rafmacalaba/datause-encoder-data huggingface dataset:
percent of GDP) on education than most northeastern states. Only Pernambuco spends less (1.4 percent of GDP). The states of the Northeast that are poorer than Bahia spend up to 5 percent of their GDP on education.

In Bahia, spending per student was R$637 at the preschool level (the highest spending among northeastern states), R$450 at the primary level (a fifth of what was spent in the Federal District), R$488 at the secondary level, and R$7,642 at the tertiary level (Table 6.7). This indicates a distortion in spending, biased toward the higher levels of education and regressive in nature, because far more students from the top income quintiles attend secondary and tertiary education than students from lower-income quintiles (see also Figure 6.2).

**<mark>Table 6.7:     Education Spending (Average Spending Per Student) in Preschool, Primary,</mark>** **<u><mark>Secondary, and Tertiary, 1998 (R$)</mark></u>**

||**Pre.**|**Pri.**|**Sec.**|**Ter.**|
|---|---|---|---|---|
|_Northeast_|_516_|_465_|_597_|_7,549_|
|**Bahia**|**637**|**450**|**488**|**7,642**|
|Ceará|467|493|631|6,017|
|Maranhão|427|401|505|8,900|
|Paraíba|565|439|696|7,976|
|Pernambuco|524|449|553|5,752|
|Piauí|433|450|600|6,177|
|Rio Grande do Norte|465|659|918|14,451|
|Sergipe|559|529|760|6,697|

_Source:_ Ministry of Education/INEP, based on IBGE data.

The incidence of education varies among the rich and poor and varies for different education levels. As Figure 6.2 shows, the share of poor students in the student population varies greatly across the different levels of education. The line in the figure for grade 1-4 is downward sloping for successively higher income quintiles, indicating the progressive nature of benefit incidence in basic primary education. The first quintile receives 36 percent of the primary school services. However, the benefit incidence of secondary education is regressive, concentrated in the third quintile and above. In secondary schools, the first quintile receives only 11 percent of the service, and the benefit incidence is highly concentrated in the fourth and fifth quintile. Spending on higher education is extremely regressive, with the vast majority of students enrolled in public education coming from the wealthiest quintile. The 2001 PNAD data reveal that only less than 0.3 percent and 1.3 percent from the poorest quintiles and fourth quintile, respectively, were enrolled in tertiary education.

34
-3-

recommended as successes. A list of the specific case studies that have been nominatei as successes<sup>2</sup> is shown in .A.ttachment Two.

The original intent of this project was to use the data obtained from tis mail survey to synthesize the information on the process of effective implementation. A sample of two forms received is contained in Figure 1. The amount and type of information contained .i these summaries is typical of what was received. ne information contained in these two samples is very thinly laid out and, by itself, would not have permitted an assessment of what is it about &-ese programs that have led to success. Therefore two additional measures were taken.

First, a wider audience of African policy makers and program implemerttors and others involved in nutrition activities in Africa was contacted to elicit furthelP information on **the** elements of successful implementation. A total of 78 additional persons were interviewed as part of this exercise. Each of these people were asked the same two questions initially: What works in nutrition in Africa? Why?

Simultaneously a subset of 6 projects from the original 66 was selected for further review. These 6 "in-depth' projects ,ere representative of the types of nutrition interventions that had been nominated as successful. Additional interviews were conducted with staff who had contact with these six projects to obtain more information on what contributed to their success. In addition, we were interested in whether the factors identified from the mail/telephone interviews as importarn; were also contained in these six in-depth case studies.

The remainder of this report concentrates on summarizing the information received from these different sources: the 110 institutions/individuals who responded to the initial letter, the 2 Some projects were nominated more then once.
|Table 2.1: I|NDIA, Percentage<br>Size|of observ<br>S|ations los<br>mall<br>|t due to<br>Me|missing<br>dium|values<br>La|by indu<br>rge|stry an<br>T|d size<br>otal|
|---|---|---|---|---|---|---|---|---|---|
|Industry||#Obs|%Lost<sup>(d)</sup>|#Obs|%Lost|#Obs|%Lost|#Obs|%Lost|
|Food|Sampling frame<sup>(a)</sup>|333||177||87||597||
||Complete case<sup>(b)</sup>|177|46.9|79|55.4|51|41.4|307|48.6|
||With replacement<sup>(c)</sup>|248|25.5|137|22.6|69|20.7|454|24|
|Textiles & Leather|Sampling frame|426||255||207||888||
||Complete case|251|41.1|210|17.7|139|32.9|600|32.4|
||With replacement|325|23.7|235|7.8|178|14|738|16.9|
|Apparel|Sampling frame|360||315||150||825||
||Complete case|247|31.4|267|15.2|120|20|634|23.2|
||With replacement|287|20.3|290|7.9|138|8|715|13.3|
|Chemicals &|Sampling frame|426||333||171||930||
|Chemical prds|Complete case|262|38.5|218|34.5|130|24|610|34.4|
||With replacement|337|20.9|282|15.3|150|12.3|769|17.3|
|Plastics & Rubbers|Sampling frame|279||189||12||480||
||Complete case|193|30.8|112|40.7|11|8.3|316|34.2|
||With replacement|243|12.9|157|16.9|11|8.3|411|14.4|
|Non-metallic|Sampling frame|105||63||48||216||
|products|Complete case|40|61.9|38|39.7|32|33.3|110|49.1|
||With replacement|75|28.6|50|20.6|39|18.8|164|24.1|
|Structural metal &|Sampling frame|618||252||39||909||
|metal prds|Complete case|328|46.9|131|48|21|46.2|480|47.2|
||With replacement|526|14.9|214|15.1|31|20.5|771|15.2|
|Machinery &|Sampling frame|1074||687||243||2004||
|Equipment|Complete case|749|30.3|482|29.8|160|34.2|1,391|30.6|
||With replacement|912|15.1|603|12.2|213|12.4|1728|13.8|
|Total|Sampling frame|3621||2271||957||6849||
||Complete case|2,247|38|1,537|32.3|664|30.6|4,448|35.1|
||With replacement|2953|18.5|1968|13.3|829|13.4|5750|16.1|

Notes: (a) “Sampling frame” refers to the total number of observations (firms surveyed multiplied by the number of years of information).

(b) “Complete case” refers to the complete case of production function variables (sales, materials, capital and labor), missing values in other IC variables—other than production function— are not considered.

(c) “With replacement” refers to the sample after imputing IC variables according to the ICA Method; missing values in other IC variables—other than production function—are not considered. Notice that only observations with information available in at least one of sales, labor, labor cost, materials or capital, are imputed (d) “Perc. lost” refer to the percentage of observations lost with respect to the sampling frame. Source: Authors calculations with IC data.

10
and poverty effects of changes in macroeconomic policy and/or trends in global markets. It complements a global CGE analysis with global microsimulations based on standardized household surveys. The tool pools most of the currently available household surveys covering 1.2 million households in 63 developing countries; household information from developed countries comes from the Luxemburg Income Study dataset. These micro data are complemented with more aggregate information for countries where no surveys are available; the final dataset covers 91 percent of the world’s population (see Annex 1 for details).

The paper is organized as follows. The next section sketches the methodology, assumptions, and data behind the GIDD. Section 3 presents the macroeconomic results of the baseline scenario, showing the importance of China and India for global growth and trade. Section 4 assesses the importance of growth in China and India for the changes in the global income distribution and the emergence of a global middle class. The final section offers concluding remarks.

# 2 Methodology

The empirical analysis in this paper relies on two tools developed at the Development Economic Prospects Group of the World Bank: the LINKAGE global computable general equilibrium (CGE) model and the GIDD, which combines a consistent set of price and volume changes from the CGE model with expected changes in demographic structure to create a hypothetical distribution of income in 2030. We begin with a brief description of the LINKAGE model and then proceed to introduce the GIDD framework and its ability to map macroeconomic outcomes to disaggregated household survey data.

## 2.1 LINKAGE: a global dynamic multi-sectoral model

The forward-looking scenarios in this paper have been produced with the World Bank’s LINKAGE model. At its core, LINKAGE is essentially a neo-classical growth model, with aggregate growth predicated on assumptions regarding the growth of the labor force, savings/investment decisions (and therefore capital accumulation) and productivity. Unlike more simple growth models, however, LINKAGE has considerably more structure (see van der Mensbrugghe (2006) for a detailed description). First, it is multisectoral. This allows for more complex productivity dynamics including differentiating productivity growth between agriculture, manufacturing and services and picking up the changing structure of demand (and therefore output) as growth in incomes leads to a relative shift into manufactures and services. Second, it is linked multi-regionally allowing for the influence of openness—via trade and finance—on domestic variables such as output and wages. Third, the LINKAGE model has a more diverse set of productive factors including land and natural resources (in the fossil fuel sectors), and labor is split between unskilled and skilled categories.

3
**Table 11. Correlations between Fluency and Comprehension for Ashaninka and Non Ashaninka students (Spearman Rho)**

||**Correlation**<br>**coefficient**<br>|
|---|---|
|Ashaninka students (N=40)|0.6490*|
|Non-Ashaninka students(N=435)|0.3958*|

* Sig. p<0.05. Coefficients that are not significant at p<0.1 are not shown in the table.

Now, given that the oral test behaves differently for Ashaninka and Non-Ashaninka students, it is also relevant to see how both tests correlate in these two groups. The Spearman’s Rho coefficient for non-Ashaninka students is 0.396 between oral fluency and written comprehension, while for Ashaninka students is 0.649 (see Table 11).<sup>38</sup>

# **_b. Relation between fluency and comprehension when social factors are taken into account_**

Despite the lack of normality in the distributions, we also run by multiple regressions to see how much of the variance in written comprehension is accounted for by oral fluency when controlling for relevant individual and socioeconomic variables. Table 12 shows that fluency in the oral tests explains over 28 percent of the variance in the ECE scores. The effect of fluency is significant even when keeping all other individual and school characteristics constant (age, Ashaninka language, preprimary education, the location and type of school), but the magnitude of the coefficient is slightly reduced.

**Table 12. Regression Models (adjusted)**

||**Model 1**<br>|<br>|**Model**|**2**<br>|<br>|**Model**<br>|**3**<br>|
|---|---|---|---|---|---|---|---|
|**Independent variables**|**Coef.**|**Coef.**||**Beta**|**Coef.**||**Beta**|
|Fluency (oral test)|0.15 **|0.10|**|0.36|0.10|**|0.35|
|Full school||1.69|**|0.09||||
|Ashaninka||-7.51|**|-0.22|-13.28|**|-0.39|
|Rural||-1.22|**|-0.07||||
|Preschool education||0.35|**|0.02||||
|Age||-0.27|**|-0.03||||
|Fluency*Ashaninka|||||0.18|**|0.20|
|Constant|41.19 **|45.34|**|.|44.28|**||
|Number of obs|475|473|||473|||
|Prob > F|0.000|0.000|||0.000|||
|R-squared|0.282|0.334|||0.340|||
|Adj R-squared|0.282|0.334|||0.339|||
|Root MSE|7.275|7.025|||6.979|||
|VIF average|1.1|1.44|||1.49|||

** p<.01 Coef. = Regression coefficient. Beta = Standardized regression coefficient.

Moreover, the beta coefficient suggests that fluency contributes to explain much more of the variation in the written scores than any other single social or individual variable, except the Ashaninka language, which comes very close to fluency in terms of explaining variation in the written test. This suggests that the relationship between fluency and comprehension measures may be influenced by the language spoken by the child. Model 3 in Table 12 explores this matter

> 38 There were not enough Ashaninka cases for obtaining correlation between fluency and oral comprehension measures.

37
14 and q from the perspective of the three major foreign investors and the second sect of columns gives the difference between mean E and mean q). In general the table shows that the annual movements in E are lesB consistent with PPP than are its long run movements. 10 TABLE I I

|COUNTRIES|INTE<br>|-SNORT<br>-TER (C|RACTION<br>BETWE<br>orreLation co|EN<br>DEPRECIATIO<br> <br>ef.-betGeei EL|N<br>CE)-AND-INFLAT<br>G<br>ER C`Df|ION<br>DIFFERENT<br>ference<br>twee|IAL.<br>-Cq<br>n|
|---|---|---|---|---|---|---|
|__________r.<br>___-_|;US<br>--|-<br>JAPAN|=GERMANY|.<br>.<br>US|JAPAN.GE|RMANY|
|ILATINAMERICA|||||||
|Brazil|-0.978|-0.979|-0.981|-0.061|-0.100|-0.076|
|ChiLe|-0.919|-0.915|-0.903|0.000|-0.039|-0.015|
|Mexico|-0.770|-0.775|-0.722|-0.012|-0.051|-0.026|
|SOUTN ASIA||_|.||_||
|BangLadesh|-0.715|-0.506|-0.502|-0.054|-O.085|-0.060|
|India|-0.546|-0.504|-0.385|-0.055|-o.oB6|-0.062|
|Pakistan|-0.414|-0.424|-0.292|-0.032|-0.063|-0.039|
|Sri<br>Lanka.|0.040|0.041|0.047|0.062|-0.093|-0.069|
|EASTASIA||||||l|
|Indonesia|-0.412|-0.403|-0.347|0.011|-0.079|-0055|
|Hal aysia|-0.164|0.274|0.355|0.027|-0.064|-0.039|
|Philippines|-0.596|-0473|-0.478|0.047|-0.04|-0.020|
|Thi Lwnd|0.167|0.015|0.018|0.046|-0.045|-O.OZO|

For Latin America, the correlation coefficients and the PPP indicatoro in Table II suggest that all three types of investors enjoy good short and long run predictors of exchange rates in their highly inflationary environment--this statement ia much more robust for the US investors than for the other two investors and less robust for Brazil than for the other two host countries. There is a high annual correlation between currency devaluation and inflation rates. Koreover, the sum of pE and pq is not significantly different from 0 for any of
52

# **Table 6 (continued)**

|TCHRPLAN:|System covers at least some teachers and other school employees.|
|---|---|
|PTCHR:|Plan exclusively covers teachers and other school employees.|
|ASSETS:|Actuarial value of pension system assets, typically (though not always) reported at<br>market value (million S).|
|ASSETSQ:|Squared value of ASSETS.|
|BOND:|Fraction of pension system portfolio held in corporate and government bonds (%).|
|STOCK:|Fraction of pension system portfolio held in stock (%).|
|BENNOCUT:|State has law guaranteeing benefit amounts.|
|BENTIERS:|Benefits differ according to worker hire date (usually more recent hires receive<br>somewhat lower benefits).|
|SPECTAX:|A special or dedicated tax is the source of employer contributions.|
|ISUNION:|A: least some of the employees covered by the pension system are unionized.|
|U'NEMPD:|Recent (1990) level of unemployment minus the long run (1981-89) average level of<br>unemployment level in the state (US Bureau cf the Census 1991).|

Olivia S. Mitchell - Public Pension Governance and Performance
summarize here. Thus, once we move beyond simple totals, data are presented only for the standard World Bank regions of developing countries and geographical aggregations of high-income countries.

The final version of the database, version 4c, contains 175.7 million international migrants. This figure corresponds closely to the United Nations figure for 2000 of 175 million, although our data refer to 2000-2002. Table 7 reports the top 15 migrant recipient and sending countries in absolute terms according to the dataset. We exclude three countries from the FSU, Russia, Ukraine and Kazakhstan, which we believe are not representative of "normal" migration behavior. As a single country, the USSR had considerable internal mobility (some forced); when it split up, many of the people involved were recorded as migrants as the result of the changing country definitions rather than of any movement that they chose to undertake. These data contain no bilateral information but are reported merely to demonstrate that they correspond well to the United Nations totals by country.

As is well known, the United States is the largest recipient of migrants and now has around 12.5 percent (some 35 million) of its population foreign-born. Australia, Canada and Saudi Arabia show higher percentages and Western Europe not much smaller. Among developing countries, India and Pakistan have high immigration, as did Côte d’Ivoire and Iran in our sample period. Turning to countries of emigration, Mexico, Afghanistan and Morocco show major outflows in proportionate terms and India and Bangladesh in absolute terms. Rich countries are also important sources, with the United Kingdom, Germany, Italy and even the United States within the top 15. Because we use an absolute criterion, all the countries in Table 7 are large. Large countries generally show proportionately much less migration than small countries, several of which have more people abroad than at home or more immigrants than locals among the resident population.

16
_Figure 6. Limited use of bank accounts to transfer remittances, especially for women (FINDEX 2011)_ <!-- Start of picture text --> U S E D A C C O U N T T O U S E D A C C O U N T T O U S E D A C C O U N T T O U S E D A C C O U N T T O<br>R E C E I VE R E M I T T A N C E S S E N D RE M I T T A N C E S R E C E I VE R E M I T T A N C E S S E N D RE M I T T A N C E S<br>Total Female Male Lesotho Swaziland South Africa<br>11.14 18.27<br>9.79 15.52<br>8.54<br>11.28<br>6.22 9.79 10.04<br>5.54<br>4.92<br>5.54<br><!-- End of picture text --> The results of our fieldwork indicate that formal sector employment is an important entry point for women to access financial services. Seventy percent of textile workers have bank accounts, most of which were opened with their employers. Some factories also incorporate insurance schemes into employees’ monthly salaries, automatically deducting a percentage of their wage. This initial introduction to formal banking services seems to induce participation in other financial services, such as savings groups. Domestic workers generally do not have bank accounts (4 percent of those surveyed did), mostly because they cannot afford the monthly fees or because they are paid in cash or goods and thus have no need for one. Another one‐fourth of domestic workers cited not having an ID as the main reason they do not have a bank account. While the survey did not explicitly ask about husband’s permission, none of our respondents indicated any legal challenges in opening a bank account or access formal finances services; instead, their reasons for not engaging in financial services was overwhelmingly due to the fact that they had no need and/or not enough money to maintain activity. If there were legal constraints, it was a lack of necessary formal identification. These findings indicate progress on women’s legal rights since the Lesotho Bank Amendment Act of 2008, which allowed married women to open bank accounts without their spouses' consent.

Textile workers are also more likely to participate in stokvels<sup>43</sup> : 21% of textile workers borrow money from a stokvel when struggling to make ends meet, compared to only 10% of surveyed domestic workers. Their participation with savings groups serves two main purposes. Our survey data suggest that some women look to stokvels when their monthly salary does not cover their expenses (about 20 percent). However

> 43 A stokvel is a club serving as a rotating saving scheme or credit society to which members regularly contribute an agreed monthly amount. Each month a different member receives the amount in the fund collected during that period.

46
**Table A.6: Intersections of Geography and Gender**

||**CHI**|**NA **|**IND **|**IA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
|Mobility CEF<br>Specification|Linear|Quadratic|Linear|Quadratic|Linear|Quadratic|
|OutcomeVar|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|
||||**Urban**|**Sons**|||
|Father’s Edu|-0.400***<br>(0.062)|-0.399***<br>(0.063)|-1.007***<br>(0.035)|-1.009***<br>(0.035)|-0.714***<br>(0.096)|-0.715***<br>(0.096)|
|Constant|15.510***<br>(0.604)|15.498***<br>(0.610)|20.780***<br>(0.309)|20.778***<br>(0.308)|16.389***<br>(0.808)|16.393***<br>(0.805)|
|Observations|17,823|17,823|16,697|16,697|2649|2649|
|R-squared|0.007|0.007|0.058|0.058|0.028|0.028|
|Mean ofY|13.48|13.47|15.39|15.38|10.902|10.901|
||||**Urban D**|**aughters**|||
|Father’s Edu|-0.640***<br>(0.059)|-0.638***<br>(0.060)|-0.384***<br>(0.049)|-0.385***<br>(0.049)|-0.694***<br>(0.075)|-0.691***<br>(0.076)|
|Constant|18.774***<br>(0.610)|18.761***<br>(0.616)|20.284***<br>(0.309)|20.268***<br>(0.311)|17.225***<br>(0.739)|17.196***<br>(0.744)|
|Observations|17,485|17,485|14,519|14,519|3270|3270|
|R-squared|<br>0.018|<br>0.018|<br>0.007|<br>0.007|0.026|0.026|
|Mean ofY|15.50|15.50|18.33|18.31|11.851|11.845|
||||**Rural**|**Sons**|||
|Father’s Edu|-0.631***<br>(0.053)|-0.633***<br>(0.052)|-0.793***<br>(0.039)|-0.805***<br>(0.038)|-0.323***<br>(0.083)|-0.319***<br>(0.083)|
|Constant|16.681***<br>(0.551)|16.688***<br>(0.547)|20.436***<br>(0.199)|20.425***<br>(0.200)|15.577***<br>(0.596)|15.530***<br>(0.596)|
|Observations|28,968|28,968|30,004|30,004|5909|5909|
|R-squared|0.017|0.017|0.021|0.022|0.004|0.004|
|Mean ofY|14.49|14.48|18.25|18.20|13.798|13.773|
||||**Rural D**|**aughters**|||
|Father’s Edu|-0.275***<br>(0.069)|-0.281***<br>(0.069)|0.793***<br>(0.050)|0.793***<br>(0.049)|-0.231***<br>(0.089)|-0.237***<br>(0.090)|
|Constant|16.972***|16.989***|13.383***|13.370***|15.857***|15.837***|
||(0.212)|(0.211)|(0.227)|(0.227)|(0.620)|(0.620)|
|Observations|29,883|29,883|25,528|25,528|6528|6528|
|R-squared|0.004|0.004|0.022|0.022|0.002|0.002|
|Mean ofY|15.98|15.98|15.52|15.51|13.798|14.534|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given category.

42