rafmacalaba/data-use-mentions-extended huggingface dataset:
|Honduras|2011, 2012, 2013, 2014, 2015,<br>2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares Propósitos<br>Múltiples<br>Encuesta Nacional de|7|LM|
|---|---|---|---|---|
|Mexico|2016, 2018, 2020|Ingresos y Gastos de<br>Hogares|1|UM|
|Panama|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta de Hogares|8|H|
|Paraguay|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Permanente de<br>Hogares|7|UM|
|Peru|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Nacional de<br>Hogares|8|UM|
|Uruguay|2010,2011, 2012, 2013, 2014,<br>2015, 2016, 2017, 2018, 2019|Encuesta Continua de<br>Hogares|7|H|

Note: LM: Lower-Middle Income, UM: Upper-Middle Income, and H: High Income Source: SEDLAC (CEDLAS and World Bank) for country classification: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</u> The proposed approach estimates a model of household-level income using a set of time-invariant and deterministic harmonized variables as controls to provide estimates of welfare dynamics. The harmonization procedure requires cleaning and processing each cross-section to ensure that all definitions and variables are identical in each country and year. The full list of available covariates for each country’s data is shown in Table 2.

Table 2: Harmonized variables for LASSO-PMM

|Variable|Definition|
|---|---|
|Welfare aggregate|
differences in the average hourly earnings of wage workers compared to the self-employed; the latter group earn approximately US$0.50 to US$2.00 less than wage workers in six of the eight countries. The relationship is different for Armenia and Ukraine, where the self-employed earn about US$0.20 more than wage workers; this may be due to the different nature of selfemployment in these two countries or to small-sample measurement error (the proportion of selfemployed in these countries constitutes less than 1 percent of the sample).

**Table 2. Labor market indicators, population ages 25 to 64, sample countries**

|Country|Labor force<br>participation|Employment<br>rate|Unemployment<br>rate|Observations|
|---|---|---|---|---|
|Armenia|49.5%|27.8%|43.7%|2,076|
|Bolivia|79.5|72.6|8.6|951|
|Colombia|73.7|61.9|15.9|1,391|
|Georgia|51.6|25.0|51.6|2,080|
|Ghana|84.3|76.3|9.5|1,346|
|Kenya|83.2|66.4|20.2|1,877|
|Ukraine|58.4|50.1|14.2|1,741|
|Vietnam|72.6|71.1|2.0|2,075|

_Source_ : STEP Surveys (2014).

**Table 3. Average hourly earnings, workers ages 25 to 64, sample countries** (in 2011 PPP-adjusted U.S. dollars)

||Armenia|Bolivia|Colombia|Georgia|Ghana|Kenya|Ukraine|Vietnam|
|---|---|-
materials that our results are robust to using only those counties that meet this flooding threshold.

We supplement these data with data from FEMA on the total payments made to individuals for every disaster declared by FEMA since 1954.

Our migration data come from the IRS Statistics of Income’s county-to-county migration flows. The IRS publishes data on the number of migrants leaving each county and their destination based on aggregated tax return data for each year from 1991 to 2019.

From these data sets, we assemble a balanced panel that lists all migration to and from all counties in the United States and the number of storms each county experienced from 1992 to 2017.

19
# **4 Internal Conflicts and Refugees**

A particularly serious aspect of internal conflicts is the human suffering they generate. This is not only those who are killed or injured in conflict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee flows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries.<sup>21</sup> The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this.

In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conflict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides information about UNHCR’s populations of concern from the year 1951 up to 2014. This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by location of residence and origin. We exploit only the data on “refugees”.<sup>22</sup> In particular, we are interested in the annual stock of refugees for each country of residence, i.e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number
Table 1 presents differences (across a variety of dimensions) between fi rms reporting a positive number of informal workers and fi rms reporting no use of such workers in our sample. The numbers in the table are the coefficients of a regression of different fi rm characteristics on a dummy variable that equals one if the fi rm reports using informal workers. Column (1) includes no other controls in the regression, while in column (2) we control for sector dummies and some regional characteristics.<sup>20</sup> Approximately 65% of the fi rms report using some full time informal workers. For these fi rms, on average informal workers represent approximately 36% of the permanent workforce. These fi rms are on average younger and smaller in terms of total employment, physical capital, value added, and pro fi ts than those reporting no informal employment, they have a less educated workforce and are less likely to provide formal onthe-job formal training to their workers. They are also less likely to be foreign or state owned than fully legal fi rms, they tend to be concentrated in low skilled/labor intensive sectors (like clothing, shoes and wood products) and are less likely to work in chemicals, machinery and electronic products.<sup>21</sup> Finally, fi rms employing informal workers report being more likely to pay bribes to labor officials (conditional on having been inspected) and are also more likely to underreport their sales for tax purposes than fi rms only hiring formal workers.<sup>22</sup> We combine this fi rm level dataset with administrative data collected by the Ministry of Labor in 2002 on the number of regional offices that exist within each subregion<sup>23</sup> , number of inspected fi rms and the number of fi nes issued in each city (more details are given in the data appendix). We have information on the number of fi nes for different labor violations: selective.

> 20We estimate an equation
. We identified 7053 eligible households at baseline. Using the same sampling method, we added extra households at midline in villages where the number of eligible households at baseline was below 10 (53 were added in total).

# **3.2 Data**

## **3.2.1 Survey and instruments**

For each household, a household survey, caregiver survey, and one assessment per eligible child were conducted.<sup>21</sup> At the village level, interviews were conducted with village heads and preschool teachers. In addition, at endline, we complemented the

> 16A brief monitoring survey was conducted in late 2016 to confirm that CPS construction was proceeding as scheduled.

> 1717 CPS were in construction at that date. 10 of them would be completed by the end of the calendar year 2016. It further means that the disturbance caused by the construction is probably minimal

> 18As described in Bouguen et al. (2013), construction delays occurred in a previously evaluated program in Cambodia. This considerably reduced take-up, exposure time, and statistical precision.

> 19EPI refers to the Expanded Programme on Immunization of the World Health Organization; see e.g., Henderson and Sundaresan (1982).

> 20The sample is not nationally representative. Villages were selected based on criteria such as expressed interest in the program, poverty rate, lack of a functioning preschool, and teacher availability. Sample households are exclusively from rural areas in southern and eastern Cambodia. This is because western provinces received CPS under a previous preschool construction program.

> 21The caregiver is defined as the direct relative who takes care of the child most of the time. In most cases, the caregiver is a biological parent (60.4% at baseline).

12
Figure 4 shows a negative relationship once we plot our data policy index of restrictiveness for each East Asian country against our preferred variable of innovation in the computer and related services sector that is found in the Enterprise Survey database. The figure selects the average of the h5 variable, but an equally sharp negative correlation appears for the other innovation variables that have been selected from the same database. Clearly, countries with higher levels of data restrictiveness appear to have lower innovation activities in the computer services sector. China is an interesting outlier in Figure 4. The country is very restricted in data as measured by our data policy index, but at the same time exhibits a very high degree of firm‐level innovation in computer services. This fact is little surprising given China’s fast‐moving activities in the digital field, but the figure also shows that the country is clearly an exception in the region.<sup>6</sup> Another interesting correlation is illustrated in Figure 5. We use a country‐specific variable that is plotted against our data policy index. We take a standard variable that measures how much East Asian countries import digital services as a share of their total commercial services imports. In this case too, we see that a tight negative correlation exists between the two variables. This suggests that countries which are more restricted regarding data policies exhibit a lower share of digital services imports. Although this variable of services trade is not taken up in our econometric analysis, it nonetheless points out to close the link between digital innovation and open markets. The services trade variable measures digital services imports performed over the internet such as software whilst the data policy index captures restrictive trade policies that target digital technologies such as the internet, data and online platforms.

However, in order to formally assess whether across the entire economy of each East Asian country firms in data‐intensive sectors are truly affected in their innovation activities as a result of higher data restrictiveness, the identification strategy takes into account the extent to which each
non-networked private facilities towards networked hospitals. Moreover, Powell-Jackson et al. (2014) using data from Ghana, Sood and Wagner (2018) from India, and Celhay et al. (2019) from Argentina document that these kinds of substitutions can increase the quality of care. No study to date looks at the impact of health insurance using facility-specific measures of quality: thus, it is possible that the studies that do not find any change in aggregate utilization are still missing changes on this margin.

In contrast to the widespread evidence of increased health care utilization, the impacts of health insurance on health outcomes have been mixed at best. Even when a comprehensive benefit package is offered as in Mexico with the _Seguro Popular_ program, King et al. (2009) fail to detect differences in health outcomes as measured by nine different self-assessments.<sup>9</sup> This basic result of zero to small impact resonates across a number of studies. Levine, Polimeni, and Ramage (2016) report zero impacts from Cambodia; Bauhoff, Hotchkiss, and Smith (2011) report zero results from Georgia; and Fink et al. (2013) from Burkina Faso. Similarly, Powell-Jackson et al. (2014) do not find any health impacts in Ghana and Miller, Pinto, and Vera-Hernández (2013) did not find significant effects of insurance on health outcomes in Colombia, whether these are self-reported health assessments, symptoms (fever, cough, diarrhea, blood pressure), or summary outcomes such as weight (including low birthweight), height, and mortality.

In the handful of studies that do find a positive causal impact on health outcomes, it is usually associated with the increased utilization of higher quality, especially preventive care. Sood and Wagner (2018) investigate the impact of a social health insurance program for the poor in the Indian state of Karnataka and find that it reduced mortality from heart conditions and cancer; they argue that insurance coverage led patients to seek early diagnosis. Likewise, in Camacho
(0.001)|(0.003)|(0.003)|(0.001)|
|Years of education|0.006***|0.005***|0.000***|0.006***|0.005***|0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Female|0.008***|0.014***|-0.005***|0.008***|0.015***|-0.005***|
||(0.002)|(0.002)|(0.000)|(0.002)|(0.002)|(0.000)|
|arcsinh(income)||||-0.000|0.000|-0.000***|
|||||(0.000)|(0.000)|(0.000)|
|Wage worker mean|0.119|0.109|0.010|0.119|0.109|0.010|
|Sample size|185,827|185,773|156,712|185,579|185,525|156,464|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the CFPS 2010 and 2016, ELMPS 1998, 2006 and 2012, IHDS 2005 and 2011/12, IFLS 2000, 2007/08 and 2014/15, MxFLS 2002, 2005/06 and 2009/12, LSMS-ISA 2010/11, 2012/13 and 2015/16, KHDS 1991/94, 2004 and 2010, and PSID 2001, 2007 and
agricultural output has been supplanted by more modem processing systems, thus replacing women's work. There are many possibilities, especially since "non-farm" as a sector can represent a wide range of activity types. Women's involvement in the different kinds of non-farm activities is likely to be highly varied across different countries and cultural contexts.

In this paper we find that non-farm activities are very important to women's welfare in the two countries of Ghana and Uganda. In both countries, poverty among female headed-households was significantly lower and fell more rapidly over time in those households participating in non-farm activities. In order to get a better idea of the gender dynamics, we explore the implications of these findings by looking at individual level changes in labor market participation and at changes in income by source at the individual level for Ghana. Individual-level income data is not available for Uganda, so we show changes over time in household income. We conclude by discussing our estimates of the determinants of an individual's participation in agriculture and/or non-farm activities rounding out the story of who benefits from non-farm participation and why.

The next section of the paper provides some background on gender roles and the division of responsibilities in the two countries. The third section provides a discussion of the data and methods of approach. The fourth section provides a description of rural poverty trends by gender and sector, and the fifth section describes the trends in sector participation, income, and income shares. The sixth section presents econometric estimates of the joint probability of participation in non-farm and agricultural activities. The final section concludes and recommends areas for further investigation.

3Hazell and Haggblade (1993) for Latin America and a World Bank report on the non-farm sector in 4
**A comparison of the distributions of "true" income based on EU-SILC and the imputed tax admin income reflects the underreporting of income at lower income quintiles and the underreporting of top incomes in survey data. At the median, the gap between “true” and “reported” income is 9%; thus, tax compliance is 91%.** The mean value of income imputed tax income is higher than that of survey income. This results from a significant underreporting of top income in EU-SILC data. If we censor income distribution at the 99<sup>th</sup> percentile of the distribution of labor income in EU-SILC data, the mean imputed tax income is lower than survey income. The gap between mean “reported” and “true” mean income is 6%.<sup>12</sup> our Due to challenges arising from the underreporting of top incomes in EU-SILC data,<sup>13</sup> preference is to assess tax compliance by comparing median incomes. This approach mitigates complications associated with data discrepancies and provides a clearer perspective on tax compliance levels.

**We present the income distributions in the (original) EU-SILC (gross labor income) and imputed tax income in the EU-SILC in Figure 1** . We censured the distributions at the 99th percentile to keep the graphs readable. The rounding of income is an obvious problem in the EU-SILC data. Many respondents do not provide the exact income value, only a rounded value. When comparing the distribution of income observed in EU-SILC with the imputed tax income, we observe a spike in values corresponding to the minimum wage (the annual value of minimum wage for the employees without higher education is 5,531 EUR, the annual value of minimum wage for the employees in the construction sector is 7,441 EUR). However, the imputed tax income data spike is significantly higher than that observed in EU-SILC data. The spike in the value of minimum wage may be seen as evidence that the
5

# _2. Pre-determined circumstances shape lives_

Opportunity sets begin taking form _in utero_ . Who one’s parents are, what country they live in, and how rich they are make a great deal of difference for a person’s opportunities. The opportunity to life itself turns out to depend on such pre-determined circumstances as the education and wealth of parents, whether their house has access to clean water and sanitation, and how close it is to medical treatment. Consider Figure 1, which plots group-specific infant mortality rates across countries. Each vertical line in the figure corresponds to one country, and within each country, the highest point in the line indicates infant mortality (per 1,000 live births) among children whose mothers have no education, while the lowest point gives the corresponding figure for those whose mothers have completed secondary schooling or higher. The differences are striking, not only across countries, but perhaps even more so within them. In El Salvador, for instance, babies born to mothers with no schooling are four times as likely to die before their first birthday as their counterparts with better educated mothers.

**Figure 1  Infant mortality varies across countries but also by mother’s education within countries**

<!-- Start of picture text --> 160<br>140<br>120<br>No education<br>100<br>80<br>60<br>Secondary or higher<br>40<br>20<br>0<br>ColombiaJordanSri LankaVietnamParaguayPhilippinasThailandBotswanaSouth AfricaNicaraguaPeru BrazilGuatemalaTurkeyIndonesiaEgyptTunesiaZimbabweGhanaMoroccoSenegalKenyaEl SalvadprNigeriaTurkmenistanIndiaBoliviaEritreaSudanBangladeshNepalCameroonComorosTogo HaitiUgandaYemenCambodiaCentral African RepublicZambiaPakistanMadagascarBeninBurkina FasoGuineaCote d'IvoireChadMalawiEthiopiaRwandaMozambiqueMaliNiger<br>Infant mortality rate per 1,000 live births<br><!-- End of picture text --> _Source:_ World Development Report 2006, from Demographic Health Survey (DHS) data. _Note:_ The continuous dark line represents the mean infant mortality rate in each country, while the endpoints of the whiskers indicate the infant mortality rates by different levels of mother’s education.

Parental education
predictions and because they generate larger and more accurate estimates of uncertainty. Because the cost to accuracy for in-sample municipalities is minor, the larger estimates of uncertainty increase the coverage rate for in-sample municipalities. Failing to weight by state household size when aggregating the simulation results to municipalities has virtually no effect on the estimates because the population weights only vary at the state level and the benchmarking procedure aligns the estimates with the survey-based estimates at that level.

# _6.3 Model heterogeneity_

So far, the results shown have come from one estimated national model. However, it is possible that estimating separate models would improve the performance of the model by better modeling the heterogeneity across states or across urban/rural areas. Table 6 shows how the results change due to different types of variants of the estimation and simulation procedure. The baseline model considers estimates from one national model. Mexico has separate poverty lines for urban and rural areas, however, which must be considered when estimating headcount poverty rates during the simulation stage. Because the software package used allows for only one poverty line, the baseline model inflates the per capita income of rural areas in the sample by the ratio of the urban to rural poverty line. After making this adjustment, the urban poverty line threshold is applied to all households in the synthetic census, in both urban and rural areas. The second model considers a similar approach, but instead estimates six models, each corresponding to different groups of states.<sup>21</sup> This may better capture the heterogeneity across these different state groupings. The third model takes a different approach to estimating separate urban and rural poverty lines. This approach simulates urban and rural poverty rates separately, based on a single national model, and then takes the population-weighted average of the urban and rural estimates for each municipality.<sup>22</sup> When taking this population-weighted average, we assume zero covariance between the urban and rural poverty estimates of common municipalities, which further

> 21 These are the same six groups
.81|0.57|0.34|0.25|
|Canada|0.64|0.26|0.56|0.30|
|United States|0.50|0.16|0.53|0.32|

_<u>Notes:</u>_ This table reports the gender norm _λoj_ and wage discrimination estimates in the 1970s and 2010s for the countries in our core sample. Panels A-C present average barriers by sector, occupation, and country. Averages are weighted by the total income in each occupation-sector and country-year.

**Model Validation.** As a validation, we examine whether our estimates of gender – – barriers which are inferred as structural residuals relate to measurable changes in social norms and women rights. To do so, we use the World Bank’s “Women, Business, and the Law” (WBL) database, which evaluates 35 aspects of countries’ legal code to construct indicators that measure gender equality in the labor market, at the workplace, and in the legal system over the past five decades and across 190 countries (World Bank, 2019; Hyland et al., 2020).

We merge the data sets at the country-year level and regress our estimates of wage ˆ discrimination _τojct_ or gender norms _λ_<sup>ˆ</sup> _ojct_ on the different indicators from the WBL data for each country-year, controlling for the log of GDP per capita.

occupation against GDP per capita.

26