rafmacalaba/datause-encoder-data huggingface dataset:
# 1 Introduction

A quick glance at levels of ...nancial development across countries reveals large di¤erences. Figure 1 plots for developing and advanced countries the ratios of private credit to GDP and trade openness to GDP starting in 1970. The average share of private credit to GDP is more or less three times higher in advanced countries than in developing ones throughout the period. On the other hand, trade volume as a share of GDP grew faster in developing countries, which have now surpassed the advanced ones. What explains persistent ...nancial underdevelopment? Can we say something about the relationship between ...nancial development and trade openness?

The literature has often emphasized the idea that ...nancial development is an endowment. La Porta, Lopez-de-Silanes, Shleifer and Vishny (1998) provide empirical evidence that a country’s legal origin is a strong and arguably exogenous determinant of a country’s ...nancial development. When it comes to institutions more broadly, Acemoglu, Johnson and Robinson (2001) document that the quality of institutions is largely determined by settler mortality rates during the colonial period. Applying these insights to international trade immediately suggests a pattern of comparative advantage: countries endowed with better ...nancial systems will specialize in goods that rely on external ...nance in production. Indeed, this idea has been formalized theoretically by Kletzer and Bardhan (1987), Baldwin (1989), and Ju and Wei (2005), and has found empirical support in a number of studies (e.g. Beck, 2002, 2003, Becker and Greenberg, 2003, Svaleryd and Vlachos, 2005, and Manova, 2005).

The purpose of this paper is to show the reverse link: ...nancial development itself depends on trade patterns. We argue that ...nancial development is endogenous, and that it will be determined in part by demand for external ...nance in each country. Comparative advantage in trade will a¤ect a country’s production pattern, and in turn its demand for external ...nance. Countries specializing in ...nancially dependent goods will have high demand for external ...nance and thus a high level of ...nancial intermediation. On the other hand, the ...nancial system will be less developed in countries that specialize in goods not requiring external ...nance. In this paper, we ...rst illustrate this point using a very simple model in which goods di¤er in their reliance on external ...nance. Comparative advantage implies that after trade opening, the ...nancially intensive sector expands in one country and disappears in the other. This change in production patterns in turn has implications for equilibrium ...nancial development in the trading countries.

We then demonstrate this e¤ect empirically. For a panel of 96 countries and 30 years, we use industry-level export data and information on each industry’s reliance on external ...nance from Rajan and Zingales (1998) to build a measure of the external ...nance need of exports. This measure, constructed following the methodology of Almeida and Wolfenzon (2005), summarizes the demand 2
developed economies are not doing any better.) Clearly there is a lack of willingness to enter into conflicts with operators, whether they are public or private, to be able to produce reasonable financial and accounting data. Technical and operational data is somewhat more available. But basic service cost and quality data (e.g. hours of service per day, basic reports on water quality, number and types of repair required) are much more difficult to obtain. And yet, they are necessary to be able to assess the extent to which there is cost manipulation which may result from a lack of accountability for corruption or from incompetence. This is where processes kick in.

Information collection and dissemination starts with a process. The failure to kick-start this process can be used to explain why information asymmetries, rents and the battles for a share of the rent continue to be so vivid and tend to lead to the disappointing performance of many utilities. But this is only a reflection of the tolerance for poor information reporting at the operator’s level.

Few governments consider it useful to impose information disclosure obligations on the operators or to allow their regulators to do so. And for the few who do, enforcement is often lacking for predictable reasons including operators’ desire for greater latitude. This is equivalent to the rejection by equity funds of disclosures needed to reduce the systemic risks in the financial system.

One of the reasons why the sector is not making the most of the options available to improve accountability is that regulators have their own biases as suggested by Trillas (2016). Indeed, one of the recent lessons of behavioral economics is that there is a need to question the assumption that the staff of regulatory agencies (or the experts they rely on), have the will and the knowledge to implement whatever policy is best for society. Regulators, just as operators, have biases and there are no reasons to expect the local biases to be compatible with the matching investors’ biases and expectations. The acceptance or rejection of a regulatory design is endogenous to the context and this is why one of the policy options underused so far is the framing of regulation to the local context rather than the adoption of standardized institutions and tools.

**So what?** There are many partial solutions capable of improving accountability which are technically feasible. Most of them are not easy and touch upon political and cultural issues usually ignored by reformers. This makes them challenging but not unfeasible.

A first direction for solutions to the accountability challenges is to better account for local legal and cultural preferences in the design of regulation. The public good nature of water makes it relatively 42
# **III. The PROCOMER Data**

Before conducting a detailed analysis of the microeconomics of export growth, we need to ascertain the quality of the data. Figure 1 compares the value of total Costa Rican merchandise exports in the PROCOMER data with the World Bank’s data on total merchandise exports, which come from official government sources, probably the Central Bank.

The two series are not identical, which could be worrisome, but is probably due to data manipulation by the Central Bank to conform to international balance of payments accounting practices. However, the over time correlation is very strong, and thus the PROCOMER data does capture the direction if not the exact magnitude of merchandise export growth observed in the macroeconomic data. The divergence between the two series is largest in 1997 and 1998, which converge to a constant gap in 1999. Consequently, the PROCOMER data tend to exaggerate the total export growth rates in 1998 and 1999, but the subsequent growth rates are comparable, as shown in the companion table to Figure 1. Nonetheless, the underlying microeconomic dynamics observed in the PROCOMER data are informative for understanding the sources of total export growth during this time period.

Unfortunately there are other issues with the PROCOMER data, which would affect the microeconomic analyses. Table 2 lists the steps taken to clean the data and the main features of the resulting data set.

The cleaning focused on issues that are relevant for understanding the microeconomic sources of export growth, including the role of incumbent, new and exiting firms, products and destinations, as well as unit values (the ratio of export value over quantities exported measured in kilograms). Hence we removed duplicate observations of firms-product-destinations, deleted observations where the description of the product was empty, entries where the reported quantity was zero, observations that reported Costa Rica as the destination market, etc. After this phase of the cleaning, the total number of observations of firms-products-year-destinations declined by about 6,000 observations and total export value declined by 0.26 percent. The next step was to remove any remaining duplicate observations that were due to the recording of the same firm, but with spelling mistakes or other errors in the reported name of the firm. This step reduced the total 5
Macro International. 2000. _DHS+ Dimensions:_ Vol. 2 No. 2.

- Manning, N., R. Mukherjee, and 0. Gokcekus. 2002. "Public officials and their institutional environment: An analytical model for assessing the impact of institutional change on public sector performance." Policy Research Working Paper 2427. World Bank, Washington, D.C.

- Martinez, J. and T. Martineau. 1998. "Rethinking human resources: an agenda for the millennium." _Health Policy Plan_ 13(4): 345-58.

- McPake, B., A. Asiimwe, F. Mwesigye, M. Ofumbi, L. Orthenblad, P. Streefland, and A. Turinde. 1999. "Informal economic activities of public health workers in Uganda: implications for quality and accessibility of care." _Social Science and Medicine_ 49(4): 849-65.

- Miller, K., R. Miller, A. Askew, M. C. Horn, and L. Ndhlovu. 1998. "Clinic-based family planning and reproductive health services in Africa: findings from situation analysis studies." Population Council.

- Miller, R., K. Fisher, K. Miller, L. Ndhlovu, I. Maggwa, A. Askew, D. Sanogo, and P. Tapsoba. 1997. _The Situation Analysis Approach to Assessing Family Planning and Reproductive Health Services: A Handbook._ New York: Population Council.

- Mills, A. 1997. "Improving the Efficiency of Public Sector Health Services in Developing Countries: Bureaucratic versus Market Approaches." In Colclough, ed., _Marketizing education and health in developing countries: Miracle or mirage?_ Oxford and New York: Oxford University Press.

- Mills, A. J., J. Kapalamula, and S. Chisimbi. 1993. "The cost of the district hospital: a case study in Malawi." _Bulletin of the World Health Organization_ 71(3-4): 329-39.

- Ministry of Education and Sports (Republic of Uganda). 2000. "Tracking the Flow of and Accountability for UPE Funds." International Development Consultants Ltd: Kampala, Uganda.

- Mowday, R. and R. M. Steers. 1979. "The measurement of organisational commitment." _Journal of Vocational Behaviour_ 14: 224-47.

- Mroz, T. A., K. A. Bollen, I. S. Speizer, and D. J. Mancini. 1999. "Quality, accessibility, and contraceptive use in rural Tanzania." _Demography_ 36(1): 23-40.

- Mwabu, G., M. Ainsworth, and A. Nyamete. 1993. "Quality of Medical Care and Choice of Medical Treatment in Kenya: An Empirical Analysis." _Journal of Human Resources_ 28(4): 838-62.

- National Bureau of Statistics (Tanzania) and MEASURE Evaluation. 2000. "Tanzania Reproductive and Child Health Facility Survey." MEASURE Evaluation Technical Report Series, No. 7.

- Newman, J., M. Pradhan, L. Rawlings, G. Ridder, R. Coa, and J. L. Evia. 2000. "An Impact Evaluation of Education, Health and Water Supply Investments of the Bolivian Social Investment Fund." World Bank. Processed.

- Over, M. and N. Watanabe. 2000. "Evaluating the Impact of Organizational Reforms in Hospitals." In Preker and Harding, eds., _Hospital Organizational Reform in Developing Countries._

44
Our eclectic discussion of related literature suggests that urban areas are likely to be more conducive to efficient production and enterprise growth since urban markets are better integrated and the quality of urban public infrastructure is likely to be better. Rural firms are likely to face severe supply-side constraints, such as poor infrastructure and limited public services. These may result in prohibitively high transaction costs restricting trade.

Consequently, one may expect rural firms to be smaller, to have more market power, to be less likely to grow, innovate and invest, and to be more vertically integrated. Moreover, one would expect urban areas to have higher labor productivity due to a combination of increasing returns to scale, higher total factor productivity (agglomeration externalities) cheaper inputs and possibly different technologies. Quantitatively predicting the magnitude and relative importance of these mechanisms is difficult. By empirically documenting their relative importance we hope to inform theoretical modeling of spatial disparities in economic activity.

Our approach is to shed light on these hypotheses by first documenting differences in enterprise performance and the investment climate across rural and urban areas and subsequently analyzing to what extent differences in enterprise performance are associated with differences in the rural and urban investment climate. We first focus on static characteristics before turning to an analysis of spatial differences in firms’ growth patterns.

# **3 Data & Descriptives**

# **_3.1 Data_**

This paper draws on the most recent Ethiopian rural and urban investment climate assessments. The rural data are from the 2007 Rural Investment Climate Survey Amhara

-7-
# Table of Contents

|**I.**Index Numbers for**Tariffs ...........................................**<br>**4**|
|---|
|1. 1 The Consumer Price Index and the Average Tariff**.......................................4**|
|I.2 The Trade Restrictiveness Index ...........................................<br>6|
|The Balance cf Payments Function ......................<br>6...|
|Derivation of die Trade Restrictiveness Index ........................................8|
|1.3 A Diagrammatic Exposition ..........................................<br>10|
|II.**The TRI and the Standard Indices ..........................................**<br>13|
|The TRI in Terns of Tariff Moments .............................<br>............. 14|
|The TRI and Piecemeal Reform Theorems .......................................... 16|
|tII.The Cobb-Douglas Example**..........................................**<br>19|
|IV. Lessons**..........................................**<br>26|
|**Appendix 1. Proof of Proposition 3 ...........................................**<br>28|
|**Appendix 2. Cobb-Douglas Simulations ..........................................**<br>29|
|References**..........................................**<br>30|

|6....................6|
|---|
12.3 percent of children are in the expected grade by age 12. On the other hand, the lack of effects on primary enrollment could have been expected given that the overall baseline coverage is high. Still, if improvements in adequate progression are signaling that children are better prepared to the challenges faced in primary education, the intervention may produce changes in drop-out in lower grades.

The positive effects on adequate progression are in line with Berlinski et al. (2009) who document sizable effects of pre-primary attendance on test scores in primary school in Argentina. In Uruguay, attending preschool was associated with a large increase in the probability of attending school at age 15 of 27 percentage points (Berlinski et al., 2008). However, as mentioned above, extrapolating these positive findings to rural contexts in poorer countries such as Guatemala is not straightforward. First, stimulation of skills conducive to high academic achievement is likely to be significantly worse in Guatemala’s rural households than in those of Argentina and Uruguay. For example, average mothers’ education in the Uruguay study amounted to 9.8 years, whereas in the communities included in our study average education for women aged 20 to 40 is only 3.2 years. Second, reports from several sources suggest that the quality of pre-primary education in Guatemala is low, and this could attenuate any positive benefits related to attending a pre-primary (UNICEF, 1996; Rubio et al., 2001). Finally, Guatemala has one of the highest levels of child chronic malnutrition in Latin America (Pan American Health Organization, 2008), which could potentially offset any positive effects associated to pre-primary attendance.

# **5.2 Further results**

Figure 2 presents point estimates and 95% confidence intervals for the effects of preprimary access at different ages. Results suggest negative effects of pre-primary access on primary enrollment at age 7 (p-value 0.082). This is consistent with evidence from Berlinski et al. (2008) for Uruguay. It can be the case that children aged 7 who are not adequately prepared for first grade are kept longer in pre-primary whereas their counterparts in communities without pre-primaries are enrolled in primary school. These negative effects on enrollment vanish at later ages suggesting that the mechanism at work here is delayed entry to primary school induced by pre-primary access. Regarding adequate progression, statistically significant effects only arise by age 12 suggesting accumulated effects over time.

Do effects differ across subgroups of the population? Identifying heterogeneous impacts can provide useful guidance on targeting pre-primary expansion programs to maximize expected effects. For clarity, Table 4 presents only estimated effects for the baseline specification and that controlling for department-linear time trends (columns 2 and 4). Panel A shows results from a regression where the relevant explanatory variables are a 11
# **Obesity and Food away from Home: What Drives the Socioeconomic Gradient in Excess Body Weight?**

Christoph Strupat,<sup>a</sup> Gabriela Farfán,<sup>b</sup> Laura Moritz,<sup>c</sup> Mario Negre<sup>a,b</sup> and Renos Vakis<sup>b</sup>

**Keywords:** food away from home, diet quality, obesity, health, Peru **JEL codes:** O1, I14, I15, I31, D12

__________________________________ a German Development Institute/Deutsches Institut für Entwicklungspolitik. b World Bank.

c Leibniz Institute of Agricultural Development in Transition Economies.

The authors would like to thank Cornelia Roemling, Maja Marcus and Raoul Herrmann for helpful comments. The authors would also like to thank colleagues at the National Statistics Office (INEI) in Peru, especially Nancy Hidalgo and Lucia Gaslac, for their support during data preparation and analysis. 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. All correspondence to Christoph Strupat, German Development Insti‐ tute/Deutsches Institut für Entwicklungspolitik, Tulpenfeld 6 53111 Bonn, Germany. E‐ mail: christoph.strupat@die‐gdi.de.
Table 1. Enrollment in Upper-secondary by Type of School

||_1997_|_%_|_1998_|_%_|_1999_|_%_|
|---|---|---|---|---|---|---|
|Federal (SEIT. SESIC)|1,015,636|38.97|1,032,059|<br>38.03|1,035,960|36.93|
|General Upper-secondary (Bachillerato General)|_20,781_|_0.80_|_20,373_|_0.75_|_21,375_|_0.76_|
|Upper-secondary by cooperation|_68,441_|_2.63_|_67,262_|_2.48_|_66,788_|<br>_2.38_|
|Upper-secondary (COBACH)|_83,946_|_3.22_|_89,369_|_3.29_|_88,016_|_3.14_|
|Technical Upper-secondary|_597,416_|_22.92_|_594,762_|_21.92_|_594,581_|<br>_21.19_|
|Technician (CETIS and CBTIS)|_45,073_|_1.73_|_38,947_|_1.44_|_40,154_|_1.43_|
|Technician CONALEP|_197,906_|_7.59_|_218,884_|_8.07_|_223,273_|<br>_7.96_|
|Technician (Others)|_2,073_|_0.08_|_2,462_|_0.09_|_1,773_|_0.06_|
|State|703,515|26.99|773,195|28.49|815,421|29.06|
|Autonomous (University)|374,201|14.36|369,992|13.63|367,960|13.12|
|Private|512,743|19.67|538,651|19.85|586,193|20.89|
|Total|2,606,095|100.00|2,713,897|100.00|2,805,534|100.00|

_Source:_ SEP, "Compendio Estadistico por Entidad Federativa 1999," DGPPP.

# IV. CONALEP

In December of 1978, the Mexican Government created CONALEP as a public decentralized body of the Ministry of Public Education (SEP). CONALEP was intended to provide a national network of upper-secondary schools that would prepare young people to become technicians at the upper-middle educational level. At this skill level 4 in the ISCED international classification (upper-secondary), there was a gap that was growing with the increasing demands for skilled labor. With the establishment of CONALEP, the Government also wanted to strengthen and rationalize the complex provision for technical secondary education in Mexico.

In 1979, the first ten CONALEP schools were opened, offering training in seven careers to 4,100 students. Not surprisingly, five of these careers focused on manufacturing, while the other two careers dealt with medical assistant and nursing professions. By 1982 the number of students enrolled in courses in CONALEP leading to technical qualifications increased to 72,000 and by 1989-1990 the total was 155,300. Since 1983, in addition to its career programs for technicians, CONALEP has also offered short courses for industry. This program was expanded 13
Policy ReseaRch WoRking PaPeR 4810

# **Abstract**

The paper tests three hypotheses about the causes of unemployment in the Central-East European transition economies and in a benchmark market economy (Western part of Germany). The first hypothesis (H1) is that unemployment is caused by inefficient matching. Hypothesis 2 (H2) is that unemployment is caused by low demand. Hypothesis 3 (H3) is that restructuring is at work. Our estimates suggest that the west and east German parts of Germany, Czech Republic and Slovakia are consistent with H2 and H3. Hungary provides limited support to all three hypotheses. Poland is consistent with H1. The economies in question hence contain one broad group of countries and one or two special cases. The group comprises the Czech Republic, Hungary, Slovak Republic and (possibly) East Germany. These countries resemble West Germany in that they display increasing returns to scale in matching and unemployment appears to be driven by restructuring and low demand. The East German case is complex because of its major active labor market policies and a negative trend in efficiency in matching. In some sense, East Germany resembles more Poland, which in addition to restructuring and low demand for labor appears to suffer from a structural mismatch reflected in relatively low returns to scale in matching. Finally, our data provide evidence that goes counter to one of the main predictions of the theories of transition, namely that the turnover (inflow) rate in the transition countries would rise dramatically at the start of the transition, be temporarily very high and gradually decline and approach the level observed in otherwise similar market economies such as West Germany.

This paper—a product of the Development Economics Department, Research Support Unit (DECRS)—is part of a larger effort by the World Bank’s Research Committee, in consultation with Regions and Networks, to commission surveys of recent policy research and diagnostic analyses of the current state-of-the-art in priority areas for developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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._ Produced by the Research Support Team