rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4606

# **Abstract**

Civil war, and genocide in particular, are among the most destructive of social phenomena, especially for children of school-going age. In Rwanda school enrollment trends suggest that the school system recovered quickly after 1994, but these numbers do not tell the full story. Two cross-sectional household surveys collected before and after the genocide are used to compare children in the same age group who were and were not exposed to the genocide - and their educational outcomes are substantially different. Children exposed to the genocide experienced a drop in educational achievement of almost one-half year of completed schooling, and are 15 percentage points less likely to complete third or fourth grade. Sustained effort is needed to reinforce educational institutions and offer a "second chance" to those youth most affected by the conflict.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the group to understand the causes and consequences of armed conflict. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at akresh@ad.uiuc.edu and ddewalque@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
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# 3.3 The Relationship between State Variables and Economic Variables

To Investigate the economic determinants of our estimated state variables for Mexico and Venezuela, we regressed them on a number of economic variables. Given the weeldy frequency of the bond price data which produced our state variable estimates, we choose economic variables that could also obtained at a weekly frequency: oil prices, the performance of the domestic stock market (the value of the Mexico country fund as traded in New York and an index of the Venezuela stock market in dollars) and two series of Mexican domestic interest rates (one denominated in dollars, the other in pesos). Oil prices can be expected to influence Mexico's and Venezuela's ability to repay. Domestic stock prices will reflect domestic market expectations of future corporate profit growth and in that respect be an indicator of ability to repay. The value of the Mexico country fund in New York will not only reflect expectations about Mexico's future corporate growth but also be an indication of investors' perceptions about their ability to repatriate their earnings and capital (as reflected in the discount of the fund's price from its net asset value). Domestic interest rates will indicate the restrictiveness of Mexican monetary policy, which will reflect Mexico's general access to fresh foreign funds and its ability to repay.'° Table _5_ presents the results of (typical) regressions of the bond prices and the underlying state variables on the various economic variables. The first colunmn gives the dependent variable that is used in a particular regression. The Table documents that both bond prices and state variables are only wealdy related to the economic variables. Except for some regression intercepts, no one parameter estimate is significantly different from zero. What is encouraging, however, is that for both Mexico and Venezuela, the regressions of the state variable on economic variables result in an improvement in overall fit (R<sup>2</sup> as well as the p-values for the F- statistics) compared to the bond price regressions. In addition, the signs of the parameter

> **1 IOks** (1990) finds that the Mexican domestic interest rate reflects, in addition to international interest rates and expectations of exchange rate movements, the fiscal situation of the government. In particular, he finds that the difference between domestic bonds that fall due and Mexico's foreign exchange reserves is an important explanatory variable.
# **4. Empirical results**

# **4.1 Descriptive statistics**

Table B2 in Annex II provides descriptive statistics of the households followed by the panel. In the case of Jaldhaka, the average household size is the same as the national average of 4.5 but the total monthly household consumption is BDT 6,066 which is significantly lower than the national average as found in the Household Income and Expenditure Survey (HIES) 2010. Half of the total household expenditure is spent on food. A majority of household heads does not have an education and very few households own durable assets. Asset ownership and housing characteristics are poor, and worse than an average household in Bangladesh. Over 40 percent of household heads are casual agricultural laborer. The average size of land owned is about 1.8 acres. Both these characteristics are highly correlated with extreme poverty in Bangladesh. In the case of Narayanganj, households are larger in size compared to the rural panel but per capita consumption is higher than the rural sample. Interestingly the majority of urban households had access to electricity unlike the households in the rural sample. However, a much smaller proportion had access to latrines compared to rural households. A similar pattern is observed with access to safe water: a much larger proportion of rural households had access to safe water compared to urban households. The situation is the reverse when it comes to asset ownership: more urban households owned television and fans, which is consistent with the fact that these households had more access to electricity than households in rural Jaldhaka.

Table B2 also provides a comparison between the households in the panel, and those who were not located at the time of the endline survey. We find that in the case of Jaldhaka, on average, families who disappeared between the two rounds were more vulnerable: they were poorer, with fewer assets, and more likely to rely on remittances compared to those who stayed behind. Only 5 percent of the 2401 households surveyed at baseline in rural Jaldhaka were not found at the time of the endline survey. In the case of Narayanganj, we find a similar pattern in that those households who left the project area were relatively poorer with worse access to 19
Table C.2 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Flood event is likely_×_Job density||||||0.132|||
|||||||(0.086)|||
|Observations|916|916|916|916|916|916|916|916|
|R2|0.47|0.47|0.48|0.48|0.48|0.48|0.60|0.60|

_Note_ : The dependent variable is the log of rent (combined rent paid and self-estimated rent), measured in 2017 PPP US$by year. Robust standard errors are clustered at the enumeration area level. The variable Dwelling tenure status it is not added because is collinear with tenure agreement, for the observations of Antananarivo. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
R&D, estimated at the firm level, and those at the national level suggesting substantial spillovers.<sup>6</sup> Jaffe (1986) also finds that firms whose research is in areas where there is much research activity by other firms generate, on average, more patents per dollar of R&D and he finds the magnitudes of the spillovers to be substantial.

To date, there are very few investigations at the aggregate level that would more completely capture such spillovers and none are comparable to the micro studies. Bottazi and Peri (2003) have estimated the R&D/patent relationship at the aggregate level using a cross section of European regions. As in the other cross-sectional firm literature, they find an elasticity close to 0.9 and in addition, significant, although small, regional spillover effects within 300 kilometers. Using an OECD panel on 17 countries, Furman, Porter and Stern (2002) more generally explore the relationship between a range of innovation inputs and institutions and patents but also do not offer parameters estimates comparable to the panel studies discussed above.

In this paper we construct a large panel of advanced and developing countries and generate estimates for the R&D-patent relationship at the global level that are comparable to those at the micro level discussed above. Including a large sample of emerging countries puts us somewhere between the count and continuous dependent variable literatures. Only 6.5% of the sample has observations of zero value, just under half the number found in Blundell, Griffith and Windmeijer (2002), so clearly the issue of integers and zero values is reduced, yet not so small as to be dismissed altogether. We, therefore employ the estimators recently developed in both literatures. This allows tests for robustness to the assumptions underlying the various estimators. We find that regardless of the technique employed, there is a strong relationship between R&D effort at the global level and innovation with a long run elasticity surprisingly close to 1.

We contribute to the understanding of the dynamics of knowledge creation. Hall, Griliches and Hausman (1986) argue that, at the firm level, patenting occurs virtually

> 6 One strand of the empirical literature working broadly in the growth tradition has measured the returns to R&D using both micro and cross-country data.<sup>6</sup>

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average poverty rate at the district level as estimated from Pakistan Integrated Household Survey (PIHS) 2001-02. Finally, literacy rate - the deterministic variable for program eligibility- comes from the 1998 Population census.

We construct two main outcome variables: 1) absolute change in enrollment in grade 6-8 between 2003 and 2005 and 2) percent change in enrollment in grade 6-8 between the two periods. For secondary program effects, we also analyze enrollments in grade 5 and in grade 9 separately to gauze any possible _pull_ and _push_ effects. As mentioned earlier, schools are our primary units of analysis. For a balanced comparison, we restrict our impact analysis to those schools that have non-zero enrollments in each of grade 6, 7 and 8 for both pre-program and post-program periods.

Descriptive statistics of outcome variables and other covariates are presented in Table 3. Not only are there fewer girl schools for middle schooling level in stipend districts relative to both boys and relative to non-stipend districts, their average enrollments are lower than all other groups in both 2003 and 2005. But female middle schools in stipend districts (relative to three other groups) reported the largest increase in enrollments both in absolute level and percent terms. It should be noted that while the majority of schools saw a positive growth in enrollments, there were a sizable number of schools that reported decrease in enrollments in grades 6, 7 and 8. For instance, about 20% female middle schools and 25% of male middle schools in non-program districts recorded negative changes. On the other hand, only 7% of girl schools and 13% of boy schools in stipend districts recorded such negative growth.

In terms of other covariates, average literacy rates, not surprisingly, are lower and consumptionbased poverty rates are higher in stipend-districts. While the ratio of private schooling to public schooling is higher among non-stipend districts relative to stipend districts, it is higher for girls than for boys in both control and treatment districts. School infrastructure variables, in general, are more favorable in non-program districts. As one would expect, boundary walls and toilet facilities are available in higher proportion of girl schools. In terms of changes from 2003 to 2005, on average additional 8% of girl stipend schools built toilets; the comparable figures were 9% for female non-stipend schools, 19% for both types of male schools. And finally, studentteacher ratios are lower in stipend program districts, suggesting there is some space in their absorption capacity.

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student in grade 9 in Finland would be coded as 0, whereas in Mexico the same student would be coded as -1. Thus, this variable is sensitive to country-specific grade progression and is comparable in pooled or cross-country regressions.

Information on formal school entry date cutoffs, and on how well these cutoffs are enforced, is hard to locate and verify. Moreover, school entry dates often vary within country and over time. Given these difficulties, rather than using the legal cutoffs, as a first step to screening countries we examine discontinuities in the data in relative grade by birth month. We restrict our sample to countries with large samples on both sides of the cutoff. Four countries—Mexico, the Republic of Korea, Slovakia, and Thailand—meet this criterion. We find large and significant shocks to relative grade: being born just before the school entry cutoff (versus being born just after it) exogenously increases relative grade by 0.55 to 0.6 grades. Figure 5.4 shows graphs of how month of birth indeed affects relative grade in these four countries; and the month of the discontinuity corresponds to legal school entry month. For example, in Korea the school entry cutoff is January 1<sup>st</sup> , and we see a sharp discontinuity in relative grade between December and January. In Mexico and Slovakia, the school entry cutoff date is September 1<sup>st</sup> and we see corresponding discontinuities between August and September. It is worth noting that in Mexico the discontinuity occurs right at the start of August, whereas in Slovakia this starts to occur earlier. This suggests that while this cutoff is enforced in Slovakia, it might be less strict.

# **Figure 5.4: Relative grade by birth month**

<!-- Start of picture text --> Month Month<br>Source: Authors’ analysis of PISA 2015 data.<br>Relative grade<br>Relative grade<br><!-- End of picture text --> 31
<mark>Dollar, D., & Kraay, A., 2002. Growth is Good for the Poor. Journal of economic growth, 7(3), 195-225. https://www.jstor.org/stable/40216063</mark> <mark>Dooley, M. P., 2000. A Model of Crises in Emerging Markets. The Economic Journal 110(460): 256-272. https://doi.org/10.1111/1468-0297.00500</mark> <mark>Eaton, J., Gersovitz, M., 1981. Debt with Potential Repudiation: Theoretical and Empirical Analysis. The Review of Economic Studies 48 (2): 289-309. https://doi.org/10.2307/2296886 Encovi, 2017. Espejo De La Crisis Humanitaria Venezolana. Encovi</mark> <mark>https://www.proyectoencovi.com/libros</mark> <mark>Esteves, R., Kenny, S., Lennard, J., 2021. The Aftermath of Sovereign Debt Crises: A Narrative Approach. CEPR Discussion Paper 16166.</mark> <mark>https://cepr.org/active/publications/discussion_papers/dp.php?dpno=16166</mark> <mark>Farah Yacoub, J., Graf von Luckner, C., Reinhart, C., 2022. Eternal external debt crises: A long view. Forthcoming.</mark> <mark>Fleurbaey, M., 2009. Beyond GDP: The Quest for a Measure of Social Welfare. Journal Of Economic Literature, 47 (4): 1029-75 DOI: 10.1257/jel.47.4.1029</mark> <mark>Frankel, J. A., Rose, A. K., 1996. Currency crashes in emerging markets: An empirical treatment. Journal of International Economics. 41 (3–4): 351-366. https://doi.org/10.1016/S00221996(96)01441-9</mark> <mark>Furceri, D., Zdzienicka, A., 2012. How Costly Are Debt Crises?. Journal of International Money and Finance 31(4): 726-42. DOI: 10.1016/j.jimonfin.2012.01.012</mark> <mark>Garcia Zea, D., 2020. Brain drain in Venezuela: the scope of the human capital crisis. Human Resource Development International, 23(2), pp.188-195. https://doi.org/10.1080/13678868.2019.1708156</mark> <mark>Gourinchas, P.O., Obstfeld, M., 2012. Stories of the Twentieth Century for the Twenty-First. American Economic Journal: Macroeconomics, 4(1), pp.226-265. DOI: 10.1257/mac.4.1.226</mark> <mark>Graf von Luckner, C., Meyer, J., Reinhart, C., & Trebesch, C. (2021). External sovereign debt restructurings: Delay and replay. VoxEU https://voxeu.org/article/external-sovereign-debtrestructurings-delay-and-replay</mark> <mark>Habib, B., Narayan, A., Olivieri, S., Sanchez, C., 2010. The Impact of the Financial Crisis on Poverty and Income Distribution: Insights from Simulations in Selected Countries. World Bank https://openknowledge.worldbank.org/handle/10986/10206</mark> 37
# **Figure 2:  Description of the workings of a CGE Model**

<!-- Start of picture text --> Domestic savings<br><!-- End of picture text --> <!-- Start of picture text --> Domestic savings<br>Factor<br>Markets<br>Value added Wages, rents<br>Direct taxes<br>Indirect taxes<br>Producers Households Government Savings<br>Intermediate<br>Demand<br>Social Transfers<br>Private consumption Government<br>consumption<br>Product<br>Market<br>Sales Final Demand<br>Exports Imports<br>Foreign<br>Savings<br>Rest of the World<br><!-- End of picture text --> <!-- Start of picture text --> Producers<br><!-- End of picture text --> <!-- Start of picture text --> Savings<br><!-- End of picture text --> <!-- Start of picture text --> Rest of the World<br><!-- End of picture text --> Source: Breisinger et al (2009) The paper uses a newly developed special version of the GTAP model, named the GTAPPOWER model. This is documented in Peters (2016). In this model, there is a substitution between capital and energy products as a composite commodity; there is a multi-level nesting system among different energy products, including both renewable and non-renewable energy. This accounts for substitution among these sources in response to either carbon taxes for cutting emissions or to any other economic and technological policy changes in the economy. Further to these changes, another modification is made to this model, which enriches the labor supply specification; in the standard GTAP model, one assumes a horizontal or vertical labor supply curve, whereas in this model, the labor supply curve for different countries is defined using the estimated labor supply elasticities. This helps in capturing a more realistic labor supply situation. <mark>Further in reality, the actions on climate change may affect technological innovations as well as investment levels. Therefore,</mark> the assumption is that investment and savings both are endogenous in each region in the model. The model does capture the reduction in investment due to reduced output and savings. <mark>Substitutability across energy sources does vary across sectors and regions, as the parameters and shares of different variables that influence substitutability have huge variations across sectors and regions.</mark> 7
statistically significant revisions of growth expectations. The magnitudes of these revisions are economically meaningful and close to the usual adjustments made by a professional forecaster in response to the release of a major news event. On average, an announcement of a tightening(loosening) of capital controls is associated with a downward(upward) revision of growth expectation of around 0.23 percent. This is supportive of the countercyclical capital control policy rule used in my theoretical model. The exception is the tightening announcement in March 2008, when market participants actually revise their growth expectations upward. To sum up, empirical evidence from high-frequency survey data suggests that expectations of fundamentals do respond to capital control measures. However, these results are based on mean expectations of 120 forecasters using different forecasting methodologies, and can be hard to interpret in greater details. Hence, in the next section, I take a stand on the forecasting methodology used to form expectations.

Table 4: Event study: capital controls and revisions to GDP growth expectations

|Event date|Type|Mean growth forecast revision(%)|T-statistic|
|---|---|---|---|
|3/12/2008|tightening|-0.05**|2.12|
|10/20/2009|tightening|0.02|-1.40|
|10/04/2010|tightening|0.70***|-72.79|
|12/01/2011|loosening|0.02|0.19|
|03/09/2012|tightening|0.03**|-1.98|
|06/04/2013|loosening|0.16***|4.77|

Coefficient is positive if it has the expected sign. T-statistic reported here are for a statistical test of the hypothesis that mean abnormal growth expectations is zero.

# **5.4 Using capital controls to improve GDP nowcast**

In this section, I use a conventional econometric model to nowcast Brazilian quarterly real GDP between 2006 and 2013. The GDP-nowcasts are my preferred measure of the expectation of economic fundamentals and the data series used to produce the nowcasts are my preferred measure of the forecaster’s information set. To my knowledge, this methodology is new to the empirical literature of capital controls. I add the dummy variable of policy stance and show that it improves the precision of the GDP-nowcast.

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BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking air transport indicators for Botswana and select other countries**<sup>**4**</sup>

|Country|Botswana|Namibia|Zambia|Zimbabwe|Mozambique|South Africa|
|---|---|---|---|---|---|---|
|Traffic (2007)|||||||
|Domestic seats (seats per year)|241,696|84,162|437,658|237,835|1,144,644|31,767,537|
|Seats for international travel within<br>Africa (seats per year)|435,708|877,812|1,459,766|1,109,986|582,836|6,314,557|
|Seats for intercontinental travel (seats<br>per year)|n.a.|242,736|113,217|182,585|91,637|7,707,063|
|Seats available per capita|0.357|0.574|0.168|0.118|0.087|0.954|
|Herfindahl-Hirschmann Index—air<br>transport market (%)|60.25|39.39|17.53|30.20|31.54|16.66|
|Quality|||||||
|Percent of seat-km in newer aircraft|100.0|79.0|63.8|71.4|57.0|83.8|
|Percent of seat-km in medium or<br>smaller aircraft|100.0|28.3|50.6|42.7|42.5|32.8|
|Percent of carriers passing IATA/IOSA<br>audit|0|100|0|33.3|100.0|33.3|
|FAA/IASA audit status|No audit|No audit|No audit|Failed|No audit|Passed|

_Source:_ Bofinger 2009. Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data).

_Note:_ The Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. An HHI of 100 indicates the market is a monopoly; the lower the HHI, the more diluted the market power exerted by one company/agent.

FAA = U.S. Federal Aviation Administration; IASA = International Aviation Safety Assessment; IATA = International Air Transport Association; IOSA = IATA International Safety Audit. n.a. = Not applicable.

# **Challenges**

Since no airline in Botswana flies to Europe or the United States, it is neither audited by the U.S. Federal Aviation Administration (FAA) or ranked by the European Union (EU). But safety oversight is a challenge for Botswana. Air Botswana is not on the International Air Transport Association (IATA) registry, and a 2008 International Civil Aviation Organization (ICAO) audit of the safety oversight system found noncompliance with standard and recommended practices to be over 72 percent, compared to the global average of 30.8 percent for the year. Beyond Air Botswana, tourists flying on local charter flights may also be at risk.

The air transport market is concentrated: Air Botswana dominates, carrying over 75 percent of capacity, with an additional capacity of roughly 18 percent provided by South African Airlink Express.

> **4** All data as of 2007 are based on estimations and computations of scheduled advertised seats, as published by the Diio SRS Analyzer. This captures 98 percent of worldwide traffic, but a percentage of African traffic is not captured by these data **.**

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