rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6180

# **Abstract**

This paper analyzes the evolution in socio-economic and ethnic disparities in tertiary education attainment, participation, and completion and labor market outcomes in the six countries of Central America. There is evidence of differential progress, with Costa Rica, a middle-income country, and Nicaragua, a low-income country, having improved participation of low-income students in tertiary education, while this continues to be negligible in Guatemala, El Salvador, and Honduras. Wide differentials in salaries linked to socio-economic background can signal differences in the quality of tertiary education or prior educational experiences. The analysis distinguishes between long-term and short-term constraints and the key transitions in the education cycle that impede access to tertiary education. The main obstacle to accessing tertiary education for poor students is the failure to either start or complete secondary education, suggesting different priorities for different countries in addressing long-term constraints. However, problems also arise within tertiary education, as in all countries the average tertiary education completion rate is below 50 percent, with even lower rates for students from low-income families and indigenous backgrounds. The paper uses an OECD framework for public policies for promoting equity in tertiary education to assess policies in Central American countries and concludes that many of them currently lack the policies, instruments, and institutional mechanisms to promote greater equity in tertiary education. The paper highlights how valuable insights can be obtained from analysis of household survey data in the absence of comprehensive data on tertiary education which is typical of many developing countries.

This paper is a product of the Human Development Department, Latin America and the Carribean Region. 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 author may be contacted at sbashir@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues
, _s_ ,0. Note that all components of _η_ ˆ _i_ ( ˆ _τ_ ) can be computed with information on bilateral trade flows in the base year plus measures of the bilateral trade shocks. In our implementation, ˆ ˆ _τij_ , _s ≈_ 12% if _i_ and _j_ are regions located in different countries, while _τij_ , _s_ = 0 if _i_ = _j_ or if _i_ and _j_ are regions of the same country (e.g., two U.S. states).

The AAE exposure measure provides a useful way to assess the impact of shocks on a given region by taking into account how it competes with all other regions in all possible destination markets, including its own. If a region is in autarky, a change in the _τ_ ’s has no ˆ effect and _θi_ , _s_ ( ˆ _τ_ ) = 0 for all _s_ , resulting in _ηi_ ( ˆ _τ_ ) = 0. Regions that are more open or have 19
# Firms’ Digitalization during the COVID-19 Pandemic: A Tale of Two Stories<sup>∗</sup>

Edgar Avalos Xavier Cirera** Marcio Cruz** Leonardo Iacovone Denis Medvedev Gaurav Nayyar Santiago Reyes Ortega JEL classification: D22, L20, L25, O10 Keywords: Coronavirus (COVID-19), Firm-level innovation, Digitalization, Digital divide, Innovation and Technology Policy

> ∗The authors gratefully acknowledge comments and feedback on the survey questionnaires as well as on earlier drafts of the paper. Authors thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. This was a background paper to the "Bridging the Technological Divide: Technology Adoption by Firms in Developing Countries" report. The views expressed in this article are solely those of the authors and do not necessarily reflect those of the World Bank or its Board. ** Correspondent authors: Marcio Cruz (marciocruz@ifc.org); Xavier Cirera (xcirera@worldbank.org).
preschool expansion. Measures of worker income and the number of workers enable us to test some of the mechanisms behind the identified impacts.

We also explore mechanisms utilizing Indonesia’s national labor force survey (SAKERNAS). SAKERNAS is a nationally representative survey of all working-age individuals conducted biannually in February and August. We use the August rounds of SAKERNAS data (for the 2007-14 period), which are representative at the district level, to construct indicators for employment in manufacturing, services, and wage employment for men and women, as well as an indicator for a change in employment during the 12 months prior to data collection.

Lastly, we employ data from the Indonesia Database for Policy and Economic Research (INDO-DAPOER) to conduct robustness checks. This data set was constructed by the World Bank from several governmental data sets and provides us with district-level economic and social indicators. We use total government per capita expenditure as a measure of fiscal development. To proxy socio-demographic dynamics, we take the district-level average scores on national exams of lower secondary and upper secondary students. Finally, we use the length of highways as a fraction of total road length to proxy quality of infrastructure. These variables enable us to test whether preschool expansion is correlated with other economic or social changes that may drive the observed impacts in firm productivity.

Table 1 provides summary statistics for outcome, treatment, and control variables from all the data sets.

# 3.2. Empirical strategy

Our identification of the impact of preschool availability on plant-level outcomes relates changes in performance of plants with varying degrees of exposure to changes in preschool availability. Specifically, we compare plants along two dimensions. The first is the degree of reliance on female workers. To that end we distinguish sectors (at a granular level of classification) with higher vs. lower share of female workers in the same district – hence exposed to the same changes in preschool density over time. The former should benefit more from any given level of preschool expansion, as we expect the absence of childcare services to
The rest of the paper is organized as follows. In Section 2, we discuss details behind our focus on just two countries, El Salvador and Georgia, and provide information about government support measures in these countries. Section 3 provides a general overview of the methods that we use for analysis. Section 4 describes data and main variables. Sections 5 and 6 report results from matching and panel estimation, respectively. Section 7 briefly concludes.

# **2. Selection of Countries and Government Support Measures**

In this section, we explain the reasoning behind focusing on El Salvador and Georgia to study the effect of government support during the COVID-19 crisis. We also provide a brief summary of support measures taken by the governments in these two countries.

As of June 2021, the World Bank Enterprise Surveys (WBES) team has made the COVES data available across 44 countries.<sup>3</sup> Several research papers have already analyzed this rich data in a cross-country context to study the effects of the pandemic (e.g., Muzi et al. 2021), including exploration of the effectiveness of government policies (e.g., Cirera et al. 2021). We started with the understanding that details of government policy, which varied tremendously across countries, would make cross-country analysis extremely complicated. This is not least because the types or sizes of government support varied across countries (as some of these variations can be accounted for through country fixed effects). More importantly, the targeting of policies varied drastically across countries and often used the severity of the firms’ experience with the pandemic as part of eligibility criteria. This has direct implications on the identification of the causal link between government policy and firms’ performance. In particular, the specificity of eligibility criteria to time-varying features of firms, coupled with the absence of that information in our database at the point of firms’ application for the policy, makes it nearly impossible to form a proper control group to consistently estimate the effectiveness of government
Policy Research Working Paper 5984

# **Abstract**

Evidence from a range of different sources suggests that Chinese workers lost 20–36 million jobs because of the global financial crisis. Most of these layoffs affected migrant workers, who have typically lacked employment protection, tend to be concentrated in export-oriented sectors, and were among the easiest to dismiss when the crisis hit.

Although it was severe, the employment shock was short-lived. By mid-2009, the macroeconomic stimulus and other interventions had succeeded in boosting demand for migrant labor. By early 2010, abundant evidence pointed to scarcity in China’s labor market, as labor demand was once again leading to brisk growth in wages.

The paper reviews different available sources of evidence for the effects of the crisis, and notes the biases associated with alternative ex post efforts to measure the employment effects of the crisis. In particular, the paper highlights the usefulness of household surveys with employment histories relative to surveys based on sampling through firms.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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 author may be contacted at jgiles@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._
current wealth index for the Colombian sample. Standard errors are clustered at the household level *** * p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

naire, completed by caregivers, is designed to identify acute and chronic posttraumatic symptoms.

For children and adolescents, we use the Strengths and Difficulties Questionnaire, a 25item behavioral screening tool filled out by caregivers. It evaluates a range of areas including emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship issues, and prosocial behavior.

Adolescents between 11-17 old are assessed for the General Anxi- years anxiety using ety Disorder Scale, a 7-item questionnaire that categorizes anxiety levels from minimal 37
recessions. Business confidence fell in all global recession episodes. Economic policy uncertainty increased during the two episodes—1991 and 2009—for which data are available (Caldara et al. 2019; Baker and Bloom 2013).

Differences across country groups. The impact of global recessions has varied across different groups of countries and regions (Table 4). In advanced economies, average per capita growth fell to -1.1 percent during the global recession years, from 2.7 percent during non-recession years. In EMDEs, the decline was to 0.2 percent from 3 percent (LICs on average suffered larger declines in per capita growth than other EMDEs). Thus, the drop in growth was 1 percentage point greater for the advanced economies than for the EMDEs. In addition, both trade and industrial production registered much larger contractions in advanced economies than EMDEs.

Some EMDE regions have been able to weather global recessions better than others. For example, the EAP and SAR regions continued expanding during the past four global recessions whereas the other four regions all on average experienced declines in per capita

> 24 On the basis of the quarterly data, average annual per capita output growth in the four global recessions was -2.4 percent (Table 1). The deepest recession is again seen to be that of 2009 and the least severe that of 1991: average annual growth rates in the four recessions were -1.9 percent (1974:1-1975:1), -0.9 percent (1981:4-1982:4), -0.5 percent (1990:4-1991:1), and -5.4 percent (2008:3-2009:1). Per capita growth was negative in each quarter of the four recessions, except in 1974:2 when growth picked up to 0.8 percent for one quarter only.

> 25 Oil consumption declined in only nine years of the past seventy (i.e., 1974-75, 1980-83, 1993, and 2008-09). These episodes coincided
bus and Moto taxi mapping under flooded (wet) conditions.

> 6 Unreasonably high travel speeds can occur due to small inaccuracies in the GTFS files, but are clear outliers. If two stop points along a route are very close (in reality or due to GPS inaccuracies) and a surveyor enters a travel time which is a few seconds off, resulting travel speeds might exceed 100 km/h, which is unreasonable in an urban public transport setting. In the entire data set there are fewer than 10 route segments (<0.3%) affected by such inaccuracies.

> 7 The multi-modal network is created based on both the GTFS feed and OSM data set, which provide speed limits for every road segment. For the pedestrian network, we assume 4.5km/h travel speed.

6
**<mark>Figure 5.3. Strictly Formal Employment Share comparisons</mark>**

<!-- Start of picture text --> 35<br>30<br>25<br>20 45 degree<br>Brazil<br>15 Kenya<br>10<br>Average relative deviation of RIWI: 39.12%<br>5<br>0<br>0 5 10 15 20 25 30 35<br>Comparison data<br>RIWI<br><!-- End of picture text --> <mark>Note: Each point represents the combination of formal employment share (comparative data, Internet survey data). Average relative absolute deviation is the average of relative values of absolute deviation of the Internet survey from the comparative data. Specifically, we calculate the average of abs(Internet-Comparative)/comparative.</mark>

**<mark>Figure 5.4. Self-employment share comparisons</mark>**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>45 degree<br>40<br>Brazil<br>30<br>Indonesia<br>20 Kenya<br>10 Avereage relative deviation of RIWI: 166.90%<br>0<br>0 10 20 30 40 50<br>Comparison data<br>RIWI<br><!-- End of picture text --> <mark>Note: Each point represents the combination of self-employment share (comparative data, Internet survey data). Average relative absolute deviation is the average of relative values of absolute deviation of the Internet survey from the comparative data. Specifically, we calculate the average of abs(Internet-Comparative)/comparative.</mark> <mark>Since we do not have comparative literature focusing on employment status or related indicators such as self-employment and formality rate, we
formal financial institution: in Sub-Saharan Africa 19 percent of adults report having saved in the past year using a savings club or person outside the family. But a large share of adults around the world who report having saved or set aside money in the past 12 months do not report having done so using a formal financial institution, informal savings club, or person outside the family. These adults account for 29 percent of savers worldwide and more than half of savers in 55 economies.

Analysis of Global Findex data shows that account penetration is higher in economies with higher national income as measured by GDP per capita, confirming the findings of previous studies.<sup>3</sup> But national income explains much less of the variation in account penetration for low- and lower-middle-income economies. Indeed, at a given income level and financial depth, use of financial services varies significantly across economies, suggesting a potentially important role for policy.

# **Removing physical, bureaucratic, and financial barriers could expand the use of formal accounts**

Poor people juggle complex financial transactions every day and use sophisticated techniques to manage their finances, whether they use the formal financial system or not.<sup>4</sup> We cannot assume that all those who do not use formal financial services are somehow constrained from participating in the formal financial sector—access and use are not the same thing. But the recent success of mobile money in Sub-Saharan Africa shows that innovations can bring about dramatic changes in how people engage in financial transactions. To allow a better understanding of the potential barriers to wider financial inclusion, the Global Findex survey includes novel questions on the reasons for not having a formal account. The responses can provide insights into where policy makers might begin to make inroads in expanding the use of formal financial services.

Worldwide, by far the most common reason for not having a formal account— cited by 65 percent of adults without an account—is lack of enough money to use one. This speaks to the fact
Policy Research Working Paper 10659

# **Abstract**

Innovations to date in detecting women’s employment have focused primarily on improving individual-level questions. This paper explores an alternative approach, using data on household enterprises and asking who participates in these activities. This research uses the latest waves of the Labor Market Panel Surveys for the Arab Republic of Egypt (2018) and Tunisia (2014). The research questions are (1) How do men’s and women’s employment rates change when adding enterprise-based detection questions to standard individual-level questions? (2) Was the additional market employment detected with project-based approaches classified as subsistence work with individual measurement approaches? (3) For which women is additional employment detected using project-based approaches? The paper presents descriptive results on work based on the different approaches. It also estimates changes in state (being reclassified as working) from adding enterprise-level data. The findings show large increases in employment rates for rural women in both countries when including enterprise-based detection questions.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 assaad@umn.edu or cgkrafft@stkate.edu.

_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
WHO|2003|38|43|24|16|
||Georgia|MICS3|2005|12|18|5.4|54|
||Kazakhstan|MICS3|2006|40|37|47|19|
||Kyrgyz Republic|MICS3|2006|9|9.3|8.7|37|
||Latvia|WHO|2003|82|88|67|9.8|
||Macedonia, FYR|MICS3|2005|2.5|3.8|0.5|36|
||Montenegro|MICS3|2005|2.0|2.1|1.8|32|
||Russian Federation|WHO|2005|26|9.2|59|3.4|
||Serbia|MICS3|2005|8.6|9.2|7.6|34|
||Slovak Republic|WHO|2003|80|84|74|2.9|
||Tajikistan|MICS3|2005|4.5|11|1.6|35|
||<br>Turkmenistan+|DHS|2000|96|—|—|0.3|
||Ukraine|MICS3|2005|8.1|3.4|19|9.1|
||Uzbekistan|MICS3|2006|2.2|3|2.6|16|
|**LAC**|Antigua Barbuda|WHO|2007|96|—|—|2|
been created in 1999. The new program, _Bono de Desarrollo Humano_ (BDH), is part of the Social Protection Plan of the _Ministerio de Bienestar Social_ and re-targeted the transfers under the erstwhile _Bono Solidario_ program. While the main purpose of BDH is to transfer cash to poor households, the transfers are also intended to be conditional on child enrollment and health care center visits. In its early days, compliance with the conditions was not monitored and consequently, non-complying households were not penalized. In order to

> 20 These villages were randomly selected out of 6,396 villages (4,546 considered to be in the treatment group and 1,850 in the control group) using probability proportional to size. Villages were identified on the basis of a community score based on information available from national census data regarding characteristics such as educational levels, occupational composition, and housing conditions.

> 21 Further details are available in Skoufias and Parker (2001).

> 22 The 7 states were: Guerrero, Hidalgo, Michoacan, Puebla, Queretaro, San Luis Potosi, and Veracruz.

16
# **2.4.2 Forward interest rates**

We use forward interest rates instead of current bond yields as the dependent variable. According to Shiller et al. (1983), the _푚_ -to- _푚_ + _푛_ -year forward nominal interest rate E _푡 퐿푖,푡_<sup>_푛_</sup> + _푚_<sup>is implied from the year-end</sup><sup>_푛_−and</sup> _푚_ + _푛_ −year zero-coupon interest rates as follows: We employ the 5-to-10-year forward real interest as the dependent variable.<sup>13</sup>

# **2.4.3 Expected public debt**

We employ projections of gross public debt to-GDP-ratio E _푡 퐷푒푏푡푖,푡_ + _푛_ by the IMF _World Economic Outlook_ , _Article IV Consultations_ , OECD _Economic Outlook_ , European Commission (EC) _European Economic Forecast_ , and Economist Intelligence Unit (EIU). Following Gruber and Kamin (2012) and Ichiue and Shimizu (2015), we use two-year-ahead projections of gross public debt-to-GDP-ratio as an independent variable.<sup>14</sup> Since the IMF _World Economic Outlook_ releases its public debt projections for most countries twice a year, we use biannual data. However, as OECD, EC and IMF _Article IV Consultations_ had made two-year-ahead projections once a year over the years, we interpolate the same projections to construct bi-annual data.

# **2.4.4 Foreign private holdings ratio**

We use the lagged share of private foreign investors _푓푝푖,푡_ −1 as the threshold variable and one independent variable. The composition of government bond holdings is drawn from Arslanalp and Tsuda (2014a) and Arslanalp and Tsuda (2014b). As we use forward interest rates for government debt securities denominated in the local currency, the foreign private holdings ratio _푓푝푖,푡_ −1 is measured as the foreign private holdings share of government debt securities denominated in the local currency.<sup>15</sup>

>
annual increases in the Gini coefficient are small by comparison to those recorded in European transition economies: of the six economies

> 1  Loshkin and Ravallion (2004) use a different decomposition to decompose the changes in inequality associated with trade reform in Morocco.

> 2  Growth calculations here and below assume exponential growth.  Data for per capita GDP are at constant prices, are for period 1991-2000, and are taken from the World Bank’s _World Development Indicators_ database.

> 3  China’s Gini is for household per capita income, adjusted for cost-of-living differences between rural and urban areas. Vietnam’s is for household per capita consumption, adjusted for regional price differences.