rafmacalaba/data-use-mentions-extended huggingface dataset:
readily accessible for other provinces, and urban areas could not be matched at the granular level necessary to exploit the cross-sectional variation in private school location and GSS presence that we utilize in the paper.

> 8We also augment the public schooling data from the EMIS with more recent data from the 2005 National Educational Census. We are able to match some more villages using the NEC, but cannot use this as the primary data source because of insucient information on the upgradation of schools from primary to secondary. Specically, there is a chance in the NEC data that we incorrectly assign a village to have received a GSS by 2000 when it only had a primary school that was upgraded to a secondary school after 2000. Since our empirical strategy examines the relationship between pre-existing girls secondary schools and private schools (as of 2000, the date of the private school census), the correct thing would be to classify such a village as not having a GSS.

9
currently produce a high level of greenhouse gas emissions per unit of GDP face a difficult challenge of structural adjustment. They are the ones in which more labor is likely to have to be reallocated from currently GHG-intensive activities, either by switching technologies within an industry or by moving labor between industry sectors. Given the importance of carbon dioxide emissions from energy production, energyintensive economies will comprise a large part of this group. The following scatter diagrams illustrate the wide variation across developing countries according to measures of carbon 24
: Own computations. Data: OECD TiE data, covering 51 countries and 45 sectors for the period 1995-2018. Income categories as of 1995 are used to classify countries into high- and low- and middle-income countries (see Appendix 8).

Several underlying factors could explain the decline of export-linked employment in emerging markets over the past decades. While country experiences differ and the data sample does not cover all LMICs, the trend of declining jobs in exports has been linked to increased technology-intensity of production especially within GVCs because new technologies are related to higher-quality standards and high-skilled labor, raising the barriers for low-skilled labor in LMICs to participate in GVCs (Rodrik 2018). As part of their own upgrading efforts, some countries have started to outsource labor-intensive production themselves, most notably China, to lower-cost locations within the region, also increasing intra-regional trade. In fact, the number of jobs sustained by manufacturing exports in China plummeted after the global financial crisis, plateaued at the new lower level until 2015 and declined further (Figure 10, bottom left panel). The emergence of new low-wage countries on the global trade landscape, not included in the dataset, such as Vietnam has further helped this development.

By contrast, the number of jobs in exports increased steadily in high-income countries, driven by business services exports, showing only a brief dip after the global financial crisis. The number of jobs in 14
same cluster) and a household specific error. The common error is referred to as location error. The household specific error will also be referred to as an idiosyncratic error. Empirical results from a wide range of countries indicate that spatial correlation is indeed significant, and that the approach put forward by ELL works quite well.

A violation of either of the two key assumptions will affect the precision of the SAEs. Therefore, each time the method is used, it is important that the user tests the validity of these assumptions, as this may vary from country to country. Specifically, if one decides to ignore spatial correlation, while it is in fact present, one runs the risk of significantly underestimating the standard errors, and hence overestimating precision.

# **IV. Estimates of Expenditure Poverty and Inequality**

# _IV.1 Selection of explanatory variables_

The first step in the poverty mapping exercise is to select the explanatory variables in the regression model with either expenditure or income as the dependent variable. These variables should meet the following criteria:

- Available in both the household survey and the census.

- Household survey and census are comparable (both questionnaires accommodate the same variable definition, and both data sets show similar summary statistics).

- Sufficiently correlated with household expenditure or income.

After carefully screening the questionnaires and examining the data (comparing summary statistics) of candidate common variables from the 2006 VHLSS and the 2006 RAFC, we selected 27 household variables which will be used as the explanatory variables in the models for expenditure and income.

We also constructed commune level data that was merged with the household level data. For selected household level variables from the 2006 ARFC we derived commune mean values, which were merged with the VHLSS at the commune level. For example, we construct the percentage of ethnic minorities of communes, the average household size of communes, etc. Note that these variables are comparable by construction. They are referred to as the `mean variables of communes’.

The commune (and district level
Figure 7 presents international comparison of debt rations across countries. Egypt falls below average of the international ratios. These patterns suggest that listed companies are not relying on debt as a significant source of their external finance. Figure 8 presents cross-country comparison of interest payments on debt. Egypt falls approximately in the middle of the distribution on this ratio.

**Figure 7.  Debt to Assets, Listed Firms Data**

<!-- Start of picture text --> 0.55<br>0.5 Debt to Total Assets (Mean)<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Source: Datastream, staff calculations<br>Figure 8. Interest Expense on Debt, Listed Firms data.<br>0.3 Interest Expense to Debt (Mean)<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Bahrain Virgin Islands (UK)Zimbabwe Venezuela, RBNigeriaSlovak RepublicAustralia Jordan Cayman IslandsCzech RepublicHungaryColombia South Africa Morocco Canada Poland United KingdomSweden PhilippinesUnited Arab Emirates Egypt, Arab Rep.Hong Kong, ChinaArgentinaSaudi Arabia Switzerland GermanyTurkeyChile SingaporeTaiwan Kuwait JapanQatarIreland LuxembourgMexico France Israel Estonia Netherlands Denmark Peru MalaysiaKorea, Rep.Austria Brazil New Zealand Finland Thailand Russian Federation China BelgiumItalySri Lanka Indonesia SpainIndia Pakistan Greece Slovenia Bermuda Lithuania PortugalIceland<br>JapanQatarSlovenia Cayman IslandsTaiwan Lithuania Saudi Arabia PortugalUnited Arab Emirates Kuwait Austria SingaporeRussian Federation Iceland Bermuda Finland Virgin Islands (UK)China Bahrain MalaysiaSpainKorea, Rep.Jordan Greece France ItalyHong Kong, ChinaChile Thailand Estonia Ireland Sri Lanka India Egypt, Arab Rep.Sweden Poland Denmark New Zealand BelgiumPhilippinesIndonesia Switzerland Netherlands Czech RepublicCanada United KingdomAustralia GermanyPakistan Peru Israel Morocco South Africa Venezuela, RBMexico Slovak RepublicTurkeyArgentinaColombia LuxembourgBrazil HungaryZimbabwe Nigeria<br>
Policy Research Working Paper 5657

# **Abstract**

Using a novel dataset with transactions level exports data from four African countries (Malawi, Mali, Senegal and Tanzania), this paper uncovers evidence of a high degree of experimentation at the extensive margin associated with low survival rates, consistent with high and middle income country evidence. Consequently, the authors focus on the questions of what determines success and survival beyond the first year and find that survival probability rises with the number of firms exporting the same product to the same destination from the same country, pointing towards the existence of cross-firm synergies. Accordingly the evidence is consistent with the hypothesis that those synergies may be driven by information spillovers. More intuitively and consistently with multi-product firms models, the analysis also finds that firms more diversified in terms of products, but even more in terms of markets, are more likely to be successful and survive beyond the first year.

This paper is a joint product of the Finance and Private Sector Development Department, Africa Region; and the International Trade Department, Poverty Reduction and Economic Management Network. 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://econ.worldbank.org. The authors may be contacted at Liacovone@worldbank.org, f.g.rauch@lse.ac.uk, ocadot@worldbank.org, mpierola@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
Ghana Registrar’s General Department. These establishments were located from lists compiled from the following: firm registry, list of firms paying value added tax (VAT), list of large taxpayers, Ghana Chamber of Commerce and Industry, business associations; General Department of Cooperatives, District Assembly registries, and block enumeration conducted by the fieldwork team.

The information collected refers to characteristics of the firm at the moment of the survey or to the last completed fiscal year (2012). Sales, employment, and labor productivity annual growth rates are calculated comparing data from the last complete fiscal year of each survey and recall data. Consequently, growth rates refer to the period 2010–2012. In general, the private sector firms considered were small (with an average size of just under 13 employees).<sup>11</sup> Firms have been operating for just under nine years on average and while the average firm has more or less doubled its size over its lifecycle, the median firm

> 8 Firms that are partially state owned could be included, however in our sample we have only 8 firms with at least 1% of state ownership, making any analysis by state ownership not meaningful.

> 9 For more information on methodology, see: <u>www.enterprisesurveys.org/methodology.</u>

> 10 The Ghana ES include two sectors within industry according to the group classification of ISIC Revision 3.1: all manufacturing sector (group D); and the construction sector (group F). The ES does not represent firms operating in the mining and quarrying and electricity sectors (groups C and E). Within the service sector, the ES includes: wholesale and retail trade, hotels and restaurant (groups G and H); transport, storage, and communications sector (group I); and computer and related activities (group K, subsector 72, IT). It does not represent financial intermediation (group J), real estate and renting activities (group
# **1. Introduction**

The volume of corporate debt has rapidly expanded worldwide since the early 1990s, growing faster than equity financing and gross domestic product (GDP). This growth has occurred not only in one, but in several debt markets that have become key sources of external finance for corporations. Evidence from the United States shows that most publicly listed firms borrow simultaneously from banks and bond markets, with bonds accounting for about 63 percent of the total domestic debt outstanding by the non-financial sector (Rauh and Sufi, 2010; Crouzet, 2018). Among bank loans, syndicated lending has become the main alternative to bond markets, especially for large corporations (Ivashina and Scharfstein, 2010; Cerutti et al., 2015). The borrowing activity through bonds and syndicated loans has taken place in both domestic and international markets (Henderson et al., 2006; Hale, 2007; Gozzi et al., 2015; Claessens, 2016).

Researchers have exploited the corporate debt issuance data in different markets to document how publicly listed companies in the United States substituted domestically issued syndicated loans for bonds to withstand negative shocks to the supply of bank credit (Adrian et al., 2013; Becker and Ivashina, 2014). These papers build on the idea that domestic bond financing can replace domestic bank lending during banking crises, when capital markets might act as a “spare tire” (Greenspan, 1999; Levine et al., 2016). Other research highlights that different markets are not perfectly integrated and provide different types of financing, even for the same borrower (La Porta et al., 1997; Karolyi and Stulz, 2003; Pirinsky and Wang, 2006; Japelli and Pagano, 2008; Bekaert et al., 2011; Gozzi et al., 2015; Crouzet, 2018). Hence, changes in financing across markets during supply contractions can have important implications both for the amount of credit obtained and for other debt attributes.

In this paper, we expand the existing literature by
focusing on the output multiplier and government spending, these studies exploit two conditions: (1) The studies argue that a significant fraction of government spending in developing economies is financed by loans from official creditors, and (2) these loans typically disburse over a period of many years following the initial approval of the loan. Kraay (2014), therefore, constructs a measure of predicted disbursements on loans from official creditors that reflects only fluctuations in loan approval decisions made in previous years, but not the current year. We update the instrumental variable using the same unique loan-level dataset from 1980 to 2021 (extended it from 2010) on about 79,000 individual loans from official creditors to governments in developing countries.

The key identifying assumption is that loan approval decisions in a given year do not anticipate shocks to private investment or output in subsequent years. Given this identifying assumption, subsequent predicted disbursements are uncorrelated with contemporaneous macroeconomic shocks, and are a valid instrument for changes in government investment.<sup>10</sup>

> 10 As discussed in Kraay (2012, 2014), conditional on the identifying assumption that loan approvals do not respond to future shocks to growth, our choice of instrument will be uncorrelated the error term for all h ≥ 0.

10
8</sup> and a new regional learning assessment program that took place in East Asia, SEA-PLM 2019, have been released, as have the results from AMPL-b 2021 assessment (in Zambia),<sup>9</sup> and policy linking results of national assessment in Lesotho. This new data has led to a country-level update of the pre-COVID-19 baseline Learning Poverty estimates.

- The Learning Poverty baseline has been updated. For regional and global Learning Poverty estimates, we use the new reporting window of ±4 assessment years around 2019 (the previous window was ±4 years around 2015). Some countries outside of the reporting window were included for temporal comparability with the previous global estimate. The exceptions include Afghanistan, Kyrgyzstan, Lesotho, Pakistan, Tunisia, Uganda, and the Republic of Yemen. We use population estimates for 2019 to calculated populated-weighted averages for global and regional estimates.

This paper presents updated results for the impact of school closures and mitigation effectiveness on the Learning Poverty headcount ratio, LAYS, and percent below minimum proficiency in PISA under three scenarios: optimistic, intermediate, and pessimistic. Since the release of <u>Azevedo, Hasan et al. 2021, we have</u> lowered our expectations regarding mitigation effectiveness based on experiences with remote learning over the past two years of school closures. The previous “intermediate” scenario parameters are now used for the “optimistic” scenario, the previous “pessimistic” scenario replaces the existing “intermediate” scenario, and the previous “very pessimistic” scenario parameters are used for the existing “pessimistic” scenario. In all

> 6 Learning Poverty is defined as the inability to read and understand a simple text by age 10. More information about the Learning Poverty measure can be found here. The World Bank’s Learning Adjusted Years of Schooling (LAYS) concept combines quantity (access) and quality (learning outcomes) of schooling into a single easy-to-understand metric of progress. More information about the LAYS measure can be found here.
Table 9: Small-occurrence region species spatial clustering by size class, Group 1 countries have the highest spatial concentrations of small-occurrence region species and the greatest potential for cost-effective protection, other things equal. In this group, 5% or less of WB2s account for 90% of small-occurrence region species. The remaining groups are partitioned as follows: countries have strong spatial clustering of small-occurrence region species. In an additional 49 countries, To summarize, our results identify a relatively small subset of countries where clustering of smalloccurrence region species offers exceptional opportunities for cost-effective protection. In many cases, we believe that such an assessment will be a useful future application of our GBIF species maps database. However, we perform an illustrative exercise for Brazil, which has 6,058 small occurrence region species and high spatial clustering (6.5% of municipios account for 90% of small-occurrence region species). Highlighting the degree of clustering, Table 10 shows that only 5 of Brazil’s 5,510 municipios account for <!-- Start of picture text --> —fDiamanting |<br><!-- End of picture text -->
Notably, the intensification of mining activities has primarily occurred in the south of Ghana. Between 2003 and 2013, gold mines spread from a handful of districts in the Ashanti region to many districts in the Ashanti, Western, and Central regions in the south. Ghana’s oil and gas fields are also located off the south coast. Between 2003 and 2013, the increase in the number of mining firms and in mining employment in the south is larger than in the north by 14 and 62 times, respectively.

We test two predictions related to the positive mining shock in Ghana, by combining the literature on production networks (Acemoglu et al. 2012; Carvalho and Tahbaz-Salehi 2019) with that on mining-led local development (Feyrer et al. 2017; Toews and Vezina 2020). First, we examine whether the mining output shock that primarily occurred in the south of Ghana led to a growing regional (north-south) difference in intersectoral linkages between 2003 and 2013. Second, we examine whether regional differences in intersectoral linkages following the mining shock led to regional differences in the sectoral composition of regional output during the same period.

To estimate regional differences in intersectoral linkages and the composition of sectoral outputs, we use statistics at the sector level (output, productivity, and employment elasticities in other sectors with respect to mining) and at the firm level (rate of firm entry and average firm-level employment in heavy manufacturing industries). Region-level sectoral data come from aggregate and regional five-sector input-output (I-O) tables for 2004 and 2013 that we construct from supply and use tables for Ghana. The five sectors are agriculture, mining, “other industry” (that is, industrial subsectors other than mining), wholesale and retail trade (WRT) services, and “other services” (that is, services subsectors other than WRT services). For firm-level data, we use two rounds of censuses of firms for Ghana: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).

We examine
without a contract than their male counterparts (in Greece, Cyprus and Italy this applies only to childless females).

In Eastern Europe, as well as in Israel, workers with either ethnic minority or immigrant background have a significantly higher propensity to work without a contract than otherwise similar native workers<sup>26</sup> . In Eastern Europe, this effect is less pronounced for those with only one parent being immigrant, as well as for immigrants from EU countries. In Spain and Portugal, all immigrants, both from EU and non-EU countries, face a substantially higher risk of informality than native workers (the marginal effect is 13 to 15 percentage points); however, this is not the case for second generation immigrants. In Italy, Greece, and Cyprus we found a significantly higher informality risk only for immigrants not covered by the free movement of labor provisions (the marginal effect is 21 percentage point), as well as for „mixed‖ second generation immigrants (13 percentage points). With regards to Italy this result should be interpreted carefully because the Italian data cover only year 2006, and the sub-sample of informal employees is pretty small.

In the UK, Ireland, Austria, and the Netherlands, immigrants not covered by the free movement of labor provisions constitute the only group among population with immigrant or minority background featuring a significant informality effect (5.6 percentage points above native workers). It is worth noting that belonging to this group is clearly an exogenous variable – unlike having an individual work permit.

Remarkably, in Nordic countries, as well as in Germany, Switzerland, France and Belgium, none of the minority or immigrant groups, after controlling for other observable characteristics, features a higher informality rate than the native employees. See, however, Hazans (2011a; 2011b) for a simultaneous analysis of informal dependent employment, informal self-employment, unemployment and inactivity.

In Eastern Europe (but not elsewhere), workers who are return migrants are more likely to be employed informally. This
Philippines. Since the program’s inception, Filipino migration has increased dramatically, and Filipinos now migrate in large numbers to an extraordinarily diverse range of destination countries. The top ten destinations account for approximately 86 percent of all new overseas Filipino worker (OFW) hires (see Table 1). Countries such as Saudi Arabia, the U.A.E., and Kuwait, in the Middle East, and Japan; Hong Kong SAR, China; Taiwan, China; and Singapore in East Asia, are the most common destinations, but Italy, the U.K., Canada and the U.S. are also among the top fifteen destinations. By comparison, 98 percent of Mexican migrants are in the United States (World Bank, 2011). Migration from the Philippines is largely temporary and legal, and occurs through licensed private recruitment agencies. Overseas temporary contract work is the primary channel through which Filipinos migrate, and in order to be cleared to leave the Philippines, an OFW must have a job contract in hand. Between 1992 and 2000, 83 percent of Filipinos abroad were engaged in contract work,<sup>2</sup> with most of the rest being non-temporary workers migrating through family reunification policies or other permanent migration channels. This form of legal temporary work is likely to become more common in future years as countries like Bangladesh, Indonesia, Sri Lanka and India seek to follow the Philippine model, and destination countries consider how to balance demands for labor with public concerns about migrant settlement.

# **2.2 Large Potential Supply**

Data from the 2010 Gallup World Poll suggest that there are many individuals in the Philippines who would like to work abroad but who are not currently doing so. This poll asked a representative sample of 1000 adults in the Philippines the question “Ideally, if you had the opportunity, would you like to go to another country for temporary work, or not?” Overall, 51.1 percent of adults aged 15 and over