rafmacalaba/data-use-mentions-extended huggingface dataset:
and experience accumulated overseas does not match demand in home labor markets, job search frictions may lead to higher unemployment among return migrants (Lucas 2005). On the other hand, past migration overseas may increase participation in economic activities at home by raising workers’ human and financial capital and thus workers’ productivity and earnings potential back home. The sign of the association between past migration overseas and labor supply in home labor markets is therefore an empirical question.

Few studies have investigated the labor market participation of return migrants compared to nonmigrants, and existing evidence on this question is mainly descriptive. Dustmann and Kirchkamp (2002) find that a sizeable share of return migrants from Germany to Turkey retire at an early age, presumably thanks to savings accumulated abroad.<sup>13</sup> Some studies evidenced that a longer duration of stay at destination can increase the likelihood to be unemployed after returning due to higher reservation wages (Piracha 2015). In the South Asian context, earlier work by Arif (1998) and Ilahi (1999) using data from Pakistan reports that return migrants from the Middle East are less likely to be employed than nonmigrants.

Table 2 and Figure 3 first display the labor market status of return migrants compared to nonmigrants by age in Bangladesh, Nepal and Pakistan. Labor market status is summarized by three indicators: (i) employment rate, defined, as the share of the working age population that is employed; (ii) labor force participation rate, defined as the share of working age population that is either employed or actively looking for employment; and (iii) unemployment rate, defined as the share of the economically active population (participating into the labor force) who is employed. First, employment rates are systematically lower among return migrants than nonmigrants in all three countries. The difference in employment rates between the two groups is statistically significant and larger in Nepal and Pakistan compared to Bangladesh (Table 2). This finding is consistent with the results of Arif (1998) and Ilahi
Table 4: RIF-OLS, Political Influence Relative to the Economy Median

||_β_<br>|<sup>_RIF_ </sup>at Perc<br>|entile, _τ_ =<br>|||
|---|---|---|---|---|---|
||10th|25th|75th|90th|Movement|
|Corruption Perceptions Index (n=24,344)|0.043***|0.033***|-0.029*|-0.072***|_→←_|
||(0.001)|(0.001)|(0.015)|(0.025)||
|Political Constraints (n=23,923)|-0.011*|-0.012*|-0.013|-0.032*|_←−_|
||(0.007)|(0.006)|(0.011)|(0.018)||
|Voice & Accountability (n=24,709)|-0.039***|-0.027***|0.015|0.048***|_←→_|
||(0.006)|(0.006)|(0.010)|(0.017)||
|Political Stability (n=24,709)|-0.031***|-0.023***|0.015|0.067***|_←→_|
||(0.007)|(0.007)|(0.012)|(0.015)||
|Government Efectiveness (n=24,709)|-0.031***|-0.018*|
it is systematically related to the level of development across space and time. More precisely, they found a stronger covariance between firm size and productivity in the United States than in Western European and more pronouncedly in Eastern European countries.

While the preceding papers have all focused on measuring the extent and cost of misallocation without knowing a particular policy/institution that may have caused such frictions, a number of recent papers have explicitly studied the consequences of specific policies or institutions. The theoretical contributions include: size-dependent policies (Guner et al., 2008; Garc ́ıa-Santana and Pijoan-Mas, 2014), credit-market imperfections (Midrigan and Xu, 2014), trade-related distortions (Melitz, 2003), capital-adjustment costs (Asker et al., 2014) and imperfect-information (David et al., 2014). Restuccia and Rogerson (2013) provide a good summary of this literature.

In a more recent work, Hsieh and Klenow (2014) focus on differences in the life-cycle of firms as an important mechanism by which frictions reduce aggregate productivity by distorting the incentive for firms to grow. They show that firm dynamics differ systematically across countries, with firms in developed countries growing much faster than those in poor countries over their life cycle. Hsieh and Klenow (2014) for instance, estimate that if U.S. firms exhibited the same dynamics as Indian or Mexican firms, aggregate manufacturing TFP would be roughly 25% lower.

There is a relatively smaller body of work that focuses on exploring misallocation and distortions in the business environment for firms in Sub-Saharan Africa. Perhaps the most salient contributions in this area are Kalemli-Ozcan and Sorensen (2012) and Aterido et al. (2011). The former explores capital misallocation in 10 African countries using the World Bank Enterprise Surveys and studies the extent to which access to finance can explain the dispersion in marginal returns to capital across countries. The latter explores the role of distortions in the business environment in explaining the differential employment growth across firms of different sizes.

Our paper makes two contributions to the
- World Bank. 2021a. GDP per capita, PPP (constant 2017 international $). World Development Indicators database, World Bank.

- World Bank. 2021b. Population, total. https://data.worldbank.org/indicator/SP.POP.TOTL  United Nations Population Division.

- Xu, W, Sun, J, Liu, Y, Xiao, Y, Tian, Y, Zhao, B, Zhang, X. 2019. “Spatiotemporal variation and socioeconomic drivers of air pollution in China during 2005–2016.” _Journal of Environmental Management_ 245: 66–75.

- Zhai, M and Wolff, H. 2021. “Air pollution and urban road transport: evidence from the world’s largest low-emission zone in London.” _Environmental Economics Policy Studies_ 23: 721–748.

- Zhang, J and Smith, K R. 2007. “Household air pollution from coal and biomass fuels in China: measurements, health impacts, and interventions.” _Environmental Health Perspectives 115_ (6): 848–855.

15
Central Asia. Given that the range of temperature varies across countries, we split the temperature distribution within each country into deciles and choose the 60<sup>th</sup> percentile as the baseline group. Figure A4 (Appendix A) shows that the effects of extreme weather are similar to what we observed in Figure 3.

We also provide further support to the regional heterogeneity by estimating the effect of temperature on poverty and inequality by country, adjusted by their real GDP per capital in 2018. Figure A5 (Appendix A) shows that countries bearing the largest effect of global warming are also those with the lowest income such as Uganda, Ghana, and Mozambique.

Next, we further assess the heterogeneity of the effects of temperature across different country characteristics. First, we examine whether a country’s institution may affect the impacts of temperature. This is motivated by the fact that institutions may affect adaptation to climate change through which incentives for individuals and collective action are structured. We use the democracy index from the 2020 report of the Economist Intelligence Unit and categorize countries into different types of regimes: (i) democracy; (ii) authoritarian; and (iii) hybrid. The results presented in Panel A of Table A9 (Appendix A) show evidence that countries with democracy regime appear to be less vulnerable to the impacts of global warming. We also examine the heterogenous impacts of temperature by other country characteristics. For example, countries near the equator have a higher poverty rate caused by an increase in temperature (Panel B, Table A9 in Appendix A). In addition, the effect of temperature is more pronounced in those with higher share of agriculture, while the opposite is found in countries with higher share of manufacturing (Panels C and D, Table A9 in Appendix A). Finally, we find a stronger effect among countries with lower share of trade, but our estimates are not statistically significant (Panel E, Table A9 in Appendix A).

In this paper, we are also interested
frequency of KPI review, and time frame of production targets. The answers to these questions are used to construct a management z-score following the methodology in Bloom and Van Reenen (2007). Despite covering only four of the 16 variables collected in MOPS, the FAT z-score based on this subset of questions accounts for 90.5% of the cross-establishment variance of the original MOPS z-score for Mexican establishments collected by ENAPROCE.

# **2.5 The Data**

Our analysis is based on primary data collected from establishments in 15 countries: South Korea, Poland, Croatia, Chile, Brazil (Ceará), Georgia, Vietnam, India (Uttar Pradesh, Tamil Nadu, Gujarat and Maharashtra), Ghana, Bangladesh, Kenya, Cambodia, Senegal, Ethiopia, and Burkina Faso. Several factors were considered in deciding where to implement the FAT survey. We targeted countries on different continents (Asia, Africa, South America, and Europe), with different levels of income, for which there was access to a high-quality sampling frames. In these countries, we collected data from 21,055 randomly selected establishments from the sampling frames. Table 2 shows the distributions of the sample by country, sector, and size groups and Table C.1 provides descriptive statistics. The median establishment in our sample has 9 workers, with an average of 34 workers. 20% of workers have a college degree, 19% of firms were multi-establishments, 18% are part of a multinational firm, 17% are exporters, 18% are 5 years old or younger, and 76% have electricity, computers, and internet access.

## **2.5.1 Sampling**

Our data is representative for a universe of about 2.1 million establishments. The samples are nationally representative for establishments with 5 or more workers. For each country, the sampling frame is based on the most comprehensive and up-to-date establishment-level census data available from the respective National Statistical Office (NSOs) or similar authority. The survey
(<0.4 million)|100%<br>69.7%||87.9%|24.1%|
|Medium city (0.4-2.2 million)|30.3%||12.1%|28.9%|
|Big city (>2.2 million)<br>Total|100%<br>100%||100%|46.9%<br>100%|
||**Municipality level**||||
|Share of municipalities|68.0%<br>3.9%||17.9%|10.2%|
|N|624<br>36||164|94|

Note: The sample corresponds to the universe of students who took the high school exit exam (Saber 11) in 2005. The information on population size comes from the Municipal Panel from CEDE (Center of Studies on Economic Development). The information on higher education programs and institutions is from SNIES (National System of Information on Higher Education).
sources,<br>World Bank.|
|Policy<br>uncertainty|Economic policy uncertainty index, which is based on the frequency of articles in domestic newspapers<br>mentioning economic policy uncertainty. Country-level indices are at a monthly frequency and used as<br>annual averages. Data are first converted to a 6-month moving average and then an index equal to<br>100 in 2015-18. Aggregated policy uncertainty is computed with market-weighted output as a weight,<br>based on 20 countries with available data.|Davis (2016); World<br>Bank.|
|Policies|||
|Government<br>expenditure|Government primary expenditure (i.e., government total expenditure excluding interest expense) in<br>local currency, deflated by GDP deflator. The expenditure series is first taken from the IMF and then<br>extended with data in Mauro et al. (2015). GDP deflator is taken from the IMF, PWT 9.1, and the<br>World Bank. Real government expenditure growth in aggregated groups is computed with market-<br>weighted output as a weight.|IMF; Mauro et al. (2015);<br>World Bank.|
|Policy interest<br>rate|Nominal central bank policy rates. Data are at a quarterly frequency and used as annual averages.<br>Data are taken from different sources, and the data series with the longest time coverage is used as the<br>main series. Then, the main series is spliced with those from other data sources. Data for aggregated<br>groups are computed with market-weighted output as a weight.|Bank for International<br>Settlements; Haver<br>Analytics; IMF; OECD;<br>World Bank.|

64
**Figure 8: Direct and indirect labor content of exports across sectors, forward linkages, South Africa 2011**

<!-- Start of picture text --> Trade and Transport Services<br>Other Private Services<br>Minerals nec<br>Metals nec<br>Ferrous Metals<br>Chemical, Rubber, Plastic Products<br>Machinery and Equipment nec<br>Metal Products<br>PubAdmin/Defence/Health/Educat<br>Transport Equipment<br>Agr, Forestry, Fisheries<br>Paper Products, Publishing<br>Electricity, Gas, Water<br>Energy Extraction<br>Processed Foods<br>Wood Products<br>Mineral Products nec<br>Textiles<br>Manufactures nec<br>Construction<br>Wearing Apparel<br>Beverages and Tobacco Products<br>Leather Products<br>Dwellings<br>0 2,000 4,000 6,000 8,000<br>US$ mil.<br>Direct LVAX Indirect LVAX<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

In line with other comparators, labor value added associated to exports tends to be biased towards unskilled workers. While the unskilled component of total labor content of exports is larger in most sectors, in public administration, defense, health and education and in other private services exports, the unskilled-skilled labor value added split is about even (Figure 9).<sup>14</sup> This is the case whether considering total value added computed through the backward or forward linkages method. At the other extreme, agriculture, forestry and fishing have the lowest shares of skilled labor contained in total exports.

> 14 We use 2007 as the split for 2011 is not compatible with previous years.

16
# Table 7: Analysis of classroom quality, teacher, and structural characteristics on children’s language <u>and cognitive development</u>

||no|DV: Languag|e & Cogniti|ve (Raw score<br>w/ teacher|)|
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br> <br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.129|0.107|0.111|0.102|0.132|
||(0.079)|(0.080)|(0.080)|(0.081)|(0.096)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||-0.135|-0.109|-0.127|
||||(0.558)|(0.550)|(0.544)|
|% of teachers w/ post secondary edu.|||-0.167|-0.170|-0.180|
||||(0.580)|(0.573)|(0.568)|
|Mean years of teaching|||-0.001|-0.003|-0.004|
||||(0.012)|(0.012)|(0.012)|
|% of teachers w/ no ECED experience|||0.274<br>(0.172)|0.255<br
and some cohort-based assumptions about the error terms to construct the synthetic panels. The methodology is described in detail in Annex B. Recent applications and further validations of the synthetic panel methods have been implemented using household survey data from various countries in Sub-Saharan Africa, East Asia and Pacific, Europe and Central Asia, Latin America, South Asia, and the Middle East and North Africa (see Dang, Jolliffe, and Carletto (2019); Dang and Lanjouw (forthcoming); and Garcés‐Urzainqui, Lanjouw, and Rongen (2021) for recent reviews).

We begin by identifying the potential time-invariant variables available in the cross-sectional surveys at two time points, which include household heads’ gender, age, level of education, and residence area (that is, urban or rural). These variables can usually be assumed to be time invariant if the underlying population remains unchanged over time. One way to test this assumption is to use a t-test for the equality of the means of the same variables in the two survey rounds. We have provided test results that consider the complex survey design in Table A1 for Chile and Table A2 for Colombia. The assumption of the equality of means is satisfied for household heads’ gender and secondary level of education in Chile and heads’ secondary and tertiary levels of education in Colombia. The assumption is satisfied for residence areas in both countries. Although the difference in heads’ primary level of education is statistically significant in Colombia, it has similar means over time. The differences between surveys for heads’ primary and tertiary levels of education in Chile and heads’

> 3 The years for the household surveys for Chile and Colombia were selected based on the availability of the subnational location information that can be matched with climatic layers on the geographic information system (GIS) platform.

5
as the World Bank are increasingly paying attention to data and analytics on refugees. However, this process has started only recently and will require time to bear fruits in terms of improved research on refugees.

For obvious reasons, the quality of refugee data is also very different depending on whether refugees are in camps or outside camps. In camps, refugees are assisted with shelter, education, health and any other requirement for conducting a decent life. Outside camps, refugees would normally receive cash and food assistance but they are on their own to find shelter and services. Refugees outside camps have an incentive to register because registration provides access to protection and assistance but contacts with UNHCR staff are more difficult and less frequent than in camps, whichs result in information that may be less accurate or up to date. In some countries, like Jordan and Lebanon over 90 percent of refugees are outside camps.

# IDPs

Data collection on IDPs is more scarce and fragmented than the one illustrated for refugees. By definition, IDPs remain in their nation of origin and data collection should, in principle, be easier. In reality, IDPs derive from an internal shock such as a civil conflict where a large number of people move quickly within a country. These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short‐term. In most cases, new surveys are suspended or carried out under the pre‐crisis frameworks and, in either case, information on IDPs is not collected or poorly collected.

This leaves specialized government
polygamy ( _m_ ), and unobserved type ( _μ_ ).<sup>10</sup> The utility function also incorporates a nonpecuniary cost (or benet) associated with being pregnant. This cost includes a stochastic preference shock as well as a deterministic age-dependant element ( _λ_ 4 _,t_ ). The cost of pregnancy changes if a woman was pregnant in the previous period ( _λ_ 5 ). The cost of consecutive births is allowed to change with the age of women ( _λ_ 6 ).

The specication of the model implies perfect control over conception and contraception. I could have instead introduced a cost of contraceptives, a probability of conception conditional on not wanting to become pregnant and a probability of not conceiving conditional on trying. Using data on births only, however, I cannot separately identify these elements. Instead, these elements will be absorbed into the cost of pregnancy (both deterministic and stochastic) and the preference for children. For example, having the cost of pregnancy change with age captures physiological factors which vary the propensity to conceive during dierent stages of a woman's life-cycle. The added cost of consecutive births enables the model to generate patterns of birth spacing.

# 2.3 Income and Consumption

It is assumed that households cannot borrow or save, which implies household consumption ( _C_ ) equals the household's income ( _Y_ ). The household's income is exogenous and stochastic. I specify a parsimonious household income function that is appropriate for the context of subsistence agriculture. I assume that the logarithm of income is distributed as Income depends on region of residence (Balaka, Mchinji), size of household's land plot (Land High), the age of the woman ( _t_ ), the number of children ( _N_ ) , and a time-varying income

> 10The majority of respondents in each of the three sites of the MDICP dataset are of dierent tribal groups. The tribes dier in their practices of lineage and residence after marriage which
have to be modified accordingly. For example, one common relationship between σ and _T_ is σ<sup>2</sup> = _a_<sup>2</sup> _T_ , where _a_ is a parameter representing some variability measure. Under this assumption: Finally, many projects to improve logistics are not specific to a particular bilateral flow. For example, investing in information technologies to improve customs clearance processes for an exporting country can benefit all outbound flows. In that case the evaluation of the impacts of such projects should be based on aggregate bilateral trade flows.

# **Conclusions**

Past research into determinants of international trade highlighted the importance of the basic gravity model augmented by additional variables representing sources of friction. Studies modeled these sources of friction using various proxies, including indices based on expert judgment in some cases. This paper focuses on logistics friction and draws on an extensive data set recently compiled by the World Bank. It also updates past studies by using bilateral trade data for 2003 (or the most recent data available).

After including model variables associated with earlier studies, the analysis finds that new variables directly related to logistics performance have a statistically significant relationship with the level of bilateral trade. It also finds that a single logistics index can capture virtually all the explanatory power of several logistics indicators used together. A single logistics index is simple to evaluate and allows easy benchmarking. In addition, it can be used to help direct resources to their most effective uses when there are competing initiatives to improve a country’s logistics operations.

The findings should spur organizations that have direct or indirect power over logistics performance in port operations to focus attention on reducing sources of logistics friction as a way to increase their country’s ability to compete in today’s global economy.

25