rafmacalaba/data-use-mentions-extended huggingface dataset:
savings in addition to coping mechanisms that involve selling productive assets or are potentially debt-increasing – namely, crediting purchases, delaying payments, or borrowing from friends, family, moneylenders, or other sources. Both outcomes look at use of coping mechanisms among all households since the previous survey round.

Finally, we explore potential longer-term effects on human capital accumulation by looking at food insecurity and engagement in interactive distance learning among school-age children. In this paper, we define _food insecurity_ as the share of households that reported that they had either run out of food, went hungry, or was hungry but did not eat, due to a lack of money or resources in the 30 days preceding the survey. _Engagement in interactive distance learning_ is the share of households with children attending school before the pandemic whose children either used mobile apps, online or in-person meetings/sessions with a teacher or tutor, or other online learning platforms in the week prior to the survey.

# Mobility and timing of the surveys

Across and within the seven EAP countries included in this analysis, mobility restrictions varied substantially between May 2020 and May 2021,<sup>12</sup> reflecting the variation in the severity of the pandemic and related

> 10 Looking at changes in income between rounds does not take into consideration net changes in income since pre-pandemic. For example, a household may have faced an income loss between pre-pandemic and round 1 but may have experienced an income gain between round 1 and round 2. Without information on the magnitude of these changes, it is difficult to say whether the household experienced a net loss, net gain, or income stayed the same relative to before the pandemic. Information on net losses are only available for a few country-rounds, so we look at changes in income between rounds instead.

> 11 Data from Mongolia and Myanmar, for instance, indicate that the majority of those who stop working do not have a job to return to or have a job to return to but have experienced a reduction in wages. Thus,
them multiple times during this time period (Figure 7). This likely underestimates the true likelihood of turning to repeated KUR loans, as borrowers who received loans for the first time toward the end of the study period would not have time to repay the first loan and take another loan during the study period.<sup>22</sup> Indeed, almost all borrowers who received KUR for the first time in 2015 had received multiple KUR loans during the 5-year study period (Figure 8). Regulations in effect at the time of the survey would allow a firm to continuously fund business using subsidized KUR loans for up to 48 years, assuming average loan sizes and repayment according to the loan periods allowed in the regulations.<sup>23</sup> Figure 6: Three percent of KUR borrowers graduate to commercial lending **Do Firms Who Receive KUR Graduate to Other Types of Loans?** <!-- Start of picture text --> 81.5 %<br>80%<br>60%<br>40%<br>20%<br>8.5% 6.5% 2.9% 0.5% 10.0%<br>0%<br>Only had Only had Graduated Graduated Graduated Graduated<br>KUR micro KUR small micro KUR micro KUR small KUR (any type)<br>to small KUR to non-KUR to non-KUR<br>loans loans<br><!-- End of picture text --> Source: Author calculations using survey of KUR borrowers Figure 7: Approximately half of KUR borrowers have received multiple KUR loans **Number of KUR loans borrowers received** <!-- Start of picture text --> 60%<br>50.8%<br>50%<br>40%<br>30.7%<br>30%<br>20%<br>12.5 %<br>
period is around 10 percent (reported in column (5) of Table A-3), which is similar to the underlying OLS fixed effect estimates reported in Table A-1. Second, we compare the implied average wages from our sample (calculated by dividing total wages by the number of employees with average wages reported in the Chinese Statistical Yearbook for 1998 through 2007. The results are listed in Appendix Table A-3. From Table A-3, we (i.e., 19
15 Table 2: Estimates of Elasticities of Exports to World GDP and Own GDP to Exports

||Adva|nced Econ|omies|Emergin|g Market E|conomies|
|---|---|---|---|---|---|---|
||1986‐2000|2001‐2014|_# countries_|1986‐2000|2001‐2014|_# countries_|
|Elasticities of Export Volume to Real World|||||||
|GDP (excl. own GDP):|||||||
|Gross exports of goods and services|2.5|1.9|_28_|2.7|1.9|_43_|
|Value‐added exports of goods and services|2|1.8|_22_|3.2|3.2|_6_|
|Elasticities of Real GDP to Export Volume:|||||||
|Gross exports of goods and services|0.5|0.6|_18_|0.7|1|_48_|
|Value‐added exports ofgoods and services|0.5|0.6|_18_|0.5|0.7|_7_|

_Sources:_ IMF World Economic Outlook, IMF Information Notice System, World Input‐Output Database, IMF Information Notice System. _Notes:_ Countries included in the averages passed the following filters: data to start no later than 1992; elasticities to be neither negative nor outliers. Estimations to derive elasticiy of exports to GDP include Real Effective Exchange Rate as an explanatory factor.

The results for the sensitivity of domestic growth to export growth (eg) are presented in the last two rows of Table 2. We find that for the average high-income economy, the elasticity of domestic GDP to exports is very close across the two periods (0.5 and 0.6, respectively), both in gross terms and in
# How Do Government Transfer Payments Affect Retail Prices and Welfare? Evidence from SNAP<sup>*</sup>

Justin H. Leung<sup></sup> Hee Kwon Seo<sup></sup> National University of Singapore World Bank

**Keywords:** Consumption, SNAP, food stamps, incidence, prices **JEL Classification Numbers:** E31, H53, I38

> * We are indebted to Marianne Bertrand, Michael Greenstone, Neale Mahoney, and Canice Prendergast for their invaluable advice and support. We also thank Chiman Cheung, Jos ́e Ignacio Cuesta, Peter Ganong, Austan Goolsbee, G ̈unter Hitsch, Erik Hurst, Aart Kraay, Magne Mogstad, Krisztina Orb ́an, Jung Sakong, Jesse Shapiro, Chad Syverson, Joseph Vavra, Michael Weber, Thomas Wollmann, Michael Wong, and anonymous referees for helpful comments. We thank Arthur Middlebrooks and Katie Bell at the Kilts Marketing Center; John Kirlin at the Economic Research Service; Anita Singh, RPMD HQ-Webmaster and Barbara Murphy at the Food and Nutrition Service; Karen Cunnyngham and Kelsey Farson Gray at Mathematica Policy Research; Philip Cho, Judith Denver and Beth Berendsen at the City of Chicago Department of Law and Department of Family & Support Services; and Timothy Beggs at Econometrica, Inc. for responses to questions and data requests. We thank friends at the House of Prayer for All Nations Church for sharing anecdotes about food stamps (Proverbs 31:8-9). Views in this paper do not represent those of the acknowledged institutions. Researcher(s) own analyses calculated (or derived) based in part on data from The Nielsen Company (US), LLC and marketing databases provided through the Nielsen Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the Nielsen data are those of the researcher(s) and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not
To determine whether expenditure per capita should be treated as endogenous, we performed a Hausman specification test using the amount of land owned and husband's earnings as instruments. We failed to reject the hypothesis of no endogeneity of expenditure per capita.<sup>8</sup> The community endowment of services and infrastructure, especially the concentration of health facilities and providers, may also be nonrandom.<sup>9</sup> Three theories provide insight into how public programs are allocated across population groups: (1) according to altruism theories, public resources are allocated more heavily towards more disadvantaged areas; (2) pressure group theories suggest an allocation pattern of public resources that is directed towards areas with higher demand and more lobbying power; (3) efficiency criteria and externalities may also influence the allocation of government programs so that public resources are placed in order to maximize the overall well-being of the population (Pitt, Rosenzweig and Gibbons 1994). Hausman specification tests for endogeneity in a recent analysis of health care choices in Guatemala suggest that the placement of government health posts and centers in Guatemala is likely to be exogenous (Pebley, Goldman and Rodriguez 1996). However, access to private health facilities and providers may be endogenous since private facilities and providers may concentrate in wealthier areas where children tend to lie healthier.

Nevertheless, in our analysis, we treat the placement of all (public and private) community health care facilities and providers as exogenous to child health. Failing to control for the nonrandomness of community services may lead to biased emnpirical results and erroneous conclusions about the effectiveness of such programs. In theory, the problem of endogeneity with cross-sectional data can be dealt with by the use of instrumental variables techniques. The use of appropriate instruments, that is new variables that are correlated with the endogenous covariates but are not correlated with the error term, would purge the parameter estimates of simultaneity bias. However, in the case of placement of community services, a valid set of instruments is not available from
our results for postnatal health investments using NFHS data, that does not suffer from this issue, are broadly similar.

> 22We do not use measures of state-specific penetration or adoption as they would be endogenous; see Jayachandran et al. (2010) for a similar argument pertaining to the introduction of antibiotics in the United States. Bhalotra and Cochrane (2010) document that access to ultrasound in India was widespread and that

8
# **1 Introduction**

Suppose that two nations with different levels of income per capita integrate their goods and factor markets. What are the implications for industrial performance in the less developed economy? In the standard Ricardian model of comparative advantage, both countries would gain from the reallocation of productive resources towards the industries in which they are relatively more efficient. However, integration may also induce capital flows to the less developed country, which may contribute to narrow the efficiency gap within industries. If the scope for capital flows and technology transfer is greater in the industries in which the advanced economy is specialized, the less developed country may plausibly observe stronger productivity and output growth in these sectors.

In this paper, we study the interplay between specialization patterns and foreign acquisitions in shaping the effects of deeper integration on industrial performance in less advanced economies. In particular, we investigate the extent to which this interaction played an important role in driving the patterns of catch-up growth observed in Poland following accession to the European Union in 2004—and the consequent deepening of goods and factor market integration with Germany. Using detailed data on Polish manufacturing firms during 1995-2013 and a difference-indifferences approach, we examine if and how the evolution of German acquisitions and firm performance in Poland, following EU accession, was mediated by predetermined measures of industrial specialization in Germany. If the scope for capital movements and technology transfers was higher in sectors in which Germany was relatively more specialized at the moment of accession, improvements in performance would be stronger among Polish firms operating in those sectors. Poland’s integration experience offers several interesting features for this analysis. Following the collapse of the Soviet Union in the late 1980s, a comprehensive reform program enabled the country to transform its socialist-style planned economy into a market economy. Like other post-communist nations, Poland experienced slumps in social and economic standards during this transition. But it became the first post-communist country to reach its pre-1989 income levels, which it achieved by 1995 following a period of strong economic growth. In the years preceding the
of connection for particular customers. Common implicit subsidies include the practice of charging a flat fee for service rather than using volumetric charges. Flat fees do not distinguish between consumption levels and thus subsidize large-volume customers. Illegal connections and inaction in the face of non-payment of bills are two other forms of implicit subsidies, which benefit those with illicit connections and those who do not pay their bills respectively.

The second form of targeted subsidy, which we focus on in this article, is targeting through an explicit subsidy mechanism. The most common type of explicit targeted subsidy in water and electricity utilities is the quantity-based consumption subsidy. Increasing block tariffs (IBT) – stepped tariffs that charge an increasingly higher price per unit to all consumers as their consumption increases into subsequent blocks – are a common example. Some (primarily electricity) utilities apply another form of quantity-based subsidy, termed here a volume-differentiated tariff (VDT). VDTs are composed of two or more different tariffs (e.g. the first highly subsidized and the second less or not at all) to which consumers are assigned based on their total volume of consumption. Our recent survey of water and electricity tariff structures in Latin America, 4
how remote sensing products compare to each other. MERRA-2 and ERA5 tend to report more rainfall than the other three products. Differences between products can be more pronounced when comparing across warm/cool zones or when comparing across semi-arid/sub-humid/humid sub-regions, depending on which weather metric one adopts. The takeaway is that one may end up with very different outcomes depending on what precipitation product one selects, what metric is used to measure precipitation, and what region of Africa is being studied.

The mismeasurement we see in rainfall is not as pronounced when we look at temperature. Here the three temperature products tend to agree with each other on what the temperature was in a given location on a given day. That is not to say that there is no mismeasurement in remotely sensed temperature, since all three products could be off in the same way. Short of knowing the actual temperature, what we can say is that one would expect results to be similar when using temperature, regardless of the temperature products or region of Africa is being studied.

# **2.4.2 Household Descriptives**

We seek to provide systematic evidence on mismeasurement in remote sensing data by modeling the relationship between weather and smallholder agricultural productivity. Because of this, our focus within the LSMS-ISA household data is on variables relevant to crop production. We examine both total farm production and production of the primary cereal crop within a country. The primary crop is maize in all countries except for Niger, where millet is the primary crop.

We present summary statistics of our dependent variables and controls by country, aggregated across all households for all waves in Table 6. Panel A presents summary statistics on output and inputs for the total farm (aggregating across all seasonal crops). The average value of total farm production varies from a low of $168 (Uganda) to a high of $664 (Nigeria), with most countries falling in the $100 to $300 range.<sup>8</sup> The value of
budget balances**

(budget balances in percent of potential GDP) <!-- Start of picture text --> 2.0%<br>0.0%<br>‐2.0%<br>‐4.0%<br>‐6.0%<br>‐8.0%<br>‐10.0%<br>2002 2004 2006 2008 2010 2012 2014 2016<br>Recessions Budet Balance<br>Structural Budget Balance Structural Budget Balance (SS)<br>% of potential<br><!-- End of picture text --> Source: World Development Indicators and authors’ calculations 14
investment in physical capital, tendency to export, level and growth of factor (labor, capital and total) productivity, and so on. Second, the firm-level data that we use covers formal firms with 5 or more employees. Thus, micro firms with fewer than 5 employees are excluded. This is an important exclusion given that informal firms are very small and therefore likely to be a bigger challenge for micro firms than the SMEs. Extending the empirical exercise of this paper to micro firms is a fruitful area for future research. Third, while there are several papers that analyze the extent of informal activity in a country, the size of the spillover effect from informal to formal sector firms has not been properly explored. Thus, exploring the factors that cause informal firms to compete more aggressively against formal firms rather than operate independently is another avenue for future research. Last, we found above that the impact of informal competition varies depending on exporting activity faced by the formal firms. Our motivation for exploring this was to raise our confidence against endogeneity concerns with our main results and not to explore heterogeneity per se. However, heterogeneity in the impact of informal competition across industry groups, firm-size groups within SMEs, gender of the top manager, and so on can be explored.

34
probability of food stress by 51.2 percentage points and food crisis by 16.0 percentage points. These findings underscore the importance of resilience-building measures in flood-prone regions to mitigate food security risks.

Climatic shocks also influence food prices and wages, affecting food security. For example, floods increase food prices by 3.40% annually, while agricultural wages rise by 1.80%. These wage increases are insufficient to offset higher food prices, resulting in a net negative purchasing power shock and worsening food insecurity. Droughts similarly induce purchasing power shocks. In our model, a one-in-six-year drought leads to a 5.17% annualized increase in agricultural wages but a 6.36% increase in food prices. The model shows that climatic shocks impact food security both directly, through agricultural losses and reduced food availability, and indirectly, through price and wage dynamics. This interconnectedness highlights the need for comprehensive climate adaptation strategies that address food production and market stability to improve food security outcomes.

This paper also contributes to the literature on food security modeling. Our model's predictive accuracy—correctly classifying 69.14% of food stress cases and 87.89% of food crisis cases— provides a foundation for developing early warning systems. These systems are crucial for anticipatory humanitarian actions, allowing policy makers to monitor risks in real time and take preemptive measures before crises unfold. This work builds on research incorporating climate simulations and satellite data to account for weather impacts on crop yields, food supply chains, and food security (Lobell et al., 2008; Cartuyvels et al., 2023) and connects to studies using stochastic modeling (Wang et al., 2020; 2022), threshold modeling (Penson et al., 2024), and machine learning methods such as Random Forest (Andrée et al., 2020) and Bayesian networks (Semakula et al., 2024) for predicting food security outcomes. Our model's ability to produce accurate, population-weighted 3
of magnitudes of all other patter ns<br>Ratio of magnitude of strongest repeating tempor al<br>pattern in SMS outgoing daily to sum of magnitudes ●<br>of all other patterns, after first activity<br>Ratio of magnitude of weekly repeating temporal<br>pattern in duration outgoing daily to sum of ●<br>magnitudes of all other patter ns<br>Ratio of magnitude of strongest repeating<br>temporal pattern in SMS outgoing weekly to sum of ●<br>magnitudes of all other patter ns<br>Ratio of magnitude of strongest repeating tempor al<br>pattern in duration outgoing weekly to sum ofmagnitudes of all other patter ns, after first ●<br>activity<br>Ratio of magnitude of weekly repeating temporal<br>magnitudes of all other patter ns, between firstpattern in duration outgoing daily to sum of ●<br>and last activity<br>Ratio of magnitude of strongest repeating tempor al<br>pattern in SMS outgoing daily to sum of magnitudes ●<br>of all other patterns<br>Ratio of magnitude of strongest repeating<br>temporal pattern in SMS outgoing weekly to sum ofmagnitudes of all other patter ns, between first ●<br>and last activity<br>2.0 2.5 3.0 3.5<br>Mean Decrease in Gini<br>Indicator<br><!-- End of picture text --> Ensemble of 500 trees. Mean nodes per tree: 424. Source: authors’ analysis from telecom data.

1
<sup>19</sup> Within each selected cluster, irrespective of the specific treatment, every 4<sup>th</sup> household was selected and surveyed, starting at either end of the cluster. In T1 and T2 clusters, all selected households were assigned to the respective treatment, with the exception of every 5<sup>th</sup> selected household which was left as a

> 17 These statistics are also consistent with Pakistan‘s rather dismal performance on a range of development indicators. According to the 1998 Human Development Report, for example, Pakistan ranked 138 out of 174 on the Human Development Index (HDI), 131 out of 163 on the Gender Development Index (GDI), and 100 out of 102 on the Gender Empowerment Measure (GEM).

> 18 While RCONs team members were new in the villages, MRDO staff had been working in the area, although their coverage was still low. According to Table 1, only around 11 percent of the women in the sample were MRDO members. We ensured, however, that MRDO staff did not conduct the campaign in the villages where they had been working previously. MRDO staff was also not engaged in the collection of the follow-up data. When asked informally about whether women in the household they had visited to deliver the campaign had voted and for whom, they did not know. As a result, it is clear that MRDO staff did not have any authority over the household and there was no expectation that they would obtain future favors if they casted the vote.

> 19 We kept one randomly selected control cluster in the survey sample per village. In one larger village, two control clusters were included in the survey.

10