rafmacalaba/datause-encoder-data huggingface dataset:
21 32. Kwon S. Pharmaceutical reform and physician strikes in Korea: separation of drug prescribing and dispensing. _Soc Sci Med_ 2003; **57** (3) **:** 529-38.

33. Stewart-Brown S, Surender R, Bradlow J, Coulter A, Doll H. The effects of fundholding in general practice on prescribing habits three years after introduction of the scheme. _Bmj_ 1995; **311** (7019) **:** 1543-7.

34. Maynard A, Bloor K. Dilemmas in regulation of the market for pharmaceuticals. _Health Aff (Millwood)_ 2003; **22** (3) **:** 31-41.

35. Lu J-fR, Leung GM, Kwon S, Tin KY, Van Doorslaer E, O'Donnell O. Horizontal equity in health care utilization: evidence from three high-income Asian economies, 2005.

36. Masseria C, van Doorslaer E. Income-Related Inequality in the Use of Medical Care in 21 OECD Countries. Paris: OECD, 2004.

37. Wagstaff A, van Doorslaer E, Paci P. Equity in the finance and delivery of health care: Some tentative cross-country comparisons. _Oxford Review of Economic Policy_ 1989; **5:** 89-112.
stunting and underweight of children using predicted data.

When new data is needed, it may be tempting to purposefully only collect the covariates _x_ , which are used to predict the outcome variable of interest _y_ , and to scale down the collection of _y_ itself, particularly if this yields a significant financial cost reduction. It is not uncommon that predictors of household welfare such as demographic and dwelling characteristics, education, employment, and asset ownership, can indeed be collected relatively inexpensively. In health sciences, simple oral questions and anthropometric data taken by a simple non-invasive device too may serve as a predictor of the outcome that is expensive to measure. Collecting _y_ for a sub-sample only, and the covariates _x_ for all, is referred to as “double-sampling”,<sup>3</sup> see e.g. Hidiroglou (2001).<sup>4</sup> The advantage of this approach is that the prediction model can be estimated with data for the relevant population and time period (the same population for which the covariate data is available). The alternative where the model is estimated to data from an entirely different dataset that may describe a different population at a different point in time is referred to as “non-nested double sampling”, in which case one will have to make assumptions about how the model has evolved between the two different datasets (or assume that the model is invariant), see e.g. Kim and Rao (2012) as well as Douidich et al. (2015) for an empirical application to poverty measurement. We will refer to any estimator that works with predicted data (i.e. imputations of _y_ ) as a prediction estimator.

There is a considerable practical interest in adopting a double-sampling approach and relying on predicted data in the hope of reducing financial costs while preserving a reasonable degree of statistical precision. Consider for example the recent initiative by the World Bank under the name of SWIFT (Survey of Well-being via Instant and Frequent Tracking) which “does not collect direct income or consumption data which can be both time-consuming and vulnerable to error without the right know-how and resources; instead, it collects poverty correlates, such as household size, ownership of assets or education levels, and then converts them

> 3It is also known as “two-phase sampling”.

> 4The literature on double sampling dates back to Neyman (1938) and Bose (1943). Many of the existing studies, including Hidiroglou (2001), Kim et al. (2006), Palmgren (1987), Rao and Sitter (1995), and Sitter (1997), provide analytical or simulation results on the properties of the estimators based on double sampling. While there are some explicit empirical applications of double sampling such as Tamhane (1978), Hansen and Tepping (1990), and Armstrong et al. (1993), the use of double sampling appears to be comparatively limited. Even fewer studies take into account the costs of data collection (Cochran (1977), Davidov and Haitovsky (2000), S ̈arndal et al. (2003), and Fujii and van der Weide (2013)), despite the fact that one major attraction of double sampling is the reduction in data collection cost.

3
# Table 7: Program Impact on Cognitive Abilities: WISC and Raven Tests

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School Meals|0.222|-0.083|0.512|0.134|0.04|0.257|0.561|-0.807|1.242|
||[1.066]|[1.062]|[1.137]|[1.095]|[1.067]|[1.160]|[1.201]|[1.253]|[1.423]|
|Dry Rations|-0.132|n.a.|-0.045|-0.2|n.a.|-0.179|0.086|n.a.|0.478|
||[1.222]|n.a.|[1.233]|[1.241]|n.a.|[1.258]|[1.455]|n.a.|[1.525]|
|Girl|-0.046|||-0.041|||-0.13|||
||[0.217]|||[0.239]|||[0.518]|||
|Constant|7.065|6.341|7.458|6.831|5.937|7.317|12.293|12.007|11.885|
||[1.544]***|[2.079]***|[1.545]***|[1.594]***|[2.092]***|[1.582]***|[1.844]***|[2.659]***|[2.020]***|
|Observations|2651|1057|1594|2078|841|1237|573|216|357|
|R-squared|0.43|0.49|0.4|0.42|0.49|0.39|0.41|0.45|0.41|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1%

Dependent variable is number of correct answers of to WISC and Raven colored progressive matrices.

Regressions also control for child age, orphanhood, parents education, ethnicity, religion and household assets (not reported). Village fixed effects are not included since the dependent variable is measured only during the follow up.

30
would follow. A locally-based limited partnership might therefore suffice to raise the

# requisite capital.

New England, the center of American textle manufactring and hence of Ameican industry in the early 19th centuy, illustates the point. The growing number of small industrial towns provided a fertle field for short-haul railroads.<sup>14</sup> New England was also the center of American commerce, shipping, and whaling. Important Boston traders specialized in the China trade, through which they learned how to use entrepreneurid **a** l nd manageial tecnques to overcome long spans of tie and distance.'<sup>5</sup> Much New England railway finance was raised in the same way that the region financed its textile mill, namely by relying on family, fiends, and personal contacts. Where contract enfo was problematic and information was diffilt to verify independenly, the markets made beavy use of such links.' Friends and associates would vest their confidence in iddual finaniers with reputations for honest dealing who signalled tieir commitment by putting their own funds at risk?<sup>7</sup> The danger of looting by fly-by-night opators was corepondingly reduced.

The great majorty of early American railway shares were subscribed by relaively sphisticated local manufcturs, farmers, landowners, bankers, merchants, and contractors. Not only did these indivduals have superor access to information, but they stood to benefit

> **14** Chandlr (1954, **p.25S).** Johnson and Supple (1967, p.30).

16 For detais, ae Lamuxea (1986).

> **17** Raidis (1988, p.<sup>2</sup> 10). As Jobnon **mnd** Supple (1967, p.338) put **it,** *investurt **tened** to **be** a cumulative al process in **ma** enviroment **lading** an impesonal **national** _nwcyn_ **_m -ke_**

11
- 26 -

<u>Table8. Multiple criteria in Swedish beauty contest for 3G licenses</u>

|||1st stage<br>|Swedish go|vernment|evaluation<br>2nd s<br>|tage||
|---|---|---|---|---|---|---|---|
|Bidder name|Cr|iteria|Decision||Criteria||Decision|
||Number<br>of<br>stations|Invest-<br>ment<br>(SEK bil.)||Area<br>coverage<br>(km2)|Population<br>coverage<br>(%)|Roll-out<br>speed||
|Broadwave communications AB|4,700|14.7||32,750|81|09/2002||
|Europolitan AB|20,000|27.5|Qualified|165,259|100|01/2001|Awarded|
|HI3G Access AB|20,814|36.9|Qualified|224,724|100|01/2002|Awarded|
|Mobility4Sweden AB|8,760|15.3||395,520|100|01/2002||
|Orange Sweden AB|8,635|19.7|Qualified|364,528|100|08/2001|Awarded|
|Reach Out Mobile AB|5,238|15.8||259,944|100|04/2001||
|Tele 2 AB|10,186|17.7|Qualified|112,666|100|10/2001|Awarded|
|Telenordia Mobil AB|7,200|14.0|Qualified|181,346|98|01/2002||
|Telia AB|4,100|6.8||308,661|100|01/2002||
|Tenora networks AB|7,550|11.2||290,038|100|01/2002||

Source: Andersson _et al_ . (2005).

# **V. EX POST ADJUSTMENT OR HARD INCENTIVE CONTRACT?**

Infrastructure projects require long-term commitment from owners and operators, regardless of whether public or private. In addition, they are technically complex and politically sensitive. Therefore, the circumstances surrounding the projects are likely to change over time, and it usually comes about that some unforeseen events would emerge almost unavoidably.

Traditional incentive theory involving multidimensional tasks (e.g., cost reduction and quality requirements) tells that if governments can observe some proxies of the quality of public tasks delivered before purchasing, high-powered incentives, such as fixed-payment contracts, can be compatible with quality enhancement efforts (Laffont and Tirole, 1993). This is analogous to general search goods, for which buyers can use the level of sales as a proxy of product quality, even though quality per se is unverifiable prior to purchasing. In the case of experience goods, of which quality can be observed only upon consumption, this is not possible; governments are confronted with difficulties in achieving two conflicting tasks, cost and quality, by a single policy instrument, i.e., the cost-reimbursement rule. When quality is a matter of serious concern, the optimal contract would tend toward low-powered incentives, i.e., ex post cost-plus arrangements.
were carried out by applying the MEGAS methodology<sup>42</sup> to determine national expenditures in HIV/AIDS. The last study was done with expenditure estimates obtained from a short sample of IPSs, EPSs, DTSs and international donors. Apparently, the reason why the MEGAS methodology was not used for the last study was because it would have required a longer period of time to apply—and the elaboration of the UNGAS 2010 report was urgent.<sup>43</sup> Unlike the PEN budget analyzed in the previous section that was disaggregated into 11 activity categories, the three expenditure studies were disaggregated into eight categories. These were: (i) prevention; (ii) care and treatment; (iii) orphans and vulnerable children; (iv) program management and administration strengthening; (v) human resources; (vi) social protection and social services (excluding orphans and vulnerable children); (vii) enabling environment; and (viii) research. To start with, this discrepancy in categorization makes the assessment of whether programs and services have ultimately been funded as planned extremely difficult.

There were further discrepancies in the data published by the three expenditure studies. For example, the total expenditures of the national response to HIV/AIDS for the year 2007 was 254.454.112.740 COP according to the 2008 report, but 182.849.521.500 COP according to the 2011 report. These discrepancies were present throughout all of the disaggregated figures that were published (e.g., by expenditure category; nature of financing; fraction of public expenditures by territorial level) in all three documents. As suggested by informants, a possible explanation for these discrepancies lies in the fact that each study applied a different methodology – as explained above.

Because of the problems associated with those discrepancies, the Study focused on years 2008 and 2009, which were also central years of the PEN life. Table 4 below shows HIV/AIDS expenditures for 2008 and 2009 disaggregated by expenditure categories:

> 42 MEGAS (Medición del gasto nacional relativo al SIDA) is a tool designed by UNAIDS that provides indicators of the financial country response to AIDS, and supports the monitoring of resource mobilization. Please see UNAIDS: http://www.unaids.org/es/dataanalysis/tools/nasapublications/ 43 MPS (2011) Informe UNGASS – 2010. Seguimiento de la Declaración de Compromiso sobre el VIH/SIDA, Bogotá: MPS.

40
# Suggestions for Further Research

It is not intended that this report convey an alarmist message. Nevertheless, it appears that a dangerously high level of pesticide resistance has become a feature of AP cotton and worse, appears to be spreading to other parts of India. This implies that, to manage resistance at below economically damaging levels, resistance management programs should be designed and implemented on an urgent basis.

In order to do this efficiently, we need to improve our understanding of farming systems, especially the interlinkages between crop protection and pest management. In the process of presenting a preliminary empirical analysis of the problem for Guntur cotton, this report has also highlighted several areas where knowledge is limited and needs to be augmented and where it is almost non-existent and new research needs to be initiated. In the following paragraphs, we shall mention the areas requiring research on a high priority basis.

First, resources should be devoted to e..tending the resistance monitoring program (as of now it is largely confined to ICRISAT and the Lam farm at Guntur) so that there is round-the-year monitoring for different crops and different areas. This should enable us to estimate pest population equations, migration equations, etc. and to isolate the most important determining factors for each. More extensive monitoring studies will also help in getting a better idea of the migration patterns and consequently of the affected areas which are distant from Guntur. The aim would be to analyse 101
<!-- Start of picture text --> 1000 18.00<br>900800 | 14.0016.00<br>3 12.00<br>&8 600 | 2g 10.00<br>S500 Fi<br>8 400 a<br>a 300 6.00 r] = a<br>200 | 4.00 Py =<br>-0 [ | .0.00 i | i l i| =<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 re<br>Decil Decil<br>m Benef. In-patient attendance Benef. Out-patient attendance = Benef. Communitary health m Benet. In-patient attendance Benef. Out-patient attendance = Benef. Communitary health<br><!-- End of picture text -->

<!-- Start of picture text --> 900 30.00<br>800<br>;00 25.00<br>500¥8 600 22 20.00<br><48.g&& 400300 a§ég 10.0015.00 |<br>5.00<br>0 | | | | | 0.00 a a = |lf FSF<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decil Decil<br>m Pre-school attendance Primary school attendance _mTM Secondary school attendance m Pre-school attendance Primary school attendance _TM Secondary school attendance<br>w Tertiary attendance m Special education attendance w Tectiary attendance m Special education attendance<br><!-- End of picture text -->

<!-- Start of picture text --> 1600 50.00<br>1400 45.00<br>40.00<br>1200<br>Pa 35.00<br>3 1000 5<br>aQ8&SoC g%S95§ 30.0025.00<br>2 oo & 20.00<br>4 15.00<br>400<br>10.00<br>1 2 3 + 5 6 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decil Decil<br>WAM health attendance = All education attendance WAM health attendance = All education attendance<br><!-- End of picture text -->
Table 9: Names of mineral commodities and their SITC codes by countries of production

|**SITC code**|**Minerals names**|**Price data**|**List of production countries**|
|---|---|---|---|
|120|Helium|Yes|Algeria|
|264|Garnet (industrial)|Yes|Kenya|
|273|Stones (crushed, dimension), limestone,<br>gypsum, marble, and silicon|Yes|Algeria, Angola, Ethiopia, Madagascar, Malawi, Mauritania,<br>Mozambique, Rwanda, South Africa, Sudan, Tunisia, Uganda, and<br>Zambia|
|274|Sulfur|Yes|Ethiopia, Kenya, Malawi, Namibia, South Africa, Zambia, and<br>Zimbabwe|
|277|Diamond|Yes|Angola, Botswana, Cameroon, Liberia, Namibia, Sierra Leone, South<br>Africa, Tanzania, and Zimbabwe|
|278|Barite, bentonite, clay, dolomite, fluorspar,<br>graphite, salt, vermiculite, fluorine, and<br>graphite|Yes|Algeria, Botswana, Chad, Djibouti, Ethiopia, Kenya, Madagascar,<br>Morocco, Mozambique, Namibia, South Africa, Tanzania, Tunisia,<br>Uganda, and Zimbabwe|
|281|Iron ore|Yes|Sierra Leone, South Africa, Tunisia, Zambia, and Zimbabwe|
|283|Copper|Yes|Algeria, Democratic Republic of Congo, Mauritania, Morocco, Namibia,<br>Nigeria, Republic of Congo, South Africa, Zambia, and Zimbabwe|
|287|Cobalt, chromium, niobium (columbium),<br>tantalum, titanium, zirconium, and tungsten|Yes|Botswana, Burundi, Ethiopia, Gambia, Madagascar, Mozambique,<br>Nigeria, Rwanda, Sierra Leone, South Africa, Uganda, and Zimbabwe|
|522|Arsenic trioxide, lithium, manganese,<br>pyrophyllite, soda ash, sodium silicate, and<br>wollastonite|Yes|Chad, Cote d'Ivoire, Kenya, Morocco, Namibia, South Africa, Zambia,<br>and Zimbabwe|
|523|Phosphate rock, and phosphoric acid|Yes|Malawi, South Africa, Tanzania, Togo, and Tunisia|
|661|Cement|Yes|Algeria, Angola, Benin, Burundi, Cameroon, Chad, Democratic<br>Republic of Congo, Ethiopia, Gabon, Kenya, Liberia, Malawi,<br>Mauritania, Morocco, Mozambique, Niger, Nigeria, Republic of Congo,<br>Senegal, Sierra Leone, Somalia, South Africa, Sudan, Tanzania, Togo,<br>Tunisia, and Zimbabwe|

35
WPS4121 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 10</mark>

# **The Impact of Private Provision of Public Education: Empirical Evidence from Bogotá’s Concession Schools**

**Felipe Barrera-Osorio**<sup>**1**</sup>

**_Abstract._** In 1999, the city of Bogotá, Colombia launched the Concession School program designed to broaden the coverage and quality of basic education. It consists of a contract between a group of private schools and the public educational system such that private agents provide education for low-income students.  This paper tests three main hypotheses concerning the impact of concessions on the quality of education: first, dropout rates are lower in concession schools than in similar public schools; second, other public schools nearby the concession schools have lower dropout rates in comparison with other public schools outside the area of influence; third, test scores from concession schools are higher than scores in similar public schools. The paper presents evidence in favor of the three hypotheses, using propensity score and matching estimators.

# World Bank Policy Research Working Paper 4121, February 2006

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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._ 1 Formerly Deputy Director of Fedesarrollo (Colombia); now Senior Education Economist, HDNED, World Bank. The World Bank provided financial support for this project. The Secretaria de Educación del Distrito provided generous help and data. I would like to thank the National Planning Department (Departamento Nacional de Planeaion, DNP) for providing some of the data used in this article. The first version of the paper was presented at the Harvard University and World Bank seminar “Mobilizing the Private Sector for Public Education.” I am indebted to Raji Chakrabarti, Ronald Fryer, Brian Jacob, Harry Patrinos, Katja Vinha, and one anonymous referee for very useful comments.

1
**Figure 2. Moldova is one of the poorest countries in the region**

<!-- Start of picture text --> GDP per capita in 2011, PPP<br>14 mean GDP per capita growth 2000-2011 (annual %) 30000<br>12<br>25000<br>10<br>20000<br>8<br>15000<br>6<br>10000<br>4<br>5000<br>2<br>0 0<br>GDP per capita in PPP<br>mean GDP per capita growth rate, %<br>SVN CZE SVK RUS EST LTU HUN POL HRV LVA TUR ROM BGR BLR MNE KAZ MKD SRB AZE TKM ALB BIH UKR ARM GEO MDA UZB KGZ TJK<br><!-- End of picture text --> Source: ECATSD’s calculations based on WDI data.

_Labor markets_

**Labor markets in Moldova have undergone significant changes in the 2000s (Figure 3)** . Labor force participation of men and women decreased, slightly more sharply during the crisis. Unemployment rates have fluctuated significantly over the decade. Due to the economic downturn, unemployment went from 4 percent in 2008 to 7 percent in 2010, with a declining trend in more recent years, registering 5.1 percent by the end of 2013.<sup>2</sup>

**Aside from an overall decline in employment during the 2000s, there have been changes in its sectoral composition (Figure 4)** . Agricultural sector employment has declined at an annual average rate of 8 percent, while sectors such as construction and finance, real estate and business have been on the rise in terms of employment. Later on we show that this shift away from the agricultural sector to manufacturing and services – with expanding employment in these sectors - is associated with movements out of poverty.

> 2 Data from WDI.

4
# High School Track Choice and Financial Constraints: Evidence From Urban Mexico

Ciro Avitabile<sup>_∗_</sup> Matteo Bobba<sup>_†_</sup> Marco Pariguana<sup>_‡_</sup>

> _∗_ The World Bank. E-mail: cavitabile@worlbank.org.

> _†_ Toulouse School of Economics. E-mail: matteo.bobba@gmail.com.

> _‡_ Pennsylvania State University. E-mail: map535@psu.edu.
Other studies have attempted to correct for the endogeneity of schooling by including a range of individual and community level variables in the model as controls and by using fixed effects specifications. Rosenzweig and Schultz (1982) view female schooling and health care as partial substitutes for information about diseases, treatment of illness and child-care practices. Using data from Colombia, Rosenzweig and Schultz control for access to services to show that in areas where services are readily accessible, they are used by both educated and uneducated women; the advantage conferred by schooling on health outcomes is diminished as a result. Desai and Alva (1998) examine the impact of education on child health outcomes and Elo (1992) looks at the effect on use of maternal health care services. They control for urban area of residence, access to water and sanitation services, and include community fixed effects in their models. Both methods reduce the effects of years of schooling. Kravdal (2003) incorporates variables to control for average education and relative autonomy of women at the community level. His main hypothesis is that everyone, including those with relatively low levels of education, can take advantage of the generally high level of education in a community. In general, controlling for confounders produces smaller estimates of the effects of individual schooling.

To-date, only one study has adopted an instrumental variable approach to examining the impact of schooling on health. Breierova and Duflo (2004) take advantage of a school construction program in Indonesia in the 1970s to instrument the effect of education on fertility and child mortality and to compare the effect of mother’s schooling relative to 8