rafmacalaba/datause-encoder-data huggingface dataset:
relative mobility experienced by the most disadvantaged children: 72 percent underestimation for sons and 22 percent for daughters.<sup>14</sup> Second, the estimates show important heterogeneity: while the Inpres schools improved relative mobility of the children from low educated households, it reduced relative mobility of children in the highly educated households. Since lower relative mobility implies a higher intergenerational persistence, Inpres schools strengthened the educational advantages of the more educated segment of the society across generations.

Third, in contrast to the primary completion results, there are dramatic gender dierences when years of schooling is used as a measure of children's educational attainment. The CEF is concave for sons, but it is linear for daughters in this case. More importantly, the evidence suggests strong eects of Inpres schools on sons, but there are no signicant eects on daughters.

Fourth, we explore alternative explanations for the puzzle of strong eects on primary schooling of girls, but little eects on their completed years of schooling. The 61,000 primary schools created a funnel eect because the number of primary graduates increased dramatically but the number of high schools did not expand in any signicant way (Heneveld (1979)). Thus, the Inpres graduates faced an unintended bottleneck at the high school entry level. Our estimates of the eects of Inpres on high school completion suggest that the boys experienced a positive eect (7.7 percentage points higher probability of completion), but the girls suered a negative eect (8.5 percentage points lower probability of completion). The expanded supply of primary graduate boys had crowded out the girls from the high school.<sup>15</sup> Evidence suggests that the negative eect on a girl's secondary schooling does not depend on the level of her father's education. This implies that the mechanisms responsible for the gender-based crowding out are common to all children irrespective of family background. There are two such potential mechanisms: (i) low labor market returns for girls at the secondary and higher levels, and (ii) social norms against girls in a patrilineal society such as

> 14These estimates refer to primary completion of children. For years of schooling, the eect on the most disadvantaged sons is underestimated by 97 percent, but there is no signicant eect on daughters in both linear and quadratic models.

> 15Once we take into account the dierences in the base: for boys it is 0.197 and for girls 0.179, the positive eect for boys cancels out the negative eect on girls, suggesting a one for one gender based crowding out.

4
PlIL Workua(; Puapvr Se-ies

|I';<br>1";'|H<br>u'>"''<br>B<e<br>v.;<l<br>"i<'**Ignory' **<br>I 1:,')_r9'33S.-'3'_|Se<br>C) ;er|Mj'cn**t9Yu1**|SK**ngj**W i'-or|
|---|---|---|---|---|
|WP%6?'|Son',to;e DutD BWybacKs forHany<br>1 (1t4[)*tt'<br>(\(|Er ric3 Detrag,ache<br>vx lflta'|March 1991|S K ng-Watson<br>'~~~~~~~~33<br>'30|
|WPSo '.)|_HovI _-ic!o"<br>c-<br>C,eo!orCourntes<br>Aftect Secondary Market Prices<br>for Develoir'g Country Deot|**Su44**O2Ier<br>Harry Huizinga|Marcr 1991|S King WVa!on<br>33130|
|WPS623|World Bank-Supported Adjustment<br>Programs Country Performance<br>and Etfect'veness|**Vittorio**Corbo<br>Patricio Rojas|March 1991|A Oropesa<br>39075|
|WPS624|Choosing Policy Instruments for<br>Pollut-nControl A Review|Gunnar S Eskeland<br>Emmanuel Jrmenez|March 1991|A Bhalla<br>37699|
|WPS625|How Trade and Macroeconomic<br>Policies Aftect Econom'c Growth<br>and Capital Accumulation in<br>Deveioping<br>Countries|Ramon Lopez|March 1991|K Cabana<br>37946|
|WPSo26|The Macroeconomics of the Public<br>Sector Det**_'ci_**<br>The Case of<br>Colombia|William Easterly|March 1991|R Luz<br>39059|
# The Wage E¤ects of Immigration and Emigration<sup>�</sup>

Frédéric Docquier<sup>a</sup> , Ça1⁄4glar Özden<sup>b</sup> , Giovanni Peri<sup>c</sup>

> a FNRS-IRES, Université Catholique de Louvain (frederic.docquier@uclouvain.be)

b The World Bank, Development Research Group (cozden@worldbank.org)

> c University of California, Davis and NBER (gperi@ucdavis.edu)

December 17th, 2010 Abstract Immigrants in Rome or Paris are more visible to the public eye than the Italian or French engineers in Silicon Valley, especially when it comes to the debate on the e¤ects of immigration on the employment and wages of natives in high-income countries. This paper argues that such public fears, especially in European countries are misplaced; instead, more concern should be directed towards emigration. Using a new dataset on migration ‡ows by education levels for the period 1990-2000, the results show the following: First, immigration had zero to small positive long-run e¤ect on the average wages of natives, ranging from zero in Italy to +1.7% in Australia. Second, emigration had a mild to signi...cant negative long-run e¤ect ranging from zero for the US to -0.8% in the UK. Third, over the period 1990-2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives. These patterns hold true using a range of parameters for the simulations, accounting for the estimates of undocumented immigrants, and correcting for the quality of schooling and/or labor-market downgrading of skills. All results go counter to the popular beliefs about migration, but they are due to the higher skill intensity of both emigration and immigration relative to non-migrants.

JEL Codes: F22, J61, J31.

Keywords: Immigration, Emigration, Complementarity, Schooling Externalities, Average Wage, Wage inequality.

> �This article is part of a research project on "Brain drain, return migration and South-South migration: impact on labor markets and human capital" supported by the Austrian, German, Korean, and Norwegian governments through the Multi-donor Trust Fund on Labor Markets, Job Creation, and Economic Growth administered by the World Bank’s Social Protection and Labor unit. The ...rst author also acknowledges ...nancial support from the Belgian French-speaking Community (convention ARC 09/14-019 on "Geographical Mobility of Factors"). The ...ndings, conclusions and views expressed are entirely those of the authors and should not be attributed to the World Bank, its executive directors or the countries they represent.

1
rowers as well as the long term impact of projects on the country’s development process. The IEG reports directly to the Board and, in principle, its staff are not permitted to move to positions in other parts of the Bank.

The IEG database covers projects started from 1961 to 2002. During this period, over 6,400 projects have been evaluated in all sectors and most countries. The outcomes of those projects are rated independently. Three factors are considered: (i) the relevance of the interventions objectives in regard of the country needs and institutional priorities; (ii) the extent to which the objectives have been achieved; and (iii) the efficiency of the project, which is the extent to which the objectives have been achieved without using more resources than necessary. Based on the scores, the outcome of the project is rated from 0 ( _highly unsatisfactory_ ) to 5 ( _highly satisfactory_ ).<sup>1</sup> As mentioned earlier we define success = 1 for projects rated as satisfactory or very satisfactory. Overall, 58 percent of the projects were considered successful and 3 percent very successful. IEG also separately evaluates the supervision effort by the World Bank and the preparation effort by the recipient government from highly unsatisfactory (1) to highly satisfactory (4).

We acknowledge that the quality of the data is not ideal and that working on project performance in post-conflict is difficult. There are known major weaknesses with World Bank project evaluation. Indeed, IEG evaluation are not randomized control experiments as evaluation would stand for in the economic literature but rather, evaluation is an internal process that consists in qualitative assessments relative to stated objectives. Consequently, it is likely that projects in post-conflict environment would imply lower expectations. The very same project implemented in Burundi may have lower objectives than when undertaken in Ghana. The data might well suffer from a potential bias that would affect the ratings of success but also of the quality of supervision and preparation. Having said that, the evaluation procedure is independent, staff are experienced, the process has been on-going for more than three decades, and a lot of resources are put into it. It is therefore unlikely that the data have no informative content whatsoever. In addition, the bias, at least for its larger part, seems to confirm the difficulties associated with implementing projects in post-confict environment. Indeed, results show that the coefficient for post-conflict projects is always significant and negative when it should be biased upwards, confirming that post-conflict makes indeed things harder. Regarding preparation and supervision, it is likely that the difficult environment of implementation calls for larger efforts. As a result, the same efforts of preparing and supervising might be less well rated in post-conflict than in a country at peace. If this is the reality, coefficients would be biased downwards. However the analysis

> 1Very unsuccessful: 0; unsuccessful: 1; moderately unsuccessful: 2; moderately successful: 3; successful: 4; highly successful: 5.

4
67 Administrative data and data linkage; collecting health data for econometric analysis; categorical data methods; count data; duration analysis; econometric evaluation by non-experimental methods; econometric evaluation with randomized experiments; econometrics in technology assessment; macro panels; models of health care costs; models for risk adjustment; panel data methods; productivity analysis; simulation methods and mixture models; spatial econometrics.

# 7. Demand for health and health care

Demand and need; utility and demand; demand for health as human capital; derived demand for health care; income and price elasticities; information asymmetries and agency relationships; externalities and publicness; supplier-induced demand.

# 8. Medical insurance

Demand for insurance (including long-term care, supplementary insurance); supply of insurance services; moral hazard, its control and trade-offs with risk-pooling; adverse selection and cream skimming; tax-financed health care systems; tax policy, exemptions, personal subsidies and health care financing; private and public systems of health insurance; welfare effects of ̳excess‘ insurance and the trade-off with internalizing externalities; insurance effects on health care providers; issues in coverage: services covered, individual eligibility.

# 9. Supply of health services

Monopoly and competition in health care supply; models of health care institutions (hospitals, nursing homes; for-profit, non-profit); care in the community: long term care, informal care; ambulance services; managed care; health care production functions; cost functions; economies of scale and scope; quality of care; regulation and price controls; the pharmaceutical and medical equipment industries.

# 10. Human resources

Models of professional behavior(s); doctors; nurses; other health professionals; health care managers; professional labor markets; training, continuing training and human capital; licensing; remuneration – fee for service, salary, capitation; doctors as entrepreneurs and employers; professionals and research; forecasting demand and supply.

# 11. Markets in health care

Information and markets; market failure; internal markets; rationing; waiting; discrimination; public goods and externalities; regulation (entry/exit); price controls; institutional subsidies; welfare economics and system evaluation; comparative systems.

# 12. Economic evaluation

Principles of Cost-Benefit Analysis, Cost-Utility Analysis, CEA, Cost-Consequences Analysis and Cost Analysis; techniques of CBA in health and health care; techniques of CUA and CEA in
<!-- Start of picture text --> Figure 15 Tuition Costs in the Public and Non-governmental Sub-sectors<br>26%<br>Mexico<br>1%<br>34%<br>Colombia<br>8%<br>Private<br>Public<br>63%<br>Brazil<br>0%<br>98%<br>Peru<br>0%<br>0% 20% 40% 60% 80% 100% 120%<br>Tuition / GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation Note: Findings for Brazil and Peru are based upon tuition fees of a relatively small sample of higher education institutions. The findings for these countries should therefore be considered preliminary.

These findings do not examine affordability of tertiary education for low-income families. The analysis is based on the average GDP per capita. This is important for Latin America, since it suffers from a high level of inequality. To estimate affordability of tertiary education for a low-income family, the education costs and student assistance would have to be expressed as a share of income of a low-income family.<sup>23</sup> In many cases, poor families in Latin America would not be able to find sufficient funds to cover living costs, estimated to an average of $1,950 for the four Latin American countries. On the other hand, one could find high-income families where affordability is high in particular if the student was enrolled in a tuition-free public institution.

The high-income countries provide two relatively successful financing models for affordability and accessibility: the Northern Europe and the Anglo-Saxon models. The Anglo Saxon model is successful through its combination of a medium to high education cost and a highly extensive student assistance system, in particular with high availability of student loans. A second successful model is the Northern European, which provides the most accessible and affordable education to all students. They have large student bodies, high attainment rates, and the student bodies are reasonably similar to the country’s socio-economic composition. This comes from tuition free or almost tuitionfree tertiary education combined with extensive student assistance programs. However, this model relies on a large public investment into tertiary education financed by very high tax contribution of its citizens.<sup>24</sup>

> 23 A related issue is the regional differences within each country. Generally, all countries show large variations in education costs, living costs and income between urban and rural areas.

> 24 Japan offers a third way. It features the least affordable tertiary education among the high-income country. However, tertiary education is not beyond the means of most Japanese families as household savings rates (not shown) and parental contribution for children’s tertiary education in Japan are extremely high.

28
urban poor maize is 26% of the diet and for Internally Displaced People in World Food Program camps it is 41% of their calorie intake.

# **_2.4. Using Poverty Mapping Techniques to Assess Local Impacts_**

The above analysis considers likely poverty impacts of higher food prices at the aggregate national, urban, or rural level. In some cases a distinction is made for the capital city. Yet it is also feasible to look at disaggregated impacts using poverty mapping techniques to assess which geographical areas are most affected by rising prices. As discussed by Elbers et al. (2003), the idea behind poverty maps is straightforward, but its actual implementation is complex. Essentially, a regression is estimated using household survey data and its key parameters used to predict the level of consumption of all households listed in a census. These predicted household expenditures are then used to construct poverty indicators for small geographic population subgroups. To assess how the increase in food prices is likely to affect households living in various areas of a country, Coulombe and Wodon (2008) complement existing poverty maps with new maps relying on a revised consumption aggregate that takes into account the impact of the price increase. By comparing the initial poverty map with the revised poverty map based on the new consumption aggregate, one obtains estimates at a disaggregated geographic level of the impact on poverty of the price shock.

The relationship between initial poverty and the change in poverty by area is visualized in Figure 3 for four countries: Ghana, Guinea, Niger, and Senegal. The scatter plots provide the initial level of poverty (measured through the headcount) on the horizontal axis and the change in poverty due to the increase in food prices on the vertical axis. Only upper bound impacts are provided here (the lower bound impact results are in most cases similar). In Ghana and Senegal, the authors find evidence of an inverted-U relationship between the change in poverty and the initial level of poverty. For areas with very low poverty measures, the impact of the food crisis is not very large, as most households are not poor and able to cope with the shock. For the very poor districts, the impact is also not very large, because many households are protected from the increase in prices because they are net sellers of food or rely in large part on autoconsumption in order to meet their basic food needs. The areas most affected are those who are in the middle-ground with initial poverty levels in the 30% to 60% range (many of these areas are urban). In the other two countries, there is however a clear negative relationship between initial poverty and the change in poverty suggesting that the hardest hit areas are not the poorest.
|**value-added of**<br>**industrial**<br>**output**||||||
|---|---|---|---|---|---|
|**Increase of**<br>**water efficiency**<br>**coefficient in**<br>**agricultural**<br>**irrigation**|Water<br>Resource<br>Bureau|0.5|E|0.5|0.53<br>E|
|**Farmland**<br>**reserves**|Land Bureau|120 million<br>hectares|R|121.2 million<br>hectares|121.2 million<br>hectares (or 1.8<br>billion mu)<br>R|
|**Comprehensive**<br>**utilization rate**<br>**of industrial**<br>**solid waste**|Planning and<br>Construction/<br>Urban Utility|60%|R|69%|N/A|

Note: Population targets are excluded. (R) refers to restricted target; (E) refers to expected target. N/A indicates that no target was stated in the respective FYP.

# **8. References**

- Ajzen, I., 1991. The Theory of Planned Behavior. _Organizational Behavior and Human Decision Processes_ , 50(2), 179-211.

- Banyuetan Network, 2013. 环保局处罚央企,何时不再是新闻? _Huanbao ju chufa yangqi, he shi bu zai shi xinwen?_ EBP penalizes central SOE, when is it no longer news? <u>http://www.gesep.com/Hot/show_183_339809.html, last accessed on June 14th, 2013.</u>

- Bulkeley, H. and Mol, A. P. J., 2003, Participation and environmental governance: Consensus, ambiva-lence and debate, _Environmental Values_ , 12(2), 143–154.

China Daily, 2006. Unexpected' pollution comes as no shock. 16 October.

- <u>http://www.chinadaily.com.cn/china/2006-10/16/content_708760.htm, last accessed on</u> April 20th, 2013.

China Daily, 2013a. Fixing the environment is in our hands.

<u>http://usa.chinadaily.com.cn/weekly/2013-04/26/content_16451383.htm, last accessed</u> on May 17th, 2013.

- China Daily, 2013b, 'Spring' in the air for NGOs? <u>http://usa.chinadaily.com.cn/china/2013 04/17/content_16413494.htm, last accessed on May 17th, 2013.</u>

China Daily, 2013c, First-tier Chinese cities barely livable, report says.

<u>http://www.asianewsnet.net/First-tier-Chinese-cities-barely-livable-report-sa-46976.html,</u> last accessed on May 30th, 2013.

China Daily, 2013d, Environment may be issue at two sessions, http://usa.chinadaily.com.cn/china/2013-02/26/content_16255385.htm , last accessed on May 30th, 2013.

China Daily, 2013e, Environmental journalism campaign launched, <u>http://usa.chinadaily.com.cn/china/2013-05/17/content_16507918.htm, last accessed on</u> June 23rd, 2013.

China Daily, 2013f, Promise of iron fist against pollution, <u>http://www.chinadaily.com.cn/china/2013npc/2013-03/18/content_16314997.htm, last</u> accessed on June 23rd, 2013.

China Environment Org, 2013, 长三角结成应急联动联盟, 2013-05-24, <u>http://www.cenews.com.cn/xwzx/hjyw/201305/t20130523_742130.html, last accessed</u> on June 21st, 2013.

45
2016 (Table 2). Furthermore, by 2019, Figure 7 shows that these high-wealth individuals continued to report significantly larger assets, with their declared holdings nearly tripling compared to 2015.

One concern in this analysis revolves around the possibility that disclosing assets might lead to a reshuffling of individuals across different percentile groups over time. To address this potential concern, we utilize data from Argentina’s income tax returns, where individuals must report assets held in both the current and previous tax years. This approach lets us compare asset changes while keeping individuals’ income rankings constant.<sup>7</sup> The results are depicted in Figure 8, highlighting that the most significant increases in reported assets are primarily observed among individuals in the top 0.1% of the income distribution. In this group, reported assets more than doubled, with these individuals reporting roughly one-third more assets in 2016 than in 2015. Interestingly, the scale of the asset increase reported by Argentina’s richest individuals aligns with what has been observed in other regions such as Colombia (Londoño-Vélez and Ávila-Mahecha, 2021), Scandinavia (Alstadsæter et al., 2019), and the Netherlands (Leenders et al., 2023).

A.13 individuals’ likelihood of a Lastly, Figure compares high-net-worth reporting foreign asset, and the share of foreign assets declared. This information is presented separately by the top fractile groups, decomposing the top 1% of the wealth distribution into bins of increasing assets.<sup>8</sup> High-net-worth Argentines held most of their wealth offshore: three-quarters of individuals in the top 0.01% reported a foreign asset in 2015, and their foreign assets comprised over two-thirds of all their assets. Moreover, after 2016, virtually all individuals in this group reported owning foreign assets, and foreign assets rose to represent four-fifths of all assets. Furthermore, a minority of individuals in the next 0.09% declared their foreign assets to Argentine authorities in 2015, so they experienced the largest increases in foreign asset disclosures. Overall, the share of foreign assets declared by the top 0.1% tripled.

# **3.3 Expanding the wealth and capital income tax bases**

The above results showed that the 2016 enforcement initiatives revealed substantial assets held by Argentines domestically and offshore. This section shows that, as a result,

> 7 For example, the 2016 income tax tabulation, which ranks individuals based on their income, includes data on assets held in 2015 and 2016 for each income bracket.

> 8Argentina’s wealth tabulations rank individuals based on total assets. To recover the share of foreign assets (and the effective tax rate) for each fractile, we cumulate the amount of foreign assets (and wealth taxes) by total assets, interpolate the cumulative function for each fractile with a smooth cubic spline function, and differentiate the interpolated function. Reassuringly, a linear interpolation delivers virtually the same results.

11