rafmacalaba/datause-encoder-data huggingface dataset:
that had twice as many Venezuelan immigrants receiving a regular migratory status experienced an increase of 25 percent more crime reports. These effects are not explained by higher domestic violence caused by the PEP program. In fact, the improvements in economic and mental health conditions observed among PEP participants (Ib ́a ̃nez et al., 2024) are inconsistent with patterns typically associated with increased domestic violence. Additionally, the program did not disproportionately increase female income relative to male income.

Particularly, a twofold increase in the number of Venezuelan migrants who received regular migratory status (relative to population) explains an increase of 40 percent and 70 percent in reports of sexual crimes and domestic violence, respectively. Given that the effects stem mostly from female Venezuelan migrant victims of crimes, it is plausible to conclude that these women felt more empowered to report such abuses without fear of deportation or other penalty. We support this hypothesis with data from a representative survey of Venezuelan refugees with and without the PEP that was collected as part of the Venezuelan Refugee Panel Study (VenRePs).<sup>5</sup> As we demonstrate, female migrants with the PEP increased the rate at which they filed complaints with the police and other institutions relative to their peers without the PEP. Additionally, and consistent with our main estimates, there was a mechanical increase in convictions and trials of male Venezuelan perpetrators of these crimes, who were likely partners of the victims.

Second,the program led to a reduction in total crime reports by Colombian natives and a decline in crimes committed by Venezuelans, excluding domestic and sexual crimes (which might be higher mechanically due to the increased reports). The results aligns with qualitative evidence from local officers suggesting that undocumented migrants were previously difficult to apprehend and prosecute due to legislative loopholes in the Colombian law. The regularization program addressed this gap, ensuring better enforcement of criminal laws. Moreover, documented increments in migrants’ income and con5Ib ́a ̃nez et al. (2024) collected the data in 2021.

4
One of the contributions of this paper is the construction of four new aggregate indicators related to trade facilitation from a wide range of primary indicators using factor analysis, a statistical modeling technique that explains the correlation among a set of observed variables through an unobserved “common factor.” To our knowledge, factor analysis has not yet been used to derive trade-related indicators. It not only helps to circumvent multicolinearity by reducing the dimensions of the data, it is a less arbitrary and more rigorous procedure for deriving an “aggregate” indicator compared with averaging out primary indicators. Moreover, unlike principal component analysis, it assumes an underlying analytical model of causality assuming that unobserved variables (to be estimated by the procedure) –our indicators— cause observed variables –the primary indicators—and, thus, provides a more rigorous framework. The new aggregate indicators contain the information of a wider range of individual indicators than any previous study.

Two of the four indicators are more related to the “hard” dimension of trade facilitation: i) physical infrastructure and ii) information and communications technology (ICT). The other two indicators are more closely linked to the “soft” dimension: iii) border and transport efficiency and iv) the business and regulatory environment. The indicators are derived for 101 countries over the period 2004-07, a greater coverage than previous indicators and a more recent one. The indicators are derived from a pool of 20 primary indicators collected from different sources: Doing Business (DB), World Development Indicators (WDI), World Economic Forum (WEF), and Transparency International (TI). Factor analysis is used in two stages: to select the primary indicators to be considered in each one of the four categories and to derive an indicator for each category.

We assess the impact of different aspects related to trade facilitation, as measured by our indicators, on export performance by estimating a gravity model. Our study also departs from most previous papers as we estimate the gravity with a two-stage sample selection model (Heckman 1979) as in Helpman, Melitz, and Rubinstein (henceforth, HMR) (2008) to deal with sample selection bias due to country-pairs without trade (in at least one direction) and to examine 3
## **Table 1: Characteristics of adults who applied for an account**

||**Adults who applied for an account**|
|---|---|
|Female (0/1)|-0.10***<br>(0.01)|
|Income: poorest 40% of households (0/1)|-0.05***<br>(0.01)|
|Rural (0/1)|-0.07***<br>(0.01)|
|Education: illiterate (0/1)|-0.14***<br>(0.02)|
|Education: primary (0/1)|-0.14***<br>(0.02)|
|Marital status: single (0/1)|-0.06***<br>(0.02)|
|Marital status: divorced/separated/widowed (0/1)|-0.03<br>(0.03)|
|Employment: out of workforce/unemployed (0/1)|-0.04**<br>(0.02)|
|Employment: self-employed (0/1)|0.04**<br>(0.02)|
|Age|0.02***<br>(0.00)|
|Age squared|0.02***<br>(0.00)|
|Constant|0.41***<br>(0.06)|
|States dummies|YES|
|Observations_(All Adults)_|12,000|

_Note_ : Table shows the marginal effect estimation of probit regressions, where the dependent variable is equal to one if an adult applied for an account and equal to zero otherwise. The base categories are male, richest 60 percent of households, urban residents, above primary school, married adults, and wage earners. The marginal effect for a zero/one variable is the effect of a change in the variable from 0 to 1 on the probability of an adults applying for an account, relative to the means of the other variables. Standard errors in parentheses, Asterisks ***, **, and * indicate significance at 1%, 5%, and 10% respectively.

# **3. Challenges of Applying for an Account**

We also survey reasons for not applying for an account. Of adults who never personally applied for an account, 20 percent report that they had an account opened for them by an employer or 7
among the wider population and school attendance among poor children. Females are more deprived in both instances. The employment indicator, meanwhile, sees no significant gender differences, while the gender gaps in legal identification are significant in all cases. The gender gaps are also larger among the displaced community, with the gap nearly five times as large among the displaced than non-displaced poor (24percentage point and 5-percentage point differences, respectively).

_Table 10: Proportion of deprived individuals across displacement status by gender, South Sudan_

|||IDPs<br>|||Hosts<br>||
|---|---|---|---|---|---|---|
||Male|Female|Difference|Male|Female|Difference|
|_All households_|||||||
|Years of schooling|0.43|0.48|-0.05|0.40|0.52|-0.12<sup>**</sup>|
|School attendance|0.20|0.25|-0.05|0.25|0.29|-0.03|
|Unemployment|0.05|0.00|0.05<sup>*</sup>|0.02|0.03|-0.01|
|Legal identification|0.48|0.74|-0.26<sup>***</sup>|0.60|0.74|-0.14<sup>***</sup>|
|Early marriage|0.08|0.75|-0.67<sup>***</sup>|0.10|0.53|-0.43<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|0.71|0.78|-0.06|0.72|0.65|0.07|
|School attendance|0.39|0.42|-0.03|0.47|0.67|-0.20<sup>**</sup>|
|Unemployment|0.07|0.00|0.07|0.06|0.06|0.00|
|Legal identification|0.75|0.99|-0.24<sup>***</sup>|0.86|0.91|-0.05<sup>**</sup>|
|Early marriage|0.20|0.90|- 0.70<sup>***</sup>|0.19|0.66|-0.47<sup>***</sup>|

Source: Authors’ calculations using the South Sudan HFS (2017). Significance at 1% ***, 5% **, and 10%

* levels.

South Sudan sees the largest gender gap of all countries for displaced women, among the poor and population-wide (70- and 67-percentage points, respectively). Displaced women are also more deprived than host women, although the differences are larger among the wider population.

# _5.2.5 Sudan_

In Sudan (table 11), the gender gaps in education and financial security are significant only among the wider population. Deprivations are higher for male hosts in primary school completion and female hosts in legal identification, whereas females face higher deprivations in employment in both displaced and host communities. Deprivation levels in education are low given the displacement situation. This may be due to good access to 21
**Table 6: Adherence to treatment in 2007 and 2008**

||**All**<br>|**Only HIV+**|**Alternative**|**All**<br>|**Only HIV+**|**Alternative**|
|---|---|---|---|---|---|---|
|||**2007**|||**2008**||
|**Non adherent**|46|23|41|81|61|32|
|**_%_**|_13,5_|_18,4_|_7,2_|_16,4_|_16,3_|_5_|
|**Adherent**|294|102|528|413|314|609|
|**_%_**|_86,5_|_81,6_|_92,8_|_83,6_|_83,7_|_95_|
|**Total**|340|125|569|494|375|641|
|**_%_**|_100_|_100_|_100_|_100_|_100_|_100_|

**Table 7: Evolution of adherence between 2007 and 2008**

||**All**|**HIV+ only**|**Alternative**<br>**measure**|
|---|---|---|---|
|**Became adherent**|14|12|26|
|**_%_**|_17.1_|_17.9_|_5.6_|
|**Became non adherent**|7|5|10|
|**_%_**|_8.5_|_7.5_|_2.2_|
|**Stayed adherent**|59|48|418|
|**_%_**|_72_|_71.6_|_90.7_|
|**Stayed non adherent**|2|2|7|
|**_%_**|_2.4_|_3_|_1.5_|
|**Total**|82|67|461|
|**_%_**|_100_|_100_|_100_|

26
net effect of the preceding and the succeeding interval (Callum and Cleland, 1988).

In this report, we consider again the micro-level relationship between reproductive behavior and child survival in Egypt, drawing once again on the rich set of information provided by the Egyptian Fertility Survey of 1980. The analysis we carry out is distinguished from previous analyses in the following respects.

First, we introduce breastfeeding behavior (specifically, the act of weaning) as a major component of reproductive behavior that influences both the survival of the child and the timing of the next conception. Especially in populations in which there is little regulation of marital fertility through contraception, breastfe.ding is a key variable for understanding the relationship between fertility and mortality. Palloni and Millman (1986) and Retherford et al. (1989) have investigared the contribution of breastfeeding to the estimated effect of birth spacing on child survival. In the former study, based on data from Latin America (where weaning occurs relatively early and contraceptive prevalence is relatively high), the contribution appears to be minor; in the latter study, based on data from Nepal (where the average breastfeeding duration is relatively long and contraception is virtually absent), breastfeeding appears to be the chief factor mediating the effect of birth spacing on child survival.

Second, we model fertility, child survival, and breastfeeding as a microlevel system. The estimation approach we adopt permics a full set of 3
<!-- Start of picture text --> (25a) For the pure reform case, we do find<br>Industrial S02 Loads in China that changes in scale, ownership and<br>sectoral composition induce<br>17.0 . --...............---.....-....-........ considerablr e ductions in pollution<br>15.0 _ intensity. However, these are<br>00 13L0 P~~~~is9s'='*sReform generally insufficient to prevent<br>cc 11.0 I """~-* *... - ss>, Levy S% further deterioration in ambient air<br>-. -*-- Levy 10% quality because of large increases in<br>7.0 _____-___.___ i.___ the scale of industrial production.<br>5.0--<br>1938 1994 2000 2010 2020 Figure 25 illustrates the projected<br>consequences by city: In the case of<br>25: Air Pollutant Emissions and S02, only Chongqing experiences a<br><!-- End of picture text --> Figure _25:_ **Air** Pollutant Emissions and

**Concentrations in Three** ScenariosS0,olChnqgexricsa slight improvement in the pure reform

scenario, and its air quality remains terrible. Otherwise, conditions deteriorate substantially. Beijing's S02 concentration rises from 117 ug/m<sup>3</sup> in 1993 to 159 in 2020; <!-- Start of picture text --> Beijng Shanghai<br>ISO . .......... .......................... so . .... ............. ............<br>150-<br>140 - --#-Worm .0 .-Re0 m<br>130. _S.- 5 45 ---..--------- ,|*<br>120t 45 -- 0- ' :<br>1993 2000 2010 2020 1993 2 2010 20<br><!-- End of picture text -->

**Figure 25b: Air Pollution Scenarios (cont'd)**

projected annual deaths from pollution-related respiratory disease increase from 3,400 to 6,400. Shanghai's air quality deteriorates from 50 to 59;, and annual deaths rise from 700 to 1,300. Shenyang's air quality deteriorates dramatically, from 131 to 217, and projected annual deaths rise from 800 to 1,500. Guangzhou's S02 concentration deteriorates from 47 to 54; deaths increase from 200 to 300. Finally, Chongqing's slight improvement in air quality (from 270 to 264) produces slightly lower mortality risk for a significantly larger population, and projected deaths rise from 4,100 to 5,000.

<!-- Start of picture text --> Shenyang GIuaNgzhu<br>160. ~ ~ ~ ~ ~ ~ ~~ 1 J 45 .<br>120 F- R---<br>1993 2000 2010 2020 1993 2000 2010 2020<br><!-- End of picture text -->

**Figure 25b: Air Pollution Scenarios (cont'd)**

28
**Table 4.1:  Summary of Current Direct Stimulus Programs (March 2009), and their “Green” Components**

|C|Tl|"G"|"G|Po|wer||Energy Eff|iciency|||
|---|---|---|---|---|---|---|---|---|---|---|
|ountry|ota<br>stimulus<br>(in $US<br>billion)|reen<br>Stimulus(in<br>$US<br>billion)|reen<br>Stimulus"<br>(%)|Renewable|CCS/Other|Building<br>Efficiency|Low<br>carbon<br>vehicle|Rail|Grid|Water/<br>Waste|
|Australia|26.7|2.5|9.3|-|-|2.48||-||-|
|China|586.1|221.3|37.8|-|-|-|1.5|98.65|70|51.15|
|India|13.7|0|0|-|-|-|-|-|-|-|
|Japan|485.9|12.4|2.6|-|-|12.43|-|-|-|-|
|South|38.1|30.7|80.5|-|-|6.19|-|7.01|-|13.89|
|Korea|||||||||||
|Thailand|3.3|0|0|-|-|-|1.8|-|-|-|
|EU|38.8|22.8|58.7|0.65|12.49|2.85|1.94|-|4.86|-|
|Denmark|-|1.8|-|0.9|-|-|0.9|-|-|-|
|Germany|04.8|13.8|13.2|-|-|10.39|0.69|2.75|-|-|
|France|33.7|7.1|21.2|0.87|-|0.83|-|1.31|4.13|-|
|Italy|103.5|1.3|1.3|-|-|-|-|1.32|--|-|
|Spain|14.2|0.8|5.8|-|-|-|-|-|-|-|
|UK|30.4|2.1|6.9|-|-|0.29|1.38|0.41|-|0.83|
|Other EU<br>states|308.7|6.2|2|1.9|-|0.4|3.9|-|-|0.03|
|Canada|31.8|2.6|8.3|-|1.08|0.24|-|0.39|0.79|0.13|
|Chile|4|0|0|-|-|-|-|-|-|-|
|US|972|112.3|11.6|32.78|6.55|30.74|4.76|9.92|11.92|15.58|
|**Total**|**2,796.00**|**436**|**15.6**|**38**|**20.1**|**66.8**|**15.9**|**121.8**|**91.7**|**81.6**|

Sources: Robins, Clover and Singh (2009); HSBC (2009); Jones (2009); Barbier (2009).

# **5. Conceptual Arguments on Effectiveness of Green Stimulus**

Table 5.1 below provides a first-cut conceptual categorization of different spending and activity, in terms of their principal effects. The table encompasses both “direct” and “indirect” green stimulus activities. We consider three categories of green stimulus policies, each discussed below. The first column indicates the potential for short-term stimulus effects, on the assumption that there is under-employment of labor and other factors of production. These effects would tend to taper off as the economy moves toward full employment. The other three columns are concerned with longer-term effects. The longer-term growth effects will depend on the efficiency of the investments themselves and on what other uses of resources might be crowded out.

Environmental cleanup, energy efficiency retrofits, and at least some natural resource maintenance and safeguarding measures are likely to have reasonably strong stimulus effects as well as significant positive effects on environmental and natural resources. Local environmental effects of bio-energy expansion and carbon sequestration will depend on the specific impacts on land and other natural resources and on the environmental characteristics of the fuel use. Effects on overall economic growth are likely to be limited except in cases 8
# **Figure 6: Cumulative Import Share and Skill Intensity Processing Trade**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Skill Intensity<br>Cumulative Import Share , 1992<br>Cumulative Import Share, 2005<br>Note: Data uses HS 6-digit classifications. The skill intensity is measured as the ratio<br>of non-production workers to total employment for US 4 digit SIC industries in 1992.<br>100<br>80<br>60<br>40<br>Cumulative Import share 20<br>0<br><!-- End of picture text -->

**Figure 7: Cumulative Import Share and Skill Intensity Non-Processing Trade**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Skill Intensity<br>Cumulative Import Share , 1992<br>Cumulative Import Share, 2005<br>Note: Data uses HS 6-digit classifications. The skill intensity is measured as the ratio<br>of non-production workers to total employment for US 4 digit SIC industries in 1992.<br>100<br>80<br>60<br>40<br>Cumulative Import share 20<br>0<br><!-- End of picture text --> 23
Our main interest lies on the e¤ect of migrant networks on export participation and intensity. From (7) we see that by reducing Fc networks are expected to increase the likelihood of export participation. We also see that, for a given level of e¢ ciency and ...xed entry costs, ...rms with stronger networks in a given destination – i.e. ...rms with larger ac(j) – are more likely to sell there. Finally, (8) reveals that export intensity is independent of Fc, but may nevertheless rise with migrant networks due to idiosyncratic ...rm-destination demand shocks, ac(j).

# 3 Historical background

Several historical features of Portugal facilitate the analysis of the impact of social networks on ...rm-level export outcomes. We provide a brief summary of such a background, emphasizing the aspects that are more relevant for the empirical analysis.

Current stocks of Portuguese emigrants are largely rooted in Estado Novo, the authoritarian regime that ruled the country between 1933 and 1974. The limits on individual rights and freedoms imposed by the regime, together with large income di¤erentials relative to developed countries, created strong pressures for emigration. Yet, as emphasized by Baganha (2003), for decades such pressures were ...ercely controlled by the regime’s highly restrictive emigration policy. In the early 1960s, however, Estado Novo’s attitudes towards emigration took a surprising turn. Following a shift in industrial policy favoring the development of more capital intensive industries in the Lisbon area, the regime was faced with an excess of unskilled labor supply in the Northern region and began to favor emigration of redundant unskilled workers (Baganha, 2003). By 1965, several obstacles to emigration had been removed and emigration was no longer a crime punishable by law. Largely in response to this policy shift, between 1960 and 1974 over 1.5 million, predominantly low-skilled, Portuguese left the country in search of better economic and political conditions in more developed nations (Figure 1). Following the Carnation Revolution in 1974, which overthrew Estado Novo, emigration ‡ows fell sharply re‡ecting the combined e¤ect of the introduction of democracy and, initially, more restrictive policies towards immigration in several recipient countries. As a result, and also in light of limited return migration from more developed countries, stocks of emigrants have remained largely stable over the past three decades.<sup>5</sup> The motives and timing of Portuguese emigration ‡ows alleviate concerns of reverse causality in the relationship between migrant networks and export outcomes. In addition, the concentration of emigration ‡ows in time and space

> 5 Despite the limited emigration ‡ows observed in Portugal since 1975, the Global Migrant Origin Database (described below) recorded a total of 1.95 million Portuguese emigrants in the year 2000. This amounts to nearly 19% of the population living in Portugal, which has remained fairly stable in recent years.

6
Table 1: Number of immigrants in GCC countries, 2020, in thousands

||||Destinat|ion coun|try|||
|---|---|---|---|---|---|---|---|
|Origin country|Saudi Arabia|UAE|Kuwait|Oman|Qatar|Bahrain|All|
|India|2,502|3,471|1,152|1,376|702|365|9,569|
|Bangladesh|1,278|1,095|380|317|262|115|3,446|
|Pakistan|1,484|996|339|250|236|105|3,409|
|Egypt,Arab Rep.|962|900|421|88|184|95|2,649|
|Indonesia|1,709|319|108|75|44|34|2,288|
|Philippines|645|565|197|46|170|59|1,682|
|Yemen, Rep.|770|206|71|-|36|22|1,103|
|Syria, Arab Rep.|823|53|23|-|25|6|930|
|Sri Lanka|529|121|40|30|154|13|886|
|Nepal|503|28|25|-|254|3|813|
|Other|2,249|963|355|191|161|121|4,040|
|Total migrants|13,455|8,716|3,110|2,373|2,226|936|30,816|
|(% of Population)|39%|88%|73%|46%|77%|55%|53%|

_Source:_ UNDESA International Immigrant Stock 2020 for immigrant stock; World Bank World Development Indicators for population in 2020.

Figure 3 presents data on contractual temporary migrants from two separate and detailed surveys. The first is from Saudi Arabia (Expatriate workers survey, 2015), the largest destination country in the Persian Gulf. The second is from Bangladesh (Bangladesh Return Migration Survey 2018/2019), one of the largest migrant-sending countries to the region. The panels on the left column are based on the data from Saudi Arabia and the panels on the right column are based on the Bangladesh data. The first panel shows that the largest origin country in Saudi Arabia is the Arab Republic of Egypt, followed by India, Pakistan and Bangladesh. In the case of the Bangladeshi migrants, the largest destination (Panel B) is Saudi Arabia, followed by the United Arab Emirates, and Oman. Migrants under these temporary contractual arrangements typically migrate at a young age, with a median age of 27 (Panels c and d), partly due to the physically demanding nature of the jobs offered in these destinations. Temporary migrant workers from lower-income countries, such as Bangladesh, tend to be very low-skilled (Panel f). There is a number of high-skilled migrants in Saudi Arabia (Panel E), but these tend to come from high-income OECD countries or other Arab countries like Egypt.

10
The present time for dismantling nonpayments is exceptionally good: the real devaluation has given domestic industry a tremendous boost by discouraging imports, improving their liquidity and reducing the need for subsidies; the government can no longer postpone difficult decisions because access to commercial borrowing has dried up for the time being; and the lessons learned from the meltdown are still fresh. The most important of these is that attainment of low inflation will lead to sustainable growth only if it is accompanied by genuine enterprise restructuring and fiscal adjustment. Both require the elimination of nonpayments.

Shleifer and Treisman (1999) note the difficulty of implementing Russia's vast reforms agenda. This paper shows where to start the new millennium: dismantle nonpayments, replacing it if needed with a much smaller targeted system of explicit subsidies that will not only have a clear social impact, but also promote growth (by hardening budget constraints) and macroeconomic stability (by reducing the size of the subsidies).45 Once growth and transparency revive, the investment climate will rapidly improve, facilitating medium-run institutional building. There is ground for optimism, based on using existing economic assets and public finance resources better.

> 4 McKinsey Global Institute (1999) found that 75 percent of assets in firms surveyed by them as part of an analysis of 10 manufacturing and service sectors in Russia could be utilized in the existing markets with minimal upgrading, suggesting that the Polish strategy of first creating an environment where existing assets can be used better may be feasible.
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