rafmacalaba/datause-encoder-data huggingface dataset:
# **Table of Contents**

|1.|Introduction....................................................................................................................................... 2|
|---|---|
|2.|Properties of Financial Sector Strategies.......................................................................................... 5|
|_2.1._|_Establishing financial sector development objectives_....................................................................... 7|
|_2.2._|_Identifying systemic risk in achieving targeted development objectives_........................................... 9|
|_2.3._|_Implementing the strategy_............................................................................................................... 10|
|_2.4._|_Communicating the trade-off between financial development and systemic risk_........................... 11|
|3.|Stylized Facts.................................................................................................................................. 14|
|4.|Stylized Facts Based on Country Characteristics............................................................................ 15|
|5.|Benchmarking the Properties of Financial Sector Strategies.......................................................... 17|
|6.|Discussion of Estimation Results.................................................................................................... 18|
|_6._|_1._<br>_By group of country characteristics_........................................................................................ 19|
|_6._|_2._<br>_Overall parsimonious models_................................................................................................. 21|
|7.|Benchmarking Individual Countries against Their Peers................................................................ 24|
|8.|Conclusion...................................................................................................................................... 25|
|Ref|erences............................................................................................................................................... 27|
|Figu|res and Tables in the Main Text...................................................................................................... 30|
|App|endix................................................................................................................................................. 34|

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transition—i.e. from 1989 to the present, especially key events and episodes that may have influenced their attitudes towards reforms, and collected information on individuals; family background, on their employment situation, and on coping strategies during transition. Finally, the survey also attempted to capture the extent to which crime and corruption are affecting peoples’ lives, and the extent to which individuals’ trust in other people and in state institutions has changed over time.<sup>27</sup> 52. The LiTS data set also includes several key questions of special interest for this paper. Respondents were asked to what extent they agreed with the statement: “The gap between the rich and the poor in this country should be reduced”, with responses coded using a 5-point scale (1=strongly disagree, 5=strongly agree). In addition, respondents were also asked if they thought the state should be involved in reducing the gap between the rich and the poor (1=not involved, 2=moderately involved, and 3=strongly involved), the factors they felt were most important to succeed in life (1=effort and hard work, 2=intelligence and skills, 3=political connections, 4=criminal/corrupt ties, 5=other), and finally also about their opinions regarding the main reason why there are some people in need in the country (1=unlucky, 2=lazy/lack willpower, 3=injustice in society, 4=inevitable part of modern life, and 5=other).

> 27 For more details on the LiTS as well as the preliminary survey findings, see Life in Transition: A survey of people’s <u>experience and attitudes, European Bank for Reconstruction and Development, London, 2007.</u>

27
**Table 3. Average proportion of undergraduate and masters level courses devoted to ...**

||LAC|SSA|MENA|Underg<br>EAP|raduate level<br>ECA|SA|All<br>developing<br>countries|Top 20 US<br>economics<br>departments|Non-Top US<br>economics<br>departments|Masters<br>level<br>All<br>developing<br>countries|
|---|---|---|---|---|---|---|---|---|---|---|
|Theory and<br>models|0.373**|0.383**|0.406***|0.389**|0.368**|0.338|0.374**|0.211|0.212|0.357**|
|Empirical<br>methods|0.062***|0.083***|0.112*|0.103***|0.076***|0.091***|0.076***|0.231|0.101***|0.091***|
|Empirics|||||||||||
|(findings,<br>stylized<br>facts)|0.199***|0.146***|0.183***|0.194***|0.095***|0.227**|0.179***|0.375|0.311|0.232**|
|# countries|17|14|5|4|9|4|53|1|1|33|
|# courses|60|34|9|9|12|16|140|14|17|79|

*,**,*** indicate statistically different from non-top 20 U.S. economics departments at the 10, 5 and 1 percent level respectively.

**Table 4. Material used in undergraduate and masters level courses**

||LAC|SSA|<br>MENA|Undergradu<br>EAP|ate level<br>ECA|SA|All<br>developing<br>countries|Top 20 US<br>economics<br>departments|Non-Top US<br>economics<br>departments|Masters<br>level<br>All<br>developing<br>countries|
|---|---|---|---|---|---|---|---|---|---|---|
|Textbooks|0.948*|0.867|0.889|1.000|1.000|1.000*|0.940*|0.813|1.000*|0.875|
|Stories from The Economist,<br>Financial Times, or other<br>news sources|0.153|0.100*|0.333|0.222|0.333|0.214|0.176|0.313|0.471|0.125*|
|Journal of Economic<br>Perspectives articles|0.373*|0.100***|<br>0.222*|0.556|0.167**|0.357|0.287***|0.625|0.235**|0.412|
|World Bank Research<br>Observer articles|0.119|0.167|0.222|0.222|0.167|0.071|0.140|0.188|0.059|0.213|
|Classic articles in|0.593|0.267***|<br>0.444|0.222***|0.250***|0.500|0.441**|0.750|0.294***|0.563|

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different communities. As more surveys include GPS coordinates, this will become increasingly possible.

The above two examples highlight the ability of GPS to help researchers to better control for (potentially) observable characteristics. Several recent papers are also using GPS to create instruments for use in instrumental variables estimation.<sup>7</sup> The most standard application is to use distance as an instrument. For example, Oster (2006) wishes to examine the response of sexual behavior to HIV prevalence rates in Africa. The concern is that HIV prevalence is endogenous, as places where people have a lot of risky sex are likely to have high rates of HIV prevalence. Her solution is to use the GPS information contained in the Demographic and Health Surveys (DHS) to calculate the distance of each cluster to the Democratic Republic of Congo (DRC), where the HIV virus is thought to have originated. She argues that the spread of the virus should be related to the distance from the DRC, but that after controlling for region, latitude, longitude, and country-level measures of development, distance from the DRC should not otherwise affect sexual behavior. As a further check, she uses data on pre-marital sex to show that distance from the DRC was not correlated with sexual behavior before the spread of HIV occurred.

A potential concern with using distance as an instrument is that distance to borders and major cities is likely to also determine access to markets, schools, health facilities, and other infrastructure (see section below), which in turn can have important impacts on economic behavior. As with all instruments, it is therefore important to perform robustness checks, and to control for potential threats to the exclusion restriction. However, when used appropriately, distance can perform well as an instrument. McKenzie, Gibson and Stillman (2006) compare the performance of different non-experimental estimators in estimating the income gains from migrating from Tonga to New Zealand against the experimental estimate obtained using a visa lottery. They find that using the GPS-measured distance from a household in Tonga to the location of the New Zealand immigration office in Tonga where application forms must be deposited is a strong predictor of the decision to apply to migrate. Furthermore, they verify among a sample of migrants in New Zealand that there is no relationship between their wages

> 7 Of course researchers can use geography to create instruments without using GPS, through painstaking map work. Recent examples include Woodruff and Zenteno (2007) who use distance from the capital of the state an individual was born in to the nearest station on the north/south railway lines as they existed in the early 1900s as an instrument for migration in Mexico; and Hoxby (2000) who uses the number of streams in a metropolitian area in the U.S. as an instrument for the number of school districts in examining the impact of school choice. GPS can make such applications more accurate and less time-consuming.

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migrant would then also subsequently receive attendance records and school grades from the child’s school, enabling them to better monitor the schooling being paid for.

# _3.3 Baseline Sample_

Given that Filipinos are a small minority of the overall population of Rome, we used intercept-point sampling to obtain a sample for this study. Between August 2012, and January 2013, we intercepted 2,291 Filipino migrants at common meeting points in Rome and at the main branch of BPI in Rome.<sup>2</sup> The migrant was then invited to answer some preliminary screening questions to determine their eligibility to participate in the pilot phase. To be considered eligible, a respondent had to meet two criteria: (a) the province of origin of the migrant and of his/her “mostly closely connected household” (i.e., the household where they lived before migrating, or the household they send the most remittances to) should be in one of three regions of the Philippines in which PAPSCU had agreed to assist in running the pilot;<sup>3</sup> and (b) had a relative in the Philippines aged 5-22. If the migrant did not refuse to answer further questions, the interview would begin.

This resulted in a sample of 501 migrant workers, who were then administered a baseline survey, and a lab-in-the-field experiment. At the conclusion of the survey and experiment, participants were then told about the new EduPay product, provided with some related marketing material and, if they wanted to use this new product, asked to sign two forms: (i) a general information form on the sponsorable student(s) and (ii) an authorization form required by PAPSCU to obtain all relevant information on the student (e.g., their school ID number) from the schools so as to implement EduPay.

The baseline survey collected information on demographic background, labor market status, remitting behavior, and the quality of their relationship with their most closely connected household in the Philippines. Summary statistics are provided in Table 1. The sample is73 percent female and the average (and median) age of the respondents is 42. Most (70 percent) of

> 2 Intercepts were scheduled at various times on a variety of days of the week. The intercept points were five fixed locations :the Santa Pudenziana Filipino community church, the Bank of the Philippine Islands Rome branch, the Embassy of the Philippines, the headquarters of an important Filipino NGO (OFSPES), and the central train station in Rome (Termini Station).

> 3 These regions, all on the island of Luzon, are: 1) Region 3 [Central Luzon];2) Region 4 [the provinces of Batangas, Cavite, Laguna, Quezon, Rizal, Marinduque, Occidental Mindoro, Oriental Mindoro, Palawan, and Romblon];  and 3) the National Capital Region (the group of contiguous cities comprising metro Manila).

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December 1994. Many public provincial banks lost substantial deposits immediately following the crisis.40 The resulting liquidity crunch might have changed the political calculations regarding bank privatization for provincial politicians. Aside from highlighting the weaknesses inherent in public provincial banks' operations, the crisis may have enabled providers of liquidity (i.e., the Central Bank and the World Bank) to impose pressure upon the provinces to privatize. This result is consistent with the hypothesis that exogenous shocks makes privatization more attractive.<sup>41</sup> Another plausible explanation is that provinces' decisions were influenced by privatization outcomes in other provinces. As the number of privatized provincial banks increased, pressure to privatize surviving public provincial banks might also have increased.<sup>42</sup>

# 5. CONCLUSION.

The main findings from the empirical section of the paper are:

1. Peronist (PJ) governors were, in general, far more likely to privatize their province's public provincial bank than governors who belonged the Union Civica Radical (UCR) party or other independent or regional parties. The estimated hazard rate for Peronist governors was six times the estimated hazard rate for governors from other parties. This result is consistent with the hypothesis that fiscally conservative politicians are more likely to privatize than other politicians.

model with a constant base hazard rate (X0 = X). The maximum likelihood estimator of the exponential hazard model will be efficient and consistent if the base hazard is constant over the entire period, while the partial likelihood Cox estimator will be consistent whether or not the base hazard is constant over the entire period, although it is not fully efficient. A Hausman(1980)-type test comparing the parameter estimates from the two models (for the model shown in Table 5, Column (2)) yields a X<sup>2</sup> (8) statistic of 234.8. On that basis, we reject the null hypothesis that the base hazard rate is constant. (A similar test for the Weibull model rejects this model in favor of the proportional hazards model at conventional significance levels as well). As noted in footnote 26, parameter estimates from both the Weibull and exponential models are similar to the parameter estimates from the general proportional hazard model.

40 As noted in Clarke and Cull(1997), the bank quality variables used in this section do not reflect the significant deposit losses suffered by many poorly performing banks especially well. This might mean that the crisis increased the cost of not privatizing, in a way not captured by the included bank performance variables.

## **41** World Bank (1995), Chapter 4.

42 Unfortunately, we are unable to test these hypotheses since only variables that differ across banks in any given time period can be included in the regression.

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WPS4036 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 8</mark>

**Evaluating the Impact of Mexico's Quality Schools Program: The Pitfalls of Using Nonexperimental Data**

## **Emmanuel Skoufias**

## **Joseph Shapiro**

The World Bank London School of Economics Email: eskoufias@worldbank.org Email: joseph.shapiro@gmail.com

# **Abstract**

We evaluate whether increasing school resources and decentralizing management decisions to the school level improve learning in a developing country. Mexico’s Quality Schools Program (PEC), following many other countries and U.S. states, offers US$15,000 grants for public schools to implement five-year improvement plans that the school’s staff and community design. Using a three-year panel of 74,700 schools, we estimate the impact of PEC on dropout, repetition, and failure using two common non-experimental methods: regression analysis and propensity score matching. The methods provide similar but nonidentical results. The preferred estimator, difference-in-differences with matching, reveals that participation in PEC decreases dropout by 0.24 percentage points, failure by 0.24 percentage points and repetition by 0.31 percentage points—an economically small but statistically significant impact. PEC lacks measurable impact on outcomes in indigenous schools. The results suggest that a combination of increased resources and local management can produce small improvements in school outcomes, though perhaps not in the most troubled school systems.

**JEL No** : I20, I21, I28, O22

**Keywords** : impact evaluation, school based management, matching, decentralization

# World Bank Policy Research Working Paper 4036, October 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._ _Acknowledgments:_ For sharing data and providing useful comments, we thank Manuel Avila Carrazco, Ernesto Castellano Perez, Rodrigo Garcia Verdu, Paul Gertler, Edgar Ignacio Andrade Munoz, Alejandra Macias, Harry Patrinos and Marta Rubio.
# **Foreign Direct Investment in Services and Manufacturing Productivity Growth: Evidence for Chile**

Ana M. Fernandes<sup>a</sup> Caroline Paunov<sup>b</sup> The World Bank OECD Development Centre and Queen Mary, University of London

**Keywords** : Productivity Growth, Services Liberalization, Foreign Direct Investment, Chile.

**JEL Classification codes** : D24, L8, L9, F21, F23.

> a Ana Margarida Fernandes. The World Bank. Development Research Group. 1818 H Street NW, Washington DC, 20433. Email: afernandes@worldbank.org.

b Caroline Paunov, OECD Development Centre, 2, rue André Pascal, 75 775 Paris Cedex 16, France. Email: caroline.paunov@oecd.org and caroline.paunov@gmail.com.

The authors would like to thank Ana Paula Fernandes, Jonathan Haskel, Raimundo Soto, Peter-Paul Walsh, and seminar participants at Indiana University, the Chilean Central Bank, the University of Chile, Queen Mary, University of London, the 6<sup>th</sup> International Industrial Organization Conference, and the 2008 Empirical Investigations in International Economics Conference in Slovenia for valuable comments. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or the OECD.
Estimating the impact of using the native language as the language of instruction on a student’s cognitive skills is challenging. First, countries that change their language of instruction in schools typically change it abruptly and apply the new policy to all students. Thus, it is difficult to differentiate the effect of a switch in the language from the effect of a student learning in their first or second language, as well as to find a suitable comparison group that is not concurrently affected to study the impact of the policy change. Second, because many of the language policy changes across the world occurred decades ago when standardized tests were not widely conducted, data may not be available for us to study the effect on a student’s cognitive skills. Third, when the language of instruction is changed, both students and teachers are affected. Even if one could study the impact of a policy change on test scores, it is difficult to differentiate the effect of the language change on a student’s ability to learn, from the effect of the language change on the quality of a teacher’s instruction.

This paper exploits a language policy change in Malaysia to study the effect of using nonnative language for instruction in schools on a student’s learning. In particular, we use the synthetic control method and several waves of data from the Trends in International Mathematics and Science Studies (TIMSS) to construct a counterfactual sample of Malaysian students and estimate the effects of a switch, and the use of a non-native language as the language of instruction on Malaysian eighth graders’ achievement in mathematics and science. The multiple ethnic groups of students with different mother tongues, the test score variations by gender and subject, as well as quirks in how the language policy change was applied to different cohorts of students provide us with a unique setting to differentiate between the effect of switching the language of instruction, the effect of using a non-native language as the language of instruction, and the effect that teachers have on a student’s learning by instructing in an unfamiliar language.

3
FTA; this arises mainly from the trade diversion effects that result in export opportunities moving away from these two countries and the rest of the world, in favor of the BBIN countries. India would gain the most in absolute terms and least in relative terms. Nepal may witness the biggest relative positive impact on GDP, exports, imports and investment; it strangely sees a reduction in private and government consumption, because most of its domestic production declines due to competition from imports, thereby resulting in lower consumption. For Bangladesh and Sri Lanka, the reduction in consumption stems from two effects; first, greater imports substitute domestic consumption; second, greater exports eat in the demand for domestic consumption further.

**Table 11: GDP implications of SAFTA and BBIN in South Asian Countries**

|||**BBIN**|**S**|**AFTA**|
|---|---|---|---|---|
|Country|% change|Millions of US$|% change|Millions of US$|
|Bangladesh|0.03|34.9|0.07|81.3|
|India|0.03|477.6|0.04|758.5|
|Nepal|0.08|15.7|0.07|13.8|
|Pakistan|<-0.01|-8.9|0.04|76.2|
|Sri Lanka|-0.01|-3.9|0.05|29.3|
|Bhutan|0.02|3.9|-0.07|-16.2|
|RestofWorld|<-0.01|-208.0|<-0.01|-352.0|

**Table 12: Trade implications of SAFTA and BBIN in South Asian Countries**

||**B**|Exp<br>**BIN**|orts<br>**SA**|**FTA**|**B**|Imp<br>**BIN**|orts<br>**SA**|**FTA**|
|---|---|---|---|---|---|---|---|---|
|Country|%<br>change|Millions<br>of US$|%<br>change|Millions<br>of US$|%<br>change|Millions<br>of US$|%<br>change|Millions<br>of US$|
|Bangladesh|5.96|1574.2|8.34|2362.3|3.76|1307.3|5.65|1962.2|
|India|0.18|565.3|0.34|1258.1|0.32|1694.3|0.54|2828.6|
|Nepal|36.45|521.5|36.90|531.1|1.74|99.0|1.59|90.3|
|Pakistan|-0.09|-25.7|1.96|606.2|-0.12|-66.0|1.88|1067.5|
|Sri Lanka|0|2.9|6.37|791.5|-0.06|-13.3|2.60|590.9|
|Bhutan|1.61|54.1|10.02|375.8|1.03|128.8|1.21|151.3|
|RestofWorld|<-0.01|-374.0|<-0.01|-748.0|<-0.01|-852.0|-0.01|-1542.0|

**Table 13: Other Macroeconomic Implications of SAFTA and BBIN (% change)**

||**Cons**|**umption**|**Inve**|**stment**|**Gove**|**rnment**|
|---|---|---|---|---|---|---|
|**Country**|**BBIN**|**SAFTA**|**BBIN**|**SAFTA**|**BBIN**|**SAFTA**|
|Bangladesh|-0.64|-0.83|1.4|2.38|-1.06|-1.38|

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