rafmacalaba/data-use-mentions-extended huggingface dataset:
green bonds.<sup>10</sup> And public debt levels are presently a smaller concern in that income group (Blanchard 2019), suggesting that for countries with sufficient fiscal space, green bonds might help improve the climate policy mix.

The relation of green bonds to debt levels also depends on their cost relative to traditional bonds. A critique has been that green bonds may be more expensive than conventional bonds and thus raise debt. But this finding may be driven by sample selection bias. To evaluate climate financing strategies, it is important to control for the types of investment projects and investor characteristics when comparing yields of green and vanilla bonds. The relevant point of comparison for a country which is considering the use of green bonds for financing climate projects is the yield of vanilla bonds that are used in financing these same climate projects, and not the yield of conventional bonds averaged across the broader market. With these controls, green bonds are paying lower yields (Flammer 2018; Baker at al. 2018; Karpf and Mandel, 2018, Partridge and Medda, 2018). So, given that a country is seeking to finance investments in its climate transition, green bonds appear cheaper than vanilla bonds. There is also evidence that the act of certifying a green bond through a third party reduces its yield (Bachelet et al. 2019) and that

> invested in adaptation projects. The authors also analyze the Bloomberg Green Bonds Database and find that only 2% of the certified issuers (from 2010 to 2018) declared that the volume leveraged should be used only for adaptation projects. The expected use of proceeds of each certified green bond is published by a third-party report at the time of issuance. 10Among the largest emitters, the Russian Federation was the last country to enter the green bond market in January 2019.

6
lower than before the pandemic (see Figure 2). In Burkina Faso, Ethiopia, Malawi and Nigeria, attendance rates in the latest post-COVID 19 survey were higher than those in the pre-pandemic LSMS survey. Only in Uganda, where schools had only partially re-opened when the latest HFPS round with an education module was fielded, had school attendance rates not (yet) returned to pre-pandemic levels. These aggregate rates, however, may mask significant heterogeneity in the impacts of COVID-19 on different groups of adolescents, which are the focus of this section.

Similar to the regressions in Appendix Table A3, our dependent variable is an indicator variable of whether a child attends school. In addition to a post-COVID 19 indicator variable, our vector of independent variables includes a host of current individual and household-level characteristics, such as sex, age, household size, whether the child is a descendent of the household head (i.e., a child or grandchild), whether the child is the oldest adolescent in the household, and urban locality, as well as time-invariant parental characteristics (i.e., mother’s and father’s education).<sup>15</sup> We further include a set of indicator variables that capture the child’s pre-COVID employment status and whether the household had above median-assets pre-pandemic. While both children’s employment status and family wealth are likely to have changed over the course of the COVID-19 crisis, they provide important baseline information. All explanatory variables are interacted with the post-COVID 19 indicator variable to assess whether there are differences in the associations between these variables and children’s school attendance pre- and post-COVID 19. The regressions are run separately for each of the five countries included in this section.

Table 2 summarizes the results, with a focus on whether the coefficient estimate is positive or negative, and its significance level (see Table A4 for the full set of regression results). For each individual and household-level characteristic included in the regressions, we show the sign and
set of professionals (i.e. specific job profiles), or to all individuals of RTAs’ member countries. Also, in some cases visa provisions grant the free mobility of individuals across signatory countries, or just simplify the administrative burden to obtain a visa or citizenship at destination (reduction in the bilateral migration cost). We benefit from the newly released World Bank database on the specific type of visa provision included in RTAs, and test the migration effect of specific types of visa provisions in RTAs.

This paper contributes to the strand of literature on the determinants of bilateral migration. Previous literature focused in particular on the role of origin- and destination-specific factors affecting bilateral migration, i.e. respectively _push_ and _pull_ factors. The average wage and the employment rate at destination have been shown to be key _pull_ factors (Hatton, 2005; Mayda, 2010; Grogger and Hanson, 2011; Ortega and Peri, 2013). Income dispersion and poverty are the 1
# **1. Introduction**

During the last decade, the ownership structure of banking sectors in developing countries changed substantially: most developing countries witnessed a sharp increase in foreign bank participation and a decline in government bank ownership. Between 1999 and 2009, on average, the share of bank assets held by foreign banks in developing countries rose from 26 percent to 46 percent, while government bank ownership declined from 28 percent to 19 percent.<sup>1</sup> These changes in banking structure were in part motivated by increasing evidence that while foreign bank participation brought many benefits to developing countries, especially in terms of competition and banking sector efficiency<sup>2</sup> , government bank ownership was often detrimental to the financial sector.<sup>3</sup> The recent global financial crisis reignited the debate on the ownership structure of the banking sector and its consequences for financial intermediation. Some have pointed to the presence of foreign banks in developing countries as a key mechanism for transmitting the 20082009 crisis from advanced to developing countries (e.g., IMF, 2009). At the same time, developing countries like Brazil, China, and India, where government-owned banks are systemically important, recovered quickly from the crisis, generating interest in the potential mitigating role that these banks can play during periods of financial distress.<sup>4</sup> Using bank-level data from 2004 to 2009, this paper examines the impact of bank ownership on credit growth before and during the recent crisis. We analyze the growth of banks’

> 1 These data come from the World Bank Regulation and Supervision Surveys. See http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037~pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html. 2 See Cull and Martinez Peria (2010) for a review of the literature on the drivers and the impact of foreign bank participation.

> 3
9 adaptation of foreign technologies for use in domestic production processes. Such training is necessary to monitor technological trends, assess what is relevant for the firm or economy, and assimilate new technologies. A more educated population also tends to be relatively more technologically sophisticated. This generates local quality sensitive demand for advanced goods, which in turns tends to stimulate local firms to innovate and design technologically sophisticated goods and production techniques.

Most empirical cross-country studies of long-run growth now include some measure of human capital and recent studies of international differences in output per worker<sup>7</sup> and economic growth rates have focused the role of human capital in economic development<sup>8</sup> . Regardless of the underlying model, it is a fairly robust finding that a country’s human capital is almost always identified as an essential ingredient for achieving growth<sup>9</sup> . For example, Barro (1991), using cross-section data for 98 countries for the period 1960 to 1985 and the 1960 values of school enrollment rates at the secondary and primary levels as proxies for initial human capital, found that both school enrollment rates had statistically significant positive effects on growth of per capita real GDP. Similarly, Cohen and Soto (2001), using cross-country time-series data on educational attainment or average years of school, finds statistically significant positive effects of education on economic growth. Hanushek and Kimko (2000) take an alternative approach by focusing on the effects of educational quality on economic growth. Using international test scores as a proxy for the quality of educational systems, 10 they find that educational quality does exert positive effects on economic growth .

# **An Effective Innovation System**

Economic theory (Solow, 1957; Romer, 1986, 1990b) indicates that technical progress is a major source of productivity growth and an effective innovation system is key for such technical advancement. An innovation system refers to the network of institutions, rules and procedures that influences the way by which a country acquires, creates
the median, and by union (an administrative unit).<sup>8</sup> There were two deviations from the experimental protocol. First, while programming the mobile application used for data collection, 27 villages were assigned by mistake to a treatment different from the original one. The partial re-assignment of treatments was thus due to a programming error and not to the incorrect implementation of the protocol in the field. In addition, the checks for balance in covariates are very similar based on originally assigned or actual treatment (see below). For this reason treatment status is defined as _actual_ treatment in the analysis. The second deviation is that surveyors were unable to differentiate a village from the one adjacent to it in four cases. While data were collected from households in these four villages and the ones adjacent to them, only pairs of villages could be distinguished, and both villages in each pair received the same treatment. In the statistical analysis there are thus effectively 112 clusters divided into experimental arms A (49 clusters), B (48), and C (15). For simplicity, in the rest of the paper these clusters will be referred to as ‘villages’.<sup>9</sup> It should be emphasized that, while surveyors completed a census of _wells_ in study villages, our data do not represent a census of _households_ . The choice to survey only owners—who were anyway a majority—was due to budgetary constraints, but an implication is that one cannot study whether the choice of the primary source of drinking water changed also among non-owners.

# **3 Data**

A team of testers who had at least completed secondary education was recruited locally. During the home visits when sale offers were made, testers also administered a short household baseline survey and recorded information on sales and, in case of purchase, the result of the test. Additionally, surveyors recorded GPS coordinates of all wells and noted down whether there were any visible labels attached to the
members may also discuss problems relating to the performance of teachers, or school directors, following complaints from parents, students, or teachers. Since 2008, the school director is not a member of the school board and, although he/she can still participate in meetings, he/she cannot vote.

**Figure 1**

# **Accountability Relationships in the Education Sector**

<!-- Start of picture text --> Government<br>MoES<br>RED<br>holds accountable employs<br>School director<br>and teachers<br>provides<br>School services<br>board to<br>Parents<br><!-- End of picture text --> Figure 1 depicts the accountability relationships in (pre-university) education in Albania. Participatory accountability can take the “long route”, which connects parents directly to the ruling government and only indirectly to the school, through voting in parliamentary elections. Participatory accountability can also take a shorter route, connecting parents directly to the school through the election of parent class representatives that, in turn, elect parent members to school boards.

# **3. The School Stakeholder Survey**

We conducted the Albania School Stakeholder Survey as part of the World Bank‟s Accountability for Better Governance Program. The survey was primarily aimed at investigating the role that parents play in the institutional framework within which primary schools currently operate.

# **3.1 Survey design and implementation**

The data collection took place between October and December 2009 in a representative sample of 180 primary schools, offering grades one to nine. The sampling strategy relied on stratification at the district 8
%, representing over 28 million people (89% of the country's population). Notably, 12 provinces have probabilities between 61%-70%, and two provinces exceed 71%, exposing large populations to chronic food stress. The distribution highlights widespread vulnerability, particularly in provinces with larger populations facing high food stress likelihood.

## **Table 4: Probability of Food Stress across Provinces in Afghanistan**

Table 4 shows the average probability of food stress over the full study period across Afghan provinces, broken down into ranges of 0.1. The columns provide a demographic breakdown of the provincial statistics to understand overall population exposure to relative risks. The majority of administrative areas are in the higher risk ranges, with probabilities over 50%.

|**Probability of food**<br>**stress**|**Number of**<br>**Province**|**Female Population**|**Male Population**|**Total**<br>**Population **|
|---|---|---|---|---|
|0.31-0.4|1|302,409|311,072|613,481|
|0.41-0.5|5|1,442,998|1,490,696|2,933,694|
|0.51-0.6|14|5,583,803|5,799,679|11,383,482|
|0.61-0.7|12|7,506,594|7,781,242|15,287,836|
|0.71 and above|2|906,737|943,930|1,850,667|

24
different estimation strategies to explore the impact of the two channels – replacement and marriage market – on fertility. This is due to the fact that proxies for conflict exposure measuring replacement effects are only available for post-genocide RDHS data, while conflict proxies measuring marriage market effects are available for both pre-genocide and post-genocide RDHS data. Table 2 provides a schematic summary of the estimation strategy.

> 8 Unfortunately, most information on marital history available in the RDHS refers to the current partnership. Hence, we do not know whether a woman has ever been widowed and then remarried. Such a woman would simply appear as ‘married’ in the data.
emerging economy and using a rich set of panel data with baseline immediately before the onset of COVID and follow-up across three rounds of post-COVID surveys.

# **2. Study Context**

## **<mark>2.1. Context</mark>**

<mark>This study leverages a large youth employment and opportunities project currently being implemented by the Government of Kenya (GoK) with funding from the World Bank. The Kenya Youth Employment and Opportunities Project (KYEOP) seeks to</mark> increase employment and earning opportunities among vulnerable youth in the country by providing skills training, entrepreneurship support, and access to relevant job market information.

<mark>A key component of KYEOP is a support program for self-employment that targets youth aged 1</mark> 8-29 years with low formal education levels. We are currently implementing a largescale impact evaluation of this program that provides selected youth with the following forms of support to help them start or grow a business: (a) a business grant of Ksh. 40,000 (about US$ 360) (b) business development services in the form of formal training and counseling (BDS); or (c) both grants and BDS. This study comprises 9,380 youth with the selection stratified across 15 counties in Kenya, as well as by gender.

The program and its evaluation were initiated in June 2019 with a call for applications followed by a screening process to determine eligible applicants. Among those eligible, a baseline survey was conducted in January/February 2020 and selection into the three treatment arms and a control arm was conducted right after.

Owing to the large scale of the program, the intervention rollout was organized by groups of five counties. This study focuses on the first group of five counties where BDS started in February 2020, and grants were disbursed in two tranches, with the first tranche in March 2020 and the second in July 2020.<sup>4</sup> The timing of the original
<!-- Start of picture text --> Share of Polluting Sectors:<br>Chinese Industy, 17l-1992<br>0.8 .<br>0.75 < .<br>0.65 \<\ > .<br>0.6 V .<br>0.55. _<br>r j g 8 e i z>88|c<br>IFigure_17__Trend_in_the_Output_Share_of_th Five<br>Figure 17: Trend in the Output Share of the Five<br><!-- End of picture text -->

analyzing recent trends for the five most . .heavily-polluting industry sectors:' Chemicals, Pulp and Paper, Non-Ferrous Metals, Ferrous Metals, and Non-Metallic Minerals (principally Cement). Using production and trade data for China and its trading partners, we have developed three indices of change in pollution intensity.

The first is the proportion of China's total industrial output produced by these five sectors during the era of reform. Figure 17 provides the evidence: From 1977 to 1992, the output share ofthe five 'dirty' sectors has trended downward. Within the period, rapid decline in the early 1980's was succeeded by an increase in the late 1980's and a leveling off in the early 1990s. There is no evidence of any 1long-run shift toward a pollution-intensive industry structure.

<!-- Start of picture text --> Net Imports of Polluting Products<br>25 ----- _- -------------------- ..<br>20 r '<br>.5 /<br>10 g<br>5 .<br>1.2<br>15987 Is" 199 1990 19,91 19,92 1993 1994<br><!-- End of picture text --> Figure 18: Trend in Net Imports from PollutionIntensive Industries Our second index is the
# Human Capital Accumulation at Work:

# Estimates for the World and Implications for Development

Remi Jedwab, Paul Romer, Asif Islam and Roberto Samaniego<sup>_∗_</sup> JEL: O11; O12; O15; O47; E24; J11; J31 Keywords: Returns to Experience; Returns to Education; Human Capital; Economic Development; Labor Markets; Development Accounting

> _∗_ Corresponding author: Remi Jedwab: Department of Economics, George Washington University, jedwab@gwu.edu. Asif Islam: The World Bank, aislam@worldbank.org. Paul Romer: NYU Stern, paul@paulromer.net and tracey@paulromer.net. Roberto Samaniego: Department of Economics, George Washington University, roberto@gwu.edu. An early version of this paper served as a basis for the fifth chapter of the 2019 World Development Report of the World Bank on “The Changing Nature of Work”. We thank Simon Gilchrist, three anonymous referees, Barry Chiswick, Carmel Chiswick, Simeon Djankov, Eric Hanushek, James Heckman, Aart Kraay, David Lagakos, Tommaso Porzio, Federica Saliola and Todd Schoellman and seminar audiences at George Washington, the Inter-American Development Bank, Johns Hopkins, Pacifico, Syracuse, UIUC, WALES, William & Mary and the World Bank for their helpful comments. We gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of the Senior Vice-President and Chief Economist and Social Urban Rural and Resilience Global Practice), the Cities Program of the International Growth Center (Grant number 89408), the GWU Institute for International Economic Policy and the GWU Center for International Business Education and Research. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
substantially weaken the race to the bottom incentive ex ante. When looking at trade patterns in general, for example in the context of environmental regulation, Jaffe et al. (1995), among others, did not find any significant (negative) correlation between such regulation and industry competitiveness, but stress the general costs and benefits related to regulatory activity. Evidence that firms then chose their investment locations according to environmental regulation is equally unsupported (see Drezner 2006 for a summary). An OECD study on Trade, Employment and Labor Standards (1996) concluded that there exists a positive correlation between successfully sustained trade reforms and improvements in core standards. Similarly, Bhagwati (2004) states that without the recent globalization the exploitation of workers would have been much greater in many parts of the world. In sum, corporate preferences regarding location and regulation are not straight forward but rather complex and complemented by considerations of agglomeration or income effects. Furthermore, there are potential gains from stringent regulation such as the protection of assets (e.g. via IP rights), competitive advantages vis-a-vis firms that need to adjust in light of regulatory upwards convergence (see Garcia Johnson (2000) or Maxwell and Lyon (2004)), or positive correlation between single international standards and strong corporate culture (Dowell, Hart and Yeung 2000). Davis, v. N. Whitman and Zald (2008) then conjecture that if large and important sales markets such as the EU or the US are characterized by high regulatory standards, firms will tend to adopt those high standards even abroad (export of standards) and a "race to the top" might occur.

So can strategic CSR actually help realigning empirical findings with theory? Emerging but still very preliminary evidence hints exactly at that, although one has to be cautious in light of issues related to estimation and measurement. Dam and Scholtens (2008) investigate a cross sectional firm level dataset (EIRIS, AMADEUS, WBES and WDI) covering 44,149 subsidiaries of 540 European Multinational Enterprises (
# _Institutions_

The story of Egypt’s utility restructuring is described in detail in the section on Egypts’s power sector reform strategy. In essence, in the long period through the 2014 Electricity Law’s adoption, much was done but little was achieved. Institutions were rearranged more than they were genuinely restructured. Incentives, performance, and behavior changed little.

The 2015 Electricity Law initiated a process of restructuring, with a long transition process. Eventually, sector companies will operate autonomously of the Ministry of Electricity and Renewable Energy and will be accountable to the regulator and to their owners for performance. More private participation is already happening in renewable energy and that may spread to conventional energy. The transmission company is being gradually made more autonomous in preparation for becoming an independent system operator within an eventually competitive wholesale electricity market. Of course, much of this depends on the sustainability of the gradual price and subsidy reform now underway.

Table 5. Utility restructuring index for Egypt and its comparators, 2015

||**Egypt**|**India, AP**|**India, Odisha**|**India, Raj**|**Morocco**|**Pakistan**|**International**<br>**benchmark**|
|---|---|---|---|---|---|---|---|
|**Utility Restructuring**|**31%**|**51%**|**62%**|**51%**|**0%**|**62%**|40%|
|_Vertical Unbundling_|0.40|0.80|0.80|0.80|0.00|0.80|55%|
|_Horizontal Unbundling_|0.22|0.22|0.44|0.22|