rafmacalaba/data-use-mentions-extended huggingface dataset:
Bank (2010), p. 3) This point brings us to one of the core issues of this report. Politics is at the heart of stable peace and recovery.

# **8.3.3 Political Exclusion**

We find both at the country and ethnic group levels that the share of excluded population as measured by the GROWup can predict conflict. Other recent studies support the idea that political exclusion has real effects on the distribution of resources, i.e. it could be part of a latent conflict within countries. Exclusion implies huge risks as it amplifies economic imbalances and could in this way increase the willingness to engage in violence. In the GROWup data we find that countries which exclude more than 20 percent of their population from power are markedly more likely to experience violence and destabilization. This is particularly interesting as it fits perfectly to the observation made by Rodrik (1999) more than a decade ago: sudden economic reversals can be explained by how societies deal with internal conflict.

Strikingly, political exclusion is also a key variable in a recent literature in political science that tries to forecast conflict out of sample. Goldstone et al. (2010), for example, come up with a list of risk factors which predict the risk of conflict onset when comparing two countries on the same sub-continent. The list includes state-led discrimination. A more recent project, the International Crisis Early Warning System (ICEWS), provides a 3 month prediction of the incidence of events like rebellions, ethnic conflict and insurgencies. Again, the structural variables identified in this forecasting exercise include the number of excluded groups and the share of excluded population.

But can this mechanism be actually addressed by policy? A lot of research has gone into showing that institutions are essential ingredients for economic development generally and the results in this report also indicate that they might be key mediating factors. However, in a post-conflict setting changes in formal institutions might not appear possible or even risky.<sup>57</sup> It
country-level data still found a positive correlation between life expectancy and schooling (Cervellati and Sunde 2013; Hansen 2013; Cohen and Leker 2014).

This paper makes several improvements over the previous cross-country studies of life expectancy’s effect on human capital investments. First, the analysis is based on a much larger number of 111 developed and developing countries. The analysis is conducted at each cohort level so that there is a one-to-one correspondence between a cohort’s year of birth in a country and the corresponding life expectancy at birth. Secondly, the schooling attainment of each cohort per country was calculated from household survey data for each country in place of the noisier approximations based on school enrollment data, extrapolated estimates for missing values, and _ex post_ adjustments for mortality that were used in previous studies. Estimates are generated separately for men and women and for urban and rural residents to establish the robustness of the findings. Thirdly, estimates of parental life expectancy at birth are incorporated into the analysis to examine evidence of the intergenerational transmission of human capital from parent to child. Finally, we estimate the impact of life expectancy on both lifetime years of schooling and lifetime earnings. We find that an additional year of life expected at time of birth increases years of schooling by 0.12 year and earnings by about 1%. The implied Wald estimate of returns to schooling are 9.9% for men, 4.3% for women, 10.2% for urban residents and 2.9% for rural residents. These cross-country results are very consistent with the findings based on individual data.

The next section applies Heckman’s (1976) model of life-cycle earnings, learning, and consumption to the question of how increased life expectancy at birth will affect lifetime schooling and earnings. Section 3 explains how we utilize these implications to derive the reduced form specifications for our estimations. Section 4 elaborates on the data sources, and 5
research is needed in order to assess whether the reform increases the overall lifetime welfare of women who were exposed to it.

# REFERENCES

Agarwal, B. 1994. _A field of one’s own: gender and land rights in South Asia_ , volume 58. Cambridge University Press, Allendorf, K. 2013. “Going Nuclear? Family Structure and Young Women Health in India, 1992–2006.” _Demography_ 50 (3): 853–880.

Amaral, S. 2014. “Do Improved Property Rights Decrease Violence against Women in India?” _Available at SSRN 2504579_ .

Anderson, S., and M. Eswaran. 2009. “What Determines Female Autonomy? Evidence from Bangladesh.” _Journal of Development Economics_ 90 (2):179– 91.

Anderson, S., and G. Genicot. 2015. “Suicide and Property Rights in India. _Journal of Development Economics_ 114: 64–78.

[Bertrand, M. S. Mullainathan, and D. Miller. 2003. “Public Policy and Extended Families: Evidence from Pensions in South Africa.” _The World Bank Economic Review_ 17 (1): 27–50.

Borooah, V.K. 2004. “Gender Bias among Children in India in Their Diet and Immunisation against Disease. _Social Science & Medicine_ 58 (9): 1719–31.

Botticini, M., and A. Siow. 2003. “Why Dowries? _The American Economic Review_ 93 (4): 1385–98.

Chattopadhyay, R., and E. Duflo. 2004. “Women As Policy Makers: Evidence from a Randomized Policy Experiment in India.” _Econometrica_ 72 (5): 1409–43. Chiappori, P., and Y. Weiss. 2007. “Divorce, Remarriage, and Child Support.” _Journal of Labor Economics_ 25 (1): 37–74.

Coffey, D., R. Khera, and D. Spears. 2015. “Intergenerational Effects of Womens Status:
of interest and measures the likelihood of completing primary for children whose “parents” did not complete primary net of cohort and census year effects.

This empirical approach has been used in Alesina et al. (2021) with data from Africa and delivers a measure of mobility comparable between countries that captures some long-term patterns over half a decade by netting out common (across countries) birth cohorts and census year effects.

**Downward mobility at the country level.** To estimate downward IGM, I use a similar econometric specification, pooling observations from all the censuses and countries:

where _yicoyt_<sup>_down_isadummyvariablethattakesavalueequaltoonewhenindividual</sup><sup>_i_does</sup> not complete primary education and zero otherwise. The parameters _γo_<sup>_b_,</sup><sup>_γ_</sup> _y_<sup>_b_,</sup><sup>_θt_againrefer</sup> respectively to fixed effects by decade-cohort of the generation above that co-resides with individual _i_ , decade-cohort of the individual _i_ , and census year. This regression uses a sample of individuals with ages between 14 and 18 (or 14 to 25), for whom the generation above (parents or older relatives) have on average completed at least primary education.<sup>13</sup> Hence, _αc_<sup>_down_</sup> is the parameter of interest and measures the likelihood of failing to complete primary for children whose “parents” completed at least primary school net of cohort and census year

**Upward and downward mobility at finer geographical level.** To estimate IGM at a more disaggregated level (i.e., provinces or districts), I use the following econometric specifications run country by country:

> 13I use the average attainment of the generation above instead of the maximum to make these estimates directly comparable to those in Alesina et al. (2021). However, this decision makes little difference as I explain in the robustness section later.

14
Policy Research Working Paper 9948

# **Abstract**

How do refugee camps impact the natural environment? This paper examines the case study of Cox’s Bazar, Bangladesh, a district that hosts nearly 1 million Rohingya refugees in refugee camps. Using spatially explicit data on land-use / land cover and proximity to a camp boundary, the paper quantifies land-use changes across the district over time. To evaluate the extent to which the camps triggered additional forest loss, the analysis calculates total forest loss in the district and uses a difference-in-difference model that compares areas 0–5 kilometers from a camp boundary (treatment) to areas 10–15 kilometers away (control). The findings show that the rate of forest loss intensified near camps relative to the control area. The analysis reveals that areas experiencing camp-stimulated reductions in forest cover are also experiencing faster settlement expansion relative to the control area. Settlement expansion is largely concentrated in areas outside protected areas. This enhanced settlement expansion still occurs when pixels 0–1 kilometer from the camps are omitted, which is evidence that the results are not due to camp settlements expanding beyond the official camp borders. The results suggest that camps stimulate in-migration as Bangladeshis seek new economic opportunities and improved access to resources.

This paper is a product of the Environment, Natural Resources & the Blue Economy 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/ prwp. The authors may be contacted at ndampha@worldbank.org, damph002@umn.edu. salem043@umn.edu, and polasky@umn.edu.

_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
***|0.001***|0.001***|0.003***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of education|-0.004|0.003|-0.003**|-0.003|0.003|-0.003**|
||(0.003)|(0.002)|(0.001)|(0.003)|(0.002)|(0.001)|
|arcsinh(income)||||0.000|-0.000|-0.000|
|||||(0.000)|(0.000)|(0.000)|
|Wage worker mean|0.056|0.069|0.039|0.056|0.069|0.039|
|Sample size|32,549|44,089|78,594|32,301|43,841|78,346|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the CFPS 2010 and 2016, ELMPS 1998, 2006 and 2012, IHDS 2005 and 2011/12, IFLS 2000, 2007/08 and 2014/15, MxFLS 2002, 2005/06 and 2009/12, LSMS-ISA 2010/11, 2012/13 and 2015/16, KHDS 1991/94, 2004 and 2010, and PSID 2001, 2007 and 2017. We drop respondents who are not self-employed or paid workers in the baseline wave of each survey. Level 1 administrative divisions include provinces for China and Indonesia, governorates for Egypt, states for India, Mexico, Nigeria and US, and regions for Tanzania. Level 2
<!-- Start of picture text --> c) Number of children<br>8000 10000 14000 18000 25000 35000 40000<br>Researvation wage (MKD)<br>1.5<br>1<br>.5<br>Average number of children in the household<br>0<br><!-- End of picture text --> _Source: Authors’ calculations based on LFS. Population weights accordingly used._ Similarly, reservation wage formation could be affected by demand-side characteristics, prevalently the labor-market conditions. **Figure 4** presents correlations of the reservation wages with the average skilland field-specific unemployment rates, per period. Higher reservation wages exist in areas where these rates are lower, potentially suggesting that the overall pressure existent on the labor market has a role to play for the reservation wage formation in North Macedonia. Despite, this finding may highly interfere with the personal skill level and field of education.

**Figure 4 - Reservation wage and some labor-market characteristics**

9
for the U.S. has shown that the scale-dependence of growth may be non-linear.

India does not collect comprehensive sectoral employment data at the district level. We therefore rely on micro-data from surveys. India runs two firm-level surveys, the Annual Survey of Industries (ASI) and the one conducted by the National Sample Survey Organisation (NSSO). The ASI survey has information on the so-called organized manufacturing sector (essentially comprising of firms with more than 10 workers), whereas the NSSO covers the unorganized manufacturing sector and the services sector. Both surveys, the ASI and the NSSO, overlap for the fiscal years 1989-90, 1994-95, 2000-01 and 2005-06. However, the service sector has only been surveyed more recently, in fiscal years 2001-02 and 2006-07. Given that part of our focus will be on the difference between manufacturing and services, we will use 2000-2005 for manufacturing and 2001-2006 for services.

3
about it because the conflict intensities have evolved similarly across the data sets. Any presence of recall bias, however, would bias down our estimation results.

We also compare the spatial distribution of the events between our data set and the ACLED database, which was used as our basis for selecting the most appropriate geopolitical zones for the telephone survey. By comparing figure B.2, panel a (the number of events in the ACLED data set) and panel b (household victimization in the telephone survey), we can indeed see that the geographical patterns across the geopolitical zones are similar in the two data sets.

Given that our outcomes are measured at the household level, we would ideally want to capture victimization of all household members. However, our victimization measure almost certainly excludes certain types of victimization. First, given that most of the respondents are male, events experienced by female household members might be underreported, which is supported by the fact that we have almost no reports on sexual violence. Second, we have very low reports of police or military violence; this might also be underestimated because the telephone survey was administered by the government.

The data also show that many events reported in the survey were not reported to anyone in the community, including community and religious leaders as well as authorities.<sup>12</sup> Given that the number of conflict-event reports in the survey is much higher than the number reported to authorities, we believe that despite our concerns of underreporting, our respondents show trust in the enumerators. Indeed, the households were visited multiple times by the LSMS/National Bureau of Statistics (NBS) survey teams since 2010—that is, prior to the telephone survey—which surely helped build trust. However, keeping in mind that households might be underreporting some specific types of events, our victimization measure—and therefore also our estimation results—might be downward biased.

The evolution of the consumption levels is displayed in figure 2, panel a, over the three waves of
Policy Research Working Paper 9360

# **Abstract**

This paper discusses the potential role of low-cost private secondary schools in Tanzania. The share of private enrollment has been negatively correlated with the availability of public schools. With the 2016 Fee-Free Basic Education Policy, the public secondary education system is experiencing significant demand pressures. The government has limited resources to address these pressures. Using microdata from the Morogoro region, the paper finds that private schools have excess capacity that can allow for absorption of additional students at relatively low cost through potential public-private partnerships. The paper finds no evidence that service delivery or student performance is worse in private schools relative to their public counterparts. These findings provide empirical evidence on some key enabling conditions for potential public-private partnerships for secondary education in Tanzania.

This paper is a product of the Education 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/prwp. The authors may be contacted at ssabarwal@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
It is important to note that, while the results of this study provide some evidence on the efficacy of gasoline and diesel price changes, many other avenues for reducing air pollution are available. Nonprice measures, such as technology mandates and catalyzer regulations, are underused and could improve air quality without diminishing consumption levels (UNEP 2021). And while 64 percent of countries have ambient air quality standards in legislative instruments, their distribution is lopsided toward developed countries. For example, while all EU countries have such regulation, only 40 percent of Commonwealth countries do. Therefore, encouraging the implementation of air quality legislation—such as mandatory filters and fuel mileage requirements for cars—could improve urban air quality. A single price adjustment alone will not curb air pollution; rather, a combination of measures need to work in tandem. This paper documents the role prices could play.

# 3. Data and methods

# 3.1. Data

To evaluate the relationship between fossil fuel prices and air pollution, we combine three datasets to construct a panel dataset with PM2.5 readings, GDP per capita, and fossil fuel prices and consumption for 133 countries.

First, we take surface PM2.5 concentrations in μg/m3 from van Donkeelar et al. (2021), who estimate mean annual air pollution levels globally using satellite imagery from 1998 to 2019. PM2.5 measures anthropogenic fine particulate pollution, such as exhaust emissions from fuel combustion, and is adjusted to remove naturally occurring particles, such as desert dust and sea salt. As it is possible to choose among a host of locations within a country that could affect readings, we homogenize our data to the center of each country’s capital city.

Second, we merge GDP per capita values, measured in 2017 international $ adjusted for purchasing power parity (PPP), from the World Bank’s World Development Indicators database (World Bank 2021a).

Third, we use IMF fossil fuel price and consumption data for 220 countries from 1980 to 2021 for coal,
The set of social norms variables at the subnational level include the share of women among highpaying employees in the Central Government at the department level. We interact this variable with the dummy variable of university level of education. We expect high paying public sector jobs to be more attractive for higher educated individuals. The other variables capturing social norms and attitudes towards women are the intrafamily violence against women in each municipality, and the share of males (compared to all) agreeing with gender parity statements at the department level.

The set public policy variables include the number of public preprimary centers in each municipality interacted with a dummy of having a child in 4 to 6 age range, the logarithm of the per capita spending in education in each department interacted with a dummy of having a child 0 to 12 years old, the logarithm of the per capita spending in health in each department interacted with a dummy of living with a person 60 years of age or older, and the municipality road accessibility at the municipality level.

_Id_ are department fixed effects that capture supply and demand conditions of local labor markets not controlled for with the set of FLFP drivers included in the model. We include _Id_ except when the driver of FLFP is defined at the department level. i is an error term. Model (3) is estimated by ordinary least squares and standard errors clustered at either the εε municipality or department level depending on the level of variation of the FLFP driver that is analyzed. All variables are defined as in Section 3 and Table A1 in the Appendix provides details on their construction and data sources.

# **4. Main Findings**

# **4.1 Decomposition of the change FLFP: 2002-2018**

We start by decomposing the observed change in FLFP between 2002 and 2018 using census data and following an Oaxaca-Blinder decomposition approach described in previous section. Table 1 reports the results of the Oaxaca-Blinder decomposition between 2002 and 2018 when FLFP increased 5.69 percentage points. Column (1) reports the FLFP in each year
during the interview (such as always asking open-ended questions and asking for examples), but these are not available in self-reported questionnaires. We address this issue by focusing on the topics that have a direct equivalent in the WMS to allow for a clear benchmark for our new index. If principals are reporting “good” information in these surveys that allow us to capture similar signals as the WMS scores, we should see similar distributions of scores across the common countries and a similar overall relationship between management and student test scores across countries. For PISA, we compare the distribution of scores and the performance correlations for the common countries as there are no school identifiers available. For Prova Brasil, we use school identifiers to match schools directly and hence provide a one-to-one comparison of the index standardized values.

**Validation of new indices.** As a first validation exercise, we compare the distribution of our PISA-based management index with the distribution of school management as measured by the WMS data in Figure 1 for all countries that the WMS has collected data.<sup>12</sup> The PISA and WMS distributions are reassuringly similar. The Kolmogorov-Smirnov test for equality of distributions rejects in only one of the 9 cases, Italy, where the PISA-index is somewhat more dispersed.<sup>13</sup>

As a second validation exercise, and to ensure we are picking up important variation with our management index, we conduct a basic check of the correlation between our measure and student performance. For each country we separate schools into quartiles of the management measure, and in Figure 2 we show, for each quartile, the average PISA test scores for math, reading and science (in deviations from the global mean). The graph includes all students and schools across the 65 countries available in the 2012 PISA dataset. This simple relationship suggests that students in schools in the bottom quartile of management within their country score are, on average, about 6 points lower than the PISA global mean, while students in schools