rafmacalaba/datause-encoder-data huggingface dataset:
variation in the _ex-ante_ costs and returns to migration but does not otherwise affect the household’s educational investment decision.

Our paper is, to our knowledge, the first to document a negative causal effect of parental migration on such informal payments to teachers. We show that the reduction in petty corruption occurs even though migrant households are, on average, wealthier than their non-migrant counterparts. This suggests that the income effect is overcompensated by other channels. Schoollevel variation indicates strong spillovers within schools, that could partly be due to social remittances (i.e., migrants affecting the opinions of those left behind) and partly due to migrant families’ behavior leading to a breakdown of the social norm of taking part in petty corruption. The results are neither explained by differences in public school funding nor by differences in the share of migrant children across schools. The money saved on informal payments to teachers does not translate into higher spending on out-of-school tutoring (henceforth: tutoring), which is an alternative way of teachers to make up for lost informal wage supplements. Rather, we find some evidence that main caregivers allocate more time to educational and school-related activities in migrant households. The reduction in informal payments might be explained by access to information or value change due to migration - a literature which finds that the migration experience can alter migrants’ and their left behind families’ political values, social norms, and behavior in general.<sup>3</sup> Since the underlying preferences and beliefs about the spread of corruption are unobserved, this remains a tentative hypothesis. We are, however, able to rule out several alternative explanations: income-effects, the valuation of education, non-parental caregivers, and several supply side factors, which we measure using matched school budget data, community level data as well as additional parts of the survey.

The remainder of the paper proceeds as follows. Section I anchors the paper in the literature. Section II provides information on Moldova and corruption in education. Section III describes the data used, and section IV presents our empirical strategy. The main results are presented in section V. Section VI tests alternative explanations and the robustness of the main results before section VII concludes.

> 3. For example, see the contributions of Cameron et al. <u>(2015), Beine et al. (2013), Barsbai et al.</u> (forthcoming), Spilimbergo (2009), Batista and Vicente (2011), <u>Ivlevs and King (2014).</u>

3
# 3.4 Descriptive Statistics

## 3.4.1 Background Statistics

Background statistics for our sample of sex workers are presented in Table 1. Panel A shows that the average sex worker is 28 years old, has completed over 9 education grades, and has roughly 2 children and 3 dependents.<sup>12</sup> Eighty-four percent of these women are heads of their households. Twenty-three percent of the sex workers in the sample are widowed, 20% are divorced or separated, 13% are currently cohabitating, and 44% were never married and are not currently cohabitating. In total, 43% of the women are previously widowed, divorced, or separated, which is much higher than the proportion of 10.2% found among the general population of Kenyan women aged 15-49 (Central Bureau of Statistics, 2004). The high number of previously married women is consistent with sociological and anthropological studies of sex workers in rural areas, and it is likely that many are HIV widows (Swidler and Watkins, 2007; Wojcicki, 2002a).

Panel A also presents statistics on the e¤ect that sex work has had on sex workers’perceptions of the likelihood that they will eventually marry (or remarry). Only 3% of women report that working in the commercial sex industry has made the prospect of future marriage less likely, but 41% report that working in sex work has made marriage more likely. This is notable because one explanation for the signi...cant wage premium to sex work is that it serves as a compensating di¤erential for reduced marriage market possibilities (Edlund and Korn, 2002). These results, however, seem to suggest that this explanation is unlikely to be important for this population of sex workers and is consistent with Arunachalam and Shah (2008).<sup>13</sup> Statistics on HIV knowledge are shown in Panel B of Table 1. Sixty percent of the sample has been tested for HIV, which is much higher than the national average (14.7%) or the average for in both rounds, so that pooled regressions are in most cases reported for 192 women. The actual number of observations in each regression di¤ers depending on available data.

> 12 The education level of women in our sample is similar to that of the average Kenyan woman. Fifty-seven percent of our sample have completed primary school, compared to 56% across Kenya and 67% across Western Province (Central Bureau of Statistics, 2004).

> 13 It is also possible that supplying commercial sex a¤ects marriage prospects on the intensive (partner quality) rather than extensive (...nding a partner) margin.

10
**Table 6. Shorrocks Mobility Index and Short-Term and Long-Term Inequality (balanced sample for each period)**

|||**Gini Ind**<br>|**ex**<br>||**Variance of Lo**<br>|**g Income**<br>|**No. of**|**No. of**|
|---|---|---|---|---|---|---|---|---|
|**Period**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|_M_<sup>_s_</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|**observations**|<br>**individuals**|
|1994-1998|0.18|0.44|0.36|0.41|0.76|0.45|19 120|4 780|
|1998-2004|0.22|0.40|0.31|0.45|0.63|0.34|25 452|4 242|
|2004-2009|0.18|0.35|0.29|0.38|0.47|0.29|22 956|3 826|
|2009-2015|0.15|0.31|0.26|0.30|0.35|0.24|27 174|3 882|
|1994-2004|0.27|0.39|0.29|0.51|0.60|0.29|24 111|2 679|
|2004-2015|0.23|0.31|0.24|0.44|0.37|0.21|23 868|1 989|
|1994-2015|0.34|0.33|0.22|0.58|0.42|0.17|16 680|834|

**Note** : Estimation results are obtained based on total household income per capita. All numbers are deflated with December to December regional CPIs and weighted with population weights

39
information presented in table 3.2 reveals that public spending on primary education is pro-poor. The share of the subsidies going to the poorest quintile is slightly higher than their share of the school-age population. But, the incidence of public spending on secondary education favors the non-poor. The poorest quintile, with 24 percent of the school-age population receives only 18 percent of the subsidy for secondary education. The richest captures 21 percent of the subsidy with only 16 percent of school-age population.

# **Determining Factors**

Benefit incidence analysis can help identify who benefits from public services by combining information on unit costs of provision with data on the pattern of use of these services by households. To draw proper policy implications from this type of analysis, it is important to understand what drives the observed distribution of benefits. For sure there are supply and demand factors involved here. The choice of the welfare indicator also plays a determining role. In the case of social services (health and education), one important supply factor to consider is the allocation of public spending across sub-sectors (i.e. primary, secondary and tertiary). Castro-Leal et al. (1999) study the effectiveness of public spending on social services in several African countries. In general, they find that these services are not targeted to the poor. One important reason they note is the allocation of public spending to different levels of service. For instance, they find that significant shares of health budgets go to hospital based services, which are rarely used by the poor. Thus, the composition of expenditure is a key determinant of its effect on different socioeconomic groups.

Demand interacts with supply to determine the targeting outcome for public spending. The demand for public services is influenced by such factors as _income_ , _quality_ and _costs_ (both opportunity and direct costs). The study of public spending on social services in Africa by Castro-Leal et al. (1999) found that household spending on health and education increases with income. They conclude that the demand for these services increases with income. Thus one way of improving the targeting of these services to the poor is to divert to the private sector the demand by the better-off.

30
sense of the magnitude of the effects of MNREGA’s increase in crop burning.

Jack et al. (2022) report that the monetized damages from deaths due to crop burning was USD$66 billion in 2018.<sup>32</sup> The average quantity of dry matter burned annually from 2015-2018 was approximately 65% higher than in 2006 at the beginning of MNREGA implementation (Liu et al., 2020). That suggests that mortality costs in 2006 were roughly USD$27 billion prior to MNREGA implementation and my estimates indicate that MNREGA increased burning by 21%, implying an increase in mortality costs of around USD$5 billion. Cropper et al. (2019) calculate a much smaller VSL for India than used by Jack et al. (2022), of between USD$256,000 and $84,000 (USD$2015). Using the mid-point of these VSL estimates lowers the welfare costs to roughly USD$0.85 billion.

This is a very crude benchmarking exercise, intended not to provide a precise estimate of the welfare costs of the increase in pollution but rather to provide a rough benchmark of their magnitude. The estimated magnitude of the welfare losses is large but plausible given the scope of burning, the size of the populations impacted, and the well-documented and severe costs of exposure to high levels of air pollution. The scale of the impacts is of the same order of magnitude as the annual federal expenditures on MNREGA, which has averaged between USD$4 billon and USD$11 billion between 2014 and 2021 An important dimension of these potential health effects is their potentially unequal distribution. Previous work (Lipscomb and Mobarak, 2016; Rangel and Vogl, 2016; Behrer and Mauter, 2017) suggests that while the region containing the source of emissions suffers negative consequences from those emissions, the majority of the damages may occur outside the region containing the polluting source. This is particularly true if there are major cities downwind of the emitting region. Rangel and Vogl (2016) find clear evidence that crop burning has negative effects in downwind areas but, because crop burning is driven by economic activity in the burning areas, increased burning may be associated with slight improvements in health in the areas where burning occurs.

# **8 Conclusion**

In this paper, I examine whether wage increases led to corresponding increases in pollution using the exogenous shock to wages generated by MNREGA. Because farmers appear to have responded

> 32They arrive at this figure using a value of a statistical life (VSL) of USD$1 million. It is worth noting that this is roughly 1/10 of the value used by the U.S. EPA in cost benefit calculations.

25
government has released some reports on the program implementation (see Secretaría de Movilidad 2010), there is no reliable evidence on the effectiveness of the restrictions.

Rationing car use may have other potential benefits; apart from addressing congestion and air pollution, it might also reduce the crash risk and road and parking costs.<sup>2</sup> Nonetheless, studies on the effectiveness of driving restrictions yield conflicting conclusions. Eskeland and Feyzioglu (1997a), Davis (2008), and Gallego, Montero, and Salas (2013) found that the _Hoy No Circula_ (HNC) program in Mexico City, which banned most drivers from using their vehicles one weekday per week, was ineffective. HNC induced many households to buy additional cars (mainly old and highly polluting ones). In contrast, Carrillo et al. (in press) found that driving restrictions in Quito reduced air pollution by 11 percent. In the latter case, the uncertainty about the program’s permanency may have kept households from buying a second car. Viard and Fu (2015) also report reduced air pollution after driving restrictions were introduced in Beijing. They argue that high compliance and vehicle ownership costs may explain the effectiveness of the program. However, around the time of the program implementation, other strict policies, for example, factory closures and suspension of construction projects, were imposed to cut pollution during the 2008 Olympics, and thus the improvements in air quality may not be entirely attributed to driving restrictions. Moreover, these programs are quite controversial from a cost perspective. Using a contingent valuation survey for HNC, Blackman et al. (2015) found that the regulatory costs are substantial (1 percent of drivers’ annual income).

The present study fills a gap in the literature by examining the effects of switching from moderate to drastic restrictions. Although the effect of multiple changes

> 2. Other common arguments are that such policies are easy to monitor, target the rich and the poor equally, and potentially induce use of public transport (Eskeland and Feyzioglu 1997a).

3
period 1996-2003 and contain about 14,000 firms with close to 70,000 firm-year observations. We refer to this as the sample of active firms.

We merged our sample of bankrupt firms with the financial data sample using a firm unique identifier. Out of 1,924 bankrupt firms only 1,201 firms have financial data in at least one year, resulting in a total of about 5,400 firm-year observations. Our panel is rather unbalanced and for many firms we only have one or a few years of data available; some firms have only pre-filing data and some have only post-filing data. The last three columns of Table 1A give the distribution of these firms with financial data across three types of proceedings and initial time of the filing.

For our bankrupt firms we create a timeline around the time of filing. We refer to the year of filing as year 0, the first year after filing as +1 and the year before filing as -1. To study the filing decision, we would like to use financial data at the time of filing (i.e. year 0). However, for some firms we have no data for the filing year, but have some data from previous years.<sup>12</sup> To increase our sample size we use all firms that have data for either year 0, -1 or -2. In total, we have 1,032 bankrupt firms with pre-filing financial data. We refer to this as the _bankrupt sample_ .

In addition to the filing decision, we are interested in comparing the speed of recovery after filing for reorganization before and after the new law. To do this we require pre and post filing data. To ensure that observed changes in performance are not caused by changes in sample composition, we construct a sample of firms with available financial data for the filing year and at least one year pre and post filing. Due to this stricter data requirement, we have only around 300 firms with about 2,000 firm-year observations. We refer to this as our _time-series sample_ .

# _C. Matched Sample of Active Firms_

Our filing data include years with different macroeconomic conditions, including an episode of major financial crisis in 1999. To control for differences in macroeconomic conditions affecting our bankrupt firms, we create a matched sample of active firms. For

> 12 For example, we have 794 firms with data for the filing year. In addition, we have 166 firms with financial data for year -1 (but no data for the filing year) and additional 73 firms have data for year -2 (but no data for year 0 or -1).

14
experience in Senegal. Panel B examines how impacts vary for the 38 percent of youth that had prior internal migration experience. We see no significant heterogeneity with respect to past internal migrant experience.

These results suggest that efforts to spur regional migration are likely to be more effective for youth who already have some experience or who are closer to considering this destination. The fact that ITT effects of the vocational training intervention on increased migration to Senegal were concentrated among those individuals that had prior experience migrating to Senegal further reinforces the evidence supporting that the vocational training intervention increased the salience of the construction and small appliance occupations for which training was provided, which were available in neighboring Senegal areas – but not in the rural areas of origin.

# **5.5 Comparison to migration expert and Gambian stakeholder predictions**

In July and August 2020, before the follow-up survey was collected, we invited migration scholars who had participated in the annual Migration and Development conference, along with World Bank migration experts, to provide their predictions as to the treatment effects through the Social Science Prediction Platform (DellaVigna et al, 2020). We asked for point estimates, along with a 90 percent confidence interval for key outcomes. We received responses from 36 migration experts living in 13 different countries. We also carried out a dissemination event at the University of The Gambia, and elicited predictions of the program effects from 22 academics, aid agency officials, and local journalists. We asked them their expectations of migration outcomes in each of the four groups, and calculated the treatment effects expected by comparing their control group to treatment group estimates.

Table 6 examines the extent to which our results line up with the priors of these experts and stakeholders. First, note that our ability to track the location of migrants was far greater than expected by migration experts. On average, they expected 28 percent attrition in measuring migration based on direct reporting, and 17 percent including proxy reports, with our realized rates of 11 percent and 0.03 percent attrition below the mean elicited 5<sup>th</sup> percentile.

Second, our estimated treatment effects on steps towards the backway and attempted backway migration are smaller than expected by academic experts, but within their 90 percent confidence intervals. The impacts on migration to Senegal are smaller than predicted for the Senegal treatment, and slightly larger than predicted for the vocational training treatment. The Gambian stakeholders 32
**1 Introduction**

Climate change will have significant and heterogeneous impacts on water availability throughout the world. In higher latitudes, historical data and climate models indicate that rainfall is likely to increase, while in lower latitudes overall precipitation is projected to decline. Additional warming caused by climate change will also increase overall rainfall variability, inducing longer periods of drought and increasing water stress as the agricultural, energy, and residential sectors extract more water from ground and surface reserves in order to combat increasing exposure to extreme heat and dryness (IPCC (2021)).

Although there is a growing literature on the impacts of water variability on aggregate economic outcomes such as GDP (Damania et al. (2017); Russ (2020)), there has been limited work documenting the causal impact of water variability on labor market outcomes. This paper aims to quantitatively analyze the impacts of water shocks on individual employment outcomes in Sub-Saharan Africa. Farming is the livelihood of two-thirds of the population in the region. With a high dependence on rainfed agriculture and limited resources and investment for climate adaptation, this region is particularly susceptible to climate change. Notwithstanding, Sub-Saharan Africa is growing rapidly, with its population projected to double by 2050. This demographic trend puts enormous pressure on the labor market to create enough jobs to absorb the growing workforce. All of these factors underscore the importance of understanding and mitigating the risks brought about by climate-related water shocks on jobs in Sub-Saharan Africa.

Our analysis relies on extensive, georeferenced individual socio-demographic and employment information collected by the Demographic and Health Surveys (DHS) Program. The final dataset includes over half a million individuals from 16 countries across Sub-Saharan Africa over the period from 2005 to 2018. By using a series of pooled cross-sections of DHS surveys, this paper analyzes the respondent’s employment status at the time of the survey and exploit a rich econometric specification that controls for a full set of (subnational-level) region and time fixed effects. The georeferencing also allows us to merge the DHS data with other spatial data on cli- mate conditions, water availability, the presence of irrigation infrastructure, and historic ethnic groups.

This paper measures water availability using the Standardized Precipitation-Evapotranspiration In- dex (SPEI), which was developed by Vicente-Serrano et al. (2010). The SPEI measures monthly variation in local climatic water balance and captures the influence of both precipitation and temperature on soil moisture—an important determinant of agricultural yield. Im2
Policy Research Working Paper 7579

# **Abstract**

The relative return to input-augmentation versus inefficiency-reduction strategies for improving education system performance is a key open question for education policy in low-income countries. Using a new nationally-representative panel dataset of schools across 1297 villages in India, this paper shows that the large investments over the past decade have led to substantial improvements in input-based measures of school quality, but only a modest reduction in inefficiency as measured by teacher absence. In the data, 23.6 percent of teachers were absent during unannounced visits with an associated fiscal cost of $1.5 billion/year. There are two robust correlations in the nationally-representative panel data that corroborate findings from smaller-scale experiments. First, reductions in student-teacher ratios are correlated with increased teacher absence. Second, increases in the frequency of school monitoring are strongly correlated with lower teacher absence. Simulations using these results suggest that investing in better governance by increasing the frequency of monitoring could be over ten times more cost effective at increasing teacher-student contact time (net of teacher absence) than hiring more teachers. Thus, at current margins, policies that decrease the inefficiency of public spending in India are likely to yield substantially higher returns than those that augment inputs.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jdas1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team