rafmacalaba/datause-encoder-data huggingface dataset:
XXXii INTRODUCrON Exhlbft 1 Countries and Economies Classified by Income Group (based ob GNP per capita in 1987 U.S. dollars)

**_Low_** Income _($480 or less)_

_less)_ Oceania Latin America and the Caribbean Kiribati Belize East Africa Solomon Islands Bolivia Burundi Vanuatu Chile Comoros Colombia Ethiopia Latin America and the Caritbean Costa Rica Kenya Guyana Dominica Lesotho Haiti Dominican Rep. Madagascar Ecuador Halawi El Salvador Mozambique _Lower.middlc Income_ Grenada Ruanda _($48142,0J_ Guatemala Somaliat8 Honduras Tanzanf a East Africa Ja:iaica Uganda Botswana Mexico Zaire Djibouti Nicaragua Zambia Mauritius Paraguay South Africa Peru West Africa Swazitand St. Kitts and Nevis Benin Ziffbaibwe St. Lucia Burkina Faso St. Vincent and the Grenadines Central African Rep. West Africa Chad Cameroon Equatorial Guinea Cape Verde _Upper-middle Income_ Gambia, The Congo, People's Rep. of the _($2,0004$5,999)_ Ghana C6te d'Ivoire Guinea Senegal East Africa Guinea-Bissau Reunion Liberia North Africa Seychelles Hali Egypt, Arab Rep. of Mauritania Morocco West Africa Niger Tunisia Gabon Nigeria Other North Africa Other West Africa Sao Tomb and Principe Sierra Leone Europe North Africa Togo Poland Algeria Libya North Africa Southwest Asia Sudan Gaza Strip Europe Jordan Greece Southwest Asia Lebanon Hungary Yemen, People's Demi. Rep. ofSyrian Arab Rep. Malta Turkey Portugal South Asia West Bank Romania Afghanistan Yemen Arab Rep. Yugoslavia Bangladesh Bhutan East and Southeast Asia Southwest Asia India Malaysia Cyprus Hatdives Philippines Iraq Nepal Thailand Oman Pakistan Sri Lanka Oceania South Asia South Asia Iran, Islamic Rep. of Oceania Fiji Other Micronesia Pacific Islands Papua New Guinea Tonga Western Samoa East and Southeast Asia Cambodia East and Southeast Asia Korea, Rep. of Macao Taiwan, China China (excluding Taiwan) Indonesia Lao People's Den. Rep. Hyanmar Viet Nam
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29
the size of the sample of children aged 10 to 15 is relatively small has led coefficients and standard errors to increase considerably in comparison with ordinary least squares, leading us to focus on the latter, and rely on the assumption that controlling for parental schooling reduces the effect of unobservables household characteristics linked to human capital investments and migration – e.g. how much households value human capital.

**Table 8. Access to Remittances and Children Education - OLS**

|Age Group||10-15 Y|ears Old||
|---|---|---|---|---|
|Dependent Variable||Accumulate|d Schooling||
|Sample|Ru|ral|Ur|ban|
||Boys|Girls|Boys|Girls|
|Mexico|-0.149<br>(0.129)|0.113<br>(0.095)|-0.023<br>(0.177)|-0.235<br>(0.192)|
|El Salvador|0.482***<br>(0.110)|0.312***<br>(0.097)|0.245**<br>(0.098)|-0.010<br>(0.097)|
|Guatemala|0.448**<br>(0.177)|0.313*<br>(0.164)|0.291<br>(0.230)|0.454**<br>(0.177)|
|Honduras|0.427***<br>(0.100)|0.495***<br>(0.098)|0.298***<br>(0.078)|0.360***<br>(0.080)|
|Ecuador|0.187<br>(0.149)|0.112<br>(0.151)|0.289**<br>(0.125)|0.314**<br>(0.134)|
|Paraguay|-0.012<br>(0.195)|0.208<br>(0.182)|||
|Haiti|||0.098<br>(0.107)|0.244**<br>(0.098)|
|Peru|||-0.070<br>(0.146)|0.157<br>(0.120)|
|Nicaragua<sup>1</sup>|0.437***<br>(0.153)|0.375***<br>(0.133)|||
|Jamaica<sup>1</sup>|-0.114<br>(0.150)|-0.005<br>(0.113)|||
|Dominican Republic<sup>1</sup>|0.077<br>(0.107)|0.118<br>(0.094)|||

Notes: *** Significant at 1% level. ** Significant at 5% level.

* Significant at 10% level.<sup>1</sup> Rural and Urban areas together.

The set of controls includes the age of the child, a dummy for the child being the oldest in the household, indicators for mother’s education, mother’s marital status, mother’s head of household status, and a quartic in mother’s age, indicators for the number of children of different ages in the household, presence of a 0-5 year old child, family home ownership, counterfactual income quintiles, the proportion of households with sanitary services in the county of residence, the proportion of household heads working in agricultural activities in the county of residence, and state/province indicators.

Source: Author’s calculations based on household surveys As mentioned at the beginning of the section, previous evidence on Mexico has suggested that the positive effects of remittances on schooling vary with the educational attainment of the children’s’ parents (Hanson and Woodruff, 2003; McKenzie and Rapoport, 2006), being generally larger when the latter are low. Differential effects of this sort could be due to the fact 27
In this paper, we provide a policy-oriented survey of the research carried out on internal migration in developing countries over the past five decades. Many studies have focused on how internal migrants behave at different stages of the migration process. How do migrants prepare for migration? How do they migrate? What are the difficulties they face on arriving in urban areas? What links do they maintain with rural areas?

This review synthesizes the current state of knowledge concerning internal migration in developing countries. As far as the preparation phase is concerned, studies show that potential migrants invest in education before migrating, anticipating that human capital will be needed or better rewarded in the city (Kochar, 2004). They may also gather information about jobs from migrant networks (Roberts, 2001) or search for a job from their rural base (Banerjee, 1991), which reduces both the risk of temporary unemployment and the uncertainty on the returns to migration.

The migration decision has been shown to be selective. Migration mainly concerns young adults who are more likely to have a positive net expected return on migration due to their longer remaining life expectancy, or because social norms require that young adults migrate in search of a better life (De Haan and Rogally, 2002). Family strategies can involve sending young adults to the city, and investing in a potentially remitting child (Lucas and Stark, 1985). Both low and high skilled individuals are more likely to migrate but usually for different reasons: “surplus” low-skilled individuals have strong incentives to move to the city in search of a manual job they may not find in the rural area, while “scarce” educated workers may find that their human capital is better rewarded in cities than in rural areas (Lanzona, 1998, Agesa, 2001). In most countries, internal migration concerns young men because urban job markets usually offer a large share of occupations available to rural male migrants, or because men bear lower risks of vulnerability than women when migrating. Latin America, in which the migration of female domestic workers has become predominant, stands out as an exception.

The decision to migrate also involves contextual factors, such as ‘push factors’ which force migrants out of rural areas and ‘pull factors’ which attract migrants to urban areas.

4
set (21 of the crossovers in the mixed method data set were from the KASSIDA data set; the additional crossover participants were identified through respondent‐driven/snowball sampling). For non‐ crossovers, 60 respondents were identified through a random sample of the KASSIDA data set,<sup>10</sup> and an additional 60 were matched to the crossover sample on a small list of pre‐business characteristics.<sup>11</sup> Given the mixed method and not purely quantitative approach, the objective of the matched subset was to enable a sufficient sample of individuals with characteristics relatively similar to crossovers before the inception of their business, with the main difference being that they took different paths due to a set of factors to be explained in the analysis.

We prepared semi‐structured interview instruments to capture a wide range of information about female entrepreneurs, including their background, family history, education, entrepreneurial capacity/tendency (risk aversion,<sup>12</sup> personality profile, IQ), access to networks, presence of role models, and a host of other factors. In addition to these quantitative measures, there were open‐ended questions that allowed participants to supply answers in their own words and elaborate on the path that led them to be in a specific business sector. The instruments included substantial room for probing by enumerators, leading to a rich quantitative and qualitative data set.<sup>13</sup> To complement information obtained in individual interviews, we conducted 17 focus group discussions with crossovers, non‐crossovers, clients, suppliers, and (male) employees, with approximately six members per group. Focus groups of entrepreneurs included cases where only one sector was represented, as well as others that included a mix of both crossovers and non‐crossovers. Additionally, we interviewed a set of community leaders and credit providers to gauge the structural constraints facing both female entrepreneurs in male‐dominated sectors, as well as those in traditional sectors. Including various partners in the value‐chain allows us to better measure community‐wide perceptions on female participation in entrepreneurial activities, enabling better understanding of why so few female entrepreneurs decide to cross over.

> 10 Respondents from the KASSIDA data set included both members of the treatment and control groups, the former of which had received technical and managerial training, the latter of which had not.

> 11 These characteristics included nearest neighbor on age, age squared, and completion of primary education. 12 The games on risk aversion were not considered for this analysis because half‐way through implementation, survey audits indicated problems in administering this part of the survey.

> 13 Interviews were recorded and there was space in the questionnaire for additional writing of questions and answers.

7
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3
between children of mothers with complete secondary schooling or more and those with incomplete primary schooling or less is 1.16 standard deviations. Excluding children in households where a language other than Spanish is spoken substantially increases the wealth gradient in rural Peru (from 0.77 to 0.95 standard deviations), but has little effect on the results for urban Peru, or urban or rural Ecuador.

Results that use the norms provided by the test developers (fifth row of the table) show similar wealth gradients as those we report in our main specification. Recall that the distribution of wealth in the data we use to calculate the TVIP scores is broadly similar to the distribution of wealth in nationally representative surveys for the rural areas of all five countries, and for the urban areas of Chile and Colombia. We can therefore also use these results to make (cautious) comparisons across rural-urban areas in these two countries and across rural areas in all five countries.

First, limiting the sample to rural areas, mean scores are highest in Chile (90 points), substantially lower in Colombia and Ecuador (78 and 75 points, respectively), and lower still in Peru and Nicaragua (69 and 66 points, respectively). This means that, in Nicaragua and Peru, the average child in the poorest wealth quartile in rural areas has TVIP scores that are more than two standard deviations below the reference population that was used to norm the test. The results for Peru are particularly noteworthy because GDP per capita levels in Peru are roughly comparable to those found in Colombia and Ecuador, and are approximately three times as high as those in Nicaragua. Second, children in urban areas have somewhat higher scores than those in rural areas in Chile (a difference of 6 points for those in the highest quartile), and substantially higher scores in Colombia (a difference of 26 points, more than 1.5 standard deviations, for those in the highest quartile). Of course, in interpreting these urban-rural comparisons, it is important to keep in mind that average income levels tend to be substantially higher in urban than in rural areas in most Latin American countries.

We also test the degree to which our results are sensitive to missing test data by calculating upper and lower bounds on the wealth gradients (last row of Table 3), in the spirit of Manski (1990) and Horowitz and Manski (2000). Specifically, we estimate the upper bound by excluding all children with missing test data in the richest wealth quartile, and assigning a score of zero to all children in the poorest wealth quartile who were missing the TVIP, as before. Conversely, we estimate the lower bound by excluding all children with missing test data in the poorest wealth quartile, and assigning a score of zero to all children in richest wealth quartile who were missing the TVIP, as before. Table 3 shows that the bounds that take account of missing test data are generally quite tight.

7
that their children are likely to work in as adults, and choose levels of schooling for each child accordingly.

In the basic model, parents use their own time allocation as a proxy for the time-allocation patterns that their children will choose. In section 3 this last assumption is amended. Parents forn Apectations of time-use of children as adults based on both their own work experience and the general pattern in the region of residence, as well as the probability of migration to oth.'ar regions. Section 3 also discusses the implications of adding sector-specific work experience (job training) as an additional component of human capital.

Section 4 uses household da. a from the PLSS to test the implications of the theoretical framework developed in sections 2 and 3. The advantages of using household data are that the schooling attainment of children is directly observed, and the effects of intrahousehold factors on the demand for schooling of boys and girls can be accounted for by including household information such as the education and occupation of parents.

In section 5, I test the implications of the theory using provincial data for 25 departments in Peru in the 1980s. Illiteracy rates are used as a proxy for investment in schooling. I conduct tests to ensure that department illiteracy rates are satisfactory measures of department schooling attainment. The findings confirm the main implications of the theory, and add to the evidence from household analysis.

Section 6 discusses the policy implications of the study. The policy implications are of two types: Supply-related and demand-related.

4
Policy Research Working Paper 5266

# **Abstract**

The 2003 PISA Korea sample is used to examine the association between within-school ability tracking and mathematics achievement. Estimates of a variety of econometric models reveal that tracking is positively associated with mathematics achievement among females and that this association declines for higher achieving females. No evidence of an association between males and tracking is detected. While this association for females cannot be interpreted as a causal effect, the presence of a measurable association indicates the need for further research on tracking in Korea with a particular focus on gender differences.

This paper—a product of the Education Team, Human Development Network—is part of a larger effort in the department to analyze the determinants of learning. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at hpatrinos@worldbank.org.

_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
Government ha' a very important role in Argentine society. It taxes, spen'Ts, produces a wide \,ariety of goods and services, regulates financial markets and supplies financial services, systematically resorts to incomes policies anid regulates foreign trade. While the regulatory aspects of government action defy any possible quantitative measure, we can get a feeling about the size of government in economic activity by looking at the relative participation of government spending in GDP. Figure 2 shows thaL government spending has systematically tended to grow faste- than GDP until the final crisis of the Argentine eccnomy started to develop in 1982. Since then it has started to fall, more as a consequence of a resource constraint than as a consequence of a deliberate political action.

FIGURE 2 <!-- Start of picture text --> TOTAL EXPENDITURES OF THE CPS AS PERCENTAGE OF UP<br>60<br>so . . . . . . . . . . . . . . . . . . . . .\<br>45 . . . . . . . . . . . . . . . . . . . . A<br>.. . ... .<br>1969 1965 1979 1975 1989 1985<br><!-- End of picture text --> The fall in the relative size of government spending, however, came too late to avoid a crisis that was already well into the working and that brought the country into a state of hyperinflation in 1989, when the inflation rate approached 50OOO and GDP fell by about 7%. The inflation rate in Argentina defies being graphed because of the significant changes in levels reached in the late 1980's. The December to December values of the rate of inflation in the CPI are as shown in Table la, It is clear that inflation has been an ever present phenomenon in recent decades and that it has followed an exploding path, of which the rucent hyperinflation of 1989 does not necessarily 2
<!-- Start of picture text --> Ww<br>=iEs 3.3 Wage subsidies<br>A oi 2.2 Social insurance contributions waivers<br>S «18 [) Other job protection measures*<br>< 5 Other income protection measures’<br>8 EF |) 2.6 Unemployment/out-of-work income support<br>a 5 1.1 Unconditional cash transfers<br>© se) o<br>© 14 In-kind and food transfers<br>2 5EEE 1,7 Utiity/financial waivers<br>3<br>a<br>cTM<br>2<br>o<br>2<br>2<br>a<br>a7<br>3<br>fe)<br>wn<br>oO<br>6.7 7 8 9 10 11 11.7<br>Log GDP per capita (US$ PPP), 2019<br><!-- End of picture text -->