rafmacalaba/data-use-mentions-extended huggingface dataset:
The obtained variable, now denoted _g_Statusst_ for each regions _s_ and year _t_ , becomes the dependent variable in our estimation framework, which we regress on the fraction of migrant workers in the working-age population in region _s_ and year _t_ (fst), together with region and time fixed effects. The baseline equation we estimate is given therefore by the following: S S s = γ s s (6) ss ss gg_S + αα + ττ + εε In this specification, _g_Status_ st is the adjusted measure of risk exposure among the working-age population of natives in NUTS2 region _s_ and year _t_ ; τt are the time fixed effects that account for common, time-varying technological parameters; αs are region fixed effects that account for variation due to time-invariant unobserved characteristics of the population in that region; and fst is the share of foreign workers in each region and year. Therefore, γ is the main parameter of interest in the estimation. From the EU Labour Force Survey, our sample consists of 123 NUTS regions in 14 EU countries observed with yearly frequency from 2011 to 2018.

Estimating Equation 6 using a standard OLS approach can raise endogeneity concerns, even after including fixed effects to control for unobserved regional characteristics and time patterns. In particular, OLS estimates for γ may be inconsistent if the share of immigrants in EU regions is correlated with the unobservable term in equation 6 ( st|fst] ≠0). This might arise, for instance, when migration flows to a given region are driven by unexpected contemporaneous ii. ee. EE[ε region-specific shocks. Further, OLS estimates could suffer from reverse causality if migrants choose to locate in areas where natives are employed in occupations that complement migrants’ skills. These occupations, in turn, may differ in their degree of exposure to economic and health risks due to COVID-19. Similarly, unobserved technology and demand shocks, which may differ across EU regions due to sectoral variation, may simultaneously affect the productivity of (and demand for) certain occupations as well as the demand for
0.00252|-0.0032|||||
||(0.00264)||(0.00264)|(0.00291)|(0.00155)||(0.00153)|(0.00173)*|||||
|Share of hhlds with computer||-0.0317|-0.0257|-0.0341||-0.01|-0.00512|0.00498|||||
|||(0.0331)|(0.0372)|(0.0337)||(0.0234)|(0.0261)|(0.0234)|||||
|IDN exports per worker *||||0.0014||||-0.0011|||||
|Share of hhlds with computer||||(0.00176)||||(0.00112)|||||
|Observations|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|||||
|R-squared|0.976|0.976|0.976|0.976|0.875|0.877|0.874|0.874|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per
Policy Research Working Paper 10493

# **Abstract**

This study uses the 2020 Census to explore the determinants of interstate migration in Mexico between 2015 and 2020 and the earnings gains from migration. The study analyzes both spatial characteristics (push and pull factors in the origin and destination states) and individual factors that influence the decision to migrate and where to migrate. Push and pull factors are assessed using a gravity-type model. Individual factors are analyzed using a multinomial regression model that accounts for migration reasons. Subsequently, the study measures the impact of internal migration on labor income. Earnings gains are estimated using a double selection model that accounts simultaneously for the decisions to migrate and to work. Finally, the paper discusses some policy recommendations that could help leverage internal migration potential for improving women’s labor market outcomes.

This paper is a product of the Poverty and Equity 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 tsaavedrafacusse@worldbank.org and ginchauste@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 the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
, their estimation results show that the presence of children affects neither the likelihood of work nor its intensity for women.

However, there is also a scattering of papers in developing countries which show mixed or even positive effects of fertility on labor supply. Based on data from Demographic and Health Surveys covering 59 developing countries, Porter and King (2012) report that while many women in developing countries are less likely to work when they have more children, some are more likely to work due to greater financial costs of feeding more children. Bloom et al. (2009) combine data from Demographic and Health Surveys with abortion legislation laws in each country, and also find mixed results of fertility. There is also a micro study from Priebe (2010), who uses data from Indonesia and finds a positive effect of fertility on female labor supply, which is mostly driven by women which are poorer, less educated and who live in rural areas.

All these studies report LATEs that hold consistent results for alternative populations and compliers, and so do not provide external validity for inference in other populations. However, a more recent literature suggests that the negative effect could be observed in other contexts. External validity of the labor supply-fertility local average treatment effect is extensively examined by Dehejia, Pop-Eleches and Samii (2015) and Bisbee et al. (2015), who find that quasi-experimental evidence generalizes more readily to countries which share closer geographical, education, time and labor-force participation characteristics. There is also a novel strand of literature which documents how the impact of childbearing on female labor-force participa5
second row of Table 4 shows that Tongan households participating in the RSE were 10 to 11 percentage points more likely to have made a dwelling improvement over the two years of our surveys. This represents an almost doubling of the percentage of non-RSE households which made dwelling improvements over the same period (12 percent). Home improvements were the most commonly mentioned main use of money from the RSE in Vanuatu, with participants saying that they were using money earned to do things such as build a new house, install a solar panel, add an iron roof, or renovate their house. However, although the point estimates suggest that RSE households were 7-8 percentage points more likely to make a dwelling improvement, this effect is only marginally significant when using the binary categorization of RSE status. We do see a significant positive effect using duration in RSE. Dwelling improvements are reported to be much more commonplace in Vanuatu than in our Tongan survey, with 79 percent of nonRSE households making a dwelling improvement over the two years of our surveys.<sup>15</sup> The impact of the RSE may also then be for households to make more substantive improvements, such as the transition from traditional to modern dwellings, which our surveys don’t directly capture since dwelling type was only recorded at baseline.

In the baseline survey, 65 percent of Tongan RSE households reported having a bank account. By the fourth round this had increased to 83 percent. Row 4 of Table 4 shows this represents a statistically significant 10-14 percentage point increase relative to non-RSE households over the two year period. This increase in bank account usage likely reflects bank accounts being set up for the purpose of household savings, rather than bank accounts directly being used to receive remittances. For the Tongan sample, over 90 percent of remittances were made via Melei mei Langi (a Tongan-run money transfer operator) or Western Union, and only 1 household in our sample directly transferred money through a bank. In Vanuatu we find the share of RSE households with a bank account
# **III. Evolution from commodity discovery to production**

Resource discoveries matter to the economy only insofar as they can be developed into production. However, since 1950, less than 60 percent of gold, zinc and lead discoveries have made it to eventual production, and less than 40 percent of copper and nickel discoveries (Schodde, 2014). Once developed, the market value of discoveries can be large compared to the size of LIC and MIC economies. For copper mines, for example, production in 2014 alone accounted for 6 percent of LIC GDP and 2 percent of MIC GDP, on average (Figure 3).

Depending on the commodity and the size of the discovery, during the lead time between commodity recovery and extraction, countries can accumulate sizeable vulnerabilities as investment rises and external liabilities grow. In the data set used here, investment growth increased sharply in the five to ten years before actual extraction of the resource began (Figure 3). This effect was only apparent in low-income countries. Since their economies tend to be smaller than middle- and high-income countries, the development of a large mine can create significant domestic demand pressures. Using a global database on giant oil discoveries (those exceeding ultimately recoverable reserves of 500 million barrels), including in Africa, Arezki et al. (2015a) find that investment growth rises immediately upon discovery and current account deficits widen. GDP growth and private consumption growth respond only once extraction begins. The full increase in GDP growth materializes with commercial production, when vulnerabilities unwind as exports expand.

The size of vulnerabilities depends on three factors: how mine construction is financed, whether governments borrow in anticipation of rising commodity revenues in the future, and whether private consumption and investment rises in anticipation of rising incomes. If rising imports and current account deficits are financed by FDI, which tends to be less prone to sudden stops than debt financing, short-term vulnerabilities are more limited (Levchenko and Mauro 2008). Nevertheless, a sudden stop in FDI projects could also disrupt
Supreme Court verdicts was successful. We find no evidence of such a drop (column 4).

# **4.4 Mechanisms of policy transmission**

Having identified a strong link between the Ganga Pollution Cases and the local health of both infants (Tables 2 and 3) and rivers (Table 4), we now turn to the questions of how water pollution affects infant health and what that means for Indian environmental policy. Our strategy for answering these questions relies on an estimation of a dose-response function for water pollution and neonatal mortality. We use the merged sample of water pollution and neonatal mortality in this phase of analysis, which limits us to the 1986-2004 time period. To utilize our IV, we must further restrict our geographic focus to only those districts with non-missing upstream pollution measurements. Appendix Table A2 shows how the pool of available pollution monitors and their distance from upstream counterparts varies with our definition of upstream (which is detailed in Section 2). Appendix Table A3 summarizes the merged sample using our preferred upstream definition of [75 km, 200 km].<sup>13</sup> Finally, Appendix Table

> 13The 75-km lower bound minimizes the risk of off-river spatial correlation, while the 200-km upper bound excludes locations that are so far upstream as to not physically affect downstream pollution levels. We test the robustness of the

18
unemployment to female labor force participation. While the literature is vast, papers, studies and reports tend to build on one or just a few country-specific datasets. Cross-country studies on employment exist, but they often rely on national estimates or ILO model-based estimates, without scrutinizing the differences between the primary data on which these estimates are based.

An exception is the continuous World Bank effort to harmonize the surveys used in its _World Development Reports_ (WDR), starting from the 1990 WDR on _Poverty_ (World Bank 1990). More recently, Montenegro and Hirn (2009) constructed a harmonized dataset of a few labor market indicators using primary data from surveys – including 19 from South Asian countries – for the 2009 WDR on _Reshaping Economic Geography_ (World Bank 2009). Similarly, a global harmonized household survey dataset called the International Income Distribution Dataset (I2D2) was developed for the 2013 WDR on _Jobs_ (World Bank 2012, Gindling and Newhouse 2012).

The dataset presented and analyzed in this paper is built in a similar spirit. It was developed for the Spring 2018 edition of _South Asia Economic Focus_ (World Bank 2018a), which explored the relationship between growth and employment in the region. To our knowledge, our dataset is the only harmonization effort for South Asia that ensures full transparency and replicability by explaining the coding based on actual questionnaires. The resulting database, identified in what follows as the South Asia Employment Dataset, is available upon request.

# **2. Sources and indicators**

# _2.1. Available primary data sources_

South Asian countries have conducted close to 100 population censuses, household surveys and labor force surveys that include questions related to employment since 2001. Pakistan and Sri Lanka have the most frequent and easily accessible surveys. Pakistan has carried out the Pakistan Social and Living Standards Measurement (PSLM) survey and the Household Integrated Economic Survey every alternate year since 2004-05 and, except for a few years, the labor force survey (LFS) has been conducted on an annual basis between 2001 and
available interactive dashboard.<sup>5</sup> The dashboard and its underlying microdata, henceforth referred as the harmonized HFPS data, is the basis for much of the evidence presented in this paper. The paper draws on the findings of several recent publications, some of which are updated with additional data and/or complemented with additional analysis of harmonized HFPS data.

Since phone surveys like the HFPS cannot collect reliable data on income or consumption, the analysis relies on proxy indicators, such as self-reported job or income losses, missed meals (as a proxy for food insecurity), and so on. While the HFPS are designed to be nationally representative (using reweighting methods to adjust for differential response rates among subgroups of the population), several limitations arise because of the inherent nature of phone surveys in developing country setting.<sup>6</sup> Three sources of potential inaccuracies are particularly important. First, groups with limited network coverage or no access to phones, who are also likely to be among the poorest, are likely to be under-represented in the sample – a bias that may not be fully corrected by sample re-weighting that can be only done using observable characteristics of households. Second, individual level indicators, such as employment and unemployment, are likely to have been overestimated in the HFPS relative to the full population, since the respondents to phone surveys were household heads in most countries, who are more likely to be male and older (and if a woman, more likely to be working than the general population of women in a country).<sup>7</sup> Third, the share of missing values in indicators of interests is at times quite high, and regularly higher than in household surveys. Since the reweighting has not factored variable-specific missing values into account, if indicator values are not missing at random, estimates may be biased.

These biases suggest the need for caution in interpreting the findings from HFPS data. In particular, direct comparisons between employment and labor force
potential endogeneity of the value of exports and use a measure of global demand for each industrial product, excluding Indonesian products, as an instrument.

We combine four sources of data. First, we use the Indonesian National Labor Force Survey (Sakernas survey) from 2005 to 2015, which collects main labor indicators covering the 33 provinces of the country. With this information, we generate aggregate labor market indicators (labor force participation, employment and unemployment rates) for each province and year, and we construct the occupational structure of employment within each province, industry and year that we use as an input to construct the skills measures. We separate the analysis in two subperiods, 2005-2010 and 2011-2015, to account for the change in the weighting scheme introduced in the Sakernas surveys in 2011. These sub-periods differed greatly: 2005-2010 was characterized by a boom in commodity prices and a large increase in mineral exports, while 2011-2015 exhibited a decline in exports that was larger for mining and mineral products.

Second, we use the 2015-2016 STEP Survey from the Philippines to define five skills measures at the occupation level, following previous work such as Autor et al. (2003) and Acemoglu and Autor (2011). We focus on the Philippines due to the lack of direct information on the skill content of occupations in Indonesia and because the Philippines is a country from the East Asia region with a similar development level. We also construct skills measures using information from the 2004 U.S. O*Net database that we present as a robustness exercise. The skills measures include nonroutine analytical, non-routine interpersonal, non-routine manual, routine manual, and routine cognitive skills.

Data on the Indonesian value of goods exports (trade variable) and the global demand for foreign products excluding Indonesian products (instrumental variable) for 2005 to 2015 come from the UN Comtrade database. Finally, information on computer use at the household level (technology variable) from 2005 to 2015 comes from the Indonesian National Socio-Economic Survey (Susenas).

We formulate the following hypotheses on
# 1. Introduction

Cross-border trade plays a pivotal role in academic research on international development and the complexities of policy design. This importance stems from its ability to provide insight into economic interactions, dependencies, and the dynamics of global markets. The estimation of such cross-border activities primarily hinges on trade data, which provides a quantitative assessment of goods exchanged between countries. Accurate data informs import tariff setting, guides negotiations for trade agreements, and steers monetary decisions that impact interest rates and currency valuation. Furthermore, it influences industrial subsidies, infrastructure investments, environmental regulations, and even national security considerations. Given its importance, the increasing number of studies highlighting discrepancies in reported trade flows is concerning (Gaulie & Zignago, 2010; Carrère & Grigoriou, 2015; Fortanier, 2016; Shaar, 2019; Chen et al., 2022). Reporting discrepancies are often overlooked in the design of empirical studies of international trade, compromising the validity or robustness of the obtained results (Linsi & Mügge, 2019; Linsi, Burgoon, & Mügge, 2023). This is especially important because statistical results guiding policy interventions have been shown to be sensitive to changes in the underlying data (Bahmani-Oskooee, Hegerty & Harvey, 2013; Farhad et al., 2023). Consequently, any inaccuracy or inconsistency in trade data can distort or compromise the validity of research outcomes and the formulation of policy recommendations.

<mark>Evaluating the reliability and accuracy of trade data typically relies on the principle of dual recording of bilateral transactions. Ideally, trade transactions between countries should be recorded by both the reporting country and its trading partner, resulting in dual records.</mark> However, empirical trade data often lacks this dual recording, with many transactions having no corresponding mirror (Carrère & Grigoriou, 2015; Shaar, 2019; Chen et al., 2022). Furthermore, mirror records often report conflicting information regarding the value or quantity of the transaction, resulting in a reporting discrepancy. The absence of mirror records and the prevalence of reporting
Level|Share|
|||ofEU||ofEU||of EU||of EU||of EU|
|||Average||Average||Average||Average||Average|
|Greece|2414|42|4824|57|6662|62|7565|57|10930|65|
|Ireland|3918|69|5939|70|8056|75|10837|82|13550|81|
|Portugal|2188|38|3853|45|5723|53|8487|64|11970|71|
|Spain|3620|64|6813|80|8521|79|10802|82|13740|82|

Note: The EU Average is an unweighted mean of all current EU members.

Source: The GNP data for 1994 in PPP terms is from the 1996 World Development Report.

# **III. The Model**

_* Can we make reasonable guesses as to the time it will take CEE countries to converge to a certain threshold; say, for example, 75 percent of the EU average income per capita level?_ _* To what degree can government policy influence the time frame during which income gaps are bridged?_ _* What is the impact of different policy scenarios for an economy's average long-run growth rates?_ To answer these questions we develop and estimate a growth model for a large crosssection of countries, and we then apply the results to CEE countries. Following the above
# <u>Demographic categories</u>

Using the economic categories above, households should then be further classified by their care needs:<sup>23</sup>

1. without dependents and with dependents

2. total dependents 3. dependents disaggregated by age: for example, children 0 to 3 years old, children ages 4 to 12, adults over 75, and/or other age compositions relevant in a particular country context.

> 21. Additional contextual information is available in sociological, legal, and anthropological studies and in databases summarizing the institutional and legal structures that inform gender-based equity. See for example the Social Institutions and Gender Index, Afrobarometer, the World Values Survey, and the Women, Business and the Law databases.

> 22. In those countries where same sex marriages or consensual unions are recognized and such data exist, the types could also be male/male and female/female. Consensual unions also include polygamous structures.

> 23. In the first stage of work, country pilots classified households into separate economic and care categories. However, for fiscal incidence analysis, it is important to use a single combined classification.

14