rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5.3.2 Migration**

Even if services and infrastructure across communities are distributed randomly, individuals may choose to migrate to communities that offer the preferred set of services. In particular, disadvantaged households with unhealthy children may decide to move to areas with better infrastructure and community services, a choice process likely to be reinforced by the fact that better employment opportunities are usually found in the same areas. In the absence of a valid set of instruments to explain migration decisions, we treat the households' place of residence as exogenous to child health. Moreover, since in rural Guatemala there is little migration of families with small children (96% of the children in the EGSF sample have spent all their life in the villages where they were born), community characteristics are likely to reflect the environment in which children grew up.

# **5.3.3 Clustering**

The multistage clustered sampling design adopted in the EGSF is typical of many demographic and health surveys conducted in developing countries, including the World Fertility Surveys and the Demographic and Health Surveys. In the first stage of sampling, 15 rural communities were randomly selected in each of the four chosen departments. The second stage involved random selection of about 100 households within each community." The data that we use for our empirical analysis are thus characterized by two levels of clustering: children are grouped within families and families are grouped within communities. The 2963 children for which we have complete information on all the variables that we use in regression analyses come from 1658 families distributed across 60 communities.<sup>12</sup> In our sample, the number of children per community ranges from 10 to 87 (mean = 49.4). The number of families per community ranges from 11 to 44 (mean 27.6) and the number of children per family ranges from I to 4 (mean = 1.8).

While the growth experience of children in different communities may be independent of one another,
Policy Research Working Paper 10193

# **Abstract**

Childcare services enable women who were previously unable to work due to taking care of their children to join the labor market. If some women are more productive in market work, rather than unpaid household work, the availability of childcare can potentially improve the allocation of talent across different occupations, triggering an increase in productivity. This paper tests this hypothesis using a survey of manufacturing plants and data on preschool expansion in Indonesia. The analysis relies on a triple difference estimation comparing plants in sectors with different degrees of female labor at baseline. The results suggest that between 2002 and 2014, when a rapid preschool expansion took place in Indonesia, an additional preschool per 1,000 children increased the total factor productivity of manufacturing plants by 11 percent for plants with an average fraction of female workers. The paper provides suggestive evidence that these effects were driven by better labor market matching, enabled by the expansion of female labor supply, and greater job stability for female employees. The results unveil a novel short-term economic impact of childcare services, which complements the longterm growth impact through human capital accumulation.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region and the Macroeconomics, Trade and Investment Global Practice. 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 eperova@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 in this paper are entirely those of
Docquier (2008) find an association between emigration and adult deaths from AIDS in African countries with high HIV incidence. When a larger sample covering developing countries from all regions is used, Bhargava et al. (2011) find no significant effect of medical brain drain on either infant and child mortality rates or child vaccination rates. These studies rely on cross-country panel analysis, with the associated limitations in being able to establish causal relationships. Measurement of these health externalities of skilled migration at a more micro-level is an important area for future research.

# **Conclusions**

Economists have been theorizing about brain drain for almost half a century. But until recently, there has been little empirical evidence to support or contradict these theories. The new evidence should counteract some of the myths and assuage some of the most common concerns about brain drain. Brain drain rates are not skyrocketing. Africa is not the most affected region for brain drain; small island states are. Most skilled migrants are not doctors. But neither are they taxi drivers – they enjoy massive increases in living standards as a result of migrating. The rise in skilled migration does not appear to be crowding out migration opportunities for unskilled migrants: instead, skilled and unskilled migration have increased together. Skilled migrants are remitting back about as much as the fiscal cost of their absence. Existing preliminary estimates of the production externalities of brain drain are quite small.

Yet we are still some way from a comprehensive global answer on the effect of brain drain on sending country growth and development outcomes, and further still from knowing the efficacy of policies chosen with high-skilled migration in mind. Data limitations continue to be a huge challenge to work in this area: there is a pressing need for better data which tracks the flows of high-skilled workers back and forward, as well as for specialized surveys to better understand the consequences of these movements (for discussion of these issues, see Commission on International Migration Data for Development Research and Policy, 2009). We have discussed eight key questions about
which were retained for round 2 with certainty, p1 would equal one. (Note that if whole EAs are selected lh for round 2, then p1 = ~~,~~ where _lh_ is the number of EAs sub-sampled in stratum _h_ and _Lh_ is the total Lh number of EAs in the round 1 sample in stratum _h_ .) If all round 1 households are included in the sample for round 2, then p1 is one for all households.

# _3) Probability of household selection into tracking_

In panel surveys, some households from round 1 will evolve into more than one household in round 2. That is, the set of household members from round 1 will no longer all be residing together by round 2. For the purposes of administration in the LSMS-ISA surveys, one of the round 2 households will be

> 4 For the purposes of this note, ‘parent’ generally refers to the household found at the same location as the previous round of data collection, and ‘split-off’ refers to new households entering the sample through an individual originally resident in a parent household during a previous round. Note that this difference between ‘parent’ and ‘split’ is purely administrative.

> 5 For the simplification of notation the subscript _h_ , representing each stratum _h_ , is generally dropped in the subsequent formulas despite the fact that the LSMS-ISA surveys all have stratified designs.

3
_K,K_ tial competitive equilibrium is the period-by-period sequence of **L** _t_ , **w** _t_ , **b** _t_ , and **m** _t_ = � _m_<sup>_kl_</sup> 1 _,t_ � _k_ =1 _,l_ =1 which solve the labor mobility model in every period, conditional on the initial labor allocation **L** 0 and fundamental parameters of the model.

# **4 Estimation**

In this section, we describe how we structurally estimate the labor part of our model using RAIS from Brazil. The main objects to be estimated are the moving probabilities, between-market moving costs, and the inverse labor supply elasticity _ν_ which is identified by the empirical strategy described in Section 2. With these estimates, we turn to our variables of interest: changes in relative welfare, option values, and the number of job options due to trade shocks. In our reduced-form analysis, we did not study the effect of trade shocks on these variables, because they do not have direct data counterparts. Once we estimate our model, we can use the sufficient statistic result in equations (14) and (15). In other words, we can study the effect of trade shocks on the welfare-related variables in a reduced-form way, before doing full general equilibrium counterfactuals in the next section.

We restrict the sample to the labor markets where at least 100 workers move in and out respectively in every year of our sample period 2004-2015. Since the identification of moving probabilities is based on workers’ mobility, values of labor markets with little labor mobility cannot be identified. Unlike ACM or CDP, we include corridors with zero mobility.<sup>38</sup> After this restriction, we have 804 labor markets in our sample.

## **4.1 Estimation of the Flows**

In practice, it is not possible to obtain _m_ �<sup>_kl_</sup>
2 a tax (Aronson, Johnson and Lambert 1994, Van de Ven, Creedy and Lambert 2001).<sup>1</sup> The change in the Gini coefficient is decomposed into three parts: a part capturing vertical redistribution (people at different points in the initial income distribution experiencing different proportionate changes in income), horizontal redistribution (initial equals experiencing inequalities in income after the growth), and a residual term capturing reranking between the two distributions. The decomposition shows that a near-zero change in the Gini index could have two quite different explanations: everyone experiencing similar proportionate changes in their income; and inequality-reducing vertical redistribution being offset by inequality-increasing horizontal redistribution and reranking. Knowing only that the Gini has not changed much without knowing which of these two explanations applies is problematic if policymakers are not indifferent between the two.

The decomposition method is applied in the paper to panel data from China and Vietnam. Both have experienced rapid income growth during a rapid transition from planned to market economy, China’s per capita GDP growing by an annual average 8.6% during the 1990s and Vietnam’s by 5.6%.<sup>2</sup> During these transitions, income inequality has increased in both countries (Glewwe 2003, Ravallion and Chen 2004). However, while these increases in income inequality are rightly a source of concern in both countries, it is noteworthy that income inequality has risen more slowly than per capita income in both countries: the Gini coefficient grew by 1.5% per year in China during the 1990s (Ravallion and Chen 2004) and by 1.4% per year in Vietnam between 1993 and 1998 (Glewwe 2003).<sup>3</sup> Furthermore, these annual increases in the Gini coefficient are small by comparison to those recorded in European transition economies: of the six economies

> 1  Loshkin and Ravallion (2004) use a different decomposition to decompose the changes in inequality associated with trade reform in Morocco.

> 2  Growth
reform (1978-2000) this trend continued, and the fall exceeded one-third. 25 Yao also estimated that, between 1953 and 1989, per capita arable land and crop area declined, respectively, by 2.08% and 1.69% per year. 26 Should more rural labor move out of the agricultural sector and more machinery be used in farming, China’s agricultural productivity could expect a major improvement. 27 However, considering the current industrial structure and land tenancy system, this would be a very difficult and slow process. Furthermore, based on 2000 data, China’s manufacturing labor productivity was only 92% of that in India and less than 5% of that in Brazil, France, Japan, and the United States (figure 2).

**Table 5.  Productivity Comparison in Agriculture, Industry, and Services (1999)**

<u>(Related GDP per person employed in industry, US$)</u>

||China|U.S.|U.K.|Germa<br>ny|Japan|Finland|Korea|Malays<br>ia|Brazil|Chile|Thailan<br>d|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|512|53657|62117|41332|25902|34835|10439|3733|3795|7059|885|
|Industry|2954|81077|60168|64556|80860|65977|32138|13534|19827|18244|8506|
|Services|1751|66494|48329|58205|64294|52199|16959|7365|13771|12349|5773|

Source: IMD, The World Competitiveness Yearbook 2000, Lausanne, Switzerland, pp461-463.

**Table 6. Cereal Yields (Selected Countries)**

<u>(kilograms
trative boundaries, we use the Database of Global Administrative Areas version 2.8.

# **4 Summary Statistics and Stylized Facts**

## **4.1 Summary Statistics**

Our main analysis focuses on a merged data set of nighttime light growth and GDP growth at quarterly frequency. It covers 117 countries from 2013Q2 to 2020Q4, including 33 advanced economies and 84 EMDEs. Table 1 presents the summary statistics. Across all countries, nighttime lights grew faster than GDP and both nighttime lights and GDP grew on average slower in AEs than in EMDEs. Nighttime light growth is an order of magnitude more volatile than GDP growth, as is clear from the much wider interquartile range of nighttime light growth rates. As can be seen from the 25th percentile, nighttime light growth can be negative even when GDP growth is moderately positive. This is likely a result of the remaining noise in nighttime lights dwarfing the positive contribution from GDP growth to nighttime light growth.

Table 1: Nighttime Lights and GDP Summary Statistics at Quarterly Frequency

||Mean|p10|p25|p50|p75|p90|
|---|---|---|---|---|---|---|
|AEs (33 countries)|||||||
|Night light growth|3.0|-15.6|-6.2|2.1|11.3|23.7|
|GDP growth|1.6|-1.9|0.8|2.1|3.3|4.5|
|Avg. nightly obs.|25.6|5.9|15.7|27.5|35.1|40.8|
|EMs (61 countries)|||||||
|Night light growth|6.7|-15.7|-4.1|5.0|16.9|33
# **B.2 The Uppsala Conflict Data Project**

The Monadic Conflict Onset and Incidence data set comes from the Uppsala Conflict Data Project (UCDP). In this paper, we rely on the 2013 version of the data set (Gleditsch, Wallensteen, Eriksson, Sollenberg, & Strand, 2002; Themn ́er & Wallensteen, 2014). The data set contains annual observations of all states in the international system between 1946 and 2013. The data is monadic—each observation corresponds to a country-year. It contains information on all internal and internationalized internal armed conflicts, where conflicts are located according to the government side in the Two versions of the data exist: one containing conflict level variables and one containing dyadic data. The difference between the two data sets is that the former treats all variables at UCDP conflict level, while the latter treats all variables at the UCDP conflict dyad level. For example, if there is one active UCDP conflict in a given country, the number of total conflicts in this country-year will be equal to 1 in the conflict level data set. However, in the dyad version of the data, if this conflict involves different actors, the data will instead report the number of actors involved in the UCDP conflict.

In this paper, we rely on the conflict level data set, which is at the country-year level. Importantly, we use information on the total number of active conflicts for a given country-year from the UCDP data set. The Monadic Conflict Onset and Incidence data also contains additional information on the following indicators, among others: the incidence of intrastate conflict, which is coded 1 in all country-years with at least one active conflict, the onset of intrastate conflict, which is coded 1 if the countryyear contains a new conflict, and the intensity level of the conflict in the country-year: minor conflict ( _>_ 25 deaths) versus war ( _>_ 1,000 deaths).

67
|47|49|50|48|
|Share of informal workers|67|65|79|64|63|60|59|54|54|51|49|48|46|48|
|Share of self-employed|4|5|5|5|4|4|4|4|4|4|3|3|3|4|
|Paraguay|||||||||||||||
|Share of formal workers|7|6|7|9|7|9|10|11|11|12|13|14|13|13|
|Share of informal workers|17|18|20|21|21|20|20|20|18|20|20|20|20|20|
|Share of self-employed|77|76|73|70|72|71|70|70|71|68|67|67|67|67|
|El Salvador|||||||||||||||
|Share of formal workers|34|34|32|35|33|34|36|34|34|33|34|36|37|38|
|Share of informal workers|32|31|35|33|36|33|32|33|34|35|35|33|35|30|
|Share of self-employed|34|35|33|33|31|33|31|33|32|32|31|30|29|32|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : The shares shown in this table were calculated based on total
the future, we hope to use a combination of importer country and exporter country CPIs. Final results are normalized to US dollars (at September of 2009 exchange rates). Note that real exchange rates fluctuated even within the European Union, so we use country-specific CPIs there as well (except in figures 1a and 1b, which uses a EU27-wide CPI). Nominal exchange rates and CPI data are taken from Eurostat for the EU countries and from Global Financial Data for Brazil and Indonesia. CPI data for the US is from the Bureau of Labor Statistics.

Very rarely, observations on value are missing. More commonly, data on quantity are missing. Both, of course, affect our ability to calculate unit-values. The US, EU, and Brazil all report a main quantity variable and a supplemental quantity variable. We use the main quantity variable where possible, and the supplemental quantity variable where the main measure is not reported.

To maximize data reliability, only trade as reported by the importer country is used. European Union import data are collected by Eurostat and was obtained through Tradewatch. United States data are from the US International Trade Commission (USITC) and was also obtained through Tradewatch. Brazilian data was obtained from the Ministry of Development, Industry, and International Trade (MDIC), and Indonesian data from BPS-Statistics Indonesia (BPS). For all four datasets, monthly data was aggregated into quarters. 8-digit and 10-digit tariff line data are aggregated into 6-digit Harmonized System product codes. All four datasets report trade with both other nations and some territories (e.g. Taiwan, Guam, etc.). Generally we report results with these territories included in the data as separate units of observation. An exception is the section on partner income, where we only include nations and a few large territories (e.g. Taiwan). Territories represent a very small fraction of world trade, and accordingly should not influence the results.

9
interpretation of results.

_Scale of roads_ . The scale of the source data (that is, the level of detail) for roads is unknown, but there are obvious differences between the countries and in comparison with large-scale data sets such as Open Street Map. The smaller the scale, the less likely the features in the geographic information system (GIS) data will accurately represent the geometry of the roads on the ground. As a result, the total kilometers calculated based on the GIS data will deviate, to some degree, from the officially reported statistics. In figure 11 below, scale makes a big difference in the calculated sinuosity index value.

_Spatial resolution of the elevation model_ . The spatial resolution of the elevation model also presents a challenge, particularly with respect to the average width of roads. Figure 11 below shows a road drawn with a width of 7 meters, approximately one-third of the size of an elevation cell. In this case, extracted z- values will be affected by roadside features, particularly in areas of high relief.

26
speed data of historic hurricanes and tropical storms in Central America. This novel windstorm hazard model for Central America has been developed by the WBG Latin‐American and the Caribbean Disaster Risk Management (DRM) team as part of the development of Country Disaster Risk Profiles.<sup>6</sup> The windstorm hazard model, described in more detail in Pita et al. (2015), is a wind field model to estimate surface gust wind speeds and a mechanism to generate synthetic events and trajectories. One of the key outputs of the model is the generation of maximum wind speeds along the trajectory of the hurricane. Wind speeds refer to the tangential wind component of the hurricane and are directly related to the intensity of the event. This model has been tested, calibrated, and validated with observed activity in the East Pacific and North Atlantic basins for the Central America region. The model’s forecasts conform acceptably well with the observed wind speeds, which guarantees the accuracy of the data used.

The wind speed model used is grounded in the asymmetric Holland equation, which is characterized by its simplicity (i.e. the number of model parameters) and its accuracy (i.e. the coincidence of model’s predictions with observed measurements).

The model reproduces the geographical distribution of the tangential gradient wind speeds, _Vg,_ or wind gusts at a height of 10 meters over a 1km<sup>2</sup> grid, using the Holland model as represented in Equation 1. The wind speed is measured at this height because it is here that wind speeds do the most damage.

Where: Δ� is the deficit of central and outer peripheral pressure (in Pascals); � is the air density at the gradient height (kg/m3); ��� is the radius of maximum winds (in meters); � is the radius of any location to the center of the storm (in meters); �� is the hurricane translation speed (in meters
module in a broader household survey is that it allows for an analysis of the correlates of exposure to and ability to cope with risks. In the case of this paper, for instance, we look in detail at the correlation between household financial wellbeing and ability to deal with disasters.

In a review of the literature on the relationship between poverty and disasters, Hallegatte et al. (2020) discuss that poor people are more often affected by natural disasters (which they call exposure bias), lose more when hit by a disaster (vulnerability bias), and are less able to cope with disasters. The findings presented in our paper are in accordance with these patterns, as we observe an income gradient in ability to cope with shocks, underlining the conclusion of 16