rafmacalaba/datause-encoder-data huggingface dataset:
of -0.20 indicates that a Category 3 hurricane wind ( , wind speed of 203 kph) would reduce nightlight by 0.04 units ( ≈ −0.20 ∗0.2 )—equivalent to a 0.33 percent reduction—using the inverse of the average level of nightlight in 2000.<sup>28</sup> In model 3 (purple), the effect of the presence of mangroves ff= 0.2 on hurricanes is greater and implies a 0.60 percent reduction in nightlights.<sup>29</sup> As in Figure 5, panels (b) and (c) show that hurricane damage is lower in cells with more than 1 and 2.3 kilometers of mangrove width during the sample period.

**Figure 6. Impact of hurricanes on nightlight by mangrove width, including Cuba**

<!-- Start of picture text --> 1 0.05 1 0.27 1 11.54<br>2 1.20 2 1.68 2 26.49<br>1 -0.20 1 0.17 1 8.37<br>2 0.87 2 0.64 2 20.10<br>3 1.32 3 2.15 3 29.40<br>1 -0.36 1 0.13 1 6.60<br>2 0.50 2 0.43 2 17.61<br>3 0.80 3 0.94 3 22.13<br>4 1.61 4 2.51 4 31.32<br>-2 3 0 2 4 0 20 40<br>a) Estimated impact of hurricanes b) Distribution of mangrove width (km) c) Distance to the coast (km)<br>on nightlights (NL)<br>Source: Authors’ calculations based on data from LECZ database.<br>Notes: In models 1–3, from panel (a) we discretize the mangrove width variable into various bins that correspond to its q-<br>quantiles and estimate the impact of hurricanes on economic activity for each bin. Model 1 uses q=2 and is colored green.<br>Model 2 uses q=3 and is colored orange. Model 3 uses q=4 and is colored purple. Panel (b) plots the distribution of mangrove<br>width for each bin, and panel (c) plots the distribution of distance to the coast for each bin. Regression coefficients are shown<br>in panel (a) and the average value for panels (b) and (c).<br><!-- End of picture text --> In general, the results show the potential value of mangroves as natural defenses against hurricanes in the Caribbean region, reflecting the importance of their conservation and restoration. These findings are consistent with other estimates of mangrove protection, which indicate that 2–7 kilometers of mangrove width would be needed to fully attenuate storm surge for the hurricanes that make up the bulk of our sample, particularly for Category 3. We find similar results when using a wide range of alternative assumptions for constructing the hurricane damage function, calculating mangrove width, and specifying models.

> 28 The calculation was as follows: [ ≈ −0.20 ∗0.2 ] *[1/12.1] 29 Following the same procedure to find the percentage, using the coefficient -0.36.

19
The unit of analysis for teacher attendance and activity outcomes is the individual teacher. The units of analysis for student test and whether the student received their entitlements (mid day meal, text books, school uniform and scholarship) are individual students. For outcomes from interviews of school committee members, the unit of analysis is the individual member.

To measure the effect of the campaign, for every outcome we conduct a linear “difference in differences” regression analysis comparing the change in intervention to the change in control group from baseline to follow up after adjustment of standard errors for clustering. Focus groups are analyzed by percentage of respondents to each question. Responses to open ended questions put to focus groups are presented as main themes that were mentioned.

It should be noted that all results reported below are for the study sample within the states, although reference is sometimes made to the states.

# **4. Baseline Characteristics**

Table 2 presents a summary of key variables at baseline. Baseline outcomes are reported in detail in another paper (Pandey et al, 2008). Baseline survey suggests that treatment and control villages are similar in socio-economic characteristics of the sample as well as in measured outcomes. It indicates low teacher attendance and poor student learning. Parents and village education committees are neither aware of their oversight roles nor participating in school management. However, there is substantial heterogeneity in outcomes across states. Karnataka has better student and teacher outcomes as well as higher levels of community awareness than MP and UP.

## **4.1**

## **Teachers’ Effort**

Average teacher attendance was 65 percent in UP, 67 percent in MP and 88 percent in Karnataka. Average fraction of teachers present and actively engaged in teaching is 27 percent in UP, 31 per cent in MP and 68 percent in Karnataka. In each of the states, teachers in treatment and control schools are similar in attendance, activity and other socio-economic characteristics (Tables 3A-3C).

Unlike Karnataka, both MP and UP have a cadre of contract teachers. 16 percent of teachers in MP and 41 percent in UP are contract teachers. In both states contract teachers have higher mean attendance and activity compared to regular teachers.

## **4.2 Learning Achievement in Language and Mathematics**

**What is the benchmark?** We use the minimum level of learning (MLL) framework recognized by the government of India as a benchmark for the minimum that a child in a given grade should know. The competencies tested for each grade fall either within or below the set of competencies listed by the MLL for the grade.  Tables 1-3 in appendix B

11
# **Economic impact**

The expected economic impacts of HIV/AIDS arise through several identified mechanisms. First, the increased mortality among young adults depletes the most economically productive part of the population, reducing the supply of labour across all skill categories. In addition, raised mortality among older, more experienced workers will tend to reduce the average level of work experience among the workforce. The resulting loss of skills will cause a reduction in productivity.

Second, there is an increase in morbidity among workers who are beginning to suffer the symptoms of AIDS. This will have the effect of reducing the average level of productivity, and hence the aggregate level of output.

Third, households will face reduced incomes because of the loss of income earners, at the same time facing higher expenditures because of additional demands for health care. This will have the effect of increasing the levels of poverty and reducing the levels of savings in the economy. The capital available for investment will therefore be reduced, with a negative impact on aggregate economic growth. For example, Greener, Jefferis, and Siphambe (2000a) found that income poverty over a 10-year period might be expected to increase by at least 5 percentage points over what it otherwise might have been.

At a government level, there will tend to be a reduction in revenue from taxation and other sources as a result of reduced economic growth. At the same time, there will be pressure to increase expenditures on health care and welfare to mitigate the impacts of HIV/AIDS. This will have the effect of pushing governments towards the need for deficit spending or reducing expenditure on other development areas. In particular, the costs of AIDS treatment will, in the first instance, be borne by governments. For example, Greener, Jefferis, and Siphambe (2000a) found that the net impact on the government budget over a 10year period in Botswana would be equivalent to a 20percent reduction in revenue. Pressure on health budgets may also cause a "crowding out" effect, where. expenditure on other health priorities may decrease. This effect may cause further economic impacts not directly related to HIV/AIDS.

AIDS will also cause human resource problems for firms and organisations, which may lose employees in vital skill areas and face difficulty in recruitment and training.

Numerous studies over the last 10 years have found estimates of the probable size of aggregate macroeconomic impact, mainly in African countries where the epidemic is the most serious. Results have indicated that HIV/AIDS will probably reduce the growth rate of Gross Domestic Product (GDP) by between 0.5 percent and 2.6 percent. For example, see Over (1992), Kambou, Devarajan, and Over (1992), Cuddington (1993a, b), Bloom and Mahal (1995), Arndt and Lewis (2000), Greener, Jefferis and Siphambe (2000a,b) and Bureau for Economic Research (2001). The overall conclusion of these studies is that the aggregate impact of HIV/AIDS on GDP growth is quite small and well within the 4
Policy Research Working Paper 8716

# **Abstract**

This paper compiles project-level data from the World Bank’s lending history to describe patterns and the composition of its portfolio. The paper focuses particularly on the effect of countries’ transition from International Development Association to International Bank for Reconstruction and Development status, which marks the point when countries start borrowing at near market rates, on lending for human development sectors (education, health and social protection). Using country and year fixed effects, which account for unobservable country characteristics (for example, national priorities) and time effects (for example, market interest rates), the paper finds that human development lending decreases when countries graduate from the International Development Association. The average difference in the binary indicator of lending for any sector is 27 percent while it is 60 percent for human development sectors. The share of human development lending (lending by human development Global Practices over total lending) is also 6.9 percentage points (30 percent) lower. This decline in human development lending in International Bank for Reconstruction and Development countries is accompanied by a greater use of budget support. The results are robust to controlling for non-World Bank aid, as well as various alternative specifications and estimation samples.

This paper is a product of the Human Development Global Practice. 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://www.worldbank.org/research. The authors may be contacted at rgatti@worldbank.org and amohpal@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
characteristics matter ex-ante for internationalization has been scarcer.<sup>7</sup> Various firm attributes may affect the probability of participating in international markets. Firm size might play an important role to the extent that there are significant fixed costs to accessing international markets. These fixed costs can derive, for example, from the need to comply with international accounting standards and/or the minimum market capitalization requirements to list abroad (Saudagaran, 1988). Growth opportunities may matter as firms with large unrealized growth opportunities might be more likely to go to more developed global markets (Bekaert et al., 2006). Since firms with foreign sales can pledge foreign revenues as a form of international collateral, they may be able to relax their borrowing constraint by accessing international capital markets (Caballero and Krishnamurthy, 2001, 2002). Also, to the extent that international equity markets are more developed than domestic ones, firms with high returns on capital might be more likely to seek equity capital abroad. Finally, corporate governance measures might be indicators of the willingness of firms to go to international markets and comply with stricter investor protection regulations (Doidge et al., 2006). Depending on what specific reasons motivate firms to access international equity markets, certain firm characteristics can thus be expected to relate to the probability of going abroad.

To analyze the participation of firms in international equity markets and its relation to country and firm characteristics, we compile a large sample of 39,517 firms from 111 countries covering the period 1989-2000. Of these firms, 2,546 are international firms, accounting for a maximum of 30,552 firm-year observations. The remaining 36,971 domestic firms account for a maximum of 223,740 firm-year observations. For each firm we collect a set of firm-level information, such as size, growth, performance, and foreign trade activity. We also compile aggregate country information related to domestic stock market development, internationalization, and other commonly used country attributes.

We start by conducting the analysis at the country level. This helps to understand how widespread internationalization across countries is and how country characteristics relate to internationalization. Then, the analysis at the firm level involves, besides a

> 7 A notable exception is Pagano et al. (2002), who study the characteristics of European firms listing abroad. They find that firms with high growth (potentials) and in high-tech industries are more likely to list in the U.S.

4
# **Figure 9: If we observe baseline imbalance, and control for baseline variables, is there any difference in follow-up balance?**

# **9a: ENE Labor Income Data 100 Observations**

<!-- Start of picture text -->
-4000 -2000 0 2000 4000<br>Difference in average outcome<br>Balanced Unbalanced<br>.0004<br>.0003<br>.0002<br>Baseline<br>.0001<br>0<br>.0003<br>.0002<br>Follow-up<br>.0001<br>0<br>.0003<br>.0002<br>.0001<br>0<br>Follow-up w/ controls<br><!-- End of picture text -->

# **9b: LEAPS Math Test Score Data 300 Observations**

<!-- Start of picture text -->
-100 -50 0 50 100<br>Difference in average outcome<br>Balanced Unbalanced<br>.025<br>.02<br>.015<br>Baseline .01<br>.005<br>0<br>.02<br>.015<br>.01<br>Follow-up<br>.005<br>0<br>.025<br>.02<br>.015<br>.01<br>.0050<br>Follow-up w/ controls<br><!-- End of picture text -->
national level for the latest survey available, as a function of the level of Gross National Income (GNI) per capita of the country (in thousand US$ of 2000). There is clearly a relationship between access rates and economic development. In poor countries such as Burkina Faso, Burundi, Chad, Ethiopia, Kenya, Madagascar, Malawi, Mozambique, Niger, Rwanda, Sierra Leone, Tanzania, and Uganda, more than 80 percent of the population does not use any modern infrastructure service. At the other extreme is high income and urbanized Gabon where only 16 percent of the households do not have any of the services. The two richest countries (South Africa and Gabon) have the highest access rates to piped water and electricity. South Africa also has the highest coverage rate for flush toilets and landline phones.

The simple univariate regressions in Figures 1 to 4 suggest that more than 50 percent of the variation in access rates is accounted for by the variation in GNI per capita. The regressions suggest that an increase in GNI per capita of one thousand dollars is associated with an increase in access rate at the national level of 15 percent for piped water, 11 percent for flush toilets, 18 percent for electricity, and a much lower 6 percent for landline phones (but this does not take into account the impact of income on the fast rising mobile phone sector). Still, while there is a strong correlation between GNI per capita and access rates, some countries are performing better than suggested by their level of economic development. For example, Lesotho, Namibia and Senegal perform well for landline phones, while Namibia, Senegal, and Zimbabwe do well for flush toilets. At the same time, some countries do not perform as well as expected. This is apparently the case among others for Lesotho and Cameroon for access to piped water, these two countries plus the Republic of Congo for access to flush toilet, Namibia and Lesotho again for electricity, and finally Cameroon and the Republic of Congo for landline phones. Of course, all these results should be interpreted as simple descriptive statistics, given that the relationship observed between economic development and access rates in figures 1 through 4 does not control for other potentially important determinants of access rates, such as the rate of urbanization, the population density per squared kilometer in the country, and the type of service provider or regulation system in place, among others.

In tables 4 and 6, access rates are provided for urban areas and rural areas. As expected, coverage in urban Africa fares better, especially in comparatively richer countries. Access rates are highest in capital and other major cities. By contrast, in most countries the rural population is almost completely left out of the modern infrastructure service coverage. The provision of 7
# **2.2.2 Identification of the marriage penalty**

Marriage and childbirth are correlated in time across individuals. A naive pseudo-panel event-study regression would likely conflate these two effects, particularly as the share of women with children rises with _t_ . We therefore propose a split-sample approach to separate the marriage penalty from the child penalty. The first case, the "no child sample", restricts the sample to women and men without children 5 years after marriage to explore gender norms. The second case, "the ignore child sample", matches women and men in the post marriage event space without considering the presence of children. This case combines the marriage and child penalties (or premium).

Our analysis relies on two identification assumptions to interpret _β_ as the causal effect of marriage on employment. First, the precise timing of marriage must be uncorrelated with other shocks that plausibly influence labor supply. Our split sample approach, by construction, rules out childbirth as such a confounder. However, other confounders, such as relocation in the context of patrilocality, might also be correlated with marriage.

Our split-sample approach also generates a second identification assumption: that the women in the “no child” sample are not diferentially selected in their propensity to work. This assumption is more challenging to satisfy. Specifically, women who remain childless up to _t_ = 5, particularly in a context of strong patriarchal norms, are likely to be those with a higher unobserved propensity to work. As such, this introduces a time-varying bias in the estimates, since this problem becomes increasingly severe as _t_ rises: married women without children many years after marriage are particularly selected. However, since these women likely have a higher unobserved propensity to work, the results as we move toward the end of the event window probably represent a lower bound on the true marriage penalty.

# **2.2.3 Summary statistics**

Table A2 presents marriage cohort-level summary statistics for the women in the no child and ignore child samples. About 20 percent of women in the sample are working. The average age of the cohort is 20 at the time of survey, almost 40 percent reside in urban areas, almost a third have more than secondary education. The average age at marriage is 21 and women 10
## **_Measuring the Costs of Eradication_**

The estimated expenditures of GDEC for the above-mentioned eradication activities include costs incurred by Global 2000, UNICEF, as well as WHO in the time horizon 1987-1996.10 Table 1 reports the annualized expenditure figures. In nominal terms, actual and projected GDEC expenditures in the time horizon 1987-1998 equal US$87.46 million in nominal terms and US$68.46 in 1987 constant dollars.

**Table 1. GDEC Expenditures (1987-1998a) in US$ millions**

|Year|Global**2 0 00 b**|Other Sourcesb|Totalb|Total Constant'|
|---|---|---|---|---|
|1987|1.26|0.23|1.49|1.49|
|1988|2.81|0.26|3.07|2.96|
|1989|3.12|0.30|3.42|3.16|
|1990|3.60|2.77|6.36|5.66|
|1991|5.84|2.12|7.95|6.80|
|1992|2.50|2.93|5.43|4.46|
|1993|7.68|8.95|16.63|13.15|
|1994|6.63|3.27|9.90|7.52|
|1995|8.66|3.39|12.06|8.81|
|1996|6.67|4.20|10.87|7.64|
|1997|5.60||5.60|3.78|
|1998|4.67||4.67|3.03|
|Total|59.04||87.46|68.46|

a. Expenditures for 1997 and 1998 are projected estimates.

b. In nominal termns.

c. In 1987 constant US$.

# **Methodology for Estimating Economic Benefits**

Through the prevention and elimination of infection-related incapacitation and debility among productive members of the population, the consequent additional output produced is assumed to be the primary benefit of GDEC."I Several studies have underscored the adverse economic impact of dracunculiasis. For instance, Belcher _et al._ (1975) estimated that, in southern Ghana, adult male farmers were at greatest risk of contracting the disease and, on average, untreated adults lost 5 weeks of work time. A review of twelve published studies yielded an average duration of disability caused by Guinea worm disease to be about 8 weeks (estimates ranging from 2 to 16 weeks). Furthermore, the economic impact of dracunculiasis is aggravated by the seasonality of infection -- coinciding with peak agricultural activities -- when few alternatives exist to alleviate

- 10 Expenditures borne by national governments for campaign implementation and control activities are not considered in this analysis. Information regarding national campaign expenditures is difficult to obtain and, in any case, expected to be modest (in dollar terms) in comparison to total annual expenditures of GDEC from other sources such as Global 2000, UNICEF, and WHO.

This is proposed as a _lower-bound_ estimate of the economic returns accruing from GDEC. We are ignoring, for instance, the benefits in terms of reduced infection-related expenditures among cases as well as positive effects on savings and income in the long run.

_5_