rafmacalaba/datause-encoder-data huggingface dataset:
The literature on international migrations identifies a number of other factors beyond those four, which influence the migration decision (see Massey et al., 1993, for a survey). Among them, the costs of migrating, monetary as well as psychological costs have long been considered important determinants of migrants’ decision. Empirical studies using gravity models for overall migration flows strongly support the role of monetary costs (see for example, Helliwell, 1997 and Karemera et. al., 2000). Consequently, we test for the impact of sharing a border with Switzerland using a dummy variable, which takes the value 1 for France, Germany, Italy, and Austria (C _om. border_ ). It is worth noting that all countries that border Switzerland also share their language with it, another factor seen as decreasing the cost of migrating. There is also a significant role for non-monetary costs such as cultural and psychological costs, which are at the source of the so-called network effects. The search for ways to diminish these costs materializes in agglomeration of migrants, or cultural clustering which have a positive pull-effect on future inflows (see, for example, Dodson, 2001, and Bartel, 1989). Cultural clustering may also offer more appealing employment opportunities and, thus, alleviate transition costs on the labor market (see Gross and Schmitt, 2003).<sup>13</sup> Persistence generated by network effect is taken into account by the size of the resident population from the same source country ( _Lnetworki,t-1_ ) and the effect is expected to be positive.

In their analysis of immigration flows to the United States, Hatton and Williamson (2002) and, Clark et.al. (2002) pay particular attention to institutional and legislative constraints arising from immigration policies by the destination country. We address this issue in several ways. First the two distinct samples match the change in access to new permits for

> 13  Buckley (1996) and Borjas (1999) show that migrants cluster in the United States may be the consequence of some institutional specificity such as availability of State welfare benefits.

17
**<u>Table A.7. The impact of the program on households’ income and poverty</u>**

||||Dependent<br>|variables<br>|||
|---|---|---|---|---|---|---|
|Explanatory variables|Share of<br>income from<br>wages<br>(in percent)|Share of<br>income from<br>nonfarm<br>production (in<br>percent)|Share of<br>income from<br>farm<br>production (in<br>percent)|Share of<br>private<br>remittances<br>(in percent)|Share of<br>public cash<br>transfers<br>(in percent)|Share of<br>income from<br>other sources<br>(in percent)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Program * Post-program period|2.4046|4.1918**|-15.2273***|2.6543**|1.4392*|4.5374***|
||(3.6972)|(1.7090)|(4.4861)|(1.0337)|(0.7440)|(1.3819)|
|(Poverty rate – 50) * Post-|-0.2635|-0.3696*|1.0891**|-0.1838|-0.0554|-0.2167|
|program period|(0.3709)|(0.2036)|(0.4849)|(0.1581)|(0.0634)|(0.1631)|
|Program * (Poverty rate – 50) *|-0.0163|0.3133|-0.6639|0.1126|-0.0141|0.2683|
|Post-program period|(0.4001)|(0.2077)|(0.5139)|(0.1629)|(0.0974)|(0.1726)|
|Age of household heads|-0.1432|0.1256***|1.1810***|-0.4116***|-0.4833***|-0.2686***|
||(0.1243)|(0.0379)|(0.1429)|(0.0533)|(0.0495)|(0.0426)|
|A d f hhld hd|-0.0010|-0.0022***|-0.0120***|0.0055***|0.0063***|0.0034***|
|ge square o ouseo eas|(0.0012)|(0.0004)|(0.0014)|(0.0006)|(0.0006)|(0.0005)|
|Gender of household head|-3.6036***|0.1971|8.6076***|-3.6608***|-0.3850*|-1.1553***|
|(male=1, female=0)|(0.7196)|(0.3688)|(0.7596)|(0.5531)|(0.2236)|(0.3021)|
|Ethnic minorities (yes=1,|-3.0134**|-11.8797***|15.4604***|-0.7899|1.0040***|-0.7815***|
|Kinh=0)|(1.4817)|(0.9528)|(1.7760)|(0.4945)|(0.2947)|(0.2687)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|28.6026***|13.1056***|24.7434***|14.5162***|8.9215***|10.1106***|
||(3.0573)|(1.2043)|(3.6762)|(1.6011)|(1.2172)|(0.9337)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.191|0.092|0.296|0.111|0.127|0.059|

Note: This table reports district fixed-effect RDD-DD regressions of share of income from different sources of households using householdlevel observations.

Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

57
number of parents outside the country). The final row adds all the controls from Table 4: both the income and family controls, as well as legal status, time spent abroad, and intent to return home.

We find that remittance behavior is primarily accounted for by income, and not by differences in family composition. The baseline result for total remittances from Table 3, controlling only for country of birth and dataset fixed effects, is that migrants with a university degree remit $300 more than migrants without a university degree. Controlling for the full set of covariates (the ‘all’ row) reduces the coefficient on university degree by two-thirds, and it becomes statistically insignificant. The third row adds just the family composition variables to the baseline specification. The main hypothesis for why less skilled migrants remit more is because they are more likely to have family members outside the country. Therefore, we would expect that controlling only for this (but not for other variables such as income) would increase the coefficient on education, but we find the opposite - the coefficient on education reduces to $230 from $300, and remains statistically significant. This casts doubt on the idea that low skilled migrants remit more because of their family composition. One explanation for this is the earlier observation that low skilled migrants are not only likely to have more family abroad, but they are also likely to live in households with more people in the host country. The second row of the table adds just income variables (a dummy for working and log income) to the baseline specification. The coefficient on university degree is cut by more than half, and is no longer statistically significant. This suggests that the income effect is a key channel through which education affects remittances: in short, more educated people send back more money simply because they have higher incomes.

Although we find that education is insignificant once we control for income in the pooled sample, this masks heterogeneity in the individual surveys. For example, the education coefficient remains statistically significant even after controlling for all available covariates for three datasets: the Spanish ENI survey, the USA Pew dataset, and the USA NIS survey. There are several reasons why the education coefficient might remain significant in some datasets and not others that we are not able to examine with our dataset. One key variable we cannot control for is the socioeconomic status of the family in the home country. More educated individuals might come from better-off families, and therefore not need to send back as much money. This 14
# References

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Atkinson, Anthony B. 2015. _Inequality What Can Be Done?_ Cambridge MA: Harvard University Press.

- Lopez-Acevedo, Gladys, Gordon Betcherman, Ayache Khellaf, and Vasco Molini. 2021. Morocco’s Jobs Landscape: Identifying Constraints to an Inclusive Labor Market. International Development in Focus. Washington, DC: World Bank.

- Bandiera, Oriana, and Ashwini Natraj. 2013. ÎDoes Gender Inequality Hinder Development and Economic Growth? Evidence and Policy Implications.” _World Bank Research Observer_ 28 (1): 2-21.

- Bertoni, Eleonora, Fabio Clementi, Vasco Molini, Francesco Schettino, and Hitomi Teraoka. 2016. _Poverty Work Program: Poverty Reduction in Nigeria in the Last Decade_ . Technical Report, World Bank, Washington DC. <u>http://documents.worldbank.org/curated/en/103491483646246005/Poverty-workprogram-poverty-reduction-in-Nigeria-in-the-last-decade.</u>

- Betcherman, Gordon. 2012. _Labor Market Institutions: A Review of the Literature_ . Policy Research Working No. 6276, World Bank, Washington DC. <u>https://openknowledge.worldbank.org/handle/10986/12139.</u>

- Blau, Francine D., and Lawrence M. Kahn. 2007. “Changes in the Labor Supply Behavior of Married Women: 1980-2000.” _Journal of Labor Economics_ 25 (3): 393-438.

- Burtless, Gary. 1999. ÎEffects of Growing Wage Disparities and Changing Family Composition on the U.S. Income Distribution.” _European Economic Review_ 43 (4-6): 853-65.

- Canberra Group. 2011. _Handbook on Household Income Statistics (2nd edn.)_ . New York and Geneva: United Nations.

- Choe, Chung, and Philippe Van Kerm. 2018. “Foreign Workers and the Wage Distribution: What Does the Influence Function Reveal?” _Econometrics_ 6(3): 41.

- Chopra, Deepta, and Meenakshi Krishnan. 2019. _Linking Family-Friendly Policies to Women’s Economic Empowerment: An Evidence Brief_ . UNICEF Early Childhood Development, New York NY. <u>www.unicef.org/sites/default/files/2019-07/UNICEF-Gender-Family-FriendlyPolicies-2019.pdf.</u>

- Cowell, Frank A., and Maria-Pia Victoria-Feser. 1996. “Robustness Properties of Inequality Measures.” _Econometrica_ 64 (1): 77-101.

- Dao, N.T., Dávila, J. & Greulich, A.  2021 “The education gender gap and the demographic transition in developing countries. Journal of Population Economics 34, 431–474.

- Eika, Lasse, Magne Mogstad, and Basit Zafar. 2019. ÎEducational Assortative Mating and Household Income Inequality.” _Journal of Political Economy_ 127 (6): 2795-2835.

- Elveren, Adem Y. 2014. ÎWomenÑs Labour Force Participation and Pay Inequality: Evidence from Panel Cointegration.” _Applied Economics Letters_ 21 (12): 862-65.

- Esping-Andersen, Gøsta. 2007. “Sociological Explanations of Changing Income Distributions.” _American_

22
“treatment” group only in the fact that it did not receive aid—as would be the case if aid were distributed to randomly selected individuals in the population. The causal effect of aid is hard to determine due to selection biases that arise precisely from the non-random fashion in which aid is distributed—the population that receives aid is, in general, not identical to the population that does not.<sup>6</sup> To the extent that these differences are correlated with population attitudes, a simple comparison of attitudes among those who did and did not receive aid will likely be biased: For instance, if aid was given to those who are very poor, to what extent was trust higher among recipients _because_ of the aid versus because they were poor, or (even more difficult) because the unobserved attributes that contributed to their poverty also affected their trust in others?

In the context of the earthquake, interpreting differences in current-population attributes as a _consequence_ of the earthquake-shock will be problematic if, for instance, people knew where the fault-line was and made their location decisions accordingly. Our main contribution relating aid to trust address this issue and is motivated by the fact that within the 2005 earthquake zone, the distribution of population and its socioeconomic characteristics was randomly distributed vis-à-vis the activated fault-line. To begin with, we adopt a tight specification by including district fixed-effects in our estimates, thus exploiting only _withindistrict_ variation. We then provide a number of tests to verify that those affected by an earthquake shock of greater intensity (living closer to the fault-line) were no different from those who received a shock of lower intensity (living farther from the fault-line) _prior to the earthquake_ . Thus differences in foreign aid received and attitudes towards foreigners between people further away and closer to the fault-line can be attributed _entirely_ to the earthquake shock

> 6 Aid might be given to a country or within a country to a group of people who differ on many, potentially unobservable dimensions from people who did not get aid. For instance, aid is often motivated by considerations of _realpolitik_ .  In times of conflict, it could be given to people who are allies but also could be given to influence those who might not agree with the donor. Alternatively, recipients could be chosen because they are more efficient and thus able to utilize aid better or perhaps because they are in worse conditions than others.

5
In Column (2) of Table 2, we test whether the effect of conditional cash transfers is heterogeneous by gender. To do so, we use the results from all of the studies that show impact results separately for boys and girls. We find that conditional cash transfer schemes result in a 3.3 percentage point stronger reduction in child labor for males than females. In fact, a substantial number of studies finds a significant impact on child labor among boys and no significant impact on child labor among girls. Borraz and González (2009), who examine the impact of the PANES conditional cash transfer program in Montevideo, Uruguay, are the only authors to find a significant decrease in child labor for girls, but no significant decrease for boys.<sup>31</sup> To better understand the differential impact of conditional cash transfer interventions on the work of boys and girls, Figure 6 provides results from three studies that disaggregate the overall impact of conditional cash transfers across different work activities by gender. Boys primarily experience reductions in economic activities for pay. Oportunidades, for example, resulted in a significant reduction in economic activities for pay conducted by boys, but not for girls (Skoufias and Parker 2001). Similarly, the CESSP scholarship program in Cambodia (Ferreira, Filmer, and Schady 2009) and the PRAF program in Honduras (Galiani and McEwan 2013) appear to have had a stronger impact on work for pay and work outside the home, respectively, for boys than for girls.<sup>32</sup> Girls, in contrast, appear to experience larger reductions than boys in household chores (Oportunidades in Mexico),<sup>33</sup> economic activities without pay (the CESSP program in Cambodia), and work at home (PRAF in Honduras).<sup>34</sup> Similar results are obtained by Del Carpio and Macours (2010), who focus on Atención a Crisis, a one-year randomized pilot building on the Red de Protección Social cash transfer scheme in Nicaragua. The authors test for differences in reductions in hours worked by boys and girls in different activities during the week before the interview.<sup>35</sup> Fixed effects estimates indicate that the reduction in participation in economic activities of boys as a result of the basic conditional cash transfer program exceeded that of girls by more than one hour a week.<sup>36</sup> For household chores 14
**Table 3: Determinants of changes in child mortality and school enrollment (TSLS)**

|Variable|Child morta|lity|School enrollment|
|---|---|---|---|
||1<sup>st</sup>stage|2<sup>nd</sup>stage|1<sup>st</sup>stage<br>2<sup>nd</sup>stage|
|Ln Initial child mortality|-0.074|0.362***||
||(0.114)|(0.110)||
|Ln Initial school enrollment|||0.073<br>(0.097)<br>-0.777***<br>(0.039)|
|Health spending per capita||0.130*<br>(0.071)||
|Health spending per capita (initial)|0.339***<br>(0.133)|||
|Health spending per capita (change)|0.339**<br>(0.133)|||
|Education spending per capita|||0.083**<br>(0.034)|
|Education spending per capita (initial)|||0.850***<br>(0.058)|
|Education spending per capita (change)|||0.304**<br>(0.131)|
|GDP growth|0.022|0.067**|0.055<br>0.054***|
||(0.034)|(0.026)|(0.035)<br>(0.012)|
|Undernourishment|-1.52e-03|-0.013**|1.05e-03<br>-0.012***|
||(4.28e-03)|(5.56e-03)|(5.00e-03)<br>(2.03e-03)|
|Population density|-5.39e-05|5.26e-04*|3.28e-04<br>2.00e-05|
||(4.08e-04)|(3.11e-04)|(3.88e-04)<br>(1.14e-04)|
|Control of corruption|0.204**|-0.053|0.154*<br>1.81e-03|
||(0.087)|(0.107)|(0.090)<br>(0.043)|
|Constant|1.287|-0.843|0.478<br>3.182***|
||(0.773)|(0.725)|(0.340)<br>(0.191)|
|Observations|7|9|78|
|Adjusted R-squared|0.2|25|0.884|
|First stage F-Stat (p-value)|0.0|00|0.000|
|Kleibergen and Paap (2006) test (p-value)|0.0|00|0.000|
|Hansen J overidentification test(p-value)|0.2|79|0.285|

Notes: Regressions are run on global samples including developing and high income countries. The dependent variables are the absolute log changes in child mortality (reduction) and secondary school enrollment (increase) over the period 1980-2014. Ln Public Spending per capita (on Health and education) are instrumented with their initial values and growth rates over 1980-2014.

# **E. Extensions**

We now extend our basic model to get a better sense of its applicability to oil- rich countries. In the introductory section, we had noted the fact that oil-rich countries in the MENA region tended to perform much better than predicted by their initial conditions or their income (see Figures 1, 2 and 3). We explore whether there is a MENA-specific effect by adding a dummy for the MENA region to the 14
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 12: Projected pension expenditure for civil servants (% of GDP) is quite constant but exponentially increasing starting around 2040**

<!-- Start of picture text --> 3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br>Year<br>variant 1 variant 2<br>Expenditure (% GDP)<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

**Figure 13: Projected pension expenditure for teachers (% of GDP) is only increasing during the entire simulation period**

<!-- Start of picture text --> 3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br>Year<br>v ariant 1 v ariant 2<br>Expenditure (% GDP)<br><!-- End of picture text -->

**Source** : WB calculations based on PROST

- 41 -
to measure refugee QOL. The first dimension is the overall experience of life, or "life satisfaction", and the second is the material living conditions.<sup>3</sup> The "life satisfaction" indicator assesses people's subjective well-being. It is popular for use in studies of refugees because it requires respondents to reflect on, and make an overall assessment of, their life happiness, including wealth, security, and hopes for the future. "Life satisfaction" tends to be an overall reflective evaluation that uses a Likert response scale of between 0 to 10, where 0 means not satisfied and 10 completely satisfied. However, it is left for the respondent to define what "satisfaction" means and the scaling of his/her satisfaction; thus, it is a subjective indicator of well-being.

"Material living condition" captures households' objective living conditions and opportunities, including material deprivations and housing conditions that directly affect their QOL. Material deprivations refer to the level at which households are able to have the consumption goods and services needed in a society at a given time. Several indicators could measure material deprivations. One typical indicator is the ability or inability for households to meet basic food needs at above the national abject poverty level. Other measures of deprivations may include counting the number of household assets, such as beds, air conditioners, and cooking utensils. The availability of housing and housing conditions can also be captured by calculating overcrowding and satisfaction with accommodation services, such as water and electricity.

# 4. Data and Identification Methodology

## 4.1. <mark>The survey</mark>

<mark>The World Bank Development Economics Data group conducted the Syrian Refugees and Host Communities Survey (S</mark> RHCS <mark>) in 2015 by surveying registered and unregistered refugees (Krishnan, Munoz, Riva, Sharma, & Vishwanath, 2019). The survey was designed to produce comparable findings on living conditions and quality of life of Syrian refugees and host communities in Jordan, Lebanon, and Kurdistan. The Jordanian survey is designed from the adjusted sample frame of the 2005 Jordanian population. The S</mark> RHCS <mark>assesses the socio-</mark>

> 3 Note that 3 dimensions—productive or other main activity, health, and education—were the subjects of the work of Ginn (2020). More so, the remaining 4 dimensions—leisure and social interactions, economic security and physical safety, governance and basic rights, and natural and living environments— will be discussed in a follow-up study.

9
Table B6: Nested Logit - Iceberg Costs

||(1)|(2)|
|---|---|---|
|**Costs:**|_Commuting_|_Trade_|
||Trips to Workplace|Trips to Shops|
|Minutes|-0.009***|-0.013***|
||(0.000)|(0.000)|
|Metro|-0.144*|-0.596***|
||(0.079)|(0.007)|
|Metrobus|-0.556***|-0.855***|
||(0.117)|(0.100)|
|Car|-0.220**|-0.603***|
||(0.095)|(0.073)|
|Walking|-0.352***|0.338***|
||(0.106)|(0.049)|
|_λ_ public|0.484***|0.484***|
||(0.033)|(0.009)|
|Observations|56,330|312,015|
|Trips|11,266|62,403|
|Iceberg cost before (mean)|4.095|7.217|
|Iceberg cost after (mean)|3.901|6.893|

_Notes:_ This table reports the results of a nested logit using the 2017 OD survey considering only trips that use one transportation mode. The first column reports the results to estimate commuting costs considering only trips from work to home or viceversa between 6am to 10am, and between 5pm to 9pm. The second column reports the results to estimate trade costs using trips to retail shops, outlets, and restaurants. I restrict the sample to trips after 1pm.

# **C Data and Quantification Appendix**

## **C.1 Calibration of Speeds**

This section describes the calibration of speeds across different transportation modes. I use different sources of information. For the transportation network in Mexico City, I use data from the Government of the city.<sup>55</sup> For the network of roads, I use information from the New York University digital archive in which they report different types of roads for each census tract in the commuting zone of Mexico City. The different roads include: autopistas, calles, viaductos, etc. I calibrate an average speed for each one of the roads. With this information I compute commuting times across census tracts in Mexico City using the Network analysis toolkit from Arcmap

> 55 The data can be found here.

12
# Trade Policy and Redistribution When Preferences Are Non-Homothetic

Quy-Toan Do<sup>_∗_</sup> Andrei A. Levchenko<sup>_†_</sup> _Keywords:_ trade restrictions, redistribution, non-homothetic preferences. _JEL Codes:_ F13, Q17, O24

> _∗_ World Bank (qdo@worldbank.org).

> _†_ University of Michigan, NBER, and CEPR (alev@umich.edu).