rafmacalaba/data-use-mentions-extended huggingface dataset:
|138|143|—|
|Percent of population below national poverty line<sup>b</sup>|33|31.4|41.6|—|
|Depth of financial sector (percent)<sup>c</sup>, 2001|31.0|17.4|20.4|35.2|
|Credit provided by banking sector (percent)<sup>d</sup>, 2001|4.6|0.8|9.6|74.2|

— Not available.

a. Calculated using the Atlas method.

- b. Benin-1995; Ghana-1992; Tanzania-1993.

c. M3/GDP.

d. Domestic credits provided by banking sector /GDP. _Source:_ World Bank: World Development Indicators 2003; African Development Indicators 2004

# **STRUCTURE OF THE FORMAL FINANCIAL SYSTEM**

18. The financial sectors of Benin, Ghana, and Tanzania are relatively shallow: the degree of monetization of the economy as measured by the ratio of M3 to GDP is 31 percent for Benin, 17.4 percent for Ghana, and 20.4 percent for Tanzania – somewhat below the level for Sub-Saharan Africa. Moreover, domestic credit provided by the banking sector as a percentage of GDP is rather low in each of the three countries, and especially in Ghana. Each of the countries has undergone a period of restructuring and liberalization of the financial sector, wherein the government has privatized and reorganized banking institutions that it previously owned, controlled, and managed.

## **_Benin_**

19. At the end of 2002, Benin’s formal financial system consisted of a branch of the Banque Centrale des Etats de l’Afrique de l’Ouest (Central Bank of West African States or BCEAO), 7 private commercial banks, 2 leasing and credit institutions, 7 insurance companies, and more than 600 microfinance and savings and loans associations. In the last two years two of the seven commercial banks
Table 9. Household Shares, by Comparison Groups

|||Host<br>Community|Settled Migrant<br>HHs (>4 years in<br>Turkey)|Recent Migrant HHs<br>(<=4 years in<br>Turkey)|
|---|---|---|---|---|
|2009 Population|Near the Syrian Border|16.3%|3.0%|3.7%|
|Share|Rest of the Country|83.7%|97.0%|96.3%|
|2013 Population|Near the Syrian Border|16.6%|1.8%|25.7%|
|Share|Rest of the Country|83.4%|98.2%|74.3%|
|2014 Population|Near the Syrian Border|15.7%|2.5%|22.2%|
|Share|Rest of the Country|84.3%|97.5%|77.8%|

_Notes_ : In 2014, the population weights exclude individuals under 15 due to changes in the LFS _._ However, this should not affect estimates of the number of households.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

21
# **1. Introduction**

Several Latin American countries recently implemented full-time schooling programs with the objective of reducing dropout rates and improving student learning, ultimately reducing income inequalities. However, in part given their high per student costs, these tend to be controversial reforms. Surprisingly, few rigorous studies have assessed the impacts of these reforms on educational outcomes in a context of middleincome countries. In this paper, we evaluate the impact on educational outcomes by the Brazilian federal program of extension of the school day, _Mais Educação_ ( _“More Education”_ ). Starting in 2008, the program gradually expanded nationwide, covering more than 65 percent of municipalities in 2013.<sup>2</sup> We exploit administrative data between 2007 and 2011 and a differences-in-differences matching estimator to assess the impact of the program on educational outcomes (Portuguese and mathematics test scores and dropout rates). We selected these indicators because the ultimate goal of _Mais Educação_ is to improve schooling outcomes among public schools, reduce evasion and dropout rates, and improve learning.

In recent decades, Brazil has made large progress in access to basic education, but this was not accompanied by higher quality education.<sup>3</sup> The 2012 Program for International Student Assessment (PISA) results show that the quality of education in the country remains far below international standards.<sup>4</sup> Aiming to improve Brazilian public education, in 2008 the Ministry of Education (MEC) created _Mais Educação_ , a nationwide program supporting the expansion of the school day in public basic education schools.<sup>5</sup> Although not yet covering the entire school system, the program has been growing at a fast pace and covers the three education networks: federal, state, and municipal. _Mais Educação_ has been one of the main federal policies to incentivize and expand the provision of full-time education and ultimately achieve the _Plano Nacional da Educação_ (PNE) targets.<sup>6</sup> _Mais
historical electricity consumption as a function of household income and other characteristics. However, obtaining utility data for such cross‐checking was outside the scope of the study.

The survey in Senegal asked if the household had spent anything on electricity over the last 12 months, which would more likely capture whether the household was paying something for electricity. If the question is about spending only in the last month only, no payment might have been made for a variety of reasons and the household would be registered as having zero expenditure. Similarly, some households did not answer the question (they might have found it difficult to recall the amount in the short time given to answer this question during the interview), and all missing responses were recorded as zero in the analysis. In all 22 countries, not all who cited electricity as the primary source of energy for lighting reported positive expenditures in electricity. Legitimate reasons for having no expenditures for grid electricity, the focus of this study, include electricity included as part of the rent, bundling of utility services (such as combined water and electricity bills), or free electricity being part of the compensation package for employment. It is also possible that the household happened not to have made payments during the recall period—for example, the household could have missed one bill payment, making up for it later.

Examination of spending on electricity was necessarily confined to those who reported positive expenditures on electricity. Among those who did, spending on electricity was computed as a share of total household expenditures as one measure of affordability. Because power tariffs in many countries are pan‐territorial and do not have large regional differences that are observed with food and other items, nominal expenditures on electricity and nominal total household expenditures were used to compute expenditure shares, except in Angola and Mozambique where nominal expenditures were not available and where regionally adjusted expenditures were used.

Where enough information was available on how consumption aggregates were derived for official poverty statistics, expenditures on food were disaggregated into those paid for by cash
for a household to shift from autarky to being net purchaser (seller) of land. In addition, imperfections in credit and labor markets imply that higher levels of wealth or family labor will affect a household‟s marginal productivity.<sup>11</sup>

# **4. Empirical evidence**

The frontier production function provides interesting insights, but does not support the notion that fragmentation reduces the level of productive efficiency on land currently cultivated. However, the survey points towards a significant share of land having remained idle for a sustained period of time because of land market imperfections. Descriptive and analytical evidence on land market functioning suggests that lease but not sales markets transfer land to younger and more productive farmers. Eliminating impediments to their functioning would appear to offer an opportunity to increase the level of economic activity and welfare in Albania‟s rural areas.

# **4.1 Determinants of technical efficiency in agricultural production**

> 11 As the survey includes data on land sales for the last 5 years, the number of observations is comparable to what is available for rental markets.

12
children are stunted and 9.8% are severely stunted.</mark> <mark>DHS surveys also include information on labor market participation, wealth, years of education, land/house ownership, and decision-making on health care of mothers and fathers. These are variables that can be used to test for alternative predictors of children’s stunting. They can also be used to test for mechanisms and channels of transmission of the refugee shock on less than five years old children’s HAZ or the probability of stunting. Table A3 in the annex provides all definitions of the variables used in the paper.</mark> <mark>For our main intergenerational analysis, we use the latest 2015/2016 DHS survey of Tanzania collected between August 2015 and February 2016 with migration history of mothers and fathers and information on the exact years they moved to their current area of residence. The data contain unique GPS information of each household cluster, allowing us to accurately assess the intensity of refugees’ influx by relying on the distance between households and each of the 13 historical refugee camps. In total, our analysis relies on a sample of 13,266 women ages 15-49 and 10,223 children ages 0-5 living in 608 clusters across the entire country. The sub-sample used for the analysis includes those households who never migrated (90.1%) or migrated before or during the refugee crisis (9.9%). For these households,</mark> 12
uses the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to investigate the socioeconomic implications of the COVID-19 pandemic on labor market outcomes of urban national and refugee communities in Kenya. Refugees in Kenya live in densely populated refugee camps inhabiting more than 200,000 inhabitants and in urban areas, primarily in Nairobi. Available job-market opportunities and labor market dynamics are therefore comparable to urban nationals rather than rural nationals who predominantly engage in agriculture, an economic activity refugees cannot pursue as they do not have the legal right to own land. From May-June 2020 until April-June 2021 we interviewed 6,343 households consisting of both refugees and Kenyans over five survey waves. Our data is unique in at least four dimensions: i) it leverages the high cell phone penetration and coverage throughout the country, including the refugee communities, to reach households during lockdowns when face-to-face interviews are impossible to conduct;<sup>5</sup> ii) its longitudinal nature allows not only to assess the first order impact of the COVID-19 shock but also its longer-term implications for recovery; iii) interviews cover refugees and nationals over the same period and are

> 2 Khamis et al. (2021) estimate that the work-stoppage rate in Kenya reached up to 62 percent compared to before the pandemic.

> 3 Results from socioeconomic surveys carried out by UNCHR and the World Bank in Kalobeyei settlement in 2018 and in Kakuma camp in 2019 show that 65 percent of Kalobeyei refugees and 68 percent of Kakuma refugees are poor, while at least 7 in 10 of them are highly food insecure (UNHCR & World Bank, 2020; UNHCR & World Bank, 2020).

> 4Kenya hosted around 530,000 refugees in August 2021 which makes it the second largest hosting country in Africa after Ethiopia (UNHCR Kenya, 2021). Alix-Garcia et al. (2018) estimate that the presence of Kakuma refugee camp has increased demand for low-skilled jobs and wage labor and led to favorable price changes for local producers which on aggregate contribute to a 25 percent increase in household consumption in
wage, education, age, marital status, gender, location, industry, occupation, some working conditions, and the hours worked.

The Sri Lankan data come from household and labor force surveys. The Sri Lanka Household Income and Expenditure Surveys (HIES) were carried out by the Sri Lankan Department of Census and Statistics. The paper used the 2002 and 2006 HIES data, which cover about 22,000 households each and contain similar information as CSES. The Sri Lankan Labor Force Survey Sri Lanka (LFS) is conducted quarterly by the Department of Census and Statistics. We used annual data for 2002 and 2008 covering approximately 60,000 individuals each. These surveys contain information about work-related activities (for example, employment status, occupation, industry, and wages), household characteristics (for example, size and location), and individual demographic characteristics such as age, gender, and education, among others.

> 9 For exact definitions please refer to empirical section.

8
# **2. Data Description and Empirical Strategy**

We use data from nationally representative student assessments conducted for mathematics in 2019 and 2021. In 2019, a nationally representative sample of grade five students sat for the assessment. In 2021, a nationally representative sample of grade five students from the same schools sat for the 2021 assessment, specifically designed to ensure comparability with the 2019 assessment. Additionally, the 2021 assessment traced and assessed the students who had participated in the 2019 assessment. Of the 3,922 students who participated in the mathematics assessment in 2019, 3,411 participated in the 2021 assessment leading to an attrition rate of 13 percent. In 2021, the assessment was complemented with student and teacher questionnaires providing a rich data set to analyze variables affecting learning during COVID-19-induced school closures. (For more details on the data, see the Appendix.) Using the data from student assessments, student, and teacher questionnaires, we test five hypotheses based on a reading of previous literature on learning loss due to temporary school closures or during summer recess: _H1_ : Student learning declined due to COVID-19-induced school closures.

_H2_ : Learning loss is greater in schools with longer duration of COVID and non-COVID school closures.

_H3_ : Learning loss is greater among students with less-educated parents/guardians.

_H4_ : Learning loss is greater among students with lower access to and usage of digital devices.

_H5_ : Learning loss is greater among boys than girls.

To test the first hypothesis (H1), we employ two identification strategies to analyze learning trends during COVID-19. The first strategy compares average scores from a nationally representative sample for grade 5 in 2019 to the average scores from the same nationally representative sample for grade 5 in 2021. The sample of schools was developed in 2019 according to the list of all schools in the country with students in grade 5. The same schools were surveyed in 2021. The sample frame was not renewed due to no major changes in the sampling frame over the two years. This identification strategy allows us
port-of-call data to offer a sketch of freight shipping in the Caribbean. We compare the observed supply of JA-compliant shipping services to the characteristics of the overall freight hauling fleets calling in PR and the Caribbean. We use the NBIC (2024) data to verify our conclusions about the JA fleet and to provide a better representation of barge traffic on the U.S.-PR route.<sup>18</sup> Panel A of Table 1 reports data for the Caribbean on three relevant measures of freight supply for six different types of freight-hauling maritime vessels. The statistics reported are the number of vessels calling, the number of port calls, and the DWT associated with vessels making port calls. These data illustrate the importance of self-propelled, non-containerized freight vessels in the overall Caribbean shipping market. Collectively, tankers, bulk, liquid and general cargo vessels account for 82 percent of the vessels calling in the Caribbean in 2019, 62 percent of the port calls and approximately 64 percent of the DWT. Container ships are an important part of Caribbean shipping, but collectively these other ship types arrive with greater frequency, and provide much more capacity.

Panel B of Table 1 reports the same statistics from the LLI data for vessels arriving in PR. The composition of freight vessel supply to PR is broadly similar to that of the Caribbean as a whole. Container ships and barges play a larger role (especially with respect to port calls), but the other vessel types collectively retain sizable shares of reported freight vessel traffic.

In order to distinguish JA-compliant ships from others serving PR, we use the LLI data to identify vessels that were U.S.-flagged and U.S.-owned.<sup>19</sup> Only 11

> 18The NBIC data do not report DWT, though this information is available for most vessels via public web sites like https://www.vesselfinder.com. Our data summary uses DWT data collected by Colin Grabow, who used a collection of public web sites
of observations|4668|4668|4668|4668|4668|4668|4668|
|R-squared|0.0120|0.0122|0.0143|0.0163|0.0194|0.0211|0.0277|
|F-statistics|3.6371|3.1176|3.0139|2.6566|2.4190|2.4445|2.2881|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata.

Notes: Linear probability model, pooled sample. Dependent variable is a dummy variable on child's survival status. It takes value 1 if the child died in postneonatal or infant period. For categorical variables with more than two categories, the left-out categories are: Low malaria risk zone (LMRZ), Surface water, No sanitation, Mother has no
center in the 1930s and nurtured one of the most important industrial bases in China before the Sino-Japanese War broke in 1937. The wars lasting many years blocked the path of growth. After 1949, the Communist Chinese Government began to transform Shanghai into a solid industrial base for the national economy. Starting in the early 1980s, the open door policy allowed. Shanghai to start regaining its position as finance and commerce center with the return of foreign banks in the new Pudong Financial District and the establishment of a modern stock exchange market.

**Figure 2:** The GDP and GDP per Capita of Beijing and Shanghai, 1978-2001

<!-- Start of picture text --> RM RM<br>600 40<br>500<br>30<br>400<br>300 20<br>200<br>10<br>100<br>0 0<br>Shanghai (GDP) Bei j i ng Shanghai (GDP/capi t a) Bei j i ng<br>1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> _Source_ : Shanghai Statistics Yearbook 2002; Beijing Statistics Yearbook 2002.

# **Urban centers of emerging metropolitan regions**

Since China’s reforms commenced in the late 1970s, three metropolitan regions have demonstrated the greatest dynamism and the highest growth rates<sup>3</sup> , two centered in Beijing and Shanghai respectively: Beijing is the economic heart of the Rim of North China Sea ( _Huan bohai_ ), while Shanghai lies at the center of the Yangtze River Delta. Regional development has buttressed urban growth and pushed economic restructuring in both these cities.

Table 2 shows the changing economic structure in Beijing and Shanghai from 1978 to 2001. Beijing and Shanghai are facing common challenges to sustain their growth in the

> 3  According to this report, the three metropolitan regions in coastal China are the Rim of North China Sea (Huan bohai
Pi = Population of locale i Ai = Area of locale i tij = Travel time from locale i to local j

# **4. Data**

Selection of our three country cases was guided by the availability of publicly-available Covid-19 infection data and the desire for geographic diversity. In the United States, daily county-level data on Covid-19 cases are available from multiple sources, including the New York Times national Covid-19 reporting project (NYT 2020) and a website operated by Johns Hopkins University. The data draw on the same reporting sources, and we chose the New York Times database because it provides better information about the early period of the pandemic. For the Philippines, we construct a daily database for municipalities from a public file of individual cases by date and location maintained by the Covid-19 Tracker website of the Philippines Department of Health (PDH, 2020). For South Africa’s Western Cape province, we construct a daily sub-district database from online daily reports by the Premier (WCP 2020).

We compute US county population densities from data files maintained by the US Census Bureau for county populations (USCB 2020a) and areas (USCB 2020b). For the Philippines and Western Cape province, we compute mean population densities for municipalities and sub-districts, respectively, from raster files at 100 m resolution maintained by the WorldPop project of the University of Southhampton (WorldPop 2020).

We compute travel-time-weighted infections (Wit in model (10)) in a multi-step process. We base the computation on the centroids of the relevant locales (counties in the United States, municipalities in the Philippines, sub-districts in Western Cape province).<sup>3</sup> For the centroid of each locale, we select all neighboring locales whose centroids are within 200 km. We compute all centroid-to-centroid travel times using the Open Source Routing Machine (OSRM 2020), which uses road information from OpenStreetMaps (OSM 2020) to compute the most time-efficient route between two points. Figures 2(a) and 2(b) display representative gradient
), Reinhart and Wickham (1994), Cuddington and Liang (1999). Comparing three different datasets<sup>7</sup>

> 5 Interest about a possible negative long-term trend in commodity prices has occupied development economic literature since the late 1940s. For instance in Prebisch (1950) and Singer (1950), Grilli and Yang (1988), Cuddington and Urzua (1989), Cuddington (1992), Powell (1991), Reinhart and Wickham (1994). 6  For annual data, Cashin and McDermott define large booms as a sequence of generally increasing prices that have had a price movement of at least 25 percent over the phase, and large slumps as a sequence of generally decreasing prices that have had a price movement of at least 25 percent over the phase.

> 7 The datasets are the following: (1) the annual data set of Grilli and Yang from 1900 to 1992, (2) Boughton’s dataset with annual observations from 1854 to 1990, (3) the IMF’s _International Financial Statistics_ (IFS) which covers the post World War II period with monthly observations.

3
# **2 Empirical Strategy and Data**

## **2.1 Data**

Our main data sources include multiple rounds of the Economic Censuses in India, namely the 3rd (1990), 4th (1998), 5th (2005) and 6th (2013) rounds. The census covers all economic enterprises in the country, except those engaged in crop production and plantations, and provides information on the number of workers hired by each enterprise, number of enterprises, as well as ownership (male/female) of enterprises. We aggregate this information at the district-level. However, after 1990 several new districts were created in India. As a result, the administrative boundaries of many districts changed between the various Economic Census rounds. Therefore, we reclassify the newly formed districts to their original district administrative boundaries in 1990. In total therefore, we have 433 districts in our data.

We also use National Sample Survey Consumer Expenditure (NSS CES) rounds 55 (199900), 61 (2004-05) and 68 (2011-12), for household expenditure data. To explore linkages between the tradable and non-traded services sectors, we use the Indian Input Output Transactions (IOT) Table from 2006-2007. Lastly, the trade data for world import demand for the services sector comes from the WIOD database, as described in Timmer et al. (2015).

## **2.2 Classifying Tradability**

Our discussion of tradable and non-tradable services first requires a classification. To classify sectors into tradable and non-tradable, a popular approach is to analyze the geographic dispersion of industries, following Jensen and Kletzer (2006). However, Gervais and Jensen (2019) have recently improved upon this approach by constructing a classification based on implied trade costs.

Due to data limitations, we cannot estimate trade costs in the same way as Gervais and Jensen (2019). Instead, we follow the approach proposed by Head and Ries (2001) and then adapted by Chen and Novy (2011) using data from