rafmacalaba/datause-encoder-data huggingface dataset:
expectations is on the extensive margin (fewer migrants) than the intensive margin (shorter episodes). The resulting decline in repatriated savings (left figure in Panel B of Figure 16), in turn, leads to a decline in the rate at which new businesses are created, which accumulates to a 0.8 percentage point reduction in self-employment over the life cycle (see the right figure in Panel B). Whereas individual welfare unambiguously benefits from the better choices agents can make under more accurate information about foreign earnings, overall business creation is actually enhanced by individuals’ optimistic expectations.

# **8 Conclusions**

For millions of low-skilled workers from countries in South and Southeast Asia, temporary migration to the high-income countries in the Persian Gulf and East Asia is a critical part of their working lives. Immigrant workers not only earn significantly higher wages while working abroad, but they also accumulate savings that they use to finance self-employment – and entrepreneurial activities upon their return. Workers’ decisions about whether and when to emigrate, where to go abroad, how long to stay, how much to save, and what kind – employment to seek upon their return are intricately linked, as our paper demonstrates.

This paper differs from the previous migration literature in two key respects. First, most papers in the migration literature focus on the wage and labor market gains during the migration episode. Largely due to data constraints, these studies tend to employ cross-sectional analyses to answer their questions. By contrast, our paper highlights the interdependence between workers’ decisions at each stage of the life cycle, and the dynamic effects of temporary migration on workers’ economic activity upon return. Second, the literature usually focuses on the role that economic conditions at the immigration destination play in determining immigrants’ decisions about when to return to their home countries. Our paper takes a different approach to the migration experience by modeling the central role played by self-employment aspirations upon return. These are important distinctions from the previous work. A joint, dynamic analysis of migration and self-employment decisions is crucial. For example, policies that aim to overcome financial constraints to self-employment might become less effective because endogenous adjustments in migration behavior may offset part of the intended on business creation.

Indeed, we show that this is the case. The data that underlie our findings come from a unique survey, _the Bangladesh Return Migrant Survey_ (BRMS), which was specifically designed to investigate the patterns and determinants of the temporary migrants’ lifetime employment trajectories, earnings, and welfare. We use these data to construct a dynamic framework that models the tightly intertwined nature of pre-migration labor market out51
land plots, the paper assumes that the relative change in illicit economic activity is proportional to the relative change in the land surface devoted to opium cultivation. yy<sup>AA</sup> The data on land use are generated by the Ministry of Counter Narcotics (MCN) of Afghanistan in collaboration with the United Nations Office on Drugs and Crime (UNODC). The extent of opium poppy cultivation is assessed on an annual basis through a methodology that combines field work with remote sensing techniques. The latter are needed because field work is dangerous, given the security conditions on the ground and the illegal nature of the crop. Image interpretation of geo-referenced ground and aerial photography allows to generate digital maps of poppy fields (UNODC 2007). The data on opium poppy cultivation used for this study are at the district level, for the period from 2007 to 2016 (UNODC 2016), measured in hectares per 1,000 inhabitants.

_Conflict intensity_ The intensity of conflict is derived from the number of killings, a metric associated with terrorist attacks generated by the Global Terrorism Database (GDT). This is a comprehensive source produced by the CC National Consortium for the Study of Terrorism and Responses to Terrorism, at the United States Department of Homeland Security Center of Excellence. Data on casualties for Afghanistan are available from May 1973 to December 2016. The raw data are georeferenced and available on a monthly basis; for this paper, they are aggregated at the district level. The intensity of conflict is defined as the number of killings and normalized by their standard deviation across the sample.

_Control variables_ Information on the presence of international and Afghan combat troops – and respectively – comes from the NATO ISAF placemat for each Regional Military Command (NATO 2018). Placemats report TT<sup>WW</sup> TT<sup>AA</sup> province-level information on the number of ISAF troops by the contributing nations and on the localization of Provincial Reconstruction Teams (PRTs). Placemat issues also include information on the number of Afghan National Army (ANA) troops and on ISAF and ANA major units. Although information on PRTs is systematically reported, information on ISAF major units at the provincial level is only available from 2011 onwards. Combat troops are measured per 1,000 inhabitants in the province. Foreign aid is proxied by development assistance commitments. The data come from the Development Assistance Database (DAD), the main publicly available source of information on development assistance DD in Afghanistan. Data are available by donor and sector, at the provincial level. They can be disaggregated 13
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a more meaningful increment in human capital; one additional year of schooling is unlikely to be rewarded in the labor market unless it yields the completion of a full category.

The fact that there is a maximum level of education, with very few individuals obtaining more than 21 years of schooling, introduces a ceiling effect. As countries develop and average years of schooling increase, it becomes increasingly more difficult for individuals to outperform their parents. When parents have tertiary education as well as their children, one may not want to classify these children as failing to be mobile as this may introduce a mechanical decline in absolute mobility at higher levels of development. For this reason, we will consider the subset of respondents whose parents are not in the top category.

For robustness, we also construct a measure of absolute mobility that uses education data in its most disaggregated form. The results obtained with this measure are presented in Section 6. In Appendix A, we consider two alternative measures of absolute mobility, one which considers everyone with tertiary education as mobile (denoted by _MIX_ ) and one which accounts for the magnitude of which children surpass or fall short of their parents’ outcomes (denoted by _DIF_ ).

# **2.2. Relative mobility**

_Relative mobility_ is the extent to which an individual’s position in the distribution of educational attainment is independent of the position of his/her parents. We consider the correlation coefficient between respondents’ years of schooling and the years of schooling of their most educated parent, denoted by _COR_ , as our primary measure of intergenerational persistence, and 1- _COR_ as our primary measure of relative mobility. The regression coefficient from the intergenerational regression of respondents’ years of schooling on parents’ years of schooling (denoted by _BETA_ ) denotes an alternative measure of intergenerational persistence and will serve as our robustness check (see Section 6).

An advantage of the correlation coefficient is that it is invariant to changes in the marginal distributions of child and parents’ education. The values of _COR_ , however, do not have a natural economic meaning. By contrast, the regression coefficient tells us how much one year of additional schooling of parents is transmitted to their children on average. While _BETA_ has a natural interpretation, it is sensitive to the marginal distributions. This means that changes in _BETA_ may in part reflect changes in inequality levels rather changes in the intergenerational dependence structure.<sup>6</sup> Further robustness checks are included in Appendix A where we consider: (a) the share of individuals who make it to the top quarter in terms of years of schooling of their generation out of those who were born to parents who ranked in the bottom half of their generation (denoted by

> 6 The two measures are related as follows: = C SS (( <u>chtsi))</u> ~~,~~ where S (. ) denotes the standard tt deviation. ttsst ttiitt BBEEB CC∗

7
**<u>Table 5: Estimation results for newly registered land</u>**

||<br>**Female**|<br> **– Male**|**Joint**|**- Male**|**Joint-**|**Female**|
|---|---|---|---|---|---|---|
|**Panel A: Entire sample**|||||||
|No. of registered plots (_β1_)|-0.256***||-0.482***||-0.226***||
||(0.00878)||(0.00950)||(0.0108)||
|No. of registered plots|0.439***||0.848***||0.410***||
|* post-2010 (_β2_)|(0.00889)||(0.00962)||(0.0109)||
|No. of sporadically||-0.270***||-0.512***||-0.243***|
|registered plots (_γ1_)||(0.00871)||(0.00913)||(0.0107)|
|No. of sporadically||0.338***||0.625***||0.287***|
|registered plots * post 2010 (_γ2_)||(0.00957)||(0.0100)||(0.0118)|
|No. of systematically||0.195***||0.392***||0.198***|
|registered plots (_γ3_)||(0.00145)||(0.00151)||(0.00178)|
|Constant|0.00660|0.00813|-0.00511|-0.00172|-0.0117|-0.00985|
||(0.0229)|(0.0227)|(0.0248)|(0.0238)|(0.0281)|(0.0279)|
|No. of obs (250*250 m blocks)|38,640|38,640|38,640|38,640|38,640|38,640|
|C|0.448|0.458|0.736|0.758|0.355|0.367|
|Size & sig. of linear combinations|||||||
|<br>_β1 _+_β2_|0.183***||0.367***||0.183***||
||(0.0014)||(0.0015)||(0.0017)||
|_γ1 _+_γ2 _||0.0684***||0.112***||0.0438***|
|||(0.0045)||(0.0047)||(0.0055)|
|_γ3_- (_γ1_+_γ2_)||0.126***||0.280***||0.154***|
|||(0.0047)||(0.0049)||(0.0058)|
|**Panel B: Restricted sample**|||||||
|No. of registered plots (_β1_)|-0.497***||-0.629***||-0.132***||
||(0.0239)||(0.0222)||(0.0300)||
|No. of registered plots|0.731***||0.965***||0.234***||
|* post-2010 (_β2_)|(0.0240)||(0.0224)||(0.0302)||
|No. of sporadically||-0.473***||-0.653***||-0.180***|
|registered plots (_γ1_)||(0.0233)||(0.0217)||(0.0282)|
|No. of sporadically||0.938***||0.772***||-0.166***|
|registered plots * post 2010 (_γ2_)||(0.0254)||(0.0237)||(0.0308)|
|No. of systematically||0.217***||0.352***||0.135***|
|registered plots (_γ3_)||(0.00293)||(0.00273)||(0.00355)|
|Constant|-0.00252|-0.00546|-0.0132|-0.0105|-0.0107|-0.00500|
||(0.0435)|(0.0425)|(0.0406)|(0.0396)|(0.0547)|(0.0515)|
|No. of obs (250*250 m blocks)|<br>9,100|<br>9,100|<br>9,100|<br>9,100|<br>9,100|<br>9,100|
|R-squared|0.516|0.539|0.727|0.740|0.138|0.238|
|Size & sig. of linear combinations|||||||
|_β1 _+_β2_|0.234***||0.335***||0.101***||
||(0.0029)||(0.0027)||(0.0036)||
|_γ1 _+_γ2 _||0.465***||0.119***||-0.346***|
|||(0.0114)||(0.0106)||(0.0138)|
|_γ3_- (_γ1_+_γ2_)||-0.249***||0.232***||0.481***|
|||(0.0118)||(0.0110)||(0.0144)|

Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010 25
**-3-**

Indla's economically active population finds secondary employment in nonfarm sectors.<sup>1</sup> This holds true in both rural and urban areas. Natlonal Sample Survey data place the rural figure e Xi lower (National Sample Survey 1961). Of course, standard labor force definitions, because of their emphasis on usual employment and Inability to fully capture f*e,ale participation, may obscure the extent of seasonallty and part-time nonfarm employment, as the following Income figures suggest.

8. Income data reveal a larger role for nonfarm activity, Indicating that It contributes about two-thirds of national Income compared to a one-third employment share. Similarly In rural areas, excluding rural towns, nonagriculture normally contributes 25-35 percent of total income In contrast with Its 20-25 percent share of employment (Table 2). The higher Income than employment share Implies either greater returns to labor In nonfarm activity or considerable part-time and seasonal nonfarm pursuits uncaptured in the emplo,.. ent statistics.

**1** So India (1988), Tabl* **B-6.**
Finally, it should be noted that the analysis in this paper is limited due to an absence of a quantifiable definition and good country data regarding the motivations for being in this sector and the patterns. Only once a definition is agreed upon and country statistical agencies use this definition to standardize questionnaires with questions to examine these issues, will the incidence of, reason for employment in, and quality of the jobs in home-based work be well understood.

Given the lessons learned in this paper, to lower the incidence of home-based work, it would be necessary to decrease the barriers to work outside the household. Among poor women with high domestic responsibilities, time saving devices – such as running water, electricity, and childcare services with flexible hours (for some countries) – may lower the cost of these women working outside the home. Additionally, more frequent, adequate, and safe transportation that would lower the time to travel to a job site, provide means for people with disabilities to leave their homes, and lower the danger of traveling alone would open the labor market options for women and the elderly. Finally, if, indeed, a working wife is a threat to men’s identity, socialization in schools and community services to break traditional gender tasks would decrease the stigma of an employed woman.

Information may be a key to improve the wages of home-based workers. Although this study could not explicitly explain why home-based workers (especially women) had lower hourly earnings than their counter-parts in more formal work sites, we may hypothesize that it is due to participation in an oligopsonistic labor market. In the short run, collectivization of home-based workers would lead to an exchange of information on salaries (and work conditions) throughout the market, thereby increasing 24
<!-- Start of picture text --> Property rights and access to assets<br>n “-——<br>zo ene<br>3S on<br>68 |. foeeet TEa _ —<br>Lo ~or*<br>oO . on mee aoe a<br>ae ee<br><<br>1960 1970 1980 1990 2000 2010<br>year<br>——— Unmarried women's rights to immoveable property<br>=-=—=- Married women's rights to immoveable property<br>— — — - Equal inheritance of daughters and sons<br>——— [qual inheritance of wives and husbands<br>cooenon JOint title is default<br><!-- End of picture text -->
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44
# **6. Propensity Score Matching and Sample Balancing**

# **6.1. Propensity Score Matching**

This section reports the results of estimating equation (4) with Probit methodology using data for 2007.<sup>47</sup> The results of the estimation are reported in table 4. Using these results, we compute the propensity score for schools to join _Mais Educação_ . This propensity score is important because we do not know the true likelihood of schools taking up the program, and this likely varies depending on a number of school characteristics and will be correlated also with the educational outcomes at the school level in which we are ultimately interested.

Results show that schools with larger enrollments and lower IDEB Index have higher probabilities of participating in _Mais Educação_ . Meanwhile, even if one of the program’s selection criteria is that schools are located in cities with more than 200,000 inhabitants, _ceteris paribus_ , municipal population decreases schools’ propensity score. Although the estimated probability of a school joining _Mais Educação_ is negatively associated to duration of the school day, it increases with the percentage of full-time schooling classes. Also, schools with lower average mathematics test scores and higher dropout rates have higher propensity scores. Nevertheless, schools’ average Portuguese test scores are positively associated with the probability of adhesion to _Mais Educação_ . Changes in IDEB and in mathematics scores between 2005 and 2007 have positive effects on the propensity score, and changes in Portuguese test scores have a negative effect.

# **6.2. Sample Balancing**

Table 5 reports the differences in the characteristics of treatment and control schools as of 2007, after matching, weighted by propensity score matching weights. Student’s _t_ tests were performed to detect the mean differences of the variables between the two groups (control and treatment) after matching. If the quality of the matching is good, we expect a lack of statistically significant differences in these results. Indeed, compared to table 3, table 5 suggests a good balance in observable characteristics across treatment and control schools after matching, with few characteristics having statistically program, schools that joined _Mais Educação_ in 2010 had worse educational outcomes and higher number of enrollments and were located in bigger cities. Nevertheless, between 2007 and 2009, treated schools improved their educational outcomes, with higher increases in IDEB and _Prova Brasil_ ’s test scores (see table A4).

> 47 For schools that joined _Mais Educação_ in 2008 and remained until 2011, in our short-term analysis we use the same control group selected by propensity score matching in the medium-term analysis. Thus, in the short-term analysis for the 2008 sample the results of the estimation of the propensity score are reported in table 4. For schools that joined _Mais Educação_ in 2010 and remained until 2011, a sample of schools used only in the short-term analysis, the results of the estimation of the propensity score are reported in table A5.

19