rafmacalaba/datause-encoder-data huggingface dataset:
- McKenzie, David, John Gibson and Steven Stillman. 2013. “A land of milk and honey with streets paved with gold: Do emigrants have over-optimistic expectations about incomes abroad?” _Journal of Development Economics_ 102(1): 116-127.

- Mergo, Teferi. 2011. “The Effects of International Migration on Migrant‐Source Households ‐

- in Developing Countries: Evidence from Ethiopian Diversity Visa (DV) Lottery Migrants to the US”, Mimeo. UC Berkeley.

- Özden, Çağlar, Christopher Parsons, Maurice Schiff, and Terri Walmsley. 2011. “Where on earth is everybody? The evolution of global bilateral migration 1960–2000” _The World Bank Economic Review_ 25(1): 12-56.

- Ravallion, Martin and Michael Lokshin. 2001. “Identifying Welfare Effects from Subjective Questions” _Economica_ 68(271): 335-57.

- Stillman, Steven, David McKenzie, John Gibson 2009 “Migration and mental health: Evidence from a natural experiment”, _Journal of Health Economics_ 28: 677-87.

- Stillman, Steven, John Gibson, David McKenzie, and Halahingano Rohorua. 2015. “Miserable migrants? Natural experiment evidence on international migration and objective and subjective well-being” _World Development_ , 65(1): 79-93.

- Veit, C.T., Ware Jnr, J.E., 1983. “The structure of psychological distress and wellbeing in general populations”, _Journal of Consulting and Clinical Psychology_ 51, 730–742.

26
# **Table 4**

# **Infrastructure and Income Inequality**

_Dependent Variable: Gini Coefficient (end-of-period, in logs) Sample: 87 countries, 1960-2005 (non-overlapping 5-year period observations) Estimation: GMM-IV System Estimation_

|**Variable**|**[1]**|**[2]**|**[3]**|**[4]**|
|---|---|---|---|---|
|_Infrastructure Development (synthetic indexes):_|||||
|Infrastructure Quantity (IK1) 1/|-0.0828 **|-0.0485 *|-0.0489 *|-0.0537|
||(0.034)|(0.026)|(0.029)|(0.045)|
|IK1 squared|..|..|-0.0120<br>(0.010)|..|
|IK1 * Sub-Saharan Africa|..|..|..|0.1815<br>(0.112)|
|Quality of Infrastructure Services (IQ1) 2/|..|-0.0387 **|-0.0274|-0.0312|
|||(0.017)|(0.019)|(0.026)|
|IQ1 squared|..|..|0.0086<br>(0.006)|..|
|IQ1 * Sub-Saharan Africa|..|..|..|-0.0349<br>(0.080)|
|_Control Variables_|||||
|Income per capita|0.4305 **|0.2731 *|0.1571|-0.0123|
|_(Real output per capita, in logs)_|(0.162)|(0.160)|(0.245)|(0.240)|
|Income per capita squared|-0.0213 **|-0.0112|-0.0049|0.0058|
||(0.009)|(0.009)|(0.014)|(0.014)|
|Education|-0.0031 **|-0.0032 **|-0.0029 **|-0.0034 **|
|_(secondary enrollment)_|(0.001)|(0.001)|(0.001)|(0.001)|
|Financial Development|-0.0178|-0.0079|0.0043|-0.0115|
|_(private domestic credit as % of GDP, logs)_|(0.016)|(0.017)|(0.022)|(0.025)|
|Lack of Price Stability|-0.0116|-0.0103|-0.0058|-0.0106|
|_(inflation rate)_|(0.016)|(0.015)|(0.018)|(0.016)|
|Trade Openness|0.0365 *|0.0431 *|0.0552 *|0.0485|
|_(exports and imports as % of GDP, logs)_|(0.021)|(0.027)|(0.030)|(0.035)|
|Observations|476|476|476|476|
|Specification Tests (p-values)|||||
|(a) A-B test for 2nd-order serial correlation|(0.314)|(0.347)|(0.320)|(0.551)|
|(b) Hansen test of overidentifying restrictions|(0.821)|(0.681)|(0.758)|(0.602)|
|(c) Difference-Sargan tests|||||
|All instruments for levels equation|(0.423)|(0.669)|(0.396)|(0.521)|

_Numbers in parenthesis are robust standard errors. Our regression analysis includes an intercept and period-specific dummy variables._ _* (**) denotes statistical significance at the 10 (5) percent level. Standard errors are computed using the small-sample correction by Windmeijer (2005) 1/ See the notes to Table 1 for the definition of the synthetic indices of infrastructure quantity and quality._ 39
# Pollcy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1033 Latin America and the Caribbean<br>Region (and Northern<br>America)<br>Population<br>Projections,<br>1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November<br>1992|0. Nadora<br>31091|
|WPS1034 Revising Financial Sector Policy<br>In Transitional<br>Socialist Economies<br>Will Universal Banks Prove Viable?|David H. Scott<br>:|November<br>1992|K. Waelti<br>37664|
|WPS1035 How Import Protection<br>Affects the<br>Philippines'<br>Motor Vehicle Industry|Wendy E. Takacs<br>|November<br>1992|D. Ballantyne<br>37947|
|WPS1036 Output Decline In Hungary<br>and Poland in 1990-91: Structural<br>Change and Aggregate<br>Shocks|Slmon Commander<br>Fabrizio Coricelli|November<br>1992|0. del Cid<br>35195|
|WPS1037 Vocational<br>Secondary<br>Schooling,<br>Occupational<br>Choice, and Eamings<br>in Brazil|Ana-Maria<br>Arr7agada<br>Adrian Ziderman|November<br>1992|C. Cristobal<br>33640|
|WPS1038 Determinants<br>of Expatriate<br>Workers<br>Remittances<br>inNorth Africa and<br>Europe|' Ibrahim A. Elbadawi<br>Robert de Razende<br>Rocha|November<br>1992<br>|A. Maranon<br>31450|
|WPS1039 Education, Extemalities,<br>Fertility,<br>and Economic<br>Growth|Martin Weale|November<br>1992|PHREE<br>33680|
|WPS1040 Lessons<br>of Trade Liberalizatio**n**<br>i<br>Latin America for Economies<br>in<br>Transition|Jaime de Melo<br>Sumana<br>Dhar|November<br>1992|D. Ballantyne<br>37947|
|WPS1041 Family Planning<br>Success Stories in<br>Bangladesh<br>and India|Moni Nag|November<br>1992|0. Nadora<br>31091|
|WPS1042 Family Planning<br>Success in Two<br>Cities In Zaire|Jane T. Bertrand<br>Judith E. Brown|November<br>1992|0. Nadora<br>31091|
|WPS1043 Deriving Developing<br>Country<br>Repayment<br>Capacity<br>from the Marke<br>Prices of Sovereign Debt|Stijn Claessens<br>t George Pennacchi|November<br>1992|R. Vo<br>33722|
|WPS1044 Hospital Cost Functions for<br>Devebping Countries|Adam Wagstaff<br>Howard Bamum|November<br>1992|0. Nadora<br>31091|
|WPS1045 Social Gains fom Female Education<br>A Cross-National<br>Study|: Kalanidhi<br>Subbarao<br>Laura Raney|November<br>1992|M. Abundo<br>36820|
|WPS1046 World Bank Project-Financed<br>Research<br>on Population,<br>Health,<br>and Nutrtion|J. Price Gittinger<br>Carol Bradford|November<br>1992|0. Nadora<br>31091|
- 18 -

International Trade Statistics.4 Sh is calculated as the difference between the share of current total investment in current GDP minus Sf. The data on current total investment share in GDP are from the Summers and Heston (1991) data set. We measure _SfISf_ as changes in the share of foreign capital import in GDP, _Sh ISh_ the change in the share of home investment in GDP. _Y/Y_ is real annual growth rate of GDP, which are from the World Bank's World Tables (1990). The population growth rate _N/N_ is from the population data in the 1990 World Tables. Table 1 lists all the countries in our sample and the mean values of _Sf Sf, ShISh, YlIY,and NIN._ We also list the quality rating of the data by Summers and Heston, since many of our variables are taken from their data set. This information should be useful in helping readers make judgement on the reliability of the statistical inferences from the data.

The number of developing countries included in our empirical study varies among different model specifications, depending on the variables included in a specification. Some countries may not have information on certain important variables so we have to exclude them from a particular equation.

# 3.3 The Result

# 3.3.1 Initial regressions

Table 2 presents three different regressions of the growth rate of income on the growth rate of foreign capital import, growth rate of home investment, and growth rate of population. Before 4We divide the SITC two digit level import commodity data into three main categories: capital equipment iWports (including SITC commodities 71,72, 73, part of 86, 87, and part of 9), intermediate good inport (including SITC commodities 2, 3, 4, 5, 6, and part of 9), and final consumtion good import (including commodities in the SITC groups 0, 1, 81-85, part of 86, 89, and part of 9).
|Partially homogenous|Uniform (homogenous) societies have both indexes of<br>language and ethnic fractionalization lower than the 25<sup>th</sup><br>percentile of the whole sample. Diverse societies have|Fearon and Laitinl ef variable; and Reynal-<br>Querol 2002_Qlf_variable_._|
|---|---|---|
|Partially diverse|both indexes of language and ethnic fractionalization<br>greater than the 75<sup>th</sup>percentile of the whole sample. We||
|Diverse|group together societies that are fairly diverse ethically (Ef<br>greater than the 25<sup>th</sup>percentile and smaller than the 75<sup>th</sup>||
|Homogenous|percentile) and as language composition (Qlf greater than<br>the 25<sup>th</sup>percentile and smaller than the 75<sup>th</sup>percentile)<br>and we label them as societies with cross-cutting<br>cleavages. Partially homogenous societies have either but<br>not both of the Ef index or the Qlf index smaller than its<br>respective 25<sup>th</sup>percentile. Partially diverse countries have<br>either but not both of the Ef index or the Qlf index larger<br>than its respective 75<sup>th</sup>percentile.||
|Ethnic dominance|The largest ethnic / religious group is between 45 and 90<br>percent of the population|Fearon and Laitin 2003|

43
responded “Do Not Know” were disregarded. As constructed, 32% of the survey traders indicated at least some effect.

Table 2. _Frequency of response to short- and long-term impacts_ _of_ _<u>pesticide exposure</u>_

|**_Response_**|**_Short-term impacts_**|**_Long-term impacts_**|
|---|---|---|
|Don’t know|-|-|
|No effect|-|-|
|Little effect|42|31|
|Some effect|21|23|
|Large effects|13|22|
|Fatal effects|5|6|

# _Health effects_

A detailed medical examination of sample traders was beyond the scope of the study. Instead, our analysis relied solely on self-assessed/reported health effects.<sup>10</sup> Among the traders surveyed, 54% reported frequent health problems such as eye irritation, headaches, dizziness, vomiting and skin effects (see Appendix for details). These symptoms have been associated with acute poisoning (extension Toxicology Network, 2004). A health effects variable was then constructed using the prevalence of these ailments. The variable was coded 1 if the respondent reported any one of these symptoms after handling pesticides and 0 otherwise.

# **5. Determinants of trader’s health perception**

# _The model_

To model trader perception, we used a bivariate probit framework with health effects as an endogenous dummy variable. This method belongs to the general class of simultaneous equations systems first explored by Heckman (1978), and extended by Maddala (1983) to include recursive dichotomous choice models. The bivariate probit model with an endogenous dummy has been shown to be the appropriate inference tool

> 10 Are self-reported health effects a credible measure?  Detailed information for pesticide traders is virtually non-existent, however, medical tests of the farming population in other Asian countries may be indicative. Several clinical studies conducted on rice and vegetable farmers in Indonesia, Philippines, and Vietnam revealed 58%-99% of the farmers exposed to pesticides had at least one health effect (Xuyen et al., 1998; Kishi et al., 1995; Antle and Pingali, 1994). This evidence suggests that the degree of upward bias, if at all in a self-assessment health effects in Bangladesh (traders) may not be large.

10
this main dataset with data on return migrants from household surveys in Albania, Mexico, Pakistan, and the Philippines, and with customized cross-tabulations from the 2003 Census in Oman. Full details of the source of each country’s data are provided in an Appendix. Appendix Table 1 shows the main source countries of youth migrants for each receiving country, showing significant geographic variation. Taken together these surveys allow us to construct a comprehensive picture of developing country youth migration around the world, and to cover both migration to developed and to other developing countries.

For each of the countries for which we have microdata, I try to distinguish between the _stock_ and the _flow_ of youth migrants. The stock is defined as all 12 to 24 years who were born abroad in a developing country and are now living in the country surveyed. Many of these individuals could have come to their current country of residence as babies or young children, in which case we would expect their migration decision to be made by parents or other family members. I therefore also consider the flow of youth migrants, defined as those 12 to 24 year olds who recently arrived in their host country, where “recently” is typically taken to be within the last two years. I consider only youth migrants from developing countries. For most of the countries studied developing country migrants comprise the bulk of all migrants. Developing country youth comprise 89 percent or more of the flow of young migrants into all countries but the United Kingdom, where the share is 50 percent.

- 7 -
Table 6. Impacts of PEP Migratory Status on Labor Outcomes

||Labor Outcomes<br>(Index)|Formal<br>Job|Hours Worked<br>(log)|Reservation<br>Wage (log)|Job<br>Satisfaction|Self<br>Employed|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|1[PEP_i_ = 1]|1.328|0.108|-0.090|0.096|0.119|-0.465|
||(1.209)|(0.107)|(0.308)|(0.071)|(0.248)|(0.262)|
|q-values|[0.839]|[0.864]|[0.839]|[0.864]|[0.839]|[0.839]|
|Obs. left|407|267|155|123|255|243|
|Obs. right|842|635|340|207|574|526|
|Observations|2,709|2,048|1,232|661|2,048|2,034|
|Mean values (Non-RAMV refugees)|0.000|0.001|3.937|0.564|0.324|0.430|
|Outcome Level|Individual|Individual|Individual|Individual|Individual|Individual|

_Notes:_ Dependent variables: (i) Labor Outcomes (Index) is constructed using the outcome variables of columns (ii) to (vi) using the methodology of Kling et al. (2007): (ii) Formal Job is an indicator variable [=1] if the refugee is employed, reports a pension fund, and has a written contract; (iii) Hours worked (log) is the logarithm of weekly hours worked; (iv) Reservation Wage is the logarithm; (v) Job Satisfaction is an indicator [=1] if the respondent wants to change the job they currently have; and (vi) Self-Employed is an indicator variable [=1] if main occupation is independent or self-employed. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

50
**Figure 5 Employment versus  real wage in manufacturing GVCs, 1995-2008 (1995=1).**

<!-- Start of picture text --> GRC<br>SVK<br>GBR<br>CZE<br>SWE<br>HUN<br>IRL FIN<br>DNK<br>PRT FRA POL<br>NLD<br>BEL<br>AUT<br>ITA ESP<br>DEU<br>.7 .8 .9 1 1.1 1.2<br>Change in number of workers<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>Change in real income per worker<br>.8<br><!-- End of picture text --> Note: Change in number of workers and real income per worker over 1995-2008 in manufacturing GVCs. Real income is measured as GVC labour income per worker deflated with the national CPI. Data are for all EU15 countries and Poland, Czech Republic, Hungary and Slovak Republic. Data for Czech Republic refers to 1996-2008. _Source:_ Author’s calculations based on World Input-Output Database, April 2012. CPI from OECD National Accounts Statistics.

Again, this shift in the sectoral distribution of the GVC jobs might be interpreted as the result of differential productivity growth in manufacturing and services following Baumol’s hypothesis. But while there is clear evidence that productivity growth in manufacturing is higher than in services overall, this does not necessarily hold for the services activities in manufactures GVCs. These only form a sub-set of the services sector, and involve in particular intermediate services such as wholesaling, transportation, finance and several business services.<sup>10</sup> These activities are generally open for international competition and likely to have much higher rates of innovation and productivity growth than services activities for domestic demand which are dominated by personal services, education, health and public administration (Inklaar et al., 2009). Hence it seems more likely that our findings are indicative of a fundamental shift in the type of activities carried out by European countries in the global production of manufactures, away from blue-collar manufacturing to white-collar services activities. This hypothesis is confirmed when one analyses the skill-content of GVC jobs as is done in the next section.

Taken together, the results of sections 4 and 5 show that international fragmentation in the production of manufactures has been accompanied by a rapid shift towards higher-skilled activities in the EU. These activities are increasingly carried out in the services sector, and no longer in the manufacturing sector itself. As such, it contributes to the so-called job polarization in advanced economies as the displaced manufacturing workers are likely to be absorbed in personal and distributional services where low-skilled employment opportunities are still growing (Goos, Manning and Salomons 2011).

> 10 It should be noted that these numbers exclude any jobs involved in the retailing of manufacturing goods as we analyse final demand at the basic price concept.

33
<!-- Start of picture text --> Regular rate Allernative rate<br>on<br>10- Et —_—— o<br>o<br>O-<br>treaties signed<br>=z20- Number of<br>2 15- Par<br>re 10- oboe fa] oes ; @a<br>a e e @a<br>e 6. 6 @:<br>a- @»<br>a=<br>15"<br>10= be ‘<br>a 5 aa i="a2 aeeee<br>1980 1990 2000 2010 1980 1990 2000 2010<br>Year<br><!-- End of picture text -->
Policy Research Working Paper 5647

# **Abstract**

What drives governments with similar revenues to publicly provide very different amounts of goods for which private substitutes are available? Key examples are education and health care. This paper compares spending by Brazilian municipalities on pre-primary education—a good that is also provided privately—with spending on public infrastructure like parks and roads, which lacks private substitutes. Panel data from 1995–2008 reveal how the distribution of income affects public investment. Revenue is endogenous to investment outcomes, and the analysis addresses this problem by exploiting a 1998, nationwide education finance reform and several revisions to the policy. The author constructs a variable that captures exogenous variation in revenue generated by nonlinearities of the law to instrument for observed revenue. Municipalities with higher median income and more inequality are less likely to allocate revenue to education or to expand pre-primary enrollment. They are more likely to allocate revenue to public infrastructure. There is suggestive evidence that this occurs for two reasons, hypothesized in two separate literatures. In rich and unequal municipalities, fewer total people support public education spending (the collective choice channel), and also, any given poor person wanting public education has less influence over policymakers there (the political power channel).

This paper is a product of the Human Development Department, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kosec_katrina@gsb.stanford.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>Sample<br>no outliers<br>no OECD<br>no East Asian Tigers<br>no Latin America<br>/Caribbean<br>no Sub-Saharan<br>Africa<br>no Middle East<br>/North Africa<br>Dep. Var.: Private Credit/GDP<br>EFNX<br>2.078**<br>1.133**<br>1.370**<br>1.568**<br>1.569***<br>1.518**<br>(0.874)<br>(0.518)<br>(0.612)<br>(0.779)<br>(0.590)<br>(0.695)<br>Controls<br>Estimation:<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>Observations<br>90<br>74<br>91<br>75<br>72<br>85<br>EFNX<br>1.458***<br>0.535**<br>0.843**<br>0.820**<br>1.304**<br>0.776**<br>(0.496)<br>(0.265)<br>(0.417)<br>(0.385)<br>(0.584)<br>(0.356)<br>Controls<br>Estimation:<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>2SLS<br>Observations<br>494<br>399<br>499<br>403<br>395<br>479<br>Countries<br>90<br>74<br>91<br>75<br>72<br>85<br>Country FE<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>Time FE<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>Notes: Panel A: robust standard errors in parentheses; Panel B: standard errors clustered at country level are in parentheses; ** significant at 5%; *** significant at<br>1%; Dependent variable,_Private Credit/GDP,_is credit to the private sector by banks and other financial institutions as a share of GDP;_EFNX_is the external<br>finance need of exports;_Log(Trade/GDP)_is log of exports and imports as a share of GDP;_Log(Income)_is log of PPP-adjusted real per capita income from Penn<br>World Tables;_French, German, Scandinavian, and Socialist Legal Origin_dummies are as defined originally by La Porta et al. (1998)._Predicted EFNX_is the<br>predicted external finance need of exports constructed by first estimating sector-level gravity regressions, and then generating predicted export shares for each<br>country and sector. Panel A: all of the variables are average values over 1970-99. Panel B: all of the variables are five-year averages 1970-74; 1975-97, ... 1995-99.<br>Variable definitions and sources described in detail in the text.<br>**_Panel A: Cross-Sectional Estimates_**<br>**_Panel B: Panel Estimates_**<br>Log(Trade/GDP), Log(Income), Legal Origin Dummies<br>Log(Trade/GDP), Log(Income)|
|---|

32