rafmacalaba/datause-encoder-data huggingface dataset:
# Table 4. Incumbent Victories and Conflict Casualties: Panel Regressions

|||Po<br>Depende<br>|isson Estim<br>nt variable: <br>|ates<br>Casualties<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Incumbent victory _×_ post-election|0.12**|0.10**|0.10**|0.13**|0.14**|
||(0.055)|(0.047)|(0.052)|(0.055)|(0.065)|
|Incumbent victory|0.017<br>|0.0046<br>|0.0039<br>|0.0077<br>|–0.052<br>|
||(0.037)|(0.028)|(0.027)|(0.025)|(0.043)|
|Margin _×_ post-election|–2.07*|–1.81*|–1.81*|–1.76*|–1.58*|
||(1.26)|(1.03)|(1.02)|(1.01)|(0.88)|
|Margin|0.074|0.29|0.29|0.28|0.30|
||(1.01)|(0.76)|(0.75)|(0.73)|(0.64)|
|Margin _×_ incumbent victory _×_ post-election|–0.68|–0.25|–0.25|–0.25|–0.22|
||(3.16)|(2.35)|(2.35)|(2.28)|(2.04)|
|Margin _×_ incumbent victory|–1.35|–1.47|–1.47|–1.43|–1.26|
||(1.57)|(1.27)|(1.26)|(1.22)|(1.08)|
|Post-election period (12 months)|–0.091**|–0.078**|–0.071**|–0.095***|–0.11***|
||(0.043)|(0.037)|(0.028)|(0.032)|(0.040)|
|Time trend (months)|||–0.00047<br>(0.0022)|0.0013<br>(0.0016)||
|Time trend squared (months)||||0.00026**<br>(0.00012)||
|Time trend _×_ incumbent victory|||–0.000094<br>(0.0038)|–0.0018<br>(0.0036)||
|Time trend squared _×_ incumbent victory||||–0.00026<br>(0.00019)||
|Gov. coalition _×_ inc. victory _×_ post-election|||||–0.061<br>(0.068)|
|Governing coalition _×_ incumbent victory|||||0.057|
||||||(0.039)|
|Governing coalition _×_ post-election|||||0.057<br>(0.045)|
|Governing coalition|||||0.0034<br>(0.019)|
|Mean of dep. var.|0.051|0.051|0.051|0.051|0.051|
|Control variables|No|Yes|Yes|Yes|Yes|
|No. of municipalities<br>No. of observations|3672|3672|3672|3672|3672|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ All regressions are weighted by a triangular kernel with a bandwidth of 0.05. The unit of observation is the municipalitymonth. The sample is restricted to observations within 12 months of the 2007 election (the month of the election is dropped). Marginal effects calculated at the sample means are reported. Standard errors, clustered at the municipality level, are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels. Control variables are the same as in Table 3.

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Second, perceptions do not seem fixed, confirming the original intuition of Zaller (1992). In fact, for some countries the shifts in perceptions are quite remarkable. For example, Poland and Portugal.<sup>19</sup> Finally, there seems to be some correlation between the evolution of objective inequality and subjective perceptions (more on this below).

The ISSP surveys of 1992, 1999, and 2009 also provide data on the evolution of demand for redistribution. ‘Net’ demand for redistribution is defined as the difference between the share of individuals who strongly agree with the statement: “it is the responsibility of the government to reduce income differences between people with high incomes and those with low incomes” and those who strongly disagree with it. A negative value indicates that more individuals disagree with the statement than agreeing with it, while a positive value indicates the opposite.

Figure 3 plots the evolution of demand for redistribution over time and across countries. As in the case of perceptions, some clear differences between countries are highlighted: European countries, both in the East and the West, have a stronger demand for redistribution than the rest of the world, particularly when compared to the United States, which is the only country in the sample that has a negative net demand for redistribution. This is not surprising given the differences in preferences between European and U.S. citizens well documented in the literature. Within Europe, Eastern European countries show a higher demand than most Western and Southern countries. Over time, demand for redistribution has also moved differently in the various countries, increasing from 1999 to 2009 in some countries (e.g., Hungary, Poland, and France), and decreasing in others (e.g., Bulgaria, Portugal, and Spain).

> 19 Note that we do not use panel data, so the shift in share may simply be due to a cohort effect, i.e. people from younger cohorts may have different opinions and that may explain (part) of the shift observed in the figure.

15
The remaining columns of Table 8 then examine the extent to which these same variables are correlated with other clear differences of developing courses from top-20 courses. Column 2 considers the share of the course devoted to empirical topics. We see that these same variables have much less explanatory power for this outcome, with the only significant variable being having the instructor not listed for the course (which is associated with less focus on empirical topics). Column 3 looks at the adoption of the Banerjee and Duflo (2011) book. This is more prevalent in countries with higher overall tertiary education attainment, and less prevalent in more statedominated economies. Column 4 looks at use of the Todaro and Smith (2014) book which is not used in top-20 U.S. courses. We see that it is less likely to be used in richer countries. Finally column 5 considers whether students are required to do homework that uses statistical software. This is again more prevalent in countries with higher levels of overall tertiary attainment, but conditional on this, less prevalent in richer countries.

The survey data allow us to examine one additional aspect of why development economics is taught as it is. One might hypothesize whether language is a barrier to teaching more modern materials to students, if recent research and resources are only in English. However, we find that 60 percent of the courses are taught in English, and 88 percent of the instructors surveyed say that availability of materials in the right language is not a constraint. Of course if these instructors were to seek out more empirically-oriented material they may then run across this constraint, but given the prevalence of courses in top universities in developing countries which are taught in English, we do not believe language is likely to be the principal barrier.

# **6. Conclusions and Suggestions for What Instructors Should Be Changing**

The field of development economics has changed dramatically over the past 20 years. This is reflected in changes in the topics that command most research attention, and particularly in the rapid growth in data availability and empirical analysis. Our survey of how development economics is being taught in developing countries suggests that many classes have not kept pace with this change, and are not meeting key student learning goals of teaching students to be critical users and analyzers of data to answer economic questions. This is particularly the case in poorer developing countries, and countries with relatively low levels of overall tertiary attainment.

We hope that this paper may provide some guidance for instructors in developing countries seeking to learn how the subject is taught in other countries. In addition, we have the following concrete 20
households affected by an income shock. The household's own shocks are excluded from this variable. The partial risk-sharing specification is formulated as: A negative and statistically significant δ would imply that some risk sharing takes place within the extended families.

We will assess the impact of these network shocks separately for migrant and nonmigrant households and fully acknowledge that selection into migration is unlikely to be random. The differences in the observed level of risk sharing may be caused by migration or by some unobserved characteristics that differ between migrant and nonmigrant households, or by some combination of both. As a result, these regressions do not allow us to say whether migration is causally responsible for the migrant taking on the role of insuring sedentary extended family network members, or whether the effect is driven by unobservables. In particular, we cannot make any statements about what would have happened if migrants had stayed home or the home-stayers had migrated. It is possible that in this parallel universe roles would have switched (migration is causally responsible) or not (it is driven by the unobserved differences between migrants and non-migrants). Our primary contribution lies in documenting the fact that migrants provide unilateral insurance to non-migrants, while at the same time shooting ahead of them in consumption terms. Nonetheless, we will dedicate Section 9 to shedding light on how selection fits into this and will utilize information from additional survey rounds to exclude some possible types of endogeneities.

Finally, the baseline per capita consumption variable in Equation (6) raises a concern about endogeneity. The error term εi could be correlated, for example due to measurement error, with the lagged consumption variable. This would then bias the estimate measuring the impact of the lagged consumption but it may also affect other coefficients. Fortunately, we can think of a credible instrument that allows us to assess this possibility. Rainfall is one of the main inputs in agricultural production in Kagera and poor rainfall (i.e. droughts) can have serious consequences for incomes. Excess rains are less of a problem due to the focus of the production on tree crops and 15
# **ECONOMIC INTEGRATION IN THE LOWER CONGO REGION: OPENING THE KINSHASA-BRAZZAVILLE BOTTLENECK**

_Marius Brülhart_<sup>_1_</sup> _and Mombert Hoppe_<sup>_2_</sup> Keywords: market segmentation, cross-border trade, transport services, Africa, Congo JEL classification: F13 - Trade Policy; International Trade Organizations, F14 - Country and Industry Studies of Trade, F15 - Economic Integration, O24 - Trade Policy; Factor Movement Policy; Foreign Exchange Policy Sector Board - Economic Policy (EPOL)

> 1 Marius Brülhart is Professor of Economics at the University of Lausanne

> 2 Mombert Hoppe is an Economist in the World Bank’s Poverty Reduction and Economic Management Department
<!-- Start of picture text --> 0.17<br>-0.09<br>non-exporter exporter<br>Note: Residuals form the estimation using TFP as dependent variable and controlling for firms<br>characteristics, investment climate, financing from Bank, sector and region FE.<br>Source: Authors' calculation based on Enterprise Surveys 2014<br>.2<br>.1<br>0<br>-.1<br><!-- End of picture text -->

# **Firm location and duality of the economy**

The location of firms reveals striking differences that can influence firm performance. While small firms are more evenly distributed across the country, there is a process of concentration in the northwest and southwest (Figure 12). Large firms are extremely concentrated in the Southwest region, where 73 percent of the large firms are located (Figure 13). Recall that the costs associated with bribes, crime, insecurity, and power outage are lower in the Southwest relative to the other regions (Figure 9).

Figure 12. Distribution of small firms by regions Figure 13. Distribution of large firms by regions 12
on attendance and math score, but not on enrollment) and the absence of treatment effects on the other outcomes studied.

_Correcting for attrition_ . As reported earlier, attrition of girls taking part in the household survey was slightly larger in the control group than in the treated groups. While our main outcome of interest (independently verified attendance rate at school) and teacher attendance are not affected by this differential attrition in the household survey, below we present results for the other outcomes, correcting for differences in individual girls’ probability to attrit from the household survey. More precisely, in Table 6, we ran regressions in which the school averages are obtained after weighting each girl in the endline survey sample by the inverse of the probability that she would be observed at endline, as predicted by all her individual and household baseline characteristics summarized in Table 2. Interestingly, none of the outcome variables measured at baseline predicts attrition, while older girls and girls from poorer households were more likely to attrit and girls from Manica district (of Manica province) were less likely to attrit. Reassuringly, reweighting observations by the inverse of the probability that they attrit does not change our conclusions.

_No selection of girls through school switches_ . The treatments were announced after the official enrollment period closed, and, in most cases, after the start of the school year, so that a negligible effect on enrollment was to be expected, as confirmed in our data analysis. Another potential source of selection of girls into the school registers for which the survey firm recorded spot check attendance data is through school switches. Out of the 2,687 endline survey girls whose parents reported as being enrolled for the 2016 school year, only 157 (5.84%) were reported as being enrolled in a school other than the one they were sampled from. Estimating Equation (4) using, as dependent variable, a binary indicator equal to one if the girl is reported enrolled in a different school to that from which she was sampled and zero if she was reported enrolled in her original school, no treatment indicator is individually significant (nor are they 34
# **1 Introduction**

This paper estimates the long-run benefits of maternal care and childcare. The long-run outcomes are of interest for three reasons. First, many effects of an early-life intervention remain latent for a long period; outcomes at birth or during early childhood may not fully capture these latent effects (Miller and Wherry, 2018). Second, the effects of such a program on human capital formation, economic and labor market outcomes can only be fully understood during adulthood. Third, subsequent parental investment may lead to a catch-up in the long-run. Thus, early life differences in health may not lead to similar long-term differences (Crimmins and Finch, 2006; Sohn, 2017). There are a limited number of studies on the long-run impacts of maternal care and child care interventions because datasets that track individuals over long periods of time and collect detailed data on several dimensions of health, education, and labor market outcomes, are rare. This paper uses multiple waves of the Indonesian Family Life Survey (IFLS), a detailed panel of households spanning over a period of 22 years. The long-run panel nature of the dataset helps tracking individuals from childhood to adulthood.

This paper studies the Village Midwife Program, a central intervention in Indonesia for increasing access to antenatal care, postnatal care, and safe delivery services among reproductiveage women from families of all social backgrounds. The program was introduced in 1989 and was rolled out in a phased manner, with villages in poor and remote communities getting priority (Frankenberg and Thomas, 2001; Frankenberg et al., 2005, 2009; Cas, 2012). This paper uses a difference in difference strategy that combines the spatial variation in the rollout of the program with a temporal variation by comparing individuals who were old enough (five years and older) at the time of the roll-out of the program with individuals in their early life ( _in utero_ or in their first three years).<sup>1</sup> Thus, the comparison is between these two groups born in the same community, with outcomes measured about 20–25 years after they were first exposed to the program. The regressions also control for maternal and time-varying community characteristics, in addition to the

> 1A separate indicator for whether children were four years old at the time of the program expansion in their community is included in the regressions. This allows for the impact of access to a midwife to differ for children who were four at the time the midwife began working.

2
students’ knowledge of various subjects (critical reading/language, mathematics, social sciences, natural sciences, philosophy, and English). The measure of individual-level academic readiness preSCP is the average of all the student’s SABER 11 subject scores, standardized by semester-year.

_Age_ is a variable whose meaning differs across estimation samples. Age is the student’s age when entering the program for the graduation sample; the age when she took the SABER PRO exam in the learning sample; and the age when she was employed in the labor market samples.

_Relocated to pursue a SCP_ is a variable that equals to 1 if the student moved away from the student’s high school city to participate in the SCP. We construct this variable based on the municipality codes of the student’s high school city and the city where she enrolls in her SCP.

_Entry year_ indicates the year the student started the program in the graduation sample. This year is estimated for the other samples as follows: entry year = student graduation year – average time to degree at the student’s specific program.

_Graduation year_ consists of the year when the student graduated in the graduation, employment, and wage samples; and the year when she took the SABER PRO test in the learning sample.

# **Measures of institution and program practices and other supply and local market conditions**

_Institution and program practices._ Using data from SPADIES, we define three practices that are relevant to our context:

- _Selective institution._ To proxy for institution selectivity, we estimate the average 2008 SABER 11 exam score across all programs taught by each institution providing SCPs, and rank institutions based on it. Institutions in the upper half of the ranking are classified as “selective.”

- _Institution field specialization._ For a particular field, this is the share of programs that an institution offers in that field relative to all the programs it offers. The index ranges between 0 to 1; an index close 1 indicates that the institution is highly specialized in that field. For example, if 60% of the programs offered by the institution are in Health and the remaining 40% are in Business, then the institution has specialization indices equal to 0.6 and 0.40 for Health and Business, respectively, and equal to 0 for the other fields. If the institution has branches in multiple cities, we construct these indices for each city.

50
service mix. This decision rights allocation was also in conformance with the operations of today's competitive hospitals.

Despite the great leap forward HDB made in procuring the inputs it needed to operate, this hospital and its ALSM were not as successful in complementing procurement capacity with procurement planning and systems. For example, the ALSM tried several times, but failed to adopt decision rights necessitating local competitive bids before procurement transactions were undertaken (Minutes 4.1.91; 10.VII.96).

Several reasons were given for why these rights were not adopted. The director agreed that food procurement contracts would have been preferable, but said that the nuns in charge of the kitchen were used to asking the hospital driver to go out and buy food everyday, and he was not able to impose a different system on them, especially given the expansive powers they had enjoyed in running HDB until recently. A member of the ALSM said that procurement of generic low-cost medicines was resisted by physicians who practiced at HDB and wanted their own name brands of medication. The hospital administrator said that LCB could not be practiced because they were never sure of demand, and that they preferred getting special breaks from suppliers they knew... All of these statements point to the same direction: due to lack of middle management capacity general uncertainty, HDB had a difficult time looking beyond the short term. Similar other non-complementarities existed in the area of procurement. But while at HDB attempts were made to establish systems, there is no record of such efforts in the informal experience. Under the new law, a different set of noncomplementarities exists because of excessive controls, and similar set of complementarities could come about because of inadequate hospital management hiring and board appointment practices.

# **5.4 Service delivery**

When HDB first embarked on its autonomy path, it was able to offer minor surgery if patients brought their own sutures and medicines. The sterilization equipment it had dated back to the 1940s (World Bank survey). At this time, HDB was able to offer limited opthalmological care, had an average of 10 births per month, treated war emergencies and had the capacity to carry out simple lab tests and x-rays. There was no systematic quality control, and HDB staff had little contact with the community. The little contact HDB had with other hospitals occurred when patients were referred away from HDB because it did not have the capacity to treat them. Table 5.4 summarizes HDB's decision rights allocation in the area of service delivery.

**Table 5.4. HDB's Decision Rights Equilibrium in Service Delivery**

|Rights Created|Quality control, medical services<br>Community outreach<br>Coordination with other hospitals, including int'l ones<br>Coordination with MOH|
|---|---|
|Rights Appropriated|Determination of range of services<br>Quality control, other services|

Source: Author's construction based on results from the Decision Rights Analysis Framework (Appendix A).

31
1

# Fiscal Policy and Stabilization In Brazil

by Celso Lulz Martone Table of Contents

1. The Insttutional Setfing

2. The Pubilc Secto Defcit 3. Fiscal Polcy and the External Secor 4. Fwscal Pokcy and Sablizatlon Tdbs FIgures 6 11 18 24 34