rafmacalaba/datause-encoder-data huggingface dataset:
academically prepared than their peers for whom financial barriers are not a constraint. Additionally, an influx of students could strain school resources. Each of these channels would lead to a negative effect of the policy on learning. On the other hand, relaxing financial constraints among students who would have enrolled in the absence of the policy could improve learning by reducing their need to generate income or by alleviating stress.

The treatment effect _β_ will therefore represent an average of these effects along the extensive and intensive margins, or what Glewwe and Muralidharan (2015) call the “policy parameter,” because it represents the effect of the policy inclusive of any adjustments made by households or schools in response. We therefore analyze how the policy altered the number of students taking the exam, the share of test takers in scholarship-eligible schools, and student characteristics, in order to understand selection into the test. We also look for heterogeneity in treatment effects by interacting the program dummy with observable characteristics, allowing us to check whether treatment effects differed in areas where the extensive margin (enrollment and composition) effects were likely to be largest. Our prior is that the effects of the policy on learning outcomes will be smaller, and perhaps even negative, the greater the evidence of gains in student access or of negative selection into the exam.

When examining aggregate outcomes, such as the quantity of test takers, we aggregate the data by district, as this is the relevant level for any public-private competition. These specifications also include district fixed effects because school effects are no longer identified.

We cluster all standard errors by region, the unit of treatment. Because there are only five regions in the sample, we conduct inference via the wild- _t_ cluster bootstrap (Cameron et al. 2008), using the weights proposed by Webb (2013) for samples with fewer than 10 clusters. Results tables report _p_ -values and significance levels based on these corrections. Because the bootstrap produces valid _p_ -values but not confidence intervals (Cameron and Miller 2015, 27), we also report standard errors clustered by region based on the usual asymptotic approximation (Labonne 2013) but caution that these are illustrative and not appropriate for inference.

# III. DATA

## _Sources_

Outcome data are the universe of student exam records from the West African Examinations Council (WAEC). Subject-level scores are available for each student registered for the exam between 1998 and 2012, allowing for several years of pretreatment outcomes for each region.<sup>8</sup> In addition to omitting region 1, we also omit private schools because they were ineligible for the scholarship program. However, all raw test results are converted to _z_ -scores based on the universe of results in a given year, including students from private schools and region 1. This standardization allows us to 8
various brain functions, and nervous system maintenance (FAO and WHO 2004). Small fish, commonly consumed by the poor in Bangladesh, are a particularly important source of calcium in diets that are often deficient in milk and milk products (Hansen et al. 1998), as well as zinc and iron (Thilsted et al. 2016). Pre-partum consumption of omega-3 fatty acids from fish is associated with lower risk of pre-term delivery (Imhoff-Kunsch et al. 2012), as well as higher likelihood of children’s normal cognitive development and fine motor skills (Hibbeln et al. 2007).

Several assessments for Bangladesh have established statistical associations between fish market conditions, household socioeconomic characteristics, fish consumption and measures of child undernutrition.<sup>1</sup> In aggregative cross-section and time-series studies, Dey et al. (2010) and Toufique (2015) find that fish consumption is highly responsive to income and fish prices. Grant (2016 - Figures 8.5 and 8.6) finds a higher incidence of stunting in poor households, and for children with less-educated mothers. However, it also finds significant stunting in wealthy households. NIPORT (2016) reports a similar finding: Infant and young child feeding (IYCF) practices improve with maternal education and wealth status, but remain unsatisfactory for 1/3 of children in the highest wealth quintile. After investigating traditional beliefs in very poor households, Choudhury and Ahmed (2011) suggest that improved IYCF practices may require targeted health behavior education.

In summary, extensive nutritional research has established a significant link between fish consumption and child health, and numerous aggregative studies have indicated that both fish consumption and child health vary with maternal education and household economic status. However, even relatively well-educated and wealthy mothers can have deficient IYCF practices. And more precise

> 1 These studies typically use WHO-recommended comparative physical measures based on reference populations or international standards : stunting (low height/length  for  age); wasting (low weight-for-height/length); and malnutrition (circumference of a child's upper arm, or a measure of weight for height).

3
**Non-Linearities**

In the previous section we assumed that the relationship between unobserved governance and observed indicator scores was linear. This assumption places strong restrictions on the units in which governance is measured in the various indicators in our sample. For example, consider an indicator such as Gallup which asks respondents how many cases of corruption there are among public officials, and offers the choice of four broad categories: 'none", "a _few",_ "many" and "a lot". Our observed data consists of numerical scores on a scale of one to four corresponding to these categories. The assumption of a linear mapping from unobserved graft into observed scores implies that the difference in graft between a country with a score of 4 and one with a score of 3 (i.e. the difference between "a lot" and umany" cases of corruption) rs the same as the difference between two countries with scores of 3 and 2 (i.e. the difference between "many" and 'a few"). Given the somewhat vague response categories, it is not at all clear that the assumption of linearity is warranted. Moreover, even if these categories were equally-spaced according to some appropriate metric, the fact that the observed data are discrete while our unobserved govemance indicator is continuous violates the assumption of a linear relationship between the two.<sup>13</sup> Finally, the mere fact that indicators are non-representative may also contribute to a non-linear relationship between governance and observed scores. A number of the indicators in our sample cover primarily developed countries together with a few developing countries. It is possible that the developing countries in these indicators suffer from a "curse of inclusion" in the sense that they receive worse scores than they might otherwise have received simply because they are implicitly being compared with countries in which various aspects of govemance are likely to be much better.14 For example, representative surveys such as DRI or PRS assign moderate scores of 55/100

> 13 The straighfforward solution to this problem would be to rely on an ordered multinomial choice model with individual effects in the latent variable. HoWever, for such a model to be identified, it would again be necessary to assume that the variances of the errors are identical across indicators.

> 14 This problem may be particularly acute for polls of experts who consider a large set of countries at once, since in contrast to surveys of residents, experts are much more likely to be aware of relative comparisons of countries.

22
Brenton, Paul and Miriam Manchin. 2003. “Making EU Trade Agreements Work: The Role of Rules of Origin,” _The World Economy_ , 26, 755-769.

- Bouet, A., Y. Decreux, L. Fontagne, S. Jean and D. Laborde, 2004, “A consistent, advalorem equivalent measure of applied protection across the world: The MAcMapHS6 database,” CEPII discussion paper No 2004–22, December.

- Brown, Drusilla K. 1987, “General Equilibrium Effects of the US Generalized System of Preferences _,” Southern Economic Journal_ 54: 27-47.

- Candau, Fabien Lionel Fontagne and Sébastien Jean. 2004, “The utilization rate of preferences in the EU,” CEPII, mimeo.

- Carrière, C. and de Melo, J., 2004, 'Are Different Rules of Origin Equally Costly? Estimates from NAFTA, CEPR Discussion Paper No. 4437.

- Francois, J. and D. Roland-Holst, 1997, _Scale Economic and Imperfect Competition_ in Francois, J. and K. Reinert, eds., _Applied Methods for Trade Policy Analysis: A Handbook_ , Cambridge University Press: Cambridge.

- Francois, J. 2001, _THE NEXT WTO  ROUND: North-South stakes in new  market access negotiations_ , CIES: Adelaide and Tinbergen Institute:Rotterdam.

- Francois, J. and K. Reinert, 1997, _Applied Methods for Trade Policy Analysis: A Handbook_ , Cambridge University Press: Cambridge.

- Francois, J., D. Spinanger, and J. Woertz,  2005, “The impact of ATC quota elimination on LDC exports,” mimeo.

- Francois, J.F. and I. Wooton, 2001, “Trade and Competition in Shipping Services and the GATS,” _Review of International Economics_ , 9(2).

- Francois, J.F. and I. Wooton 2005, “Market Structure in Services and Market Access in Goods,” CEPR discussion paper.

- Hansen, B. E. 2000, ‘Sample Splitting and Threshold Estimation’, _Econometrica,_ 68: 575-603.

- Herin, J. 1986, ‘Rules of Origin and Differences Between Tariff Levels in EFTA and in the EC, European Free Trade Association’, Economic Affairs Department, Occasional Paper No. 13.

- Hoekman, Bernard. 1993. “Rules of Origin for Goods and Services: Conceptual Issues and Economic Considerations," _Journal of World Trade_ , 27(4), 81-100.

- Hoekman, Bernard. 2004. “Dismantling Discrimination Against Developing Countries: Access, Rules and Differential Treatment,” CEPR Discussion Paper No. 4694.

- Hoekman, B. and C. Özden. 2005.  “Trade Preferences and Differential Treatment of Developing Countries: A Selective Survey,” Policy Research Working Paper 3566, World Bank.

- Hudec, Robert, 1987, _Developing Countries in the GATT Legal System_ (London: Trade Policy Research Centre).

- Ianchovichina , Elana, Aaditya Mattoo and  Marcelo Olarreaga, 2001. “Unrestricted Market Access for Sub-Saharan Africa: How Much Is It Worth and Who Pays?,” _Journal of African Economics_ 10(4), 410-32.

25
8 Table 3: Sensitivity of Indi,; 'ual Capital Account Net Inflows to Current Account Financing Requirement in 16 Developing Countrie3.

|Country|Direct<br>investment|Portfolio<br>investment|Long-term<br>flows|Short-term<br>flows|Public<br>sector|
|---|---|---|---|---|---|
|Argentina<br>(1975-1989)||0.14*<br>(2.2)||||
|Brazil|||0.36**|||
|(1975-1989)|||(1.8)|||
|Cb;j_e|||0.60**|_0.X6*_||
|(1975-1991)|||(2.0)|(3.5)||
|Egypt<br>(1975-1988)||||||
|India|-0.O0*||0.35**||0.39**|
|(1975-1989)|(-2.9)||(2.0)||(2.1)|
|Mexico|O.07**||0.41*|0.67*||
|(1975-1988)|(2.2)||(2.6)|(4.0)||
|Nigeria<br>(1975-1990)||||||
|Pakistan|0.05*|||||
|(1975-1990)|(2.9)|||||
|Sri Lanka||||0.21**||
|(1975-1991)||||(2.0)||
|Turkey||-0.09*||0.50*||
|(1975-1990)||(-2.1)||(2.9)||
|Venezuela|||0.62*||0.58*|
|(1975-1991)|||(2.5)||(2.5)|
|Indonesia|0.07*|0.05*||||
|(1975-1990)|(2.2)|(3.3)||||
|Korea|-0.02**||0.32**|0.39**||
|(1975-1990)|(-2.0)||(1.8)|(2.1)||
|Malaysia|0.14*||||0.26**|
|(1975-1990)|(2.5)||||(1.8)|
|Philippines<br>(1975-1990)||||||
|Thailand||||0.38*||
|(1975-1990)||||(3.2)||

Note: t statistics in parentheses. ' 95 per cent confidence level. * 90 per cent confidence level.
E. Figure shows percent of firms that were visited or inspected by tax officials or were required to meet with them over the last year.

F. “Introduce process innovation” data indicate the percent of firms that introduced any new or significantly improved process over the last three years, including methods of manufacturing products or offering services; logistics, delivery, or distribution methods; or any supporting activities for processes. “Invest in R&D” data indicate the percent of firms over the last fiscal year that invested in formal research and development activities.
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59
increased with the level of education, while they decreased for females: the RoR for primary education was 0.9 percent, it was 2.7 percent for general secondary education, 3.1 percent for vocational education, 7.5 percent for university graduates, and 11.8 percent for those with post-graduate studies. Returns to education for females decreased with higher education levels: the RoR for primary education was 9.9 percent; for general secondary, 8.2 percent; for vocational education, 6.1 percent; and for higher education, 7.4 percent. The study shows that returns to education are higher in the government and public sectors only for early education levels (primary and preparatory), while the RoRs for vocational, university, and higher education levels were superior in the private sector (El Arabi, 2010).

Wahba (1996) studies the determinants of earnings in the Egyptian labor market using the Egyptian Labor Force Sample Survey (LFSS) performed in October 1988. She uses the classical Mincerian model. Her study is consistent with earlier studies in showing an increase in returns to education with education level. She also reports variations among regions, and explains these discrepancies by differences in labor productivity characteristics.

Said (2007) uses the 1998 LFSS, 1998 ELMS, and 2006 ELMPS to study workers’ earnings in Egypt. She ran 18 multivariate regressions of log hourly wage regressed on education levels for three rounds (1988, 1998, and 2006), males and females, sector of employment, and region. Said reports considerable improvement in female wages between 2006 and 1998, due to the concentration of female employment in the government sector, where wages increased by 40 percent between these two periods compared with a 17 percent increase in the private sector. Said reports a 4.7 percent return on vocational education, 7 percent for post-secondary education, and 8.5 percent for university education, without allowing for any heterogeneity.

El Arabi (2010) uses a sample of 862 observations of household heads between 15 and 64 years old who are wage employees distributed among five Egyptian governorates to study the economic and noneconomic determinants of the demand for education. El Arabi also uses both the classical and extended Mincerian models. El Arabi finds that the return on education in Egypt is very low compared with other developing countries, which explains why school drop-out rates are high among the poor.

3
and thus to increase labor demand in agriculture. This in turn raises agricultural wages and reduces a major incentive to migrate to non-farm jobs. We then ask whether global trade liberalization, by decreasing inter-sectoral migration, would have an adverse impact on poverty reduction. By contrast, by reducing the inflows of agricultural workers searching for jobs, it may relieve some of the pressure on incomes in the non-agriculture sectors, or even have some direct poverty reducing effect through raising agricultural incomes (that more than compensates for lower migration flows). For the case of Brazil, we find that trade liberalization scenarios can significantly reduce the flow of workers out of agriculture but that the net final effect is that more individuals, rather than less as initially hypothesized, are able to escape poverty.

To resolve the ambiguity of the poverty impacts of trade reform we rely on the mentioned microsimulation approach and combine it with a recursive dynamic computable general equilibrium (CGE) model. In a first step, we build a Business as Usual (BaU) scenario that ‘replicates’ for a 15 year future period the main economic trends – including changes in the skill composition of the population and in the sectoral distribution of employment – observed in the recent decade. We then simulate, for the same time horizon, counterfactuals involving trade reforms and link key CGE’s aggregate results (on relative factor prices and resource movements from agricultural to non-agricultural sectors) to the microsimulation model. This is finally used to estimate new counterfactuals income distributions and poverty and inequality statistics can be calculated and compared with those of the BaU scenario.

This macro-micro model enables us to analyze the long-term poverty and distributional impact of the different growth patterns implied by trade liberalization scenarios versus a Bau scenario, but can potentially be used to study other policies as well. Among the additional key findings, our approach demonstrates that, at least for high inequality countries such as Brazil, anti-poor changes in the distribution can easily dwarf the poverty reducing potential of growth and that successful poverty reduction strategies need to include re-distribution or precise targeting measures. This confirms what, in different contexts, recent research has also shown: growth can differ tremendously in its power to reduce poverty both across countries and over time;<sup>1</sup> according to Bourguignon (2003) changes in per capita incomes only explain, at least in his large sample of countries, 26% of the variance of observed changes in poverty headcounts.

> 1 See Bourguignon (2004), Ravallion (2001), Ravallion and Datt (1999), World Bank (2001), or Kappel, Lay and Steiner (2005).

4
support this view. The pattern of job turnover in private firms is very much different from that in public and privatized (“collectively owned”) firms.<sup>15</sup> Let’s focus on the non-agricultural business sector. The job creation record of state firms is dismal. They create less than three new jobs per every 100 existing jobs (per year). Privatized firm perform better, with the annual gross job creation rate at close to 9 percent. However, only the genuinely private firms expand at a relatively fast pace: they create almost 12 new jobs per 100 existing jobs. This clearly shows that ownership matters, and the strong job creation potential exists only in the private firms.

This claim is reinforced when one looks at the job destruction rates. Privatized firms perform badly on this count, destroying close to 12 percent of their jobs. Both state owned and private firms destroy around 7 percent of their jobs per year.

As a result of a high job creation rate and a modest job destruction rate, the private sector creates jobs on a net basis, and increases its employment (at some 4 percent per year). In contrast, both the state owned and privatized sectors destroy jobs on a net basis, i.e. decreases their employment (about 3 to 5 percent). Again, this shows that only the private sector is the provider of job opportunities and the source of employment growth.

Interestingly, the job reallocation rate is the highest among the privatized firms. Within this segment of the labor market, about nine percent of all jobs are moved away from the declining firms toward the expanding firms. This points to a high degree of heterogeneity among the privatized firms: some of them behave like the private firms and

> 15 The classification of ownership used in this paper differs from that used in the official Moldovan statistics.  According to official statistical sources, the so called “collective property” is part of the private sector.  However  the bulk of “collective firms” are formerly state owned enterprises turned into joint stock companies and partly or fully privatized.  (In addition, “collective firms” comprise cooperatives, firms owned by organizations, etc.).  Analysis shows that these privatized firms behave very much differently from genuinely private firms and therefore lumping them together may be misleading.  Accordingly, in order to obtain a clear picture of job turnover by firm ownership, the paper presents data on “collectively owned” firms” separately from those on private firms.  Note that as a result of this separation of “collective firms” the share of private sector in employment is substantially smaller than officially reported.

19
survey. Finally, another third of the beneficiary households received, in addition to the basic CCT, a grant for productive investments aimed at encouraging recipients to start a small non-agricultural activity with the goal of asset creation and income diversification. This grant was conditional on the household developing a business development plan, outlining the investments outside of subsistence farming in new livestock or nonagricultural income generating activities. This package included technical assistance and training in basic commercial skills. The beneficiaries of the productive investment package had received the largest amount of benefits at the moment of the follow-up survey: 2-3 months before being surveyed they had received $175 to invest.<sup>5</sup>

# _II.2. Program randomization_

The program was targeted to 6 municipalities of the drought region in the northwest of Nicaragua. These were municipalities that met both criteria of having been affected by a drought the previous year and by the high prevalence of extreme rural poverty based on the national poverty map. From the list of all communities in the 6 municipalities, 56 intervention and 50 control communities were randomly selected through a lottery to which the mayors of the 6 municipalities were invited to attend and participate. Baseline data were then used to define program eligibility based on poverty and vulnerability, resulting in the identification of 3,000 households to participate in the program.<sup>6</sup> Finally, from each eligible household, the female household member that was reported as the children’s primary caregiver was invited to a registration assembly.<sup>7</sup> At the end of each assembly, all the beneficiaries participated in a lottery process through

> 5 The remaining $25 was to be paid on the next payment day (after survey completion).

> 6 The eligibility criteria were determined using the proxy means methodology developed for the RPS and based on the national household data from 2001 (EMNV). Additional discussions with local leaders from each intervention community were conducted to identify possible exclusion or inclusions errors. Based on this, the list of eligible households was finalized. Based on the discussions with leaders, 3.72% of all the households considered were re-assigned from non-eligible to eligible, and 3.65% from eligible to noneligible. To avoid any possible selection bias resulting from the re-assignment by the leaders, all estimates in this paper are intent-to treat estimates, using the intent-to-treat as defined by the proxy means methodology.

> 7 Only in the few cases that there was no adult female in the household, an adult man was selected as the program recipient.

7