rafmacalaba/data-use-mentions-extended huggingface dataset:
greater omitted-variable bias in the other parameters of interest if we deleted the occupation dummy variables for this reason. We were also reassured by our test results on sector-selection bias (Appendix). Some of our specification choices were determined by data limitations, both in terms of the variables collected by the HES and the survey's sample size (which limits how finely one can identify geographic effects, for example.) However, there were other variables that one might want to include. For example, we tested robustness to a specification which included secondary occupations (of the head, or spouse), though recognizing that this is likely to raise further concerns about endogeneity (while one might argue that the primary occupation of the head can be taken to be exogenous to consumption decisions, this is clearly more problematic for secondary activities.) However, only a few of the variables describing secondary activities were significant, and the main results (described below) on consumption gains from switching primary sector were very similar. We dropped the secondary activities from all specifications.

Table 2 suggests a number of similarities between the determinants of consumptions in the two sectors. For example, better education and more land increase per capita consumption, while larger households tend to have lower per capita consumption. The consumption returns to owning land are not significantly different between households who farm land themselves and those who rent it out (recalling that there is little secondary farming activity amongst primarily non-farm households). Similarly, while there are gains to having a better educated spouse, they are no different between the two sectors. And the impacts of most household size variables are similar between sectors. So none of these variables have a significant impact on the gain to a farmer from switching to the non-farm sector.

However, there are a number of sectoral differences in the process determining 14
countries and often find that higher demographic similarity to coworkers is positively correlated with worker retention (Elvira and Cohen, 2001; Hirsch _et al._ , 2020; Leonard and Levine, 2006; Sørensen, 2004; Zatzick _et al._ , 2003). Causal evidence is provided by Linos _et al._ (2024), who exploit random team assignments within a high-wage professional services firm in the U.S. and find very similar results to ours: A larger share of white peers only increases the turnover of black women with no effects on other race and gender groups. We add to this literature by studying the effects of racial coworker composition on workers’ retention using high-quality administrative data from Brazil. Exploiting exogenous changes in the peer group composition from unexpected worker deaths, we can isolate these changes from other confounding factors, such as firm management practices, and study the causal effects of racial coworker similarity for a large and diverse set of firms in a middle-income country.

Second, we speak to a large body of literature that studies cross-race interactions and racial segregation in various social contexts. Many papers have tested the “white flight” hypothesis to understand residential segregation, examining how neighborhood choices of whites are shaped by changes in the local exposure to minorities (Bayer _et al._ , 2024; Boustan, 2010; Card _et al._ , 2008; Shertzer and Walsh, 2019), and to understand school segregation, studying how white parents react to changes in the racial composition of schools (Baum-Snow and Lutz, 2011; Caetano and Maheshri, 2017; Cascio and Lewis, 2012; Reber, 2005). We complement this body of work by shedding new light on the retention effects of cross-race interactions at the workplace. In doing so, we also reveal important asymmetries in inter-group dynamics. While many studies focus on how the majority group reacts to increased contact with the minority group, our results indicate that the retention effects of working with same-race coworkers are not symmetric across racial groups and are more
# **Did Program Support for the Poorest Areas Work? Evidence from Rural Viet Nam**

Hai-Anh H. Dang, Klaus Deininger, and Cuong Viet Nguyen<sup>*</sup>

**JEL** : C15, D31, I31, O10, O57

Key words: poverty, targeting, household surveys, Viet Nam

> * Dang (hdang@worldbank.org; corresponding author) is a senior economist in the Living Standards Measurement Study Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, London School of Economics and Political Science, and University of Economics Ho Chi Minh City, Vietnam; Deininger (kdeininger@worldbank.org) is a lead economist with <mark>the Sustainability & Infrastructure Unit,</mark> Development Research Group, World Bank; Nguyen (vietcuong@vnu.edu.vn; corresponding author) is a researcher in International School, Vietnam National University, and Thang Long Institute of Mathematics and Applied Sciences (TIMAS), Thang Long University, Hanoi, Vietnam. We would like to thank Alessandro Barattieri, Paul Burke, Hal Hill, Duong Le, Hoa Nguyen, Chi Ta, Peter Warr, and participants at seminars at Australian National University and the World Bank Chief Economist’s Office for East Asia and Pacific Region for helpful feedback on earlier drafts. We are grateful for funding support from the United Kingdom Foreign Commonwealth and Development Office (FCDO)’s Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program and University of Economics Ho Chi Minh City, Vietnam (UEH).
Despite the lack of modern asset registries in the region, banks do not seem to be particularly worried about the registration of security interests in property, especially when it comes to registering mortgages on immovable assets. Only 16% of banks consider the registration of mortgages problematic, while almost 50% of banks consider the registration of security interests in movable property a key constraint (see Table 8). One would expect that given the lack of modern registries in the region, the percentages for registering movable property should be higher than 50%. Possible explanations for this are: (i) the relatively modest use by financial institutions in the region of movable property registries (given the fact that movable property is only taken as complementary or secondary collateral), and; (ii) the general lack of knowledge from financial institutions (specially domestic banks) on how good functioning registry systems work elsewhere.

**Table 8: How Problematic is it for Banks in the MENA Region to Register Interests in Property?**

|**Region**|**Percentage of Banks Reporting Problems Registering Rights on Assets**<br>**Movable**<br>**Immovable**|
|---|---|
|**MENA Average **|49%<br>16%|
|**GCC Countries**|56%<br>19%|
|**Non GCC Countries**|43%<br>14%|

_Source:_ World Bank MENA SME Lending Survey, 2010 To conclude with what the situation is in MENA with regard to technology infrastructure, there is no reason to believe that the infrastructure limitations in MENA preclude the development of effective registries. In MENA’s middle income countries, internet use is widespread enough to suggest that the infrastructure necessary for an electronic registry sharing data across multiple locations is feasible. Internet penetration is reported to be 28.3% overall in the Middle East, with variation among countries:
# **References**

Aguiar, Mark and Manuel Amador (2014). Sovereign Debt. In: Gopinath, Gita, Elhanan Helpman, and Kenneth Rogoff (ed.) _Handbook of International Economics_ , Vol. 4, Amsterdam: Elsevier, pp. 647-687.

- Aguiar, Mark, Manuel Amador, Hugo Hopenhayn and Iván Werning (2019). Take the Short Route: Equilibrium Default and Debt Maturity. _Econometrica_ , 87(2), 423-462.

- Aguiar, Mark and Gita Gopinath (2006). Defaultable Debt, Interest Rates and the Current Account. _Journal of International Economics_ , 69(1), 64-83.

Aguiar, Mark, Satyajit Chatterjee, Harold Cole, and Zachary Stangebye (2016). Quantitative Models of Sovereign Debt Crises. In: Taylor, John B. and Harald Uhlig (ed) _Handbook of Macroeconomics_ , Vol. 2, Saint Louis: Elsevier, pp. 1697-1755.

Alquist, Ron (2010). How important is liquidity risk for sovereign bond risk premia? Evidence from the London stock exchange. _Journal of International Economics_ , 82(2), 219-229.

Altman, Edward I. (2011). Default Recovery Rates and Lgd in Credit Risk Modelling and Practice. In: Lipton, Alexander and Andrew Rennie (ed) _The Oxford Handbook of Credit Derivatives_ , Oxford: Oxford University Press, pp 39-66.

Amador, Manuel (2012). Sovereign Debt and The Tragedy of the Commons. Mimeo.

Andritzky Jochen R. and Julian Schumacher (2018). Long-Term Returns in Distressed Sovereign Bond Markets: How Did Investors Fare? Paper presented at the 2018 VfS conference.

- Arellano, Cristina (2008). Default Risk and Income Fluctuations in Emerging Economies. _American Economic Review_ , 98(3), 690-712.

Asonuma, Tamon and Hyungseok Joo (2018). Sovereign Debt Restructurings: Delays in Negotiations with Risk Averse Creditors. Mimeo.

Asonuma, Tamon and Christoph Trebesch (2016). Sovereign Debt Restructurings: Preemptive or Postdefault. _Journal of the
0.99|0.84|2,506|
|Child does not have skin problems|0.00|( 0.01)|0.83|0.93|2,505|

Every row corresponds to a separate estimation (equation 1). The vitamin index is based on three variables: the child has received vitamin A, ferrous sulfate and deworming medicine during the last six months (yes/no questions). The hygiene index captures the hygienic condition of the child observed by the test administrator. The Attitudes index is calculated based on answers by the caregiver to the following questions: 1. Do you answer your child’s questions? 2. Do you think boys can play with dolls? 3. Do you think girls can play with cars? 4. Do you think children start talking by nature? 5. Does the child’s brain develop from gestation or when the child starts going to school? Each outcome variable is standardized using the mean and standard deviation for control households. The rows shows ITT estimates. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and dummy variable for a set of households surveyed in 2016, as well as village fixed effect. Standard errors reported in parentheses. p<0.1, p<0.05, p<0.01 60
Poverty rates are high among these households; understanding the reasons behind these low levels of economic activity is critical for identifying pathways out of poverty. On one hand, it could be that the high labor intensity of household tasks in areas with few services keeps women from actively contributing to agriculture or other market-oriented activities. On the other hand, these low rates could reflect an undermeasurement of female economic activity. With this in mind, the study explores two complementary questions designed to improve the quality of labor statistics in highly informal areas. First, the study aims to identify women’s activities and their labor intensity (time and effort), including: 1) work done outside of the home, both paid and unpaid; 2) production activities undertaken in the home to increase income or consumption; and 3) tasks undertaken to increase the well-being of the household, such as childcare, cooking, cleaning, and fetching water. Second, the study seeks to understand how individuals in these communities determine which activities and tasks are identified as “work” or “employment”.

The study finds that most women who report not being economically active are in fact participating in various types of economic activities, including production for own consumption, production of market goods, and remunerated market activities in services and commerce. Most women in the study reported working the equivalent of a part-time schedule across different economic activities, including agriculture, commerce, and services for pay. Based on the finding that many women consider their economic activities as part of their household tasks, simulations suggest that the rural female labor force participation rate in Honduras is more likely between 50.1 and 66.4 percent, depending on the assumptions applied.

The study identifies two main explanations behind this observed discrepancy. First, women are identifying themselves (and being identified by others) primarily as housewives, and the concepts of housewife and employment are taken as mutually exclusive. Second, given this duality between housework and employment, women define ‘employment’ based on a set
weighting, we take one half the shares of both exports and imports as the weights. We obtained the data for the import and export shares from the COMTRADE database. The resulting elasticities are reported in table C1. XXX For the sensitivity analysis, we must take upper and lower bounds of the elasticities. Unfortunately, Broda et al. only report point estimates in their on-line Excel file. In the paper itself, however, Broda et al. (2006, table 4, p. 36) list the standard error of the median DixitStiglitz estimate for the country. The standard errors are rather small for most countries. In the case of Lithuania, the median Dixit-Stiglitz elasticity is 3.8 with a standard error (of the median estimate) of 0.23.

Although we don’t have standard errors at the product line level, we use the overall estimate of the standard error to guide our choice of upper and lower values of the estimates. The low standard error suggests that we do not need to take very large ranges to check for robustness. For central values of 3.8 or less, we took plus or minus three times the average standard error of 0.23. CGE modelers have often doubled and halved the central estimate in the sensitivity. For central values greater than 3.8, we took plus or minus 50% of the value of the parameter, which gives a wider band than plus or minus three times the standard error. For these elasticities, this presents a tougher test of the robustness of our model.

Broda, Christian , Joshua Greenfield and David Weinstein (2006), “From Groundnuts to Globalization: A Structural Estimate of Trade and Growth,” National Bureau of Economic 94
# **1 Introduction**

Tax evasion by firms in middle-income countries is often thought to be one of the key reasons why revenue levels as a share of GDP remain well below those in high-income countries (Slemrod, 2019; World Bank, 2023). However, it can be challenging to measure how pervasive tax evasion is by firms by drawing on tax administrative and survey data (Slemrod and Weber, 2012; Timofte (Coca) et al., 2019). Estimates based on administrative data typically rely on tax declarations being checked for accuracy with the limited third-party information that is available in these settings, and it can be unclear whether lower-than-expected tax declarations are due to changes in economic activity, misreporting, or genuine evasion (Pomeranz, 2015; Carrillo et al., 2017). On the other hand, relying on surveys of taxpayers also has its challenges as respondents may be hesitant to directly admit to evading tax, which leaves many studies using questions about ”tax morale” as a proxy for compliance (e.g., see discussion in Luttmer and Singhal, 2014; Ali et al., 2014; and Hoy, 2022). The absence of reliable information about tax evasion levels can undermine revenue authorities’ ability to improve firms’ compliance and identify the types of firms they should focus their enforcement efforts on (Slemrod, 2008; Mascagni et al., 2023).

To overcome these challenges, we embedded an experiment that indirectly captures levels of tax evasion as part of a nationally representative survey of 2,955 firms in Indonesia. Specifically, we use a list experiment (also known as the “item count technique”) whereby respondents only state the number of statements on a list that apply to them but do not reveal directly which of the statements are true (see detailed explanation of this approach in Ahlquist, 2018, Blair and Imai, 2012, Imai, 2011 and Rosenfeld et al., 2016). We draw on best practices in measuring sensitive topics, such
which social protection and safety nets benefit recipients are ambiguous (Banerjee et al., 2020).<sup>1</sup> Third, we assess the impact of the pandemic on the diets of women and pre-school children. As is well understood, the impact of shocks on households can have unequal effects on individual household members (Alderman et al., 1995; Hoddinott 2006) and our data allow us to assess some of the intra-household impacts of COVID-19.

In March and August 2019, we conducted face-to-face surveys with mothers of children under the age of 24 months to assess how access to Ethiopia’s flagship social protection program, the Productive Safety Net Program (PSNP) had affected their food security and nutritional status. In June 2020, we re-interviewed these mothers by phone. Our data cover drought-prone areas of Ethiopia, spanning the four main highland regions of the country. Having access to pre-pandemic data allows us to assess the extent to which household food security and diets of individual members have changed following the start of the pandemic in Ethiopia. The longitudinal nature of our data, together with a sample that included both PSNP and non-PSNP households allows us to combine a difference-in-difference approach with a household fixed effect estimator, allowing us to control for a wide range of confounding factors.

We find that the percentage of households reporting a food gap, a widely used measure of food insecurity in Ethiopia, increased by 11.7 percentage points and the size of the food gap by 0.47 month. Participation in the PSNP offsets virtually all of this adverse change; the likelihood of becoming food insecure increased by only 2.4 percentage points for PSNP households and the duration of the food gap increased by only 0.13 month. The protective role of PSNP in food security is higher for poorer households and those living in remote areas. Results are robust to definitions of PSNP participation, different estimators and how we account for the nonrandomness of mobile phone ownership. PSNP households were less likely to reduce expenditures on health
# **2 Background on Temporary Migration from South Asia**

Bangladesh, Nepal and Pakistan are major migrant-sending countries globally, both in terms of the absolute magnitude of flows and stocks as well as their incidence in the working age population at origin (Ahmed and Bossavie 2021). In Nepal, the incidence of international migration is especially high: 27 percent of households have at least one member working abroad, and over a third of the male working age population has ever worked abroad (Bossavie and Denisova 2018; Bulmer, Shrestha, Marshalia, et al. 2020). In Bangladesh, about 15 percent of the male working age population is either currently working overseas or did in the past, according to data from the Household Income and Expenditure Survey (HIES). In absolute terms, Pakistan and Bangladesh respectively rank 5th and 6th in the global ranking of countries with the largest diasporas overseas. Migrants from South Asia account for 15 percent of all global migrants, and for two thirds of migrants in the Gulf Cooperation Council (GCC) countries.

Labor migration from South Asia is regionally concentrated in a handful of destinations in the GCC economies, and to a smaller extent in Southeast Asia. The GCC is a major destination of migrants globally hosting over 10 percent of the total stock of international migrants. It attracts many foreign workers, primarily from lower income countries, to fill labor shortages in low-skilled occupations in the private sector which are unattractive to natives. They provide large wage gain opportunities for workers from South Asia who can expect to earn three to four times what they earn back home (Ahmed and Bossavie 2021). Migration from Bangladesh, Nepal and Pakistan is especially concentrated in Saudi Arabia and the United Arab Emirates: over 80 percent of Pakistanis and over half of Bangladeshis working overseas are employed in one of these two countries, while half of temporary migrants from Nepal are employed in Saudi Arabia alone.

Given the low skill level of the workforce in Bangladesh, Nepal and Pakistan, combined with high demand for low-skilled
There are certainly many contributing factors to the resulting emphasis of research focused on the US antidumping process, for example. First, the United States is one of the most frequent “users” of the policy instrument. Until the mid-1990s, there were only four primary users of antidumping was– the US, EU, Canada and Australia. A second explanation for focus on the United States is the economic size of the market at stake and the value of imports affected by the policies. A third factor is that there is also readily available complementary data required for political-economic empirical analysis, such as data on product-level trade flows, disaggregated industrial level economic activity, and even political activity such as lobbying expenditures or political influence on Congressional committees that may affect the process.<sup>5</sup> In addition to these explanations, we believe that a fourth factor is an important determinant of substantial research on the US antidumping process relative to other countries: policy transparency and data availability. Researchers are more likely to scrutinize a policy when detailed information as to how it is being implemented is publicly available, which is the case for the United States’ use of antidumping.<sup>6</sup> Furthermore, since 2000, Bruce A. Blonigen at the University of Oregon has provided an additional boost to research in this field by posting on the Internet a collection of well-organized and comprehensive Finally, papers examining the ramifications of antidumping on international trade flows use include Bown and Crowley (2005a, 2005b, forthcoming) and Prusa and Durling (forthcoming).

5 For example, historical data at high frequencies (e.g., monthly) on product level (at the 10-digit Harmonized Tariff Schedule) trade flows is publicly available in the United States from the US International Trade Commission (http://dataweb.usitc.gov/ ). See also Feenstra, Romalis and Schott (2001). Furthermore, historical data on industry level indicators of economic activity at the 4-digit Standard Industrial Classification (SIC)
Remote sensing of violent conflict: eyes from above. _International Journal of Remote Sensing_ , _36_ (9), 2326-2352. https://doi.org/10.1080/01431161.2015.1035412.

- Witmer FD and O'Loughlin J (2011) Detecting the effects of wars in the Caucasus regions of Russia and Georgia using radiometrically normalized DMSP-OLS nighttime lights imagery. _GIScience & Remote Sensing_ , _48_ (4), 478-500. <u>https://doi.org/10.2747/1548-1603.48.4.478.</u>

- World Bank (WB) (2020) Remotely-sensed data of Nighttime lights. In _<mark>Open Night Lights</mark>_ <mark>tutorial. A</mark> vailable at <u>https://worldbank.github.io/OpenNightLights/tutorials/mod1_2_introduction_to_nighttime_light_data.ht ml (accessed 10 December 2021).</u>

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