rafmacalaba/data-use-mentions-extended huggingface dataset:
22.66|
|p-value of Anderson-Rubin chi-sqared test of endog|enous regressors||0|1.20e-05|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.

Table 3 shows the results of running this analysis for our sample of emerging and advanced economies. Column (3) looks at the correlation of public consumption to primary revenue and shows how spending grows at more or less the same rate as revenue (beta estimates are very close
the head of the household. δδ Outcome variables i focus on participation in land markets and land-related investment as well as δδ credit market access. YY Aerial photography from before (2012) and after (2016) the intervention allows extracting of finished or unfinished structures at two points in time as an ‘objective’ measure of investment that may suffer less from bias than household survey responses.<sup>14</sup> Appendix figure 3 provides some examples for such changes. We use a main road separating treatment and control in the area of interest (figure 9) to implement a spatial discontinuity design along this road by estimating (1) with area under finished or unfinished structures as outcome variable for different bandwidths. This is a plausible design if, before the project, households either side were similar in observed and unobserved characteristics.

> 13 Estimated mean and median bias are lowest for kernel-based vs. to one-to-one or two-nearest neighbor matching, an impression confirmed by visual inspection. Standardized percentage biases, defined as the percentage difference of sample means in treatment and control groups (unmatched or matched) as a percentage of the square root of the average of the sample variances in the treatment and control groups (Rosenbaum and Rubin, 1983), for different covariates as displayed in appendix figures 1 and 2 illustrate that, compared to being widely spread for the unmatched sample, biases are concentrated around zero in the matched sample. 14 Imagery was available for 2012 (at 20 cm resolution) and 2016 (at 35 cm resolution) to guide program implementation and census mapping, respectively. Finished and unfinished structure were hand digitized. Unfortunately, the resolution proved insufficient to robustly discern changes in characteristics such as presence of fences, walkways, and complexity or type of roofs that could be made out from drone imagery using machine learning (Gevaert _et al._ 2020).

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# **II.V – Tajikistan L2T Baseline Survey for 2015**

Although the Tajikistan HBS survey is made available to the World Bank and other development partners, the sampling design used is not well-suited to the purposes of poverty mapping because the geographic coverage of the survey is limited in comparison to other options. Geographic coverage is crucial in the context of poverty mapping, and as an alternative, a survey with a more widely distributed sample was used. The baseline for the L2T Survey was selected because it: i) used the 2010 national census sample frame for the L2T baseline survey conducted by the national statistical agency, and ii) used a widely distributed traditional stratified two-stage clustered sample design. In the first stage, 150 clusters were selected, with a probability of selection proportional to size. In the second stage, 3,000 households were selected to participate in the survey.

The sample was designed to be nationally representative for consumption and expenditure. The survey was conducted over a two-month period beginning in March 2015. CAPI systems were used and all data were collected in person. As with the L2CU study, continuous data collection was conducted following the baseline survey, however, no comprehensive consumption or expenditure model was included in follow-up rounds. The interviews were implemented under the supervision of the Ministry of Health and Social Protection of Tajikistan and the World Bank.

# **II.VI – Satellite and Administrative Data**

The main source of satellite and administrative data for this study is the AidData project at the college of William and Mary.<sup>3</sup> The spatially aggregated data available for Kazakhstan from this source include:

- 1) World Bank project locations

- 2) Yearly VIIRS day night band nighttime lights data (without stray light correction)

- 3) Version 4 DMSP-OLS Nighttime Lights composites. The lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. Calibrated across sensors and years using Elvidge
predict that, other things being equal, older entrepreneurs are likely to have larger ...rms because they have had more time to accumulate and learn, and thus reach their long-term steady state. The data support this. Parental education is added as a control variable for initial household wealth and socioeconomic status. The regressions also include an indicator that the owners is female. We ...nd that female-owned businesses are signi...cantly smaller in terms of baseline capital stock.

In sum, the regressions on Table 2 suggest that baseline capital stocks are consistent with several of the predictions made by the RTI model. The signi...cance of pressure to share with others in the household suggests that factors outside the RTI framework may also be important. We now turn to the experimental data to investigate whether exogenous shocks to household capital are invested in ways which are consistent with model predictions.

21
spending OOP (World Bank 2015). Projected real GDP per capita through 2030 were obtained from the analysis carried out by Patrick Huang‐Vu Eozenou (see Annex B for details). Historical data on OOP expenditures per capita (1995‐2013) were calculated from health expenditure and OOP percentage data. Estimates for future OOP per capita were obtained by projecting the OOP percentage of total health spending from 2014 to 2030 by selecting the models giving the smallest predicted error for each country (see Annex A for details). Projections of future workforce demand were made for 165 countries for which both demand and supply input data were available.

To obtain the estimates for nurses and midwives and all other workers, we use the approach taken by WHO to fill missing data by multiplying the projected physician density for each country by a constant 2.517 ratio of nurses and midwives to physicians based on the WHO Global Observatory data (WHO 2016). This estimation assumes that the production function for health care workers will stay constant and is the same for all countries. To obtain estimates of all other workers, we similarly applied constant multipliers, but did so stratified according to World Bank income groups (high income = 0.373; upper middle income = 0.406; lower middle 8
Newson, R. B. (2010). Frequentist q-values for multiple-test procedures. _Stata Journal_ , _10_ (4), 568-584.

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Piopiunik, M., & Ruhose, J. (2017). Immigration, regional conditions, and crime: Evidence from an allocation policy in Germany. _European Economic Review_ , _92_ , 258-282.

Roodman, D. (2011). Fitting fully observed recursive mixed-process models with cmp. _Stata Journal_ , _11_ (2), 159-206.

Ruiz, I., & Vargas-Silva, C. (2018). Differences in labour market outcomes between natives, refugees and other migrants in the UK. _Journal of Economic Geography_ , _18_ (4), 855-885.

- Shergold, P., Benson, K., & Piper, M. (2019). Investing in refugees, investing in Australia. Department of Home Affairs (Australia).

Singhal, S. (2019). Early life shocks and mental health: The long-term effect of war in Vietnam. _Journal of Development Economics_ , _141_ , 102244.

- Slewa-Younan, S., Rioseco, P., Guajardo, M. G. U., & Mond, J. (2019). Predictors of professional help-seeking for emotional problems in Afghan and Iraqi refugees in Australia: Findings from the Building a New Life in Australia Database. _BMC Public Health_ , _19_ (1), 1-11.

- StataCorp. (2019). _Stata: Release 16. Statistical Software_ . College Station, TX: StataCorp LLC.

- Staubli, S., & Zweimüller, J. (2013). Does raising the early retirement age increase employment of older workers? _Journal of Public Economics_ ,
— in Figure S4 is that ultra-poor individuals use their phones very differently than nonultra-poor individuals, and machine learning algorithms can use those differences to predict ultra-poor status.

Our main analysis uses a gradient boosting model, which generally out-performs several other common machine learning algorithms for this task (see Table S3). The feature importances for the trained model are shown in Table S2. To limit the potential for overfitting, probabilistic predictions are generated via 10-fold cross-validation, with folds stratified to preserve class balance.<sup>4</sup> Additional details on the machine learning methods are provided in Appendix A.

**Combined Methods** We also evaluate several approaches that use data from multiple sources to predict ultra-poor status. Our main _combined method_ trains a logistic regression to classify the ultra-poor and non-ultra-poor households using the predicted ultra-poor probability from the CDR-based method (i.e., the output of the gradient boosting algorithm described above), as well as asset and consumption data collected in the TUP survey. For comparison, we similarly evaluate the performance of methods that combine only two of the available data sources (i.e., assets plus consumption, assets plus CDR, and consumption plus CDR). Predictions for each of the combined methods are pooled over 10-fold cross-validation.

# **2.5 Targeting Accuracy Evaluation**

**Evaluation on Matched Subsample** Our main analysis focuses on the 535 households for which we observe both CDR and survey data, and evaluates whether machine learning methods leveraging CDR data can accurately identify households designated as ultra-poor by the TUP program (using the two-step hybrid approach described in Section 2.1). We compare the performance of the CDR-based method to the performance of methods based on the wealth index, consumption data, and combinations of these data sources.<sup>5</sup> Each targeting method is evaluated based on classification accuracy, errors of exclusion (ultra-poor households misclassified as non-ultra-poor) and errors of inclusion (non-ultra-poor house-

> 4While cross validation is a
more<br>Prejudice Prevalence Prejudice Prevalence<br>Prejudice Prevalence Prejudice Prevalence<br><!-- End of picture text --> _Notes:_ The figure depicts the average level of prejudice by level of education for both the Colombian population and the aggregate Latin American population. The Prejudice Prevalence variable is constructed using the following indicator variables: (i) Accepting Venezuelan migrants will have a negative impact [=1], (ii) Belief that migrants have a negative impact on the host economy [=1], Belief that migrants compete with hosts for jobs [=1], (iii) Belief that migrants contribute to increased crime [=1], and (iv) Belief that migrants impose a burden on the state [=1]. The At Least One Variable Equals 1 Index equals 1 if any of the aforementioned variables is 1, and the Average Index of Variables is the mean of these variables. The data is extracted from the Latinobarometro survey for the year 2020, which is an annual public opinion survey representing the populations of 18 Latin American countries.
country.

Relative to these studies, our paper provides the first analysis of the labor market conditions of Venezuelan migrants in a Latin American context on the basis of a survey specifically designed for this purpose. We also use this information to analyze policies that are tailored to the specific characteristics of Venezuelan migrants in Ecuador. In this sense, our paper is also related to Clemens et al. (2018). This paper analyzes the effects of granting formal labor market access to refugees, highlighting the potential for substantial economic gains for the host country but also the potential costs for certain groups of the host population, which will depend importantly on the characteristics of the migrants (e.g. education) as well as features of the host labor markets. In the US context, some recent studies have analyzed the economic effects, on wages and GDP, of providing legal status to undocumented workers (Edwards and Ortega (2017) and Ortega et al. (2019)).

3
still includes ephemeral light, for example from fires and gas flaring, which we filter out. Following Beyer et al. (2018) and Beyer, Franco-Bedoya, and Galdo (2021), we identify different clusters by removing monthly outliers, averaging the brightness of cells over time, and clustering areas based on their light intensity.<sup>16</sup> We then define a background noise mask based on these clusters and only include lights outside the mask. In practice, this approach amounts to setting to zero cells that are distant from homogeneous bright cores.<sup>17</sup> The cleaned monthly data are aggregated to the district level and standardized by area.

# _ATM Transactions_

We obtained monthly data on ATM transactions at the Postal Index Number (PIN code) level from the National Payments Corporation of India, which is an umbrella organization set up by the Reserve Bank of India for operating retail electronic payment and settlement systems.

# _Credit Disbursements and Deposits of Scheduled Commercial Banks_

We use district-level credit and deposits data from the Reserve Bank of India, which are available at quarterly frequency. The scheduled commercial banks (SCBs) provide these data as part of the Basic Statistical Returns.

# _Household Income and Expenditure_

We obtained data on household-level income, expenditure, and their subcategories from the Consumer Pyramids Household Surveys (CPHS) database maintained by the Centre for Monitoring Indian Economy. This database is based on a large-scale survey that includes around 160,000 households covering all Indian states and union territories. The Centre for Monitoring Indian Economy visits each household once every four months and records

> range and onboard calibration correcting for saturation and blooming effects, the data are more comparable over time than previous nighttime light products.

> 16The advantage of using a background mask is shown clearly by Gibson and Boe-Gibson (2021b), who compare results using masked and unmasked VIIRS data, for both within and between estimators on a panel of about 3,000 U.S. counties.

> 17Our approach basically replicates the
# **I. Introduction**

Recent empirical research, especially in the context of developing countries, argues that democracy mitigates political favoritism (Hodler and Raschky, 2014; Burgess et al., 2015). Burgess et al. (2015), for example, document the prevalence of political bias in the geographic distribution of roads in post-colonial Kenya and conclude that “favoritism disappears during periods of democracy.” These findings, however, contrast with a large literature linking distributive politics to electoral competition, a defining feature of representative democracy.<sup>1</sup> In a democracy, politicians can often resort to opportunistic budget allocation by rewarding specific electorates or special interest groups in exchange for votes and rents (Grossman and Helpman, 1996).

In this we combine road and election data to the paper, geo-referenced investigate extent of distributive politics in the spatial allocation of federal highways in Mexico. Specifically, we examine whether municipalities that vote for legislative candidates from the president’s party subsequently receive a disproportionate share of federally-funded highways. We focus on the post-1990 period, an era that many commentators portray as one of genuine political competition in Mexico (Font et al., 2003). During the same period, the country also embarked on an aggressive public investment strategy marked with the implementation of the National Solidarity Program (Pronasol) in the 1990s and the launch in 2007 of the first National Infrastructure Program (NIP).<sup>2</sup> This unique institutional and policy context over more than two decades gives us the opportunity to shed light on the interplay between politics and public investment.

To identify political bias in the geographic distribution of federal highways (‘ _Highway_

> 1 See Golden and Min (2013) for a comprehensive review of literature on distributive politics in different institutional environments.

> 2 The National Solidarity Program (Pronasol) is a poverty reduction program launched in 1988, which devoted US$15 billion to highway construction and improvement projects. The National Infrastructure Program (NIP)
_Note_ : Table compares types of observed poverty in 2010 and 2013. Estimates account for complex survey design, and are expressed in percentage terms.

Table 2 provides descriptive statistics for our variables of interest for Malawi as a whole, as well as the Northern, Central and Southern regions. The ranking of regions in terms of poverty differs if we use consumption versus income per capita. In terms of consumption per capita, Central has the highest consumption figures, followed by Southern, with Northern quite a bit lower. Income per capita is also highest in Central, but then followed by Northern and then Southern. The pattern for maize harvest per capita follows income per capita, with the highest amounts in Central followed by Northern. Households in Southern obtain approximately half the amount of maize harvest per capita as Central. Households’ cropland holdings dedicated to maize production are also much lower in Southern, at .69 hectares on average, versus 1.34 and 1.38 in Northern and Central, respectively.

# **_Shocks_**

The precarious material conditions of most rural households in Malawi are compounded by their high exposure to rainfall, price, health and off‐farm employment shocks. To capture the effect of rainfall shocks, we use the absolute value of the percent difference between flowering season<sup>5</sup> rainfall in 2012/2013 and the long‐term mean during the flowering season. Historical rainfall data were obtained from the ARC2 database of the National Oceanic and Atmospheric Administration (NOAA). The absolute value is used since values far from the mean in either direction are expected to correlate with worse welfare outcomes. For maize price shocks, we had to rely on a shorter time period, 2005 – 2013, using administrative data on monthly maize prices observed in 72 markets across the country. We use deviations in 2012 from the mean price during the lean season January‐March, since this is the period prior to the harvest when households are most likely to need to purchase maize. We
# 1. Introduction

Afghanistan is at a critical juncture, with escalating food insecurity demanding attention as the country faces multiple crises threatening livelihoods and development. According to recent reports, hunger levels continue to rise, and progress toward the Sustainable Development Goals (SDGs) has stalled. In 2022, an estimated 12.6 million people—30.6 percent of the population—experienced severe food insecurity, according to the United Nations Food and Agriculture Organization (FAO). Of these, 2.9 million were at emergency levels (classified as Phase 4 or above by the Integrated Food Security Phase Classification, or IPC), on the brink of starvation due to economic crises and droughts, as highlighted in the latest IPC analysis (March-April 2024). The World Bank’s World Food Security Outlook (Andrée 2022; 2023c) projects that Afghanistan will not achieve Sustainable Development Goal 2 (zero hunger), with severe food insecurity persisting beyond 2030.

The choice between investing in resilience or responding with humanitarian aid represents a dilemma in the development and humanitarian nexus. Investing in resilience aims to build long-term sustainability and reduce vulnerability, while aid provides immediate relief in crisis times. Striking the right balance is essential, as focusing solely on aid can perpetuate dependency, while underinvesting in resilience leaves communities exposed to future shocks. Important in this tradeoff are investment opportunities, the magnitude of preventable agricultural losses, and the role of locally produced food for food and nutrition security.

Global costs associated with natural disasters and climate extremes are rising. Newman and Noy (2023) quantify the global costs of extreme weather events attributable to climate change, highlighting economic burdens, particularly human loss of life. Addressing climate shocks can be an essential part of resilience strategies, as the agriculture sector offers opportunities for proactive adaptation and mitigation measures to reduce vulnerability (Diogo et al., 2017). Challinor et al. (2014) conducted a meta-analysis of 1,700 simulations to evaluate yield impacts of climate change and adaptation globally. Across different climate