rafmacalaba/datause-encoder-data huggingface dataset:
4. _Forcibly displaced children_ : These migrate due to involuntary conditions such as conflict and natural disasters. We explore their unique challenges, including trauma, disruption in education, and adaptation to new environments.

The review encompasses papers published in the top five economic journals and specialized journals in migration, education, and early childhood development.<sup>1</sup> We completed the search by consulting Google Scholar to capture any significant articles missed initially. Additionally, we examined references within each selected article’s literature review, focusing on the most relevant ones. This approach yielded a robust collection of 113 pertinent scholarly works.

This literature review discusses various methodologies, including experimental, quasi-experimental, and qualitative approaches, among others. Three papers employed experimental methods, specifically randomized controlled trials. Quasi-experimental designs were used in 95 papers, indicating causal studies not strictly experimental in nature. Within these, 51 papers used ordinary least squares, 19 used instrumental variables, 13 applied difference-in-difference approaches, 4 implemented regression discontinuity designs, and 25 incorporated fixed effects. Other methodologies included the Cox proportional hazard model, the Todd and Wolpin (2023) value-added model, the Blinder-Oaxaca decomposition technique, and survival analysis. Notably, only 3 qualitative studies were cited. (Refer to the appendix for detailed descriptions of each paper).

This review comprises five additional sections. Section II delves into research on the effects of migration on _migrant children_ , focusing on education, health, early childhood development, and the influence of immigration policy. Generally, this research shows that migrant children fare better than those in their origin countries but still fall behind native children in host countries. This gap often extends to poorer long-term labor outcomes, partly due to lower educational attainment and language barriers. Age at migration significantly influences the disparity between native and migrant children, and immigration policies are pivotal in narrowing or widening this gap.

Section III examines the impact of migrant parents on their _left-behind children_ . The evidence reveals several key points. First, remittances usually benefit the education and physical health of these children by stabilizing income. This stability tends to reduce child labor, bolster school re-

> 1 Included journals are the _International Migration Review_ , _Journal of Development Economics_ , _Journal of Population Economics_ , _Economics of Education Review_ , and _Early Childhood Research Quarterly_ .

3
in the household survey is consistent with the value of consumption per capita being higher in household survey data than in the national accounts.<sup>34</sup> For example, the national accounts are likely to underestimate the value of consumption from own agricultural production, which we estimate to be 20% of total consumption from the household survey data in Malawi. This suggests that national accounts might not only be underestimating consumption growth, as suggested in Young (2012), but also the consumption level.<sup>35</sup> The ranking of consumption across countries is maintained in the macro and the micro data: Malawi is the poorest, following Tanzania, and Uganda.

# **3 Cross-Sectional Inequality in Sub-Saharan Africa**

Here we investigate the distributions of CIW separately for the rural and urban areas of Malawi, Tanzania, and Uganda. First, we investigate the level and distributional behavior of CIW in Section 3.1. Second, we explore the behavior of saving rates and CIW conditional on income in Section 3.2. Third, we study the top and bottom of the distributions and their respective welfare in Section 3.4.

## **3.1 Rural and Urban Differences in Levels and Inequality**

We present the mean for CIW split between rural and urban areas in Table 2. We find that differences in the levels of income and consumption between rural and urban areas are an order of magnitude larger than the differences across these countries. Across rural areas, the largest difference in mean per capita consumption is between Tanzania (US$280) and Uganda (US$321) — a 15% difference<sup>36</sup> — while the mean per capita consumption in Malawi is US$297. The largest difference in mean income per capita in rural areas is between Tanzania (US$221) and Malawi (US$246) — an 11% difference.<sup>37</sup> In contrast, in urban areas, mean per capita consumption and income in urban Malawi is, respectively, US$648 and US$618, which implies a difference of 118% and 151%, respectively, compared with its rural Malawi counterparts. The magnitude of the difference between rural and urban areas is even higher for Tanzania and Uganda. In all, the rural-urban gap in consumption and income within countries is at least 10 times larger than the

> 34ISA provides close to gold standard measures are based in short-recall periods (Deaton (1997) and Gibson et al. (2014)), see our discussion Section 2.

> 35However, note that our measure of consumption is entirely different from that in Young (2012), who uses the ownership durables (and other education and health measures) to proxy for real consumption. We use actual consumption data.

> 36These two estimates are statistically different. The confidence intervals are, respectively, [US$271; US$290] and [US$299;US$343].

> 37The respective confidence intervals are [US$203; US$239] and [US$236; US$256].

17
We find that these women are less likely to work and less likely to work in agriculture. We also test if it the case that the effect of mines differs between societies with high and low participation of women in the service sector. To this end we use data from the ILO on share of women in the service sector (ILO, 2011) and interact an indicator variable for being in a country with a high share of women working in services (above the median in the ILO data is defined as a high share). The results are shown in Table A.14 in the Appendix. We confirm that women are more likely to work in service sector jobs in these countries, but the interaction effect of being in a high female service country and in an active mining area does not increase the effect further. If anything, there seems to be less of an effect in countries with high participation of women in the service sector.

Employment opportunities matter for women. For welfare, it also matters what types of jobs are offered. We try to rule out the possibility that the increase in female employment in the service sector is driven by engagement in the sex industry. Using lifetime number of sexual partners, which should increase with sex trade activity, we find no indication of sex trade among women in active mining areas (Table 10). In fact, there is a clear negative effect of mine openings on the number of sexual partners. Considering groups that may be at more risk, such as young women (aged under 25), women working in the service sector, and women without a partner, there is also a decrease in the number of sexual partners. Finally, we find no statistically significant difference in the likelihood of the woman never having sexual intercourse, and no change in the use of a condom in the last intercourse.

Table 10: Effects of mining on women’s lifetime number of sexual partners and condom use.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Lif|etime numbe|r of sexual par|tners|Never sex|Used condom|
|Sample|All|Under 25|In services|No partner|All|All|
|VARIABLES|||||||
|active (20 km)|-0.106*|-0.107**|-0.726***|-0.223|0.011**|-0.006|
||(0.055)|(0.051)|(0.151)|(0.142)|(0.005)|(0.005)|
|inactive (20 km)|0.771***|0.708***|0.492**|0.691***|0.001|-0.014*|
||(0.172)|(0.175)|(0.224)|(0.156)|(0.009)|(0.008)|
|Observations|210,456|64,672|12,049|49,128|512,922|350,797|
|R-squared|0.093|0.087|0.076|0.079|0.252|0.142|
|F test: active-inactive=0|23.61|20.02|20.15|19.17|1.145|0.749|
|p value|1.20e-06|7.76e-06|7.36e-06|1.21e-05|0.285|0.387|

Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education and religious beliefs. Please see Table 3 for more information about coefficients of interest. *** p<0.01, ** p<0.05, * p<0.1 23
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**Appendix 5** : _Commercial Insurance Enrollment, Consolidation, and Antitrust Concern Levels in US State Health Insurance Markets, 2002-‘03_

|State|Commercial<br>Insurance<br>Enrollment<br>(000’s)|Share of<br>Largest<br>Health Plan<br>(%)|Share of<br>three largest<br>health plans<br>(%)|Herfindahl-<br>Hirschmann<br>Index of<br>Market<br>Concentration|Antitrust<br>concern<sup>41</sup>|
|---|---|---|---|---|---|
|Alabama|2,836|71|81|5,054|High|
|Arizona|3,020|30|84|2,461|High|
|Arkansas|1,343|56|74|3,283|High|
|California|19,677|27|67|1,842|Moderate|
|Colorado|3,651|19|41|883|Low|
|Connecticut|2,507|57|78|3,629|High|
|Delaware|543|59|84|3,931|High|
|DC/NOVA|1,837|69|98|5,495|High|
|Florida|8,583|30|65|1,758|Moderate|
|Georgia|4,916|43|65|2,184|High|
|Idaho|836|32|75|2,041|High|
|Illinois|7,905|47|64|2,471|High|
|Indiana|3,951|46|57|2,258|High|
|Iowa|1,983|66|78|4,405|High|
|Kansas|1,738|37|56|1,587|Moderate|
|Kentucky|2,562|46|75|2,685|High|
|Louisiana|2,217|41|66|2,058|High|
|Maine|766|70|97|5,312|High|
|Maryland|3,833|34|92|2,921|High|
|Massachusetts|4,350|47|81|2,883|High|
|Michigan|6,563|47|61|2,393|High|
|Minnesota|3,745|53|89|3,535|High|
|Mississippi|1,584|47|59|2,356|High|
|Missouri|3,812|36|71|2,111|High|
|Montana|476|59|67|3,490|High|
|Nebraska|1,112|54|72|3,160|High|
|Nevada|1,203|17|40|664|<br>Low|
|New Hampshire|860|66|100|5,275|High|
|New Jersey|6,113|39|60|2,260|High|
|NewMexico|884|25|65|1,541|Moderate|

_Source:_ Robinson (2004)

> 41 Antitrust concern is based on the Federal Trade Commission (FTC) & Department of Justice (DOJ) thresholds.  An HHI below 1,000 denotes a low level of antitrust concern, between 1,000 and 1,800 denotes a moderate level of antitrust concern, and higher than 1,800 denotes a high level of antitrust concern.

34
48 labour and capital, and output value. On each of these counts there Is not complete agreement between the various studies concernIng the productivity of the two tenure sectors.

105. Which Is the most logithnate (or reliable) Indicator of relative productivity? Yates (1981b:748-750) argues that, since the relative scarcity of land Is now probably the same for both the ejido and the private farm sectors, It Is logithate to focus on land productivity when comparing the performance of the two sectors. Moreover, the most recent ovidence suggests that, comparing same-size holdings, there Is no significant varlatIon between tenure categorles In average land quality (Tables 9 & 10). However, It has been suggested that, In the pre-1970 period, Institutional blasses tended to starve the Individual ejldos of good-quality (Irrigated) land (Reyes Osorlo et al,1974; Hewitt de Alcintara,1978); this needs to be borne In mind when assessing the pre-1970 evidence on relative productivity.

106. Dovring (1970) found that the rates of growth of crop output were about the same for both sectors In the 1950s; referring to the same decade, Mueller (1970) made a sknilar fInding, concluding that, In terms of output growth per unit of total measured Inputs, ejidos performed at least as well as private farms.

107. There Is less agreement about relative crop yields. Dovring (1970) concludes that, In 1960, there was no significant difference In crop yields between ejidos, on the one hand, and private farms over five hectares on the other. Also referrlng to the 1960 census, Ecksteln and his associates find that, In terms of physical crop yields, ejldos are less productive than either small or large private farms. However, In terms of the per hectare value of crop output, ejldos are superior to small private farms and Inferior to large private farms, reflecting the proportion of high revenue crops In the crop mix (1978:Appendix C,p.1).
<!-- Start of picture text --> 70<br>J<br>60 j<br>50 al<br>Zl<br>40 S<br>A<br>a wv J<br>0<br>1300 1400 1500 1600 1700 1800 1900<br>ss*+ America —- ‘Europe Africa — - -Asia<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60 on eer<br>50 a<br>40 aa ee<br>a2 a ere<br>0<br>1900 1920 1940 1960 1980 2000<br>Year<br>—Latin America --- Middle East / North Africa ---Asia Africa<br><!-- End of picture text -->
participant his name and caste before randomly assigning him to a treatment and sending him to a large classroom, where participants were entertained for up to an hour while waiting for all the cars bringing participants from other villages to arrive. The focus of the experiment was on the effect of three conditions that varied the publicness and salience of caste identity in the session, which was always led by a high-caste young woman experimenter: _Caste Not Revealed_ (the control condition). A session was composed of 3 H and 3 L. No personal information about the participants was revealed.

_Revealed Mixed_ ( _i.e_ . caste revealed in a mixed-caste session). The composition of a session was the same as in the preceding condition, but now the experimenter began a session by saying that she would like to confirm some information with each participant, who should nod if it is correct. Then the experimenter turned to each participant and stated his name, village name, father’s name, grandfather’s name, and caste.

_Revealed Segregated_ ( _i.e._ caste revealed in a segregated session) _._ This was the same as the preceding condition except that a session was composed of either 6 H or 6 L.

The priming mechanism reflects a way in which caste identity is actually made salient in classroom settings. This increases the external validity of our results. Although an individual’s caste is widely known in a village, publicly referring to a child’s caste is not uncommon in rural schools. There is anecdotal evidence of teachers telling low-caste children to not drink from the tap at the school, lest it pollute the water for others. While implementing this study, we came across some such instances. Caste is commonly recorded in school enrollment books, often using different colors for high and low castes, to identify caste-targeted entitlements such as stipends and uniforms provided by state governments. In villages, people are frequently called by their caste names. Following the common usage in this area and also the way that caste is recorded in school enrollment books, in revealing caste identity we used the traditional name for each caste (Thakur, Chamar, etc.).<sup>5</sup> We next describe the incentive schemes. Participants were given a packet of 15 mazes to solve in each of two 15-minute rounds.<sup>6</sup> Some participants had piece rate incentives in both 5 In the 1998-99 Indian National Family Health Survey, households were asked to name their caste. Most low-caste respondents gave their actual caste name ( _e.g._ Chamar), but a few used the more generic and politically correct names, Dalit, _harijan_ , or Scheduled Caste (Marriott 2003).

6 The mazes are Xerox copies from <u>http://games.yahoo.com/games/maze.html, level 3. Gneezy, Niederle,</u> and Rustichini (2003) showed that individuals do not solve mazes just for fun, they respond to incentives.

11
# **4. Data and Research Design**

# **4.1 Conjoint Experimental Survey**

In order to measure Colombian policy preferences, we conducted a fully randomized choicebased conjoint experiment. Specifically, the conjoint design allows estimation of causal effects of different treatment components simultaneously in a multidimensional manner (Bansak et al. 2021; Hainmueller et al. 2014; Rodon and Sanjaume-Calvet 2020). As noted in Bansak et al. (2021) and Horiuchi, Markovich, and Yamamoto (2021), evaluating several attributes simultaneously helps to obscure the intent of researchers as well as allows respondents to justify their choices along any number of the dimensions. Indeed, evidence shows that fully randomised conjoint designs do mitigate social desirability bias (Dahl 2018; Horiuchi et al. 2021).

We presented respondents with hypothetical pairs of packages comprising policies towards Venezuelans that reflect current Colombian policy as well as options that policymakers in the country could plausibly implement. Each package contained randomly selected levels from within each of the six policy dimensions shown in Table 1. Then, each respondent was asked to choose which of the two policy packages they preferred more, as well as to rate each package on a 1-7 scale, where 1 indicated “absolutely dislike” and 7 indicated “absolutely like.”<sup>9</sup> Each respondent completed five trials (Figure A2 provides an example of the task within the survey interface). In line with good practice, we randomised the order of the dimensions between respondents to reduce ordering effects (where respondents disproportionately focus on items appearing near the beginning of a list), but kept it constant across the five trials to minimize survey fatigue. We also ask a series of questions that allow us to explore the level of concern regarding economic _vs_ humanitarian aspects, as well as respondents’ levels of social contact. <u>Table A2 and Table A3 in the</u> Appendix explain each of these variables in detail.

# **4.2 Survey Sample**

> 9 The introductory paragraph and exact wording of the questions were: “ _Imagine that the Colombian government is considering various packages of measures to address migration from Venezuela. Next we will show you two possible options of packages, A and B. Read their descriptions carefully.”_ Then, in each conjoint task they are asked: (1) _Which package is closest to what you would prefer?,_ and (2) _On a scale from 1 to 7, where 1 means “you absolutely dislike” and 7 means “you absolutely like”, how would you rate each of these packages?_

14
|||Table3:ContributionofHealth Insurance to Hea(thFirace<br>Rt Hlth Edit ^||||
|---|---|---|---|---|---|
|||ecurren ea xoernure. by Source<br>PLbiIC<br>|||<br>|
|||<br>Private<br> <br> <br> <br> <br> <br>|ShareofOut-<br>|ShareofPrivate<br>|<br>Shareof Forml<br> <br>|
|Comatrv|Year Currenc|NON<br>Social Security<br>Other<br>Pubtic 0ut-of-<br>Private<br>other<br>u<br>S<br>udoet<br>ludoet<br>Pocket<br>Insurnce<br>Private<br>Total|of-Pocket in<br>Total|Insurace in<br>Total|Pulic<br>insurance<br>In Total|
|lurkin8 Faso|1961<br>FCFA|3.06<br>203'<br>3 <sup>,60</sup>7<sup>b</sup><br>2<sup>,</sup>35 2<sup>C</sup><br>92U4<br>( s) t only avaltable for goverrmnt sqloyees.<br>binctudes: (I) OtherNinistries,<br>1,068; (II)<br>Foreign Aid, 2,349;<br>(Mii) tocal goverrmnt, 190.<br>Clcluis:<br>(1) Dru purcha.<br>2.298; (ii)<br>Paymnt of hospital fets, 54.|25.4S|--|0.220S|
|5luwi|1966<br>Flu<br>(millios)|127<br>489b<br>1<sup>,</sup>07<sup>0</sup>6<br>62ad<br>2.984<br>aIncludes:<br>Ci) NON 1,077; (Mi)NOE181; (Mii) NSA 9.<br>binclujs:<br>(1)Nutuellede la Fonctfon Publiqu, 476;_and_<br>(if)<br>ocial<br>security,<br>13.<br>CForeign aid.<br>iri<br>l<br>ud<br>**aamissim**<br>(210 Flu).whichare almost totally<br>financd by<br>out-of-pocket (210 Flu of 215 FBu).|<br>21.0X||16.4S|
|C_nroon|1963<br>FCFA 2<br>(millions)|2,140<br>--<br>331a<br>--<br>--<br>6<sup>,</sup>100<sup>b 28,571</sup><br>*Foreign assistance for recurrentcosts.<br>bio informtion<br>on private sector available for Ca_roon, except for<br>the IOOs that snt<br>the 6,100 million.||||
|Cote dlIvoire|1965<br>FCFA 2<br>(millions)|9,085<br>300a<br>b<br>**39 58 5 **<sup>**c**</sup><br>562<br>69.532<br>*CNIPS<br>(Caisse lationale de Prevoyance Sociele).<br>%sutu lle Gwwrale de Fontiomnaire<br>andcontained inNUbudget.<br>cOf this<br>xmont, 32,641 for drugs.|569X|.0811|043U|
|Ethiopia|1986<br>BIRR<br>(mllions)|**79 0 **<sup>**'**</sup><br>--<br>20.0<br>196.5<br>0.6<br>5.7<br>301.8<br>Govern,nteeployees receive 50 discount at Goverrtent facllities.|65.01|_.021_|--|
|_Ghab_|1987 Cedi<br>(billions)|5.5<br>--<br>--<br>11.0<br>-<br>2.5<br>19.0<br>iON erployees and trainees receive all carefree<br>and 40S of all drug<br>Issuedat asmple of hospitalsstudied<br>In 1988.|57.9X|--|--|

W
Table 2-C. Cross-sectional Determinants of Dollarization (Dependent Variable: Calculated Trend Dollarization)

|Equation:|1.1||1.2||1.3||1.4||1.5||1.6||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|<br>Variable|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant|30.4|***9.0|49.9|***9.6|44.8|***7.7|40.8|***5.3|31.6|***3.8|34.0|***4.1|
|Restrictionsondoll|-5.77|***3.3|-5.26|***3.8|-6.47|***4.0|-6.90|***4.3|-5.98|***3.8|-6.70|***4.3|
|Min. var.portfolio|40.8|***3.5|29.4|**2.5|29.2|**2.1|25.7|*1.8|26.8|**2.0|24.3|*1.8|
|Inflation(log)|||7.96|***4.4|4.98|**2.2|3.97|1.5|2.92|1.1|3.17|1.2|
|<br>Institutions|||||-8.14|**2.1|-9.23|**2.3|-8.38|**2.2|-6.16|1.5|
|Transition|||||||8.26|1.4|15.0|**2.4|14.7|**2.5|
|Legalprotections|||||||||11.2|**2.0|11.3|**2.1|
|InflatTargeting|||||||||||-10.1|1.6|
|<br>R-squared/NOBS|0.184|84|0.381|77|0.409|75|0.429|75|0.457|75|0.477|74|
|AdjustedR-squared|0.163||0.355||0.375||0.387||0.409||0.422||
|S.E.ofregression|22.7||20.1||19.9||19.7||19.3||19.2||
|<br>Loglikelihood|-380.0||-338.2||-328.0||-326.7||-324.8||-319.5||
|Equation:|1.7||1.8||1.9||1.10||1.11||1.12||
|Variable|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|Coeff.|t-Stat|
|Constant|36.4|***4.6|34.2|***4.1|35.7|***4.0|36.5|***4.1|48.5|***6.1|38.0|***4.3|
|Restrictionsondoll|-6.26|***4.0|-6.72|***4.3|-5.56|***3.7|-5.57|***3.7|-5.83|***3.4|-5.21|***3.5|
|Min.var.portfolio|25.2|**2.0|24.3|*1.9|18.2|1.4|19.0|1.4|19.6|1.4|14.8|1.2|
|Inflation(log)|4.76|**2.1|3.48|1.4|3.88|1.4|4.03|1.5|5.64|*1.9|4.95|*1.8|
|Institutions|||-4.40|1.0|-1.82|0.4|-1.84|0.4|-6.04|1.4|1.37|0.3|
|Transition|14.8|**2.5|15.2|**2.5|11.9|**2.0|10.9|*1.9|1.16|0.2|15.8|***3.0|
|InstitutionsxTransn|-11.5|**2.2|-8.41|1.4|||||||-18.8|***3.0|
|Legalprotections|12.8|**2.3|12.0|**2.2|16.7|**2.5|16.2|**2.3|||17.6|**2.5|
|InflatTargeting|-12.1|**2.0|-10.2|1.6|-10.7|1.3|-9.73|1.1|||10.1|1.2|
|GDP-hedge|||||-3.33|0.7|||-7.64|1.3|-4.49|0.8|
|<br>GDP-hdgxTransn|||||||||12.44|1.3|4.69|0.5|
|<br>R-squared/NOBS|0.478|74|0.485|74|0.492|59|0.494|59|0.423|60|0.524|59|
|Adjusted R-squared|0.422||0.422||0.411||0.401||0.345||0.425||
|S.E.ofregression|19.2||19.2||18.8||19.0||19.7||18.6||
|Loglikelihood|-319.5||-318.9||-251.9||-251.8||-259.6||-250.0||

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.