rafmacalaba/datause-encoder-data huggingface dataset:
Chile can be examined relative to findings from similar studies using data from the English-speaking Caribbean<sup>1</sup> , Brazil, and Honduras from which we can draw regional lessons about which factors matter most for LAC youth.

A caveat should be made up front: the majority of the US and LAC studies, and this study, only have available cross-sectional data for a single year, so it is not possible to establish a causal relationship between the factors and behaviors. Thus, the findings of this paper are most useful for establishing hypotheses regarding which factors may be the most important in explaining behaviors in Latin America and the Caribbean; they are “best guesses” given the existing data.

The paper starts with a description of the conceptual framework in Section II followed by the estimation methodology in Section III. Section IV presents a description of the data and gives a brief characterization of youth in Chile and Mexico. Section V presents the results by identifying which factors are most important in explaining seven different behaviors and Section VI concludes.

# **II. Conceptual Framework and Empirical Literature**

# **Conceptual Framework**

This study borrows its conceptual framework from the public health literature. The ecological risk framework posits that youth are a product of individual (personal), micro-, and macro-environmental factors (Bronfenbrenner 1979). The individual factors are those skills, behaviors, and ideas that are “hardwired”, rather than formed, such as rage, optimism, or general health. The micro factors include preferences taught and formed by the family, peers, community, and local institutions and the constraints imposed by the same, including household poverty. The macro factors include more general influences and constraints, such as gender/race discrimination, armed conflict, national poverty and economic inequality. These factors are commonly classified into two groups: the set of individual, macro, and micro factors that increases the risk of

> 1 Antigua, Bahamas, Barbados, British Virgin Islands, Dominica, Grenada, Guyana, Jamaica and St Lucia participated

3
governance and investment climate assessments, and fiduciary analyses sponsored by donors (OECD 2003: ch. 2). Authors of these reports frequently are unaware of recent studies on the same topic in the same country funded by a different donor (Easterly 2003: 15).

The second category of costs is more insidious and long-lasting, involving donor practices that tend to undermine the quality of governance or retard the development of public sector capacity. A few examples of these practices include providing aid through projects rather than through budget support, bypassing central government units (for example, by the use of project implementation units), relying on expatriates instead of subsidizing “learning by doing” by hiring local staff, and funding investment projects that in the aggregate imply unrealistically high recurrent expenditures in future years. In the latter case, donors in effect treat the budget for recurrent expenditures as a common-pool resource (Brautigam 2000), producing a tragedy of the commons in which roads are built but not repaired, and schools are built but not staffed. Donors engage in these practices to increase the visibility of their efforts and the short-term appearance of success for their individual projects, at the expense of coherent policy making and capacity building in the recipient country’s public sector (World Bank 1998: 84). It is well-known in the aid business that however successful a project appears on its own terms, it will have little or no sustained impact in a poor sector-policy environment, and where it is not integrated into other donor-funded or government projects (Easterly 2003: 7; Kanbur and Sandler 1999: 29). However, where there are numerous donors, any one of them would gain only a small share of the total benefits, in terms of project success, from efforts to improve administrative capacity in the country, which would in effect subsidize the success of other donors’ projects.

The remainder of this paper examines in more detail another much-discussed practice of this sort, donor “poaching” of qualified local staff. Section 2 summarizes the related literature, including anecdotal evidence, and presents a simple formal model of poaching as a collective action problem among donors. Cross-country data for testing the model’s predictions are described in section 3, with results reported in section 4. Results from these tests support the hypothesis that aid undermines the quality of the government bureaucracy more severely in recipient countries where aid is fragmented among more donors. Section 5 summarizes and briefly suggests a few possible solutions, but notes the political difficulties in implementing them.

# **2. Poaching**

Because of donors’ need to show tangible results for their projects, excessive recourse is made to expatriate experts, especially long-term advisers. To the extent 3
we compute separately for treated and untreated observations. other statistics, such as the variance.

Interpreting Quantile Effects Quantile effects are often misinterpreted, which can result in unwarranted conclusions for policy. This section discusses and illustrates two common pitfalls: implicit assumptions of rank invariance and extrapolating treatment effects to different populations. It is easy to conflate quantile effects (i.e., changes in the distribution of outcomes) and individual effects (i.e., the distribution of changes in outcomes). One might naively reason: ment.” Recall that the quantile effect is the difference in quantiles between treated and untreated participants. Thus, we implicitly assumed that John’s outcome would be equal to the (cf. Part 2). Rank invariance is a strong assumption, which is implausible when treatment effects differ across observably identical participants. Assumptions of rank invariance are often subtle. For example, researchers might argue that all participants benefitted from the intervention if all quantiles effects are strict y positive. They would have invoked rank invariance across treatment status. One mig t also equate quantile effects and changes with respect to baseline outcomes instead of interpretation requires rank invariance both across treat states and over time. For illustration, consider Figure 2. It shows estimate eatment effects, based n data from Simulation 1 (see Appendix 1 for details). We set the average treatment effect at each quantile of basel tcomes is close to the overall average effect, as can be seen from the line labeled “ATE at Quantile of Baseline Y”. Y_ , quantile effects are increasing. The QTT (dashed line) clearly differs from both avera e effects at baseline values (thin line) and quantiles of the distribution of individual effects (thick line). This discrepancy underlines the notion that implicitly assuming rank invarian e and misinterpreting quantile effect may lead to incorrect conclusions. Note, moreover, th t all quantile effects are positive, even though some participants are worse off after treatA different concern is the comparison of treatment effects across (sub-)populations. This part has surveyed the estimation of changes in the distribution of potential outcomes. As Section 4.2.2 argued, RCTs ide these marginal distributions, which allows us to compute treatment effects under mini a ssumptions. However, these effects depend on the unidentified joint distributio of pare results between diff ren ( b-)populations, unle s we — n account for differences in both the distributions of a e relation between these discrepancies arise rom differences in respons s tot intervention between men they can stem fro gender differences in the unknown distribution of treatment effects, in the distribution of | or both. We can estimate the marginal distribution of outcomes, which As before, it is possible to estimate the QTT from a weighted quantile regression. The slope gives the QTT.
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20 Table 2 (continued)

||N<br>Cn|o. of<br>otrles|Period|
|---|---|---|---|
|10) Balassa, B.<br>(1985)|"Exports, Policy Choices and Economic<br>Growth in Developing Countries After<br>the 1973 Oil Shock," JouXnal gf<br>Develjpment Economics.|43|1973-79|
|11) Balassa, B.<br>(1981)|"The Newly Industrialized<br>Developing<br>Economies After the Oil Crisis,"<br>Weltwirtschaftliches<br>Archiv.|12|1973-78|
|12) Jung, W.&<br>Marshall, P.<br>(1985)|"Exports, Growth and Causality in<br>Developing Countries,"<br>Journal of<br>Development Economics.|37|1950-81|
|13) Hutchinson, M.<br>and Singh, N.<br>(1987)|"Exports and Growth in Developing<br>Economies: Identifying Externality<br>Effects," UCSB Working Papers.|34|1950-81|
|Group C: Studies Us<br>Exgort Per|ing Kravis (1970) "Decomposition<br>Method<br>formance in IDCs|" to Anal|vze|
|14) Love, J.<br>(1984)|"External Market Conditions, Compe-<br>titiveness and Diversification<br>in LDCs<br>Exports," Journal of DeveloRment Econ.|27|1950-78|
|15) Kavcussi, R.<br>(1985)|"International<br>Trade and Economic<br>Development: Recent Experience of<br>Developing Countries," JournaL of<br>Developing Areas.|52|1967-73<br>1973-77|
|16) Singer, H. and<br>Gray, P. (1988)|"Trade Policy and Growth of Develop-<br>ing Countries: Some New Data,"<br>World Developmnent.|52<br>61|1967-73<br>1977-83|
|Grouo D: Studies Us<br>Trade and|ing Heckscher-Ohlin<br>Framework to Analyz<br>Factor Productivity|e Patter|n of|
|17) Krueger, A.<br>(1983)|Trade and Emnlgvment in Developing<br>Countriea, Synthesis volume of NBER<br>project on "Alternative<br>Trade Strateg-<br>ies and Employment.|12<br>|1960-75|
|18) Havrylyshyn, 0.<br>(1985)|"The Direction of Developing Country<br>Trade: Empirical Evidence,"<br>Journal of Development Economics.|45|1963-78|
8 between farmers and government irrigation officials which has increased the farmers assurance that irrigation officials will delivec the promised level of service. Conflict among farmers has declined substantially while system improvement havb. provided greater water supplies for tailenders, Careful to separate their organizations from partv pol .cs, the farmers h.ve also eased ethnic tensions. In one area, cooperating farmers cleared a canal allowing the dry seasoa cultivation of 1,000 hectares which had previously been left fallow. This benefitted over 300 ,anilies and demonstrated that participation and cooperation can providv substantial benefits and that the farmers have a strong concern for fairness.

Much like Sri Lank° and the Philippines, Ehe government of Indonesia has instituted a policy to turnover small-scale irrigation systems of under 500 hectares to WUAs. This was supported by the World Bank funded Irrigation Sub-Sector Project (ISSP). The WUAs were granted formal legal status to enable ihem to take on management responsibilities. The government carefully prepared the turnover process by bringing in the farmers for discussions about rehabilitation and redesign so as to provide farmers with a sense of ownership and responsibility. The International Irrigation Management Institute studied two pilot turnover projects under the ISSP and found that, overall, the maintenance performed was more or less what was required with no long-term threat to deterioration of the canals (Burns and Atmanto, 1992). By the middle of 1991, the government had transferred over 400 irrigation systems covering 34,000 hectares to WdJAs. Success for the program relies on early inclusion of farmers in the design and construction phase with flexiuiity in the formation of WUAs.

In Ne al a somewhat different approach was used to establish accountability and responsibility in irrigation system management. Farmer-managed irrigation schemes have been a long tradition in Nepal, with 70 percent of all irrigation under farmer control. However, more recently, the government has been heavily involved in managing new irrigation systems with poor results.
census model, the estimation method for the geospatial model, and the use of two proprietary geospatial indicators.

# A. The Size of the Partial Registry

Given the impressive predictive performance of the partial registry predictions, one might wonder whether increasing its size would further improve performance. Figure 7 compares the performance of the partial registry predictions for partial registries of 444 villages (10 percent), 666 villages (15 percent) and 888 villages (20 percent). Overall, expanding the size of the hypothetical partial registry offers only limited improvements in predictive accuracy, and is not worth the added expense it would entail.

**Figure 7.** Expanded sample for the partial registry method

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1.00<br>0.89 0.88 0.89<br>0.90<br>0.78 0.80<br>0.80 0.75<br>0.70 0.63 0.64<br>0.57<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Rank correlation AUC R-squared<br>Sample 1: 10%-90% Sample 2: 15%-85% Sample 2: 20%-80%<br><!-- End of picture text -->

# B. Village welfare measure

Second, we consider how the results are affected by the choice of welfare measure. This is important because the results until now have used a non-standard welfare measure, namely the average predicted per capita consumption of the bottom half of households in the village. This was based on a conscious decision to match the UBR administrative data, which only contains PMT scores for the bottom 50 percent of households in each village. This section considers how the results change when we consider mean village consumption, taken across all households, as the main welfare measure.

Changing the welfare measure has three main implications for the methodology. First, the census model must now be retrained using all households, not just the bottom half, in the survey. This of course also changes the predicted values of household per capita consumption in the simulated partial registry, which is equal to the benchmark welfare model for villages included in the registry, which entails re-estimating the geospatial model. Finally, we re-estimated the IHS plus geospatial model to train it against average per capita consumption across all households in the survey, rather than just the bottom half.

20
|Homs and Hama Sewerage Project||
|---|---|
|Homs and Hama Sewerage Project||
|Homs and Hama Sewerage Project||
|Homs and Hama Sewerage Project||
|National Agriculture Extension - IFAD||
|National Agriculture Extension - IFAD||
|National Agriculture Extension - IFAD||
|National Agriculture Extension - IFAD||
|Aleppo Sewerage Project|Mario Antonio Zelaya Najera|
|Aleppo Sewerage Project|Mario Antonio Zelaya Najera|
|Aleppo Sewerage Project|Mario Antonio Zelaya Najera|
|Aleppo Sewerage Project|Mario Antonio Zelaya Najera|
|MEDITERRANEAN POLLUTION CONTRO|Fathi Ben-Slimane|
|Solar Water Heating|Nourredine Bouzaher|
|Solar Water Heating|Nourredine Bouzaher|
|Solar Water Heating|Nourredine Bouzaher|
|Solar Water Heating|Nourredine Bouzaher|
|Solar Water Heating|Nourredine Bouzaher|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Demonstration Project for the Phaseout of Ozone DepletingSubsta|nAlaa Ahmed Sarhan|
|Rural Road Project||
|Rural Road Project||
|Fisheries Project(02)||
|Fisheries Project(02)||
|Fisheries Project(02)||
|Urban Development Project(02)||
|Urban Development Project(02)||
|Urban Sewerage Project(02)||
|Urban Sewerage Project(02)||
|Urban Sewerage Project(02)||
is heaviest in the populous countries of South and East Asia and a few African countries.

All regions display a broad pattern of variation.

# **3.3 Vulnerable Populations on Fragile Lands**

> The Bank's World Development Report 2003 has identified the vulnerability of human populations on fragile lands as a critical poverty-environment problem. Approximately 1.4 billion people live on fragile lands that are steeply-sloped, arid, or forested, and many of these people are very poor (WDR, 2003). Research on povertyenvironment links in this context is not highly developed, but policymakers and researchers generally agree that people on fragile lands bear a high risk of natural resource degradation and impoverishment. For this study, we highlight the overall problem by computing total population on fragile lands using GIS techniques. Figure 3 displays the results, which indicate a particularly heavy incidence of this problem in East and South Asia. With some visible exceptions, vulnerable populations are generally smaller in Eastern Europe, Sub-Saharan Africa, and Latin America.

# **4. A Measure of Project Success Probability (pi)**

A country's optimal environmental investment share depends on the probability that a project or program will succeed, as well as the scale of its environmental problems. To estimate country success probabilities, we have drawn on a large database maintained by the World Bank's Operations Evaluation Department (OED). Since 1990, OED has rated the outcomes of over 3,000 World Bank projects in 146 countries. OED rates projects in eight categories: Highly satisfactory, satisfactory, moderately satisfactory, marginally satisfactory, marginally unsatisfactory, moderately unsatisfactory, unsatisfactory, and highly unsatisfactory. We interpret the first four ratings as "successful" for our 9
where _NFA = FA - FL_ denotes the net foreign assets of the central bank expressed in domestic currency. These may become large and negative if, for instance, the central bank borrows actively abroad, or manages schemes to attract the remittances of nationals working abroad, or still incurs in foreign exchange liabilities as a result of the implementation of foreign exchange insurance schemes. The reason net foreign assets turn out to be negative in these cases is that the central bank does not hold an equivalent stock of foreign assets. That, in turn, results from sales of previously acquired foreign exchange to the government or the private sector, and the channelling of the revenues from the sales towards credits denominated in domestic currency.

The variations in the central bank's net worth are identically equal to the difference between the central bank's revenues and expenditures, including the capital gains and losses due to exchange rate devaluations. Thus, these variations are obtained through a combination of its income statement and revaluation accounts:

- (8) _NWtb = (i + E)NFA E+isCs ipCp -iRS-A_

The first term on the right hand side of (8) is a depreciation-adjusted interest revenue or cost, depending on the sign of net foreign assets. Here we make the simplifying assumption that the central bank earns on its foreign assets the same international interest rate that it pays on its foreign liabilities. The second and third terms are the interest revenues collected on credits to the government and to the private sector, respectively. Next comes the interest expenses on bank reserves, and finally the dividend payments to the government, **4IO.** Equation (8) does not cover all possible operations that might affect the central bank's profits. For instance, the central bank can run a profit 'Olt is assumed that the government is the central bank's sole shareholder.

13