rafmacalaba/datause-encoder-data huggingface dataset:
where is a standard error term with mean zero and variance . We predict the expected U earnings of Mexican migrants (mm M ) if they migrate to the U.S. using the estimated coefficients εε σσ<sup>2</sup> ( ) and characteristics of Mexican workers ( M ) from the ENOE data. When predicting ww� migrant wages in the US, we assume that Mexican migrants in the U.S. are employed in the mm ββ̂<sup>U</sup> XX private sector (not working for the federal, state, or local government, and not in the armed forces). We also assume that all migrants are Hispanic, single (for the purpose of work migration), and have no U.S. Citizenship or permanent residency status. The expected earnings in the U.S. of Mexican migrants ( MU ) is then: �MU Mww� and MU �MU ⁄2] (8) MM mm MM where is the unbiased estimator of ln ww = ββ̂<sup>U</sup> XX from (7) ww� = exp [ln ww(Wooldridge 2012+ σσ�<sup>2</sup> <u>).</u> We postulate that a migrant makes a migration decision assuming that his earnings in the σσ�<sup>2</sup> σσ<sup>2</sup> We postulate that a migrant makes a migration decision assuming that his earnings in the U.S. are functions of his specific human capital characteristics and his occupation in Mexico. Here, the migration decision is also a function of migrant’s professional experience in his home country. A Mexican electrician plans to work in that occupation in the U.S., forming his wage expectations ( MU ) based on information about wages of electricians in the U.S. The other explanatory variables U � include information about age, gender, marital status, race, the ww� highest level of education, citizen status, job classification, and whether a worker works full- or mm �XX part-time. The detailed regression results for (8) can be found in the Appendix (Table A2).

We estimated a second specification that drops the worker’s occupation, on the grounds that this is endogenous. Causal inference is not the objective of the predictions, but it is nonetheless of interest to see if the results change much if we do not condition on occupation, which may well change in the U.S. The results are in the Appendix, and are very similar for this alternative restricted specification. The discussion in Section 4 focuses mainly on the full model.

The BIG may act like a reservation wage rate, a higher value of which will reduce labor supply (at any given wage rate) for workers earning above the basic income. Thus, their wage rates will rise in equilibrium. (This is similar to the expected spillover effects on other wage rates of a higher statutory minimum wage rate.<sup>11</sup> ) The impacts on the U.S. income distribution then depend on the wage elasticity of labor demand. Our calculations assume that the distribution of

> 11 See, for example, Neumark et al. (2004) and Dube (2018).

9
|6|Zimbabwe|206|607|1,031|741|13|25|55|209|193|582|976|532|61.4|69.2|75.2|32.4|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|7|Angola|104|6|5|4|104|159|216|415|0|-153|-211|-411|0.0|-9.3|-8.1|-5.8|
|7|Cameroon|823|738|1,002|1,315|33|55|102|227|790|683|900|1,088|59.1|59.5|59.2|60.3|
|7|Congo, Rep.|127|181|126|318|17|34|89|127|110|147|36|191|21.6|23.3|4.3|14.7|
|7|Equatorial Guinea|23|39|109|98|9|7|11|29|14|32|98|68|26.9|45.6|33.7|8.1|
|7|Nigeria|340|318|362|590|633|75|649|1,163|-293|243|-287|-573|-2.1|4.0|-3.0|-3.4|
|7|Sudan|476|372|328|506|96|111|132|251|379|261|195|255|25.3|25.5|12.5|5.9|
|8|Congo, Dem. Rep.|275|227|59|101|63|95|69|154|212|132|-10|-53|21.7|12.5|-1.8|-4.2|
|8|Eritrea|..|..|2|5|..|..|30|40|..|..|-28|-35|..|..|-16.5|-11.3|
|8|Liberia|242|159|185|166|51|41|35|72|191|118|150|94|5.9|2.8|3.3|2.0|
|8|Sierra Leone|56|20|7|16|22|17|27|36|34|3|-19|-20|14.3|1.9|-5.9|-4.1|
|8|Somalia|139|86|90|105|65|26|53|57|74|60|38|48|19.9|33.5|13.4|13.9|
||Memo Items:|||||||||||||||||
|2-8|All SSA Countries|11,030|11,044|14,495|19,352|2,545|2,676|4,882|8,038|8,485|8,367|9,612|11,314|16.8|18.2|12.8|9.0|
|2|Middle-income SSA, All|2,602|2,828|4,214|5,396|519|686|1,355|2,043|2,082|2,141|2,860|3,353|14.2|14.2|10.0|6.7|
|6|Low-income SSA, All|8,428|8,216|10,280|13,956|2,026|1,990|3,528|5,995|6,402|6,226|6,753|7,961|17.9|20.2|14.6|10.5|
|7|Oil Exporters|1,893|1,654|1,932|2,830|894|441|1,200|2,213|999|1,213|732|617|6.0|12.9|7.0|3.3|
|8|Civil Conflict States|712|492|344|394|201|178|214|359|511|313|130|34|10.5|5.6|2.2|0.5|
||Other Low-income|5,823|6,070|8,004|10,733|931|1,370|2,114|3,423|4,892|4,700|5,890|7,309|39.6|31.8|24.5|19.8|

Note: Food and agricultural materials are defined as raw food plus agric raw materials, excluding processed food products and seafood (see details in Appendix Table 1). 2 = Middle-income; 6 = Low-income; 7 = Oil exporters; 8 = Civil conflict countries (see detailed classifications in Appendix table 4).

Source: Based on partner data from UN COMTRADE Statistics.

47
allows calculation of an intent-to-treat effect, which in turn allows isolation of the pure effect of movement on wages.

In the sample of McKenzie, Gibson, and Stillman (2006), the mean weekly income of Tongan non-applicants to the lottery is NZ$70. The OLS estimate of the income gain to migration—which controls only for education, age, sex, height, and birth on Tonga’s principal island—is NZ$360 per week. The experimental estimate is NZ$274. Controlling for observables, then, the wage ratio _Ro_ is (70+360)/70 = 6.14, while the true wage ratio _Re_ controlling for both observables and unobservables is (70+274)/70 = 4.91. In other words, the predicted wage ratios of observational equivalent workers overstates the true ratio of selected movers by a factor of 6.14/4.91 = 1.25.

While currently there are no other estimates from policy-induced migration channels free of selection, there is evidence comparing destination-country incomes among migrant streams that plausibly differ in their basis and degree of selection. Cortes (2004) finds that refugees who arrived in the US between 1975 and 1980 had 6% lower earnings in 1980 than economic migrants who arrived in the same period—controlling for education, age, language ability, marital status, and region of residence. If indeed the unobserved traits _x_ ′ of migrants differ systematically from the true unobserved traits<sup>_x_</sup> _r_<sup>′of non-</sup> migrants, then estimates of _Re_ based on (3) are biased by _E_ [φ ˆ _fh_ ( _x_ ′)] _E_ [φ ˆ _fh_ ( _xr_ ′ )] . The more refugees’ unobservable determinants of earnings resemble those of the average nonmigrant, the more closely this bias term is approximated by the Cortes (2004) finding of 1/(1–0.06) = 1.064. This means that either (i) if refugees are much less positively selected on unobservables than other migrants, then the selection bias in our estimates of _Re_ is not large, and is not far above 1.1, or (ii) refugees are selected on unobservables as much as other migrants, in which case their earnings relative to other migrants are not informative about the degree of bias that selection produces in our estimates.<sup>19</sup>

# 3.4 _Bounding the effects of natural barriers_

The wage ratio _Re_ measures the wedge between an individual’s wages after and before migration, and reflects the cost of moving. As in equation (1), part of this cost we term “natural” ( δ _n_ )—which is to say largely independent of government policy—such as transportation expenses, psychic costs of an unfamiliar environment, costs of language acquisition, and so on. The rest is the policy-induced cost of moving ( δ _p_ ).

> 19 Cortes (2004) also finds that by 1990, the same refugee cohort had 20% _higher_ earnings than economic migrants, controlling for the same observable traits. Measured at this time-point, then, if refugees are much less positively selected on unobservables than economic migrants, our wage ratios are not only overestimated to a small degree—they are underestimated.

25
5 Between 1998 and 2001, all four economies increased their share of GDP devoted to health, with Hong Kong (China) increasing its share most slowly and Taiwan (China) most quickly ( **_Table 1_** ). Over the same period, Singapore’s share fell further below its expected share, but the other Tigers all moved closer towards their expected share, Taiwan (China) most quickly. It was over this period that Japan moved from being on the regression line to above it. In sum, while the degree to which the Tigers ‘under-spend’ on health care has—with the exception of Singapore—diminished somewhat recently, this trend would have to continue much longer before one could begin to question the ability of these systems to keep aggregate spending under control.

_Table 1: Changes in actual and predicted share of GDP spent on health, 1998-2001*_

|||Share of|GDPspent on<br>Percentage|health<br>Pt|Actualsha|re as percentag|e ofpredicted share|
|---|---|---|---|---|---|---|---|
|||||||||
||1998|2001|point<br>change p.a.|<br>change p.a.|1998|2001|% change|
|Hong Kong (China)|5.6|5.7|0.02|1%|78%|82%|4%|
|Japan|7.2|7.8|0.12|3%|99%|109%|11%|
|Korea|4.3|5.1|0.16|6%|68%|79%|16%|
|Singapore|4.2|3.9|-0.06|-2%|60%|57%|-5%|
|Taiwan(China)|5.5|6.0|0.11|3%|82%|91%|12%|

# **_How is health spending financed?_**

The five health systems vary in how they raise the revenues used to finance health care (Figure 3). At one extreme is Hong Kong (China), which finances just over half of its health spending through general tax and non-tax revenues. At the other extreme is Singapore, which finances as much as two thirds of its health spending through out-of-pocket payments. In the middle are Japan, Korea and Taiwan (China), all of which rely on social insurance for half (or nearly half in the case of Korea) of their health spending. Noteworthy is the fact that out-ofpocket payments account for over 30% of health expenditures in all systems, with the exception of Japan, and that private insurance accounts for a fairly small share in Hong Kong (China) and Taiwan (China), and a negligible share in the other three systems.

* The predicted figures are from the same regression as used in Figure 2. Data for 1998 are from the same sources as in Figure 2.
13 country sources. All time-averaging is done by taking simple averages of annual variables, rates or ratios. All real interest rates are ost, that is, deflated by inflation over the same year.

For 11 of the countries it is also possible to obtain local stock market price indices from the IFC data base for an average of 8 years up to - 1985, and these are also included in the data base.

Figure 3 shows the average real interest rate for the 34 countries relative to the rate on real 3-month US T-Bills for the period 1965-85. Real rates in the developing countries have usually been below the real T-Bill rate by a considerable margin, and this widened in the second half of the period. US interest rates rose after 1979, and rates in many developing countries increased sharply but fell behind accelerating inflation after 1971. Only by 1985 were there signs that the average rate in developing countries was approaching the US rate. The mean real rate for the 34 countries declined from -2% over 1965-73 to -5% in 1973-85. But the median fell less sharply, from -1% to -2% because rates declined precipitously in a few countries.

Some of the changes in averages between these two periods are indicated in Tables la to ld. Table la includes the whole sample. Table lb shows cross-country averages for the higher and lower-growers over the whole period. Table lc shows averages for the higher-growing halves of the sample in the two sub-periods, and Table ld averages for the lower-growing halves.

Table la shows that after 1973, although investment rates increased from 21% to 24% of GDP, performance deteriorated sharply. The average incremental output-capital ratio (IOCR) fell by over 50%, and growth slumped from 6% to only 3.6%. Inflation soared from 18% to 34%, and the mean real interest rate fell from -2% to -5%. Nevertheless, as countries continued to develop and financialise, the mean ratio of M3 to GDP rose from 28% to 35%. The Tables also show a notable increase in the cross-country variability of performance in the second period. The gap between good and poor performers widened, especially for inflation (which rose little for the high growers). Tables lc and ld show large differences in the level of real interest rates between the higher and lower-growth countries, and that these widened in the second period. Before 1973, nominal interest rates in the high-growth countries equalled inflation and in the low growers real rates were negative 4%; after 1973 these real rates fell to -1% and -9% respectively. For the overall period, the low growers' real rates were 6 percentage points below those of the high growers and their ratio of M3/GDP was one third lower; their investment rate was somewhat lower (19% versus 26% of GDP) and their efficiency of investment (as measured by the IOCR) only 16% as opposed to 27%.
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# Winning Hearts and Minds through Development? Evidence from a Field Experiment in Afghanistan<sup>1</sup>

**Andrew Beath**<sup></sup> **Fotini Christia**<sup>†</sup>

**Ruben Enikolopov**<sup>‡</sup>

1 We are indebted to Chad Hazlett, Hamid Gharibzada, and Maiwand Siddiqi for excellent research assistance, to Jason Lyall for generously sharing the data on security incidents, and gratefully acknowledge the generous cooperation and assistance provided by Tariq Ismati and Abdul Rahman Ayubi of the National Solidarity Programme; H.E. Wais Barmak, Minister or Rural Rehabilitation and Development; Ehsan Zia, Former Minister of Rural Rehabilitation and Development; staff of AfghanAid, C.H.A., InterCooperation, IRC, NPO/RRRAA, Oxfam UK, and People-in-Need; and Kamran Akbar, Josephine Bassinette, Ladisy Chengula, Philippe Dongier, Susanne Holste, Nicholas Kraft, Qazi Azmat Isa, Dean Jolliffe, Zishan Karim, Elliot Mghenyi, Norman Piccioni, Mariam Sherman, and Mio Takada of the World Bank. We would also like to thank Mike Callen, Rex Douglas as well as colleagues and seminar participants at Brown University, Georgetown University, MIT, NYU, and Princeton University’s NMACPIR conference for helpful comments. The study was financially supported by the Food and Agriculture Organisation (FAO), World Bank, and the National Solidarity Programme. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the Government of the Islamic Republic of Afghanistan, FAO, World Bank, the World Bank’s affiliated organizations, members of the World Bank’s Board of Executive Directors, or the countries they represent. **_JEL Classification_** : H11; H12; H41; H43; H56; H84; D74; D78. **_Keywords_** : Civil Conflict; Government Legitimacy; Government Scope; Crisis Management; National Security; War; Afghanistan. **_Sector Boards_** : Social Development (SDV); Agriculture and Rural Development (ARD)

>  Office of the Chief Economist for East Asia and the Pacific, World Bank (abeath@worldbank.org)

† Department of Political Science, Massachusetts Institute of Technology (cfotini@mit.edu) ‡ New Economic School (REnikolopov@nes.ru)
# **Appendix D: Balance Tests, Before and After 2011**

Table D1: Gender randomization checks

||(1)<br>Judge maj. female|(2)<br>Judge maj. female|(3)<br>Judge maj. female|
|---|---|---|---|
|Pla. maj. female|0.0114|0.0116|0.00739|
||(0.00863)|(0.00860)|(0.00679)|
|Def. maj. female|0.00351<br>(0.00764)|0.00335<br>(0.00755)|-0.000446<br>(0.00651)|
|DV mean|0.363|0.363|0.363|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies|No|Yes|Yes|
|Other controls|No|No|Yes|
|Observations|20394|20394|20394|

The regressions test whether female plaintiffs/defendants are more likely to be matched with female judges than male judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 1. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, maj. = majority.

38
<u>Table 2 (cont.)</u>

|||Unaid<br>Wom|en|Leisure<br>Second|arylevel|Unaid<br>M|en|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|1.99|5.29|11.27|5.40|4.66|0.67|12.06|6.59|
|Argentina|2.46|4.26|n.a.|0.03|5.41|1.29|n.a.|0.02|
|Bangladesh|1.42|5.19|13.56|3.78|5.37|1.26|13.13|4.23|
|Colombia|3.25|4.74|10.15|5.93|6.39|1.12|9.86|6.45|
|Ecuador|2.51|5.47|9.74|4.95|5.55|1.27|9.50|4.95|
|Ghana|2.95|5.16|12.40|4.81|3.11|3.45|11.95|6.64|
|Guatemala|2.96|5.38|10.59|3.95|5.74|1.06|10.78|4.56|
|Iraq|1.38|4.78|n.a.|5.34|6.77|1.51|n.a.|5.28|
|Mexico|3.21|5.88|n.a.|13.72|6.54|1.68|n.a.|13.54|
|Moldova|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|3.38|5.99|11.45|3.37|4.63|4.23|11.44|4.16|
|Pakistan|0.54|5.06|13.25|5.11|4.33|1.65|12.27|5.71|
|West Bank and Gaza|0.71|3.05|11.17|6.60|5.32|0.38|11.16|6.81|
|Peru|2.81|5.54|9.77|5.39|6.28|2.09|9.57|5.60|
|South Africa|2.36|4.44|12.30|5.13|4.14|2.20|12.09|6.08|
|Timor|0.91|2.42|n.a.|n.a.|3.20|1.17|n.a.|n.a.|
|Turkey|1.04|4.25|11.09|5.45|3.99|0.40|10.61|5.10|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|2.52|3.10|10.99|6.59|4.04|2.03|10.65|6.83|

|||Unaid<br>Wom|en|Leisure<br>Tertiar|ylevel|Unaid<br>M|en|Leisure|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|Market<br>work|<br>p<br>domestic<br>work|Personal<br>care|<br>,<br>social and<br>study|
|Albania|3.94|3.27|11.35|5.42|5.02|0.59|11.68|6.71|
|Argentina|3.21|3.27|n.a.|0.04|4.81|1.33|n.a.|0.05|
|Bangladesh|2.50|5.30|13.11|3.05|6.41|1.20|11.58|4.78|
|Colombia|4.49|3.43|10.01|6.14|6.15|1.34|9.69|6.78|
|Ecuador|3.79|4.42|9.63|5.96|5.43|1.42|9.34|6.08|
|Ghana|4.49|3.99|10.96|5.63|6.05|1.37|10.55|7.01|
|Guatemala|4.49|3.64|10.51|3.99|6.46|1.27|10.38|4.00|
|Iraq|2.68|4.17|n.a.|5.43|6.36|1.59|n.a.|5.95|
|Mexico|4.44|4.79|n.a.|14.65|6.58|1.81|n.a.|14.12|
|Moldova|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|4.77|4.77|11.48|3.07|6.49|2.55|11.54|3.73|
|Pakistan|1.78|4.62|13.07|4.51|5.53|1.01|12.19|5.26|
|West Bank and Gaza|1.70|3.86|10.93|5.35|5.52|0.57|11.18|6.20|
|Peru|4.05|4.49|9.50|5.66|6.23|2.05|9.18|6.24|
|South Africa|4.70|3.59|11.53|4.48|6.03|1.91|11.43|5.26|
|Timor|1.43|1.80|n.a.|n.a.|2.02|1.05|n.a.|n.a.|
|Turkey|1.01|3.50|10.93|4.83|2.80|0.76|10.79|5.27|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|4.07|3.33|10.64|4.92|5.54|2.13|10.12|5.64|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

29
In regions with an average total daily consumption of less than 2500 kcal per capita (SubSaharan Africa and South Asia) the situation improves somewhat, but in 2050 average calorie intake is still significantly lower than in other regions. In regions with high average calorie intake, consumption remains stable or increases only slightly. In middle-income regions (South East Asia, Central America, South America), food consumption slowly rises towards the level of OECD countries. As already noted, differences in the consumption of animal products are much greater than in total food availability, particularly between regions. Food demand for livestock products essentially doubles in Sub-Saharan Africa and South Asia, from some 200 kcal per person per day in 2000 to around 400 kcal per person per day in 2050. On the other hand, in most OECD countries that already have high calorie intakes of animal products (1000 kcal per person per day or more), consumption levels will barely change, while levels in South America and countries of the Former Soviet Union will increase to OECD levels (Van Vuren et al., 2009).

Cultural issues can play a substantial role in diets: at equivalent incomes, cultural differences become conspicuous drivers of food quality and type (FAO, 2002), such as low pork consumption rates in some regions and low beef consumption rates in others. In addition, in some of the OECD countries particularly, organic agriculture has been increasing and further expansion in the future seems likely. This is certainly likely to be the case if actual food production costs become more nearly reflected in agricultural prices, and if carbon footprint and food miles labeling become common, as is probable. There would also seem to be some prospects for niche marketing of specific livestock products (for example, "conservation beef" produced in tropical rangelands in ways that enhance the conservation of wildlife). Some traditional and indigenous cultures may well be sources of agricultural knowledge that is useful for devising novel and sustainable production systems.

In the future, ethical issues may also have some impact on patterns of demand for livestock products. Biotechnology may have considerable benefits for society, but in some countries its use raises ethical concerns about food and environmental safety. There is not always much logic in the way that biotechnological or "high-science" fixes to certain challenges are tolerated by the general public but not others. In general, there is a serious disconnect between the science of many of today's development challenges and the perception of these problems on the part of a considerable portion of the general public. In Britain, for example, polls tend to find no great enthusiasm on the part of the public for restrictions to their 13