rafmacalaba/data-use-mentions-extended huggingface dataset:
six workers) also invest in generators during power crises, though using a generator exacerbates productivity losses by diverting capital away from other inputs ( **?** ). In the sample studied in this paper, only 3% of firms report having access to a generator during the panel data collection period and only a single firm purchased a generator in the year during which the sample was surveyed.

This study also differs from the existing literature in the frequency of the labor input data it exploits. Using high-frequency (daily) data, this paper shows that microenterprises cope with power outages in the short term by shifting labor demand for workers and labor supply of firm owners. In this, it builds on **?** , which presents indirect evidence that electrification enables self-employment in South Africa and **?** , which show that place-based infrastructure grants increase employment in India.

This article studies a period of heavy load shedding and frequent, unpredictable blackouts in March and April of 2015, at the tail end of a three-year power crisis known as _Dumsor_ , or the “Lights Off, Lights

> 1For example, **?** consider access to capital, finding high rates of return to capital in microenterprises in Sri Lanka; **?** show an impact of randomly offered consulting services on the productivity of small firms in Mexico; and **?** present experimental evidence from Ghana that small firms in their context are labor constrained.

> 2The World Bank Enterprise Surveys in Ghana happen to have been collected during two periods of extreme power crisis, in 2007, during the 2006-07 power crisis and in 2013, near the beginning of the 2012-15 Dumsor crisis. As such, their estimates of the firm-level burden of lacking electricity may reflect that particular timing. In the 2013 survey, 61% of firms cite electricity as a major constraint to firm performance, as compared to 43% citing corruption and 62% citing access to finance. This figure is fairly constant across the three major firm
weighted average tariff difference in Manufacturing. This result supports our assumption that, in addition to tariffs, there are other barriers to trade between CEFTA and EU. Comparison between the tariff data and our border differential estimates suggests that Mining and Agriculture are subject to more significant NTMs as compared to Manufacturing, where the difference between the tariffs and the

> 4We find this heterogeneity intuitive. The border between CEFTA and EU is likely to impose the most trade costs on agricultural products for two reasons: (1) most tariffs between EU and CEFTA countries have gone to zero for all industrial products, but for some agricultural products there are still import tariffs for CEFTA products into the EU and vice versa, and (2) even with low tariff barriers, the EU imposes high SPS conditions which results in traders facing long delays at the borders crossing into EU (for e.g. from Serbia into Croatia). The large border differences in services trade can be due to various unified standards within the EU, which impose barriers to services trade with outsiders. Another possible explanation is that trade in services is highly localized consumption. Finally, a possible explanation for the relatively small borders in Mining and Manufacturing is that the barriers to trade in those sectors have been decreased.

4
# **4.1. Identification using department and year of birth: Age-cohorts from ENV-2008**

According to the ENV-2008 survey data, for more than 90 percent of the individuals who earned the CEPE (completed six years of primary education), it took between 6 to 10 years. This suggests the majority of the students in the primary school are in the 6 to 16 age group with the plausible assumption that primary education normally starts at the age of six. To identify the potential victims of war, we construct a young cohort including all primary school goers who were exposed to the conflict between 2002 and 2006. Based on this, the individuals aged between 10 and 22 years constitute the young cohort in the ENV-2008 survey. Using ENV-2008, we compare average years of education for individuals in the young cohort against an older cohort, aged between 23 and 32. The individuals in the old cohort are likely to be over the age of primary school goers between 2002 and 2006. We use the year of birth and the department of birth to identify an individual‟s exposure to war. To begin with, a straight forward difference-indifference of average years of education is calculated based on year and department of birth.

Table 4.1.1 Means of Years of Education by Cohort and War Prevalence

||Ye|ars of educ|ation|
|---|---|---|---|
||No War|War|Difference|
|Old Ch Ad 23 32 i 2008|7.84|9.18|-1.34|
|oort (ge to n )|(0.14)|(0.08)|(0.18)|
|Y ht Ad 10 t 22 i 2008|6.32|6.46|-0.14|
|oung coor (ge o n )|(0.06)|(0.04)|(0.07)|
|Diff
<mark>enumeration units) and a secondary sampling unit (dwellings).</mark><sup>23</sup> <mark>There is stratification in the first</mark> stage of sampling, based on 88 strata (urban and rural areas plus counties) (ibid).

# **<mark>The EU-SILC survey instrument is structured to gather the AROPE indicators at both household</mark>**

**<mark>and individual levels, enabling comprehensive AROPE monitoring across the 27 EU countries.</mark>** <mark>While this dataset enables the assessment of multiple deprivations among several vulnerable groups, it does not allow the assessment of deprivations among the Roma population due to the unavailability of ethnicity information. Very few EU countries currently have ethnic identifiers in the official household surveys. In addition, they suffer from significant data challenges (see Annex 1), underscoring the need for careful consideration when interpreting and utilizing ethnic data collected as part of the official household surveys.</mark>

**To monitor AROPE changes over time, we build a measure of absolute multidimensional poverty (“anchored AROPE”).** The at-risk-of-poverty measures income inequalities rather than direct poverty, as the poverty threshold varies with income distribution **.** Therefore, we anchored the poverty line to the year 2019 to track absolute income poverty and deflate the monetary welfare aggregates with the HCPI (Harmonized Consumer Price Index). This adjustment in the monetary component helps to ensure that the poverty threshold remains fixed or anchored and consistent across different years, allowing for more accurate comparisons of poverty rates over time.<sup>24</sup> Together with material and social deprivation<sup>25</sup> (the absolute component of the AROPE) and low work intensity, which captures exclusion from the labor market, they constitute the anchored AROPE, an absolute measure of multidimensional poverty.<sup>26</sup>

**<
# **1 Introduction**

The global spread of a coronavirus has led to renewed calls for greater investments in public health systems around the world. Policymakers are being advised to increase public spending on health to combat the disease as well as provide a fiscal stimulus to the economy (Wolf, 2020). A previous disease outbreak, of Ebola, had led researchers to conclude then that public health systems in poor countries need Community Health Workers (CHWs) as the frontline health workers to prevent, detect and manage disease outbreaks (Miller et al, 2018; Lo et al, 2017; Moon et al, 2015). This is the workforce that can ultimately implement any public health policy strategy advised by senior scientists and health professionals. Without the institution of such a workforce the world remains at risk of not being able to effectively manage and prevent disease outbreaks, especially those that originate in and spread from low capacity, poor countries.

This paper provides new ideas for policymakers to invest in frontline public health workers using economic theory of "principal-agent" relationships, in which one type of actor, the agent, takes actions on behalf of another, the principal. Insights from economic theory are supported by survey evidence from Bihar, the poorest state of India, a place which is representative of the challenges involved in building capacity of public health systems in poor environments. These ideas are: to combine steady wage structures, as opposed to high-powered incentives where salaries are conditioned on specific inputs or outputs of health workers, with management to strengthen peer-to-peer monitoring and professional norms; and political engagement of local leaders around public health. These ideas can immediately be put to work in policy responses to the COVID-19 crisis and strengthen public health systems to prevent future pandemics.

Policy-makers in poor countries, such as India, have typically relied on voluntary or quasivoluntary CHWs, rather than contracting CHWs to perform specified tasks under a wage structure. Any remuneration that is provided is typically incentivized on indicators of service delivery, such as in the case of
Starting from a broader set of at-risk taxpayers, we performed two restrictions to obtain the final experimental sample. First, in order to avoid spillovers between treatment and control units, only taxpayers with a unique primary email address were included in the experimental sample<sup>19</sup> . Second, power calculations exercises suggested we could significantly increase minimum detectable effects (MDE) by dropping extremely large taxpayers. Figure A5 reports MDE for our four main outcomes of interest when we trim our sample at different percentiles. Considering all primary outcomes, we decided to trim the sample at the 97th percentile of declared revenue distribution, arriving at our final sample of 31,396 taxpayers<sup>20</sup> .

We implement a stratified randomization, at the taxpayer level, using 60 strata defined by whether third-party information was available or not; whether the taxpayer was a corporation; municipality of operations defined as Distrito Central (capital Tegucigalpa), San Pedro Sula (second largest city, often referred as the industrial capital) or other; and five risk levels as defined by the tax authority (2*2*3*5 = 60 strata). In each strata 49 percent of taxpayers were allocated to the control group and the remaining 51 percent in three equally sized treatment arms. Following Bruhn & McKenzie (2009) we deal with "misfits" (remaining taxpayers in each strata) by randomly assigning them to one of the four groups (control + 3 treatments) using the above weights as assignment probabilities<sup>21</sup> .

# **3.3 Benchmarking results: Expert forecast survey**

In order to benchmark the magnitude of our results, we follow DellaVigna & Pope (2018) and DellaVigna et al. (2020) and surveyed academics and policymakers on their predictions about the impacts of this experiment. We focused on collecting forecasts from three groups of subjects: academic economists (faculty, PhD students and researchers at academic institutions); public sector workers in Honduras,
very similar to those from JOIN in 2013. However, for other countries experiencing changes in employment rates, the methodological changes are not well documented. These measurement issues also motivate using a more specific definition of employment in the full UHCI (discussed in the next section).

# **3.2 Data — Full UHCI**

In addition to the HCI and employment-to-working-age-population (basic utilization) rate, the full UHCI requires data on the share of employment in better jobs (SEBJ) as in Equation 12.<sup>33</sup> The SEBJ is then multiplied by the basic utilization (employment) rate to form the Better Employment Rate (BER, as in Equation 11). For those countries with HCI data, the BER is available for 161 countries (and 12 other countries with missing HCI data).

The primary source of data on employers, non-agricultural employees and total employment is ILO series “Employment by sex, status in employment and economic activity (Thousands)”, using the most recent year available.<sup>34</sup> The “Status in Employment” used is defined by the International Classification by Status in Employment, 1993 (ICSE-93), which breaks total employment into Employees, Employers, Own-account workers, Members of producers' cooperatives, Contributing family workers, and Workers not classifiable by status, where I use data on the first two categories as parts of better employment.<sup>35</sup> In terms of “economic activity”, I use the broad sectors “Total” (for employers and total employment) and “non-agriculture” (for employees). The SEBJ calculated using ILO data is multiplied by the employment-to-population ratio from the ILO used in the basic UHCI.<sup>36</sup> For countries with HCI data, these ILO data are available for 115 countries or 71% of the sample.

The secondary source is the JOIN database microdata. At the time of writing, the public JOIN data set provides
>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database Figure 19. Cumulative change in weighted average log labor productivity, 2014-2021 (wholesale trade, constant 2010 prices) <!-- Start of picture text --> 0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>-1.6<br>2014 2015 2016 2017 2018 2019 2020 2021<br>Within-firm Between-firm Entry* Exit* Total<br>Log-changes<br><!-- End of picture text --> Note: Averages are weighted by firms’ share of labor. Source: World Bank and ZRA staff calculations based on CIT database 12
<br>GCC Other oil Oil importers Rep.<br>exporters<br>SSA SAR MNA LAC EAP ECA High-income<br>countries<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

# D. Health spending below EMDE median

Sources: Rozenberg and Fay (2019); Global Infrastructure Outlook; World Economic Forum Global Competitiveness Index; World Health Organization; World Bank.

Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa; GCC = Gulf Cooperation Council.

A. Investment needs in a preferred scenario as defined in Rozenberg and Fay (2019).

B. Unweighted averages of survey data from the World Economic Forum Global Competitiveness Index. Data was collected using the question: “How would you assess general infrastructure (e.g., transport, telephony, energy) in your country? (1 = extremely underdeveloped—among the worst in the world; 7 = extensive and efficient—among the best in the world).” Oil importers include Egypt, Jordan, Lebanon, Morocco, and Tunisia. Non-GCC oil exporters include Algeria, the Islamic Republic of Iran, Libya, and the Republic of Yemen. GCC countries include Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates.

C. Unweighted average. Based on Universal health coverage services coverage index.
The relationship between absolute mobility and national income may similarly be non-monotonic as opposing forces are at play. On the one hand, we hypothesize that absolute mobility decreases with national income on the grounds that richer countries have more educated populations, making it harder for children to surpass the educational attainment of their parents. On the other hand, however, the capacity to educate children increases as countries become richer, which suggests a positive effect of national income on absolute mobility. The relationship between absolute mobility and national income will thus be determined by which of these two effects is stronger at different stages of national development.

# **3. Data**

## **3.1. Identifying relevant surveys**

To construct our Global Database on Intergenerational Mobility (GDIM), a comprehensive review of surveys that ask respondents about their parents’ educational attainment was conducted. Eighty percent of the surveys selected were conducted after 2011 such that respondents born in the 1980s had a chance to complete their education by the time the survey data was collected. Only individuals aged 21 or more are included in the sample to minimize the fraction of individuals still enrolled in school while not removing too many individuals from the sample. If more than one relevant survey was found for a country, the survey was selected based on sample size and the quality of the parental education information.

Broadly speaking, three different types of surveys are used. For most developing countries, crosssectional household income or expenditure surveys are used. For most countries in Europe and Latin America, social surveys such as the European Social Survey (ESS), the Latinobarometro, and the Life in Transition Survey (LITS) are used.<sup>8</sup> For a selected number of high-income countries, annual panels such as the Panel Study of Income Dynamics in the United States and the Labor & Income Panel Study in the Republic of Korea are used. A table with the list of surveys and years used can be found in Table B.1 in Appendix
0.0392**|-0.0290***|0.000661|
||(0.0180)|(0.0185)|(0.0193)|(0.00862)|(0.0196)|
|Observations|3,668|3,668|3,668|3,668|3,622|
|Dependent mean|0.400|0.510|0.780|0.910|0.740|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to public goods provision. Panel A presents results related to the supply-side provision of public goods and services. Panel B shows outcomes related to household demand for public goods and services. Column 1 shows whether the canton has received public investment for any social project (FISDL), mostly related to construction or infrastructure updates. Columns 2, 3, and 5 report the number of hospitals, schools, and public buildings per 100k inhabitants, respectively. Column 4 shows the road density in each census tract, which is measured as the length of all roads in the unit divided by their area. Columns 6–9 report the treatment effect on the share of households with any of the marked services within each census tract. Column 10 shows whether the household receives water daily or not. Information in the latter columns comes from the Population Census of 2007. Information in Columns 2 and 4 comes from Google maps. The unit of observation in Column 1 is the canton, but for the rest of the columns it is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects that represent the closest evenly spaced break in the guerrilla-controlled boundary. The
# **I. Introduction**

Income distribution as a topic of analysis and measurement has mushroomed in recent decades, since Anthony Atkinson argued in his presidential address to the Royal Economic Society in 1997 that the subject should be ‘brought back in from the cold’ (Atkinson 1997). There has been a veritable explosion of interest in the subject among researchers, policy makers, and commentators alike. A virtual industry has emerged focusing on the documentation and analysis of inequality trends at the national and global levels. The interest has been spurred in both the developed and the developing world.

While there has been much progress in the production of inequality statistics around the world, offering expanding opportunities to track trends over time and to make comparisons across countries, the underlying processes that characterize changes in inequality merit further and continued investigation. Notably, it is still rarely the case that the patterns of relative income mobility that underpin changes in inequality are documented, let alone well understood, particularly for developing countries. And yet the normative view one takes of a rise in inequality may differ depending on whether one is observing a simple stretching out of the income distribution—leaving individuals in the same position within the distribution—or the increased inequality is accompanied by individuals or households moving up and down in relative terms within the income distribution. As noted by Paul Krugman, ‘if income mobility were very high, the degree of inequality in any given year would be unimportant, because the distribution of lifetime income would be very even’ (Krugman 1992 00).

Assessing the degree of income mobility poses significant data and measurement challenges. In particular, it is necessary to work with panel data, as only such data permit the tracking of households over time. Collecting such data, however, can be very costly, and can also pose logistical and capacity-related challenges. These constraints are particularly acute in developing countries. The scarcity of panel data has thus rendered the analysis of welfare dynamics difficult, if not impossible, in many developing country settings
# **Non-technical summary**

Despite the long relationship between the European Union and the African, Caribbean and Pacific (ACP) countries aimed at encouraging their exports while stimulating growth and investment, the ACP states still face difficulties in integrating into the world economy. This paper examines the non-least developed ACP countries preferential trade with the EU using data on EU member states' imports eligible for preferences under the Cotonou agreement for the period 2001 at the 8-digit level. Using data on tariffs and preferential quota applicable on each 8- digit product for the year 2001 ad-valorem tariff rates were calculated. The objective of the paper is to explain the determinants of preferential exports of ACP countries towards the EU. The paper also investigates the existence of a threshold in the offered duty reduction under which traders have no incentives to ask for preferences since the costs of obtaining these exceeds their benefits.

Our results showed that the higher the value of preferences offered, the higher the probability that preferences are requested. We also provided evidence that there exists a minimum value of preferences needed for traders to request preferences. Thus if the difference between preferential and third country tariff rates are lower than 4%, there are no incentives for traders to request preferences since the costs of obtaining the preferences are expected to be higher than the benefits from obtaining the preferences. Our results additionally indicate that country specificities also play an important role in the decision whether requesting preferences or not and how much to import.

ii
Manufacturing, Utilities and Construction):** There is evidence of unconditional

- convergence in many of the industry sectors. The finding of unconditional convergence in the manufacturing sector is line with Rodrik (2013) using UNIDO data.<sup>10</sup> However, the estimated convergence rate is low. Diao et al. (2021) reveal a dichotomy between larger firms in the manufacturing sector that exhibit

> 9750 years ≒(ln(0.9)/ln(0.1)-1)/0.00127, using Table 2.

> 10However, Rodrik (2013) acknowledges that the “convergence results that follow should be read as applying to the more formal, organized parts of manufacturing and not to micro-enterprises or informal firms. In developing countries, enterprises with fewer than 5 or 10 employees are often not included.” UNIDO reported that there is a significant difference between UNIDO and the national account.

8