rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5370

# **Abstract**

Using data they collected in rural Burkina Faso, the authors examine how children’s cognitive abilities influence resource constrained households’ decisions to invest in their education. This paper uses a direct measure of child ability for all primary school-aged children, regardless of current school enrollment. The analysis explicitly incorporates direct measures of the ability of each child’s siblings (both absolute and relative measures) to show how sibling rivalry exerts an impact on the parents’ decision of whether and how much to invest in their child’s education. The findings indicate that children with one standard deviation higher own ability are 16 percent more likely to be currently enrolled, while having a higher ability sibling lowers current enrollment by 16 percent and having two higher ability siblings lowers enrollment by 30 percent. The results are robust to addressing the potential reverse causality of schooling influencing child ability measures and using alternative cognitive tests to measure ability.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to better understand the determinants of education decisions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ddewalque@worldbank. org, akresh@illinois.edu , emilie.bagby@gmail.com and harounan.kazianaga@okstate.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
all children younger than 16 and all women of reproductive age (between 15 and 49) in the follow up round. The field work differs from many school feeding evaluation studies, not only in its randomized assignment of treatments, but also in that it surveyed children not in school.

We summarize our key variables at baseline in table 1. The first three columns report the averages for the villages with school meals, take home rations and for the control villages. The last two columns (4 and 5) report the tests whether these variables are statistically different across treatment and control groups. We consider child level variables, which include educational, and health outcomes as well as socioeconomic characteristics, and household level variables which include the household head socioeconomic characteristics and household wealth.

It is apparent that prior to treatment, the groups were similar on most variables including enrollment, child health and nutritional status, household and socioeconomic characteristics. Out of the 86 differences reported in columns 4 and 5, there are 5 instances where the estimated differences are statistically significant. Overall, we conclude that the random assignment of villages to treatment and control groups was reasonably successful.

These summary statistics also show that these villages are characterized by low enrollment rate and poor child health. At the time of the baseline survey, 28 percent of children in the school meals villages, 24 percent in the take home villages and 24 percent in the control villages were enrolled in schools<sup>4</sup> . In both treatment and control villages, only a small fraction (about 17 percent) of those children who are enrolled has access to all the required books. If this is any indication of the learning environment, one could conjecture that other school inputs are constraining as well.

On the math tests, children scored less than 2 points which correspond to getting half the answers correct. The anthropometric data point to severe food shortage, with weight-for-age and height-for-age 2 standard deviations below the reference population5,6. The figures in table 2 (top panel) indicate that prior to the treatment, more than half of children were underweight or

> 4 As previously noted, these differences are not statistically significant as shown in columns 4 and 5.

> 5 We use the World Health Organization Child Growth Standards Package (WHO Multicentre Growth

> Reference Study Group, 2006).

> 6 As previously noted, these differences are not statistically significant as shown in columns 4 and 5.

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FIGURE **Al** <!-- Start of picture text --> ''1 4-d COTE d'h/OIRE: INTEREST AND INFLAT)ON<br>to to<br>4<br>I'~~~~~~~~~~<br>ISO<br>i4O<br><!-- End of picture text --> <!-- Start of picture text --> to0- GHANA INTEREST AND INFLATION RATES<br>70<br>12 83 t 64 88 *8 6<br>170<br>-o .,,,so--<br>140<br>70 KENYA: INTEREST AND INFLATION RATES<br>40<br>'40;<br>62 83 84 68~~~8' of<br>K~YA: NTEREST AND 5NLTO AE<br><!-- End of picture text -->
|(9)<br>PCA<br>index<br>cols. 1-8<br>-0.811<br>(0.728)|0.12<br>332<br>-1.189<br>(0.900)<br>0.00<br>921<br>-0.270<br>(0.498)<br>0.04<br>1471<br>les.|
|---|---|
|(8)<br>coping<br><br>strategy<br>index<br>-1.639<br>(2.550)|0.05<br>335<br>2.507<br>(3.223)<br>0.02<br>959<br>0.380<br>(2.324)<br>0.05<br>1489<br>th percenti|
|(7)<br>food<br>insecurity<br>score<br>0.004<br>(0.409)|0.03<br>335<br>0.670<br>(0.532)<br>0.00<br>959<br>0.118<br>(0.237)<br>0.04<br>1487<br>10th and 90|
|(6)<br>any day<br>w/reduced<br>d<br>meals<br>-0.098<br>(0.185)|0.06<br>336<br>0.253<br>(0.183)<br>0.03<br>960<br>0.062<br>(0.116)<br>0.03<br>1493<br>sorized at the|
|(5)<br># food<br>groups<br>consume<br>-0.361<br>(0.444)|0.09<br>337<br>-0.375<br>(0.636)<br>0.05<br>981<br>-0.162<br>(0.375)<br>0.07<br>1487<br> Top-up.<br>01<br>ies are Win|
|(4)<br>per adult<br>equivalent<br>calories<br>-549.642<br>(531.664)|0.09<br>338<br>-351.598<br>(277.377)<br>0.00<br>983<br>-257.588<br>(218.456)<br>0.05<br>1499<br> and PWP *<br>_, ∗∗∗p <_0_._0<br>. Total calor|
|*|WP<br>05<br>d 8|
|(3)<br>food<br>cons.<br>score<br>-12.501*<br>(5.060)|0.00<br>337<br>-7.461<br>(5.551)<br>0.00<br>981<br>-1.268<br>(3.773)<br>0.08<br>1487<br>d with P<br>_, ∗∗p <_0_._<br>s 6, 7, an<br>d efects.|
|)|te<br>0<br>n<br>e|
|(2)<br>ln (p.c.<br>food cons.<br>last week<br>-0.447*<br>(0.236)|0.00<br>338<br>-0.257<br>(0.275)<br>0.03<br>983<br>-0.050<br>(0.148)<br>0.05<br>1499<br>n instrumen<br>vel. _∗p <_0_._1<br>rity of colum<br>-interview fx|
|(1)<br>ln (p.c.<br>food exp.)<br>last week<br>-0.509<br>(0.375)|0.24<br>336<br>-0.349<br>(0.527)<br>0.03<br>945<br>0.149<br>(0.262)<br>0.01<br>1495<br> participatio<br>at the EA le<br>se food secu<br> and week-of|
|Dependent variable:<br>Panel A: North<br>HH participated in PWP<br>in past 30 days|R-squared<br>Observations<br>Panel B: Center<br>HH participated in PWP<br>in past 30 days<br>R-squared<br>Observations<br>Panel C: South<br>HH participated in PWP<br>in past 30 days<br>R-squared<br>Observations<br>2SLS estimates. HH PWP<br>Standard errors clustered <br>Higher values indicate wor<br>Estimates include district|

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**Table 3:** Predictors of Rural Non-Farm Employment

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Pr(Self-Em|ployed = 1)|Pr(Casual|Work= 1)|Pr(Salari|ed Work = 1)|
|Female|-.043<sup>_∗∗∗_</sup>|-.043<sup>_∗∗∗_</sup>|-.059<sup>_∗∗∗_</sup>|-.059<sup>_∗∗∗_</sup>|-.015<sup>_∗∗∗_</sup>|-.015<sup>_∗∗∗_</sup>|
||(.004)|(.004)|(.004)|(.004)|(.003)|(.003)|
|Literate|.044<sup>_∗∗∗_</sup>|.044<sup>_∗∗∗_</sup>|.002|.002|.034<sup>_∗∗∗_</sup>|.034<sup>_∗∗∗_</sup>|
||(.003)|(.003)|(.003)|(.003)|(.003)|(.003)|
|Educated|.011<sup>_∗∗∗_</sup>|.011<sup>_∗∗∗_</sup>|-.025<sup>_∗∗∗_</sup>|-.025<sup>_∗∗∗_</sup>|.117<sup>_∗∗∗_</sup>|.117<sup>_∗∗∗_</sup>|
||(.004)|(.004)|(.003)|(.003)|(.004)|(.004)|
|Age|.008<sup>_∗∗∗_</sup>|.008<sup>_∗∗∗_</sup>|-.001<sup>_∗∗∗_</sup>|-.001<sup>_∗∗∗_</sup>|.007<sup>_∗∗∗_</sup>|.007<sup>_∗∗∗_</sup>|
||(.001)|(.001)|(.000)|(.000)|(.001)|(.001)|
|Sq. Age|-.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|-.000|-.000|-.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|
||(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|
|SC/ST|-.032<sup>_∗∗∗_</sup>|-.032<sup>_∗∗∗_</sup>|.015<sup>_∗∗∗_</sup>|.015<sup>_∗∗∗_</sup>|-.009<sup>_∗∗∗_</sup>|-.009<sup>_∗∗∗_</sup>|
||(.003)|(.003)|(.004)|(.004)|(.003)|(.003)|
|Muslim|.053<sup>_∗∗∗_</sup>|.053<sup>_∗∗∗_</sup>|.016<sup>_∗∗_</sup>|.016<sup>_∗∗_</sup>|.000|.000|
||(.009)|(.009)|(.007)|(.007)|(.004)|(.004)|
|Marginal Landowner|.086<sup>_∗∗∗_</sup>|.086<sup>_∗∗∗_</sup>|.066<sup>_∗∗∗_</sup>|.066<sup>_∗∗∗_</sup>|.048<sup>_∗∗∗_</sup>|.048<sup>_∗∗∗_</sup>|
||(.003)|(.003)|(.004)|(.004)|(.003)|(.003)|
|Fraction rural||.018||.022||.105<sup>_∗∗∗_</sup>|
|||(.023)||(.039)||(.018)|
|Fraction salaried workers||-.092<sup>_∗∗_</sup>||-.098||.586<sup>_∗∗∗_</sup>|
|||(.036)||(.065)||(.051)|
|Fraction public employment||-.033||.335<sup>_∗∗∗_</sup>||.002|
|||(.022)||(.053)||(.019)|
|Fraction manufacturing||.307<sup>_∗∗∗_</sup>||.102<sup>_∗_</sup>||.084<sup>_∗∗∗_</sup>|
|||(.044)||(.059)||(.031)|
|Fraction educated||.048||.017||-.064<sup>_∗∗∗_</sup>|
|||(.034)||(.055)||(.022)|
|Branch per capita (Log)||.016<sup>_∗∗_</sup>||.024<sup>_∗∗_</sup>||-.002|
|||(.007)||(.011)||(.005)|
|Credit per capita (Log)||-.001||-.030<sup>_∗∗_</sup>||.007<sup>_∗_</sup>|
|||(.007)||(.012)||(.004)|
|Observations|453161|453161|453161|453161|453161|453161|
|R<sup>2</sup>|.08|.08|.09|.09|.11|.11|
|Dep Var Mean|.08|.08|.06|.06|.06|.06|
|Dist Controls|No|Yes|No|Yes|No|Yes|

This table shows the impact of individual and district characteristics on rural non-farm employment by employment type. The unit of observation is rural individuals aged 15-65, who participate in the labour force. Educated refers to individuals with secondary education or better. Maginal landowners are workers in households with less than 0.5 acres of land holding. Standard errors in parentheses, clustered by district.

44
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<!-- Start of picture text --> Figure 9. Change in Household Consumption-to-Income Ratio<br><!-- End of picture text --> <!-- Start of picture text --> 8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>-12.0%<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Bangladesh Ghana Moldova Nepal<br>Peru Romania Thailand<br>Source: Authors’ calculations with data from RIGA, and National Household Surveys.<br>percentage change<br><!-- End of picture text -->

# Figure 10. Decomposition of Changes in Moderate Poverty

<!-- Start of picture text --> 140.0<br>120.0<br>100.0<br>80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>-20.0<br>-40.0<br>US$1.25-a-day US$4.00-a-day US$5.00-<br>a-day<br>Share of adults Employment + earnings Nonlabor Income Consumption-to-income ratio<br>share of total poverty reduction<br>Bangladesh, 2000–10 Ghana, 1998–2005 Nepal, 1996–2003 Argentina, 2000–10 Brazil, 2001–09 Chile, 2000–09 Colombia, 2002–10 Costa Rica, 2000–08 Ecuador, 2003–10 Honduras, 1999–2009 Panama, 2001–09 Paraguay, 1999–10 Peru, 2004–10 Thailand, 2000–09 Moldova, 2001–10 Romania, 2001–09<br><!-- End of picture text -->

**_Source:_** Authors’ calculations with data from SEDLAC, RIGA, and National Household Surveys. **_Notes:_** “Labor income” refers to the change in employment and earnings per adult; “nonlabor income” refers to transfers, pensions, capital, and other income not from labor. Consumption-based measures of poverty are used in the case of Bangladesh, Ghana, Nepal, Peru, Thailand, Moldova, and Romania. Income-based measures of poverty are used in the case of Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Panama, and Paraguay.

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40 fell from 18 percent before the Uruguay Round to about 3 percent after.<sup>2</sup> '

# D. Possible Effects of the Round's NTB Liberalization

The Uruguay Round's elimination of NTMs applied under the Multifiber Arrangement is clearly a positive development for developing countries as a whole, yet there may be negative implications for individual exporters. Under the MFA, and its predecessor the Short-TermTextile Arrangement (STA), developing countries were allocated quotas for their textile and clothing exports to industrial countries. Because of these assigned quotas some developing countries may able continue exporting even though they may have lost the comparative advantage in this sector they previously possessed. When the MFA quotas are phased out textile and clothing trade will be subject to intense international competition and the displacement of many established suppliers could occur. Stated differently, some regional countries may be uncompetitive in this new international environment and could find their exports displaced by more efficient producers whose trade is now restrained by the MFA. As such, some ME countries may have to give a high priority to restructuring their industries, reducing costs, and improving quality to compete in a post-Uruguay Round world.

The tariffication and of agricultural NTBs (and reduction in associated levels of protection) could lead to increases in world prices of previously subsidized agricultural products, including cereals, meat, dairy products and sugar. Price increases should occur because of the increased international demand for agricultural products associated with a lowering of OECD trade barriers and the new Uruguay Round regulations regarding subsidies. These changes would benefit developing countries which are important exporters of these products. Some studies have concluded that prices of some previously subsidized products could rise by 4 to 10 percent in total when the full effects of the Round are felt.

A number of developing countries which are net importers of food have expressed concern about possible higher food prices. Provided that these higher prices are passed on to farmers there will be an offsetting increase in domestic production. Nevertheless, if world food prices do rise overall individual countries that remain net importers of food will incur higher costs. Probably the best way to counter such a development is through the adoption of efficiency and cost cutting reforms to help st.mulate domestic agricultural production.

2tGiven the Round's accomplishments regarding NTMs, what remains to be done if a further post-UR liberalization is attempted? Analysis of the Bank's NTM data shows that antidumping and countervailing duties (which may be far more widely used in a post-UR world), followed by QRs should be the most important<sup>remaining</sup> restrictions. These are mostly concentrated in chemicals (SITC 5) and miscellaneous manufactures<sup>(SITC 8). In</sup> short, the focus of post-Uruguay initiatives on NTMs would shift markedly in terms of the types of measures applied, the sectors affected, and the overall importance of these restrictions.
community-based providers. Similarly, a _transport section_ aimed to establish user and access patterns to public transport as well as contentment for their use.

The _labor market_ module included questions as to economic activity of all household members (by sector and firm), formality of labor contracts, time worked, and search behavior for employment, as well as several questions on household earnings from work and transfer incomes. The sample population interviewed were asked about their _budget priorities_<sup>_6_</sup> for the city in two ways: what programs they would suggest to be expanded under a budget increase and what programs (of an identical list) should be cut if the need were to reduce the overall city expenditures. A _civic participation_ module aimed to assess the degree of participation in local organizations, ranging from Cali-specific ‘Juntas de Administration Local’ to community self-help groups, NGOs and sports clubs. Reasons for participation/non-participation, willingness of interviewees to become active in specific programs, as well as willingness to participate financially in service provisions were explored.

The survey covered 1,912 households (8,461 individuals) in the city, being representative of five areas and socio-economic strata as defined by the Colombian statistical institute DANE. The sample frame used was that of the last Colombian census (1993) which was updated with the latest local area population estimates which the statistical institute provided. Based on that sampling frame, area selection and determination of the number of households per area was designed so that representative results could be drawn for socioeconomic strata as well as by geographical areas. Sampling, digitalization (using double entries to provide immediate consistency checks during the data entry process), data cleaning as well as basic tabulation was carried out by an experienced Colombian survey firm. The definition of socio-economic strata followed the definition of DANE.

The survey has elements of contextual as well as non-contextual data collection instruments<sup>7</sup> and in such draws on elements of in-depth multi-topic surveys (like the Living Standard Measurement Surveys<sup>8</sup> ), rapid monitoring surveys like the Core Welfare Indicator Questionnaire<sup>9</sup> and village surveys. It is contextual in the sense that it was developed to capture the specific economic, social and economic environment of the municipality of Cali. It did not, however, employ open-ended questions but used standard, pre-formulated questions

> 6 The list of programs included all those for which the municipality had a full or partial implementation mandate.  The list comprised education, health, public transport, water supply, garbage collection, sanitation, public lightening, childcare, polices, social housing, sport arenas, and public works programs

> 7 See Booth et al (1998) and Hentschel (1999).

> 8 See Grosh and Glewwe (1999) and the Living Standards Measurement Study website (www.worldbank.org/lsms/).

9
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# Portugal

Portugal is another country that wants to increase emigration to alleviate unemployment problems. Portugal still has a high emigration potential because of its low productivity and relatively high fertility (Simon, 1987). Many experts believe that there will be a substantial outflow from Portugal when free movement of labor within the European Community becomes operative in the early 1990s. A few, however, think there is a potential for Portugal to become a net immigration country within ten years, again because of its position in the European Community. On balance, I -ould recommend fixing net emigration at 50,000 in 1985-1990, 40,000 in 1990-1995, and 30,000 in 1995-2000.

# Sweden

The decline in net immigration in the previous World Bank figures over time seems too rapid, particularly in the light of a resurgence in net immigration among the foreign population from 1983 to 1987 (SOPEMI, 1988; Salt, 1987). In 1986, 25,100 non-Nordic citizens immigrated to Sweden, the highest level since 1970. The number of asylum seekers has also been inching upward, from 12,000 new arrivals in 1984 to about 15,000 in 1987 (SOPEMI, 1988). I would recommend increasing net immigration to 35,000 in 1985-1990, 30,000 in 1990-1995, and 20,000 in 1995-2000.

# Turkey

Despite cutbacks in guestworker programs in Europe, Turkey still has a considerable potential for emigration. Turkey is burdened with a very high rate of natural increase, a saturated employment market and high inflation (Simon, 1987). However, it is increasingly difficult for Turkish citizens to find host countries to move to. The 1980-1985 period saw decreasing emigration from Turkey to Europe and the Middle East and large-scale return migration from Europe. Under the circumstances, it seems that the net emigration figures previously used by the World Bank for 1980-1985 are too high. I would recommend net emigration assumptions of 75,000 in 1980-1985, 67,000 in 1985-1990, 50,000 in 1990-1995, and 30,000 in 1995-2000.

# Romania

Between 1985 and 1987, Romanian immigration to. the United States and Canada averaged 5,828 persons per year and few Romanians can be expected to return to their country of origin soon. Therefore, net emigration on the order of 30,000 persons per year is in order for each five-year period after 1985.