rafmacalaba/data-use-mentions-extended huggingface dataset:
firm exports by destination. We manually matched both databases for the period 2001–2005. As a result, we built a 5-year panel database of Chilean manufacturing firms.

The data have several modules. The main module contains information on industry affiliation, ownership type, sales, exports, input use, imports of materials, workers and wages. Industry affiliation is defined at the 4-digit ISIC Revision 3 level, which totals 113 industries.

We are mostly interested in the employment information. The data on workers are presented at detailed categories, which allows us to explore the demand for different skills and tasks. To do this, we base our empirical analysis on recent models of international trade where goods are produced with tasks using assignment rules (Acemoglu and Zilibotti 2001; Acemoglu and Autor 2011; Costinot and Vogel 2010). Production involves many different tasks, such as managing, accounting, clerical activities, design, packaging, logistics, sales representation, operational production, input control, monitoring, supervision, services. Tasks can be executed by workers with different skills. We argue that the most skill-intensive tasks, such as managing or directing, are typically filled with skilled workers. In turn, the most unskill-intensive tasks, such as cleaning services, are instead performed by unskilled workers. The remaining tasks, mostly involving production and design, can be done by either skilled or unskilled workers. This depends on factor prices as well as on firm characteristics such as productivity. Because of this, exporters and non-exporters may choose different skill employment composition even for similar tasks. For instance, a machine at an exporting firm may be operated by a skilled engineer, while the same task could instead be performed 3
the coefficient on the age group dummy 40-49 between return migrants and non migrants, suggesting that the age effect is concentrated among individuals that are close to retirement age. When interpreting our results, it is important to keep in mind that our sample of return migrants only consists of return migrants who came back home in the last 5 years. As a result, inference from our analysis can only be drawn for the population of recent returnees. In particular, older returnees as captured by the survey data are also individuals who returned home at an older age.

# **5.2 Occupational choice**

Conditional on being employed, the occupational choices of return migrants may also systematically differ from those of nonmigrants. We first examine the sectoral choice of return migrants relative to nonmirants, which has not been much examined by prior literature. As migrants from South Asia acquire sector-specific experience overseas, primarily in the construction sector (Ahmed and Bossavie 2021), they may be more likely to operate in those sectors after returning home to take advantage of the wage returns to experience. We first look at the unconditional distribution of return migrants across sectors of activity, compared to nonmigrants. As shown in Table 2, return migrants to South Asia are more likely to be employed in the construction sector back home compared to nonmigrants in Nepal and Pakistan, but not in Bangladesh. The difference in sectoral distribution between nonmigrants and return migrants is statistically significant in Nepal and Pakistan, while it is insignificant in Bangladesh. In Table 6, we estimate the likelihood of being employed in four main sectors of activity in a multinomial regression setting, controlling for workers’ observable characteristics. In all three countries, being a return migrant increases the likelihood of being employed in the construction sector. Return migrants thus disproportionately select into occupations they were employed in at destination when they return home.

A growing body of literature also shows that return migrants are likely to become selfemployed or entrepreneurs in various contexts. When options for formal wage employment at home are limited, temporary
>Norway 11.6%<br> 12.1% South Africa<br> 12.3% India<br> 12.3% Indonesia<br>Australia 15.2%<br> 15.2% Peru<br> 15.6% Brazil<br> 15.9% Bulgaria<br> 20.8% Russia<br>0% 25% 50% 75% 100%<br>A-rated issuers (AAA to A-)<br>B-rated issuers (BBB+ to BB-)<br><!-- End of picture text --> capital is a function of (inflation-adjusted) unit rents, production quantities and the difference between deforestation and re-/afforestation (World Bank, 2018).

A main critique point of the wealth data is its low, annual frequency. While this characteristic may be undesirable for financial markets, where subsecond data can be used to arbitrage, the same property turns out to be an advantage in our case. Even if crop yield data would be available on a daily frequency, it would be conceptually meaningless, let alone the concerns about measurement errors and low signal-to-noise ratio.6 In this study, the low frequency alleviates an econometric concern. Suppose bond yields and natural resources are related, which way does the effect go? With monthly, stationary bond series on the one side and annual, persistent wealth data on the other side, the estimated effect is more likely to originate from the latter. Moreover, the discounted resource rents are calculated with lagged, 5-year moving average rents (World Bank, 2018). This effectively makes the wealth variables predetermined. These technical arguments, however, do not preclude the existence of long-term trends in the bond data or the presence of other confounding and possibly unobserved variables that influence bond prices as well as natural resources.

The wealth accounting indicators used in this study were compiled on an annual frequency and will be made available together with the accompanying report (
**Figure 5.1: Competition policy indicators and rule of law**

<!-- Start of picture text --> Competition policy indicators, 2013 Rule of law, 2015<br>1.1 1 1.1<br>1<br>0.9 0.8<br>.8 0.9 .5<br>0.4<br>.6<br>0 0.1 - 0 .1 - 0.1 -0.1<br>-0.2<br>.4 0.4 0.4 -0.4<br>.2 0.3 0.3 0.3 0.3 -.5 -0.7<br>0.2 0.1<br>-1<br>0<br>IDN ZAF IND OECD BRA HUN POL TUR BGR RUS CZE CZE POL HUN ZAF IND TUR BGR BRA IDN RUS<br>Indicator 0-6<br>Indicator -2.5 - +2.5<br><!-- End of picture text --> Source: OECD; author’s calculations. Note: higher values on the competition policy indicator denote less conducive to competition.

**More intense competition in service sectors could lead to 1 percentage point of additional growth in value added in all service‐intensive sectors** (World Bank, 2013). Increasing competition in services would have positive spillover effects downstream on nearly all sectors of the economy. Conservative estimates indicate that reducing regulatory and competition constraints on professional and transport services would result in benefits of over TL 1 billion in additional value added to the economy per year.<sup>16</sup>

**Professional services should be opened to competition** . Turkey’s regulatory framework governing the liberal professions (professions requiring special training in the liberal arts or sciences, such as notaries, lawyers, engineers, or accountants) does not
identifying national weaknesses that may have serious repercussions at the regional level.

# Roads

Data from 2008 reveal that around 73 percent of roads in EAC are paved. A much lower 57 percent of the regional road network in East Africa is paved (table 2.3). This section takes a national perspective on the regional road network. For this purpose, the regional road network is defined as the network needed to connect all national capitals with one another and with the major deep sea ports. In EAC (and East 17
Policy Research Working Paper 9993

# **Abstract**

Did increases in mask supply slow the spread of COVID19? Rwanda licensed and incentivized textile manufacturers to produce high-quality masks at the start of the COVID-19 pandemic. This paper exploits spatial variation in exposure to mask manufacturing through textile trade networks within an event-study design using receipt-level tax data. Licensing domestic mask manufacturers conservatively reduced mask prices by 8.8% and reduced monthly growth in COVID-19 infections (proxied by demand for anti-fever medicine) by 12%. The dynamics of the results suggest that increased mask quality explains reduced infections, in a context where there was strict enforcement of mask mandates and informal markets for masks.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fkondylis@worldbank.org, jloeser@worldbank.org and kbyrne@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Please note that urban _kabupatens_ that are part of large metropolitan areas may be treated just like cities ( _kotas_ ) and grouped with cities. Meanwhile, the fiscal capacity and fiscal needs would be calculated as follows:

**Fiscal capacity** is defined to include potential revenues from own sources (PAD) if the jurisdiction applied group average tax effort to own bases plus tax shares and transfers (DBH Pajak) plus potential natural resource revenues (DBH SDA) plus other grants. A simple way to calculate potential own source revenues would be to apply national average effective tax rate to local nonresource GRDP (in this simulation, it is proxied by using non-oil and gas GRDP). Potential resource revenues will be similarly calculated by applying national average effective resource tax rate to local resource based GRDP only (in this simulation, it is proxied by using oil and gas GRDP). Due to instability of resource reveues, and higher public expenditure needs with resource exploitation and exhaustible nature of some resources only 50% of resource revenues will be counted towards revenue capacity of resource rich regions as done in Canada. Revenues from specific purpose grants will be fully included. Capital grants and loans earmarked to finance spefic projects to finance centrally determined infrastructure deficiencies will  be excluded from such calculations.

**Fiscal need** is calculated for a representative expenditure system of about 10 functions comprising most of local operating expenditures. This expenditure system will be dfferentiated by size class of local governments and will have service population indicators as determinants with weights based upon aggregate group local government expenditure for the specified function based on a 3 or 5 years group moving average. Table 8 provides illustrative expenditure functions, service indicators and weights based upon 2008 expenditures only. Since the data for public housing is unavailable, its weight is distributed to other field of services.

The allocation for Alternative 1 is then determined by a simple gap-filling for the per capita fiscal gap, that is the difference of per capita fiscal capacity and per capita fiscal need. The per capita fiscal gap for
**Figure 4.** Guinea: Correlation between sectoral productivity and change in employment share during the period 2006-15

<!-- Start of picture text --> β = 0.413; t = 2.17<br><!-- End of picture text --> <!-- Start of picture text --> β = 0.413; t = 2.17<br>min<br>pu<br>con tsc fire<br>man<br>cspspswrt<br>ag<br>-6 -4 -2 0 2<br>Change in employment share<br>Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> Fitted values<br><!-- End of picture text --> Source: Authors’ calculations based on data from the INS.

_Note:_ Size of circle represents employment share in 2006; agr= agriculture; min=mining and quarrying; man=manufacturing; pu=public utilities; con= construction; wrt=wholesale and retail trade, hotels and restaurants; tsc=transport and telecommunications; fire=finance, insurance and real estate; csps=community, social and personal services; ps= government services. β = coeff. of independent variable in regression. ln(p/P) = α + βΔEmp. Share.

**Table 2.** Guinea: trends of labor productivity <u>growth and its components</u>

||**2006-15**|**2006-10**|**2011-15**|
|---|---|---|---|
|Labor productivity growth rate|-0.58|-1.02|0.27|
|Within component|-1.57|-1.53|-0.04|
|Structural change component
(1) Y=Z—AZ In equation (1), the term Y denotes a final demand vector, Z denotes a gross output vector, and A denotes a matrix of intermediate use coefficients, which can be calculated using data from IO tables. Equation (1) therefore defines final output with respect to intermediate input requirements.

The first matrix that is calculated is the widely used Leontief inverse matrix, also known as the multiplier matrix M. With some manipulation we arrive at the Leontief inverse matrix:

# �������<sup>��</sup> ����

The multiplier matrix M measures the inputs contained in a unit of final output. This M matrix contains both direct and indirect inputs. In particular, if we assign the sector indexes i,j to the M matrix, then a representative element of the M matrix, ���, gives the direct and indirect inputs (and thus receipts) of sector i linked to each unit (for example each dollar) of sector j receipts in the data. This implies real production activities measured by value of output. For our purposes, it provides a means to trace through these income flows, the flow of gross activity (and eventually labor value added) from intermediate to final goods and services, ostensibly across borders as well as sectors. Because linkages will vary by industry, each industry will be characterized by different multipliers.

Next, we need to calculate a diagonal matrix that measures the labor value added (compensation of employees) share of total gross output, which can be calculated using data from SAMs. To focus on labor value added, rather than gross activity, we note first that in terms of gross output values Z, some share of this involves labor value added within each sector. We define �<sup>�</sup> as the diagonal matrix indexed over i,j with diagonal elements equal to the compensation of employees’ shares of output Z. These shares
**Figure 12: Index of Diversification of Destinations for Peruvian Exports: 1993-2009**

<!-- Start of picture text --> 0.21<br>0.19<br>0.17<br>0.15<br>0.13<br>0.11<br>0.09<br>0.07<br>0.05<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>Traditionals Non Traditionals Total<br>index HH<br><!-- End of picture text -->

# **5. Conclusions**

Peru‟s exports exhibited rapid growth during the past decade, largely as a rapid increase in external demand for Peruvian exports – associated with the substantial pace of growth of the global economy -- and of growing supply as a result of the implementation economic reforms since the early 1990s. This has resulted in a tangible increase in the degree of openness of the Peruvian economy (as measured for example by the export/GDP ratio). However, the country has not yet caught up with what would be expected for its income level, as indicated by an empirical analysis that adjusts for a number of other factors associated with openness.

Based on a breakdown of the price and volume effects in traditional and non-traditional exports, this study finds that a significant part of the export value increase of Peru is related to the growth of volumes. The effect of export supply growth has been particularly important in explaining the expansion of the value of non-traditional exports, for which prices have played only a marginal role. Even for traditional exports, and despite booming international price levels after 2000, the increase of volumes has accounted for around 30% of the growth -- around 45% for the entire period since 1990. This suggests that the Peruvian export boom has also stemmed from investments in supply expansion, many of which made prior to the commodity price boom.

The analysis also finds
Our main contribution is to shed light on the solutions to environmental health challenges of the upcoming decades. We quantify the role of household-level tools for adaptation, underscoring the positive consequences of recent development policies in mitigating climateinduced disease. Our findings underscore the critical role of sanitation as an adaptation strategy in the face of escalating environmental challenges. However, the results also shed light on the urgency of a diversified portfolio of adaptation strategies to address the complex health risks posed by environmental factors in Cambodia. Without other strategies, only universal toilet ownership would temporarily contain the climate-driven increase in diarrhea incidence.

The remainder of this paper is organized as follows: In Section 2, we delve into the dataset used for our analysis and provide descriptive statistics. Section 3 outlines our baseline empirical strategy. In Section 4 we present the impacts of weather on health and explore the heterogeneity of these impacts and potential climate adaptation actions. Section 5 shifts our focus to assessing the impact of climate change on diarrhea incidence and evaluating the effectiveness of toilet ownership as a climate adaptation strategy. Section 6 summarizes our key findings and discusses their broader implications.

# **2 Data**

The Demographic and Health Surveys (DHS) have been conducted in low- and middle-income countries worldwide for over three decades, providing nationally representative household survey data on a wide range of population, health, and nutrition indicators (Cambodia NIS, MOH, and ICF, 2023). The DHS is a repeated cross-section of geo-referenced data, allowing researchers to track spatial changes over time. We use the 1998, 2000, 2005, 2014, and 2021 Cambodia DHS rounds as our main source of information on individual-level health outcomes for children. The DHS questionnaire separately asks whether children have experienced these symptoms in the past two weeks, allowing us to assess recent health status accurately.

To measure exposure to environmental hazards, we use two datasets, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and its analogous version for temperature (CHIRTS), published by Funk et al.
largest emitters per capita with a population of at least 1 million (both measured on average from 1980-2000). The resulting donor pool is listed in Appendix B.<sup>10</sup> This third restriction speeds computation and reduces concerns of interpolation bias. Including all economies with complete data does not meaningfully change the chosen weights, and therefore does not affect the results.

> 9 https://carbonpricingdashboard.worldbank.org. The Australian carbon tax, incidentally, does not appear in the World Bank’s carbon tracker data.

> 10 We finalize the donor pool with one further exclusion - we drop the United States due to the adoption of hydraulic fracturing in the mid- to late-2000s, which displaced coal-fired generation and caused a drop in emissions around 2010. 12
Policy Research Working Paper 9762

# **Abstract**

This paper examines spatial heterogeneity in the impacts of the early days of the COVID-19 pandemic on urban household incomes in Ethiopia and Kinshasa, Democratic Republic of Congo. Combining new panel household surveys with spatial data, the fixed-effects regression analysis for Ethiopia finds that households in large and densely populated towns were more likely to lose their labor incomes in the early phase of the pandemic, and their recovery was slower than other households. Disadvantaged groups, such as female, low-skilled, self-employed, and poor, particularly suffered in those towns. In Kinshasa, labor income-mobility elasticities are higher among workers—particularly female and/or low-skilled workers—who live in areas that are located farther from the city core area or highly dense and precarious neighborhoods. The between- and within-city evidence from two Sub-Saharan African countries points to the spatial heterogeneity of COVID-19 impacts, implying the critical role of mobility and accessibility in urban agglomerations.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at snakamura2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
investment needed to close half the gap is 3 percent of GDP a year or less, given the larger initial infrastructure stock (IMF 2020).

ECA’s sizable investment gaps are related partly to shortcomings in the efficiency of public infrastructure investment relative to EU peers (figure 9.B). In Bulgaria, for instance, the same public investment outcomes could have been achieved with considerably less investment spending (less by about 2 percent of GDP), had the efficiency of public investment and quality of infrastructure been closer to peers (IMF 2022a).

Education. Although average years of education in ECA are among the highest of the EMDE regions, there is significant scope for increased investment, beyond gross fixed investment, to improve basic and tertiary education in ways that would raise labor productivity (World Bank 2020b). The OECD’s PISA scores and learning-adjusted years of schooling suggest that the subregions and countries where improvements in the quality of basic education are needed most are Central Asia (Kazakhstan, the Kyrgyz Republic, Tajikistan, and Uzbekistan); the South Caucasus (Azerbaijan and Georgia); the Western Balkans (Albania, Bosnia and Herzegovina, Kosovo, Montenegro, and North Macedonia); Moldova; and, in Eastern Europe, Bulgaria and Romania, which are among the EU countries that invest the least in education, including public expenditures on teachers and training, education infrastructure, digital learning, and equity and inclusion. Early childhood education is also important. On average, children who attend preschool stay in school nearly a year longer and are more likely to be employed in high- skill jobs. Highquality interventions in the early years have a high benefit-cost ratio and can deliver annual returns of about 13 percent on investment (García et al. 2016).

In some economies in ECA, particularly in Central Asia, inadequate investment in human capital has left parts of the workforce poorly equipped for rapid technological change (Flabbi and Gatti 2018). Low educational attainment among the workforce and inadequate skills have often been cited