rafmacalaba/data-use-mentions-extended huggingface dataset:
DHS surveys (e.g., Ethiopia, Cambodia, etc.) and the health module of national population censuses and other surveys. Traffic injuries are part of broader transport issues, as lack of access to safe transport is faced by many LMICs, which has implications in participation in economic activities. While tracking progress towards SDG 3.6 of reducing traffic deaths and serious injuries poses a measurement challenge in most LMICs due to large discrepancies between reported official statistics and estimates from global health measurement studies, censuses and household surveys can fill the information gaps in traffic-related injuries.

The Household Budget Surveys (HBSs) are nationally representative household surveys with a focus on consumption expenditure. For most low- and middle-income countries, HBS is the main data source for poverty estimation and consumer price index weights calculation. Some HBSs, such as Kosovo’s and the Republic of Yemen’s, only collect the expenditure evidence, while some HBSs with additional modules, such as Albania’s, can capture more information on transport such as the vehicle ownership and number of cars in the household through the standalone section of transport and communication.

16
Unfortunately, there is little systematic information available to China’s top leaders and economic planners. China’s policy makers require data on the number of laidoff and/or unemployed rural workers, and information about the consequences of job loss for workers in order to gauge the seriousness of the crisis. The estimates that do exist concerning the impact of the crisis on rural labor range widely. One analyst projected that 12 million workers would be laid-off (Sheng et al., 2009) and another placed the estimate at 20 million (Chen, 2009). The nature of the reporting, the definitions of “impacts” and sources of the data, however, were not always clear.

Somewhat surprisingly given the importance of the question, there have not been many independent attempts by researchers to estimate the impact of the financial crisis on China’s rural labor force. The existing research to date in this area faces data issues or other shortcomings. For example, Kong et al. (2009) used factory data to estimate lay offs. Relying on factory data alone, however, fails to account for labor market flexibility: workers may have lost their jobs, but found others elsewhere. This, of course, is the problem of measuring layoffs when one is also interested in unemployment. Enumerating workers who are still in the factories makes it impossible to monitor the status of those who left the factories. There are also questions of representativeness.

Another paper (Wang et al., 2009) estimated employment using data derived from a nation-wide dataset. The paper also reported employment rates of rural individuals in the sample. However, the paper made no effort to measure the full impact of the crisis by comparing actual employment to what employment rates would have been under a “business as usual” counterfactual. In addition, there was no attempt to track exactly what happened to those that were laid off from their jobs.

3
MONGOLIA'S INFORMAL SECTOR Reasons for Growth _24_

# **The Result: Incomes of Informal Sector Activities**

61. Having discussed the leading supply and demand factors, we can now examine the end result: incomes in the informal sector. Differences in income levels between the informal and formal sectors draw people toward, or push people away, from the informal sector and therefore motivate growth0 But the levels actually observed are the result of an interaction of the all of the factors outlined above. (State sector incomes will be used as representative of formal sector incomes, simply because the data is readily available.) 62. Employees in the formal sector are guaranteed by law certain benefits that informals do not receive, such as holidays, paid vacations, and sick leave.' Such guarantees must be addressed before comparing formal and informal incomes; they will be treated as an effective 19% increase in base salary of formal sector employees. Also, formal sector employers must make contributions, on behalf of employees, to the state's Health Insurance and Social Insurance Funds. The value of such contributions to the employees will be approximated by the amount of the contributions, 24% of income.<sup>7</sup> " 63. Figure 4 illustrates the relative profitability of state employment versus informal sector work. Even after incorporating the value of employer paid benefits, holidays, annual leave, etc., state salaries seem extraordinarily low, considering the relative requirements of education and the relative prestige of the jobs. It is not at all uncommon to find doctors cleaning houses or skilled engineers driving taxicabs. Indeed, the informal sector surveys revealed numerous examples of people who said they didn't want to work in the formal sector simply because salaries were so low?<sup>2</sup> It is particularly instructive that kiosks, which are viewed by local officials as a means of alleviating poverty (see the section on kiosks in Appendix A), generate average incomes greater than salaries earned by
understanding of the socio-economic factors of the migrants and migrants’ origin households in determining their international labor migration. This section presents descriptive examinations of the profiles of migrants and migrants’ origin households, including their trend over times. Some key characteristics of the migrants and migrants’ households are analyzed to identify who among migrants are likely to search for jobs overseas, and from which households they are, differentiating between male and female migrants. This

> 6 The 2000 data also identifies a fourth source—siblings—but for consistency purposes, we keep the same definition between the 2000 and 2007 data.

7
# **Figure 1. GDP, unemployment, and inflation**

# **A. GDP in major advanced economies**

<!-- Start of picture text --> Index, 2007Q1=100<br>115<br>Euro Area<br>Japan<br>110<br>UK<br>US<br>105<br>100<br>95<br>90<br>2008 2010 2012 2014 2016<br><!-- End of picture text -->

# **C. Unemployment rate in NIRP economies**

<!-- Start of picture text --> Percent Latest 2008Q2 Post-crisis peak<br>16<br>12<br>8<br>4<br>0<br>E. 2016 inflation forecasts for NIRP<br>economies<br>Percent<br>3 Latest Jan-16 Jan-15<br>2<br>1<br>0<br>-1<br>Euro Area Sweden Hungary Denmark Switzerland Japan<br>Switzerland Japan Euro Area Denmark Hungary Sweden<br><!-- End of picture text -->

# **B. Inflation in major advanced economies**

<!-- Start of picture text --> Percent<br>Current<br>3<br>Target<br>2003-07 average<br>2<br>1<br>0<br>-1<br>Euro Area Japan UK US<br>D. 2016 growth forecasts for NIRP economies<br>Percent<br>4 Euro Area Sweden Japan<br>Switzerland Denmark Hungary<br>3<br>2<br>1<br>0<br>F. 2020 inflation forecasts for NIRP<br>economies<br>Percent<br>3 April 2016 April 2010<br>2<br>1<br>0<br>Jan-15 Apr-15 Jul-15
# 1 Introduction

This paper investigates how existing diasporas or networks (i.e. stock of immigrants of own national background already resident in a given destination) impact the number, skill composition and geographical concentration of new migrants. The role of the diaspora on migration flows is well known and undisputed. The contribution of this paper is both methodological and empirical. Using global aggregate data, we show that diasporas not only influence the future flows but also their other characteristics, such as composition and concentration. We present various econometric and data challenges that are relevant in this literature, discuss how we try to resolve them and show how different empirical methods influence the results. In order to guide our thinking on the diaspora effects, we construct a unified yet simple theoretical framework based on various bilateral factors that influence migration costs. Among the main determinants of migration costs are distance, linguistic overlap, political bonds such as colonial links.

We argue that diaspora externalities influence migration patterns (flow size, skill composition and concentration) through their effect on bilateral migration costs. These diaspora externalities operate through two main channels. First, diasporas reduce assimilation and information costs for newcomers. They help them with jobs, housing, education and various cultural adjustment issues. Second, diasporas attract new migrants through family reunion programs and other venues that lower legal migration barriers. Assessing the combined effect of these two channels is key to understand the dynamics of the size and composition of migration flows.

Until recently, the analysis of diaspora externalities has been conducted at the micro level (see Boyd, 1989, Massey, 1993, Munshi, 2003, McKenzie and Rapoport, 2010). Individual or household level micro data have multiple advantages such as detailed information on demographic, human capital, social and economic parameters. However a major drawback of those micro approaches is that they can only focus on a limited number of migration corridors at time (e.g. the Mexican-US corridor) and can hardly be generalized to other country pairs. An exception is provided by
.

The data presents some limitations, which require making the reasonable assumption that only individuals 15 years of age or older can be considered as workers, which also implies that households with positive labor income must be deemed to have at least one working adult.<sup>6</sup> More specifically, we assume that there is at least one adult-worker per household in any activity that reports positive earnings for the household.<sup>7</sup> Likewise, in order to assess accurately the relative contributions of different income sources to welfare improvements, we consider only those activities that generate earnings as “work.” Finally, we consider individuals as workers if they were engaged for at least 240 hours in a given activity during the last year; activities can include taking care of animals, working on the household farm or business, or for a wage or salary.

We restrict ourselves to households in the IHDS balanced panel—that is, about 83% of the households from IHDS-I were re-interviewed in IHDS-II—which comprises of 34,643 out of a total of 41,554 households surveyed for both periods. In order to get a one-to-one matching of households, we

> 5 This is most easily seen by a thought experiment of two households with vastly different incomes, where the incomes switch perfectly from one period to another (household A’s income in period 1 becomes household B’s income in period 2, and vice-versa), while every other household’s income remains constant. With anonymity, no change can be detected; whereas in a panel data, there will be change.

> 6 In some cases there is information on labor income even when no household member appears to be working, or only children (14 years of age or less) are reported as working. These problems can occur due to measurement error and/or child labor. The former is quite likely, as in some cases we find individuals as young as 3 years of age working.

> 7 In
# **APPENDIX**

## **Figure A1: Trends in CPI inflation, year-on-year**

<!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>May June July Aug Sep Oct Nov Dec Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Jan Feb<br>2015 2015 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017<br>Percent<br><!-- End of picture text --> Source: South Sudan National Bureau of Statistics<sup>11</sup>

## **Figure A2: High inflation in all categories of goods between June 2015 and June 2017**

<!-- Start of picture text --> Communication<br>Clothing and footwear<br>Restaurants and hotels<br>Health<br>Miscellaneous goods and services<br>Food and Nonalcholic beverages<br>Furnishings, household equipment<br>Housing, Water,Electricity, Gas and other fuel<br>Alcoholic beverages and Tobacco<br>Recreation and culture<br>Transport<br>Education<br>0 500 1000 1500 2000 2500<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on CPI data collected by South Sudan National Bureau of Statistics

> 11 <u>http://www.ssnbs.org/cpi/</u>

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: LMPS 2016.|80.2|19.8|70.2|29.8|94.5|5.5|

attainment this may not be the case. Moreover, the female advantage does not carry over to non-PFPS workers. The CMMHH data reveals largely the reverse pattern for them: a higher percentage of female irregular workers became unemployed or left the labor force during under COVID-19 conditions than male ones (See _Table 5-17 Jordan: Percent of Non-Permanent Private Sector Workers Employed Pre-COVID No Longer Employed as of February 2021, by Gender_

||Unemployed,|2021<br>Out of labor force,2021|
|---|---|---|
|By Gender|||
|Female|34.8|9.2|
|Male|15.3|2.5|

Source: CMMHH, 2021.

Table 5-16).<sup>49</sup> By the first quarter of 2021, 35 percent of them were unemployed relative to 15.3 percent of males; and over 9 percent had left the workforce, whereas only 2.5 percent of men did so.

**Age** : As shown in Table 5-14 above, our method generates little age-related dispersion in job loss rates and therefore does not do well in predicting the large difference in job losses for older versus prime-age PFPS workers as captured in the CMMHH (38 percent versus 20 percent). These effects are most likely due to age-conditioned employer dismissals or labor

_Table 5-16 Jordan: Percent of Non-Permanent Private Sector Workers Employed Pre-COVID No Longer Employed as of February 2021, by Age Group_

|Age Group|Unemployed,2021|Out of labor force,2021|
|---|---|---|
|below 25|25.3|5.1|
|25-45|15.4|2.5|
|above45|15.8|4.4|
|Source: CMMH|H, 2021
Additionally, the table, using USGS price data, reports statistically insignificant impacts on average annual lights per capita growth, population density, and spatial inequality in the treated districts engaged in extraction of low-value minerals. Nonetheless, the impacts on the Sen Index are positive and statistically significant. Similar to Table 7a, when we fit the models using the World Bank’s real price data, the results remain qualitatively unchanged. Further, except for the changes in coefficient sizes, the sign of the impacts of minerals prices and the expectation effects remain similar to those in Table 7a.

We now turn our attention to the heterogeneous effects based on the different types of mining activities; namely, extraction or transformation activities. Table 7c presents these results again, indicating statistically insignificant impacts on all our outcome variables in the treated districts compared to control districts engaged in the minerals extraction activities. These results remain qualitatively robust using both the USGS and the World Bank data. Furthermore, when we fit the models using both the USGS and the World Bank data, the results indicate similar insignificant impacts on all the outcome variables in the treated relative to control districts engaged in minerals’ transformation activities. Again, except for the changes in coefficient sizes, the sign of the impacts of minerals prices and the expectation are qualitatively similar to those in Tables 7a and 7b.

20
in the short run with respect to the baseline headcount poverty rate of 24.21 percent and up to 0.80 in the long run because of the direct effect of lower prices (table 3).<sup>22</sup>

**Table 3. Poverty and Inequality Effects, Loss of Ethio Telecom Monopoly, Current Users**

|_Scenario_||_Poverty indic_<br>|_ators_<br>||_Inequality indicators_<br> <br>|
|---|---|---|---|---|---|
||_Headcount_|_Poverty gap_|_Squaredpoverty gap_|_Gini_|_Atkinson(epsilon = 0.5)_<br>_Entropy (theta = 0.0)_|
|Baseline|24.21|6.86|2.83|33.3|0.0934<br>0.1871|
|Reform scenario|23.99|6.78|2.80|33.5|0.0947<br>0.1898|
|Difference|−0.22|−0.08|−0.03|0.23|0.0013<br>0.0026|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ The reform scenario corresponds to a reduction in the market share of Ethio Telecom from 100 percent to 45 percent. The Gini index is based on consumption.

The results also show an average relative welfare gain of 1.09 percent among all current users of telecommunication services (table 4). The richest decile will gain Br 474.8 (in birr of 2015) on average, relative to Br 9.8 among the poorest decile. This corresponds to a relative gain of 1.7 percent among the top decile and 0.3 percent among the bottom decile. This suggests that households in
Japan and France is relatively small. And, although trade complementarity with Germany is rising, Russia’s services trade with Germany is not as intense as that of all the other countries that export to Germany.

There is also scope for developing services across regions as well as for engaging in more services activities. Services are concentrated in the central regions, but the propensity of firms to export is highest in the Far East region (4.5) and smallest in the North Caucasus region (1.6). Average export revenue per region is only about 3 percent of the total revenue of firms, and most regions have export revenue from only a few services sectors, mainly transportation and professional services. Outside the Central region, regions like the Far East or the North Caucasus are very much dependent on just a few sectors for export revenue.

Building up Russia’s services exports will depend to a large extent on supply‐side policies. In terms of services trade policies—those that limit or discriminate against foreign providers—Russia’s regime is relatively open and will become even more so as the services commitments adopted on accession to the WTO come into effect. Despite these efforts, there is also scope for additional liberalization of certain investment measures in the services sectors. For examples, the OECD FDI Regulatory Restrictiveness Index shows that many services activities maintain still important levels of restrictiveness compared to other OECD countries. This may benefit the emergence of new and efficient service activities by increasing competition and imports of services as well supporting current and new exports. This will also affect the enabling factors that have great influence on services: institutions, governance, the rule of law, infrastructure supportive of services, and human capital.

In investigating what affects Russia’s services performance, Russia was found to still have relatively onerous restrictions in some services sectors. Moreover, Jensen, Rutherford and Tarr (2004) estimate 21
data come from a report compiled by the United Nations Development Program (UNDP) in 2009.<sup>15</sup>

# **V Empirical Methodology**

## **V.1 Identification Strategy**

In the absence of endogeneity concerns, the simplest empirical strategy to estimate the effects of repatriations on labor outcomes using municipal data is given by the following specification where _Yimt_ is the outcome of interest for firm _i_ located in municipality _m_ in period _t_ . Repatriation Share _mt_ stands for the population share of returnees that arrive in each municipality _m_ at period _t_ . _Xmt_ is a vector of time-varying municipal characteristics, that include the homicide rate. _αm_ and _λt_ account for municipality and time fixed effects and the standard errors are clustered at the municipality level. Such an empirical strategy, thus, relies on comparing firm outcomes in locations that are exposed to varying levels of returnee inflows.

The estimate of _θ_ in this specification, however, is biased since repatriated individuals will likely choose their locations in El Salvador or else it is chosen for them by migration authorities for reasons that may be correlated with our outcomes of interest. One concern is that, for example, returnees can go disproportionately to municipalities with more employment opportunities, less labor market competition, or higher wages.

Since we have information on both the municipality of return and the municipality of birth for our 2010-2019 sample, we were able to document that more than 70 percent of the returnees go

> 15El Salvador has not conducted a census since 2007 and reliable and recent data for all of the country’s municipalities is lacking. The 2009 UNDP report contains the most detailed, recent demographic and socioeconomic measures that cover all of El Salvador’s 262 municipalities.

12
framework and internationally agreed indicator on empowerment we adopted, we include them as part of the broader women’s empowerment index.

5. **Time spent working** : Total time spent working in the past 7 days in all occupations (agriculture, livestock, household chores and other paid and unpaid work), separately for the primary man and woman of the household.

# **Design Integrity**

## Baseline Balance

We test for baseline balance using the same empirical specification used for the outcome analysis, which compares baseline treatment and control households, controlling for randomization stratification (133 PRA dummy variables) to increase precision. Table A3 of the <u>Online Appendix</u> presents these comparisons. We find no statistically significant or economically meaningful differences across groups for all main outcome indicators at baseline. Across 23 variables that could influence our primary outcomes of interest, we observe statistically significant differences for land ownership and engagement in livestock activities at the 5% level. Treatment households are 2 percentage points less likely to engage in livestock production (control: 5%) and are significantly more likely to own land and/or dwellings based on a principal component score aggregating these assets. While the differential livestock activity at baseline may result in an underestimate of treatment effects, the significantly higher land ownership could overestimate effect sizes if this is an important capital input for generating value from the program. To assess whether these imbalances influence any of the main results, we include these variables as covariates in a robustness regression for main outcomes, presented in the Online Appendix. Results with and without these covariates do not differ in sign or significance.

## Compliance

We track monitoring data collected by CHA/MISFA on all TUP recipients throughout implementation and include an extensive compliance module in the follow-up survey of all treatment and control households. Table A4 of the <u>Online Appendix summarizes the</u> treatment compliance results, as reported in the survey. We find that the program was successful in delivering its