determination of employment on EGS can we hope to answer the question of whether or not the imposition of higher minimum wage rates led to a significant and substantial change in the scheme, whereby an unfulfilled demand for relief work emerged on a large scale.

Our econometric investigations suggest that this is what happened. We have presented three empirical observations to support that conclusion; i) A model of EGS employment (measured by an attendance count) was estimated on monthly data over 13 years prior to the doubling of the minimum wage rate in mid-1988. The model behaves pretty much as one would expect over this period; EGS attendances respond positively to the EGS wage rate, negatively to foodgrain output, and have the expected response to unusual rainfall patterns ar.d the normal seasonality of agricultural operations. However, this changes markedly after mid-1988. The model predicts substantially higher EGS attendances than those actually observed after the
23 increase in the minimum wage rate, and allowing for the fact that 1988/89 was a good ab,ricultural year. Our results suggest a structural break in the process of employment determination, consistent with the introduction of rationing. Assuming that our model adequately captures the notional demand function for EGS work, we estimate that the EGR met only 43% of that demand in the 12 months after the wage increase. 86% of the difference between mean employment per month in 1988/89 and that for the preceding 13 years was due to rationing; otherwise we would have expected to see an attendance figure some 3.3 million persons higher in 1988/89. We do not know whether there was any systematic pattern in the rationing (whether, for example, it tended to be the poorest participants). An investigation of the spatial pattern of rationing across districts may throw light on this question.

ii) A further test is possil..e for a three year period up to early 1990 for which we have better employ-f,ent data and a monthly series on the number of EGS works in progress. For this recent period, we find little evidence to suggest that the observed series of EGS employment can be interpreted as the workers' notional demand for local work on the scheme. The number of works in progress has a sizable and significant effect on EGS employment, which is not what one would expect if the scheme was simply accommodating the notional demand for local EGS employment. Also, unlike the period prior to the increase in minimum wage rates, we find a seemingly perverse response of emplovment to EGS wage rates. And agricultural wage rates do not appear to have influenced employment on EGS in recent years.

iii) Finally, we investigated the effect on agricultural labor markets. If EGS really was absorbing the excess supply of labor to alternative work then one would expect to see a substantial impact on wages for that work
24 stemming from shifts in EGS wages. However, we find no sign of an equilibrium relationship between the two wage rates. Little more than lCX of an increase in the EGS wage was passed on in the agricultural wage rate in either the short-run or long-run. Rationing of EGS emplovment appears to have dampened the expected second-round income effects arising through agricultural labor markets.

Whether one deems these changes to have been desirable or not depends crucially on one's judgement of the desirability of an employment guaiantee. The concept of assured employment, albeit at a low wage, does have a number of potentially attractive features from the po'nt of view of poverty alleviation: it generally allows scarce resources to go to the poorest first (at least those able to work), it maximizes the insurance benefits to the poor, and it helps undermine some of the possibilities for their corruption on such schemes, and for their exploitation in labor ;irkets and tenancy contracts. However, achieving these benefits with limited budgetary resources must entail a sufficiently low wage rate. There can be no presumption that higher wage rates without the necessary budgetary resources will be in the interests of the poor.
25

# Notes

1. There is a large literature on the sc-heme, including Acharya and Panwalkar (1988), Basu (1981), Bhende et al., (1990), Dandekar and Sathe (1980), Dreze (1990), Echeverri-Gerit (1988), Herring and Edwards (1983), and Lieberman (1985). More general discussions of the economics of this type of policy can be found in Besley and Coate (1990), Dreze and Sen (1990), Narayana et al., (1988), Ravallion (1990b), and World Bank (1990, Chapter 6).

2. The existing evidence is more conclusive on porformance in screening poor from non-poor, than on transfer and stabilization benefits, recognizing that the latter are net of participants' forgone incomes, whicn are difficult to measure. See Ravallion (1990b) for a recent survey of the evidence on the scheme's performance in poverty alleviation.

3. Exceptions can arise if non-wage cost per worker is sufficiently high, or if a high value is placed on participants crossing the poverty line (rather than raising the lowest incomes). Ravallion (1990a) examines the theoretical case for and against wide coverage in a poverty alleviation scheme.

4. A Times of India News Service report from Pune, October 18, 1988, stated that: "Slums mushrooming on the periphery of several towns in Maharashtra's sugar belt indicate the large scale migration of landless laborers from the drought-hit areas in search of livelihood. The migration does not appear to be seasonal like the earlier ones, with the result that the slums may well develop into permanent settlements." The report identifies failure to find EGS work as the main cause.

5. Prior to this period, only limited data are available on a monthly basis. From April 1987, computerized records have been kept of person-days of employment and other variables, such as works in progress. Prior to this date, only an attendance count is available on a monthly basis. There were clear errors in a few observations, usually under-reported employment evident as extreme average wage rates; 15 of the 875 district/month observations in the data set (25 districts bv 35 months) had to be deleted for this reason. Our aggregate series on EGS were built up from the district data, deleting these obvious errors.

6. For example, suppose (to simplify the exposition) that there are just two activities, "digging" and "shifting", with the rate for digging set above that for shifting. The relative wage is fixed (it did not change before and after May 1988), and we shall assume that the administrator cannot influence total employment. The value of output is f(s,l-s) per unit of total employment, where s denotes the proportion of labor allocated to shifting which is paid at the rate W. (The production function is quasi-concave and homogeneous of degree one.) The surplus per unit of employment is then f(s,l-s)-[s+(l-s)r]W where r > 1 denotes the wage relativity (digging/shifting). The surplus is maximized when the difference in marginal, social products between shifting and
26

- digging is equal to the difference in wage rates, f,-fl_, (l-r)W, and it is readily verified that the surplus maximizing value of s is a strictly increasing function of W.

7. This is also observed between sub-perioos I and 2, though this may partly reflect seasonality; we shall model seasonality explicitly in the next section.

8. From April 1987 to February 1990 the two variables are very highly correlated (r-.996 in levels, .989 in logs).

9. Data on labor attendance on EGS and the EGS time wage were obtained from the Planning Department, Government of Maharashtra.

10. Foodgrain output data disaggregated by kharif and rabi seasons are available only up to 1987-88. Since we are particularly interested in seeing how the model predicts labor attendances after mid-1988, we are unable to use the seasonally disaggregated data on foodgrain output.

11. Ideally, we would also like to include variables for the non-foodgrain output. We are however constrained by the non-availability of data for the last two tears of our sample period. Foodgrains nevertheless still account for about 70 per cent of the gross cropped area in Maharashtra (GOM 1987, 1989).

12. The fact that we do not have a monthly series on the EGS wage rate means that there is measurement error in this variable, which will bias OLS estimates. We also estimated a generalized instrumental variables estimator, in which the EGS wage was not used as its own instrument. The instrumental variables included the average EGS wage rate of the previous year and the rank of the current year's EGS wage, as well as all other RHS variables. The IV estimator gave very similar results to Table 3, though with a slightly lower wage elasticity (0.15 rather than 0.20). In view of this, and the fact that we gain 11 degrees of freedom using the OLS estimator, we decided to stay with the latter.

13. The model parameters also contain a dummy variable for December 1977. We believe there is an error in the data for this month. Excluding that variable had little effect on other parameters, though it did yield significant nonnormality of residuals, as indicated by the LM test.

14. As evident in Figure 1, the actual increase in the wage rate was staggered over June to September 1988, so starting the post-sample forecasting in July (rather than June) is reasonable. This also coincides with the beginning of the new crop-year.

15. 1988 was not, of course, the first instance when piece rates on EGS increased, though it was the largest increase. However, in the earlier cases we do not find any evidence of structural instability, as indicated by the evolution of recursive residuals since December 1978. Both the CUMSUM and CUMSUMSQ statistics (Brown et al., 1975) are well within the 95% confidence intervals.
27 16. We also estimated the following model over the entire period, so as to focus on any possible changes in the attendance-wage relationship in the postsample period:

- 0.00066t + other terms as in equation (1) (1.476)

R<sup>2</sup> -0.95; SEE-0.1115; Mean d.v.-13.0571; N-167 LK tests: AR(12)-0.704; RESET(l)-5.66; HETERO(l)-2.84; NORM(2)-l.273 where DPS is a dummy variable taking the value **1** for the post-sample period, and 0 otherwise. Thus, a significant positive relationship between attendances and the EGS wage tends to vanish in the post-sample period. This disappearance of the attendance-wage relation for a period when the wage itself increased appreciably is suggestive of there being some rationing of EGS employment.

17. For example, even in normal times it has been said that the scheme is less effective in reaching poor tribals in some districts, such as Thane.

18. Two types of forecasts can be made: (i) static ("one-step") forecasts, which are made one month ahead, and so are based on the actual values of the previous month's attendance count, as well as the observed current values of all other variables in the post-sample period, and (ii) dynamic forecasts, which do not assume that any attendance counts are known for the post-sample period; unlike static forecasts, they use the forecasted values of the previous month's attendances. The forecasts in Figure 2 are dynamic forecasts. These are our best estimates of the time series of attendances that one would have observed in the post-sample period if there had not been any change in the model determining employment. Unlike static forecasts, dynamic forecasts do not "build in" the effects of any employment rationing since the increase in the minimum wage rate.

19. All variables except the monthly dummies are in natural logs. The wages are in real terms. The consumer price index for agricultural laborers was used to deflate the nominal wages. The base date is April 1987.

20. These data are unpublished. They are available in the form of a fortnightly series of daily wages by gender for various agricultural operations for each of 75 monitoring points spread over rural Maharashtra. For our purposes, we have used the simple mean of all wage *servations available for a given month as the estimate of the average wage r>,e for Maharashtra in that month.

21. The choice of these instruments can be justified within a partial adjustment framework (or error correction model) expressing the actual number of projects as a suitable function of its own lagged value and the current and lagged values of the desired number of projects, and where the latter is a function of the expected and unexpected components of the notional demand for EGS employment.
28 22. The Durbin-Watson test on the OLS residuals from the static regression of real agricultural wage rate against real EGS wage rate is .86.

23. The Engle-Granger methodology involves first testing for unit roots in each series, and then (if unit roots are indicated) testing for stationarity in the OLS residuals from the "static" regression in the levels of the two series. We used the augmented Dickey-Fuller tests proposed by Engle and Cranger. Unit roots were indicated in both the EGS and agricultural wage series. The first difference of the residuals from the static regression was then regressed against its own lagged values (first and second order), and the lagged level of the residual. The t-ratio on the latter was 1.06, which is well below the critical value (for larger sample sizes) given in Engle and Granger (1987). The test was repeated using nominal wage rates, and with both real and nominal, wage rates in log form. In no case could the null of no cointegration be rejected (t-ratios were all similar to the above figure). While it is recognized that these tests can lack power in small samples, the rejection of cointegration would still seem convincing.
29

# **Reference&**

- Acharya, Sarthi and V.G. Panwalkar (1988): "The Maharashtra Employment Guarantee Scheme: Impacts on Male and Female Labour". Paper prepared for the Population Council Program on Women's Roles and Gender Differences.

- Basu, Kaushik (1981): "Food for Work Programmes: Beyond Roads that Get Washed Away", Economic and Political Weekly 16:37-40.

- Besley, Timothy and Stephen Coate (1990): "Workfare vs. Welfare: Incentive Arguments for Work Requirements in Poverty Alleviation Programs", mimeo, Woodrow Wilson School, Princeton University.

- Bhende, M.J., T.S. Walker, S.S. Lieberman and J.V. Venkatarpm (1990): "The EGS and the Poor: Evidence fr l Longitudinal Village Studies", Processed, International Crops Reseaich Institute for the Semi-Arid Tropics.

- Brown, R.L., J.Durbin and J.M. Evans (1975): "Techniques for Testing the Constancy of Regression Relations over Time", Journal of the Royal Statistical Society B 37:149-92.

- Dandekar, Kumudini and Manju Sathe (1980): "Employment Gudrantee Scheme and Food for Work Programme". Economic and Political Weekly 15 (April 12): 707-713.

- Dreze, Jean (1990): "Famine Prevention in India", in Jean Dreze and Amartya Sen (eds), The Political Economy of Hunger. Oxford: Oxford University Press, forthcoming.

- Dreze, Jean and Amartya Sen (1990): Hunger and Public Action. Oxford: Oxford University Press.

- Echeverri-Gent, John (1988): "Guaranteed Employment in an Indian State. The Maharashtra Experience". Asian Survey 28:1294-1310.

- Engle, Robert F. and C.W.J. Granger (1987): "Co-Integration and Error Correction: Representation, Estimation and Testing". Econometrica 55:251-276.

- Government of Maharashtra (GOM) (1984): Report of the Fact Finding Committee on Regional Imbalance in Maharashtra. Bombay: Planning Department, GOM.

(1987): Selected Indicators for Districts in Maharashtra and States of India 1984-85. Bombay: Directorate of Economics and Statistics, GOM.

(1989): Selected Indicators for Districts in Maharashtra and States of India 1986-87. Bombay: Directorate of Economics and Statistics, GOM.
30

- Granger, **Colin** W. J. and Paul Newbold (1986): Forecasting Economic Time Series. Second Edition. San Diego: Academic Press.

- Herring, Ronald J. and Rex M. Edwards (1983): "Guaranteeing Employment to the Rural Poor: Social Functions and Class Interests in Employment Guarantee Scheme in Western India". World Development 11: 575-592.

- Lieberman, S. S. (1985): "Field-Level Perspectives on Maharashtra's Employment Guarantee Scheme". Public Administration and Development 5:109-127.

- Narayana, N.S.S., K.S. Parikh, and T.N. Srinivasan (1988): "Rural Works Programs in India: Costs and Benefits". Jourral of DeveloDment Economics 29:131-156.

- Pesaran, M. Hashem, and Bahram Pesaran (1987): Microfit. An Interactive Econometric Software Package. Oxford: Oxford University Press.

- Programme Evaluation Organization (PEO) (1980): Joint Evaluation Report on EmRloyment Guarantee Scheme of Maharashtra. Planning Commission, Government of India, New Delhi.

- Ravallion, Martin (1990a): "On the Coverage of Public Employment Schemes for Poverty Alleviation". Journal of Development Economics, forthcoming.

- (1990b): "Reaching the Poor through Rural Public Employment:

- A Survey of Theory and Evidence". Discussion Paper 94, World Bank, Washington DC. (Shortened version forthcoming in World Bank Research Observer.)

- (1990c): "Market Responses to Anti-Hunger Policies: Wages,

- Prices, and Employment". Forthcoming in Jean Dreze and Amartya Sen (eds) The Political Economy of Hunger. Oxford: Oxford University Press.

- Subbarao, K. (1989): "Interventions to Combat Household-Level Food Insecurity: A Review of India's Experience", mimeo, World Bank.

- World Bank (1989): India: Poverty. Employment and Social Services. A World Bank Country Study, Washington DC: World Bank.

- _(1990): World Development Report 1990. Poverty. Washington DC:

- World Bank.
31 Table **1:** Summary Data on EGS Before **and** After the Wage Increase.

||Sub-peri|od:||
|---|---|---|---|
|1<br>April 87<br>May 88|2<br>- June 88-<br>November 88|3<br>December 88-<br>February 90|Percentage<br>change<br>1 to 3|
|Total cost<br>132.6<br>(RpxlO <sup>6</sup>/month,<br>April 1987 prices)|<br>123.6|105.3|-21|
|Of which:<br>Wage cost<br>88.7|<br>91.0|76.0|-14|
|Non-wage cost<br>43.9|<br>32.6|29.2|-33|
|Employment<br>(Person-daysxlO<br>6/month) 9.9|6.5|6.1|-38|
|Average wage rate<br>9.0<br>(Rp/day, April 1987<br>prices).|15.1|12.4|37|
|Average non-wage cost<br>4.6<br>per worker (Rp/day,<br>April 1987 prices)|5.9|4.9|7|
32

**Table 2. A** Decomposition of **the** Change in Operating Cost.

||Changeinaverage<br>monthly cost due to:||
|---|---|---|
|(3)-ml)klLl/pl|increase in minimum nominal wage rate|**89.0**|
|+(P3-pl)alklLl/pl<br>2|increase<br>in price level|-17.8|
|+(k3.kl)m1L1/pl|change in work composition|-37.9|
|+(X3-X1)LI|change in real unit non-wage cost|2.6|
|+(L3-LI)V1|changein employment|-51.8|
||residual (interaction<br>effects)|-11.5|
|-C3-C1|change in real cost<br>(RpxlO6/month,<br>April 1987 prices)|-27.3|
33

**Table 3: Parameter Estimates for**<sup>**the EGS Monthly Attendance Model, 1975-88**</sup>

|Explanatory<br>variable|Parameter estima<br>(absolute t-rat|te<br>io)|
|---|---|---|
|Intercept|4.907<br>||
|L-1<br>W<br>Q<br>t|(4.51)<br>0.805<br>(22.25)<br>0.196<br>(2.30)<br>-0.284<br>(2.93)<br>-0.001<br>(1.60)||
|Month<br>Mj|REi|RDi|
|July<br>-0.116|-0.758<br>|-0.572<br>|
|(1.74)|(1.86)<br>|(1.41)|
|August<br>-0.087|-0.406<br>||
|(2.06)|(2.06)<br>||
|September|-0.239<br>(3.05)||
|October<br>-0.163<br>(4.33)|||
|November||-0.161<br>|
|||(2.64)<br>|
|December<br>0.376|-0.075|-0.182<br>|
|(5.39)|(1.57)|(2.01)<br>|
|January<br>0.106<br>|0.069<br>|0.085<br>|
|(1.77)|(1.13)<br>|(1.02)<br>|
|February|0.063<br>|0.226<br>|
||(2.01)|(3.96)|
|March<br>0.275||-0.206<br>|
|(4.82)||(1.94)|
|April<br>0.071|||
|(1.82)|||
|May<br>0.113|-0.090||
|(2.59)|(1.65)<br>||
|June|-0.301||
||(1.61)||

R<sup>2</sup> -0.923; SEE-0.1096; Mean d.v.-13.1286;<sup>N-155; Durbin h-0.445;</sup> LM tests: AR(l)=0.0479 ; AR(12)-0.6761;<sup>RESET(l)-0.7574 ; NORM(2)-06949;</sup> HETERO(l)-2.638 Post-sample forecasting: Chi<sup>2</sup> 1<sup>(12)-30.59; Chi2</sup> 2<sup>(12)-67.33</sup>
34

**Table 4: Estimated Employment Rationing on EGS After the Wage Increase**

||Actual<br>attendances|Unfulfilled<br> <br>demand|
|---|---|---|
||('000|persons)|
|July 1988|248.1|70.6|
|August 1988|147.7|151.7|
|September 1988|<br>97.5|163.7|
|October 1988|79.3|168.4|
|November 1988|85.1|188.2|
|December 1988|130.1|283.2|
|January 1989|182.1|327.2|
|February 1990|238.9|385.0|
|March 1990|308.9|461.3|
|April 1990|342.5|401.6|
|May 1990|371.3|382.7|
|June 1990|322.5|357.7|
|Mean 1988/89|212.8|278.4|
35 Table 5: Further Tests for Employment Rationing 1987-9.

|Elasticity of EGS<br>employment w.r.t.:||1<br>(OLS)|Estimation method<br>2<br>3<br>(IV)<br>(IV)|
|---|---|---|---|
|The number of works<br>in progress (P)|b5|1.094<br>(3.83)|1.243<br>1.136<br>(3.08)<br>(4.85)|
|The average EGS<br>wage rate (W)|b2|-.514<br>(2.30)|-.523<br>-.479<br>(2.32)<br>(2.92)|
|The average ag.|b3|-.011<br>|.102<br>n.a.<br>|
|wage rate (WA)||(.017)|(.144)|

Note: Absolute t-ratios in parentheses, 31 monthly observations, R<sup>2</sup> -.97 for 1,2 and 3. Regressions 1 and 2 included 11 monthly dummy variables and lagged dependent variable. Regression 3 excludes highly insignificant variables (including agricultural wage rate). All regressions passed LK tests for serial correlation, functional form, normality, and heteroscedasticity. See text for further details on the model.
36 Figure 1: EGS and Agricultural Wage Rates <!-- Start of picture text --> Rupees per day, April 1987 prices<br>20<br>10 ,7 '**B*_<br>Sub-period 1 Sub-period 2 Sub-period 3<br>1 987 1 1988 1989/90<br>i EGS real wage 9 Ag. real wage<br><!-- End of picture text --> Figure 2: EGS Attendances by Month <!-- Start of picture text --> '000 persons<br>1200<br>1000 f V After wage<br>1000 ~~~~~~~~~~~~~~~~~Increase<br>800K<br>600<br>400 W<br>1978/7 1978/9 1980/1 1982/3 1984/5 1988/7 1988/9<br>- Actual i Fitted Forecast<br><!-- End of picture text -->
# PRE Working Paper Series

||futbzALgho|||Contact<br>for pae|
|---|---|---|---|---|
|WPS554|Korea's Labor Markets Under<br>Structural Adjustment|Dipak Mazumdar|December 1990|M. Schreier<br>36432|
|WPS555|The Macroeconomics<br>of Price Reform<br>in Socialist Countries: A Dynamic<br>Framework|Simon Commander<br>Fabrizio Coricelli|December 1990|0. del Cid<br>39050|
|WPS556|Taxing Choices in Deficit Reduction|John Baffes<br>Anwar Shah|December 1990|A. Bhalla<br>37699|
|WPS557|The New Fiscal Federalism in Brazil|Anwar Shah|December 1990|A. Bhalla<br>37699|
|WPS558|Alternative Instruments for<br>Smoothing the Consumption of<br>Primary Commodity Exporters|Kenneth M. Kletzer<br>David M. Newbery<br>Brian D. Wright|December 1990|J. Carroll<br>33715|
|WPS559|Fiscal Policy and Private Investment<br>in Developing Countries: Recent<br>Evidence on Key Selected Issues|Ajay Chhibber<br>Mansoor Dailami|December 1990|D. Bilkiss<br>33768|
|WPS560|The Persistence of Job Security in<br>Reforming Socialist Economies|Milan Vodopivec|December 1990|CECSE<br>37188|
|WPS561|The Labor Market and the Transition<br>of Socialist Economies|Milan Vodopivec|December 1990|CECSE<br>37188|
|WPS562|Anticipated Real Exchange-Rate<br>Changes and the Dynamics of<br>Investment|Luis Serven|December 1990|S. Jonnakuty<br>39076|
|WPS563|EmpiricalinvestmentEquations in<br>Developing Countries|Martin Rama|December 1990|E. Khine<br>39361|
|WPS564|Costs and Benefits of Agricultural<br>Price Stabilization in Brazil|Avishay Braverman<br>Ravi Kanbur<br>Antonio Salazar P. Branda<br>Jeffrey Hammer<br>Mauro de Rezende Lopes<br>Alexandra Tan|December 1990<br>o|C. Spooner<br>30464|
|WPS565|Issues in Socialist Economy<br>Reform|Stanley Fischer<br>Alan Gelb|December 1990|CECSE<br>37188|
|WPS566|Measuring Outward Orientation in<br>Developing Countries: Can It Be Don|Lant Pritchett<br>e?|January 1991|K. Cabana<br>37947|
|WPS567|Macroeconomic<br>Management and the<br>Division of Powers in Brazil:<br>Perspectives for the Nineties|Antulio N. Bomfim<br>Anwar Shah|January 1991|A. Bhalla<br>37699|
|||PRE Working Paper Series|||
|---|---|---|---|---|
||idle|Author||Contact<br>forpaWer|
|WPS568|Higher Wages for Relief Work Can<br>Make Many of the Poor Worse OH:<br>Recent Evidence from Maharashtra's<br>'EmploymentGuarantee Scheme"|Martin Ravallion<br>Gaurav Datt<br>Shubham Chaudhuri|January 1991|C. Spooner<br>30464|
|WPS569|Domestic Purchase Requirements for<br>Import License Allocations in Mali|Wendy E. Takacs|January 1991|S. Fallon<br>37947|
|WPS570|Debt Concentration and Secondary<br>Market Prices|Raquel Fernandez<br>Sule Ozler|January 1991|S. King-Watson<br>31047|