rafmacalaba/datause-encoder-data huggingface dataset:
Figure 4: Total Factor Productivity Distributions by Exit Status <!-- Start of picture text --> (a) Years with Little False Exit<br><!-- End of picture text --> <!-- Start of picture text --> (b) Years with a Lot of False Exit<br><!-- End of picture text --> <!-- Start of picture text --> TFP Distributions for Survivors and Potential Exiters TFP Distributions for Survivors and Potential Exiters<br>Years with Little False Exit Years with a Lot of False Exit<br>−15 −10 −5 0 5 10 −15 −10 −5 0 5 10<br>TFP TFP<br>Survivors Potential Exiters Survivors Potential Exiters<br>(c) Years with Little False Exit: Top and Bottom 1% (d) Years with a Lot of False Exit: Top and Bottom<br>Truncated 1% Truncated<br>TFP Distributions for Survivors and Potential Exiters TFP Distributions for Survivors and Potential Exiters<br>Years with Little False Exit Years with a Lot of False Exit<br>−4 −2 0 2 −3 −2 −1 0 1<br>TFP TFP<br>Survivors Potential Exiters Survivors Potential Exiters<br>1.5 1.5<br>1 1<br>kdensity kdensity<br>.5 .5<br>0 0<br>1.5 1.5<br>1 1<br>kdensity kdensity<br>.5 .5<br>0 0<br><!-- End of picture text -->

- (c) Years with Little False Exit: Top and Bottom 1% Truncated

Distributions of total factor productivity (TFP) for firms identified as survivors versus potential exiters. Panel (a) shows distributions for two years (1999-2000) in which observed and predicted exit rates are similar, indicating little false exit in the panel. Panel (b) shows distributions for two years (1995 and 2004) in which observed exit rates are much higher than predicted exit rates, indicating substantial false exit in the panel. In Panels (c) and (d), the top and bottom 1% of the TFP distributions are not shown in order to focus on the parts of the distribution where most of the mass is contained.

39
often the poverty line.<sup>3</sup> Vulnerability to expected utility estimates the intensity of vulnerability by measuring the gaps between the utility attained under conditions of certainty and the expected utility (Calvo and Dercon 2013; Gallardo 2020; Günther and Maier 2014; Ligon and Schechter 2003; Magrini, Montalbano, and Winters 2018). Lastly, vulnerability as uninsured exposure to risk estimates the degree to which exogenous shocks reduce household consumption (Amin, Rai, and Topa 2003; Cafiero and Vakis 2006; Cochrane 1991; Dutta, Foster, and Mishra 2011; Jalan and Ravallion 1999; Povel 2015; Townsend 1994). Despite their methodological differences and various data requirements, all these methods aim to estimate expected household consumption and its variance.<sup>4</sup> Estimating the mean and variance of household consumption is challenging because of the intertemporal nature of vulnerability. Long panel data at the household level are necessary to obtain accurate estimates, but such data are often unavailable in developing countries. If panel data are not available, householdspecific mean consumption and its variance may be estimated from cross-sectional data under certain restrictions. Chaudhuri, Jalan, and Suryahadi (2002) propose a method for estimating the mean and variance of household consumption on cross-sectional data by assuming that the variance of consumption can be modeled as a function of observable household and community characteristics, while the error term in a consumption regression captures the unexplained variability caused by household- and community-specific income variance. Given the low availability of panel data, it is not surprising that a large number of published studies rely on cross-sectional data to estimate vulnerability (Chaudhuri 2003; Christiaensen and Subbarao 2005; Dang and Lanjouw 2014; Günther and Harttgen 2009; Kamanou and Morduch 2002; Suryahadi and Sumarto 2003; Tesliuc and Lindert 2004).<sup>5</sup>

# 3. Data and methods

Estimating a map of poverty and vulnerability requires a household survey and a population census. In the case here, the map is constructed using the Harmonized Survey on Household Living Standards 2018–

> 3 This measure of vulnerability has an intuitive interpretation: the probability of falling into poverty. However, its simplicity comes at the cost of violating desirable axiomatic properties (see Calvo and Dercon 2005). 4 See Gallardo (2018) for an in-depth review of the literature.

> 5 Following Günther and Harttgen (2009), various studies apply multilevel models to estimate the vulnerability to poverty in various countries. Examples include Échevin (2014), who applies a two-level model to study idiosyncratic and covariance shocks in Haiti; Mina and Imai (2017) and Pham, Mukhopadhaya, and Vu (2021), who apply a threelevel model in the Philippines and Viet Nam, respectively, and Skoufias, Vinha, and Beyene (2024), who examine the vulnerability to poverty in the drought-prone regions of Ethiopia.

6
and the additional transit component is equal to where _dist_ ( _j, k_ ) is the distance between countries _j_ and _k_ , _lang_ ( _j, k_ ) is an indicator variable that is equal to 1 if countries _j_ and _k_ share a common language and _gdp_ ( _j_ ) is the GDP per capita in country _j_ . Total fixed moving cost of moving from _j_ to _k_ for an agent born in _i_ is, thus, given by where _C_ 1<sup>_s,j,j_</sup> = 0, _C_ 2<sup>_s,i,i_</sup> = 0, and the indicator function **1** _i_ = _j_ = 1, if _i_ = _j_ and 0 otherwise. In other words, the fixed moving cost is equal to zero for stayers independent of their birth country; it is equal to _C_ 1<sup>_s,j,k_</sup> for people moving from their birth country to country _k_ and it is equal to _C_ 1<sup>_s,j,k_</sup> + _C_ 2<sup>_s,i,k_</sup> for transit migrants - people born in country _i_ , living in _j_ and moving to _k_ .<sup>7</sup>

# **2 Data**

Among the main constraints faced in estimating dynamic migration models is the availability of detailed and comprehensive data. Most global bilateral migration data sets are based on censuses and population registers of the destination countries where the migrants currently reside. These data sources record only the country of birth or citizenship of migrants. Other important variables, such as the year of arrival or migration status, are not included in most surveys. Detailed migration histories tend to be available only in small and specialized surveys

> 7In reality, both direct and transit cost components might be functions of the time a migrant spends at the current location. Furthermore, the random component might show persistence over time. We abstract away from these issues in our model and estimation since it is not feasible to identify duration of stay of migrants at a given location.

13
Figure 2. Evolution of Potential Growth A. Potential growth <!-- Start of picture text --> Percent<br>8 Potential growth<br>Actual growth<br>6<br>1998-2017 potential growth<br>4<br>2<br>0<br>World AEs EMDEs<br>C. Contribution of potential growth and<br>business cycle to actual growth<br>Percent<br>8 Growth Potential Cycle<br>6<br>4<br>2<br>0<br>World EMDE<br>E. Per capita potential growth<br>Percent<br>8 Potential growth<br>6 Actual growth<br>1998-2017 potential growth<br>4<br>2<br>0<br>-2<br>World AEs EMDEs<br>1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27<br>2003-07 Contribution 2013-17 2003-07 Contribution 2013-17<br>1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27<br><!-- End of picture text --> <!-- Start of picture text --> B. Potential growth<br>Percent Potential growth<br>10 Actual growth<br>1998-2017 potential growth<br>8<br>6<br>4<br>2<br>0<br>G7 EM7 LICs<br>D. Share of economies and GDP with potential<br>growth below 1998-2017 average<br>Percent<br>Share of countries<br>100<br>Share of GDP<br>80<br>60<br>40<br>20<br>0<br>World AEs EMDEs<br>F. Per capita potential growth<br>Percent<br>8 Potential growth<br>Actual growth<br>6 1998-2017 potential growth<br>4<br>2<br>0<br>-2<br>G7 EM7 LICs<br>1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27 1998-2002 2003-07 2008-12 2013-17 2018-27<br>2003-07 2013-17 2003-07 2013-17 2003-07 2013-17<br>1998-2017 2003-07 2008-12 2013-17 2018-27 1998-2017 2003-07 2008-12 2013-17 2018-27 1998-2017 2003-07 2008-12 2013-17 2018-27<br><!-- End of picture text --> Sources: Penn World Tables, World Bank. Notes: Based on potential growth derived using production function approach. A. B. C. E. F. GDP-weighted average. A. C. E. Sample includes 30 advanced and 50 emerging market and developing economies. B. F. G7 includes Canada, France, Germany, Italy, Japan, the United Kingdom and the United States. EM7 includes Brazil, China, India, Indonesia, Mexico, Russia, and Turkey. LICs includes 6 countries.

C. Blue bars denote average actual global growth during 2003-07 and 2013-17. Red bars denote the contribution of global potential growth to the change in actual growth between the two five-year periods; orange bars denote contribution of the cyclical component of global growth to change in actual global growth between the two five-year periods. D. Number of economies and their share of global GDP among 30 advanced economies and 50 EMDEs with potential growth in each period below its longer-term average (19982017). Horizontal line indicates 50 percent.

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<!-- Start of picture text --> 1000008a NI A 3008<br>300008 —_. ~~ Z 2502)<br>—— i<br>F 600008 5<br>Fr 4000021<br>OF OF<br>Year?<br><!-- End of picture text --> <!-- Start of picture text --> ied80008bea<br>a a 3508<br>60008] — 4008)<br>55 a_ aaa<br>me on A 3008 ee<br>° (ola .<br>eet SC=S;=«C<br>en<br>-60008) (oa<br>20048) 20058 20068] 20078<br>= Excessitiemandi 84402 -41562 5138 38408<br>—& Worldiricel$/tonl 304.678 260.088 313.258 427.338<br>Yearil<br><!-- End of picture text --> <!-- Start of picture text --> (ola 40081<br>-500008 :<br>& -1000008 2508 s<br>8Ss -1500008 —— 1508)<br>:<br>7 -2000008-2500008I— (ola1008]<br>20022 | 20032) 20048} 2005%}| 2006%| 20078<br>——ExcessHemandil | -174389H) -156333@ -158755@ -177495@ -2051798 -2031252<br>= Worldiprice/tonil 196.892) 200.862] 244.498) 290.5A| 311.242] 334.45f<br>Year?<br><!-- End of picture text -->
# **<mark>Figure 13. Undervaluation and Export Growth, Ethiopia and Selected Countries</mark>**

<!-- Start of picture text --> China India<br>0.6 0.4<br>0.4<br>0.2<br>0.2<br>0<br>0<br>-0.2<br>-0.2<br>-0.4<br>-0.4<br>-0.6<br>-0.6<br>-0.8 -0.8<br>1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011<br><!-- End of picture text --> <!-- Start of picture text --> Republic of 0.4 Thailand<br>0.8 Korea 0.3<br>0.6<br>0.2<br>0.4<br>0.2 0.1<br>0 0<br>-0.2<br>-0.1<br>-0.4<br>-0.6 -0.2<br>-0.8 -0.3<br>-1<br>-0.4<br>1 Argentina 0.6 Brazil<br>0.4<br>0.5<br>0.2<br>0<br>0<br>-0.2<br>-0.5 -0.4<br>-0.6<br>-1 -0.8<br>-1<br>-1.5 -1.2<br>1 0.6<br>Mexico Peru<br>0.8<br>0.4<br>0.6<br>0.2<br>0.4<br>0<br>0.2<br>-0.2<br>0<br>-0.4<br>-0.2<br>-0.4 -0.6<br>-0.6 -0.8<br>1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010<br>1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011<br>1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011<br>1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 1951 1954 1957 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011<br><!-- End of picture text --> Note: Undervaluation zone is above zero. Source: Nguyen (2014) and Rodrik (2008).

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12 is to scale the year-by-year growth rates from the national accounts for each sector-quantile such that they cumulatively produce the growth observed in the survey for that sector-quantile. When year-by-year growth rates calculated this way by sector and quantile are applied in the simulation, starting with the initial year survey data, they should produce a distribution in the final year which closely matches the final year survey data.

In these simulations, each quantile’s mean household consumption moves proportionally with the GDP per capita in the national accounts. This procedure is the natural extension of the inequality adjustment Datt and Walker apply in one version of their method. In the approach outlined here, rather than adjust the distribution to generate a specified change in a single inequality measure, we set the growth rates quantile-by-quantile to produce a particular distribution in the final year.<sup>7</sup> This procedure is approximately (though not exactly) equivalent to setting the growth rates for individual quantiles by shifting the national growth rate pattern up or down. To illustrate this, in Figure 2 below, the true growth rates for Tanzania from the national accounts are plotted along with growth rates calculated using the procedure for two hypothetical quantiles with cumulative growth of 1% and 40%. Recall from Table 2 that cumulative GDP per capita growth 1993-2001 in Tanzania was 6.1%.

# **Figure 2**

## **Simulated Growth Rates for Hypothetical Quantiles Using Two Survey Method**

<!-- Start of picture text --> 7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0% Per Capita GDP Growth (6.1%)<br>Quantile w/ 1% Cum. Growth<br>1.0% Quantile w/ 40% Cum. Growth<br>0.0%<br>-1.0%<br>-2.0%<br>-3.0%<br>-4.0%<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Year<br>Adult Equivalent<br>% Change in Household Consumption per<br><!-- End of picture text --> Note that the initial and final years of the simulation will match the “true” distributions in the survey data, and consequently the simulation’s cumulative change in mean consumption will match the change implied by the survey data. If cumulative growth in GDP per capita differs from 7 The “two survey” approach has a natural relationship to growth incidence curves: the simulations scale growth for each quantile to match the average growth rates that are plotted in the GICs.
<!-- Start of picture text --> Grit (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Georgia Ghana Kenya<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Serbia Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> <!-- Start of picture text --> Hostile attribution bias (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-4 -2 0 2 4 6 -4 -2 0 2 4 6 -4 -2 0 2 4 6<br>Georgia Ghana Kenya<br>-4 -2 0 2 4 6 -4 -2 0 2 4 6 -4 -2 0 2 4 6<br>Ukraine Vietnam<br>-4 -2 0 2 4 6 -4 -2 0 2 4 6<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Decision making (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Georgia Ghana Kenya<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Serbia Ukraine Vietnam<br>-6 -4 -2 0 2 4 -6 -4 -2 0 2 4 -6 -4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> <!-- Start of picture text --> Risk taking (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Colombia<br>-2 0 2 4 -2 0 2 4 -2 0 2 4<br>Georgia Ghana Kenya<br>-2 0 2 4 -2 0 2 4 -2 0 2 4<br>Ukraine Vietnam<br>-2 0 2 4 -2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> Time preference (Z score)'<br>Males and females aged 25-54<br>Armenia Bolivia Georgia<br>-2 0 2 4 -2 0 2 4 -2 0 2 4<br>Ghana Kenya Ukraine<br>-2 0 2 4 -2 0 2 4 -2 0 2 4<br>Vietnam<br>-2 0 2 4<br>Males Females<br>Kernel densities of standardized scores<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> 63
evaluation efforts. Randomization also ensures that treatment is distributed independently of any other determinants of the outcome, which works towards eliminating omitted variable bias.

However, participation in anti-poverty programs, such as NE, is seldom random. Absent randomization, accounting for all observable and unobservable differences between the program recipients and non-recipients becomes essential for credible impact evaluations. Specifically, in order to compute consistent estimates of program impact, the researcher has to deal with estimation bias arising from (i) self-selection of treatment recipients, and (ii) nonrandom program placement, i.e. purposive program targeting based on a set of exogenous individual, household and/or community attributes. As is later, rationing in NE benefits increases the likelihood of finding non-participants in the data that are similar to the treated units in observable and unobservable characteristics. Thus, dealing with bias due to nonrandom program placement, as opposed to self-selection, becomes the more important issue for computing consistent impact estimates.<sup>12</sup>

# **5.1 Cross Sectional Analysis:**

We pool the ALSMS02 and ALSMS05 cross sections, and estimate, via propensity score matching (PSM), the separate effects of household participation in NE and old-age pension on household consumption and subjective measures of poverty.<sup>13</sup> Since consumption data was not

> 12 Household pension receipts mostly originate from pensioners’ contributions throughout the communist era when all employment was formal and workers automatically contributed to the pension fund. Thus, bias due to selfselection should not be an issue for pension impact estimates.

> 13The objective variables of welfare chosen as outcome variables in household cross sectional analysis are (i) a dummy variable equal to 1 if a household is poor, (ii) poverty gap, (iii) poverty severity, (iv) real per capita monthly total consumption, and (v) real per capita monthly expenditures on food, nonfood, education, durables, and utilities. The following subjective measures of poverty, as evaluated by the household head, were used as outcome variables in cross sectional analysis: dummy variables equal to 1 if (i) household is satisfied with the current financial situation, (ii) the financial situation has improved in the past 3 years, (iii) the financial situation is expected to improve in the next 12 months, (iv) household overall consumption is adequate, (v) household food consumption is

17
Table 2: Sample descriptives and sample balance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Control|SL - Control|P-value|N|
|HH head religion: Hinduism (%)|67.8|-2.47|0.642|2821|
||(3.57)|(5.28)|||
|HH head religion: Islam (%)|18.6<br>|3.74<br>|0.507|2821|
||(3.90)|(5.61)|||
|HH head religion: Buddism (%)|12.8|-0.95|0.774|2821|
||(2.35)|(3.28)|||
|Nr of HH members|5.01|0.030|0.792|2821|
||(0.084)|(0.11)|||
|HH head caste: Backward (%)|34.2|-2.55|0.636|2821|
||(4.08)|(5.38)|||
|HH head caste: Scheduled (%)|41.3|-1.23|0.840|2821|
||(4.11)|(6.04)|||
|HH head caste: General (%)|24.0|3.34|0.570|2821|
||(4.04)|(5.86)|||
|Gender of the HH head - male (%)|89.8|1.54|0.269|2821|
||(1.02)|(1.38)|||
|Age of the HH head in years|45.3|0.17|0.777|2821|
||(0.48)|(0.60)|||
|Years of education HH head|5.86|0.14|0.627|2821|
||(0.20)|(0.28)|||
|HH head is married (%)|91.4|1.02|0.408|2821|
||(0.93)|(1.22)|||
|Dweeling owned by HH members (%)|96.1|0.64|0.619|2821|
||(1.03)|(1.28)|||
|Dwelling structure: Pucca House (%)|17.7|2.54|0.423|2821|
||(2.41)|(3.15)|||
|Dwelling structure: Semi-pucca house (%)|65.6|-0.71|0.863|2821|
||(3.09)|(4.09)|||
|HH owns a BPL card (%)|59.1|-1.15|0.731|2821|
||(2.06)|(3.32)|||
|HH owns an APL card (%)|27.8|-1.29|0.674|2821|
||(1.88)|(3.04)|||
|Primary activity HH: agriculture (%)|52.0|3.55|0.503|2821|
||(4.11)|(5.28)|||
|Primary activity HH: Waged employment (%)|27.5|-2.00|0.552|2821|
||(2.34)|(3.34)|||
|HH owned a toilet at baseline (reconstructed) (%)|25.1|3.23|0.281|2821|
||(2.04)|(2.97)|||

_Notes_ : SL equals sanitation loan arm. Standard errors clustered at the village level are shown in parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level. HH stands for household. Column 2 reports mean and standard deviation (in parenthesis) for each variable in the control group. Column 3 reports differences in means between SL and Control arms. Toilet ownership at baseline is reconstructed from toilet construction dates reported at endline. If a toilet was in the dwelling when household moved in we consider number of years HH head lived in the household as a proxy of construction date.

13
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