rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure A1.4: Role of the Private Sector**

<!-- Start of picture text --> Not Consulted on Design<br>Consulted on Design<br>0% 20% 40% 60% 80% 100%<br>Private Sector Involved (No) Private Sector Involved (Yes)<br><!-- End of picture text -->

# **Annex 2: Hexagon-Level Database**

The methodology to create the hexagon-level database of nighttime lights and human settlements around transport corridors involves the following steps.

## **Step 1: Create a GIS layer of transportation corridors**

We use a tabular data set, which indicates the latitude and longitude coordinates of major nodes along the corridors of interest, to create a GIS layer representing the spatial characteristics of each corridor (including extent, form, and length). Each corridor is divided into multiple segments (figure A2.1). For each segment, we record the construction starting and end dates. The GIS layer of the corridors does not represent the exact path of each corridor along the land; it only includes segments of straight lines that connect the known nodes, a beginning and an end point of a project’s segment).

## **Step 2: Calculate buffer zones around each corridor**

We calculate a buffer layer spanning 200 kilometers around each corridor (100 kilometers on each side of the corridor). The buffer layer consists of 10 rings around each side of the corridor, in intervals of 10 kilometers (a total of 20 rings around each corridor) (figure A2.1).

## **Step 3: Create a hexagonal tessellation grid around each corridor**

We evaluate the wider economic benefits of transportation corridors by measuring changes in nighttime light luminosity around the corridors as a proxy for economic activity. To do this, rather than relying on the pixel as the unit of analysis, we calculate a Sum of Light (SOL
after therapy, GC announced and held a private draw for $200 grants among the full sample, in blocks of roughly 50 men. Finally, a third organization (Innovations for Poverty Action) ran endline surveys 2 and 5 weeks, and then 12 and 13 months, after grants.

The sample were very mistrustful of authority, and we randomized by individual draw rather than computerized assignment to maximize trust, transparency, and staff safety. Men in each block took turns drawing colored chips from a fabric bag.<sup>18</sup>

**Balance** This resulted in 25% assignment to cash only, 28% to therapy only, 25% to both, and 22% to neither (Table 2).<sup>19</sup> Treatment is balanced along covariates. Table 1 reports tests of balance for teach treatment, for selected covariates (see Appendix A.1 for all, and for endline respondents only). Of 57 covariates over three treatments, 14 (8.2%) have a difference with _p < ._ 05, and within treatment arms the covariates are not jointly significant.

**Compliance** Both interventions had high compliance, in part due to NEPI’s persuasive efforts and street credibility. Of men assigned to the grant, 98% received it. Of men assigned to therapy, 5% attended none, another 5% dropped out within the first 3 weeks, and two thirds attended most sessions (>80%) (Appendix A.4). Those who dropped out early had less schooling, less self control, and were less likely to exhibit antisocial behaviors like substance abuse or stealing (Appendix A.3). Thus the highest-risk men seem more likely to attend over poorer, noncriminal men.

**Phased implementation** For logistical reasons we recruited, treated, and studied the men in three phases, as seen in Table 2. A pilot phase recruited 100 men, to ensure that the therapy and
Bown and Crowley (2013, in press) interpret US TTB use as increases to the “cooperative” trade policy as in Bagwell and Staiger (1990), and they present evidence that both antidumping and safeguards can be viewed as cooperative tariff increases in response to trade volume shocks. Their estimation of the determinants of US _industry_ -level use of antidumping and safeguards covers 1997-2006 and therefore also includes substantial TTB application of both policies on the US steel sector. Second, at the aggregate level, there is evidence that safeguards can be an important contributor to modern commercial policy adjustments in the face of macroeconomic fluctuations. Bown and Crowley (2011, in press) examine United States TTB policies in higher frequency data over 1988-2010 and find that, relative to models estimated on antidumping alone, the _combination_ of antidumping and safeguards has both a stronger countercyclical response to macroeconomic shocks and a stronger reaction to exchange rate fluctuations.<sup>6</sup> Our results also contribute to the body of research that examines different implications of US use of its safeguards policy. With respect to the 2002 US steel safeguard, Liebman and Tomlin (2007, 2008) provide evidence that government announcements that are associated with the policy affected share prices for at least three different types of firms: steel producers; downstream, steel-consuming industries; and firms that are otherwise unrelated to steel but are connected to the safeguard through the channel of “retaliation” against their exported products that is made possible by European Union tariff threats under a WTO trade dispute. Durling and Prusa (2003) describe the distributional impacts that resulted from the new 2002 safeguard import tariffs on steel slab, which were expected to raise the costs of not only foreign firms, but also of _domestic_ rivals, thereby benefiting US mini-mills at the expense of a number of US vertically-integrated firms.

This paper also relates to other studies of more general questions regarding import protection and the US steel industry. Blonigen et al. (2013), for example, use plant-level data from the US Census
ascertain and formally register location and occupancy of land leased to estates, extinguish rights to ‘ghost’ estates, adjudicate overlapping claims, and as much as possible allow market-based reassignment of unused land to more productive uses. Impacts on own-source revenue generation could be very large: Our estimates suggest that, beyond supporting greater efficiency of land use and opening up avenues to access land via lease markets, incremental public revenue to be generated through realistic lease fees could potentially be very large, amounting to up to US$50 mn. per year, equivalent to 5% of public revenue.

Follow-up research will be of interest in two areas. The first relates to determinants of estates’ productive performance and their impact on neighboring smallholders in ways going beyond averages and exploring heterogeneity across estates. This could combine survey data on location and actual occupancy of estate land with more specific methods of imagery recognition to assess not only current land use but to rely on historical imagery to also provide evidence on changes over time. Beyond providing a basis for any effort at systematic lease updating, this could help assess estates’ effect on neighboring smallholders in a way that would complement existing methodologies such as Ali _et al._ (2016c) with evidence on channels via which such effects may be transmitted. A second area for follow-up research relates to exploring ways in which smallholders’ tenure insecurity could be reduced in different settings and to assessing productivity impacts of such interventions. This could improve not only understanding of production relations in Malawi, but also help ensure that efforts at systematic tenure regularization address these so as to have the desired impact on smallholder productivity and welfare.

14
65%|14%|
|Normal Superior|44,062|233|1,032|-98%|343%|
|_Bachelor in specific subjects***_|_31,365_|_12,197_|_13,022_|_-58%_|_7%_|
|Mathematics|2,502|237|719|-71%|203%|
|Portuguese|7,146|1,347|1,213|-83%|-10%|
|**_All higher education programs_**|**_730,484_**|**_973,839_**|**_1,150,067_**|**_57%_**|**_18%_**|

* _Licenciatura_ courses in typical secondary education fields, like math, Portuguese, physics, biology, etc. ** E arly childhood education and early grades of primary education training. ***Bachelor's degree courses in typical secondary education fields. Source. Synopsis of the Census of Higher Education - INEP / MEC.

In the aggregate, the number of graduates in pre-service training courses practically stagnated and the bachelor's degree in typical secondary education fields dropped sharply in the period (Table 1). There was a decrease in the presence of potential teachers with a bachelor’s degree in favor of the _licenciatura_

> 5 Data from the Unified Selection System (SISU) of the Ministry of Education. The most recent data available in a complete and systematic way for all public higher education institutions that participated in SISU were from year 2014.

> 6 Dada from Synopsis of Tertiary Education Census - INEP/MEC shows that the average number of candidates per offered place in _licenciatura_ face-to-face courses is 0.68 and in bachelor’s degree face-to-face courses is 1.45.

4
Policy Research Working Paper 8753

# **Abstract**

Does “infant industry” preferential access durably boost exports? Using country-product-year data for 1992–2017 and triple-differences regressions, we show that the African Growth and Opportunity Act (AGOA) enhanced apparel exports of African countries on average. But the impact leveled off after the Multi-Fiber Arrangement unleashed competition from Asian countries. Furthermore, the positive average impact masks regional heterogeneity: East Africa’s late-bloomers offset Southern Africa’s boom-bust pattern. Firm-level data reveal that even East Africa’s export growth was driven by entrants rather than incumbents who received large preference margins during the early AGOA years. Overall, the authors find little evidence that preferences durably boosted exports.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at afernandes@worldbank.org, aforero@worldbank.org, hmaemir@worldbank.org, and amattoo@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
(0.00933) (0.00934) We also looked for time‐series dynamics between real growth and financial markets subindexes. Our results indicated that on average, a one‐unit increase in the FMAI Index is associated with a 0.16 percent change in real growth. This result is a bit stronger than the result for the broader FMI Index.

# **ARDL for the FMAI Index:**

DLRGDPPC = + 0.72 + 0.249*LRGDPPC_1 – 0.327*LRGDPPC_2 + 0.157*FMAI_2 (SE) (0.28) (0.151) (0.137) (0.0591)

# **6. Conclusion**

This paper investigates the association between financial development and economic growth in Egypt by analyzing trends between 1980 and 2016, using the new data set on financial sector development indexes introduced by Svirydzenka (2016).

Modeling of bivariate regressions for real growth per capita with the measure for financial development that is broadly used in the literature (M2/GDP) provides strong support for the association between real income growth and financial development. However, the regressions do not yield statistically significant results regarding the presence of time‐series dynamics between real income growth and indexes relating to financial institutions and banking or error correction. This finding can be attributed to the low credit growth to the private sector.

The Financial Markets Index used in this paper shows a strong association with real economic growth. This index is an aggregate index of three subindexes, on financial market depth, efficiency, and access. Bivariate modeling for real growth confirms that the financial markets access index—which includes data on the percentage of market capitalization outside the top 10 largest companies and the total number of issuers of debt (domestic and external, nonfinancial and financial corporations) per 100,000 adults—is associated with real income growth. Capital market development yields statistically significant results for an association with economic growth.

The main policy implications of the empirical results of this
Figure 2. Dynamic treatment effects on payroll reported by firm <!-- Start of picture text --> Soft letter Strong letter<br>Filing period Filing period<br>.3 .3<br>.25 .25<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>0 0<br>-.05 -.05<br>Marginal effect on payroll (w.r.t control)<br>-.1 Marginal effect on payroll (w.r.t control) -.1<br>-.15 -.15<br>-.2 -.2<br>Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text --> Source: Authors' calculations using historical tax declaration records. Coefficients from random effects model plotted.

# Evaluating impacts on employment & average wages

The impacts on the firm wage bill highlighted above can be decomposed into two changes: a change (likely positive) in the average wage per employee and a change (likely negative) in the total number of workers employed ( _reported_ ) by the firm. The letters were meant to address suspiciously low wages for select employees, so the expected impact of these letters was an increase in the total wage bill due to a higher wage reported for targeted employees. However, at the time of the study there was anecdotal evidence that some firms continue to report wages for employees no longer employed by the firm (administrative error in filing), so some employees would be “let go” as a result of these letters. However, a rational decision by the firm would also be to either terminate employees because of the letters (to hide the suspiciously low pay from the authorities) or as a result of the pandemic (lower
such as the automotive sector. The tradable sector is an important source of economic growth and jobs for many regional economies in Mexico. Thereby, any permanent changes to trade flows between these two economies can have profound consequences in Mexico. The results of this article highlight that while the employment impacts of foreign and domestic automation are still small, they hit unskilled workers the most.

The rest of this article is structured as follows. Section 2 provides a literature review and Section 3 presents the econometric model. Section 4 describes the data sources and stylized facts on automation, trade and employment patterns. Section 5 presents the econometric results, with Section 5.1 focusing on the impacts of US automation on Mexican exports, and Section 5.2 focusing on the impacts of US and Mexican automation on LLM outcomes. Finally, Section 6 concludes.

# **2 Literature Review**

This article contributes to the empirical literature on the extent of reshoring and its drivers, which is scarce. Since reshoring is not typically captured by official statistics, researchers have to rely on alternative ways to measure it. De Backer et al. (2016) report different estimates of the share of companies in Europe and the United States that are reshoring their production processes. Data from manufacturing surveys show that around 4 percent of companies in selected European economies moved part of their activities back home between 2010 and 2012 (Dachs & Zanker 2015). However, the extent of new offshoring processes continues to be substantially more important than that of reshoring. Evidence for the United Kingdom suggests that about 15 percent of firms are engaged in reshoring, while that figure is about 20 percent for large companies in the US. Reasons reported for reshoring vary. Dachs et al. (2017) find that large companies in high-tech sectors are more likely to reshore their activities, and that one of the main reasons for this is the lower quality of goods from foreign suppliers. Fratocchi et al. (2014) report that logistics costs are also an 5
# 1 Introduction

Surviving in the export market is not just about entering into, but also about not exiting from foreign markets. The rapidly growing literature on international trade at the level of individual ...rms has mainly focused on the role of ...xed costs and ...rms’ characteristics–productivity and ...nancial constraints–on exporting status (Melitz, 2003, Eaton et al., 2004; Bernard and Jensen, 2004; Chaney, 2005; Manova, 2008, Eaton et al., 2009). Yet, empirical evidence on the relevance of these determinants on ...rms’ability to sustain once they entered is rather scarce. This is surprising given that sustained trade relationships are the driving force of ...rms’export performances and subsequent economic growth (Besedes and Prusa, 2006a; Brenton and Newfarmer, 2007; Brenton et al., 2009). In countries as di¤erent as Columbia or China, higher export performances are to be expected from the securing (survival) and deepening of existing trade ‡ows rather than the creation of new ones (Eaton et al., 2008; Manova and Zhang, 2009).

In this paper we try to ...ll this void of the literature and investigate the role of ...nancial development in promoting export survival of agri-food exports sensitive to food safety regulations of destination markets. The focus on the agri-food sector is dictated by the availability of a measure of ...nancing needs at the product level. We rely on a measure of product sanitary risk developed by Jaud et al., (2009a). Additionally, the agri-food sector is particularly relevant to developing countries. International trade in high-value food products–including fresh and processed fruits and vegetables, ...sh, live animals and meat, nuts, and spices–from developing countries has expanded enormously over the last two decades o¤ering opportunities for development. However, trade in agrifood is governed by a
Policy Research Working Paper 9605

# **Abstract**

An unprecedented number of Venezuelans have left behind the worsening economic and social crisis at home to look for better future prospects. Brazil is hosting about 261,000 Venezuelans as migrants, asylum seekers, or refugees, which, at 18 percent, constitutes the largest share of Brazil’s 1.3 million refugees and migrants population (as of October 2020). Although previous literature on other host countries found that Venezuelan refugees and migrants are struggling to secure high-paying jobs that are commensurate with their education, little is known about their access to education and social protection. This paper fills this gap by analyzing various administrative and census data to explore whether Venezuelan migrants and refugees face differential access to education, the formal labor market and social protection programs. It finds that even though there is minimum legal constraints and work permits are relatively easy to obtain, Venezuelan refugees and migrants face challenges integrating into the education system, social protection programs and the formal labor market. The results suggest that Venezuelan refugees and migrants have faced downgrading in grades at school and occupations at work. They are more likely to attend overcrowded schools than their host community counterparts and more likely to do inferior jobs characterized by temporality, lower wages and higher hours worked. Overall, the results suggest that improvement in school capacity, accreditation of Venezuelan education or degrees and relocation to places with favorable employment opportunities may facilitate integration.

This paper is a product of the Social Protection and Jobs 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 pacosta@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
manufactured products such as textile, footwear or machinery (Table A.3). We also show in Figure A.1 that the sample for which trade policy data (and especially NTM data) are available is comparable to the rest of the sample in terms of market structure.

# **3.4 Additional data**

In our robustness exercises we use additional information on the characteristics of products (manufacturing, consumer good, differentiated or homogenous), and destination countries (gravity type variables including distance, income level, market potential information). We also use aggregated trade data to replicate our analysis at the country-pair-product level. These data and their sources are detailed in the corresponding section as well as in the online appendix Section B.1.

# **3.5 Descriptive statistics**

Table 2 shows descriptive statistics for the variables used in the regressions (see online appendix Section B for additional statistics). Our final sample contains around 55,000 firms. Unsurprisingly, trade-policy variables have the largest proportion of missing values. Both tariffs and the estimated ad valorem equivalents of NTM are low around 5 percent to 7 percent on average.<sup>19</sup> The median firm in our sample exports 2 HS 8-digit products and serves 1 destination against 6 products and 3 destinations for the average firm. This skewness in the distribution of products and destinations is consistent with stylized facts documented by the literature over the last decade: most exporters export only one product to a single destination and exports are dominated by a few very large, multi-products, multi-destinations firms (see for instance Mayer and Ottaviano, 2007).<sup>20</sup> Table 2: Descriptive statistics

||Obs.|Mean|S.D.|Q1|Median|Q3|
|---|---|---|---|---|---|---|
|unit value (USD)|648,566|835