rafmacalaba/datause-encoder-data huggingface dataset:
clustered at the firm level to allow for potential autocorrelation in the choice of export destination at the level.

As a second step, we explore whether the third-country effect of RTAs is stronger for deep trade agreements by estimating the following equation: where _Exp Other Depthij,t−_ 1 = max _k_ = _j {Exporting Dummyik,t−_ 1 _× Depthjk,t}_ captures the depth of the deepest RTA that country _j_ has with a country to which firm _i_ exported in _t_ -1. We exploit the richness in the content of RTAs to construct measures of depth and investigate different aspects of third-country RTAs that generate stronger spillover effects for firms that already exported to one of the member countries. The first indicator is the aggregate depth, measured as the additive number of provisions that are strictly legally enforceable. This aggregate depth measure is normalized between 0 and 1 so that ( _β_ 1 + _β_ 2) captures the impact of prior export experience of firm _i_ to a country, to which firm _i_ exported in _t_ -1 and has the deepest RTA with country _j_ , on its export participation and entry to country _j_ .

Going beyond the aggregate depth measure, we further investigate whether certain provisions are particularly important in driving the spillover effect of RTAs. Specifically, we separately estimate the effects of RTA depth based on provisions that are nondiscriminatory (or most favored nation-MFN) and preferential. This distinction is interesting and important for our analysis, since we focus on third-country effects of RTAs. This means that _Depth_ in equation (3) becomes a vector of MFN-depth and preferential-depth, measured as the additive number of MFN and preferential provisions respectively, again normalized between 0 and 1.<sup>20</sup> As above, the interaction variables are constructed such that they capture the maximum MFN and preferential depth of country _j_ ’s RTAs among the set of countries that firm _i_ exported to in _t_ -1 . We expect RTAs that are “deep” in MFN-provisions to generate larger spillovers to non-membercountry firms. Finally, to test if third-country effects are driven by the harmonization of standards, we check if the effects are stronger for RTAs that include SPS or TBT provisions.

# **_Results_**

Table 3 presents the first set of results. The dependent variable is the export participation dummy in columns (1)–(4) and the export entry dummy in columns (5)–(8). First looking at export participation, column (1) estimates equation (2) and column

> 20See Appendix A.1 for the list of MFN and preferential provisions.

10
# **Introduction**

The recent external review of World Bank research noted that “perhaps the most important role of Bank research is to learn what works, and to widely disseminate the results” (Banerjee et al. 2006, p. 148). Rigorous impact evaluations, which compare the outcomes of a program or policy against an explicit counterfactual of what would have happened without the program or policy, are one of the most important tools that can be used along with appropriate economic theory for understanding “what works”. Despite this, until recently impact evaluations have been rare, especially outside the areas of health and education.<sup>1</sup> This is now particularly apparent in the area of finance and private sector development, where the recent financial crisis has prompted renewed attention to knowing what works in terms of getting finance to consumers and firms, and in getting the private sector growing again.<sup>2</sup> One reason for the lack of impact evaluations in this area is the perception that many finance and private sector development (hereafter FPD) policies and projects lend themselves less to formal evaluations.3 Changes in laws or regulations may occur at an economy-wide level, or a large loan may only be given to one or two banks or firms. However, in many cases it is still possible to formally evaluate FPD policies or projects. Regulations may be implemented in some regions and not others, or apply only to firms of a certain industry or size. Generally available programs or policies may have low takeup that can be raised through targeted interventions. And in a non-trivial number of cases it will indeed be feasible to implement a randomized experiment. The purpose of this paper is to demonstrate the feasibility of such impact evaluations, distil the lessons of these new evaluations for policymakers and practitioners, and help prompt new impact evaluations for projects going forward.

> 1 For example, the Development Impact Evaluation (DIME) Initiative has until recently focused on topics in health and education. See <u>http://web.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTDEVIMPEVAINI/0,,menuPK:3998281~p</u> ~ ~ <u>agePK:64168427 piPK:64168435 theSitePK:3998212,00.html</u> [accessed February 4, 2009]. 2 See also the recent World Bank Policy Research Report on Access to Finance which calls for more impact evaluation (Demirgüç-Kunt et al. 2008).

> 3 A second reason may be that research on FPD has historically worried less about the challenges of identification that are a prime concern of the labor and applied microeconomics literature. Financial economists are much less likely to be exposed to impact evaluation methods in their graduate classes than health, education, or labor economists. A further purpose of this paper is thus to better expose practitioners in the FPD field to the ideas and possibilities of impact evaluations.

- 2 -
To ensure that our study population was generalizable to the national population of Pakistan that has access to a mobile phone, post-stratified weighting was applied. The weighting corrected for differences in the achieved sample’s profile and what was expected as per the latest population census (2017). For creation of weights, two strata were used, namely: 1) Province (Punjab, Sindh, KP, and Balochistan) and 2) region (urban or rural). Thus, a total of 10 weights were created.

# Key Variables and Tools

The study focuses on the following key risk variables: parental distress, psychosocial stimulation, food insecurity, maternal education, ECE enrollment,<sup>3</sup> and living in a rural area. The two main outcome variables were child development for children under 36 months and school readiness for children 36-72 months. The age at which a parent believes their child should begin school was also analyzed to understand the association between parental belief and ECE enrollment. The following variables were constructed. A description of the independent variables follows.

## Outcome Variables

## **_Child Development for Children 0-3_**

The Caregiver-Reported Early Development Instrument (CREDI) is a globally validated, population-level, developmental assessment tool for children aged 0-3 years (McCoy et al., 2018). It has been used in over 17 countries and is designed to be culturally and linguistically neutral. The short form questionnaire was chosen given that data were collected using a phone survey and respondent fatigue was a concern. The short form is also recommended for population-level surveys. This version of CREDI has 20 questions to be completed by household parents or caregivers<sup>4</sup> about a selected child. The scale captures development across four domains: motor, language, cognition, and social-emotional development. There are 20 items per form, with items representing developmentally appropriate skills for each of six age groups ranging from 0-2 years (0-5, 6-11, 12-17, 18-23, 24-29, and 30-35 months). Forms are standardized by age such that a question about social-emotional development looks different for a child under 6 months than a child aged 30 months, and so forth.<sup>5</sup> Differences in child development are interpreted in standard deviation units. A child who scores more than 2 standard deviations below the mean is typically considered to be developmentally “off-track."

> 3 This item is only asked for children old enough to be enrolled in school – i.e., children aged 36-72 months. 4 The terms “parent” and “caregiver” are used interchangeably in this paper to refer to key adults in a child’s household providing care and supervision, including mothers, fathers, grandparents, and other household adults. 5 The short and long-form CREDI questionnaires are open access and available online via: https://credi.gse.harvard.edu/credi-materials.

10
Which households are more likely to be unequal? Is it related to the total amount of expenditure? To the household structure? To the head’s characteristics? In this section, we examine the correlates of being an unequal household focusing on the household structure, and show how the specificity of the PSF questionnaire allows to gain new insights on the determinants of intra-household inequality.

In table 5, we present the correlates of the within household Theil index, both for consumption as a whole and then separately for food and non-food expenditures.

Household structure clearly plays a role in explaining intra-household inequality. It is worth noting that this might be difficult to pin down with standard household surveys that only record the relationship of each household member to the household head. The first 2 columns of table 5 compare the description of the relation between inequality and household structure obtained from a regression using only variables that could be retrieved from a traditional survey, to what can be exhibited with the more detailed information on household structure that PSF contains. Column (1) describes household structure with variables counting the number of children below age 5, those between 5 and 15, the number of adult women between 15 and 65 years of age, and the number of elderly members. It also includes a dummy variable if the household doesn’t embrace any horizontal extension, i.e. if all members are either the wife, the parents, the children or the grand-children of the household head. The first column only indicates that the level of intra household inequality increases with the number of children aged 5 to 14. As this might reflect difference in needs that are not captured by the per capita measure of individual consumption, it is not very revealing. The second column makes clear that, even controlling for the size and the age-gender composition of the household, its precise organisation matters. Extended households with more than two cells, whether they are polygamous households or include cells headed by relatives in addition to the head’s conjugal unit, have more unequal resource allocation than any other household types. On the other hand, 18
**Table 20: Criminal Offences by Brazilians and Peruvians**

||1992|1998|1999|2000|2001|2002|increasing rate|
|---|---|---|---|---|---|---|---|
|The number of foreign resident|1,281,644|1,512,116|1,556,113|1,686,444|1,778,462|1,851,758|4.1%|
|Brazilian|147,803|222,217|224,299|254,394|265,962|268,332|0.9%|
|Peruvian|31,051|41,317|42,773|46,171|50,052|51,772|3.4%|
|Number of Offence|12,153|31,779|34,398|30,971|27,763|34,746|25.2%|
|Number of Criminal Offence|7,457|21,689|25,135|22,947|18,199|24,258|33.3%|
|Brazilian|222|3,278|5,110|3,273|3,457|4,967|43.7%|
|Peruvian|331|1,023|1,250|482|425|436|2.6%|

Source: Japanese Ministry of Justice.

42
# Equity in Tertiary Education in Central America: An Overview<sup>1</sup>

By Sajitha Bashir and Javier Luque<sup>**2**</sup>

**Keywords: Inequality, Tertiary, Education, Central America**

**Classification code:  I240**

**Sector Board: Education**

> 1 This article is based on analysis undertaken in the context of the World Bank’s study on Equity in Tertiary Education (J. Salmi and R. Bassett, “Opportunities for All? A Global Study on Equity in Tertiary Education” (forthcoming)). The analysis was partly funded by a Trust Fund financed by The Dutch Government for the purpose of studying equity in developing countries. The findings, interpretations, and conclusions expressed herein are those of the authors, and do not necessarily reflect the views of the International Bank for Reconstruction and Development / The World Bank and its affiliated organizations, or those of the Executive Directors of The World Bank or the governments they represent.

> 2  Sajitha Bashir (Sector Manager, Education - Africa Human Development Department - Email: sbashir@worldbank.org) Javier Luque (Senior Education Economist - Latin America and the Caribbean Human Development Department - Email: <u>jluque@worldbank.org</u> ). The authors wish to thank Pablo Adrian Garlati Bertoldi for excellent research assistance, Jessica Acosta for formatting the paper and  ICEFI for undertaking data collection and analysis on public expenditures on education in Central America.
When it comes to reports, one can make use of digitalized USDA forecasts for each marketing year, from 1975 to 2016. These forecasts are for demand and yield per acre. To obtain �<sup>Zpu</sup> t forecasts from demand forecasts, we regress these last ones against forecasted yields, then use the residual as a demand shock estimate. The reason for this is because given that a simple demand forecast contains also an endogenous forecast, since a higher yield would endogenously result in lower prices and higher consumption, one can eliminate this endogenous component of the demand forecast by regressing it against the yield forecast.

This section calibrates the parameters to simulate and show quantitative results. Given the data available (described above), the section proceeds as the following. First, calibrate the parameters necessary to calculate �<sup>q</sup> i<sup>which are �; �I; �II;A;�Z�y;Z�q; !; and �: Demand and supply elasticities are</sup> estimated as indicated in the previous step. The persistence of supply and demand fundamentals are calibrated by estimating AR(1) processes, and volatility is obtained by taking the residual’s standard deviations.

Storage cost parameters �<sup>I</sup> ; �<sup>II</sup> are estimated by choosing values that minimize residuals. That is, the previous set of equations will give theoretical prices as the result for each quarter. The di¤erence between these and observed prices are the residual. Parameters �<sup>I</sup> ; �<sup>II</sup> were calibrated such that the total sum— in absolute values— of the residuals is minimized.

The next step is to calibrate public and private signaling parameters. Regarding public signals, it is important to say that these appear in speci...c days, which are publicly known in advance. That is, at a certain time, information becomes publicly known in a report and markets react almost instantaneously Price changes for those "announcement" days can be seen, and even though private information is always present in markets, it is reasonable that by constraining price changes to the speci...c announcement day isolates public signal’s e¤ect on prices.

The precision of public forecasts is estimated by comparing USDA demand and supply forecasts versus ex post realizations. The most non-straightforward task, however, is to obtain a value for the precision of private signals. The section proceeds to identify them as follows. First, calculate �<sup>I</sup> 4<sup>and �I</sup> 5<sup>by usingthe method of undetermined coe¢cients (also use this to obtain all parameters</sup> �<sup>q</sup> i<sup>).Second,obtainthereleasedatesforpublicsignals(USDAforecastreports)andthesame-day</sup> price reactions. Then, regress same-day price changes against the changes in forecasted values in the released reports— with respect to the previous report— for that day. This allows to estimate the 29
12 stands for total employee compensation divided by -assets. This measure- of host country compensation has declined for developed and developing countries alike. The tax and wage data together suggest host countries, and especially developing countries, are receiving progressively fewer direct benefits from the presence of foreign direct investment.

Table 3 provides information on how U.S. foreign direct investment differed by industry for 1982. First, payroll and income tax rates are highest in the petroleum industry, followed by manufacturing and wholesale. Lowest taxes are paid by the finance and service industries. The least leveraged industry is manufacturing, while the most highly leveraged is finance. High-tax industries tend to have high plant and equipment expenditures as a share of assets. Apparently, the presence of fixed capital assets enables tax authorities to raise the average rate of taxation. High tax payments also appear to coexist with high employee compensation relative to assets. Apparently, the same factors that allow tax authorities to extract tax payments allow labor to bargain for its compensation. Secondly, the last column provides information on research intensity, as measured by the volume of research and development expenditure for the affiliate divided by affiliate assets.<sup>7</sup> The manufacturing industry is shown to be the most research intensive.

Of course, the information, as presented in the table, does not allow us to infer precisely the relationships between financial structure and taxation. To address these questions, we next present the results of regressions that attempt to explain the financial structure choice of the U.S. multinational, on the one hand, and the country's taxation decision on the other.

# _Regression Results_

To start, Tables 4 and 5 examine the leverage decision for U.S. foreign affiliates. I The regressions in Table 4 result from ordinary least squares. The country's taxation decision, however, is argued in Section 2 to be endogenous to the firm's leverage d&csion. To adjust for this, Table _5_ reports two-stage-least-squares regression results, where the P EXP/A variable is used as the instrument for the T/A variable. The data are aggregated to the host country level. The dependent variable is the ratio of affiliate liabilities to assets. Column 1
# **Table 15.  Impact of WTO Accession on Output by Sector and Regional Market**

(percentage change)

|Good|Central|Siberia|South|North|Urals|Far East|Moscow|St.<br>Peters.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|FME|33.1|13.1|44.8|23.7|15.2|19.7|43.6|352.3||10.5|
|CHM|8.3|21.5|10.4|-1.9|12.7|0.1|2.9|4.9|45.3|28.6|
|NFM|10.9|4.5|33.8|10.2|22.7|6.1|35.4|-0.1||-11.6|
|TMS|9.2|5.0|9.0|16.6|-2.3|16.7|-4.3|15.8|11.3|16.8|
|TRK|7.7|5.3|6.9|9.6|3.1|11.2|2.9|10.8|7.0|11.9|
|COL|5.6|3.7|5.5|5.7|4.8|5.2|||||
|TRD|6.0|4.1|4.4|4.2|3.9|5.3|4.3|6.5|2.7|8.0|
|ELE|1.9|1.6|1.8|1.0|1.4|0.7|0.4|1.9|0.3|4.0|
|SCI|-0.6|1.3|-0.1|2.9|-1.2|3.7|-0.1|4.8|-0.5|4.5|
|HOU|0.9|1.2|1.9|1.8|0.4|1.9|1.4|1.3|2.2|1.6|
|PST|1.5|1.0|2.0|1.6|0.5|2.1|1.0|1.1|1.7|1.6|
|HEA|1.2|1.2|1.6|1.4|1.0|1.6|1.4|1.6|1.2|1.7|
|CRU||0.9|1.4|1.7|0.9|1.6|||1.6|1.3|
|RLW|3.2|-0.1|0.9|0.4|0.8|0.8|-1.1|4.6|0.7|2.6|
|OIL|4.0|-2.5|3.6|2.2|-1.7|3.0|-0.7|-2.8|6.2||
|GAS||-1.4|-0.2|-0.5|-1.0|-0.6|||1.1|-1.9|
|CON|0.5|-1.6|0.6|-1.4|-0.1|-1.4|1.6|-2.7|2.2|-4.7|
|TPP|-7.0|-4.6|-10.1|-0.6|-2.3|29.9|-13.2|-8.9|15.3|-10.6|
|AIR|-1.4|-5.1|-1.4|1.1|-3.4|0.6|-2.5|-2.6|4.8|-2.7|
|CLI|-1.8|-4.5|-2.1|-3.1|-3.2|-2.9|-3.8|-6.1|1.3|-7.1|
|PIP|-2.4|-4.6|-3.1|-4.6|-2.8|-4.6|-5.9|-6.0|-0.2|-1.3|
|MWO|-9.4|0.4|-1.7|-6.1|-9.0|-4.0|8.7|-2.4|-1.7|-11.2|
|OTI|-3.8|-6.2|-2.6|-4.5|-4.3|-5.9|-0.3|-3.6|-1.5|-6.0|
|AGR|-3.2|-4.0|-2.2|-4.6|-1.8|-5.4|-3.7|-9.7|2.8|-11.4|
|FIN|-6.2|-6.7|-6.1|-6.6|-6.7|-6.3|-6.6|-6.0|-7.1|-5.3|
|MAR|-4.3|-11.7|-6.8|-4.9|-6.1|-3.6|-9.3|-6.0|-5.8|-8.1|
|TRO|-6.2|-11.0|-7.5|-2.3|-15.6|-1.8|-19.4|-2.2|-10.0|-1.1|
|CNM|-7.4|-11.4|-5.6|-8.0|-7.1|-9.0|-12.7|-14.1|1.6|-15.7|
|OTH|-7.0|-13.9|-8.7|-13.3|-6.9|-14.7|-7.0|-17.2|-4.6|-21.1|
|FOO|-13.5|-11.9|-10.6|-8.8|-11.3|-12.5|-17.1|-13.9|0.4|-17.0|

See Tables 1 and 2 for sector and regional market definitions.

Source: Authors' calculations.

47
Policy Research Working Paper 6137

# **Abstract**

This paper exploits the staggered rollout of Vietnam’s hospital autonomization policy to estimate its impacts on several key health sector outcomes including hospital efficiency, use of hospital care, and out-of-pocket spending. The authors use six years of panel data covering all Vietnam’s public hospitals, and three stacked crosssections of household data. Autonomization probably led to more hospital admissions and outpatient department visits, although the effects are not large. It did not, however, affect bed stocks or bed-occupancy rates. Nor did it increase hospital efficiency. Oddly, despite the volume effects and the unchanged cost structure, the analysis does not find any evidence of autonomization leading to higher total costs. It does, however, find some evidence that autonomization led to higher out-ofpocket spending on hospital care, and higher spending per treatment episode; the effects vary in size depending on the data source and hospital type, but some are quite large—around 20 percent. Autonomy did not apparently affect in-hospital death rates or complications, but in lower-level hospitals it did lead to more intensive style of care, with more lab tests and imaging per case.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at awagstaff@worldbank.org.

_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._ Produced by the Research Support Team
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