rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Abstract**

This paper examines whether the relationship between traffic fatalities and per capita income is the same for different classes of road users and investigates the factors underlying the decline in fatalities per vehicle kilometer traveled (VKT) observed in high-income countries over recent decades. Formal models of traffic fatalities are developed for vehicle occupants and pedestrians. Reduced-form approximations to these models are estimated using panel data for 32 high-income countries over 1964-2002.

The results suggest that the downward-sloping portion of the curve relating traffic fatalities per capita to per capita income is due primarily to improved pedestrian safety. Our more detailed models shed light on some factors influencing pedestrian fatalities per VKT but much of the reduction in pedestrian fatalities remains unexplained; however, increased motorization and a reduction in the proportion of young drivers in the population clearly played a role.

Occupant fatalities, however, do not show a significant decline with income. What does explain declines in occupant fatalities per distance traveled are reductions in alcohol abuse and improved medical services, and a reduction in young drivers. The importance of demographic factors suggests that in countries where young persons (between 15 and 24 years of age) comprise an increasing share of the driving population, adopting policies to improve young driver education and reduce speeds will be crucial.

1
MVAC forecasts of the number of people who are expected to suffer from food deficits in the upcoming lean season using data from a wide range of primary and secondary data collected by dedicated team, which feed into a Household Economic Analysis model, and model results form the basis for the first-level of geographic targeting (MVAC, 2013; Svesve, 2015; FEWSNET, 2016; Babu et al., 2018). Though it is unclear exactly which rainfall estimate data was used to quantify hazards in the MVAC model – or that are subsequently taken into consideration by FARP – we tested a number of rainfall variables constructed using different sources and covering different periods noted in those documents. We chose to retain those variables that had the best predictive power in explaining aid distribution and that included:

- (i) the percent difference in total crop season (last dekad of November through the final dekad of March) rainfall realizations when rainfall was below the long- mean using the decadal rainfall estimates from the University of California at Santa Barbara’s Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data set (covering 19812015),

- (ii) the percent difference in total season rainfall realizations when rainfall was above the same long-term mean derived from the CHIRPS data set,

- (iii) the median 3-month Standardized Precipitation Index (SPI) values for the period December-January, as provided by the Instituto Pirenaico de Ecología, and

- (iv) the median total crop season Normalized Difference Vegetation Index (NDVI) constructed from Climate Data Record of Advanced Very High-Resolution Radiometer (AVHRR) Surface Reflectance, as provided by the National Oceanic and Atmospheric Administration (NOAA).

MVAC also bases its needs assessment on extension planning area (EPA)-level agricultural production estimates. Given data quality issues that a range of specific crops – and not knowing how these were handled – we instead use third-round EPA-level maize yields estimates for the main growing 14
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# Evaluating public per-student subsidies to low-cost private schools: Regression-discontinuity evidence from Pakistan

## Felipe Barrera-Osorio Dhushyanth Raju

_JEL classification codes_ : I21; I28; O10 _Keywords_ : education; Pakistan; regression-discontinuity design; subsidies; private schools Contact information: Barrera-Osorio: World Bank, <u>fbarrera@worldbank.org;</u> Raju: World Bank, <u>draju2@worldbank.org. We thank the staff of the Punjab Education Foundation, in particular Usman Tariq Bhatty,</u> Mian Kashif Ijaz, Shahid Hafiz Kardar, Allah Bakhsh Malik, Haroon Raheem, and Huma Rizvi, for extensive discussions on program design and implementation and assistance with the program administrative data; Nabiha Asghar, Hummayun Javed, Kashif Mahmood, and Sehar Sarah Sikander Shah for assistance with the follow-up data collection; Corinne Saiens for assistance with the National Education Census data; Amit Dar, Michelle Riboud, Sofia Shakil, and the Government of Punjab School Education Department/Project Management and Implementation Unit for their encouragement and support of the research project; and Salman Asim, Elizabeth King, Leigh Linden, and Quynh Nguyen for helpful comments and suggestions. Financial support for the follow-up data collection from the World Bank’s Education Program Development Fund and Bank-Netherlands Partnership Program Trust Funds is gratefully acknowledged. The findings, interpretations, and conclusions expressed herein are our own and do not necessarily represent the views of the World Bank, its Board of Directors, or any of its member countries. All remaining errors are our own.
The relationship between the market value of dwellings on the one hand and their specific characteristics and those of their localities on the other is known as a hedonic price function. In the proposed approach, this function is specified as: where captures the physical characteristics of each dwelling, those of the locality it sits in, and the average value of dwellings in the areas. The last term, , is a stochastic disturbance XX<sup>ii</sup> LL<sup>ii</sup> with zero mean that is independently distributed across households but whose variance may be different across locations. VV<sup>ii</sup> εε<sup>hii</sup> The approach proposed in this paper to assess the cost of disasters is to use recent microeconomic data on , and to estimate equation (8). Then, data from a field survey or satellite imagery are used to assess how the characteristics of each individual dwelling might have been affected. VV<sup>h</sup> XX<sup>h</sup> LL<sup>ii</sup> With this assessment at hand, it is possible to use the estimated equation (8) to predict the postXX<sup>h</sup> disaster values of and, by extension, of . This information then allows computing the decomposition in equation (6) for all areas affected by the disaster. VV<sup>h</sup> VV<sup>ii</sup>

# **4. The Gorkha earthquakes**

Triggered by a slip in the Main Frontal Thrust, the Gorkha earthquakes were Nepal’s worst natural disaster since 1934. The first wave struck on the 25<sup>th</sup> of April 2015, with a Richter magnitude of 7.5. Its epicenter was in Barpak, in Gorkha District. It was followed by a major aftershock on the 12<sup>th</sup> of May 2015,
an exogenous government spending shock using a news variable that captures episodes that led to large military buildups in the U.S..<sup>8</sup> More recently, Owyang et al. (2013) use the same approach to estimate spending multipliers contingent on the state of the economy in Canada and in the U.S.. However, this approach is limited by the number of observations of the news variable for countries such as Canada, which rarely participate in large military operations. Romer and Romer (2010) use narrative records, such as Congressional reports and presidential speeches, to identify the main motivations for all major post-war tax policy changes in the U.S.. Although this approach is more appropriate for identifying exogenous changes in tax revenue, its implementation remains in practice more cumbersome.

Another common technique is the recursive approach. It consists of imposing the condition that government spending or tax revenue is not responsive to business cycle fluctuations for at least one quarter (this approach is used by Blanchard and Perotti (2002), Perotti (2004), Monacelli and Perotti (2010), Auerbach and Gorodnichenko (2012)). The logic behind this assumption is that fiscal variables need more than one quarter to adjust in response to unexpected changes in GDP. However, this assumption is often criticized because it imposes a systematic non response of fiscal variables to a business cycle shock.

# **2.2 The sign restrictions method**

I extend the methodology employed by Mountford and Uhlig (2009) in the case of a small open economy by assuming a block exogeneity in the VAR model. Sign restrictions are imposed on the IRFs of a set of variables to identify structural shocks. The advantage of this approach is that it does not assume a lack of contemporaneous effects of unexpected changes in GDP on fiscal variables. This strategy is also used to identify exogenous technology shocks (see, for example, Dedola and Neri (2007), Enders et al. (2011)) and
MONGOLIA'S INFORMAL SECTOR Size and Trends 12 25. In summary, the official employment and unemployment statistics demonstrate a large decline in official employment, concurrent with an expansion of the working age population, and relatively low official unemployment rates. But due to the perception that work in the informal sector is not "employment," participants in the informal sector are counted as neither employed nor unemployed. The informal sector surveys conducted by the author support this notion, finding that 75-85% of informal employment is not reflected in official statistics. The SSO household survey also supports the contention that informal income is more important, and informal activities are more prevalent, for households in which the head considers himself to be "unemployed," and for households with a greater number of unemployed adults.<sup>23</sup>

# **The Current Situation: Employment in Ulaanbaatar's Informal Sector**

26. The previous section discussed the trends in informal employment. We now turn to the issue of the current size of informal employment in Ulaanbaatar. Statistics derived from the Annual Labor Force Balance will be combined with the results of the informal sector surveys to estimate that 70,000 to - 100,000 people are engaged in Ulaanbaatar's informal sector as their primary occupation including part-timers, moonlighters, and working pensioners, an estimated 105,000 to 130,000 people are engaged in informal activity, equal to roughly 34-42% of the "able-bodied population of working age" in Ulaanbaatar.<sup>24</sup> An analysis of the data from the SSO household survey generates similar estimates, finding that 30-33% of the working age of population are engaged in informal activities.

27. The estimates of employment in the informal sector are derived from a combination of official employment statistics and the informal sector surveys conducted as part of this study. The informal sector surveys suggests that for every informal who reports to the _duureg/soum_ that they are employed, there are 2 to 6 informals who report that
both economics (Olson 1982) and sociology (Banfield 1958), so that one would expect to find a great deal of empirical research on the distinction between bonding and bridging social capital. Instead, there are few studies on this distinction, and they are especially rare in economics. Putnam (2000: 23) frankly admits that _“I have found no reliable, comprehensive, nationwide measures of social capital [for the US] that neatly distinguish ‘bridgingness’ and ‘bondingness’.”_ The few economic studies to have considered the bonding/bridging distinction in the analysis of the positive effects of social capital focus on _economic growth_ . A much-cited study in this regard is Knack and Keefer (1997), which is based on the World Value Survey (WVS). Its main finding is that trust of the survey’s respondents impacts positively and significantly on a country’s economic growth. The subsequent literature has largely confirmed this result (e.g. Zak and Knack 2001; Beugelsdijk et al. 2004; Dearmon and Grier 2009). Knack and Keefer then attempt to apply the bonding/bridging distinction of social capital, though they do not use this terminology. In fact, they distinguish people’s participation in social organizations depending on the kind of organization, i.e. whether it is of the Putnam variety, like cultural organizations which enhance cooperation and solidarity, or of the Olson variety, like political parties, which are rather motivated by rent-seeking behavior. However, although the estimated coefficients in the overall GDP growth equations differ between the two kinds of organization, they are not significant.

Two other very recent studies focus on regional growth in the US and the EU respectively. Knudsen et al. (2010) apply factor analysis to distinguish among the different kinds of participation in social organizations. However, they only find that participation is not of the same kind, so that they are obliged to fix an _a priori_ criterion, and to plead for “experts” for its
> Wealth Index of Lee & Braithwaite (2022) (Figure A2). Since the original estimate is only available

> 28The value for _γ_ is informed by estimates of the costs of road delays to African truckers by Teravaninthorn & Raballand (2009). The value of _β_ is informed by estimates on the cost of traffic congestion by Wang et al. (2011). Fajgelbaum & Schaal (2020) use _β_ = 0 _._ 13 and _γ_ = 0 _._ 1, but their characterization of infrastructure ( _Ijk_ ) in terms of road-lane km yields significantly larger values than the formulation in terms of km/h adopted from Graff (2024).

> 29Fajgelbaum & Schaal (2020) page 1439 simulate an IRS case by inverting the ratio of _β_ and _γ_ keeping _β_ fixed, which, for _β_ = 1, yields _γ_<sup>_′_</sup> = 1 _._ 057. This, however, yields very similar solutions to _γ_ = 0 _._ 946, thus I set _γ_ = 1 _._ 2 to induce stronger effects. There are, as far as I am aware, no studies or empirically accessible data on road congestion in inter-city or international transport.

> 30Graff (2024) sets _N_ = 6 _, k_ = 2 (with one international good produced outside the country and one agricultural good produced by smaller cities) and conducts simulations for every African country. Fajgelbaum & Schaal (2020) set _N_ = 10 _, k_ = 1 and conduct simulations for France, Spain, and Western Europe.

> 31Graff (2024) uses nightlights divided by city population to form the productivity measure. While this is a viable approach, the task of measuring differences in city productivity across countries encourages the use of richer data sources. Lee & Braithwaite (2022) combine daylight satellite imagery and OSM data in an iterative ML framework that has proven able to predict wealth within and across SSA countries reliably. As an attempt to verify, taking a
**Table 1. List of skills surveys**

|**Dataset**|**Countries**|**Year**|
|---|---|---|
|PIAAC|Austria, Belgium (Flanders), Canada, Czech Republic, Denmark,<br>Estonia, Finland, France, Germany, Ireland, Italy, Japan, Korea,<br>Netherlands, Norway, Poland, Russian Federation, Slovak Republic,<br>Spain, Sweden, United Kingdom (England and Northern Ireland),<br>United States|2011/2012|
||Chile, Greece, Israel, Lithuania, New Zealand,<br>Singapore, Slovenia, Turkey|2014/2015|
||Ecuador, Hungary, Kazakhstan, Mexico, Peru|2017|
|STEP|Bolivia, Colombia, Lao PDR, Sri Lanka, Vietnam|2012|
||Armenia, El Salvador, Georgia, Ghana, Kenya, North Macedonia,<br>Ukraine|2013|
||Serbia|2015/2016|
||Kosovo, Philippines|2015|
|LMPS|Tunisia|2014|
||Jordan|2016|
||Egypt|2018|

Table 2 shows the types of tasks used to estimate the WFH index, Appendix 1 describes the rationale for choosing these tasks, and Table A 1 in Appendix 2 shows the complete list of variables. Such variables are slightly different across the three data sets. For example, while STEP has information on whether the job requires contact with customers, such information is not collected in the LMPS for Jordan and Tunisia. Thereby, while the indexes can be compared across countries within the STEP, PIAAC and LMPS data sets, comparisons are not possible across them.

6
Figure 4 documents the results. Panels A and B present our general female and male segmentation indices—how similar is the gender composition of a firm’s workforce to a focal employee—by firm size and also their simulated counterparts. The dark red bars are the actual segmentation levels, while the light blue bars represent predicted segmentation based upon India’s workforce composition and firm size. For women, segmentation levels are highest among the smallest firms and largest firms. By contrast, Monte Carlo simulations predict a uniform decline in firm size; for the largest firms, the expected segmentation levels are basically the same as those contemplated above for the economy as a whole. This pattern is quite interesting as it suggests root causes of abnormal segmentation are most prevalent among small enterprises and very large establishments, which have different economic properties and competitive environments. Panel B, by contrast, shows that gender segmentation for men is consistently higher than anticipated by the Monte Carlo simulations but also uniformly declining in firm size.

Panels C and D turn to our estimations of segmentation that focus on the match of employees to the gender of the owner. We can only calculate these metrics for establishments with ten employees or fewer. It is, of course, possible to simulate expected levels of owner-based segmentation for larger establishments, and we do this extension so that we have baselines than are comparable to Panels A and B. Owner-based segmentation for women-owned enterprises shows its greatest deviation from expectation for the smallest firms of 1-3 employees, with deviations shrinking substantially for larger establishments. By contrast, male-owned establishments have a more modest deviation from baseline for the smallest firms, but this appears to widen somewhat for larger establishments. Said differently, it appears women business owners are increasingly likely compared to random hiring to pull workers of both genders while scaling small enterprises, while men have a greater capacity to continue scaling with a focus on male employees.

We have also investigated a comparison point to the United States using the Longitudinal Employer-Household Dynamics (LEHD) database. The LEHD
the coefficient on the interaction term is not only insignificant, but is negative, not positive as implied by an argument for reverse causation.

In our second variant of this identification strategy, we distinguish donors by selectivity in aid allocations. Specifically, we posit that donors that are more sensitive to quality of country policies and systems when allocating aid will also be more sensitive to the quality of systems when deciding whether to rely on them for managing their aid. In contrast, there is no particular reason to expect the strength of any reverse causation from use of country systems to vary with donors’ sensitivity to policy in allocating aid. An empirical finding of heterogeneous impacts in this instance can be explained better by the causal argument than by reverse causality (from use of systems to system quality). Equation 5.2 includes an interaction term, to test the hypothesis that PFM Quality will affect use of country systems more for donors that are more policy selective in allocating aid among recipients.<sup>18</sup> For this purpose, we use the donor Policy Selectivity scores from Knack, Rogers and Eubank (2011), based on aid disbursements in 2007. The interaction coefficient is positively signed, as hypothesized, and significant at the .01 level. These heterogeneous impacts are more consistent with a causal explanation from PFM Quality to use of PFM systems than with an argument for reverse causation from use to quality.

Endogeneity bias cannot be entirely ruled out by the results in Tables 4 and 5, or of course by the use of lagged values of PFM quality in all of the regressions. However, these results are all consistent with a direction of causality running predominantly from PFM Quality to use of country PFM systems, and much harder to square with an argument of reverse causality.

> 18 The interaction term is equal to the product of the deviations of PFM Quality and Policy Selectivity from their respective means.  The coefficient on PFM Quality thus indicates its marginal effect conditional on the mean value of Policy Selectivity.  Because
mark>

<mark>The regressions are run on data from 2017 and include 30 countries, which is the largest sample for which all variables are available. The estimation is based on within-2-digit-sector variation. The R-squared decomposition is computed using Shapley and Owen values.</mark>

**<mark>Source</mark>** <mark>: WTO (2019),</mark> _<mark>World Trade Report 2019: Services – The New Trade Frontier</mark>_ <mark>, Geneva: World Trade Organization.</mark>

> 26 Both variables come from the Centre d’études prospectives et d’informations internationales (CEPII).

> 27 World Bank Group.

> 28 World Economic Forum.

> 29 International Telecommunications Union.

> 30 World Bank Group.

> 31  Centre d’études prospectives et d’informations internationales (CEPII).

> 32 CESIfo.

> 33 Worldwide Governance Indicators (WGI).

> 34 Both variables are taken from the Regional Trade Agreements Database from Egger and Larch (2008).

> 35 Worldwide Governance Indicators (WGI), World Bank Group.

> 36 Worldwide Governance Indicators (WGI), World Bank Group.

18
# **Introduction**

The demand for high-quality early childhood development (ECD) support is growing in Eastern Europe and Central Asia. Indeed, many post-transition countries have similar issues in regard to preschool service delivery and the provision of educational facilities. In the Russian Federation, ECD remains a high priority for federal and regional policy makers. According to national statistics, 66.2 percent of children aged 0 to 7 are enrolled in the Russian early childhood education system (UniSIS, 2015). The Russian government recently introduced a set of reforms to improve the quality of the early childhood education system and its operational management at the regional level (Bennett et al, 2012). The new federal state standard of preschool education, adopted in 2013 (Government of the Russian Federation, 2013), required regional ECD systems to become more child-centered, diverse, and flexible in terms of their content and learning environments. Furthermore, aiming to increase enrollment in kindergartens, the Russian federal government has introduced policy interventions to support the new construction and the maintenance of existing educational facilities in the regions, as well as to equip the kindergartens with relevant modern technology. At the same time that these federal initiatives are happening, many policy makers at the regional level strive to reduce the construction and operating costs related to preschool facilities.

This paper is the first attempt to reflect on the practical application of the new ECD standard norms to the design of preschools in Russia. Starting in 2013, the World Bank and the Department of Education and Youth Policy of the Khanty Mansyisk Autonomous Region of Russia (hereinafter referred to as Khanty-Mansyisk) began a pilot project aimed at increasing the efficiency and improving the quality of the region’s ECD system 2