rafmacalaba/datause-encoder-data huggingface dataset:
second shows the equivalent in the whole US population. In principle, the numbers for each decade should decline across censuses due to return migration or mortality. Counter-intuitively, it can be observed that samples sometimes increase over time. For example, the 1980s arrivals counting 1,668,342 people in 1990, increase to 1,704,575 in 2000. The biggest increase in cohort size occurs between Census 1980 and Census 1990 for the 1970s arrivals (22,893 individuals). This may be due to measurement errors, including undercounting of illegal migrants and methodological differences across censuses, issues that will be detailed below. Despite the upward bias, the relative constancy in the shares of migrants by half-decade of arrival, as well as the stable proportion of the highly educated individuals (4+ years of college) across samples are positive signs that the data capture the same people over time.

As indicators of occupational placement, I employ three alternative variables. Two of them are available from the IPUMS samples and the third is based on own calculation. The first, labeled Prestige Index, does not vary by census, and measures the prestige associated with an occupation.<sup>25</sup> The second, labeled Education Index 1, indicates the educational content of each occupation and is based on the share of individuals with undergraduate and graduate degrees who declare an occupation.<sup>26</sup> Both the Prestige Index and Education Index 1 take values from 1 to 100. The last variable, labeled Education Index 2, indicates the educational content of an occupation and is calculated as the weighted average of the number of education years of all individuals, male and female, native and foreign-born alike, who declare an occupation. The two indices of educational content are sample-dependent; therefore they vary by census, unlike the prestige indicator, which is constant across periods. On the other hand, the Prestige index which is derived from individual answers to a survey might be more sensitive to modifications in the survey respondents’ profile by country of origin. However, the fact that the Prestige index is correlated up to 90% with the two Education indices provides assurance that the potential bias is not significant. All three indices increase with the education level as can be seen from Table 3.

The study uses, in its first stage, 122 countries of origin for which data are available in all three Censuses. To ensure readability, only the 80 most important sending countries are displayed in graphs and used as basis for the regressions of predicted indices on country-specific variables. These 80 countries represent 95% of the male immigrants who were in the US as of

> 25 Details regarding its content and calculation are available at http://usa.ipums.org/usaaction/variableDescription.do?mnemonic=PRENT

> 26 Details about calculation and interpretation at http://usa.ipums.org/usaaction/variableDescription.do?mnemonic=EDSCOR90

16
# 2. The Story in Pictures

Income of the poor has a very tight link with overall incomes. The top panel of Figure 1 shows the logarithm of average income in the poorest fifth of the population plotted against the logarithm of average income for the whole economy (per capita GDP). The graph includes 418 observations covering 137 countries, and multiple observations for a single country are separated by at least five years over time. The slope of this relationship is very close to one, and all of the observations are closely clustered around this regression line. This indicates that as overall income increases, on average incomes of the poor increase equiproportionately. For 285 of these observations, we can relate growth of income of the poor over a period of at least five years to overall economic growth, as shown in the bottom panel of Figure 1. Again, the slope of the relationship is slightly larger than one, and although the fit is not quite as tight as before, it is still impressive.' There are 149 episodes in which per capita GDP grew at a rate of at least 2% per year: in 131 of these episodes, income of the poor also rose. Thus, it is almost always the case that the income of the poor rises during periods of significant growth. There are a variety of econometric problems with simple estimates of the relationship between incomes of the poor and overall income, which we take up in the following section. Even after addressing these, the basic result that growth in the overall economy is reflected one-for-one in growth in income of the poor turns out to be very robust.

One can use the data in Figure 1 to ask a closely-related question: what fraction of the variation across countries and over time in (growth in) incomes of the poor can be explained by (growth in) overall income? In terms of levels of per capita income, this fraction is very large. The data in the top panel of Figure 1 imply that over 80 percent of the variation in incomes of the poor is due to variation in overall per capita incomes, and only 20 percent is due to differences in income distribution over time and/or across countries. To us, this reflects nothing more than the commonsense observation that poor people in a middle-income country like Korea enjoy much higher living standards than poor people in a country like India, not because they receive a significantly larger share of national income, but simply because average incomes are much higher in 5
“post”-intervention period would be enough to account for this time series inconsistency in standard errors. Tables AII and AIII in the Annex shows that when the DD is performed using a sample with data collapsed into two periods, before and after PAE, the results are practically unchanged. The effects on math (Spanish) test scores range from 0.18 _σ_ (0.14 _σ_ ) with the full sample and no controls to 0.14 _σ_ (0.09 _σ_ ) when the controls are added and 0.13 _σ_ (0.08 _σ_ ) when the sample is restricted to schools within the OB, in all cases significant at the 95 percent confidence level.

As mentioned in Section 3 one of the most important limitations of the DD approach as a strategy to evaluate the impact of PAE is that program effects can be confounded with mean-reversion noise caused by a treatment selection based on ranking of schools at the baseline. Although the RD is a robust strategy to address this concern, we also perform a simulation showing that mean-reversion is not driving the results presented so far. If mean-reversion is at work, then reproducing PAE’s eligibility criteria but using the ENLACE results of 2008 (as opposed to 2009) to rank and select schools should result in PAE schools advancing faster than non-PAE schools between 2008 and 2009. Figure AII in the Annex shows the average evolution of learning outcomes between PAE and non-PAE schools using the alternative selection criteria based on the school ranking of 2008 (top figure) and compares it with the evolution under the actual selection of PAE and non-PAE schools. The simulation and actual trends on average test scores in Figure AII are based on schools within the OB. Using the ranking of 2008 to assign schools into PAE and non-PAE produces no differentiated evolution in test scores after the assignment, suggesting that the post-2009 PAE versus non-PAE differences in trends observed after 2009 can be attributable to the program with little or no mean-reversion effects.

# **4.2 RD Approach**

The first strategy followed within the RD approach is a graphical representation of the discontinuity using local linear or kernel regressions on both sides of the cutoff. Figure 6 illustrates the relationship between average math performance at the school level in 2010 (vertical axis) and the _forcing variable_ , the simple average ENLACE results at the school level in 2009 relative to the test score (horizontal axis). The PAE schools are to the left of the cutoff, which was used for their eligibility into the program, and the non-PAE schools are to the right. A mild discontinuity appears at the cutoff (469 points of ENLACE): there is a general pattern showing that schools slightly below the cutoff (the PAE schools) display greater test scores in 2010 than schools slightly above the cutoff (the non-PAE schools), although their scores in 2009 were very similar. Zooming into the discontinuity reveals that the difference in the regression at the cutoff is around 10 ENLACE points, very close to DD estimates. This graphic illustration suggests a positive effect on learning 13
multiple rounds of Nepal’s population census, only about 3 percent of the population was abroad in 2001, an increase of 0.7 percentage points since 1981. By 2011, over 7 percent of the population was abroad. In terms of households, one in four households had a migrant abroad and almost a fifth (18 percent) of the households had migrants outside India.

The majority of the migrant population is young males. In 2011, about 90 percent of the migrants outside India is male. The fraction of males is only slightly lower (85 percent) for India migrants. The median age of migrants is only 25 years old and almost 90 percent of migrants are aged between 15 and 45 years. Absentee males are 13 percent of the resident male population. Since migrants are mostly of working age, more than a fifth (22 percent) of the national male population aged 15-45 are outside the country. The educated migrants, especially outside India, earn and remit more money to their families in Nepal. Most of the migrants went for work. In particular, over 97 percent of the migrants to Malaysia and the Gulf countries migrated for work. The share of work-related migration is a bit lower at about 85 percent, for India and other destinations.

The pace at which remittances from abroad have increased in Nepal is unprecedented. Until the late 1990s, personal remittances received were less than 1 percent of GDP, lower than that for Bangladesh or India. The first half of the 2000s saw a drastic increase in this share, from 2 percent in 2000 to 15 percent in 2005, 22 percent in 2010 and as much as 29 percent in 2014, while the neighboring countries experienced an increase at a much more modest rate (Figure 7, panel b).<sup>19</sup> Expressed in per capita terms in current US dollars, Nepalis on average received less than $5 of remittances from abroad in 2000 but it reached $205 by 2014, by far the highest in South Asia and more than twice as much as Bangladesh (94 dollars).<sup>20</sup> Foreign exchange earned from migration exceeds the sum of export receipts and official aid (World Bank 2011).

**Figure 7: Personal Remittances Received in Nepal and Selected Countries**

19 Personal remittances received as percent of GDP is 8.7 percent for Bangladesh and 3.4 percent for India as of 2014.

20 Even this figure may be an understatement of the prevalence of remittances from abroad as it includes the population working abroad in calculating the per capita value.

15
the entry e¤ect of preferences. The e¤ects of such policies are, of course, blunted by the presence of quotas in other markets.

In our estimation, we simulate our model and then match the generated distributions to those in the data.<sup>4</sup> In this paper, we use only cross sectional price and quantity information and are able to generate bootstrap standard errors for our estimates. The advantage of this approach is that such cross sectional data is commonly available, which makes our procedure widely applicable in contrast to the structural dynamic approach taken in recent work, such as Das, Roberts and Tybout (2007) and Aw, Roberts and Xu (2011), which is limited to where data is available over a period of time.

Finally, some caveats. Our model, which we believe captures the essential aspects in question, as usual, has a number of limitations. We make a number of modelling assumptions like constant marginal costs, restricting ourself to a static model etc., which simpli...es things considerably. These assumptions let us separate what happens in the two markets at any point of time. If, for example, marginal costs were not constant, as would be the case with capacity constraints, pricing in one market would depend on demand shocks in the other, resulting in our model being mis-speci...ed and our estimates being biased. However, if capacity constraints were binding, the correlation of sales of ...rms that serve both the US and EU markets (AUS ...rms) should be negative. For the 155 AUS ...rms in our data, this correlation is +0.32 (and signi...cant at the 5% level), consistent with no binding capacity constraints.<sup>5</sup> Our structural approach interprets the data through the lens of a heterogenous ...rm model. Such models have been widely used in recent work. The literature has focused on whether ...xed cost changes or marginal cost changes drive increases in trade into the export market.<sup>6</sup> This is not the focus of our work, though we consider the e¤ect of both changes

> 4Demidova, Kee and Krishna (2012) take advantage of a natural experiment in trade policy that provides clean predictions regarding how ...rms should sort themselves across markets in this augmented Melitz model. They then show that these predictions are consistent with the data.

> 5This could be because ...rms in apparel can subcontract out and thereby relax capacity constraints at low cost. The absence of such evidence of capacity constraints helps to motivate our assumption of constant marginal costs.

> 6The importance of marginal trade costs, such as tari¤s, in explaining the expansion of the extensive

6
# **Table 1: PSF1 sample of ever married adult women.**

|**Marital status**||Rural|Urban|TOTAL|
|---|---|---|---|---|
|First marriage|N|1,168|985|2,153|
||%|71.35|64.89|68.24|
|Remarried widow|N|95|60|155|
||%|5.80|3.95|4.91|
|Remarried divorcee|N|115|132|247|
||%|7.03|8.70|7.83|
|Widow|N|210|220|430|
||%|12.83|14.49|13.63|
|Divorcee|N|49|121|170|
||%|2.99|7.97|5.39|
|**TOTAL**||**1,637**|**1,518**|**3,155**|
|Remarried widows in:|||||
|_a leviratic marriage_|N|51|20|71|
|_a non-leviratic marriage_|N|36|34|70|
|_Unknow type of union_|N|8|6|14|

Note: Adults are defined as 15 and older. For 14 remarried widows, we have no information on whether the current husband is a relative of the deceased husband. Source: Authors’ calculations using PSF1.

32
provide little security. The model predicts that when firms believe that political alternation is more likely, they will invest less in influence.<sup>2</sup> We test this prediction for the 27 post-communist in Eastern Europe and the former Soviet Union by constructing a measure of the frequency of change in power among distinct leaders and among ideologically distinct groups (parties) that is comparable across political systems. We use lagged cumulative political alternation as a measure of expected future political alternation.<sup>3</sup> To measure the quality of governance in the post-communist countries, we use the indicator for the rule of law constructed by the World Bank (Kaufmann, Kraay and Mastruzzi 2007; hereafter, KKM). A higher score corresponds to better governance, and the mean and median are normalized at zero and the standard deviation at one. Figure 1 shows that there is great diversity of governance outcomes for the post-communist countries relative to the diversity of outcomes in the world. Postcommunist countries with the highest rule of law scores (Estonia and Slovenia) are in the 75<sup>th</sup> percentile of all countries in the world, whereas those with the lowest scores (Kyrgyzstan and Turkmenistan) are in the bottom 10<sup>th</sup> percentile.

> 2 A related role for political alternation in the quality of governance is studied in Dixit, Grossman and Gull (2000).   In their model,  two parties expect to alternate in power indefinitely, and that prospect makes it in the self-interest of each party to share wealth broadly rather than privileging their own constituents. 3 In principle, how alternations are spaced in time should also be taken into account, but in our sample, we did not have examples of quick successions in power followed by a long reign (or the reverse),  so that relatively little information is lost by considering only the cumulative number of alternations.

4
**Figure 5.  Gini index changes between 1980s and 2000s**

<!-- Start of picture text --> Mid 1980-s Early 2000s<br>50<br>40<br>30<br>20<br>China Russia Hungary Poland Georgia Mexico Austria Sweden USA United<br>Kingdom<br>Gini index<br><!-- End of picture text --> _Source_ : ECAPOV2, Ravallion and Chen for China, LIS (www.lispoject.org) for other countries.

_Note_ levels of Gini index are not comparable across countries as different concepts and definitions of welfare are used. In ECA for 2002s: current consumption per capita without housing rental values, correcting for regional price differences and without outliers. Data for early 1980s come from published sources and refer to total expenditures, not correcting for price differences. China – total incomes per capita, correcting for price differences. OECD – per equivalent adult total money incomes, correcting for regional price differences without outliers.

How different drivers interact with each other is very much a question of particular country circumstances, initial conditions, and most importantly, policy choices. The taxonomy of drivers offers some basic insights into the reasons behind the variation across countries with respect to increases in inequality. Models of transition provide further guidance regarding the role of policies.

# **Models of restructuring**

Aghion and Blanchard (1994) proposed a theoretical model of transition dynamics describing the reallocation of productive resources in transition. The transition is formalized as a reallocation of labor and capital across _state_ and _private_ sectors, with unemployment as a transient step between the two<sup>7</sup> , and highlights ways in which endowments and policies in transition affect the distribution of income (Commander and Tolstopiatenko [1996]).

In the CIS, with an ungenerous benefits regime, and low initial values for closure and restructuring, the reallocation of labor to the private sector is protracted, and inequality rises gradually and steadily to high levels. More generous benefit regimes with higher probabilities of restructuring, as in Central Europe, lead to unemployment peaking at higher levels, but given a rapid movement of workers into the private sector and a generous floor in the form of unemployment benefits, the rise in inequality is less pronounced, reaching a hump at a lower level than that observed in the first scenario (Figure 6).

> 7 This state can also be re-interpreted as subsistence or informal sector employment (Radulovic 2003).

16
# **Works Cited**

Abadzi, Helen. Efficient learning for the poor: New insights into literacy acquisition for children. _International Review of Education_ , 54:5-6, November 2008.

Adekanmbi, Adebimpe, Moussa P. Blimpo, and David K. Evans. The state of lower basic education in the Gambia: A baseline survey report prepared for the ministry for basic & secondary education, the Gambia. _APEIE, World Bank_ . Available upon request to authors, 2009.

Banerjee, Abhijit, Rukmini Banerji, Esther Duflo, Rachel Glennerster, and Stuti Khemani. Pitfalls of participatory programs: evidence from a randomized evaluation in education in India. _American Economic Journal: Economic Policy_ , 2:1–30, 2010.

Bardhan, Pranab, and Dilip Mookherjee. Relative capture of local and central government: An essay in the political economy of decentralization. Center for International and Development Economics Research, Institute of Business and Economic Research, UC Berkeley, 2002.

Barrera-Osorio, Felipe, Tazeen Fasih, and Harry Anthony Patrinos with Lucrecia Santibez. Decentralized Decision-Making in Schools: The Theory and Evidence on School- Based Management. 2009.

Bayona, E.L.M and B. Sadiki. An investigation into appropriate ways of implementing institutional development (whole school development). University of Venda School of Education research report, _Accessed at http://www.jet.org.za/publications/pei-research_ on 31 July 2010, 1999.

Bjorkman, Martina, and Jakob Svensson. Power to the people: Evidence from a randomized field experiment on community-based monitoring in Uganda. _The Quarterly Journal of Economics_ , 124:735–769, 2009.

Bloom, Nicholas, Renata Lemos, Raffaella Sadun, and John Van Reenen. Does Management Matter in Schools? Working paper. 2014.

Bruns, Barbara, Deon Filmer, and Harry Anthony Patrinos. Making Schools Work: New Evidence on Accountability Reforms. World Bank. 2011.

Bruns, Barbara, and Javier Luque. Great Teachers: How to Raise Student Learning in Latin America and the Caribbean. World Bank. 2014.

Conn, Katharine, Esther Duflo, Pascaline Dupas, Michael Kremer, and Owen Ozier. Bursary targeting strategies: Which method(s) most effectively identify the poorest primary 23
19 _Table 5: Further results for 1991-2000 CHNS panel_

||High ou<br>|t-of-pocket pa<br>|yments<br>|Catastrophic<br>|out-of-pocket<br>|payments<br>|
|---|---|---|---|---|---|---|
||Sample|Urban|Rural|Sample|Urban|Rural|
|Public insurance|0.237|0.413|0.052|-0.033|0.265|-0.275|
||(1.04)|(1.26)|(0.16)|(0.12)|(0.65)|(0.75)|
||_0.050_|_0.078_|_0.012_|_-0.008_|_0.065_|_-0.068_|
|Worker insurance|0.385|0.843|-0.340|0.367|0.850|-0.367|
||(1.61)|(2.64)|(0.85)|(1.33)|(2.31)|(0.79)|
||_0.079_|_0.144_|_-0.079_|_0.088_|_0.197_|_-0.091_|
|Dependents’ insurance|-0.422|-0.171|-0.246|-0.104|0.200|-0.330|
||(0.45)|(0.17)|(0.09)|(0.11)|(0.19)|(0.11)|
||_-0.098_|_-0.035_|_-0.057_|_-0.026_|_0.049_|_-0.082_|
|Work unit insurance|0.328|0.279|0.380|0.420|0.370|0.467|
||(1.41)|(0.56)|(1.42)|(1.63)|(0.66)|(1.58)|
||_0.067_|_0.052_|_0.080_|_0.100_|_0.090_|_0.109_|
|Cooperative medical insurance|0.670|0.129|1.084|0.638|-0.077|1.097|
||(1.38)|(0.18)|(1.62)|(1.20)|(0.09)|(1.58)|
||_0.127_|_0.025_|_0.191_|_0.148_|_-0.019_|_0.228_|
|Other health insurance|0.912|1.472|-0.324|1.149|1.945|-0.865|
||(1.71)|(2.29)|(0.28)|(1.97)|(2.59)|(0.65)|
||_0.162_|_0.199_|_-0.076_|_0.243_|_0.355_|_-0.212_|
|Insured but type unknown|-0.199|-0.523|0.467|-0.427|-1.043|0.325|
||(0.36)|(0.65)|(0.57)|(0.62)|(0.96)|(0.31)|
||_-0.045_|_-0.115_|_0.095_|_-0.106_|_-0.249_|_0.077_|
|N|14905|5128|9796|14905|5128|9796|
|Av. no. waves|2.4|2.2|2.5|2.4|2.2|2.5|
|Log likelihood|-904.926|-290.578|-604.867|-768.446|-220.295|-539.586|
|Chi squared|304.086|79.956|243.092|271.275|69.127|219.277|

Note: Models are fixed effects logits, estimated on the 1991, 1993, 1997 and 2000 waves of the CHNS. Equations include in addition to health insurance variables all the covariates in Table 4. Income expressed in millions of RMB for the estimation. The first number for each covariate is the coefficient, the second (in parentheses) is the _t_ - statistic, and the third (in italics) is the marginal effect (conditional on a zero fixed effect).