rafmacalaba/data-use-mentions-extended huggingface dataset:
L. ∆Prefik ≡0, i.e., we assume there is no change in the lagged bilateral level of import protection.17 We also summarize the complete characterization of the key explanatory variable in Table 2.<sup>18</sup>

# **3.2 Econometric Methodology, IV Estimation, and Data**

The earlier literature has identified a number of econometric issues that arise when estimating models of the relationship between changes in levels of import protection that a country applies to PTA insiders and PTA outsiders. We briefly present them here; for a more in-depth discussion see Estevadeordal et al. (2008), as we essentially adopt their motivation and approach to dealing with these concerns. In particular, we focus on a subset of their 10 country sample and analyze the policies of Argentina and Brazil, the two largest MERCOSUR economies, over the period of 1990-2001.

First consider the measures of import protection. We lag ∆Prefik one year since the bilateral tariff component is scheduled by the terms of the MERCOSUR agreement, and thus predetermined relative to MFN tariffs (or TTBs). In addition, adopting a lag helps lower simultaneity bias. Second, we define the preferential tariff in industry _i_ in a given year as the minimum of the preferential tariff that the country applies on sector _i_ in that year against any of its MERCOSUR partners. Finally, in the estimation, we drop the observations for which the MFN tariffs are set at zero. If the MFN tariff is zero, the preferential tariff would also have to be set at zero and that may bias the results.

The preferential tariff data component in ∆Prefik comes from the MERCOSUR tariff schedules, which establish how tariffs are to be reduced over time in each country and product. The tariffs are aggregated as simple averages into roughly one hundred four-digit ISIC industries.<sup>19</sup> In the period preceding the year in which a country grants the first preference in a given sector, the preferential tariff 17 Implicitly
|Autonomy ‐ establishing student assessment|0.84|0.44|0.77|0.76|
|---|---|---|---|---|
|Autonomy ‐ determining courses content|0.73|0.41|0.29|0.32|
|Autonomy‐ decidingwhich courses are offered|0.73|0.49|0.11|0.15|

Notes: authors’ own computation with data from PISA. All means take into account PISA survey design (see Annex A for details) The selection of variables to be included in the estimation of the production function was restricted to those which were included in all four rounds of PISA. Some variables that were included in the four rounds were excluded from the estimation when 20 percent or more of the observations had missing values. The parameters of the production function were estimated with a variance-covariance matrix accounting for the additional variation in PISA results due to the plausible values methodology followed in the test design (see Annex A for details).<sup>9</sup> All the estimations and subsequent simulations are based on reading learning outcomes since it is the only subject area for which a 2000-2012 comparison is valid.

The estimated results are presented in Table 4. The proportion of explained variance by the production function (as captured by the R<sup>2</sup> ) is relatively high at around 40 percent, a level close to the one reported in Santos (2007). The model’s goodness of fit varies little across years. This might be the outcome of restricting the estimation of the production function to the same variables across surveys and using only those variables that have relatively low levels of missing values (less than 20 percent).

As expected, student characteristics and socioeconomic background variables are important determinants of student learning outcomes. In all four years, girls get significantly better reading results than boys, averaging close to
− industries performs better than an enclave a small pocket of firms. We measure the density of economic activities by (1) ln(no. firms relative to workers) = ln(no. formal firms / no. workers in formal firms), and (2) ln(population density).

The basic estimation results from decomposing the residuals of Table 5-2 (1) are reported in Table 7. The basic structure is that city growth residuals between _t_ and _(t-1)_ years are affected by city characteristics in year _(t-1)_ . However, when data in year _(t-1)_ are not available, we use the city characteristics in year _t_ assuming long-lasting persistence of city characteristics across years. In any case, the estimation result should be interpreted as associations of contemporary variables rather than a causal relationship. We find that population growth is higher in cities with better enforcement of land use and zoning laws – the estimates suggest that city growth is associated with increases in the percentage of city population under land zone laws.<sup>15</sup> However, we do not find any statistically significant association between city growth and existence of laws to collect IPTU (property tax). This is most likely because there is almost no variation in the IPTU collection data – most cities have laws to collect the property tax. A richer set of inter industry linkages is also associated with growth – the OLS coefficient for the number of (formal) firms relative to (formal) workers is statistically significant and has the expected sign. A higher number of firms relative to workers stimulate competition and collaboration among firms and workers in a city, and is associated with higher city growth.

> based industrial system. The Route 128 region, in contrast, is dominated by autarkic (self-sufficient) corporations that internalize a wide range of productive activities. She concluded that this difference in regional socio-economic structure accounts for the divergent prosperity of two regional economies, in spite of their common origins in
land records and land registration (and the integration of the two), and institutional reforms in land settlement and adjudication. Some of these are considered in greater detail in the Orissa study (Mearns and Sinha 1998). A One-Man Committee on Records of Rights in Land under the Indian Planning Commission has proposed that a pilot land title registration program be established in one or two districts of selected states, beginning with areas in which revisional surveys have recently been conducted and in which records of rights in land are therefore more up-to-date (Wadhwa 1989). This proposal has met with widespread written support from leading judges, Chief Justices, academic and practicing lawyers, former ministers of finance, revenue, and urban development, planning commissioners, and managing directors of housing and urban development corporations<sup>3</sup> '. The major components of such a pilot program would include the amendment of legislation, provision for a responsible public agency, and appropriate institutional strengthening. Other types of legal reform may need to be considered as preconditions, particularly where legislative restrictions on land transactions are unusually cumbersome. Under this proposal, the current, 'presumptive' titles (based on revenue records) would be registered as conclusive titles during revisional surveys. It is recommended that the process start with undisputed titles, and that land settlement be conducted systematically from village to village within a given district. Given the expected volume of disputes, one possibility may be to establish special courts for land adjudication, to relieve some of the existing burden on civil courts.

Little practical progress has been achieved in implementing this proposal to date, however. The Chief Minister of Bihar is reported to have attempted to introduce title registration but failed owing to opposition from rural landlords who feared loss of land to tenants. A former 31 File of correspondence held by Professor D C Wadhwa.

31
|No earners|64%|29%|63%|51%|40%|50%|
|---|---|---|---|---|---|---|
|Remittance recipients|||||||
|only|19%|4%|16%|8%|7%|8%|
|Female single<br>earner|47%|15%|41%|52%|12%|47%|
|Male single earner|54%|27%|44%|23%|8%|19%|
|Majority female earners|66%|1%|43%|57%|3%|50%|
|Equal contribution|46%|6%|27%|24%|10%|20%|
|Majority male earners|41%|4%|16%|16%|5%|13%|
|Panel B. N.E Nigeria|||||||

|Panel B. N.E Nigeria|%<br>|of monetary po<br>|or<br>|% of m<br>|ultidimensiona<br>|l poor<br>|
|---|---|---|---|---|---|---|
||IDPs|Non-IDPs|All|IDPs|Non-IDPs|All|
|Female hhh|91%|91%|91%|28%|8%|14%|
|Male hhh|90%|76%|80%|21%|22%|22%|
|De jure female hh|99%|77%|96%|32%|9%|20%|
|De facto female hh|85%|80%|89%|
- Ray, D. (1998). Development Economics. Pricneton University Press.

- Redding, S. and Turner, M. (2015). Transportation Costs and the Spatial Organization of Economic Activity. pages 1339�1398.

- Robinson, R. and Thagensen, B., editors (2004). Road Engineering for Development. Spon Press: Taylor and Francis Group.

- Singh, R., editor (1995). Grand Trunk Road: A Passage Through Time. Aperture Books.

- Sokolo�, K. L. and Engerman, S. L. (2000). Institutions, Factor Endowments, and Paths of Development in the New World. Journal of Economic Perspectives, 14(3):217�232.

- Todaro, M. and Smith, S. (2015). Economic Development. Pearson.

- van Maarseveen, R. (2020). The urban-rural education gap: do cities indeed make us smarter? Journal of Economic Geography, 21(5):683�714.

- Vanneman, R. and Barnes, D. (2000). Indian district data, 1961-1991: machinereadable data �le and codebook. Center on Population, Gender, and Social Inequality. University of Maryland.

- Verdin, K. L., Godt, J. W., Funk, C., Pedreros, D., Worstell, B., and Verdin, J. (2007). Development of a global slope dataset for estimation of landslide occurrence resulting from earthquakes. Technical report.

- World Bank (2018). The Web of Transportation Corridors in South Asia. Technical report, World Bank Group, Washington DC.

30
.

## **3.1 The High-Frequency Phone Surveys**

One of the reactions to understand the impact of the pandemic was to collect new data, given that regular national household surveys were not well-suited to deal with this novel situation. One of the more ambitious initiatives was led by the World Bank, which implemented or supported several waves of high-frequency phone household surveys in over 100 countries, and harmonized the results. Thirteen Latin American countries participated in the survey: Argentina, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Peru. These countries represent around 60 percent of the region’s population.

The HFPS have a panel structure over three rounds conducted between May and August 2020.<sup>2</sup> The surveys collect information on multiple dimensions, such as changes

> 2The first round was conducted between May 8 and June 14, 2020, the second round from June 5 until July 16, and the third round from July 5 until August 25, 2020. In Ecuador there was a fourth round, which was collected between August 15 and 25, 2020.

5
, Morduch and Sicular (2000), Parish, Zhe and Li (1995) and Parish and Michelson (1996). 3Morduch and Sicular (2000) provide one exception among these studies in that they use a longitudinal data set (1990 to 1993), albeit from one county in Shandong province, to show that, after controlling for time-invariant unobservable factors, village cadre households earned approximately 20 percent more than non-cadre households.

3
the characteristics of the manager of the firm and

> 1Business administration corresponds to human resources, finance, and accounting. Appendix A lists each technology associated with each GBF in the FAT survey. See also ( _17_ ).

> 2The list of key business functions and technologies associated with each of them were developed in collaboration of over 50 sector and technology experts. The FAT survey follows several procedures to minimize bias and measurement errors, both in its design and implementation. Moreover, several ex-post robustness checks for Brazil, Senegal, and Vietnam, including validation of the technology sophistication measures with external data are described by ( _17_ ). Data were collected through face-to-face interviews with top managers. The survey was implemented by the General Statistics Office (GSO) in Vietnam, by the State Industry Association (FIEC), in Cear ́a-Brazil, and by Kantar-public, in Senegal.

5
with which the EU had signed PTAs increased UK trade by 42 percent –not differently from other countries.

The data in WIOD allow to split total trade into trade in goods and trade in services. Columns 2 and 3 report the coefficients of depth and the interaction with the UK dummy for goods and services

> 8 Since trade data from WIOD are limited to the 1995‐2011 period, the deepest agreement in our sample is the EC

> (27) Enlargement with 41 provisions.

14
it respects and protects their religious, ethnic, economic, linguistic, and other rights, including gender and family rights, personal freedoms, and freedoms of the press, belief, and association. The _Survey_ divides the world into three broad categories: "Free" (countries whose ratings average 1-3); "Partly Free" (countries whose ratings average 3-5.5); and "Not Free" (countries whose ratings average 5.5-7). {See Karatnycky, Adrian (1999)}

**Figure 3 Country Classification: Political Rights and Financial Openness**

|||Financial<br>Low|Openness<br>High|
|---|---|---|---|
||Low<br>Low|~<br>23<br>(8.86)|9<br>(9.09)|
|Democracy||||
||High<br>High|<br>32<br>(18.24)|37<br>_(25.59)_|

Drawing on the above measures of democracy and financial openness, Figure 3 provides a 2 by 2 classification of sample countries along the democracy and financial openness axis. The figure shows the number of countries in each category, as well as the mean value for the countries in each quadrant of average government expenditures on health, education and transfer payments as a percentage of GDP for the period 1991-1997 (in parenthesis). A high level democracy means _democracy index >_ 0.6. A "High" level of financial openness is defined by a score of >1.6 in the Financial Openness Index.<sup>3 0</sup> From the matrix, it could be argued that the difference in means between the two groups (High-Democracy & High Financial Openness group versus Low Democracy &-Low Financial Openness) was highly significant at 0.1% level.

Table 2 reports the
participation in government programs. Individual data consist of information on demographics, education, employment, health, and migration. It should be noted that the VHLSS contain data on the year and month but not the full date of birth of individuals.<sup>4</sup>

# **<mark>3. Childcare systems and descriptive analysis</mark>**

# _3.1. Childcare in Vietnam_

In Vietnam, kindergartens are generally available for children age 3 to 5, but some <mark>kindergartens also admit children age 18 months or older.</mark> Vietnam’s law on universal primary education require that children 6 years old and older attend a primary school, with the exception of children with health problems or those living in isolated areas. It should be noted that the admission age for school entrance in Vietnam reflects a child’s current age rather than completed age. Following this practice, hereafter, when we discuss a child’s age for admission to childcare, we refer to running age rather than completed age.

Children under the age of three can attend early childcare centers, but access to early childhood care centers remains limited in Vietnam, with only 26% of villages in rural Vietnam providing such centers.<sup>5</sup> Kindergartens are more widely available, with 49% of villages having at least a kindergarten. The VHLSS does not collect data on the availability of childcare centers for urban areas. However, using individual-level data, we can compute the proportion of urban and rural children attending childcare centers and kindergartens. In 2016, 44% of urban children age below 6 attended childcare centers and kindergartens, while this rate for rural children was lower at 35%.

In Vietnam, children are entitled to a place in a kindergarten from the age of 3 through just before attending primary school. School, including kindergarten, in the country starts from September each year. Thus, for example, if a child was born in 2000 (
# I. INTRODUCTION

A large number of developing count-ies rely heavily on one or two primary commodity exports as an important source of national income and foreign exchange. As Table 1 shows, the share of primary commodities (including fuels, minerals, and metals) in tot.. exports of all developing countries was 60 percent in 1985. Although this ratio has fallen gradually from 80 percent in 1965 to its present level, primary commodity exports continue to loom large in the export activity of low-income and middle-income LDCs.

In virtually all of these countries, commodity market developments have important effects on the government budget. Not only do trade taxes generate a large fraction of total government revenue. In many countries the government has a direct ownership stake in the production of primary commodities, especially in the cases of fuels, metals and minerals. In others it benefits indirectly through its role as an international marketing agent for private producers, because it passes on less than 100 percent of the world price to producers.

The booming world commodity markets of the 1970s and the post-war lows for real commodity prices in the mid-1980s have created complex economic management problems for commodity exporters. 1/ The experiences of commodity-exporting LDCs during the 1970s have made it abundantly clear that I/ In July 1987, the IMF's index of 39 primary commodity prices hit its lowest level in the post World War II period (IMF Survey 16, 14 (July 13, 1987), p. 209). In the third qi'arter of 1987, some commodity prices, notably those for metals and agricultural raw materials, moved upward sharply, contributing to a general improvement in the index before the October 1987 stock market collapse impacted the commodity markets.

1
of the DHS (the same data source as used in Section 3) is used to construct measures of education and Christian religion. The DHS asks respondents (women between the ages of 15 and 49) about their highest level of education achieved: no education, primary, secondary, or tertiary. Three measures of education at the cell level are constructed: the average level of education, the fraction of respondents with at least primary education, and the fraction of respondents

> 33This is the same logic used when controlling for present-day population.

37
estimated from these regressions. Second, we calculate for each cell the variance of the residuals of the first-stage household-level regression. Third, we regress the residual variance on cohort dummies (created by crossing household head date of birth, education and location dummies) and a polynomial function of age (generally of two degrees or more if statistically significant). From the age profile of the residual variance, we calculate the slope which is an estimate of the variance of asset shocks. Note that this slope should necessary be positive (i.e. the amplitude of shocks grows with age) since the estimated variance should always be positive. This is generally the case. However, when it is not, contiguous cells have been regrouped for the estimates. Finally, once the variance of shocks is estimated for each cohort then the last estimation step consists in drawing values of shocks within the standard normal distribution and estimating the household vulnerability index using coefficient estimates.

Poverty and vulnerability rates are reported in Table 8 where a household is considered as poor when its asset index is below the 80<sup>th</sup> percentile of the 1995 distribution of asset index. An extremely poor household is one whose asset index is below the 40<sup>th</sup> percentile of the 1995 distribution of asset index. A household is considered as vulnerable if the probability to be poor or extremely poor is higher than 0.5, whereas it is considered as highly vulnerable for a probability higher than 0.8. At the national level, poverty headcount (71.5%) is not different from the estimated fraction of the population who is vulnerable to poverty. Moreover, 34.5% of the population is extremely poor, while is 34.7% vulnerable to extreme poverty. Whatever the threshold indeed, poverty and vulnerability do not appear very different from each other. If we look at non-poor people, 10.6% are vulnerable to poverty. Among the population that is vulnerable to poverty,
where betterpaid and better-educated workers were almost fully insulated from the shock.

We focus on migrant workers, who represent an under-examined category of workers within the literature that has analyzed the labor-markets impacts of the pandemic thus far. A handful of studies have looked at this group, finding that immigrant workers tended to be more exposed to the negative labor market consequences of the pandemic (Boeri et al. 2020; Borjas and Casidi, 2020; Fassani and Mazza, 2020a; Fasani and Mazza, 2020b; Yasenov, 2020). However, the drivers of increased exposure among migrant workers have yet to be analyzed in detail. In contrast to previous contributions, this paper provides a detailed descriptive analysis of the potential drivers of the higher economic and health risks faced by migrants during the pandemic. It also examines actual job losses that followed the pandemic by migrant status, in addition to ex-ante measures of vulnerability to the pandemic. We focus on migrant workers in the EU, one of the main destinations for immigrants; the region hosts some 60 million of the world’s estimated 272 million immigrant workers. By taking advantage of the harmonized labor-force data available for multiple destination countries in Europe, we examine potential drivers at a granular level, taking into account migrants’ personal characteristics, occupations, and origin countries.

The paper also contributes to a literature examining and comparing the occupational choices of immigrant and native workers, and the risk levels for the two groups that stem from these occupational choices. The literature has primarily focused on occupational hazards and has shown that immigrant workers are over-represented in occupations with higher rates of injuries and fatalities. This has been evidenced in the US context (Orrenius and Zavodny 2009; Hersch and

> 1 For evidence on the labor market effects of COVID-19 in developing economies, see for example Kamis et al. (2021) and Kugler et al. (2021).

4