rafmacalaba/datause-encoder-data huggingface dataset:
might have been instrumental the in staffing of private primary schools and, thereby contributing to the growth of the private sector.

In the PESRP program document<sup>6</sup> , it notes that: a) female middle school enrollment rate increased from 43% (baseline 2003) to 53% in 2005, b) share of female enrollment in government primary and middle school increased from 45% in 2003 to 50% in 2005, and c) female dropout rates between grade 5 and 6 decreased by 25%, and that in middle school decreased by 20%. While these performance indicators point towards an increase in enrollments and decrease in dropouts, none of them specify the intended net impact in a counterfactual framework (in program districts relative to non-program districts, or in girls schools relative to boys schools). Furthermore, given that post and pre reform school census data forms the basis of these figures (rather than household-based data), combined with the fact that school census data do not have individual student identifiers, the dropout rates should be taken with several grains of salt, at best.

Given that PERSP covers the entire province, evaluating the overall impact of the reform on enrollments becomes quite difficult in the absence of a suitable counterfactual. The State Bank of Pakistan (2006)<sup>7</sup> , SBP, recently conducted an impact analysis of the Punjab Education Reform program using the entire province as the treated unit and the other three provinces of Pakistan as counterfactuals. The analysis is based on a difference-in-difference estimator of progress in gross and net enrollment indicators reported in the Pakistan Social and Living Standards Measurement survey (2004-05). They conclude that the program contributed significantly to improving gross and net enrollments and that suggest that extension of similar program activities elsewhere in the country would substantially improve the country’s education access outcomes. Because SBP analysis attempts to evaluate the overall PERSP program and not any specific intervention such as the stipends program, it is difficult to attribute its usefulness to the impact analysis of girls’ stipends program. While their use of relevant data and technique is a noteworthy start, the choice of other provinces as counterfactuals (controls) is problematic because of significant social, political, and institutional differences between Punjab and other provinces. Moreover, their analysis does not include schooling outcome indicators for middle school grades– the relevant indicator for female stipends program. Precise estimates (proper statistical inference such as including standard errors) of these net impacts are missing in their analyses.

> 6 _Annex B_ of Program Document for Education Sector Adjustment Credit for the Government of Punjab Province, January 8, 2004.

> 7 State Bank of Pakistan (2006), Special Section: Impact Analysis of Punjab Education Sector Reforms in _First Quarterly Report for FY06, The State of Pakistan’s Economy_ .

5
The differences in educational attainment between the poor and the rich lead to much higher differences in incomes due to the convexity of returns to education, as illustrated in Figure 11. This Figure shows, indeed, that the returns to education increase significantly after finishing secondary school –a condition rarely met by children from lower quintile households. This fact not only helps explain the strong persistence of high income inequality given the low observed educational mobility, but actually –in conjunction with the presence of credit constraints- contributes to explain the low educational mobility itself. Poor parents, who face the need to keep children in school for an extended period of time to actually reap the benefits of their investment in education, also face very high opportunity costs from income forgone from children work, specially during periods of adverse income shocks. The high desertion rates observed among children from poor households should hence not come as a surprise. Actually, this is the main economic rationale of the Conditional Cash Transfers programs that have become so popular in many countries of Latin America in recent years. By lifting the credit constraint, such transfers help effectively reduce the opportunity cost of keeping children in school for poor households that enter the program.

To complicate matters further, Figure 11 shows that educational returns for the children of the poor are lower than for children of the rich. This maybe due to a variety of factors: lower quality of schools, lower availability of assets that are complementary to education in income generation (land, public infrastructure, credit), discrimination in labor markets and unobservable factors (linked to differential access to pre schooling facilities, in nutrition levels, etc). Such differential (and probably more uncertain) returns also help explain the high difference in educational outcomes among children of the poor and the rich, adding to the complexity of the problem and of its solutions. Of course, in many countries in the region there are in addition severe limitations in the supply of schools and teachers in poor neighborhoods, especially in rural areas.

**Figure 11. Nicaragua. Returns to education by level.**

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values<sup>8</sup> . The second one is the correlation between the diaspora size and the error term, due to the presence of some unobservable bilateral components that a¤ect both the size of the diaspora and migration ‡ows. One important aspect of the whole analysis is the robustness of the main results to alternative estimation techniques.

# 4.1 Size

The ...rst question we ask is on determinants of migration ‡ows and the role of the diaspora size. In equation (4), the dependent variable is ln [Nij(h)], i.e. the log of the migration ‡ow between 1990 and 2000 from country i to country j of individuals with skill level h. We proxy it by taking the di¤erence of the migration stocks observed in 1990 and 2000.

Among the main determinants of migration ‡ows in equation (4) are the wage differential (speci...c to each skill level), migration costs and the factors in‡uencing visa costs and other legal barriers. In Appendix B, we report the data sources and the way we construct measure the explanatory variables that proxy determinants of migration ‡ows. We have good estimates for skill prices in destination countries (wj) but fairly imprecise data on wages at origin (wi) in order to construct the wage di¤erential variable (wj � wi). One way of resolving this problem is to include origin country dummies �i that capture the combined e¤ect of all unobserved characteristics of the origin country i on the migration ‡ow to country j. These origin country dummies also capture the role of stock of residents with education level h (ln [Nii(h)]) as well all migration costs speci...c to the origin country (xi) in equation (4). Pair-speci...c factors in‡uencing migration costs are captured by geographical distance between the two countries, colonial links (a dummy variable) and linguistic proximity. We also introduce a dummy variable indicating whether the two countries are subject to the Schengen agreement favouring the mobility of persons within the European Community. The set Aj includes destination-speci...c variables that a¤ect the attractiveness of country j in terms of migration such as population sizea and social expenditures as a share of GDP (as a measure of the extent of social welfare). The proxy for selective immigration policies is measured by the share of refugees in immigrants admitted in 1990 by country j. Finally, we capture diaspora e¤ects by size of the diaspora in 1990 and denoted by the variable Mi;j: It should be clear that the estimated impact of Mi;j in the estimation is a combined e¤ect through Cij (network e¤ects that lower migration costs) and the impact on Vij (family reuni...cation e¤ects that lower visa costs).

Introducing these variables, we get a ...rst speci...cation for the migration ‡ow with observable destination speci...c variables: 8 Some reported zeros might not re‡ect the actual absence of migrants. Due to con...dentiality and disclosure rules, some national statistics o¢ ces report zero when the diaspora size is below a threshold level. We are not able to distinguish these cases from "true" zeros.

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increasing the range of households who remain in autarky, again likely because of more limited credit supply.

# **5. Conclusions and policy implication**

An emerging literature has identified high levels of land holding fragmentation as an important constraint to higher productivity in Eastern European countries that radically restructured their agricultural sector. While consistent with _prima facie_ evidence, detailed study to assess productivity impacts as well as the channels through which they materialize has been limited. This makes it difficult to derive more specific policy recommendations regarding conditions that need to be in place for consolidation to be costeffective and sustainable, as well as the magnitude of expected benefits which would in turn be relevant for justifiable levels of program costs and performance indicators.

Although it is not based on a special survey explicitly designed for this purpose, our analysis allows better understanding of constraints to higher rural productivity. We find little effect of fragmentation on productivity of currently cultivated land. At the same time, with some 10% of potentially productive land lying idle, patterns of land holding affect economic outcomes. One-off consolidation programs will be appropriate if a structure with many uneconomically small plots is an impediment to productive land use and land market operation. However, if other barriers prevent land markets from transferring land to more efficient producers, such programs may be ineffective and/or unsustainable. In the case of Albania, 99% of the area where land remains uncultivated can be attributed to ill-functioning land markets rather than reasons that would be amenable to systematic consolidation efforts. While the generic nature of the LSMS data limits the ability to explore obstacles to land market functioning in more detail, it allows formulation of hypotheses and identification of proxy indicators that could be used to test these. Having data on transactions in markets (land type, intensity of use, potential productivity, contract terms, level of formality, length, price, transaction cost incurred etc.) would allow for doing so easily and at modest cost.

Land fragmentation and abandonment in the context of apparent economic decline and ageing of rural areas are key issues for policy makers in most countries of the region. Vast cross-country differences in farm structure, productivity, and history caution against premature generalization of the results obtained here. Still, the main insight that in a setting where large land areas remain unutilized or have been abandoned consolidation may only be part of the answer that needs to be viewed in a broader context of potential institutional and land governance impediments to land market operation, is of broader applicability. Such a focus will be important not only to ensure economic attractiveness and viability of rural areas, but also to provide the basis for supply to respond to broader changes in commodity prices.

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# **2. The proposed measure and its antecedents in the literature**

The simplest way one might measure the capacity for redistribution is by normalizing the aggregate poverty gap—the sum of all income shortfalls from the poverty line per capita—by the overall mean income. This has long been interpreted as a measure of a “country’s _potential_ ability to meet the challenge of poverty” (Sen, 1981, p.190).<sup>8</sup> A variation on this measure is instead to normalize the poverty gap by the aggregate income of the non-poor defined as those living above that poverty line; the aggregate income can be either gross income or income net of the poverty line. This can be interpreted as the tax that would be needed on the non-poor to cover the poverty gap. The earliest examples I know of are Anand (1977) and Kawkani (1977) both using survey data for Malaysia in 1970. Anand (1977, p.11) reported that “..if poverty were to be eliminated by a transfer from the non-poor to the poor, the non-poor would need to sacrifice 8.3 percent of their income (or 12.7 percent of their income in excess of poverty line income).” However, when we confront the problem of how an aid donor might assess a developing county’s capacity for redistribution, one can question whether these past measures use an appropriate normalization. Aid donors will surely not be indifferent to how incomes are distributed above the poverty line when assessing the capacity for redistribution to the poor. It is clearly not acceptable to say that a country has a high capacity if (given its income distribution) redistribution would require putting almost all the tax burden on people living just above the poverty line. Some countries have a greater affluence—with more people living well above the poverty line—than others, and this must be brought into the picture.

The desirability of financing assistance for the poorest by taxing the middle class is questionable on both intrinsic and instrumental grounds. In assessing a poor country’s capacity for redistribution it can be argued that citizens of a rich country would find it intrinsically (ethically or politically) unacceptable to expect a poor country to address its poverty problem by taxing people who would be considered poor in the rich country. Popular judgments about “inequality” appear to give greater weight to redistributions from the rich to the poor than redistributions amongst the middle class or from the middle class to the poor.<sup>9</sup> The instrumental arguments point to the expected role of the middle class as agents of progress and also the likely

> 8 Also see the discussion in Kanbur (1987).

> 9 Amiel and Cowell (1999, Chapter 4) study attitudes to redistribution in a series of experiments, in which people were asked to say which of two hypothetical distributions was more “unequal,” and this was repeated for various redistributions. Respondents were found to be much more likely to identify redistributions from one income level to a higher one as making the distribution “more unequal” as the income gap between the two levels rose.

4
monthly level. We focus on maize prices, because maize is the predominant crop (grown by approximately 96 percent of rural households) in Malawi. The IHS3 panel and IHPS households are geo-referenced allowing us to map households to food prices found at the closest markets. In this way, we can measure changes in maize prices that households faced in the 12 months leading up to their survey date.

# _2.2 Context_

Malawi is among the poorest countries in the world, with limited resources and an economy relying heavily on single-crop maize agriculture. Maize is by far the most important staple crop, both in terms of the number of farm households cultivating this crop and in terms of total area harvested in the country (Dabalen and others, 2017). Maize also accounts for more than 50 percent of the daily calorie intake in Malawi (Minot, 2010).

The country’s population is estimated to be 17.6 million in 2015, and about 80 percent of the people live in rural areas. GNI per capita is US$ 350 as of 2015<sup>11</sup> and 71 percent of the people are estimated to live on less than US$ 1.90 per day (2011 PPP prices) as of 2010.<sup>12</sup> Malawi is relatively small in land size and densely populated. This, combined with a high population growth rate, has created significant pressure on available land for smallholder farming and on the environment and natural resource base.

Such precarious conditions are compounded by the country’s high and recurrent exposure to drought, floods and food price shocks. Compared to other countries in SubSaharan Africa, food prices in Malawi are highly variable and subject to spikes, with maize

> 11 This estimate is from the World Bank’s World Development Indicators online database (databank.worldbank.org/WDI), and is expressed in nominal 2015 US$, converted from local currency using the Atlas method (based on market exchange rates).

> 12 This estimate is from PovcalNet, an online tool provided by the World Bank for global poverty measurement, see http://iresearch.worldbank.org/PovcalNet.

6
# **2.2 The ENLACE Panel**

ENLACE created a unique personal identifier ( _Clave Única de Registro Poblacional_ or CURP) for all test takers, enabling the construction of a panel of students with learning outcomes at different points in their education trajectory. In addition to these outcomes, the ENLACE dataset included a school identifier, socioeconomic information for each school based on its geographical location,<sup>2</sup> and age and sex of the student. Using the CURP, we merge the information from all Grade 6 students who took the exam in 2007 with their results in the 2010 (Grade 9) and 2013 (Grade 12) exams. We begin the panel in 2007 because of the relatively low take-up in 2006 and the lack of availability of the unique identifier in some states during that year. Among the 1,986,190 students who sat for the exam in 2007, we were able to identify 71 percent three years later in ENLACE Grade 9 of 2010 and 32 percent in the Grade 12 exam in 2013 (Figure 2).

The large attrition observed in the panel is caused by (1) grade repetition, (2) school drop-out, (3) exam take-up rates of less than 100 percent and (4) imperfect matching due to administrative data or coding errors. If a large share of the attrition in the panel is caused by a low test take-up or imperfect matching, we would not be able to identify accurately the effects of Grade 6 test scores on lower- and upper-secondary on-time graduation rates. To quantify the magnitude of the causes behind attrition, we use administrative data from the annual school census ( _Formato 911_ ) to estimate the expected survival rates given state-level repetition and drop-out rates in lower and uppersecondary. Given the school trajectories implied by administrative data, 77 percent of the student population that completed Grade 6 in 2007 was expected to finish Grade 9 in 2010 and 39 percent to graduate from upper secondary in 2013 (Figure 2). Therefore, our ENLACE panel has a survival rate that is 6 and 7 percentage points lower vis-a-vis the survival rate implied by administrative data, in lower- and upper-secondary, respectively. This difference is the result of both less than 100 percent test take-up and imperfect

> 2The National Population Council ( _Consejo Nacional de Población_ , CONAPO) ranks all of Mexico’s localities (an administrative and/or geographic entity according to a marginality index, a weighted average of literacy, access to basic public utilities, household infrastructure and average wages. Rankings range from very high marginalization to high marginalization, medium marginalization, low marginalization, and very low marginalization. For methodological details regarding Mexico’s marginality index, see www.conapo.gob.mx.

8
_If a temporary Community Committee can be immediatelyformedfrom among these leaders, this would be an excellent start, as the objective is to have all community activities planned and coordinated by a Community Council comprised of representatives from existing community groups with co-opted members from amongst the citizens._ _In any case, with the help of local leaders, a Social Survey should be undertaken which will provide salient facts about social and economic conditions, and existing community structures. Based on this, decisions will be taken about what can be done to encourage and to extend existing community organisations, and to help in arranging trainingfor their leaders and in planning their programme along concrete lines, including the formation ofpractical projects._ These sixty year old guidelines seem to have had a clear influence in the manner in which the Jamaican Social Investment Fund (JSIF) currently operates. JSIF is an autonomous govemment agency that reports directly to the Ministry of Finance. It solicits proposals from communities by widely disseminating information via radio and television. NGOs, community based organizations (CBOs), and central govemment agencies facilitate the applications by helping key actors organize the community to decide on and apply for a project. These NGO, CBOs and key actors within conmmunities often have a past association with the Jamaica Welfare Society and follow similar procedures. The proposal usually includes a social appraisal outlining the problems faced by the community, how this intersects with social and economic constraints within the community, and the role that a sub-project can play in improving living standards. JSIF screens the applications on the basis of its target criteria which mandate a focus on the poorest communities, and then undertakes a series of field visits to identify through research (e.g: PPAs, animation, sample surveys) a realistic sense of the needs of the community.

Like other Social Funds, communities are usually required to make a contribution to construction costs either in cash or in kind. Projects are supposed to be decided in broad consultation with the community and must fall within a menu of projects that JSIF will support. There are however, notable exceptions to this menu-based restriction as we will demonstrate below. The projects are generally executed by contractors employed by JSIF, and then managed by the appropriate ministry (e.g. Education, Health). It is expected that the conmnunity, having a greater sense of ownership of the project, will participate in its maintenance and management thus improving project sustainability.

Having seen a bit of the history and background of CDD in Jamaica and looked at JSIF's guidelines, let us now turn to an examination of how these guidelines work in practice. **2. Oualitative Analysis**<sup>**3**</sup> 3 The names of three of the five communities have been changed. Two communities, Port Royal and Arnett Gardens, are extremely well known and difficult to disguise. Furthermore, understanding the impact of JSIF project in them