rafmacalaba/data-use-mentions-extended huggingface dataset:
We report error terms robust to individual heteroskedasticity, i.e. clustered at the individual level, since treatment was randomized at the individual level. We present standard p-values from regression estimations, but also rely on p-values from randomization inference tests to account for the relatively small sample size (Young, 2019). We account for multiple inference by calculating q-values for coefficients of interest within pre-specified families of outcome variables, limiting the false discovery rate (FDR) using the method proposed by Benjamini and Hochberg (1995). We report both uncorrected p-values and FDR-adjusted q-values for each regression.

The results presented below are all robust to inverse probability weighting to account for the very minor differential attrition by baseline characteristics as shown in Table 1; tables available on request. The results are also robust to a post-double LASSO procedure allowing selection from all of the baseline covariates presented in Table 1; tables available on request.

# **6 Results**

## **6.1 Impacts on Credit Constraints**

Table 2 presents the treatment effect of access to psychometric-appraised loans on women entrepreneurs’ access to credit. Panel A, Column 1 uses the MFI Wasasa’s administrative data to confirm that the MFI managed near-perfect compliance with the randomized assignment. Just 3% (two respondents) of the control group received a WEDP-X loan due to an administrative error, while two treatment-group respondents did not receive the WEDP-X loan (the administrative data does not distinguish whether this was an administrative error or the borrower declining the final offer). Altogether, treated women were 93 percentage points more likely to have received the uncollateralized loan product than the control group. Three control-group respondents eventually accessed regular, collateralized WEDP loans at Wasasa, compared to zero in the control group; thus treatment leads at most to very minor substitution into uncollateralized borrowing at Wasasa from clients who might have gone 12
integrate into the region’s educational sector or the extent to which they benefit from public services.

In this paper we analyze the educational transitions of young forced migrants in Chile. We examine students’ educational trajectories and the institutional roadblocks that could impede their transition to higher education. We use data from Chile’s Ministry of Education (2017–2020) to identify such roadblocks to access and advancement. We also employ a regression discontinuity design (RDD) to compare access to financial resources for higher education between migrants and non-migrants.

Our results show that offering financial aid effectively increases access to higher education, but there are significant gaps between native-born and migrant (and especially forced migrant) students. We also identify gaps between students in general education versus vocational schools. Moreover, and rather counterintuitively, financial aid has a greater impact on migrants in general education schools than their native counterparts.

These results advance the literature on migrants’ educational outcomes in at least three important ways. First, in addition to confirming prior national and international findings regarding the widening gap in educational enrollment between natives and migrants, we identify forced migrants as an even more vulnerable group. Second, our results complement the literature on forced migrants in continental Europe by focusing on a developing country to identify some of the problems developing nations have when accepting migrants into their already crowded public education systems. Third, we highlight the importance of information and access to financial resources in helping migrant students access higher education.

This paper is part of a World Bank series that analyzes the educational and labor market integration of migrants, forced migrants, and refugees in various countries and its effects on different forms of inequalities between groups. For instance, for the Malian case, (Foltz and Shibuya, 2021) analyzes the results of a household survey and concludes that an influx of internally displaced persons (IDPs) is associated with ’non-decreasing’ wealth and is uncorrelated with inequality and poverty. Similarly, in Switzerland, (Mullera et al., 2021) analyze the labor market
duties and financial transaction taxes in Argentina exhibit a negative correlation with the economic cycle. Historically, export duties were initially implemented as emergency measures to address the inflationary impact of significant peso depreciation, typically during balance of payment crises. In line with this, export duties were reintroduced in 2002 following a sharp devaluation of the peso. However, unlike previous crisis episodes, export duties have remained a regular source of tax revenue even after the recovery from the 2002 crisis, persisting for more than two decades. The structure of export duties is complex, with varying tax rates for each product or sub-product. Notably, export duties differ across sectors and even within agricultural products. For the purpose of this study, an export tax rate index was constructed, assigning weights to each product based on its contribution to total exports. **Figure 15** illustrates significant changes in export duties over the years. These fluctuations in tax rates demonstrate a negative correlation with real GDP (-0.26), suggesting a procyclical behavior ( **Figure 17** ). However, the correlation is weak and not statistically significant. Similarly, the financial transactions tax was initially introduced as an emergency tax during the 2001-2002 crisis but has since become a regular tax with a constant rate of 1.2 percent. With minimal changes since its implementation, the correlation with the economic cycle is very weak, albeit negative (-0.12), as it was introduced during the economic recession of 1999-2002.
value of exports as a share of GDP in 2021. The export data is taken from the UN-Comtrade Database (UN-CT), which provides information on export value for all 11 commodities and all 56 countries, with a time series that usually starts in 2002.

As in the standard LTGM, the initial stock of capital-to-GDP ratio of 3.6 is calculated using the most recent observation from PWT 10.01. The initial capital stock is split across activities to equalize the initial marginal revenue product of capital across the copper and non-resource sectors.<sup>8</sup> In the resource sector, the reserves are calibrated using the initial stock of reserves in industry is taken from the U.S. Geological Survey Database (USGS) for mining industries, such as copper, gold, and iron. As a stock variable, initial ii reserves are set to match the most recent observation (usually 2017).

The model requires the paths of exogenous variables from 2021 until 2050, and we assume that the variables will follow recent trends in the long-term. Discoveries of natural resources are calibrated using data on annual production and reserves of the resource good , taken from the USGS. The time series of discoveries of good in period is computed as the change in reserves from period to plus production in period . In the baseline, the trajectory of discoveries of good ii from 2021 to 2050 is set to ii tt tt− 1 tt match the historical average over the past 15 years of 6.5 million metric tons (mt). tt ii The trajectory of copper prices is from Chile’s Advisory Committee projections for the Copper Reference Price 2023, which is a survey of 20 experts that publishes their expected prices of copper until 2032. We use the average of the 20 responses and convert it to real prices of 2015 in US$/mt. For the trajectory of prices between 2032 and 2050 we assume copper prices are constant at their 2032 value.

> 7 N P × G /G 0 where
debate here beyond stating that our findings are in line with those found elsewhere."<sup>8</sup> The most robust finding is that of a band across the North-Western States of India (and which extends into several provinces in Pakistan) in which there are large disparities in child mortality rates. The high gender disparities in Northern India and Pakistan have been pointed out before, perhaps most notably in Miller (1982, 1989, 1993), Murthi et al (1995), and Agarwal (1997) in which the focus was on juvenile sex ratios. Moreover, Miller (1982) points out the persistence of the disparity, going back to evidence from the 1872 census. Miller (1982) attributes the mortality differential to the relative neglect of girls in the allocation of food, medical care, and "love and walmth." Others have focused more specifically on the role of the quantity or quality of medical care (e.g. Das Gupta, 1989, Wyon and Gordon, 1971). In our results, however, there is a puzzle as to how these differentials come about: although we find that female disadvantage in absence of health care is higher in South Asia than elsewhere, neither differences in absence of health care nor differences in nutritional status (as reflected by wasting and stunting) show nearly as pronounced a band across the Northern States as mortality.<sup>19</sup> Our finding that gender disparities are not systematically decreasing with income is also in line with other studies (see Murthi et al, 1995, for a description of this debate), although the result is perhaps not surprising given that gender disparities are observed to be A useful summary of the claims and counter-claims and an assessment of these can be found in Murthi et al (1995).

9 As pointed out before, the latter of these non-findings might be due to the fact that one is essentially looking at the tails of distributions of which may be hard to get reliable estimates.
or mortality. Counter-intuitively, it can be observed that samples sometimes increase over time. For example, there are 1,668,342 migrants arriving in the 1980s according to 1990 census and they increase to 1,704,575 in 2000 census. The biggest increase in cohort size occurs between Census 1980 and Census 1990 for the 1970s arrivals (22,893 individuals). This may be due to measurement errors, including earlier undercounting of undocumented migrants and methodological differences across censuses. These issues will be detailed below. Despite the upward bias, the relative constancy in the shares of migrants by half-decade of arrival, as well as the stable proportion of the highly educated individuals (4+ years of college) across samples are positive signs that the data capture the same people over time.

The US Censuses are among the richest databases available. However, as mentioned before, they have caveats. First, it is impossible to follow the exact same individual through

> 15 Because variable “years since migration” is identified by half decades, the 1975 and 1985 arrivals refer to intervals 1975-1979 and 1985-1989 respectively.

> 16 To ensure readability, only the 80 most important sending countries are displayed in graphs and used as basis for the regressions of predicted indices on country-specific variables.  These 80 countries represent 97% of the male immigrants who were in the US as of Census 1990, and meet the criteria by which we constructed this paper’s dataset: employed, not living in group quarters, not in school, having entered the US in the 1970s or the 1980s.

10
sets. The statistical matching techniques have been used by Robayo-Abril and Rude (2024) in the Romanian context.

**The data sources used in this research do not share common identifiers.** Therefore, we cannot apply record linkage and must use statistical matching techniques. We imputed tax income in the EU-SILC data set based on common characteristics. In tax data, employees' gross salary is reported by their employers. Our income concept is total gross labor income. If more than one employer employs one individual, we sum up the income from all employment contracts. The EU-SILC captures various income sources and distinguishes labor and non-labor income. In the EU-SILC data, we also use total gross labor income as our income concept. Both tax data and EU-SILC record the information on gender, year of birth, and region,<sup>8</sup> sector of the economy,<sup>9</sup> and employment status (full-time vs. part-time).   Although the list of the common variables is not long, it is not necessarily a limitation. Too many matching variables can generate unnecessary noise (Donatiello et al., 2016b).

**Among data fusion approaches, we utilize model-based methodologies.** Following the approach of Bacher and Prander (2018), we first estimate an empirical functional relationship between X and Z using the donor dataset, which in our case is the tax administrative data, as follows: The functional form of depends on the population parameters XX= ff(ZZ; θθ; δδ) and the imprecision with which Z might have been measured, denoted as An example of such a functional form could be a ff(ZZ; θθ; δδ) θθ linear regression: δδ. 0 1 1 pp pp XX= ββ + ββ ZZ + ⋯+ ββ ZZ + δδ

8 In the EU-SILC , the information is representative at the NUTS-2 level, while in the tax data, the information is at the NUTS3 level. More detailed classification in tax data
# **1. Introduction**

Trade – and exports in particular – play a crucial role in supporting jobs and increasing labor earnings, consistent with the fact that 90% of jobs in the developing world are associated with private sector activities (World Bank, 2012). This role of exports is particularly important as jobs and labor earnings are key drivers of poverty reduction and increased shared prosperity in many developing countries (Azevedo et al., 2013). Indeed export growth played a significant role in the dramatic reduction in poverty rates in East Asian countries in recent decades.<sup>2</sup> Despite this, systematic data on the contribution of exports to employment and labor earnings across countries and sectors is not readily available, particularly for developing countries. How much labor income is supported by automotive exports in South Africa? How much of that is via linkages to sectors providing inputs for automotive? How many domestic jobs does $1 million of processed food exports support in Brazil? Is that higher than in comparator countries? Answers to such questions are typically not readily available. Yet this information matters. First, such information would provide some reference to the relative importance of exports for employment and wages in specific countries and sectors. Second, it would shed light on the extent to which the interconnections between sectors matter for jobs and wages supported by exports. However for the majority of governments in developing countries today, it is difficult to know how many jobs and what size of earnings their countries’ exports support domestically, let alone the contribution of individual export sectors.

This paper helps fill this gap by computing the domestic value of labor (i.e. total wages) and the number of jobs contained in exports for a large sample of developed and developing countries and sectors for intermittent years between 1995 and 2011.<sup>3</sup> The resulting Labor Content of Exports (LACEX) database, consisting of two data sets on the labor value added and the jobs content of exports
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.6. Comparative port performance across African regions**

||**East Africa**|Southern Africa|West Africa|Global best<br>practice|
|---|---|---|---|---|
|**Performance**|||||
|Container Dwell Time (days)|**5–28**|4–8|11–30|<7|
|Truck Processing Time (hours)|**4–24**|2–12|6–24|1|
|Crane Productivity (containers per hour)|**8–20**|8–22|7–20|20–30|
|Crane Productivity (tonnes per hour)|**8–25**|10–25|7–15|>30|
|**Charges**|||||
|Container handling (US$ per TEU)|**135–275**|110–243|100–320|80–150|
|General cargo handling charge (US$ per tonne)|**6–15**|11–15|8–15|7–9|

Source: AICD ports database.

Performance of individual ports in East Africa varies (table 2.7). Mombasa (Kenya) and Dar es Salaam (Tanzania) exhibit generally good performance that is within global best practices on some indicators. On the other hand, Port Sudan (Sudan) and Djibouti (Djibouti) exhibit much lower port efficiency levels. Container dwell times in Mombasa and Dar es Salaam are within global best practice benchmarks, but the dwell

**Table 2.7. Comparative performance across East African ports**

||Djibouti|Mombasa|Port<br>Sudan|Dar es<br>Salaam|
|-
# **2 Data and Methodology**

By way of background, zones and other cluster-based programs have steadily increased in South Asia. Starting from the first export processing zone (EPZ) in Gujarat in 1965, India has developed over 3,350 zones, according to the Ministry of Commerce and Industry. Bangladesh, Nepal, and Bhutan have all announced plans to establish and expand Special Economic Zones (SEZs), industrial districts and parks. The passage of SEZ Acts and Policies signal more to come (Galal, 2021).

However, despite the growing prevalence of these programs, a lack of data has obstructed their systematic assessment. This paper is able to do so thanks to a unique World Bank survey designed to provide detailed information on firms within and outside zones. The survey covered 1,201 firms inside different types of zones and 1,166 firms outside zones in Bangladesh, Bhutan, India, and Nepal. For data collection, the sample frame for firms outside zones was based on administrative data from government sources.<sup>1</sup> The sample frame for firms inside zones relied on a database collected from zone operators. The same process was followed in all four countries. Fieldwork was conducted between September 2020 and January 2021 and successfully surveyed 2,367 of the 2,400 target enterprises. Maps of the number and types of zones are presented in Figure 1. For the sake of simplification, the term zone is used in the rest of the paper as a generic umbrella to cover all zone types, but when necessary, the specific zone type will be mentioned explicitly.

Table 1 presents sample descriptives for firms inside and outside zones. The survey was designed to ensure that firms outside the zones were from the same districts as the zones and representative of their district. Within each district, on average 52% of firms were located inside zones. Most firms in the survey are located in India, followed by Bangladesh, Nepal, and Bhutan. The largest sectors represented among
10 While countries hosting a greater number of FDP tend to have a greater number of publicly available FDP datasets, some countries defy this overall pattern – having no FDP dataset despite a relatively large size of FDP they host. Figure 3 Panel A plots the relationship between the size of FDP (or the total of refugees, Venezuelan refugees and migrants, and IDPs combined) and the number of publicly available FDP datasets.<sup>27</sup> The upward slope seen in the plot indicates that overall there is a positive correlation between the number of FDP and the number of publicly available FDP datasets. Bangladesh, Cameroon, Kenya, Lebanon and Uganda top the list in terms of the number of publicly available FDP datasets. It is worth highlighting that there are a number of countries that, despite having a sizable number of refugees or IDPs, have no FDP dataset identified through this analysis. Notable cases include the Syrian Arab Republic, Türkiye, Yemen, Pakistan, and China where the number of FDP exceeds 1 million, but no publicly available FDP dataset is found in the MDLs.

FDP data is also relatively lacking among countries in fragile and conflict-affected situations (FCS). Indeed, the level of conflict intensity is negatively correlated with the number of publicly available FDP datasets, meaning that the more fragile and violent a country, the fewer datasets it has available in the MDLs. This pattern is certainly not unique to FDP microdata. For instance, comparable poverty measures over time are often lacking in FCS countries (World Bank 2021).

A confluence of factors explains data deprivation in FCS countries. To inform discussions about policy responses towards FDP in a given country, the surveys must be representative at least for the target population of interest to policy makers. However, a reliable sampling frame is often missing in such conflict situations where population census rarely takes place, and registration data may also be obsolete, thereby complicating the implementation of sample household survey (Aguilera et al. 2020). Furthermore, collecting data in
**Figure 1 Ranking of 36 SIDS in the ND-GAIN sub-index of Exposure to Climate Change**

<!-- Start of picture text --> 250<br>200 180 182 185 186 187 188 189 190 192<br>169 173<br>158<br>152<br>150 120 125 126 129 131 136 139 143<br>105 107<br>100 90 94<br>85<br>79 79<br>73<br>66 66<br>60<br>50 53 54<br>50 46<br>0<br>Dominica Grenada Cape Verde Saint Lucia Barbados Guyana Trinidad & Tobago St Vincent & Grenadines Bahrain Suriname Bahamas Saint Kitts and Nevis Haiti Dominican Republic Jamaica Fiji Comoros Antigua and Barbuda Sao Tome & Principe Samoa Mauritius Vanuatu Papua New Guinea Cuba Guinea-Bissau Timor-Leste Palau Tonga Solomon Islands Nauru Marshall Islands Seychelles Micronesia Kiribati Tuvalu Maldives<br><!-- End of picture text --> _<u>Note: There are a total of 192 countries ranked in this sub-index. Exposure captures the physical factors</u> external to the system that contribute to vulnerability. It is a component of vulnerability independent of the socio-economic context. A higher ranking denotes greater exposure._ _<u>Source: ND-GAIN</u>_ Although SIDS are a heterogeneous group of countries, many of them share common factors which expose them to the hazards of climate change. Most are located geographically in tropical cyclone regions. Many also have extensive low-lying coastlines. Most also have small undiversified economies heavily dependent on a single export sector, such as tourism. Small islands are unique in that almost all of their territory is exposed to a natural hazard and their small land area means that a single disaster can have a systemic impact.

The main type of climate-related natural disasters<sup>3</sup> which affect most SIDS—both in terms of incidence and especially in terms of the economic damage caused
significant effects, only emerge after the program ended. One possible explanation is that the program affected which types of workers subsidized firms kept vs. let go. That is, subsidized firms may have kept the workers with the most specialized and relevant skills in greater proportion than firms in ineligible industries. However, data from the ENOE do not support this argument. Instead, the data suggest that the employees who were laid off during the crisis were predominantly those with the lowest job tenure, who presumably have the lowest levels of job specific skills, both in eligible and ineligible industries.

Another potential explanation is that it was not the firing restrictions that allowed employment to recover faster after the crisis, but rather the subsidy funds. These funds may have provided liquidity for hiring back workers, thereby allowing firms to take advantage of market opportunities at a time when other funding sources were scarce. In fact, firms typically received the subsidy many months after they limited layoffs, and the time when funds started to be paid out corresponds to the time when employment in eligible industries started to increase again after its initial drop during the crisis.

The remainder of this paper is structured as follows: Section 2 describes the wage subsidy program; Section 3 the data and identification strategy; Section 4 the results and robustness checks; and Section 5 concludes.

3