rafmacalaba/datause-encoder-data huggingface dataset:
assistance. 2) Legacy-VA score: the humanitarian scoring model used before 2018 to predict household vulnerability and determine eligibility for CLCI cash assistance. 3) SEVAT score: the humanitarian scoring model used since 2019 to predict household vulnerability and determine eligibility for CLCI cash assistance. 4) PPMT-Legacy-VA score: a model which predicts eligibility for MOLSA programs based on the common variables from the Legacy-PMT scoring model. 5) PPMT-SEVAT: a model which predicts eligibility for MOLSA programs based on the common variables from the SEVAT scoring model. The first three scoring models are the actual models used to determine MOLSA and CLCI eligibility. The two pseudomodels are used to predict likely cross-eligibility when only partial scoring information is available in humanitarian databases.

**<u>Step 2. PMT scores correlation analysis:</u>** The second step is analyzing the correlation between the PMT scores. The correlation is done using Spearman's ranked correlation to see the relationships, whether positive or negative, between the calculated PMTs (the two CLCI-PMTs and PPMTs) and the MOLSA-PMT scores. We also constructed a kernel density distribution plot to examine the relationship among the PMTs further.

**<u>Step 3. Single eligibility calculation</u>** <u>: In step three, we identify households eligible to receive support under</u> each of the three main PMTs; MOLSA-PMT, Legacy VA, and SEVAT, and calculate their proportion against the total sample size. Eligible households are the households whose PMT score falls below a specific cutoff determined by the relevant agency. For the MOLSA-PMT, all households below the bottom 18 percent of the PMT score are considered eligible. The bottom 18 percent of the population is the national poverty line, and it implies that this population consumes below IQD110,000 per capita per month.  It should be noted that other cutoff levels, including bottom 15 percent, 20 percent, 25 percent, 30 percent, and 35 percent, are included in this paper for comparison. The Legacy-VA and SEVAT have three cutoffs that are synonymous with CLCI’s three levels of vulnerability and support. For example, households with scores below the first cutoff, 4.84, in the SEVAT in Table 2, implies they consume below 70,000 IQD per person per month, identify as the most vulnerable households, and qualify to receive three cash transfers. While those between the first and the second cutoff points are eligible for two transfers and that those between the second and third are eligible for one support. Households above the third cut-off are deemed nonvulnerable and do not qualify for any support.  The cutoffs and share of households eligible under each category are shown in Table 2.

**-** **<u>Step 4. Cross eligibility calculation:</u>** Cross-eligible households are the households who are eligible for humanitarian support (α) and also identified to be eligible for government assistance ( β ) under the full

12
was a shift from using short-term leases to using multi-year leases of water and permanent sales of water rights.

4. **Controversies with Reallocations** . In both developed and developing countries, reallocations of irrigation water are often controversial. In some cases, especially where water rights have been poorly defined and/or enforced, reallocations are carried out by administrative decisions and without properly consulting and/or compensating the agricultural water users. The resulting _political and social tensions_ tend to be in proportion to the political clout of the constituencies that may lose in the transfer—in particular the farmers and possibly also the surrounding communities (Molle and Berkoff, 2009). But even in regions where water rights are well defined and enforced, such as the western United States, reallocations (especially those involving larger amounts of irrigation water) have been controversial. It has been often questioned whether the reallocations are _economically feasible_ , i.e. whether the benefits resulting from these transactions are large enough to outweigh the associated costs (see, for example, U.S. National Research Council, 1992).

5. An underlying problem is that the benefits and costs associated with reallocations are usually not readily observable. In a market economy with perfect competition, direct impacts could be measured by the price for water, since the price would indicate the marginal (net) benefit gained or foregone in the respective uses. In the absence of a perfectly competitive market, when the prices are either distorted or not observable, the respective price needs to be estimated using different approaches. Estimates of the economic effects of irrigation water transfers to alternative uses have been presented in the literature since the 1960s—including estimates of direct benefits (DB) and, equivalently, foregone direct benefits (FDB) in irrigated agriculture, and also of potentially associated secondary and indirect benefits and foregone benefits. However, the assessments have shown relatively wide-ranging estimates of the effects, in particular with regard to how much worse off agricultural producers would be from irrigation water transfers (i.e., with regard to the magnitude of FDB). In part, the differences in estimates have been caused by the use of different methodologies (or estimation techniques); even when the same methodology has been used, differences in estimates may have been the result of different model specifications. Independent of the methodology and model specification used, previous estimates have almost exclusively focused on water delivery rather than consumption as measure of water quantity.

6. **Objectives of the Paper** . The paper intends to shed some light on the problem of assessing the direct economic effects of irrigation water reallocations to alternative uses—with a focus on FDB in irrigated agriculture. This includes: first, reviewing the methodologies and model specifications used in the literature for estimating FDB; second, illustrating of the impact of different model specification on 2
private schools gained significantly in the share of overall primary enrollment and their gains were across all provinces, across rural and urban areas and for both the rich and the poor. Nevertheless, towards the end of the 1990s, there were still large areas of the country, such as rural Balochistan and Sindh, where private schools had not made any inroads. This uneven spread of private schools allows us to better understand the causes of their spectacular growth as well as its limitations.

# **Section IV. The Private Schooling Story**

Seven figures and tables illustrate different facets of the private schooling story; together they present a landscape where (a) educational outcomes are closely linked to the presence of a private school and (b) the presence of a private school relies critically on the availability of educated women in the village. A key message emerges: where private schools arise, they are affordable and used by the poor, but private schools do not arise everywhere. Indeed, the same mechanism that ensures their affordability also constrains where they locate.

# <u>Schooling Outcomes in Villages with and without Private Schools</u>

The first table shows differences between villages with and without private schools in schooling outcomes and the relative use of private schools by the rich and the poor using matched data from the census of private schooling (PEIP, 2000) and the Pakistan integrated household survey (PIHS, 1998). We present results for the country as a whole (Column 1, Table 1) and the four main provinces separately (Columns 2-5, Table 1). Several features of this comparison are noteworthy.

As expected, private school enrollment is higher in villages with private schools, and in both villages with and without private schools the rich use private schools more than the poor (note that even in villages without private schools, some parents send their children to private schools outside the village). In all four provinces, the share of private enrollment is _twice as high_ in villages with private schools compared to those without: in Punjab, for instance, in villages with private schools, 23 percent of enrollment is in private schools compared to 11 percent in villages without. For the four main provinces, the share of private schooling among the rich is double that of the poor in villages with private schools, and almost four times as high in villages without. The pattern repeats across all four provinces, with the exception of Balochistan, where the size of the sample is too small for meaningful comparisons.

However, the use of private schools by the poor has as much to do with their availability as their costs conditional on location. Where there are private schools in the village, a sizeable fraction of 10
WPS3795 made. At one extreme are countries such as the U.S. that have a well-developed set of commercial laws that include blanket filings on accounts receivable and inventory (Article 9 of the Uniform Commercial Code) and well-defined electronic registration systems that temporally define lien filings. At the other extreme are countries with, at best, recently implemented commercial law that leaves collateral priority and notification ambiguous. Empirical evidence suggests that the legal efficacy of collateral – in particular, the ability of lenders to seize collateral in the event of default – is associated with increased use of collateral (Qian and Strahan 2005). Although progress is being made on this dimension, many of these countries – such as those in Eastern Europe – are still deficient in key areas such as the scope of assets that can be secured, registration and filing, priority, and enforcement (EBRD 2003).<sup>14</sup> The efficiency of the judicial and bankruptcy systems are also critical to credit availability. Recent theoretical and empirical research suggests that judicial inefficiency (i.e., high-cost judicial procedures) is associated with decreased access to credit (Jappelli, Pagano, and Bianco 2005). It has also been shown that countries with greater legal procedural formalism – typically associated with civil law countries – take longer to enforce some types of financial contracts (Djankov, La Porta, Lopez-de-Silanes, and Shleifer 2003). The Czech Republic offers an illustration of this problem. The cost of enforcing commercial claims was considerably higher in the Czech Republic than in five other transition economies that have joined the EU, the 15 non-accession EU countries, and the U.S. Pursuing claims in a bankruptcy environment is also quite difficult in this country (World Bank 2003).

The length of time in bankruptcy is a particularly important dimension of efficiency. Also important is the degree to which the application of bankruptcy procedures are associated with adherence to absolute priority. The power of collateral _ex ante_ ultimately depends on the whether the priority rights of secured lenders are upheld in bankruptcy _ex post_ . Details of the laws that are often missed in academic analyses can be extremely important. For example, the rights of secured lenders in the U.S. may at first seem relatively weak because an automatic stay is immediately invoked upon acceptance of a bankruptcy petition by the

> 14 In many of these countries, lenders cannot file a single lien on all currently existing and future accounts receivable and inventory and instead must identify by invoice number and serial number each receivable and inventory item as it is generated.  This effectively renders asset-based lending infeasible.

17
# **Main References**

Angrist, J, Bettinger, E, Bloom, E. King, E and Kremer, M (2000): “Vouchers for private schooling in Colombia: evidence from a randomized natural experiment”, The World Bank, ( _mimeo)_ Borjas, G and Acosta O L (2000): “Education Reform in Colombia”, Working Paper Series N.19-FEDESARROLLO, Colombia Carciofi, R, Cetrangolo, O and Larranaga, O (1996) _“Desafios de la descentralizacion de_ _<u>la educacion y la salud en Argentina y Chile”</u>_ , ECLAC – Santiago de Chile.

Di Gropello, E. (2002) “An assessment of the Impact of Decentralization on the Quality of Education in Chile”, in _<u>World Bank’s Economist’s Forum</u>_ <u>, Washington DC</u> Di Gropello, E. (1997) “Descentralización de la Educación en América Latina: Un Análisis Comparativo”, Serie Reformas de Política Publica N.57, ECLAC- Chile Di Gropello, E and Marshall, J (2004): “Teacher Effort and Schooling Outcomes in Rural Honduras”, The World Bank, _(mimeo)_ Eskeland, G., and Filmer, D. (2002) “Autonomy, Participation and Learning in Argentine Schools: Findings and their Implications for Decentralization”, The World Bank ( _mimeo_ ) Espinola, V (1994): “La descentralizacion de la educacion en Chile: continuidad y cambio de un proceso de modernizacion”, CIDE, Serie Gestion Escolar, N.2 Faguet, J.P. (2000) “Does Decentralization Increase Responsiveness to Locl Needs? Evidence from Bolivia”, The World Bank ( _mimeo_ ) Gauri, V (1996): “Market forces in the public secor: Chilean educational reform, 19801994”, Princeton University Ph.D Dissertation Gallego, F (2002): “Competencia y resultados educativos: teoria y evidencia para Chile”, in _<u>Cuadernos de Economia</u>_ <u>, Year 39, N.118, pp.309-352.</u> Gomez, A and Vargas, JE (1997): “La Descentralizacion de los Servicios de Educacion en Colombia”, Serie Reformas de Politicas Publicas N.50 - ECLAC, Chile Galiani, S. and Schargrodsky, E. (2002) “Evaluating the Impact of School Decentralization on Educational Quality”, in _<u>Economía</u>_ <u>, Vol.2, N.2, Journal of the Latin</u> American and Caribbean Economic Association.

Hsieh, CT and Urquiola, M (2002): “When schools compete, how do they compete ? An assessment of Chile’s nationwide school voucher program”, Princeton University, Cornell University Jiménez, E. and Sawada, Y. (1998) “Do community school managed schools work? An evaluation of El Salvador’s EDUCO Program”, in _<u>World Bank Economic Review</u>_ <u>, Vol.</u> 13, N.3, pp.415-441 32
**Table 1: Mean and standard deviation of Body Mass Index (BMI) by age group, KHDS and selected developing and industrialized countries**

(Standard deviations in parentheses)

|**Location**|**60-69**|**Age group**<br>**70-79**|**>=80**|
|---|---|---|---|
|**Men**||||
|**_Kagera, Tanzania (1991-94)_**|**_20.3_**_(3.0)_|**_19.3 (2.6)_**|**_18.1 (2.0)_**|
|Brazil (1989)|23.7 (5.4)|22.9 (5.0)|22.4 (4.1)|
|China|20.8 (3.0)|21.7 (3.9)|20.9 (2.6)|
|Guatemala (rural)|21.3 (2.6)|20.2 (2.2)|19.6 (2.3)|
|United States|26.4 (4.0)|25.6 (3.7)|24.6 (3.8)|
|**Women**||||
|**_Kagera, Tanzania (1991-94)_**|**_21.3 (4.3)_**|_19.7 (3.3)_|_19.7 (3.9)_|
|Brazil (1989)|25.8 (6.7)|25.0 (7.4)|23.9 (4.9)|
|China|21.7 (3.9)|20.7 (3.6)|19.6 (3.1)|
|Guatemala (rural)|22.4 (3.3)|21.4 (4.4)|20.7 (4.1)|
|United States|26.5 (5.3)|25.7 (4.9)|24.5 (5.0)|

Source: Kagera: authors' calculations on observations of the elderly from wave I of the KHDS; Other countries: WHO Expert Comrmittee (1995). The KHDS results describe the sample and are not weighted to be representative of Kagera region.

# **Independent variables**

The independent variables represent measures of adult deaths and arguments of the reduced form demand for health. The descriptive statistics in Table 2 are for two samples - the samnple of 586 adults over 50 the second tine they were observed and the 1512 "differenced" observations in adjacent periods, leaving from 1-3 observations on 695 adults over 50.10 '° We use the second observation on each individual for the single-wave estimnates because we believe that the accuracy of the reporting of the timing of the adilt death and classification of the individual as a household member is better during the follow-up interviews than at the baseline, when there was no concrete reference point like the previous visit of an interviewer. The differenced means are the mean of the difference between the value of each variable in time t and in time t- 1. With a maximum of four observations per individual, the maximum number of 'first differences' is three. The total number of observations on the 695 elderly before differencing was 2235. Since a difference requires two adjacent observations in time, those individuals seen only once drop out.

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provision of adult education; graduation time; and participation in international cooperation. To calculate the amount of performance funding, target figures are multiplied by field‐specific cost factor, agreed for a three year contract period (Holtta and Rekila, 2003).

The Netherlands provides 50% of university financing based upon output criterion. Each university’s grant is based upon the number of students who completed their programs multiplied by the normative study duration (4.5 years) plus the number of students who drop out multiplied by an administratively set study duration of these dropouts (1.35 years). The total amount of funding is then calculated by multiplying calculated graduation numbers by a fixed reimbursement per student with engineering receiving a higher amount per student compared to other programs (CPH and CHEPS, 2001).

Sweden specifies minimum number of students in general and in science and technology in particular for each institution to receive financing. 40% of the financing is based on enrollments and the remaining 60% based upon accumulated credits.

In the USA, most states provide a small amount of funding based upon performance. South Carolina is the only state where all university financing is based upon 37 specified indicators. The State of Tennessee provides 6% of university finances using four performance criteria: academic testing and accreditation with 60 % weight; satisfaction surveys with 15% weight; planning and collaboration receiving 10% weight and student outcomes in terms of retension, job placement and assessment implementation receiving 15% weight (Bogue, 2002).

# **Results‐Based Education Finance: Developing and Transition Country Examples**

Conditional non‐matching output‐based transfers to ensure national minimum standard in merit goods or for fiscal need compensation are rarely used in DTEs.

19
- 10 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

cannot be compared because of the lack of the sensor’s on-board calibration (Cao et al., 2016; Hsu et al., 2015; Pandey et al., 2013; Zhang & Seto, 2011). An empirical intercalibration was performed to enable the comparison of these images. We follow (Hsu et al., 2015) and used the same area as reference: Los Angeles metropolitan area. This area was selected for two reasons: it has for long been a mature metropolis and hence the light change is negligible; second, because of its size and the large variability of light intensity within its extent, it provides samples of high DN values in the city center, as well as low DN values in suburban areas. We use the 2000 image as reference, and estimate linear regression models and secondorder regression models. The second-order models showed only negligible improvements compared to the linear model, which is preferred for its simplicity (Table 1).
Nevertheless, although increased children‘s school enrollment and time in school are important inputs for the formation of human capital, they do not automatically translate into attainment of more education and improved learning outcomes. First, if the school supply remains fixed, schools may get congested due to the rise in enrollment, increasing teacher pupil ratios and overcrowding in the classroom. Second, the marginal children who are brought into school by the transfers and conditions of the program could have lower expected returns to school compared to those already enrolled since they may be, for instance, less motivated, come from lower socio-economic background, and have less capacity or time devoted to school work. Another plausible reason for limited effects on school attainment and performance is that CCTs are often geographically targeted to poor areas where the teaching and school quality may be relatively lower. Despite the substantial amount of work devoted to assessing the educational impacts of CCTs, little is known about their long-term effects on the stock of human capital, i.e. educational attainment and academic performance in early adulthood.

This paper seeks to help fill in this knowledge gap and identify the expected but empirically uncertain link between school participation and educational achievement through an evaluation of the long-term educational impacts of a Conditional Cash Transfer program in Colombia. More specifically, we investigate whether multiple cohorts of children who are covered by _Familias en Acción_ (FA) and who have different degrees of program exposure (ranging from one to nine years) complete more years of education –measured by the probability of completing high school– and perform better in a national standardized test at the end of high school. Although these are not necessarily final outcomes since they do not reflect the ultimate educational achievement, they are close determinants of human welfare and economic growth. In addition, we examine whether there is heterogeneity in program impacts by location (urban and rural) and by the gender of child. Finally, this paper explores possible indirect effects on the human capital of older children (18 years and older) who by the rules of the program are ineligible to the transfer but who reside in households with younger participant children.

Identification of program impacts is derived from two different empirical strategies that use a panel of household surveys, a census of the poor and administrative data from 3
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hours, others up to several generations. Gamb4;n f&rmers live in a credit economy, and most are perpetually involved in a complex web of debts and credits to relatives, neighbors, friends, and merchants.

Anyone involved in lending co smallholding farmers should understand, then, that farmers have their own personal hierarchies of creditors, and that the newest, most distant, and least familiar lenders rank at the bottom. (And seen from a 'target grotup", even big guns, when far enough away, become very small.) This understanding puts into perspective pejorative judgments about *moral hazards' and "delinquency **w** here farmers fall into arrears or default on institutional loans.

# Other Basic Principles

West African farmers are usually not just farmers. Multiple occupations (sometimes intermittent and often missed in questionnaire surveys) cut subsistence risks, even out income through the year, and put family labor to beneficial use. Real rural people do not live in 'sectors", and these notions, as embodied for instance in the divisions between corridors of large international aid agencies, are not as real or important to Gambian farmers as to some development planners from northern countries. Saved and borrowed resources are exchangeable and substitutable (to economists, "fungible") between agriculture, for instance, and housing, trade, health, or education, in ways lenders cannot easily control and should probably not attempt to control.

The social divicions common to the several larbest Gambian (and southern Senegalese) ethnic groups include not just nucle-ted villages with elected headmen, but also patrilineages and divisions into cross-cutting castes, classes,