rafmacalaba/datause-encoder-data huggingface dataset:
11 maize, rye, sorghum, millet, barley, and mixed<sup>grains. Food aid data2are in the sa</sup> units of grain equivalent comparable to the<sup>food production variable. The ti</sup> dimension of the aid and import variables is different<sup>from that of the production dat</sup> the last two are calendar year while the first<sup>is crop year. It is assumed that t</sup> crop year leads the calendar year by two to three<sup>months.</sup> The dynamic relationship between food aid and<sup>food production is explored</sup> investigating the characteristics of the VARs<sup>for these variables. First, a model</sup> estimated in which food production appears on the left-hand<sup>side, and its own lags a</sup> lags of food aid appear on the right-hand side. The regression<sup>does not include a</sup> information on variations in climate, soil quality, production<sup>technology, or any other</sup> economic characteristics, although all of these may<sup>affect food production. To t</sup> extent that these variations can be regarded as<sup>'country effects," however, the</sup> omission should not cause any problem. In essence,<sup>the estimation procedure discuss</sup> above eliminates these effects via differencing.<sup>In addition, the equation contains</sup> dummy variable for each year. The system of dummies will<sup>capture any underlying tre</sup> in the data as well as important influences common<sup>to all countries in a given year</sup> such as a drought.

Two other estimation problems are important.<sup>The first is whether t</sup> parameters are stationary over time. The above<sup>model (and virtually all work analyzi</sup> panel data), assumes that the parameters are constant<sup>not only across different uni</sup> but also over time. Similarly, each individual<sup>effect is time invariant. Holtz-Eakin</sup> and others (1988) derive a more general specification<sup>that allows all the parameters</sup> depend on the time period. Since this amounts<sup>to estimating a separate equation f</sup> 2The FAO and the World Food Program provided the data on food aid and commercial food imports.
Table 3: Reallocation of Workers across Firms and Sectors due to GDP Decline in Firms’ Destinations

||Baseline<br>efect|Initial<br>frm|Other frm in<br>same sector|Other frm in<br>tradable<br>sector|Other frm in<br>non-tradable<br>sector|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A: Average months worked_||||||
|GFC frm shock|-0.0226***|-0.0535**|0.0187|0.0085|0.0036**|
||(0.0054)|(0.0210)|(0.0152)|(0.0053)|(0.0018)|
|Observations|921,977|921,977|921,977|921,977|921,977|
|R-squared|0.0423|0.0452|0.0144|0.0259|0.0165|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|Yes|
|_Panel B: Average real wages_||||||
|GFC frm shock|-0.0089**|-0.0194***|0.0066|0.0030|0.0009|
||(0.0036)|(0.0056)|(0.0040)|(0.0025)|(0.0012)|
|Observations|916,397|916,397|916,397|916,397|916,397|
|R-squared|0.4518|0.2404|0.0284|0.0508|0.0299|
|2-digit industry X Year FE|Yes|Yes|Yes|Yes|Yes|
|State X Year FE|Yes|Yes|Yes|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. FE stands for fixed effects.

Table 4: Hours Worked and Renewal of Fixed-Term Contracts

||Average<br>hours<br>worked|Indicator for not<br>renewing fxed-term<br>contract|
|---|---|---|
||(1)|(2)|
|GFC frm shock*2009|-0.0048***<br>(0.0018)|0.0021**<br>(0.0009)|
|GFC frm shock*2010|-0.0056***<br>(0.0019)|0.0008<br>(0.0011)|
|GFC frm shock*2011|-0.0052***|0.0012|
||(0.0020)|(0.0009)|
|GFC frm shock*2012|-0.0047**|0.0002|
||(0.0020)|(0.0007)|
|GFC frm shock*2013|-0.0041**|0.0001|
||(0.0021)|(0.0010)|
|GFC frm shock*2014|-0.0039*|0.0001|
||(0.0020)|(0.0013)|
|GFC frm shock*2015|-0.0037*|-0.0008|
||(0.0020)|(0.0018)|
|GFC frm shock*2016|-0.0032*|0.0001|
||(0.0020)|(0.0015)|
|GFC frm shock*2017|-0.0031|0.0005|
||(0.0019)|(0.0019)|
|Observations|3,046,065|42,807|
|R-squared|0.3213|0.0371|
|2-digit industry X Year FE|Yes|Yes|
|State X Year FE|Yes|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, a d * indicate significance at 1%, 5%, and 10% levels, respectively. All specifications control for vectors of worker and firm characteristics as of 2007 described in Section III. FE stands for fixed effects. The estimating sample in column (2) includes only workers with contracts.

36
# **3.3 Data**

In December 2019, we conducted a baseline survey of female respondents, any cohabiting partners, and children aged 0 to 6, through face-to-face interviews. The baseline questionnaire asked women participants about their household composition, demographic background, economic activities of the respondent and any co-resident partner, consumption, dwelling and assets, knowledge and practices on child health and nutrition, input into productive decisions of the respondent and any co-resident partner, freedom of movement, acceptance towards domestic violence, sharing of housework, happiness, mental health,<sup>8</sup> and time use. We directly interviewed the woman and cohabiting partner. We also asked about the primary caregiver for each child for each hour over the past 24 hours. We measured child development using the Development Milestone Checklist (DMC III), which was adapted and validated in Burkina Faso for children aged 0 to 8 (Prado et al., 2014). The DMC III includes questions on gross motor, fine motor, and language skills. Due to the time demands of administering the DMC, we randomly selected one child aged 0 to 2 and one child aged 3 to 5 from each household to include in the sample, instead of administering it to all children in each household.<sup>9</sup> The baseline sample includes 2,150 households and 3,126 children. The baseline survey occurred approximately one month after the first childcare centers opened, we therefore control for baseline outcomes in our econometric specifications.

In February 2021, we conducted an endline survey by phone, due to COVID-19 social distancing requirements. We interviewed each female respondent about herself, her partner, and her children. We were able to contact 1,990 respondents from our baseline sample (a 92.6% tracking rate). We have some differential attrition by treatment status with a slightly higher attrition rate in the treatment group (8.6%) compared to the control group (6.4%). We address this in our analysis. Figure 3 indicates the timeline of implementation and data collection activities. All 18 sites that were randomized to receive a childcare center ended up opening a childcare center, so we have perfect compliance with assignment to open a childcare center.

> 8 We measure depressive symptoms using the Center for Epidemiological Studies Depression Scale Revised Short Form (CESD-R-10) (Miller, Anton and Townson, 2008; Radloff, 1977). The CESD, a screening test for depression and depressive disorder, is one of the most used instrument to measure depression in ECD studies in low and middle income countries (Evans et al., 2022).

> 9We selected children up to age 5, even though children up to age 6 could attend the childcare centers, so that we could ensure that these children would still be eligible to attend the childcare centers at the time of the follow up survey. At baseline, two children were selected regardless of the age categories; i.e. two children aged 3 to 5 could be selected if there were no children aged 0 to 2. At endline, we collected data on children sampled at baseline, for only one randomly selected child in each age category.

11
the standard for one additional month - the month of harvest - after the implementation of NREGA. Figure 5 shows the event study of the share of district _×_ months that exceed the standard. There are no obvious pre-trends and a clear increase after the implementation of MNREGA.

This discussion of pollution concentration has focused on the level of pollutant concentrations in the districts in which MNREGA is implemented. That leaves out the change in concentration levels that are downwind of the implementing district. These downwind effects may be more severe than the impacts in implementing districts (Behrer and Mauter, 2017). Even if they are less severe, considering pollution only in the implementing district presents only a partial picture of the impact of MNREGA on pollutant concentrations.

# **7.2 Welfare consequences of increased pollution**

There are a variety of negative economic and health consequences associated with exposure to air pollution. Mani et al. (2022) review many of these consequences in the context of South Asia while Aguilar-Gomez et al. (2022) provide a more comprehensive review. While existing studies of MNREGA’s impact on health find generally positive effects, it is possible that these effects would have been larger absent the increase in pollution. Previous work has estimated substantial infant mortality and reductions in birth weight from exposure to cropland fire smoke (Pullabhotla, 2018; Rangel and Vogl, 2016; Singh et al., 2019; Garg et al., 2022) and forest fire smoke (Jayachandran, 2009; Pullabhotla et al., 2022).<sup>31</sup> Rangel and Vogl (2016) in particular find meaningful negative impacts on infant health based on changes in concentrations similar to what I observe after the implementation of MNREGA.

My results combined with previous literature raise a natural question: what was the magnitude of the monetized damages due to the increase in agricultural fires caused by MNREGA? The difference-in-differences framework I use cannot estimate the impact of the increase in agricultural fires on health outcomes separately from the total impact of MNREGA. MNREGA likely impacted both pollution and health outcomes through channels in addition to changing agricultural fires. Estimating the total health impacts of MNREGA is well beyond the scope of this paper. Instead, I can roughly benchmark the health costs against the total spending on MNREGA to provide a

> 31Additional work suggests that those same forest fires reduced the later in life wages of those children who were exposed but survived and that the pollution exposure rates they experienced may be comparable to the exposure rates residents of Delhi experience from crop burning (Tan-Soo and Pattanayak, 2019).

24
**Figure 8 No gender gap in HE ownership**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>Female<br>0<br>Male<br><!-- End of picture text --> _Source: Annex table 3_ In the richer, more urbanized countries (R. Congo, urban areas, and Ghana), more than 70 percent of HEs are reported as primary employment for owners, while in other countries 54 percent or more of HE owners only operate a HE as a secondary activity. The pattern is strongly related to location with urban HE owners being much more likely to report HEs as primary employment than rural HE owners (75 percent versus 34 percent on average) - obviously driven by farming being primary employment for in rural areas, especially in less diversified economies. Consistent with HE being the primary employment for urban dwellers, urban HE owners work long hours - often more than 40 hours per week. In Tanzania, about 1⁄4 of rural HE owners work more than 40 hours a week on the enterprise, compared to 67 percent in urban areas. A similar pattern is observed in Ghana and Rwanda. Of course, since primary and secondary employment are self-defined, it is not clear if the fact that an individual spends a lot of time on the job causes an activity to be defined as primary, or vice versa, and what is the role of income earned per hour worked in the decision on which activity is reported as primary. Rural HE owners often can only work on weekends, when markets are open and foot traffic is heavier. Without electrification, working after sunset for the rural HE owner is usually impossible.

**Figure 9 Number of hours operated per week**

<!-- Start of picture text --> Primary employment Secondary employment<br>100% 100%<br>80% 80%<br>60% 60% More than 50<br>40% 40% 40 to 50<br>20% 20% 30 to 40<br>0% 0%<br>20 to 30<br>10 to 20<br>Less than 10<br>Ghana RwandaTanzania Ghana RwandaTanzania<br>Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural<br><!-- End of picture text --> _Source: Annex table 1 and authors‟ calculations_ 16
30 Figure 6: Commodity Trade and Welfare Reduction Indexes, markets of 19 African focus countries, all covered products<sup>a</sup> , 2000–04 (percent) <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>‐20<br>‐40<br>‐60<br>WRI TRI<br><!-- End of picture text --> Source: Anderson and Croser (2009) based on NRA and CTE data in Anderson and Valenzuela (2008) a. Products with a WRI of less than 30 percent in 2000–04 are omitted from the chart; and camel — which has the highest WRI in 2000–04 — is also omitted.
Table 2. Mothers’ Characteristics of 8- to 13-Year-Olds with and without Younger Children, by Marital Status.

1990–2011

||||Single|mothers|||
|---|---|---|---|---|---|---|
|||A.|||B.||
||No yo|ungerch|ildren|Withy|oungerc|hildren|
||1990|2000|2011|1990|2000|2011|
|**Labor outcomes**|||||||
|Labor participation|0.68|0.78|0.79|0.56|0.64|0.68|
||(0.47)|(0.42)|(0.41)|(0.50)|(0.48)|(0.47)|
|Worked last week|0.63|0.70|0.75|0.52|0.55|0.63|
||(0.48)|(0.46)|(0.43)|(0.50)|(0.50)|(0.48)|
|Hours last week|<br>50.75|<br>45.55|<br>40.00|<br>43.04|<br>44.56|<br>34.54|
||(17.16)|(16.04)|(15.29)|(19.87)|(18.26)|<br>(16.09)|
|**Background**|||||||
|<br>Schooling|8.54|10.51|11.12|7.67|9.97|11.01|
||(4.35)|(4.12)|(3.48)|(3.98)|(3.57)|(3.05)|
|Poor|0.48|0.22|0.21|0.73|0.44|0.41|
||(0.50)|(0.42)|(0.41)|(0.44)|(0.50)|(0.49)|
|Age|39.40|39.34|40.37|33.40|34.01|34.45|
||(7.06)|(6.57)|(7.21)|(5.84)|(5.12)|(5.64)|
|Number of children 8 to 13|1.08|1.09|1.07|1.28|1.26|1.16|
||(0.52)|(0.53)|(0.49)|(0.54)|(0.53)|(0.39)|
|Number of children 14 to 17|0.48|0.39|0.38|0.26|0.28|0.24|
||(0.71)|(0.63)|(0.58)|(0.56)|(0.51)|(0.48)|
|Urban|0.89|0.91|0.91|0.84|0.89|0.91|
||(0.31)|(0.29)|(0.28)|(0.36)|(0.32)|(0.28)|
|Other adult female in the household|<br>0.57|<br>0.60|<br>0.75|<br>0.59|<br>0.51|<br>0.67|
||(0.50)|(0.49)|(0.43)|(0.49)|(0.50)|(0.47)|
|Number of people working at|||||||
|<br>household|1.43|1.54|1.54|1.25|1.39|1.39|
||(1.13)|(1.05)|(1.05)|(1.08)|(1.09)|(1.08)|
||||Married|mothers|||
||No yo|ungerch|ildren|Withy|oungerc|hildren|
|**Labor outcomes**|||||||
|Labor participation|0.32|0.43|0.51|0.25|0.34|0.41|
||(0.46)|(0.49)|(0.50)|(0.43)|(0.47)|(0.49)|
|Worked last week|0.30|0.39|0.48|0.24|0.30|0.37|
||(0.46)|(0.49)|(0.50)|(0.42)|(0.46)|(0.48)|
|Hours last week|46.36|42.01|39.06|44.09|41.94|37.13|
||(17.00)|(17.33)|(15.23)|(19.49)|(18.30)|<br>(16.70)|
|**Background**|||||||
|<br>Schooling|8.28|9.91|10.70|9.10|10.39|11.21|
||(4.20)|(3.89)|(3.67)|(3.89)|(3.50)|(3.35)|
|Poor|<br>0.38|<br>0.19|<br>0.11|<br>0.54|<br>0.32|<br>0.23|
||(0.49)|(0.40)|(0.32)|(0.50)|(0.47)|(0.42)|
|Age|40.49|40.60|42.22|33.50|34.72|35.17|
||(6.49)|(6.21)|(6.60)|(5.10)|(4.95)|(5.48)|
|Number of children 8 to 13|1.24|1.15|1.07|1.35|1.27|1.16|

31
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 -->
Figure la.

Figure 1a: Indlces of trends In soil quality <!-- Start of picture text --> 300<br>250 -4 Avall.P<br>-4Soil pH1<br>g200 A - -- Organic matter<br>-<br>\, Soluble salt<br>XC100 *s -_<br>0 1<br>Al A3 <k A% q %lb %lb bA %q 5 9¢b<br>Year<br><!-- End of picture text --> _Source: Authors' own calculation_ Figure lb.

Figure lb: Indices of trends in tubewell water quality <!-- Start of picture text --> 250<br>150<br>-100<br>x<br>xl 50<br>O-~~~ . ... .. .. .. .. .. .. ...<br>+ + < 4 <9~~t 0N otb b %A0 9 f<br>Year<br><!-- End of picture text --> _Source: Authors' own calculation_ 13
in inequality among better secured good jobs is evident in Armenia, but not in Azerbaijan or Georgia.

Table 1: Contribution of effort and hard work and circumstances to total inequality in access to good jobs (%)

|||Effo<br>Age|rts and<br>|hard<br>Ed|work<br>ucati|on|G|end|er|F<br>ed|<br>athe<br>ucat|Circum<br>r's<br>ion|stance<br> <br>p<br>af|s<br>Parent<br>olitic<br>filiati|'s<br>al<br>on|Et|hnicity|Tot<br>e|al un<br>leme|fair<br>nt|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Type ofgoodjob*|1|2|3|1|2|3|1|2|3|1|2|3|1|2|3|1|2<br>3|1|2|3|
|Armenia|9|6|5|27|44|44|46|11|17|11|22|18|5|14|15|1|2<br>1|63|49|51|
|Azerbaijan|1|0|1|15|44|18|68|38|59|13|13|17|2|2|3|2|2<br>3|85|55|82|
|Georgia|21|1|5|45|74|55|9|0|5|20|17|29|1|2|1|4|5<br>5|34|24|40|
|.<br>BMU & Central|||||||||||||||||||||
|Asia|17|17|14|33|41|44|23|6|8|12|18|17|5|8|6|11|10<br>12|50|42|42|
|S.E. Europe|19|12|17|34|43|39|20|11|13|16|18|16|3|3|2|8|13<br>13|47|45|43|
|EU-11<br>GER, ITA, RUS,|21|16|22|38|40|40|7|8|5|18|21|18|4|4|4|11|11<br>10|41|44|38|
|TUR|24|24|28|27|28|21|22|14|22|18|19|16|1|3|1|9|14<br>13|50|49|51|

Note: *Type 1 is working 20 hours or more a week; type 2, working 20 hours or more a week with contract; type 3 working 20 hours or more a week with tenure Nonetheless, the share composition of the various factors contributing to the unfair element in the inequality in access to good jobs in South Caucasus varies across categories of good jobs and across countries. The gender factor plays a sizable role in Armenia and Azerbaijan, while Georgia is among the countries with the smallest share of the gender factor in the inequality in the labor market. Thus, 68 percent of the inequality in access to jobs involving work of 20 hours or more a week in Azerbaijan derives from differences in gender, while this is true of a smaller, but still considerable share of jobs of 20 hours or more a week with contracts or tenure (see table 1). Armenia shows a slightly different pattern: in jobs of 20 hours or more a week, the share of the gender factor in inequality is 46 percent, but this declines significantly, to 11 percent and 17 percent, respectively, in jobs of 20 hours or more a week with contracts and tenure.

Parental educational attainment, one of the proxies for family social status, plays a considerable role in inequality, especially in Georgia. The shares of the differences in parental educational attainment in inequality in Georgia are 20 percent for jobs of 20 or more hours a week, 17 percent for jobs of 20 or more hours a week with contracts, and 29 percent for jobs of 20 or more hours a week with tenure. The contribution of parental educational attainment to labor market inequality in Armenia and Azerbaijan is between 11 and 22 percentage points depending on the category of good jobs. Meanwhile, parental political affiliation is a relatively important 23
kina Faso in West and Central Africa (WCA).<sup>**7**</sup> Details on the historical and recent developments of the cotton by‐products industries of the four countries can be found in Appendices A through D. The analysis was supplemented by a brief discussion of the by‐products industry in the United States (Appendix E) and a short survey (see Appendix F).

Table 5 provides a summary comparison of the industries in the African countries. **Uganda** is a small cotton producer, averaging about 25,000 tons of lint annually (very little compared to the global production of 25 million tons of lint), almost all of which is exported. It has five privately‐owned cottonseed processing companies, of which four are independent and one is associated with a ginning operation. Uganda has 50 ginneries each processing small volumes— which explains why most of the crushing facilities are independent entities. While Uganda’s cotton production is spread around the country, all the seed‐ processing facilities are in or close to Kampala, the key oil consuming area.

**Tanzania’s** cotton sector is much larger than Uganda’s, producing about 100,000 tons of lint annually. It consists of 20 privately owned cottonseed processing companies, all but one of which are attached to ginneries. (Tanzania has 73 ginneries.) Almost all Tanzania’s cotton production and all its processing facilities are located in the Mwanza region. Most of the country’s cotton oil out‐ put is consumed in this region, though limited quantities go to Dar es Salaam.

All cottonseed in Uganda and Tanzania is processed, with most of the oil going for human consumption and the meal for animal use. Neither Uganda nor Tanzania exports cotton oil, and both are net importers of edible oil. In both countries there are established markets in the sense that cotton seeds, oil, and meal are all tradable commodities. The survey evidence shows that seed and oil prices move in concert within each country, indicating that the marketing struc‐ ture functions well. Cotton oil prices spiked during the 2008 boom and declined in 2009 following the decline of world edible oil prices.

Cotton is a key cash crop in both Benin and Burkina Faso. **Benin’s** cotton lint production averaged 114,000 tons during the past three seasons. **Burkina Fa‐ so** is SSA’s largest cotton producer; its output of cotton lint averaged more than 200,000 tons during the past three seasons and reached almost 300,000 tons in 2006/07. Both countries have undertaken many policy reforms in the cotton sec‐ tor during the past decade, including privatizing the former parastatal that han‐ dled all ginning and marketing. In both countries, cotton prices are set in a pan‐ seasonal and pan‐territorial manner.

> **7** The cotton sectors of these four SSA countries are also analyzed in the multi‐country cotton study (Tschirley, Poulton, and Labaste 2009).

**_Page 18_**
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