rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.2. Data**

The main sample consists of 25 countries (EU27 countries minus Ireland and Lithuania) for which we have country level data and NUTS2 level data.<sup>19</sup> Our sample generally runs from 2000-2018 (programming periods 2000-2006, 2007-2013 and 2014-2020), though the sample starts later for some countries and specifications due to missing data.<sup>20</sup> The main data required for Equation 1 are real GDP and real EU payments, both measured in real euros. Real investment data are required for variants of Equation 1 explaining investment rather than GDP. Each real variable is constructed from nominal data taken from Eurostat for GDP and Investment and the European Commission for EU payments. Specifically, nominal GDP data are “Gross domestic product (GDP) at current market prices by NUTS2 regions” (downloaded 19-Oct-2022),<sup>21</sup> EU payments are from “Historic EU payments - regionalised and modelled” (downloaded 09-Sept-2021)<sup>22</sup> and Investment data are “Gross fixed capital formation by NUTS2 regions” (downloaded 19-Oct-2022).<sup>23</sup> In order to convert nominal series denominated in euros ( ) to a real series ( ), we take the nominal value of each of the series for NUTS2 region _i_ that belongs to country nn _c(i)_ , at time rr _t_ and divide it by that ii,tt ii,tt xx xx country’s GDP deflator at time _t_ measured in euros ( € ) i.e = €nn, ~~.~~<sup>24</sup> The deflator data are measured rr xxii tt in euros, and are taken from Eurostat (downloaded 14-Oct-2022). ppcc(ii),tt xxii,tt<sup>25</sup> ppcc(ii),tt

**Data Cleaning.** Growth data are often highly volatile, and so extreme
# **Background**

There is a growing consensus that poor quality care is a major constraint in further improving health outcomes in low- and middle-income countries (LMICs).<sup>1–3</sup> Based on the most recent estimates, 8.6 million people per year in LMICs die from conditions that could be treated by health systems, and a majority of these deaths are due to receipt of poor quality care.<sup>4</sup> Measuring quality at various levels of the health system—especially where the system fails the people in need—remains rare in LMICs. Existing indicators of structural quality, such as availability of medicine, equipment, and qualified health attendants, are not sufficient to adequately capture the quality of care being offered.<sup>1</sup> The lack of quality measurement can help to explain both why there is limited research on how to effectively transform low-quality health systems into high-quality ones, and why there are few such successful reforms in LMICs. Actionable and credible data on quality, if properly disseminated, can play an important role in holding country health systems accountable for effective and safe care and can form the basis for quality improvement.<sup>5</sup> Our paper reports on large-scale assessments of clinical knowledge, health provider absenteeism, and means to provide care in Sub-Saharan Africa. We use data collected through clinical vignettes, unannounced visits, and visual inspections of over 20,000 providers in over 8,000 facilities in 10 Sub-Saharan African countries, which together represent over 40 percent of the region’s total population. We focus on conditions that account for a large proportion of maternal and child mortality. (We include the STROBE checklist for cross-sectional, observational data analysis in Appendix Section S1.<sup>6</sup> ) We use these data to measure dimensions we would argue are essential for providing quality care. Clinical vignettes tell us what providers know. Unannounced visits to facilities tell us if
of the UNHCR, registry data are very rarely shared. A few exceptions to this rule occur when UNHCR implementing agencies need information for running programs with refugees or if the UNHCR needs to carry out a study on a particular group of refugees. Recently, the UNHCR has also collaborated with the World Bank to carry out a study on the welfare of Syrian refugees and for this purpose the UNHCR shared some of its data with the World Bank.<sup>8</sup> However, unit records, anonymized or not, are not available online or upon request from the UNHCR. This is perhaps the main

> 8 See Verme et al. (2016) and the following section.

15
the model, while AFR has mixed results, with Botswana and South Africa showing significantly higher public employment levels.

We examine the potential role of political ideology driving these deviations, under the hypothesis that left-leaning governments would have larger public employment levels. Hence, we correlate the deviations with a variable that captures the political ideology of the government in office (left, center of right) obtained from the Database of Political Institutions 2017 from the Inter-American Development Bank. The deviations from the predicted level do not appear to be driven by political ideology, as suggested by

> 11 The model is estimated at an annual frequency (not five-year averages) to examine the public employment along the business cycle. Results are not presented to save space but are available from the authors upon request. 12 To increase the number of countries, in cases where general government employment data are not available, we substitute it with public sector employment.

> 13 Figure 16 shows the comparison using only the last available 5-year window.

19
We use _OCCISCO_ variable which records the person’s primary occupation<sup>2</sup> coded according to the major categories in the International Standard Classification of Occupations (ISCO) scheme for 1988 and have 11 categories: _Legislators, Senior Officials and Managers; Professionals; Technicians and Associated Professionals; Clerks, Skilled Agricultural and Fishery Workers; Crafts and Related Trades Workers; Plant and Machine Operators and Assemblers; Service Workers and Shop and Market Sales; Elementary Occupations; Armed Forces_ and _other occupations and no identified occupations_ . Table 1 lays out the categories we include in more detail.

Autor (2010) and Autor and Dorn (2013) map these 3-digit categories into a distinct set of skill sets listed in figure 1 to better capture “routine” tasks. Hence, in the original ISCO categorization, operators of machines in manufacturing appear in “Plant and Machine Operators, and Assemblers” (category 8) but manufacturing workers who do not operate machinery appear in “ elementary occupations” (category 9). Both may be more routine than, for instance, food preparation or personal care, also found in category 9, which require potentially less skill, but which are also less easy to automate. As we are working with numerous countries with varying degrees of disaggregation and sometimes inconsistent or ambiguous categorizations across time that have been standardized by the census categories, we work directly with those. As we show, for the United States, this does not change the conclusions appreciably.

The available census data for developing countries for which we can follow employment in a substantive way before and after 1990 are limited, but not unrepresentative. From Latin America we have Brazil, the Dominican Republic, Ecuador, El Salvador, Mexico, Nicaragua, Panama, and Peru, most of which have had a manufacturing tradition. From Asia we have India, Indonesia, Vietnam, and Fiji. From the Middle East: Arab Republic of Egypt, Mo2For someone with more than one job, the primary occupation is typically the one in which
# **Figure 5: Domestic Mobility as Measured by Current Residence Migration History**

<!-- Start of picture text --> Toshkent City Year of Last Departure<br>Sirdaryo<br>Andizhon % of total migrants<br>Karakalpakstan 5.0<br>Horazm<br>4.5<br>Buhoro<br>4.0<br>Navoiy<br>Jizzakh 3.5<br>Fargona 3.0<br>Namangan 2.5<br>Kashkadaryo 2.0<br>Surhondaryo 1.5<br>Toshkent region<br>1.0<br>Samarkand<br>0.5<br>0% 5% 10% 15% 20% 25% 30%<br>0.0<br>Destination Out-Migration<br>1940 1954 1958 1963 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016<br><!-- End of picture text --> _Notes: Mobility estimates are derived from survey responses in the L2CU Baseline. Author’s Calculations. The left shows source/destinations as a share of all those people who report ever having moved (domestically). The right-hand graph reports the year that they report last having moved._ Most self-reported migrants came from other regions of Uzbekistan (Figure 6). However, about 23 percent moved from another country, most commonly the Kyrgyz Republic, Tajikistan, and the Russian Federation. About 71 percent report that the primary reason for their move was family-related (either moving with family, or to join family) followed by employment-related reasons at 19 percent. Relatively small shares of respondents reported moving due to government relocation or security concerns, and only about four percent for the purposes of studying.

There is imprecision associated with these population estimates, which is magnified by the lack of census data for Uzbekistan (no census has been conducted since independence, though one is planned for 2022). In particular
# **1 Introduction**

Household consumption expenditure surveys conducted by the National Sample Survey (NSS) organization are the main source of poverty and inequality statistics in India. These surveys support the development of major data-driven policies in India and are used as inputs in the estimation of GDP and India’s consumer price index (CPI).<sup>1</sup> The latest NSS expenditure survey that is publicly available for India is from 2011. As the Indian economy has undergone significant changes since then, the release of the 201718 round of the survey had been eagerly anticipated. Unfortunately, it was ultimately decided to withhold the unit level survey data and its main results.<sup>2</sup> Using leaked estimates of the empirical distribution function of household consumption, Subramanian (2019) shows that poverty increased in rural India between 2011 and 2017 and that consumption inequality moderated (both in rural and urban areas). The rise in rural poverty neither sits well with consumption trends reported in national accounts data nor with proxy indicators of household welfare derived from official and non-official sources (including labor force surveys, surveys on agricultural household incomes, national family and health surveys of DHS, nighttime lights, etc.).

In the absence of an official consumption survey, several studies have attempted to fill the gap in poverty and inequality data by exploiting alternative data sources. Newhouse and Vyas (2019) and Edochie et al. (2022) impute household consumption into different choices of non-expenditure surveys, namely the Survey of Expenditure on Services and Durables (conducted in 2014-15) and the Survey on Social Consumption on Health (conducted in 2017-18). Chen, et al. (2018) and Felman, et al. (2019) predict growth in mean household consumption based on national accounts data.<sup>3</sup> Bhalla, Bhasin and Virmani (2022) build predictions using night-time lights and changes in state gross domestic product data. Desai (2020) estimates poverty using consumption data
**Figure A.6. Probabilities of Female-Headed Households Escaping Poverty in Second Year Conditional on Being Poor in First Year (percentage), Mauritania**

<!-- Start of picture text --> Mauritania 2004-2008 Mauritania 2008-2014 Mauritania 2014-2019<br>FHH wo/children FHH w/children Non-FHH<br>Self-reported Females>0.5 Potential Educated fem. Self-reported Females>0.5 Potential Educated fem. Self-reported Females>0.5 Potential Educated fem.<br>50 50 50<br>45 45 45<br>40 40 40<br>Percentage (%) Percentage (%) Percentage (%)<br>35 35 35<br>30 30 30<br><!-- End of picture text --> Note: Estimates are obtained with synthetic panel data and weighted with population weights where the second survey round is used as the base year. The figure shows the percentage of the population that moves out of poverty in the second year. FHH characteristics are measured in second period. Dashed red lines represent the national average for each period. Household heads' ages are restricted to 25-55 for the first survey round and adjusted accordingly for the second survey round. Standard errors are estimated with 1,000 bootstraps.

65
- Dagnelie, Olivier, Anna Maria Mayda and Jean-Franc ̧ois Maystadt. 2023. Refugees, Children’s Health and Malaria Transmission in Africa. Technical report.

- Davis, C. Austin, Paula L ́opez-Pe ̃na, Ahmed Mushfiq Mobarak and Jaya Y. Wen. 2024. “Refugees Are Hosted in Highly Vulnerable Communities.” AEA Papers and Proceedings 114:75–79.

- Friedl, Mark A, Damien Sulla-Menashe, Bin Tan, Anita Schneider, Navin Ramankutty, Adam Sibley and Xuecao Huang. 2010. “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets.” Remote Sensing of Environment 114(1):168–182.

- Gibson, J, S Olivia, G Boe-Gibson and C Li. 2021. “Which Night Lights Data Should We Use in Economics, and Where?” <u>Journal</u> of Development Economics 149:102602.

- Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21stCentury Forest Cover Change.” Science 342(6158):850–853. **URL:** _https://glad.earthengine.app/view/global-forest-change_

- Henderson, J. Vernon, Adam Storeygard and David N. Weil. 2012. “Measuring Economic Growth from Outer Space.” American Economic Review 102(2):994–1028.

- HRW. 2003. “Burmese Refugees in Bangladesh: Still No Durable Solution: Historical Background.” Human Rights Watch 12.

**URL:** _https://www.hrw.org/reports/2000/burma
nearest coast, and concentration of Particulate matter of 2.5 micrometers or smaller – PM2.5) from the NASA Socioeconomic Data and Applications Center (SEDAC). We provide descriptions and summary statistics of all variables in Table B1.

# **References**

- <mark>Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyses of climate change.</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 181–198.</mark>

- <mark>Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(3), 740–98.</mark>

- <mark>Heyes, A., & Saberian, S. (2022). Hot Days, the ability to Work and climate resilience: Evidence from a representative sample of 42,152 Indian households.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>155</mark>_ <mark>, 102786.</mark>

- <mark>Iizumi, T., & Sakai, T. (2020). The global dataset of historical yields for major crops 1981– 2016.</mark> _<mark>Scientific Data</mark>_ <mark>,</mark> _<mark>7</
do not hire unrelated workers. The owners of these low-probability firms have below secondary or secondary education. These kinds of informal firms have more than a 50% chance of being in the lowest quartile of the probability of formality and almost no chance of being in the top quartile. These relatively young small firms with no hired workers constitute about 19% of all informal firms inside establishments in the ELMPS. Older one-person firms with relatively low revenue per worker come next in terms of representation in the lowest quartile, irrespective of owner’s education. These constitute an additional 20% of all informal firms. Adding these two groups together suggests that approximately 40% of all informal firms operating in establishments have a low chance of formalization.

Results based on Model 2 using EcC data, shown in Table 7, confirm this pattern. Here, young one and two-person firms with no wage workers are also the most highly represented category in the lowest quartile of the probability of formality, irrespective of the amount of capital they have and irrespective of their value added per worker. These young small firms represent 34% of informal firms in the EcC. It appears that being young and not hiring wage workers are the primary predictors here, since larger firms with these characteristics are also highly represented in the bottom quartile although they only constitute another 1% of informal firms.

The cluster analysis highlights additional variables that characterize the informal firms that have the lowest probabilities of formality. As shown in Figure 14, the analysis using ELMPS data highlights low probability of formalization clusters than can be characterized as “women-owned retail firms” (8% of informal firms in ELMPS, 62% of which are in the lowest quartile of predicted formality) and “youth-owned young firms” (10% of informal firms, 42% of which are in the lowest quartile of predicted formality). These are followed by a cluster we term “smaller professional firms” (4% of informal firms, 39% of which are in the lowest quartile of predicted formality)
Table 5: Relationship between the green goods trade and earning across gender and skill levels (with industry fixed effects)

||All|Male|Female|High skilled|Low skilled|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||**A**|**nnual Earn**|**ings**|||
|Green Goods Export Share|0.000644|0.00007|0.00128|-0.00014|0.00154|
||(0.44)|(0.06)|(0.69)|(-0.16)|(0.73)|
|Green Goods Import Share|-0.00141|-0.0003|-0.003|0.00019|-0.00259|
||(-0.49)|(-0.19)|(-0.64)|(0.10)|(-0.70)|
|Industry FE|Yes|Yes|Yes|Yes|Yes|
|Workers control|Yes|Yes|Yes|Yes|Yes|
|N|77544|52868|24676|15708|61836|
|Adj. R2|0.441|0.444|0.456|0.267|0.255|

_Note: Results are based on repeated cross-sectional labor force survey data that are merged with industry level trade data. Industry fixed effects are at two-digit ISIC Rev 4 level. Worker’s control includes age, age squared, gender, marital status and 8 groups of educations in column (1). In column (2) and (3) gender is excluded while in column (4) and (5) education is not included as these regression
# **2. Setting and Experimental Design**

# **2.1 Study Area and Sample**

This study focuses on Ethiopia, which has one of the highest rates of child labor in the world, with 54 percent of rural 5 to 14-year-old children involved in economic activities, mostly as unpaid workers in family farms (Guarcello and Rosati 2007).<sup>3</sup> Our survey design experiment is carried out in two different regions in Ethiopia (Jimma and Sidama) that produce two different varieties of Arabica coffee with high demand in international markets. We focus on coffee cultivation since it is a child labor-intensive crop due to the characteristics of the tasks associated with the pre-harvesting and harvesting production process (Kruger 2007). Before the harvest season, it has been documented that children participate actively in pruning, weeding, and fertilizing. At harvest, coffee producers employ children mainly as pickers of red coffee beans which must be picked immediately upon ripening to maximize their quality (ILO 2004).

Our population framework is based on 5100 smallholder farmers who are active members of four Fairtrade coffee cooperatives that are spread out over 12 different districts (Kebeles). Within each selected region, we selected two representative Fairtrade Coffee Cooperatives, one characterized as of ‘high’ productivity and the second as of ‘low’ productivity to improve the external validity of the sample.<sup>4</sup> Sample selection is based on a 2x2 stratified random design: we split the population of farmers into high- and low-production groups according to whether they were above or below household median coffee production in 2014 based on administrative records. As variation in household coffee production could entail different combinations of adult/child work, this approach yields a sample that is representative of low and high coffee production 8
gaps in the literature that go beyond external constraints and endowments, and include internal constraints, preferences, and non-economic outcomes. Allison et al. (2023) provide data evidence that women-led firms tend to underperform compared to men-led firms around the world. Several policyoriented reports focus on policy recommendations and include useful reviews of the literature (Kevane et al. 2021; Koshy and Sanchez 2021; and Siegrist 2022). This paper contributes to this literature by reviewing a large set of recent RCTs with the purpose on analyzing what works to support grow-oriented womenled businesses.

# 2. Motivating facts and analytical framework

## 2.1 Motivating facts

A series of empirical studies documents that women-led firms face differential constraints and have lower levels of labor, total factor productivity, and profits than men-led firms (e.g., Aterido et al. 2011, Bardasi et al. 2011, Campos et al. 2019, Islam et al. 2020, World Bank 2021, Fang et al. 2022, Allison et al. 2023). Moreover, recent studies show that the COVID-19 pandemic has had a disproportionate negative impact on businesses led by women (Goldstein et al. 2020, Kugler et al. 2021, Liu et al. 2021, Torres et al. 2021). However, there is some emerging evidence indicating that gender gaps in profits or productivity are reduced or disappear for enterprises of larger size (World Bank 2021, Fang et al. 2022). This points to the importance of studying constraints restricting the growth of women-led firms, which can generate misallocation of resources and have major implications for economic growth and development (Chiplunkar and Goldberg 2023).

Such constraints are evidenced by the fact that the share of businesses led by women decreases as the size of the firm increases, as shown in Figure 1. According to calculations using data from World Bank Enterprise Surveys, women-led businesses constitute 22 percent of formal businesses globally.<sup>3</sup> However, the share of women-led firms diminishes from 24 percent
# <u>Post-Stratification</u>

Post-stratification adjustment matches population/household shares of subnational units between a phone survey and a nationally representative reference survey. If some subnational household shares are largely different from the nationally representative reference survey, even if SWIFT poverty estimation is accurate at the subnational levels, the aggregates or the national average poverty rates could differ largely. PSW cannot fully address these differences-even if subnational level indicators have been included in the PSW procedure, the shares of some subnational units may still exhibit non-negligible differences between the two surveys. Poststratification, on the other hand, modifies weights so that the population or household share of each subnational unit is exactly matched between the phone survey and the reference survey.

Under the circumstance that maxentropy or raking is executed at the national level and where subnational household or population shares are included as part of the constraints, post-stratification is not necessary. This is because national-level maxentropy or raking matches the subnational shares by including subnational unit indicators. In contrast, if maxentropy or raking is conducted at a subnational level (within each subnational unit), the adjusted household share of each unit will not be matched to that of the reference survey. Therefore, it is important to implement post-stratification after subnational level maxentropy or raking to eliminate any remaining bias due to mismatched population/household shares.

_Table 1 Post-Stratification Illustrative Example_

|**Reference Survey Hou**|**sehold Distribution**|**Phone Survey House**|**hold Distribution**|
|---|---|---|---|
|District 1|P1|District 1|S1|
|District 2|P2|District 2|S2|
|District 3|P3|District 3|S3|
|District 4|P4|District 4|S4|
|District 5|P5|District 5|S5|
|District 6|P6|District 6|S6|
|District 7|P7|District 7|S7|

Table 1 illustrates an