rafmacalaba/data-use-mentions-extended huggingface dataset:
after RTA enactment with later years indicating insignificant increases in forest loss. While some leading coefficients for RTAs with provisions appear to differ from leading coefficients without provisions, neither group has leading coefficients that differ from zero, nor is there evidence of differential trends prior to enactment. The estimates using the rate of forest loss as an outcome variable are qualitatively similar and presented along side our estimates on log loss in Appendix Table A3.

We evaluate the cumulative effects three years after enactment on forest loss for RTAs with and without conservation provisions in Table 6. These estimates come from the sum of coefficients for the year of and three years following RTA enactment from Appendix Table A3, with the appropriately adjusted standard errors. For both forest loss measures, RTA enactment leads to significant increases in cumulative forest loss for agreements without conservation provisions, whereas increases in cumulative forest loss from RTAs with conservation provisions are small and statistically indistinguishable from zero. The lower panel presents the cumulative differences between those without and those with provisions. Using our log forest loss measure, the reduction in forest loss is sizable and statistically significant at the 5 percent level. In our rate of forest loss measure, the reduction associated with provisions is nearly half of the total increase from RTAs without provisions, but this difference is not statistically significant.

The results from our country-level analysis are qualitatively consistent with our agreement-level approach and, indeed, these results are quantitatively quite similar as well. As we discuss above, the country-level panel with country-level fixed effects, exogenous timing of entry into force, and dynamic effects together at least partly mitigate the selection bias issue of provision inclusion. The stability and persistence of our main findings across an entirely different identification strategy suggest that our results are not driven by a single empirical approach but represent a common, underlying causal response to the inclusion of environmental provisions in RTAs.

25
**Figure 9:** Yearly Survival rates for firms (left) and products (right) in Senegal, Tanzania, Mali, Malawi and Lao PDR

<!-- Start of picture text --> 100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>2nd 3rd 4th 5th 6th 7th 8th 2nd 3rd 4th 5th 6th 7th 8th<br>year year year year year year year year year year year year year year<br>Senegal Tanzania Mali Senegal Tanzania Mali<br>Malawi Laos Malawi Laos<br><!-- End of picture text --> _Source:_ For Lao PDR, authors‟ calculation based on Lao PDR Customs data; for the African countries, Cadot, et al (2010) For all countries, the slope in the early stages is very similar, despite their different starting points. For firms, Malawi begins with the lowest rate (~30 percent) and Laos the highest (65 percent), but by the fourth year they are all in the 70-80 percent range, even though this means that Lao experience grew at a slower pace. A different picture emerges for products, where Laos starts off with an incredibly high rate (~75 percent, compared to 10 percent for Malawi) and then seems to level off quite rapidly, meaning that here too it grows at a slower pace. A cautious conclusion, given the short time period, is that Lao firms improve their survival rates at a relatively slower pace, albeit starting from higher levels.

We have seen in this section, through various methods, that exit rates for all units (firms, products, FPDs etc.) are very high, although in a similar range to those countries for which we have comparable data. In the next section we analyze survival per se and its determinants, with a view to
Database. Where both censuses and population registers are available, censuses receive priority.

Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices.<sup>7</sup> The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis.

Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed. Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997).

# _The Raw Data_

The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some
in gross income for farmers annually and the increased wheat production generates an additional 49 million | in gross income.

Using data on production costs from Bhattarai et al. (2021) and CACP (2011) we can also estimate the change in farm profits that comes from this increase in production. Accounting for production costs suggests that the increase in production results in an additional 9 million | from rabi rice production and 12 million | from rabi wheat production. These estimated changes in profits are similar to the benchmark that a back of the envelop calculation using data from Ryan and Sudarshan (2020) would suggest. They find that an increase in depth to groundwater in Rajasthan reduces farm profits by 155 |/HA per meter. Using this estimate and the total area planted on average in rabi rice and rabi wheat in Star states prior to MNREGA we estimate an increase in farm profits of approximately 17 million | annually based on our actual change in depth to groundwater.

All of these estimates of the change in gross farm income and farm profits are a rough approximation of the benefits of the reduction in depth to groundwater. These calculations make significant assumptions about the symmetry of effects of reducing vs. increasing depth to groundwater and use state or nationwide averages for production, profits, and costs. However, our estimates provide a ballpark figure for the benefits that farmers realized from easier access to groundwater after the implementation of MNREGA.

# **7 Conclusion**

Access to reliable sources of groundwater for irrigation remains a critical challenge for Indian farmers. Ensuring long-term sustainability of groundwater withdrawals will likely require policies that encourage conservation (Ryan and Sudarshan, 2020). However, in areas with aquifers that have high surface level connectivity, the construction of surface level infrastructure that encourages recharge may provide a short-term solution to alleviate pressure on groundwater levels.

Our results suggest that surface level water conservation infrastructure constructed as part of the MNREGA program led to meaningful reductions in depth to groundwater in the states that implemented the largest number of these projects. This reduction
sectors differently than donors that report a larger portion of their aid. Similarly, this method considers that, for a given donor, the sectoral allocation of unreported aid may differ from that of the reported portion. The method ensures that the distribution of aggregate aid across sectors for each donor-year closely follows the sectoral allocation in DAC5, which contains complete disaggregated aid data. Subsequently, the main assumption is that the donor-year-specific sectoral allocation of the total residual applies equally to each recipient that receives aid from the donor in that year that is not accounted for in CRS.

In the final step of the data construction, I scale the sectoral aid variables such that their sum matches the aggregate aid received, similar to the scaling performed in previous studies (recall equation (5)). However, because the sectoral disbursements prior to scaling are based on more extensive information than in previous studies, these disbursements are more likely to provide a useful guide to the true sectoral allocation of total disbursements. Therefore, the scaling should be less problematic. On average, the constructed disbursements before scaling constitute more than 76% of the complete aggregate disbursements, whereas this value for CRS 8
Moreover, it is common that such multi-purpose surveys will be fielded over 12 months, to account for seasonality in consumption expenditures. In this case, the recall period will vary across households depending on the month during which the household is interviewed. In the case of surveys from year-long fieldwork, such as the three examined here, there is variation up to 11 months between the time of the harvest and the data collection, and recall periods for input use are several months longer.

Even if all sample households are surveyed once over a very short period just after harvest, events early in the season will be reported with a recall of several months (e.g. fertilizer application). This raises concerns about the reliability of information reported for earlier events. Substantial recall effects resulting from this gap in time could introduce bias or measurement error into data collected further from events of interest, and the problem may be further aggravated by variable recall periods across sample households.

The objective of this paper is to investigate the extent of recall bias, using data from three national household surveys conducted in East and Southern Africa with fieldwork over about 12 months. We use the variation in recall period and random assignment of households to month of interview (and, therefore, recall period) to examine several important agricultural indicators within the three datasets. Specifically, we explore whether we find any differential in reporting of input use and harvest amounts for main crops based on the time elapsed between events and reporting. In general, we find little evidence of any significant recall bias, in terms of under/over reporting. The findings suggest that farmers’ reports of harvest, crop sales, and input use are not significantly different when collected more than 8 months later as opposed to just after the harvest. Although this is _not_ evidence that agricultural data are not of poor quality, it does address at least one aspect of data collection – length of recall period – which could compromise data quality.

The next section briefly reviews the literature on survey reporting
residents must travel to reach a bank branch. Poor infrastructure and telecommunications, and heavy branch regulation, also restrict the geographical expansion of bank branches (CGAP, 2009).<sup>5</sup> Indeed financial inclusion is positively and significantly correlated with access points measured as commercial bank branches per 100,000 people (Figure 4). Sub-Saharan Africa economies are at the low end of the spectrum with low number of commercial bank branches per 100,000 adults and low account penetration.

**Figure 4: Financial Use vs. Access Points**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>ROW<br>40%<br>SSA<br>30%<br>NA<br>20%<br>10%<br>0%<br>0 20 40 60 80 100<br>Number Of Commercial Bank Branches Per 100,000 Adults<br>Account Penetration<br><!-- End of picture text --> _Source_ : Demirguc-Kunt and Klapper, 2012 and IMF Financial Access Survey (FAS). _Note_ : 2009 values from FAS are used as more economies are covered. Data excludes Mauritius.

# **_3.3 How and Why Are Formal Accounts Used?_**

The majority of adults with a formal account in Sub-Saharan Africa and North Africa make deposits or withdrawals only one to two times in a typical month. However, in North Africa 36% (29%) of adults report they do not deposit (withdraw) any money into (from) their accounts in a typical month. Automated Teller Machines (ATM’s) are the main mode of withdrawal for the majority of account holders in Eastern and Southern Africa (even excluding South Africa), while account holders in Western and Central Africa are more likely to make withdrawals over
activities ( _urbanization economies_ ) tend to enhance the productivity of urban areas. Here we first examine the concentration of different types of industries across cities. Then we examine city specialization per se. Finally in the next section we examine how manufacturing overall has tended to decentralize out of the largest cities over time. Due to limitations in the available employment data we cannot investigate changes over time and therefore focus only on patterns in 2000.

A typical measure of concentration of industry _j_ spread over _m_ urban areas is a variation of the well-known location quotient (e.g., Ellison and Glaeser, 1999): where _Sij_ is the share of city _i_ in industry _j_ employment nationally and _Ei_ is the share of city _i_ in total national employment. This index has a value of zero, if an industry is spread evenly across all cities according to their sizes, as is typical for personal and retail services. If an industry is highly concentrated it has a value approaching two. Table 7 shows urban concentration for each two-digit level industry across the urban hierarchy.

19
market. They suggest that though relatively large rms are the main recipients of BNDES nancing, the impact of BNDES on smaller rms may be dierent.

In fact, Cavalcanti and Vaz (2017) use a variation in access to a targeted loan program from BNDES to assess the causal eect of better credit conditions on investment and productivity of Brazilian manufacturing rms. The estimated causal eects point to positive eects on investment and productivity for small and medium rms. The authors argue that their results support the hypothesis that nancial constraints for long-term investment matter for small and medium rm's productivity and investment decisions. In addition to signicant government intervention in credit markets, Brazil also has very large private lending spreads (the dierence between lending rates and the deposit rate). According to the International Financial Statistics, the average interest rate spread is approximately 0.7 percent in Japan, 3 percent in the United States, 10 percent in Uruguay and 40 percent in Brazil.<sup>16</sup> High spreads may arise due to several reasons, such as poor collateral, credit taxes, regulatory costs and lack of competition. The banking sector is very concentrated in many countries, but concentration in Brazil seems to be particularly high and increasing over time. Between the mid-1990s and 2016, the share of assets held by the 5-largest banks in Brazil increased from 50 percent to more than 85 percent.<sup>17</sup> Joaquim and Van Doornik (2019) use heterogeneous exposure to large bank mergers to estimate the eect of bank competition on both nancial and real variables in local Brazilian markets. Specically, they use merger and acquisitions episodes of large Brazilian banks between 2005 and 2015 as a source of exogenous variation of competition in local banking markets to identify the causal eect of bank competition. The authors nd that a reduction in bank competition results in a signicant increase in lending spreads and a decrease in loan volume. They also
conditions over time or other time‐variant household factors. The model regresses the household specific difference of the income in one specific year from the mean in years with observations using the difference of weather conditions in one year from their means in all observed years.<sup>7</sup> By taking differences, all time‐invariant commune and household‐fixed effects are taken out eliminating any omitted variable bias caused by time‐invariant factors.

To take advantage of the full data set and test the robustness of the results, additional models are fitted based on the _Pooled_ data set, which estimate the determinants of income differences between households. First an Ordinary Least Squares model (OLS) is fitted that controls for observable commune characteristics in _Z_ ( _Pooled OLS_ ). These commune controls include long‐term rain and temperature levels and variability calculated from the CRU data and other location‐specific factors (e.g. altitude, slope, soil conditions, tree cover, distance from city and roads) calculated from other geo‐spatial data sets.<sup>8</sup> Whereas the set of commune controls reduces omitted variable bias caused by time‐invariant commune characteristics, it is unlikely to eliminate it. To further reduce this bias, a fixed‐effects linear model is fitted by including a dummy for each commune in _Z_ ( _Pooled FE_ ). This specification can control for commune‐ fixed effects, but not for unobservable household‐fixed effects.<sup>9</sup> Where unobserved household‐factors are correlated with weather deviation (e.g. cultural preferences or risk aversion), they can also lead to omitted variable bias, which can only be reduced through the _Panel FE_ model.

These regression models are estimated for various sub‐samples to disentangle differences between socioeconomic groups and climate zones. To identify weather impacts on different income activities, the regression models are also applied to the household sub‐sample participating in the respective income activity.<sup>10<
eligible households the estimate of impact of micro-credit on subsidy uptake is very similar to that of impact on toilet uptake. Just as the impact that we observed for toilet uptake in column 4, the observed impact on subsidy uptake is close to being significant at conventional statistical levels (p-value of 0.105). Similarity in impacts on toilet uptake and subsidy uptake for subsidy eligible households suggests that subsidy eligible households that constructed a new toilet were able to obtain a subsidy. Therefore, lack of access to the subsidy scheme is unlikely to have been the only barrier, if one at all, of sanitation investments for subsidy eligible households in our study area. Unsurprisingly, we do not observe any impacts on subsidy uptake for the subsidy ineligible group.<sup>23</sup>

# **5 How does micro-credit support the subsidy program?**

The findings thus far indicate that, first, both subsidy eligible and subsidy ineligible households took the sanitation loans. Second, this translated into a significant increase in toilet ownership and usage and a reduction in open defecation practice, mainly for the subsidy ineligible group, but to some extent also for the subsidy eligible group. Combined, these findings suggest that the sanitation micro-loan program was able to support the SBM program’s over-arching goal of reducing open defecation. An important question for policy design is _how_ the micro-credit program supported the subsidy program, which we address in this section.

## **5.1 Determinants of subsidy eligibility status**

We first investigate whether the micro-credit supported households who are vulnerable but are erroneously classified as ineligible for the SBM subsidy since they were recorded as having a toilet at SBM baseline.<sup>24</sup><sup>_,_25</sup> Comparing toilet ownership rates at SBM baseline and at survey baseline for VG households, we see in Table 8 that over half (83 out of 115) of vulnerable MF client households identified as owning a toilet in the 2012-2013 SBM data (making them
prices are estimated from a basket of 39 food products with weights reflecting West European consumption patterns. New York food prices are set equal to 1. Real food wage (in New York food prices) is estimated by dividing the nominal after-tax dollar wage by the food price index (not shown here). Annual number of hours worked is equal to the weekly number of hours of work given for each profession and country separately (UBS, 2009, pp. 34-35) multiplied by 52 weeks, and reduced for the number of official and paid vacation days per year for each country (UBS, 2009, p. 30).

1/ Unskilled or semi-skilled laborer, about 25 years of age, single.

2/ Skilled worker with vocational training and about 10 years of experience, working in a large company in the metal working industry, approximately 35 years of age, married, two children.

3/ Employed in an industrial firm in the electrical engineering sector, university or technical college graduate with at least 5 years of experience, about 35 years of age, married, two children.

4/ Rich are New York and London; poor are Beijing, Delhi and Nairobi.

15
within‐survey multiple imputation (Pape and Mistiaen, 2018, 2015). The lower amount of time spent collecting consumption data allowed the HFS to devote more time to collecting complementary data. Indeed, the HFS questionnaires contained additional modules covering asset ownership, education, labor market outcomes, perceptions of government performance and provision of public goods and services, psychological well‐being, perceptions of violence and safety, allowing a well‐rounded depiction of welfare and livelihoods.

The rapid escalation of the conflict in the summer of 2016, including several violent incidents affecting international humanitarian and development staff, led to the closure of the World Bank Office in South Sudan, disrupting the implementation of the third wave of the HFS. Therefore, the NBS implemented the third wave of the survey more independently relying mainly on remote support. A multitude of challenges had to be met, including large inflation, fuel unavailability, electricity shutdowns, insecurity, delay in payment of staff salaries, high NBS staff volatility, and cash flow limitations. Even though the NBS and the World Bank project team managed to mitigate a number of those challenges, the final sample reached only about 50 percent of the intended sample size. Nevertheless, this paper will argue that despite the enormity of challenges faced during fieldwork and the slight methodological departures from established approaches to poverty estimation (e.g. Deaton and Zaidi, 2002), the data collected by the HFS provide the best‐possible insights on welfare and livelihoods during a critical period of the country’s history.

The data from the HFS are complemented by video testimonials providing a glimpse of the lives of the people of South Sudan. At the end of the interviews, respondents are offered to provide a short video testimonial where they can share their views and give a sense of their lives. The testimonials capture the dire situation on the ground and provide a much richer qualitative picture that accompanies and complements the quantitative data. While the data may help the government fine tune its policies, the videos may reach a