rafmacalaba/datause-encoder-data huggingface dataset:
Collier, Paul, Hoeffler, Anke, and Soderbom, Mans. (2004) “On the Duration of Civil War,” _Journal of Peace Research_ 41(3): 253-273.

Cunningham, David. 2004. “Veto Players and the Duration of Civil Wars,” Dissertation. University of California, San Diego.

Devarajan, Shantayanan and Ianchovichina, Elena. (2017) “A Broken Social Contract, Not High Inequality, Led to the Arab Spring,” _Review of Income and Wealth_ (forthcoming).

Doyle, Michael W. and Sambanis, Nicholas. (2000) “International Peacebuilding: A Theoretical and Quantitative Analysis,” _American Political Science Review_ 94(4): 779-801.

Doyle, Michael W., Ian Johnstone & Robert C. Orr. (1997) _Keeping the Peace: Multidimensional UN Operations in Cambodia and El Salvador_ . Cambridge University Press: Cambridge.

Elbadawi, I. and Sambanis, N. (2000) “External Interventions and the Duration of Civil Wars,” Policy Research Working Paper No. 2433, The World Bank, September.

Ellingsen, Tanja. (2000) “Colorful Community or Ethnic Witches’ Brew? Multiethnicity and Domestic Conflict During and After the Cold War,” _Journal of Conflict Resolution_ 44(2): 228-49.

Esteban, Joan and Ray, Debraj. (2011) “Linking Conflict to Inequality and Polarization,” _American Economic Review_ 101: 1345-1374.

Esteban, Joan, Mayoral, Laura, and Ray, Debraj. (2012) “Ethnicity and Conflict: An Empirical Study,” _American Economic Review_ , 102(4): 1310-1342.

Fearon, James D. (1995) “Rationalist Explanations for War,” _International Organization_ 49(3): 379-414.

Fearon, James D. and Laitin, David D. (2003) “Ethnicity, Insurgency, and Civil War,” _American Political Science Review_ 97(1): 75-90.

Gershenson, D. (2002) “Sanctions and Civil Conflict,” _Economica_ 69 (274): 185-206.

Gleditsch, Kristian Skrede (2007) “Transnational dimensions of civil war.” _Journal of Peace Research_ 44 (3): 293-309.

Gleditsch, Kristian Skrede and Beardsley, Kyle. (2004) “Nosy Neighbors: Third-Party Actors in Central American Conflicts,” _Journal of Conflict Resolution_ 48 (3): 379-402.

40
# **4. Econometric Research on Labor Market Effects of Climate Policy**

Many studies have used empirical methods to investigate the relationship between environmental regulations and jobs. The initial studies largely focused on pollution control regulations, such as the Clean Air Act of the United States. As climate policies have been put into place over the past 10-15 years, newer studies have begun to estimate empirically the impacts of climate policy on jobs. Collectively, this empirical research provides mixed evidence on the environmental regulation/climate policy impact on jobs.

## **A. Micro-econometric Models**

The majority of econometric studies on environmental regulation and jobs has used micro-econometric methods and data (i.e., data on individual workers, firms, or industries affected by regulation).

## _i. Effects on Employment_

Micro-econometric estimates of environmental regulations and/or climate policy impacts on employment are generally focused on developed countries. First, developed countries are much more likely to have quality data on employment at the plant and industry levels. Second, developed countries have been far more ambitious in implementing environmental regulations and climate policies.

Morgenstern et al. (2002) find that environmental regulations, as measured by expenditures on environmental compliance activities, have a small positive, but statistically insignificant, impact on jobs in “four heavily polluting industries: pulp and paper mills, plastic manufacturers, petroleum refiners, and iron and steel mills.” The study uses plant-level data from 1979–91 for a pooled cross-sectional estimation that essentially compares firms within an industry with each other: firms that spent more on environmental compliance had slightly more workers.

If environmental regulations and/or climate policies reduce the entire size of an industry, then the Morgenstern et al. method will not be able to capture these effects.

16
# **Introduction**

The 2000 Millennium Development Goals (MDGs) created a powerful global consensus to improve the development of poor countries by 2015. Central to this promise are the MDGs related to educational outcomes: (1) Ensure that all children complete primary education by 2015. (2) Eliminate gender disparities in primary and secondary education by 2005. By 2006, most countries have already fallen well behind the necessary targets to meet these goals. These worrying trends led to renewed calls for greater public investment in schools through school construction, teacher training programs and cash transfers for children. Notably absent in the proposals has been any role for private schools.<sup>2</sup> One reason why private schools have never been seen or used as an instrument for mass education is that they typically serve the elite. In the US, 4 percent of low-income families send their children to private schools compared to 19 percent among the rich. Of parents with less than high school education, only 3 percent send their children to private schools compared to 19 percent for parents with graduate or professional degrees.<sup>3</sup> Moreover, the share of private schooling is higher in tertiary and secondary education compared to primary education, by which time a large share of children from poor families have dropped out. These patterns are not unique to the US. The share of private schooling is higher in secondary compared to primary education in the majority of countries worldwide; in others the difference is fairly low and usually associated with the high state funding of private schools (Belgium, Spain, Netherlands and Chile are four such examples).<sup>4</sup> In contrast, South Asian countries such as Pakistan, Bangladesh and India all show high shares of private enrollment at the primary level. Countries such as Zimbabwe in Sub-Saharan Africa as well as Lebanon and the gulf-states in the Middle East also stand out, with both high private school enrollment and a larger private sector share at the primary level.

We focus our attention on Pakistan, a country that is severely off-track in achieving the MDGs and where the role of private schools is strongly debated in academic, popular and policy writings.<sup>5</sup> A substantial part of this debate is framed in terms of the “rising fees” and limited access to these schools in rural areas, concerns strongly voiced in the government’s recent policy statement on private schools:

> 2 The United Nations (2005)

> 3 NCES (1995), Table 1.5

> 4 Based on the World Bank’s Edstats database, available at <u>http://www1.worldbank.org/education/edstats/</u> 5 See Rahman (2005) and Abbas (2006)

2
However, the above report and other studies of fossil fuel subsidies, such as one by Kojum and Koplow (2017), focus on _oil consuming/importing countries_ or _fossil fuel consuming sectors of the economy_ . The oil and gas producing countries, on the other hand, face a very different structure that has been far less investigated. For one, they sell at world market prices far above their cost of production. For example, in 2016 the average cost of production in Saudi Arabia and the Islamic Republic of Iran was about $8.5 per barrel,<sup>5</sup> while the price of oil averaged to about $43. Adding the cost of refining of $3-$5 per barrel (Favenec, 2022) and estimating domestic transport cost from refining to retail by pipelines for domestic use (approximately 37 cents per barrel),<sup>6</sup> the total cost would be about $14 in 2016, as the unsubsidized retail cost of petroleum. This is far less than the international price. How does the classical case of oil subsidy work in this case? For one thing, an explicit subsidy is when the price is below the cost of production, while a typical MENA oil producer enjoys an international price far above the cost of production as we have seen above. Naturally, while governments can use the production cost of approximately $14 per barrel in the above example as a point of reference to subsidize the price, oil producing companies may consider any price below the international price as foregone profits and thus lobby for subsidy levels relative to the _international_ market, rather than the domestic marginal cost of production. In such a case, the level of subsidy will vary depending on the prevailing world oil price.

To better understand the relation between these variables for a typical oil exporter, Figure 3, developed by the authors, provides further insights. To streamline the modeling, we abstract from the monopoly power of individual oil producers and assume that all sell at a set international price of P*. This is partly because, except for Saudi Arabia, no other single OPEC member has full price setting capability. Further, even in the case of Saudi Arabia, we can imagine that the geopolitical process by which P* is arrived at _precedes_ the value of P* and focus instead on the implication of this for subsidies. For a typical oil producer, especially those in MENA, P* exceeds the marginal cost of production at that point, as captured in the figure. The market is segmented into the domestic and the world market. But the price faced by domestic users differs from the world price, due to two factors; that the cost of production is far less than the world price, and that the governments further subsidize the production and transportation of oil for domestic consumption, but not world exports.

> 5 - - Petroleum Watch (2021)  https://www.energy.ca.gov/sites/default/files/2021 <u>09/2021 09_Petroleum_Watch_ADA.pdf</u>

6 Authors’ calculations. See Appendix 1.

8
# Big Data in Transportation: An Economics Perspective

Harris Selod and Souleymane Soumahoro<sup>*</sup>

**Keywords:** Traffic, Congestion, Covid-19, Bayesian Structural Time-Series. **JEL Classification Numbers:** R41, C55, and D62.

> *The World Bank 1818 H Street, NW Washington, DC 20433, USA. The authors are grateful to the big data analytics firm QuantCube (www.q3-technology.com/), and especially to its CEO, Thanh-Long Huynh, and its Lead Data Scientist, Alice Froidevaux, for collaboration on the empirical application in Section IV-B. They also thank Gilles Duranton, Alejandro Molnar, and participants to the workshop “Leveraging new data for better urban management and policies” and the 6th World Bank/GWU/IDB/IGC Urbanization and Poverty Reduction Research Conference (Washington, D.C., September 9-10, 2019) for useful comments. Funding from the Knowledge for Change Program is gratefully acknowledged.
Monetary policy normalization in developed countries could limit the availability of loanable funds via Eurobond issuances. Countries in the region have enjoyed favorable external conditions for several years but could well confront a tightening in financial conditions due to the move to normalization of monetary policy in advanced countries, a decrease in other sources of funding, and rising sovereign risks in the region. International bond market conditions are still bullish; however, it is questionable whether these conditions will hold in the future.

From 2021 onwards, international bonds will begin to mature and the large repayments pose significant refinancing risk to the region ( **Figure 17** ). About US$3.7 billion in debt per year is set to mature in Sub-Saharan Africa during 2019-20. The amount of maturing debt will exceed US$8 billion in 2024. Countries with bond debt maturing in the coming years could face greater refinancing risks if international financial market conditions tighten and global investors lose interest in rolling over existing debt or purchasing new debt issuances. In addition to refinancing risk, liquidity risks have increased in several Sub-Saharan African countries as changes in the level and composition of PPG external debt raised debt service payments. Several countries breached their respective thresholds, indicating potential liquidity bottlenecks.

**Figure 17. International Bond Repayments for select LICs and LMICs in Sub-Saharan Africa** _(US$ billions, estimates)_

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033<br>Angola Ethiopia Gabon Ghana Cote d'Ivoire Kenya Mozambique<br>Namibia Nigeria Cameroon Rwanda Senegal Tanzania Zambia<br><!-- End of picture text --> _Source: World Bank (2018) Africa’s Pulse, Volume 18. Washington, DC: The World Bank_ 20
# Shortcomings in the Market for Developing Country Debt

# by John Wakeman-Linn

# Table of Contents

|T.|Introd|uction|2|
|---|---|---|---|
|II.|Unenfor|ceabilitv|6|
||II.a.|The Nature of the Problem|6|
||II.b.|Impact on the Loan Participants and the<br>Loan Contract|11|
|III.||Information Asymmetries|19|
||III.a.|Information About Ability to Pay|19|
||III.b.|Information Regarding Willingness to Pay|21|
||III.c.|Information About Use of the Loan Proceeds|25|
|IV.|Conclus|ion|26|
|Appe|ndix||29|
|Refe|rences||41|
-- as in Switzerland or France. They base their assertions on qualitative evidence. Comparative assessments of poverty are difficult enougb in the 1980s to expect them tt we any better a century ego. Adelman and Morris may turn out to be right, but quantitative comparative conclusions are hasty on the basis of the qualitativ **e** videnc' ow available.

The facts are that I are is as yet no data which makes it possible to compare the numbers in poverty across nations in the 19th century, let alone the living standards of the poor. Thus, this essay will limit its coverage to changes in poverty over time where comparative judgments are more tractable. While other countries are more poorly documented, we are able to get some impression of trends in and composition of 19th century British and American poverty by exploiting official statistics on poor relief. What little we do know about otber countries does not appear to conflict with British and American experience. Use of official statistics on poor relief is fraught with difficulties, some of whicb will be discussed below in Section III, but, in combination with the famous poverty surveys done by Booth, Rowntree, and others, they at least give us some basis on which to assess poverty. Unfortunately, it is very difficult to identify changes in the composition of the poor by this means, but we can get static impressions of wbo was poor at various points in time. Indeed, it is more useful to look at how certain groups were affected by industrialization than to search for some elusive statistics documenting overall poverty.

The second question is harder. Now might industrializatioa **n** d modern economic growth diminish poverty? On the face of it, the answer eeems obvious. If by growth we mean an increase in per capita income, and if there is no cbanae in the distribution of that income, then by definition the incomes of the poor will rise along with everyone else, and the rate of escape from poverty will exhibit the same performance. This "trickle down" theorem

-2-
Table 7: Hours Worked Tobit Results

|||||Work Within|Market Work<br>Within the|Household|<br>Market||Agricultu|re|Nona|gricultural|activities|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Total Hours|Wage work|the Household|<br>Household|Production|Work|Wage work|Household|All|Wage work|Househol|d All|
|||(1)|(2)|(3)|(4)|(5)|(6)|(1)|(2)|(3)|(4)|(5)|(6)|
|Ages 6-15||||||||||||||
||Rice Price|-10.014<br>[2.594]**|na|-9.517<br>[2.553]**|-8.214<br>[3.938]**|-3.151<br>[1.775]*|-9.417<br>[3.998]**|na|-6.353<br>[3.843]*|-6.947<br>[3.896]*|na|na|na|
||Rice Price*Male|7.403||6.566|5.712|2.852|7.549||6.545|7.109||||
|||[2.715]**||[2.674]**|[4.073]|[1.876]|[4.128]*||[3.977]*|[4.027]*||||
||Observations|9532||9532|9579|9539|9579||9579|9579||||
|Ages 16-24||||||||||||||
||Rice Price|-9.716|-14.579|-13.173|-9.915|-4.63|-6.843|na|-9.912|-9.708|-8.715|8.191|-0.676|
|||[3.797]**|[9.183]|[4.077]**|[3.937]**|[1.972]**|[3.660]*||[3.721]**|[3.735]**|[15.589]|[13.911]|[9.453]|
||Rice Price*Male|0.878|19.882|3.118|1.406|1.631|-0.662||-0.661|0.346|9.433|1.267|5.118|
|||[3.934]|[9.466]**|[4.228]|[4.083]|[2.080]|[3.792]||[3.861]|[3.867]|[16.151]|[15.000]|[9.978]|
||Observations|6040|6043|6040|6042|6056|6042||6042|6042|6043|6043|6043|
|Ages 25-64||||||||||||||
||Rice Price|-9.303|7.093|-13.034|-10.13|-3.621|-6.308|0.66|-9.157|-7.688|19.846|-7.197|0.86|
|||[2.262]**|[6.281]|[2.513]**|[2.410]**|[1.264]**|[2.148]**|[8.239]|[2.314]**|[2.338]**|[10.146]*|[8.086]|[5.821]|
||Rice Price*Male|3.315|9.288|3.169|1.323|1.877|1.439|16.621|0.895|1.118|-9.135|1.332|1.689|
|||[2.467]|[6.497]|[2.742]|[2.631]|[1.394]|[2.343]|[8.721]*|[2.529]|[2.553]|[10.165]|[8.808]|[6.157]|
||Observations|13301|13305|13301|13305|13319|13305|13305|13305|13305|13305|13305|13305|

Note: See comments to table 3 and 5. Cells labeled by na represent cases in which too few observations report positive hours worked to estimate the regression with Tobit (i.e. no convergence is achieved).
Table 5: Program employment effects, difference-in-differences estimates

|Outcome:|**Employed [0/1]**||
|---|---|---|
|Sample<br>All registrants|MDIC registrants<br>Unemp. Benefc.|All others|
|(1)|(2)<br>(3)|(4)|
|**Panel A: Em**|**ployment at any frm**||
|Post-course * completed<br>0.023***|0.031***<br>-0.042***|0.005|
|(0.003)|(0.006)<br>(0.006)|(0.003)|
|Mean of outcome<br>0.565|0.748<br>0.604|0.533|
|St. dev. of outcome<br>0.496|0.434<br>0.409|0.499|
|_R_<sup>2</sup><br>0.40|0.45<br>0.39|0.43|
|**Panel B: Employ**|**ment at requesting frms**||
|Post-course * completed<br>0.004***|0.020***<br>-0.003**|0.004***|
|(0.000)|(0.004)<br>(0.001)|(0.000)|
|Mean of outcome<br>0.041|0.237<br>0.009|0.025|
|St. dev. of outcome<br>0.198|0.425<br>0.094|0.156|
|_R_<sup>2</sup><br>0.76|0.82<br>0.42|0.69|
|**Panel C: Employm**|**ent at nonrequesting frms**||
|Post-course * completed<br>0.018***|0.010<br>-0.038***|0.001|
|(0.003)|(0.007)<br>(0.006)|(0.003)|
|Mean of outcome<br>0.524|0.511<br>0.595|0.508|
|St. dev. of outcome<br>0.499|0.5<br>0.491|0.5|
|_R_<sup>2</sup><br>0.42|0.60<br>0.39|0.44|
|_N_ (all panels)<br>10,243,541|912,053<br>1,872,837|7,458,651|

Notes: Table presents present difference-in-differences IV estimates of course completion. Heteroskedasticity-consistent robust standard errors two-way clustered by class and month*year reported in parentheses. All specifications include an unreported constant term and vectors of individual and month*year fixed effects. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

33
ULB is added to the estimated own revenue capacity to get the revenue capacity numbers for a ULB (Figures 2 and 4, Table A 2).

**Table 11 Ratio of Own revenue to Gross City products of UAs**

|Urban Agglomeration|Ratio of Own Revenue to GCP<br>(Median for all ULBs)|‘Standard’ Rate of Maximum Own<br>Revenue Capacity to GCP|
|---|---|---|
|Hyderabad|2.7%|3.25%|
|Chennai|1.7%|2.5%|
|Kolkata|1.15%|2.5%|
|Delhi|1.4%|2.25%|
|Pune|1.5%|3%|

**Source: Authors’ Computations**

# **Figure 1 Estimated Gross City Products (Rs, 2004-05)**

<!-- Start of picture text --> 1,000,000<br>900,000<br>800,000<br>700,000<br>600,000<br>500,000<br>400,000<br>300,000<br>200,000<br>100,000<br>0<br>Central City Non Central Central City Central City Non Central Central City Non Central Central City Non Central<br>City City City City<br>Kolkata Delhi Pune Hyderabad Chennai<br>M illio n s<br><!-- End of picture text --> 30
those in Schultz (2003) as well as the OLS results conditional on the inclusion of cognitive ability in Vogl (2014). Thus, while conceptually an IV approach is preferred to OLS for measuring human capital impacts, the results here reinforce rather than challenge the core evidence from OLS studies in the literature.

However, drilling down a bit into the manner by which height explains labor choices indicates that there also are some differences compared to previous studies. For example, height seems to have no role in selection into wage work in Pakistan and, of course, given the bivariate choice in the choice of occupations this also implies it has no role in selection into other selfemployment activities including those in which brawn is assumed to be more central.

The differences with other results in the literature likely reflect context; there are too few studies that include both height and a range of other measures of human capital from which to generalize. In addition, the data required for precise estimation of selection and endogenous choice over the life-cycle are daunting. Still, given that the paper also shows that socio-emotional skills have additional explanatory power, a finding that is more commonly noted in studies from high income countries, the three relatively accessible measures of skills studied here confirm not only that ability is multi-dimensional but that insights into these dimensions are available with judicious modifications of standard labor force surveys.

13
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poverty tends to fall with economic growth can be found in World Bank (1990, 2000), Ravallion (1995, 2001), Ravallion and Chen (1997), Fields (2001) and Kraay (2005).

There are a number of reasons for caution in interpreting this finding of a lack of correlation between changes in inequality and growth. Firstly, finding that there is no change in overall inequality can be consistent with considerable “churning” under the surface, with gainers and losers at all levels of living. This cannot be seen in cross-sectional surveys. The (more limited) panel data sets available point to churning.<sup>1</sup> Simulations of the impacts of specific policy changes intended to promote economic growth also point to such heterogeneity, or “horizontal inequality,” in the impacts of reform. In the context of trade reform, Ravallion (2005a) reviews evidence on the extent of horizontal inequality, as indicated by the dispersion in welfare impacts of reform at any given level of pre-intervention income. This dispersion reflects differences in variables such as household demographics and location that influence the net trading positions in relevant markets and (hence) the welfare impacts of trade reform.

Secondly, the measures of “inequality” in this literature are typically measures of relative inequality, whereby multiplying all incomes by a constant leaves the measure of inequality unchanged. Finding that a relative inequality measure is unchanged during an aggregate economic expansion is perfectly consistent with large increases in absolute income disparities. Growth in average income tends to come with higher absolute disparities between the “rich” and the “poor” (Ravallion, 2003). Arguably, it is the absolute changes that are more obvious to people living in a growing developing economy than the proportionate changes.<sup>2</sup> So it may well

> 1 A useful compilation of studies using panel data can be found in the August 2000 special issue of the _Journal of Development Studies_ ; see the introduction by Baulch and Hoddinott (2000).  The churning also stems in part from time-varying measurement errors, though plausible covariates have been evident in the studies that tested for this (see, for example, Jalan and Ravallion, 2000).

> 2 For further discussion see Amiel and Cowell (1999), Atkinson and Brandolini (2004) and Ravallion (2004).

3