rafmacalaba/datause-encoder-data huggingface dataset:
rollment with number of students tested). For some schools and years, proficiency is not available for one of the following three reasons: 1) the school only includes early childhood enrollment; 2) the school only includes grades that are not tested; 3) the school includes grades that are tested but enrollment in those grades is below the minimum threshold for reporting requirements. The last reason is the most prevalent cause of missing proficiency. See below for the imputations made in those cases.

- Math proficiency: percent of students who are proficient in math. Constructed similarly as reading proficiency.

- Year of Opening: year the school opened if it was open after 2003. Using the CCD “status” variable and web searches we verify the school’s initial year, which is the first year for which we have records.

- Year of Closing: first year that the school is no longer open. We verify the content of this variable using the CCD “status” variable and web searches.

- Year of Merge: year the school merges with another school. The variable stores the first year that the school no longer operates separately, which is the first year for which we have joint records.

Ethnic composition and low-income status of the student body are missing for 2 and 4 (out of 701) observations respectively; these are schools with very low enrollment. To the cases of missing ethnic composition we impute the school’s average ethnic composition over the years for which we do have data. When possible, we impute the predicted value coming from a school-specific linear trend. Achievement is missing in 16 out of 701 observations. To these observations, we impute the predicted achievement coming from the regression of school-level proficiency rates on year dummies, ethnic composition variables, percent of low income students, enrollment, and school fixed effects. In cases in which we have no proficiency data at all, we run a similar regression excluding school fixed effects and including dummies for school level and use the resulting predicted values for our imputations.

# **A.1.2 Charter Schools**

As with public schools, the starting point for this dataset is audited enrollments from the District of Columbia Office of State Superintendent of Education (OSSE). This gives us the list of charter schools along with their grade- and school-level enrollment. For consistency with public schools, we exclude alternative schools. We also exclude schools in which ungraded or adult students constitute the majority of the student body, and schools with residential programs.

By law, charter schools cannot serve special education students exclusively. However, they can offer services targeted to specific populations. We exclude schools whose services target special ed students. We exclude early childhood schools only if they never add regular grades during our sample period. We also exclude online campuses. Some non-early childhood charters opened an early childhood campus during the sample period; we only included these campuses if at some point during the sample period 42
# **Table 3: Impact of Quality Ratings on Choice of Business Service Provider**

|All<br>Providers|HR<br>Providers|Marketing<br>Providers|Accounting<br>Providers|
|---|---|---|---|
|**Panel A: Average Star Rating of Top-ranked Providers (out of 5**<br> <br>|**)**|||
|Ratings Only<br>0.200***|-0.014|0.296***|0.294***|
|(0.043)|(0.091)|(0.061)|(0.070)|
|Negative Comments<br>0.386***<br>(0.049)|0.414***<br>(0.100)|0.334***<br>(0.060)|0.408***<br>(0.073)|
|All Comments<br>0.345***|0.430***|0.281***|0.345***|
|(0.048)|(0.093)|(0.059)|(0.071)|
|Mean of Information Only Group<br>3.44|3.04|3.91|3.34|
|Sample Size<br>660|665|668|667|
|P-value: test of equality<br>0.000|0.000|0.588|0.233|
|**Panel B: Proportion of Providers Chosen with Top Rating of 5**||||
|Ratings Only<br>0.067***|0.048**|0.137***|0.024|
|(0.018)|(0.020)|(0.031)|(0.020)|
|Negative Comments<br>0.107***|0.081***|0.173***|0.074***|
|(0.020)|(0.024)|(0.032)|(0.023)|
|All Comments<br>0.073***|0.080***|0.119***|0.029|
|(0.020)|(0.023)|(0.032)|(0.021)|
|Mean of Information Only Group<br>0.170|0.210|0.210|0.085|
|Sample Size<br>660|665|668|667|
|P-value: test of equality<br>0.164|0.222|0.309|0.088|
|**Panel C: Proportion of Providers Chosen with Low Rating (3 or**|**lower)**|||
|Ratings Only<br>-0.092***|-0.050*|-0.069***|-0.148***|
|(0.015)|(0.026)|(0.020)|(0.029)|
|Negative Comments<br>-0.142***<br>(0.016)|-0.184*** <br>(0.029)|-0.064***<br>(0.020)|-0.175***<br>(0.029)|
|All Comments<br>-0.146***|-0.185***|-0.073***|-0.183***|
|(0.016)|(0.027)|(0.019)|(0.029)|
|Mean of Information Only Group<br>0.352|0.531|0.126|0.400|
|Sample Size<br>660|665|668|667|
|P-value: test of equality<br>0.000|0.000|0.837|0.369|

# Notes:

Regressions also control for city. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. Test of equality tests equality of the three quality treatments: providing star ratings only, also providing any negative comments, and also providing positive and negative comments.

22
The government met strong resistance in the congress when it proposed reform on tax, social security, administrative systems. Now the Real Plan relies on an over-valued exchange rate, which in the long run is harmful. (4) Even though the per capita income is high, the quality of Brazilian education is poor. Teachers are underpaid. Dropout rates are high. Of the children who start primary school, only 60% reach grade 4, 43% grade 8. This is inadequate for the requirements of an industrial state.

## Reference

1. World Bank. _Country Brief: Brazil._ February 23, 1996.

2. Economic Intelligence Unit. _Country Profile: Brazil._ London, United Kingdom. 1991/92, 1992/93, 1993/94, 1995/96, & 1996/97.

3. World Bank. _Brazil Country Econonmic Memorandum. Brazil: An Agenda for Stabilization._ October 7, 1994.

# Colombia

## **History**

Colombia declared its independence from Spain in 1810 while the Napoleon's French forces occupied Spain. After the defeat of Napoleon, Spain tried to restore its colonial control. Under the leadership of Simon Bolivar, Colombia won the war against the loyalists and gained the full independence in 1819. Colombia, Venezuela and Ecuador formed the Republic of Gran Colombia, and Panama was annexed in 1821. Then Venezuela and Ecuador ceded from the republic and become independent countries in 1830. Panama, supported by the USA, declared independence during the war over 1899-1902. The Colombian domestic politics was characterized by the rivalry between the Conservatives and the Liberals. After "La Violancia" over 1948-58, which claimed 250,000 lives, the two parties decided to settle their differences by forming the National Front. From 1957 to 1973 the two parties rotated the presidency. This power-sharing system between the two parties excluded other sections of the population from the domestic politics. Guerrilla groups were formed against the government in mid-1960s. Only some of the guerrilla groups were disarmed in 1991 after the cease-fire agreements were reached in 1989. The negotiations with the two best armed and most extreme groups, the Fueraz Armadas Revolucionarias de Colombia (FARC) and the Ejercito de Liberaci6n Nacional (ELN), have dragged on without any results.

## **Coffee Bonanza (1970-1979)**

Colombia was for many years heavily dependent on the export of coffee even though it has abundant natural resources, such as agricultural land, water of irrigation, energy resources (oil, natural gas, and coal), and mineral resources (nickel, gold, and emeralds). The GDP growth was at 6% annually on average during 1970s thanks to the more than fourfold coffee price increase over a period of twvo years, which is known as the coffee bonanza of 1975-80.

49
# 1. **Introduction**

Many have pointed out that prior to 1990 the Colombian econorny was characterized by the presence of many microeconomnic distortions and prudent macroeconomic management (see, among others, World Bank (1991), Urrutia (1994)). As a consequence, growth only had a decent performance and inflation and budget deficits were moderate. For most Colombian policymakers, the lack of a major macroeconornic crisis and the failures of different economic experiments throughout Latin America were sufficient evidence that no serious economic reform was needed. However, during the period 1990-1994 several structural reforms were undertaken in areas such as foreign trade, foreign investment, labor markets, financial markets and public utilities. The ultimate objective of the reforms was to foster growth by strengthening the role of the government in the provision of security and justice and by allowing the private sector to compete in former protected markets and expanding its role in the production of "public goods" (Montenegro (1995)).

The structural reforms carried out by the government were accompanied by a significant decline in private savings. Until 1992 this behavior was offset by public savings and consequently the national saving rate remained stable (Figure 1). Although the deterioration of private savings started in 1988, its extension in the 1990s was associated with the implementation of the structural reforms'. Until 1989 around 70% of disposable income accrued to liquidity constrained consumers (L6pez (1994)). Given

**Figure 1: Savings in Colombia**

<!-- Start of picture text --> 25<br>10<br>1951 1958 1965 1972 1979 1986 1993<br>Private Saving ---- Public Saving - National Saving<br>* Source: National Accounts<br><!-- End of picture text --> XAn additional hypothesis not related with the reforms has been a reassessment of permanent income due to the oil bonanza (see, among many others, L6pez (1995)).

l
Industry Restructuring and Regulation - Building a base for sustainable growth <!-- Start of picture text --> 2000000 Number of users and Density 1S<br>waiting list<br>1800000 14<br>1600000<br>1400000<br>1200000' 10<br>12000000<br>1000000<br>6000004<br>I~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>400000|<br>1987 1988 1989 1990 1991 1992 1993 1994 1995<br>Year<br>I-Lines in Service -Wailing list - - Density (lines per 100 people)j<br><!-- End of picture text -->

_Source:_ Table 8-3 _Chile: Recent Policy Lessons and Emerging Challenges_

# _2.2.3 Water and Sewerage Services_

A sector that has attracted much less attention, primarily because of the paucity of the data that exists, is the water and sewerage sector. Few water privatizations or restructurings have a sufficient track-record of information than that required to undertake these types of review. However, one concession that has attracted significant attention and for which sufficient information does exist is the Buenos Aires' concession.

Some partial information on the impact of the concession was provided by Crampes and Estache - summarized in Table 9 below.

|**TAble**9:<br>**1miact**<br>**oa indicatoi y**<br>**993 1oDeember 19**<br>|
|---|
|_Indicator_<br>_<br>_Change_|
|Increase in production capacity (%)<br>26|
|Water pipes rehabilitated (kilometers)<br>550|
|Sewers drained (kilometers)<br>4,800|
|Decline in clogged drains (%)<br>97|
|Meters upgraded and installed<br>128,500|
|Staff reduction (%)<br>47<br>|
|Residents with new water connections<br>642,000|
|Residents with new sewer connections<br>342,000|

13
# **4. Results of scenario analyses of key water related macro-micro policy linkages**

As seen from the discussion in section 2 above water allocation between WMA’s and between competing economic uses within WMAs remains governed by a number of quantitative restrictions and non-market factors. The developed Water CGE model will be useful for evaluating the net impacts of potential shifts in water policy towards more market-based allocation regimes which the NWRS aspires to promote. The SA Water CGE model is accordingly employed in this section to examine a number of water-related issues in SA. The economy-wide (micro and macro) impacts of the following policy scenarios have been evaluated: <u>Scenario I</u> simulated intraregional irrigated-water-market liberalization to examine the impact of liberalizing local water allocation among crops so as to equalize the SP of irrigation water across crops within each WMA. This scenario does not introduce changes in total water use at the WMA-level (i.e. implying current inter-region water transfers are not changed) and also does not change allocation of available water between irrigation and other uses (e.g. industry and domestic users). The regional irrigation water market liberalization ( _Regional Irrigation Market_ ) scenario however, allows for more efficient allocation of water resources among crops within WMAs based on crop-specific water demands (VMP). We expect the model to allocate relatively more water to those activities that in the base solution had relatively high shadow price (SP) values (i.e. water restricted). However, since the elasticities of water demand vary by crop and are affected by product market adjustments (with product price adjusting less if the crop is relatively foreign trade intensive), some SP values may rise or fall by lager magnitudes relative to the base than other SP values. Consequently, the initial SP values only provide a partial prediction of the direction of the final result. This scenario leads to estimation of general equilibrium SPs for irrigated water for the various WMAs; <u>Scenario II</u> allows for changes in inter-regional transfers of water for irrigation use based on existing water transfer schemes in addition to liberalizing regional (within WMA) irrigation water markets (Scenario I). Water allocation between irrigation and nonagricultural use remain unchanged in this scenario which liberalizes national irrigation 22
Table 5: Changes in per-pupil funding in public schools, before/after FPE

|||Schooling|level of hou|sehold head||
|---|---|---|---|---|---|
||None|Some<br>primary|Complete<br>primary|Some<br>secondary|Some<br>tertiary|
|% of households, 1997|26.90|40.50|6.40|22.70|3.60|
|Change in fees per pupil, 1997-2006 ($)|-2.50|-3.00|-4.60|-10.40|-25.50|
|FPE grant per pupil ($)|11.90|11.90|11.90|11.90|11.90|
|Scenario 1: EMIS data, perfect sorting||||||
|TPR change<br>|-0.0016|-0.0053|-0.0084|-0.0128|-0.0313|
|Monetary value of TPR change ($)|-4.99|-16.52|-26.34|-40.37|-98.35|
|Net change in funding per pupil ($)|6.51|-5.52|-16.94|-36.77|-109.85|
|Scenario 2: survey data, perfect sorting||||||
|TPR change|0.0023|-0.0001|-0.0023|-0.0056|-0.0194|
|Monetary value of TPR change ($)|7.29|-0.47|-7.09|-17.50|-61.12|
|Net change in funding per pupil ($)|18.79|10.53|2.31|-13.90|-72.62|
|Scenario 3: survey data, no sorting<br>TPR change|-0.0003|-0.0003|-0.0003|-0.0003|-0.0003|
|Monetary value of TPR change ($)|-0.89|-0.89|-0.89|-0.89|-0.89|
|Net change in funding per pupil ($)|10.61|10.11|8.51|2.71|-12.39|

Source: Authors’ calculations as described in the text. Fee levels are based on the 1997 Welfare Monitoring Survey and 2006 Kenya Integrated Household Budget Survey. Pupil teacher ratios are based on administrative data from the Education Monitoring Information System (EMIS) for 2002 and 2005. Salary figures are taken from the Kenya National Examination Council (2010).

26
# **Household Vulnerability to and Preparedness for Disasters in Haiti**<sup>*****</sup>

Gustavo Canavire Bacarreza<sup>†</sup> Naraya Carrasco<sup>‡</sup> Marlen Cardona Botero<sup>§</sup> Olive Nsababera<sup>**</sup>

**_Keywords_** _: Disaster Risk Management, Poverty and Climate Change, Haiti_ **_JEL Codes_** _:  I3, D6, D8_

> * The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the World Bank, its Board of Directors, or the countries it represents. This paper and the data collection received financial support from the European Union in the Caribbean Regional Resilience Building Facility framework, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR). We thank Guillermo Gomez for superb research assistance.

> † World Bank.  Poverty and Equity Global Practice and Universidad Privada Boliviana. <u>gcanavire@worldbank.org</u>

> ‡ World Bank. Urban, Disaster Risk Management, Resilience & Land Global Practice. <u>ncarrasco@worldbank.org</u>

> § World Bank.  Poverty and Equity Global Practice. mcardonabotero@worldbank.org

> ** World Bank.  Poverty and Equity Global Practice. onsababera@worldbank.org
## C.1.14 Preferences and Welfare

The parameters _αq_ and _βq_ dening the varying marginal budget shares of goods and services _q_ in the consumers' total real expenditures in equation (B.45) are estimated by maximum likelihood as described in Craneld et al. (2003) and Yu et al. (2004). The parameters _<u>q</u>_ dene the subsistence level of consumption of goods and services _q_ were calibrated to match the initial allocation of land resources. The social discount rate is the same as in the Dynamic Integrated model of Climate and the Economy (DICE), version 2007.<sup>9</sup> We parameterize the scrap value function as where the parameters _π_ 1 and _π_ 2 denote the scrap prices of unmanaged and commercial forests at the beginning of period _T_ . We calibrate the values of _π_ 1 and _π_ 2 , so that forest replanting rates are stable over time and unmanaged natural lands are not depleted over 50 percent of their initial amount during the time horizon of the problem.<sup>10</sup>

# C.2 Construction of Counterfactual Scenarios

The values of the model parameters corresponding to the three counterfactual scenarios are summarized in Table C.1 and Figure C.2.

To quantify the impact of climate change impact on potential crop yields we follow Steinbuks and Hertel (2013), who obtained results from runs of the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model (Jones et al. 2003), run globally on a 0.5<sup>_◦_</sup> grid and weighted by agricultural output of four major food crops (maize, soybeans, wheat and rice) under most optimistic (RCP2.6) and pessimistic (RCP8.5) Representative Concentration Pathways GHG forcing scenarios (Moss et al. 2008) and alternate assumptions on CO 2 fertilization eects over the period between 1971 and 2099. For scenario A, we calculate the change in potential agricultural yield for food crops assume the pessimistic RCP8.5 without CO 2 fertilization eect.

> 9For a detailed description of the DICE model see Nordhaus (2008). DICE 2007 model parameters can be accessed at the following website: http://nordhaus.econ.yale.edu/ DICE2007.htm

> 10We have tried setting dierent values of _π_ 1 _,_ and the optimal path of natural land conversion was not signicantly aected over the rst 100 years.

24
our analysis. Among 15 to 19 year olds, the intra-household correlation of the years of schooling attained ranges from 0.70 to 0.95 across these surveys, with a mean of 0.86. This suggests a very high degree of correlation among siblings in school attainment.

# **3) Results**

# **3.1)  Aggregate trends in adult mortality**

Figure 3 summarizes the aggregate trends in under-5 (solid lines) and adult mortality (dashed lines) for countries from the Sub-Saharan African region and countries from other regions.<sup>12,13</sup> Under-5 mortality (which are obtained from the World Bank’s World Development Indicators database) is shown for two sets of countries.<sup>14</sup> First “all” countries for which under-5 mortality data are available and for which GDP per capita never exceeds $10,000 (in real 2000 dollars) during the periods under consideration; and second, restricting the set of countries to those for which we also have adult mortality data (referred to as “overlapping” countries). A comparison of adult mortality trends to under-5 mortality trends in overlapping countries provides comparison of these two mortality trends in the same set of countries.

Comparing under-5 mortality between the full and “overlapping” samples suggests that our DHS sample is fairly representative of the regions as a whole: indeed, the levels and trends in these mortality estimates are very similar. This is consistent with the fact that the countries surveyed in Sub-Saharan Africa cover a high share of the population in that region. It also suggests that while the countries outside of Sub-Saharan Africa are not “representative” of developing countries, they do have similar levels and trends of under-5 mortality.

> 12 When there are multiple estimates of adult mortality for the same country and same period (based on different rounds of the DHS) the estimates are averaged, weighting each estimate by the inverse of the variance of the estimate so that more precise estimates are given more weight.

> 13 These regional averages are population weighted, where the weights are the total population at the beginning of the period in question.

> 14 Under-5 mortality for each period is the average of the rate reported for the beginning and end of the period.  So, for example, under-5 mortality for 2000-04 is estimated as the average of the WDI reported mortality rates for 2000 and 2005.

9
We first use historical data on 2729 IPF operations evaluated by IEG between 1995 and 2012 to estimate the relationship between a small set of project characteristics and project outcomes as assessed by IEG. These project characteristics include project size, preparation time, effectiveness delays, planned project length, country performance as measured by the CPIA ratings, and the track record of the project manager (known as a “task team leader” or TTL), as measured by IEG ratings on other projects managed by the same TTL. Most of the predictive power of the model for project outcomes comes from these last two variables.

We document the in-sample predictive power of this model by comparing its predictions with the actual IEG evaluations, and find that it can successfully predict between 40 and 46 percent of unsatisfactory project outcomes. While this is far from perfect, the prediction model does a substantially better job of predicting outcomes than do ISR-DO ratings. For example, ISR-DO ratings in the first quarter of the life of a project correctly anticipate only 3 percent of unsatisfactory IEG ratings, while those in the second quarter do so correctly only 17 percent of the time.

Given this evidence on the ability of the prediction model to anticipate unsuccessful project outcomes, we next use it to predict project outcomes in the portfolio of 1591 active IPF operations as of end-FY13. The predictions of the model are based on real-time-observable information on project characteristics as of end-FY13. We calibrate the prediction model to generate a 27 percent rate of predicted unsatisfactory projects, consistent with the historical experience between 1995 and 2012. This identifies a set 430 projects at risk of ultimately having unsatisfactory IEG ratings. Of these, only 77 are rated by the ISR-DO rating as making unsatisfactory progress towards meeting their development objectives. We document how the incidence of these predicted-to-be-unsatisfactory projects varies across regions and across projects in different stages of implementation.

# **2. Institutional Setting**

This paper focuses on the Bank’s Investment Project Financing (IPF) loans, credits and grants, which can be provided for a wide range of activities aimed at creating the physical and social infrastructure necessary to reduce poverty and promote sustainable development. While each Bankfinanced project is a country-led effort, Bank staff typically monitor progress and provide inputs at each stage of the project (illustrated by Figure 1). The preparation phase usually begins once a proposal to address a development issue is identified and discussed between the borrower and the Bank. The Project Concept Note (PCN) is followed by Project Appraisal Document (PAD), which is subject to 3