rafmacalaba/datause-encoder-data huggingface dataset:
Wallsten, S. and K. Kosec. 2005. “Public or Private Drinking Water? The Effects of Ownership and Benchmark Competition on U.S. Water System Regulatory Compliance and Household Water Expenditures” AEI-Brookings Joint Center Working Paper No. 05-05, AEI-Brookings Joint Center, Washington, DC **.** Whittington, D., J. Davis, L. Prokopy, K. Komives, R. Thorsten, H. Lukacs, W. Wakeman, and A. Bakalian. 2009. “How Well is the Demand-Driven, Community Management Model for Rural Water Supply Systems Doing? Evidence from Bolivia, Peru, and Ghana.” _Water Policy_ 11 (6): 696–718.

World Bank (2004). “Reforming Infrastructure: Privatization, Regulation and Competition.” World Bank and Oxford University Press, Washington, DC.

World Bank. 2010. “Modernizing Public Water Services in Uruguay through a long-term Partnership,” Working Paper, World Bank, Washington, DC.

24
Table 1 presents oil prices under various scenarios and corresponding percentage changes from the current (i.e., 2009) price level. For example, a 50% increase in oil price from the baseline in 2020 refers to a 147% increase as compared to the 2009 level.

**<u>Table 1: Oil prices and percentage changes from the current (i.e., 2009) price level</u>**

|Year|Baseline|Scenario||
|---|---|---|---|
||25%|50%|100%|
||US$/barrel(2008 price)|||
|2009|56|||
|2010|67|||
|2015|87<br>109|130|174|
|2020|93<br>116|140|186|
||% Changefrom 2009level|||
|2009|0|||
|2010|19|||
|2015|54<br>92|131|208|
|2020|65<br>106|147|230|

Source: EIA (2009) and IEA (2009) for baseline data for period 2009-2015.

# **3.1 Impacts on biofuel production and penetration in transportation fuel mix**

Table 2 presents the production of biofuels in monetary value in the baseline and the percentage increase from the baseline under various scenarios for oil price increase. As can be seen from the table, global biofuel production in 2020 would be more than double than that in year 2009. Middle/low income countries are found responsible for a larger share the output in 2020 than high income countries. It is interesting to note that production of biofuels is highly sensitive to oil price. For example, a 25% increase in oil price from the baseline in 2020 (i.e., a 107% increase from the 2009 level), would cause a 20.4% increase in global biofuel production. Similarly, a 100% increase in oil price from the baseline in 2020 (i.e., a 230% increase from the 2009 level), would cause a 77.3% increase in global biofuel production. The impacts, however, vary significantly across countries (see Table 2 for the detailed results).

The penetration of biofuels in the transport sector under various scenarios for oil prices are presented in Figure 2. In the base case, the share of ethanol and biodiesel in total liquid fuel consumption in the road transport sector would increase from 2.4% in 2009 to 5.4% in 2020. If, starting from 2012, oil price increases by 25% from that in the baseline, the penetration of 13
# Debt, Debt Relief, and Growth: A Bargaining Approach

by Daniel Cohen (CEPREMAP, Paris and CEPR, London) and Thierry Verdier (DELTA, Paris) Table of Contents

|I.|Introduction|1|
|---|---|---|
|I.|Framework of Analysis|2|
|-II.|Characterization of the Memoriless Subgame Perfect Equilibrium|5|
|IV.|The Role of Reserves|10|
|V .|Debt Relief and Debt Equity Swaps|15|
|Appe|ndix 1: Existence and Uniqueness of the Stationary<sup>Subgame</sup><br>Perfect Equilibrium|18|
|Appe|ndix 2: Proof of Proposition 2|21|
|Appe|ndix 3: Proof of Proposition 3|24|
|Refer|ences|27|

The paper was started while both authors were visiting the<sup>Debt and International Finance</sup> Division, International Economics Department of the World<sup>Bank. The authors thank</sup> Jonathan Eaton for his useful comments on a preliminary draft.
Geographic Imbalance, Search Frictions, and Regulation: Causes of Empty Miles in Freight Trucking<sup>∗</sup> Ron Yang JEL Classification: L92, R41 Keywords: Transportation, Trucking, Search Frictions, Economic Geography

> ∗Version: May 15, 2024

> 1Contact: ron.yang@sauder.ubc.ca. I thank Aiga Stokenberga, Matias Herrera Dappe, Mathilde Lebrand, Harris Selod, and Atsushi Iimi for helpful comments and discussions. This paper was prepared as a background paper for the Global Transport Practice Flagship Report, “Shrinking Economic Distance,” led by Matias Herrera Dappe, Aiga Stokenberga, and Mathilde Lebrand.
Here, PPP*i could be computed from the 1988 price comparison study among former CMEA countries and also 1990 ICP data.

16. E* could be also computed from actual foreign trade statistics, where traded items are evaluated at both domestic and "foreign" prices. The foreign prices here are the actual "invoice" prices converted from foreign currencies into rubles at the official exchange rates. The estimated E* should be close to the so-called commercial exchange rate.

17. In many developing countries, the ratio of the average market prices of non-tradables to tradables may not equal to the same ratio in resource cost terms, because of various government interventions to markets. Thus, K for FSU in equation (9) in para 7 above should be also adjusted to approximate the average K value of developing economies.

37
<!-- Start of picture text --> Chronic poverty Non-poor in both rounds<br>Ge a<br>= rs<br>a<br>_=‘ uw2<br>)a is x+ ni sSe siN id sbe sts s=)<br>es a rr aa be “A a ¥ a<br>Moving out of poverty Moving into poverty<br>un un<br>“at “er<br>Pe a<br>TM TM<br>a. —§<br>=ci Py -1b =i =Se 8a - xb otte si]<br>s a e £a S§b £ SFA se €a 8<br>WO Range between upper and lower bound<br><!-- End of picture text --> Shares are based on synthetic panel estimates for household heads aged between 25 and 55 in the first round of each interval. The vertical axes give the joint probability; note that the scales vary across graphs.
Considerable variation remains in teacher absence rates across states with estimates ranging from 12.9 percent in Tamil Nadu to a high of 45.8 percent in Jharkhand. Teacher absence rates declined in 14 out of 19 states with significant reductions in 12 states. Five states (Tamilnadu, Punjab, Maharashtra, Orissa, and Chhatisgarh) now report teacher absence rates below 15 percent. On a population-weighted basis, the largest contributor to reductions in all-India teacher absence was Bihar, which is consistent with the widely-reported improvement in governance in the state during this period (Chakrabarti, 2013). In contrast, the significant increase in teacher absence in India’s most populous state Uttar Pradesh (population over 200 million), partly offset the reduction in other states.

Since Chaudhury et al. (2006) find a strong negative correlation between GDP/capita and teacher absence rates (both across countries and within Indian states), one way to interpret the magnitude of these changes is to compare them with the expected reduction in teacher absence that may be attributed simply to the economic growth that has taken place in this period. Using a growth accounting (as opposed to causal) framework, we can decompose the change in teacher absence into a component explained by changes in GDP/capita (as a proxy for ‘inputs’) and one explained by a change in governance (a proxy for TFP). Cross-sectional estimates from the 2003 data suggest that a 10 percent increase in GDP/capita is associated with a 0.6 percentage point reduction in teacher absence.<sup>14</sup> In the period between 2002 and 2010, real GDP/capita in India has grown 38 percent. Thus, growth in GDP/capita over this period should have by itself contributed to a reduction in teacher absence of 2.4 percent. Our estimate of the change in teacher absence rate is exactly in this range, and suggests that the reduction of teacher absence we document is consistent with a proportional increase in ‘inputs’ into education, but a limited improvement in TFP in this period. We discuss the policy implications of this in the conclusion.

Finally, to interpret the cost of teacher absence to students, we note that the effective attention a student receives from a teacher can be increased both by reducing teacher absence as well as by hiring more teachers. To account for the reduced attention that students receive when teachers are absent, we define the “effective student teacher ratio (ESTR)” as: We use official DISE data on total enrollment and total number of teachers, combined

> 14The cross-sectional relationship is estimated by regressing village-level teacher absence on the log of district-level per-capita consumption (from the National Sample Survey) in the 2003 survey. Estimates without state fixed effects are larger (and equal -1.17) whereas estimates with state fixed effects are smaller but still significant (and equal to -0.63). Our default estimate is based on using state-fixed effects since cross-state variation in per-capita income is much more likely to be correlated with unmeasured governance quality. Tables are available on request.

9
Policy Research Working Paper 9084

# **Abstract**

The degree of concentration and market power in South African markets has been the topic of much policy discussion. However, there has been little evidence on what drives market power and the impact of the degree of competition in South African markets on economic outcomes. This paper improves on previous markup estimates for South Africa using a methodology developed by De Loecker and Warzynski (2012) applied to tax administrative data for 2010–14. The paper then explores the firm-level determinants of the estimated markups and assesses the link between competition and firm-level outcomes, including productivity, employment, and wages. The analysis finds that average markups across the economy appear to have risen between 2010 and 2014. Larger firms, higher-intensity exporters, and firms with greater sales shares charge higher markups than comparator firms in South Africa, even after controlling for efficiency. Moreover, lower product market competition has a significant, negative effect on productivity growth, employment growth, and wage growth in South African manufacturing industries. Higher sales-weighted and value-added-weighted average industry-level markups are associated with lower industry-level entry rates. The findings highlight the importance of implementing sound pro-competition government interventions and the significant economic benefits associated with such policies.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. 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 sdauda@worldbank.org or snyman@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
that remains stuck in poverty and is somehow unable to participate in the processes of upward mobility.

Our analysis studies the period between 2004/5 and 2011/12, and draws on three “thick” rounds of National Sample Survey data referring to 2004/5, 2009/10, and 2011/12. As noted above, these data sources indicate a substantial decline in poverty over the entire 7-year period, with a sharp acceleration occurring after 2009/10. Our analysis suggests that during the first episode of poverty reduction between 2004/5 and 2009/10, poverty decline was accompanied by an increase in the share of the population that can be considered vulnerable, or facing a heightened risk of falling into poverty. Between 2009/10 and 2011/12, poverty decline accelerated further, and this was now accompanied also by a decline in the fraction of the population that was particularly at risk of falling into poverty.

We show further that aggregate trends in poverty reduction mask a considerable degree of entry into, and exit out of, poverty, but that a substantial core of the poor have remained poor over the duration of the study period. We document some of the key household characteristics of those who have managed to escape poverty and vulnerability, and contrast these with those who have fallen into this undesirable welfare status during this period.

Before turning to a detailed discussion of our empirical findings, we confront in this paper several methodological challenges that have typically held back investigations of the kind we are attempting here. The key difficulty is that analysis of poverty transitions and of the likelihood of escaping, or falling into, poverty depends on the availability of panel data that permit the analyst to follow households over time. Yet in India, as in many other countries, nationally representative panel data are not available. The existing data sources underpinning poverty analysis—the NSS 3
intensive sampling in adjacent PSUs (Salehi and Seber, 1997). PSUs are selected randomly first, with or without stratification, and—based on a trigger threshold—adjacent PSUs are then enumerated. As such, the process is no less naïve in the initial selection of PSUs than other sampling approaches, but ACS exploits the information revealed in the process to efficiently target the population of interest when that population is clustered and/or rare (Thompson and Seber, 1996). The sampling process to survey informal businesses follows a two-stage method. In the first stage, ACS provides a probabilistic sample of PSUs which are fully enumerated to list all informal businesses. In the second stage, businesses are randomly selected from the list for an indepth interview, discussed more in detail in Section 5.

# **3. Technical Foundations of Adaptive Cluster Sampling**<sup>15</sup>

ACS begins with the definition of PSUs, . For the sake of generalization, let be a well-defined geographic area, divided into a grid of equal-sized squares. For this reason, PSUs will ii∈NN NN also be described in this application of ACS as block areas, (henceforth BAs).<sup>16</sup> The first step draws an initial sample of BAs of size ; all informal businesses are fully enumerated in these BAs. Implementers set some condition , which is an expansion threshold: if a BA contains nn sufficient units of the target population ( ), additional BAs adjacent to all squares where CC the count of units that meets or exceeds that threshold are sampled. Implementers can set specific ii∈ii ∑jj ≥CC rules for this expansion, with the most straightforward being all eight adjacent neighbors. If these newly sampled BAs meet condition , they trigger additional sampling of adjacent squares, and so on. CC Define a **_neighborhood_** as a collection of BAs that includes both an initially selected BA, and in the case that it satisfies , an expansion into additional, adjacent BAs. In this case, we use the pre-set rule of expanding into all eight, adjacent BAs; additional neighborhood expansions are CC shown in Appendix Figure A.1. A **_cluster_** is the collection of all connected neighborhoods that are enumerated because of the initial selection of BA . Figure 1 below shows clusters highlighted in ii

> 15 Note that the emphasis in this section is in the first stage of sampling informal businesses where BAs are selected using ACS and then fully enumerated. The second stage is just a simple random selection from the resulting enumeration list, which can be implemented via any number of random selection algorithms.

> 16 This term is also to differentiate from city blocks or public squares, with a ready comparison of ‘enumeration area.’

7
Table 4 presents GMM estimations of the PPML—see Windmeijer and Silva (1997). Column (1) presents the first-stage results. Note that the value of the F-test statistic of the first stage, 344.58, is well above 10, which is usually considered a good threshold, and so the instruments cannot be judged as weak. Moreover, Hansen J statistics for mutual consistency of available instruments are provided at the bottom of columns (2) and (3), and they do not reject the null hypothesis that the excluded instruments are valid and uncorrelated with the error term, so there are no overidentification problems. Column (2) shows GMM estimates using co-inventorship as the dependent variable. It shows a positive and statistically significant relationship between inventor diasporas and international co-inventorship. The GMM results are slightly stronger in terms of magnitude of estimated coefficient relative to former PPML. Thus, analysis suggests that ignoring the endogeneity issue tends to underestimate the effect of migration on international co-inventorship. Note, however, that the difference is small and therefore the coefficients are comparable. On the other hand, results for the case of R&D offshoring remain positive but not significant (column [3]). I interpret these differentiated results as further evidence of the critical role of highly-skilled diasporas for worker-to-worker co-inventorship and their less important effect for applicant-to-inventor relations. Personal face-to-face relations and trust building are critical to explain co-inventorship, where contracts are usually more tacit and contract enforcement is difficult. However, in more formal and explicit relationships where written contracts are the rule, such as R&D offshoring, highly-skilled, technical immigrant workers do not play any role. Of course, one cannot rule out the possibility of measurement error of the diaspora variable due to the naturalization issue discussed above—in fact, results in appendix S3 using ethnic name recognition algorithms point to a larger coefficient on the estimated diaspora-R&D offshoring relation.

29
coincided with sharp increases in infant mortality rates; however, they attribute this at least partly to the collapse in public spending on health, which occurred during the same time. There is some evidence that the 1997-99 financial crisis in Indonesia had adverse impacts on neonatal mortality rates in both urban and rural areas (Rukumnuaykit 2003; see also Simms and Rowson 2003).<sup>4</sup> Stillman and Thomas (2004) show that weight-forheight of children deteriorated during the 1998 economic crisis in Russia. Bhalotra (2010) shows that in rural areas in low-income countries, infant mortality rates increase during economic downturns. Based on data from World Development Indicators (2009), an analysis by Buvinic (2009) of countries’ progress toward the Millennium Development Goals (MDG) suggests that girls in poor households in the countries with pre-existing high child mortality rates (and/or low female schooling) are highly vulnerable to the effects of the global economic and food crises. Their situation is especially precarious in the subset of 15 countries, mostly in Africa, that are affected by both low female schooling and high infant and child deaths, as well as decelerating growth (Buvinic 2009).<sup>5</sup> Unlike schooling impacts, however, there is strong suggestive evidence in lowincome countries of gender differences in the way recessions affect infant mortality (Baird, Friedman, and Schady 2007; Friedman and Schady 2009). Baird et al. (2007) use Demographic and Health Survey data from 1986-2006 on mothers’ reports of births and deaths from 59 low-income countries in Sub-Saharan Africa, Latin America, and South

> 4 This evidence is slightly controversial, since other studies do not find impacts of the Indonesian crisis of 1997-98 on child health outcomes (Strauss et al. 2004, Frankenberg et al. 1999).

> 5 Countries with high infant mortality rates are those in the highest quartile of the distribution of under-five mortality rate scores (per 1,000 live births) for 151 developing countries  in the year 2007.  Similarly, countries with low female schooling are those in the lowest quartile of the distribution of the ratio of girls to boys in primary and secondary enrollment for 131 developing countries for the latest year between 2004 and 2007 (World Development Indicators 2009). Countries with decelerating growth identified by using World Economic Outlook 2009 projections and calculations by World Bank Staff.

17
# V – Do Migration and Remittances Increase Well-Being and Satisfaction?

Like the other countries of Central Asia, Uzbekistan has high average life satisfaction for its income level. Figure (6) below presents the average levels of life satisfaction from the LITS 2016 survey, which was conducted in a comparable way across nearly all countries in Europe and Central Asia. By that measure, Uzbekistan has the highest average life satisfaction of any country in the region.

**Figure 6: Average Life Satisfaction in Europe and Central Asia**

<!-- Start of picture text -->
1.0<br>UZB<br> 0.9<br>TJK<br> 0.8<br>DEU<br> 0.7<br>KGZ<br> 0.6<br> 0.5<br>TUR<br> 0.4<br> 0.3<br>UKR<br> 0.2<br> 0.1<br> ‐<br>3.3 3.5 3.7 3.9 4.1 4.3 4.5 4.7 4.9<br>log GDP per capita<br>Overall Life Satisfaction<br><!-- End of picture text -->

_Source: Life in Transition Survey 2016_ Although it is measured using a different scale (from 1= not satisfied at all, to 5= completely satisfied), the finding that there is relatively high life satisfaction in Uzbekistan is largely replicated in L2CU. In any given round of the L2CU panel survey, about 90 percent of respondents were moderately satisfied to completely satisfied, while only about 10 percent of respondents were completely unsatisfied or not satisfied. However, although the aggregate statistics on life satisfaction remain quite stable at the national level, there is substantial churning at the individual level. The descriptive statistics presented in table (12) report transitions in life satisfaction at the individual level. Stability ranges from 20.5 percent for those who were not at all satisfied in the previous round (i.e., the majority improve in subsequent rounds) to 86 percent who were “moderately satisfied” in the previous round (i.e., about 14 percent either improve or deteriorate in the subsequent round).

26