rafmacalaba/data-use-mentions-extended huggingface dataset:
migrants are almost never allowed to take their families with them. This generates additional costs and further incentives to return. Low-skilled migration from many other major migrant sending countries globally, such as India, Pakistan, Nepal, the Philippines or Egypt, shares similar features.<sup>5</sup>

# **2.2 Data**

Our main data source is a new and unique nationally representative survey, called the Bangladesh Return Migrant Survey (BRMS). The survey was specifically developed to analyze the relationship between workers’ situation before, during and after their migration episodes. A special module focused on economic activities of the migrants after their return, including entrepreneurship. BRMS was conducted by the World Bank in 2019 and consists of a sample of 5,000 temporary migrants who had returned to Bangladesh at the time of the survey.<sup>6</sup> The BRMS collected unique information on full employment histories, migration expenditures, demographic characteristics of migrants, sources of financing, and economic activities after returning to Bangladesh. It is one of the largest datasets on temporary migration conducted to date and, to the best of our knowledge, it is the first of its kind in a country from where migration is almost exclusively based on temporary contracts. BRMS is also the first comprehensive survey on temporary migrants who have returned to Bangladesh. The survey was designed for an analysis of the economic activity of recently returned, temporary migrants in rural and semi-urban areas of Bangladesh. It covers all districts in the country.<sup>7</sup> The data set has several features that enable us to study the role of temporary migration in overcoming the institutional voids in underdeveloped capital markets. The survey includes detailed retrospective questions on the entire employment histories of migrants, both in Bangladesh (before and after the migration) and while they were abroad. It thus allows us to construct full employment and migration trajectories, including the destination country, dates and duration of stay, and labor market outcomes (such
# **1) Introduction**

In developed countries, premature mortality is negatively associated with income and education (Deaton and Paxson 2003; Lleras-Muney 2005). The same gradient is expected in developing countries, although, given the lack of vital registration systems it has been less easy to demonstrate. What has generally been shown is that the poor are more likely to experience poor health and less likely to have access to health services (Filmer 2005, Gwatkin, Wagstaff and Yazbeck, 2005). Additional information is available over a wide range of socioeconomic differences in health, nutrition and population including indicators on child mortality, malnutrition, fertility, immunization coverage, treatment of diarrhea and acute respiratory infections, antenatal care visits and delivery attendance, use of modern contraception as well as knowledge of HIV/AIDS prevention (Gwatkin, Rutstein, Johnson, Suliman, Wagstaff, and Amouzou. 2007). However adult mortality is not included among the indicators reported. Since adult mortality is the most objective measure of adult health (as opposed to self-reported health status or diseases), analyzing the socioeconomic gradient in premature adult mortality adds to the knowledge about the disparities in health in developing countries.

Bicego (1997), Timaeus and Jasseh (2004), Gakidou and King (2006) and Obermeyer, Rajaratnam, Park, Gakidou, Hogan, Lopez and Murray (2010) have analyzed adult mortality using various data sources (including the Demographic and Health Surveys—DHS—that we use in this study) and have gradually improved the methods for doing so, but they have limited their analysis on overall trends and on differences by gender and by age.

Part of our analysis looks at the extent of adult mortality in countries affected by conflicts and as such generalizes earlier analyses of the distribution and consequences of excess adult mortality in Cambodia (de Walque 2005, de Walque 2006) and Rwanda (de Walque and Verwimp, 2010). The HIV/AIDS epidemic is another important factor contributing to adult mortality, especially in Sub-Saharan Africa.
Policy Research Working Paper 9242

# **Abstract**

The paper uses Google mobility data to identify the determinants of social distancing during the 2020 COVID-19 outbreak. The findings for the United States indicate that much of the decrease in mobility is voluntary, driven by the number of COVID-19 cases and proxying for greater awareness of risk. Non-pharmaceutical interventions such as closing nonessential businesses, sheltering in place, and school closings are also effective, although with a total contribution dwarfed by the voluntary actions. This suggests that much social distancing will happen regardless of the presence of non-pharmaceutical interventions and that restrictions may often function more like a coordinating device among increasingly predisposed individuals than repressive measures per se. These results are consistent across country income groups, with only the poorest countries showing limited effect of non-pharmaceutical interventions and no voluntary component, consistent with resistance to abandon sources of livelihood. The paper also confirms the direct impact of the voluntary component on economic activity, by showing that the majority of the fall in restaurant reservations in the United States and movie spending in Sweden occurred before the imposition of any non-pharmaceutical interventions. Widespread voluntary de-mobilization implies that releasing constraints may not yield a V-shaped recovery if the reduction in COVID risk is not credible.

This paper is a product of the Office of the Chief Economist, Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed
3,457|3,457|
|R-squared|0.014|0.031|0.041|0.040|0.038|0.040|0.041|0.038|
|Number of households|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|NO|NO|NO|NO|NO|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-roundFE|NO|NO|YES|YES|YES|YES|YES|YES|

_Note_ : Dependent variable is the CSI. Data used are from the six visits of the GHS and telephone survey for conflict. The conflict recall is one year. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 xxi
*|
|productivity|(0.791)|(1.096)|(1.538)|first stage|(0.733)|(1.062)|(0.988)|(0.962)|(0.825)|
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
equal to 1 if the respondent answered “yes” to the question of whether they were concerned that a household member may fall ill with COVID-19 or that the household would experience financial distress due to COVID-19. The binary variable is regressed on indicators for each country, with Malawi as the base case, and each pre-COVID-19 household annual per capita consumption quintile, with the lowest consumption quintile as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country or that the coefficient on one quintile indicator equals the coefficient on an indicator for a different quintile. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi and Uganda, and round 1 in Nigeria. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

47
appreciated by nearly 20 percent in the two-year period.<sup>12</sup> By mid-19819 the government replaced the annual foreign exchange allocation system with a weekly system and tightened exchange contrels in an attempt to protect reserves. These moves led to development of a substantial black market in foreign exchange (Jeanneney, 1986, p.184).

- The following subsections (3.1 3.3) analyze more carefully some of the structural problems that tend to give rise to fiscal unsustainability in SSA.

# 3.1 Budgetary Fragility: **The** Tan3i Effect

Tanzi (1977) pointed out that if taxes are levied in nominal **terms** today and collected in nominal terms after some time lag, the real value of the taxes will have been eroded in the interim by **any** domestic inflation. Failure to index the tax system for collection lags of this sort can reduce the sustainable steady state level of expenditures. This is illustrated by the - - curves DD' and zz in Figure 2, which treat the real primary deficit, d (g t), as an inkreasing function of the inflation rate: d - d(Cf), d'>0. The diagram shows a situation that is unsustainable precisely because of this effect. In the stable lagged adjustment case (curve zz), reliance on the inflation tax (by the early 1980i, reserves were exhausted in many SSA countries and access to international borrowing was zero on the margin) will lead to steadily rising inflation (e.g., the path starting at B).<sup>13</sup> 12 Narrow money is currency plus demand deposits of the banking system; the inflation rate Ai the change in the GDP deflator. All data are from the IMP International Financial Statistics Yearbook, 1987.

13 As has been done in Brazil, indexation of the tax system to domestic prices will protect the real deficit from collection lags (while simultaneously removing, the fiscal
see that courses in countries with higher levels of per-capita income are more similar to those offered in top-20 U.S. departments, while courses in countries with more involvement of the state in the economy are less similar. Instructors who are more research active also are more likely to cover topics closer to those in top-20 departments, while courses without an instructor listed (and for which the instructor is less likely to be a researcher) are further from the frontier. The general tertiary attainment level in the country is positively correlated with similarity to the top programs, but this is not statistically significant. The R<sup>2</sup> of 0.33 shows that just a few correlates account for a sizeable share of the variation in topic choice across classes.

> 7 This data is taken from the World Bank education statistics, and is the Barro-Lee measure. We also attempted to use PISA test scores but they were not available for most of the developing countries in our sample.

> 8 Ideally it would be useful to correlate the age and place of PhD of these instructors with their course content, but we were unable to collect this information for the majority of our sample.

19
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 45 bootstrap method, based on the bootstrap procedure for finite populations introduced by Gonz ́alez-Manteiga et al. (2008). This method is described below.

# **Bootstrap procedure for MSE estimation:**

1. Fit model (5.1) to the sample data **y** _s_ = ( **y** 1<sup>_′_</sup> _s_<sup>_, . . . ,_</sup><sup>**y**</sup> _Ds_<sup>_′_)</sup><sup>_′_,obtaining</sup> ˆ

estimates of the model parameters, **_β_**<sup>ˆ</sup> , _σu_<sup>2,and</sup><sup>_σ_ˆ</sup> _e_<sup>2.</sup> 2. Generate bootstrap area effects as follows: Generate, independently of _u_<sup>_∗_</sup> 1<sup>(</sup><sup>_b_)</sup> _, . . . , u_<sup>_∗_</sup> _D_<sup>(</sup><sup>_b_),bootstraperrorsasfol-</sup> lows: Then, generate a bootstrap population (or census) of the response variable through the model, 3. Define the census vector of response variables for area _d_ , **y** _d_<sup>(</sup><sup>_b_)</sup> = ( _yd_<sup>(</sup><sup>_b_</sup> 1<sup>)</sup><sup>_, . . . , y_</sup> _dN_<sup>(</sup><sup>_b_)</sup>
Table B6: Nested Logit - Iceberg Costs

||(1)|(2)|
|---|---|---|
|**Costs:**|_Commuting_|_Trade_|
||Trips to Workplace|Trips to Shops|
|Minutes|-0.009***|-0.013***|
||(0.000)|(0.000)|
|Metro|-0.144*|-0.596***|
||(0.079)|(0.007)|
|Metrobus|-0.556***|-0.855***|
||(0.117)|(0.100)|
|Car|-0.220**|-0.603***|
||(0.095)|(0.073)|
|Walking|-0.352***|0.338***|
||(0.106)|(0.049)|
|_λ_ public|0.484***|0.484***|
||(0.033)|(0.009)|
|Observations|56,330|312,015|
|Trips|11,266|62,403|
|Iceberg cost before (mean)|4.095|7.217|
|Iceberg cost after (mean)|3.901|6.893|

_Notes:_ This table reports the results of a nested logit using the 2017 OD survey considering only trips that use one transportation mode. The first column reports the results to estimate commuting costs considering only trips from work to home or viceversa between 6am to 10am, and between 5pm to 9pm. The second column reports the results to estimate trade costs using trips to retail shops, outlets, and restaurants. I restrict the sample to trips after
first define the reference period for labor outcomes. This corresponds to the seven days prior to the interview date. We call this reference period week _t_ . Then we obtain the average level of a pollutant from monitoring stations in week _t −_ 1.<sup>20</sup> Thus, we use exposure to pollutants in the week before labor outcomes are realized. This responds to previous evidence

> 16We cannot use other years due to lack of geographical information. 17The ENAHO survey also contains self-reported information on health conditions, such as illnesses or accidents in the last 4 weeks. However, we do not use this information due to its longer time horizon, which difficult to match with environmental conditions, and because it does not distinguish between respiratory and non-respiratory diseases.

> 18The collection dates in a given week were randomly chosen. The collection process was done by trained personnel of the Ministry of Health using active air sampling procedures. The detailed data collection protocol is available at `http://www.digesa.sld.pe/norma_consulta/protocolo_calidad_de_aire.pdf` .

> 19As mentioned in Section 2.2, the meteorological agency (SENAMHI) started regular, hourly, collection of air quality indicators only since late 2011. We cannot use these new air quality data due to lack of georeferenced labor data for this period.

> 20We also examine the results using other lags and leads. We find that the only significant effect is obtained when using one-week lag. These results are available upon request.

8
16 In Kerala, people frequently refer to “Gulf Wives Syndrome.” This is a way of indicating the plight of a woman whose husband has left to work in the Arabian Gulf, leaving her with a stream of money at the emotional cost of separation and the responsibility of managing the family’s affairs in his absence (Zachariah et al. 2003).

> 17 The decision to conduct a panel survey was made only after the 1998 KMS was completed. The survey team selected the 2003 panel of households from 125 of the original 200 communities surveyed in 1998. In each community, they interviewed 40 households. Attrition in the panel stemmed from being unable to locate certain households due to a combination of incorrect addresses, and entire households having moved during the inter-survey period. The survey team did not identify which households had moved.

9
Russian Longitudinal Monitoring Survey (RLMS). The RMLS has less than 5,000 observations and is not representative of the population on the regional level. But is has extensive information on individual and household sources of income: wages and profits from first, second and third jobs; pensions and unemployment benefits; profits and dividends from accumulated assets.

We have employed both small area estimation (SAE) and Matching techniques (see Elbers et al., 2003; Rao, 1999; Moriarity and Scheuren, 2003) to generate sources of income data for all 55,098 households in the HBS. We describe our procedures in Appendix B of Rutherford, Tarr and Shepotylo (2005). Results from both techniques yield similar results.

The key point is that we chose characteristics of the two datasets that are common to both datasets and which we expect influence factor shares of income. These characteristics, which can be found in both the HBS and the RLMS, are personal, household and geographic characteristics. Personal characteristics include age, gender, skilled or unskilled worker, head of the household, primary, secondary, and other occupation, and income. Household characteristics are family size, members of the household who work and gender of the head of household. Geographic characteristics are the region of Russia and urban or rural.

Using the RLMS data, we then estimate regression equations where the independent variables are the characteristics mentioned above and factor shares are the dependent variables. In the SAE procedure, we assume that the estimated equations based on the RLMS data apply to all the households in the HBS. Using the data on the household characteristics in the HBS, we thereby generate factor shares for the larger HBS. Factor shares and consumption shares aggregated to deciles are presented in Rutherford, Tarr and Shepotylo (2005).

# **Reconciliation of the National Account and Household Budget Survey Data**

66
occupation. The model incorporates the drivers of employment choices that have been emphasized in the macro-labor and structural transformation literature, which include heterogeneity in workers’ ability and hence their comparative advantage, gender-specific barriers, differential returns to ability across occupation-sectors (Hsieh et al., 2019; Cassan et al., 2024), as well as non-homothetic demand, sector-specific technological change, and changing human capital supply (Ngai and Pissarides, 2007; Herrendorf et al., 2014; Boppart, 2014; Ngai and Petrongolo, 2017; Comin et al., 2021; Porzio et al., 2022; Feng et al., 2023).

The paper is organized as follows: Section 2 describes the data and provides new evidence on the link between economic development and gendered labor market outcomes. Section 3 presents the theoretical model and Section 4 describes the identification and model quantification. Sections 5 and 6 present the estimation results and the counterfactual simulations. Section 7 examines mechanisms and tests the robustness of our analysis. Section 8 concludes.

# **2 Empirical Facts**

## **2.1 Data**

**Data Sources and Sample Description.** We use data from the Integrated Public Use Microdata Series (IPUMS International, 2020), which provides harmonized individuallevel data on demographic and employment variables from nationally representative censuses, and household and labor force surveys for many countries and years. We extract employment information by occupation, sector, and gender for 91 countries and 305 country-years. The time coverage ranges from 1960 to 2020 and includes, on average, 3-4 rounds of data for each country. Online Appendix C shows that the data has good coverage over the time period and the entire development spectrum. We complement the IPUMS data with labor force surveys from the World Bank Global Labor Database (GLD) and the World Bank i2d2 database.<sup>6</sup>

– For our quantitative exercise, we use a core sample of six large economies India, – Indonesia, Mexico, Brazil,
treatment indicator and the interaction of this variable with a dummy for girls. Similarly, in Panel B the treatment indicator is interacted with a dummy that signals that the school has a higher enrollment than the median school. Panels C and D report results from analogous regressions when splitting the sample by prevalence of indigenous population and by the share of adults with no education. Finally, in Panel E we explore the extent to which malnutrition could be attenuating the positive effects of pre-primary attendance. We matched data at the municipality level from the Second National Census of Height of 2001 ( _Segundo Censo Nacional de Talla_ ). They measured the height for all first grade students aged 6 through 9, constructing an indicator of prevalence (%) of stunted children at the municipality level.<sup>10</sup> There are several noteworthy findings. First, we find that the effect of pre-primary attendence does not appear to differ significantly by gender (Panel A). Documenting similar effects between genders is in line with the aforementioned studies for Argentina and Uruguay (Berlinski et al., 2008; Berlinski et al., 2009). However, evidence from the United States document larger effects of preschool programs for girls (Oden et al., 2000; Anderson, 2008; Cascio, 2009). Second, we do not find consistent differences in effects on enrollment when classifying communities by percentage of indigenous population, share of adults with no education or prevalence of chronic malnutrition (Panels C to E); .

Similar to the effects on enrollment, when considering progression we find no statistically significant differences for populations with higher proportions of indigenous population or with a high prevalence of chronic malnutrition. However we do find that the positive effects of pre-primary access are attenuated in communities where a high share of adults has no education. This may be due to several factors associated with higher educational attainment in the community. For example, pre-primary take up rates may be higher in communities with higher education since it may be valued more by parents.