rafmacalaba/data-use-mentions-extended huggingface dataset:
installments, in accordance with the schedule, in the nearest NRSP village branch and take a repayment receipt.

# **Appendix B: Variable definitions**

Data used in this paper come from two surveys: a baseline conducted in November 2006, a follow-up survey in December 2008. We also used administrative data about loan takeup, obtained from NRSP’s internal records.

# _<u>Treatments and Take Up (from administrative records)</u>_

- _Female picture brochure_ takes the value of 1 if the member was shown a brochure with female business owners on the cover, 0 if the brochure showed the same businesses with men.

- _Eligibility_ , takes the value of 1 if member is eligible for the loan lottery, 0 otherwise.

- _Business Training_ , a dummy taking the value of 1 if the individual was offered business training, 0 otherwise.

- _Applied for larger loan_ , 1 if the member actually requested a loan, 0 otherwise.

- _Approved_ , conditional on applying for a loan, takes a value of 1 if the member was approved by NRSP, 0 otherwise.

- _Borrowed_ , takes a value of 1 if the individual actually borrowed money from NRSP; while conditional on approval, not all those that applied and were approved took actually a loan.

- _Amount borrowed_ , is measured in thousands of Rupees.

# _<u>Baseline characteristics</u>_

# _<u>Individual</u>_

- _Female_ equals 1 for women and 0 for men.

- _Age_ is respondent’s age in years.

- _Years of education_ is years of completed schooling, and is top-coded at 16.

- _Married,_ a dummy taking the value of 1 if member is married, 0 if single, divorced or widowed.

- _Digital span recall_ reports the number of digits correctly recalled after being shown an eight digit number for 30 seconds.

- _Member of a mixed group,_ dummy takes the value of 1 if the member belongs to a borrowing
displacement, as well as data on their socio-economic welfare (broadly consistent with data collected and analyzed for poverty work, i.e. demographics, income and expenditure data, prices, living standards, access to infrastructure, services and local governance etc.); and

- (d) Rigorous impact assessments of interventions in various contexts to address the development impacts of forced displacement.

There is a significant opportunity to address some of these gaps by mainstreaming forced displacement into household surveys, focusing on international survey instruments.<sup>99</sup> Sample surveys can potentially provide a rich source of data on displaced populations. Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status.

Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include:

> 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner
# **A Solow-Swan growth model**

One of the implications of the Solow-Swan growth model (Solow, 1956; Swan, 1956) is that countries with low levels of productivity should catch-up to those at the frontier. In an economy characterized by the standard production function, consisting of technology ( _At_ ), capital ( _Kt_ ) and labor ( _Lt_ ):<sup>20</sup> the rate of growth in the capital stock per worker ( _gk_ ), and therefore output per worker, is decreasing in _kt_ = _Kt/Lt_ (capital per worker). Formally: where s is the fraction of output that is saved, _δ_ denotes the depreciation rate of capital, n is the growth rate of population, and _g_ is the growth rate of technology.<sup>21</sup> Countries with lower initial capital should, therefore, grow faster, converging to the productivity level of high-income economies. However, this is contingent on several assumptions: that there are decreasing returns to capital intensity, saving rates ( _s_ ) are homogenous across economies (Mankiw et al., 1992); and, that technology ( _A_ ) is costless to replicate across borders regardless of country-characteristics.

# **B Data**

Throughout the document, productivity is measured as output per worker, measured at 2010 prices and exchange rates to the US dollar. Labor productivity measured as output per worker can mismeasure output per unit of labor input when workers or employers adjust their working hours. Total hours worked is a more accurate measure of labor input than the number of workers, but data is available for only 30 EMDEs. As a result, sizable changes in hours worked over the business cycle can generate cyclical swings in measured labor productivity per worker.<sup>22</sup> Data on other macroeconomic aggregates such as GDP are from the World Bank’s World Development Indicators (WDI) database, with data on employment from the Conference Board’s Total Economy Database (TED)
# **1. Introduction**

Lending in low-income countries is notoriously difficult. Clients typically lack adequate collateral and lenders often have limited information about the profitability of their customers. Information asymmetries coupled with costly enforcement of repayment severely limits the profitability of lenders. The problem is particularly acute in agriculture because the nature of production precludes the use of many of the mechanisms used in microfinance. For example, lenders cannot schedule frequent repayments because cash flows are only received after harvest, several months after the loan is taken. In addition, all farmers need cash at the same time, so allowing some farmers to borrow only after others have repaid their loans is problematic because some farmers would end up receiving credit when they do not need it. Even if all clients were allowed to borrow at the same time, joint liability may be ineffective if most production shocks are covariate. Finally, and perhaps most importantly, lenders may lack the ability to deny access to future loans to defaulting clients in the absence of a national system that allows individuals to be uniquely identified.

When this happens, loan defaulters can often avoid sanction by simply applying for new loans under different identities. Lenders respond by limiting the supply of credit, due to the inability to sanction unreliable borrowers and, conversely, to reward reliable borrowers with expanded credit. As a result, many smallholder farmers are severely constrained by the inability to finance crucial inputs such as fertilizer and improved seeds, particularly for export crops.<sup>1</sup> In this paper we implement a randomized field experiment to estimate the impact of biometric identification (fingerprinting) in a context—rural Malawi— characterized by a lack of a unique identification system and limited access to credit.

According to the 2006 Doing Business Report, Malawi ranked 109 out of 129 countries in terms of private credit to GDP, a frequently-used measure of financial development. Malawi also gets the lowest marks in the “depth of credit information index” which proxies for the amount and quality of information about borrowers
29 This change may take several dimenslons. Food Items may be of different nutritional quality. They may also requlre different levels of preparation. The lower panel of Table 3.2 indicates, for example, that Indonesian hotuseholds of higher income groups, pay higher prices for their foods than their lower income counterparts.

The new economic theory of the household emphasizes one qualitative dimension of food preparation through **i** ts preoccupation with the shadow price of a diet that includes also the price or value of time. As Indicated previously, the cost of any particular diet is not just a matter of objective market food prices but also of the subjective value of time. It follows that whatever will cause a rise In the value of time, such an increase in household incomes, through employment opportunities and wages of women in particular, will Induce time-saving production of diets. This can imply a host of behavioral changes from substitution of breast feeding for bottle feeding to substitution of labor intensive home-cooking for appliance-intensive cooking, ready-made foods, and foods eaten away from home. Data from the Phillipines indicate that women working outside the home are more likely to initiate mixed feedings by adding breast milk substitutes after the third month (Akin, J.S. et al. 1985). Additional data presented in Table 3.4 based on the Indonesian experience, show that more educated homemakers, presumably with higher incomes and value of time, tend to have lower- consumption of nutrients, all other things equal.
intensity score ranging from _−_ 10 to +10, ranking events from the most hostile to the most cooperative.

We construct our panel dataset of events by selecting all hostile entries in the ICEWS dataset—i.e., those with negative intensity values—that are classified as violent according to Amodio et al. (2021).<sup>11</sup> This includes a broad spectrum of political violence and conflict episodes, even those that did not result in fatalities. Our final dataset comprises a total of 1.1 million political violence events occurring between 2006 and 2019. A detailed breakdown of event types and their frequency over the period of analysis is provided in Table A6, while Figure 2 displays their geographical distribution.

We exploit this data to construct our firm-specific measure of exposure to political violence, labeled _Conflict Exposure_ . This variable is defined as the logarithm (log of 1+) of the number of political violence events that occurred within a 20 km radius of the firm’s location during the 12 months preceding the end of the last fiscal year in each country. In absolute terms, the mean is 13, the standard deviation is 7.3, and the median and third quartile are 13 and 79, respectively.

As robustness checks, we construct alternative measures of conflict exposure: i) by using different radii for the buffer around each firm or weighting by inverse distance; ii) by exploring different types of conflict events; and iii) by employing alternative data from the PRIO/Uppsala Armed Conflict and Location Event Dataset (ACLED) (Raleigh et al., 2010). Like the ICEWS dataset, ACLED provides the date and geolocation of several types of conflict-related events. However, ACLED’s country coverage during our period of analysis is significantly smaller.<sup>12</sup> The bottom panel of Table A6 reports the number of conflict events by type over our period of analysis.

Our measures of conflict exposure may be subject to measurement error
exports accounted for 4 percent of the (net) new jobs that exports created during the study period.

Finally, human capital accumulation is also an important determinant of long-term productivity growth. However, access to roads can alter rural households’ opportunity costs of human capital investments. On the one hand, better transport infrastructure may reduce students’ travel costs to school, improving attendance. On the other hand, greater transport connectivity also exposes students to more immediate job opportunities, potentially incentivizing them to drop out of school to join the labor market. Li et al (2019) look at the impact of highway investments on educational choices in rural China. Using 2005 population census data, the authors find that highway access significantly reduces the enrollment rate of senior high schools in rural areas by as much as 9 percent, without affecting the enrollment rates for junior high and elementary schools. This negative effect is more significant on students in rural regions than in urban regions, indicating that better transport connectivity may improve the income of rural households in the 31
The context of our study is Cambodia, a country with a tropical monsoon climate encompassing distinct wet and dry seasons and a high incidence of floods and droughts. In Cambodia, national-level climate change models predict a considerable increase in mean annual temperature, ranging between 0.7° and 2.7° by 2060 and 1.4° and 4.3° by 2100. Mean annual rainfall is expected to rise, particularly during the wet season (Thoeun, 2015). The associated extreme weather is likely to influence the prevalence and transmission of various diseases, particularly water-borne illnesses<sup>2</sup> like diarrhea (Davies et al., 2014). We study how weather shocks impact diarrhea incidence in this paper. Although Cambodia’s share of global greenhouse gas (GHG) emissions was only 0.14 percent in 2019, they grew at an average annual rate of 8 percent from 2010 to 2019 (World Bank, 2023b). The country’s rapid industrialization and urbanization have increased emissions and the other face of the same coin, air pollution levels, creating a pressing need to investigate the relationship between pollution and health outcomes.<sup>3</sup> In what follows, we choose air pollution’s impact on cough in children since lower respiratory infections are the second cause of death in Cambodia (IHME, 2023). Studying the link between climate change and health is even more important in Cambodia, where increased morbidity is likely to have significant consequences on households’ finances, particularly for the poorest. Indeed, out-of-pocket expenditure represents 60.6% of current health expenditure (WHO, 2023; World Bank, 2024).

This study leverages high-resolution satellite data measuring environmental hazards, as well as geo-referenced individual-level indicators from the Demographic and Health Surveys. We use flexible binned specifications that simultaneously consider the impact of temperature, precipitation, and air pollution. The latter is a crucial dimension of environmental degradation and one of the negative consequences of transport and industrial emissions.
than a simulation exercise.

We start with over 300 studies compiled from a database produced by Evans and Yuan (2019), which draws studies from a wide range of reviews (Evans & Yuan, 2019; Ganimian & Murnane, 2016; Glewwe et al., 2011; Kremer et al., 2013; Krishnaratne et al., 2013). We then add 13 studies from the World Bank Strategic Impact Evaluation Fund (SIEF), as well as four additional recent studies that have rigorous evaluation methodologies and high-quality impact and cost data (Eble et al., 2021; Piper, Destefano, Kinyanjui, & Ong’ele, 2018; Romero, Sandefur, & Sandholtz, 2020; Sabates, Rose, Alcott, & Delprato, 2021).<sup>9</sup> Our inclusion criteria are that the study should be based on a credible causal inference strategy, using either randomized controlled trials or quasi-experimental methods, such as differences-in-differences, instrumental variables, regression discontinuity, fixed effects, or propensity score matching. To aggregate across outcomes, we code outcomes such that positive impacts always represent an improvement; for example, a reduction in absenteeism is coded as an 8Given that there are substantial difficulties when comparing cost data across contexts and interventions, we believe the field will benefit from efforts to standardize how these costs are reported, for example by consistently using $PPP.

> 9We also check for robustness of our results with the new data from the 2023 GEEAP report (Akyeampong et al., 2023). These results are analysed in Appendix C.

14
# **3 Main Results**

Our baseline specification is as follows: where _c_ is for country, _y_ is for year. **1** ( _Incumbent Stays_ ) _c_ , _y_ takes the value of 1 if the incumbent party wins the election. ∆ ln( _Crude Oil Index_ ) _c_ , _y−_ 1 captures an oil price shock one year ago. Country fixed effect, _αc_ , capture time-invariant difference in political systems and institutional quality. The specification also includes year fixed effects, _βy_ , which capture global shocks over time, such as the worldwide downturn due to the Global Financial Crisis. Finally, the standard errors in our main tables are clustered at the country level, to address the correlation of oil price shocks within each country. In a robustness exercise, we show that the results are robust to multiway clustering across countries and years (Table A2).

We also examine the effect of changes in import commodity prices on election outcomes: where ∆ ln( _Import ComPI_ ) _c_ , _y−_ 1 captures the lagged change in the import commodity price index. Finally, using the polling data, we test the specification: _Voting Intention Incumbenti_ , _m_ = _αi_ + _βm_ + _μ_ ∆ ln( _Crude Oil Index_ ) _i_ , _m−_ 12 + _εi_ , _m_ (3) where _i_ denotes the election and _m_ denotes the month. _Voting Intention Incumbenti_ , _m_ captures the voting intentions for the incumbent party in a given month.

∆ ln( _Crude Oil Index_ ) _i_ , _m−_ 12 captures the country-specific change in the crude oil price index with a 12-month lag. Lastly, _αi_ and _βm_ are election and month fixed effects.

Table 2 presents our main results on the impacts of oil price shocks on electoral turnover. Column 1 shows that an increase in crude oil prices in the previous year systematically and negatively affects the reelection chance of the incumbent party. A 1% increase in crude oil index reduces the reelection chance by 5.9 percentage points depending on
9 suffered; the next 15 towns ranked below the median damage score and the remaining ones were slightly above<sup>8</sup> . This means that these towns were generally not entitled to significant compensatory financial transfers. However, all young men belonging to specific cohorts were still granted the exemption from military service<sup>9</sup> . Second, geographic proximity between neighboring towns across the border of the quake region implies that they largely shared the same economic environment. Thus, they were exposed in a similar fashion to the deep economic recession ensuing the quake. More broadly, any general equilibrium effect of the quake probably had the same impact on similar individuals across the border. This implies, in particular, that changes in schooling choices induced by changes in wages and employment opportunities are of the same magnitude and accounted for in comparisons of similar individuals across the border of the quake region.

The 1981 population census allows us to assess whether towns just outside the quake region represent the appropriate counterfactual for towns just inside the region<sup>10</sup> . In particular, census data allow us to compare educational achievements and employment rates in the two groups of towns. Unfortunately, the Italian census does not collect earnings and income data. We focus on the adult population, specifically people aged 30-50 at the census date.

Figure (3) shows the age-specific differences in the percentage of high school graduates among the adults living in inner and outer towns as of 1981 together with the associated 95 percent confidence interval. Differences are generally small and never statistically significant at customary confidence

> 8Specifically, the lowest level of the damage scale corresponds to situations in which no one was injured or died because of the quake, less than 2 percent of the population lost their home, temporary shelter was needed for no more than 5 percent of the population, less than 1 percent of residential estate was damaged and less than 0.5 percent was declared inhabitable. Analogously, the highest damage level assigned to
temperature and precipitation events accurately. In addition to climate data, we incorporate monthly pollution data from the Atmospheric Composition Analysis Group at Washington University in St. Louis (Van Donkelaar et al., 2021), which has a spatial resolution of 0.1 degrees and provides annual and monthly estimates of ground-level fine particulate matter ( _PM_ 2 _._ 5).

To establish hazard exposure, we leverage the geographic coordinates collected by the DHS. We create 2km and 5km buffers around urban and rural households, respectively, and then extract the average temperature, precipitation, and pollution levels from the satellite data within these predefined radii.<sup>4</sup> We employ larger buffers for rural households, taking into account the random displacement of coordinates conducted by the DHS to ensure the privacy of respondents in sparsely populated areas. Our comprehensive dataset for analysis encompasses household-level covariates and health outcomes spanning the years 1998, 2000, 2005, 2014, and 2021, along with pollution exposure data covering the period from 1997 to 2021. We merge the DHS data with the temperature and precipitation data at the cluster, day, month, and year levels. The associated weather variables represent the average temperature and precipitation in the month leading to the interview. Air pollution data is at the monthly level. We merge it at the cluster, month, and year levels. The resulting variable measures air pollution in the month of the survey.<sup>5</sup> Table 1 furnishes an extensive overview of key variables essential to our empirical analysis. In Panel A, environmental data, including mean daily maximum temperature, average monthly precipitation, and average monthly ground-level fine particulate matter ( _PM_ 2 _._ 5), are presented. Panel B sheds light on health outcomes, specifically, diarrhea and cough prevalence. Notably, data on these health indicators were exclusively available for children, constituting 25.28% of the overall sample. For children aged 0-4, data on diarrhea was accessible for nearly 99.
After describing our data for China’s 35 largest cities in section 2, we outline our model of program participation in section 3. We then look for evidence of behavioral responses in section 4. The targeting performance of DB and its impact on poverty are the subjects of section 5. Section 6 concludes.

# **2. Data**

We use China’s Urban Household Short Survey (UHSS) for 2003/04. The UHSS was done by the Urban Household Survey Division of the National Bureau of Statistics (NBS) as a first step in constructing the (smaller) sample for the regular Urban Household Survey (UHS), which has a much longer questionnaire. We use the UHSS sample for the 35 largest cities, giving a total sample of 76,000, varying from 450 (in Shenzhen) to 12,000 (in Beijing). For these 35 cities, the definitions of geographic areas in the UHSS coincide with those for the DB lines and the entire data set has been cleaned by NBS staff and made available for this research.<sup>8</sup> While the UHSS is a relatively short survey, it allows us to measure a fairly wide range of household characteristics. The survey also included household income, as obtained from a single question, “What is your household’s total income?” (though respondents were also asked how much of their income came from wages). This is unlikely to give as accurate a measure of income as the UHS, which builds up its income aggregate from many questions. So we must expect measurement errors. Questions were added to the survey on subjective perceptions of welfare, namely a question on whether the respondent felt that the household’s income was

> 8 Outside these 35 cities, the local DB lines are not coded or use different codes, and in many cases use different boundaries to the geographic areas used by UHSS.  So it is not feasible to assign DB lines to households outside the 35-cities sample