rafmacalaba/data-use-mentions-extended huggingface dataset:
While our analysis provides useful insights into service levels and performance across sectors and cities in Kenya specifically, the results of our analysis have implications that extend well beyond urban Kenya. We argue that our data provide compelling evidence for the importance of defining new measures of access that take into account how services are actually being used. This requires looking beyond easy-tomeasure indicators of nominal access, to capture additional data on both effective and quality-adjusted access.

Our efforts to capture quality dimensions of service build on numerous ongoing initiatives at the sectoral level. The uniqueness of our research lies in its attempt to develop a more systematic, comprehensive and comparable reporting of quality across sectors. Our research introduces a common language for discussing service access and performance across sectors in a manner that key stakeholders can meaningfully interpret. The results can be broadly applied in both policy and practice. For example, at a policy level this common ‘language’ could be used by the Minister of Finance to assess how services and Ministries are performing relatively and to identify key priorities in each sector. At a more granular level within a country or city these indicators can be used to highlight which cities/neighborhoods are doing poorly relative to others. Within a sector it can be used to identify development needs and the relative priority of expansion versus quality improvement efforts. It can also be analyzed to develop a more nuanced understanding of demand and willingness to pay for different types and quality of services, and for identifying factors that contribute to expanding access.

Our research demonstrates the value of collecting, compiling and analyzing indicators for each of the three levels of access for all cities/countries. As a next step, it would be useful to test and adapt this methodology, expanding it to other countries and testing alternative faster and more economical means of collecting data that do not rely on large-scale expensive household surveys. In particular, it would be worthwhile to investigate ways of incorporating observational data (e.g. nightlights to look at coverage and quality of infrastructure services).

Finally,
of-province family migration in both stipend and non-stipend districts is rather uncommon, particularly in rural areas. Instead, most migration is in the form of temporary out-migration of a member of the household — often of working men. It is very unlikely that this migration was affected by the stipend program. Finally, some simple econometric models give no evidence that the FSSP affect either type of migration after controlling for most of the covariates used in the analysis.

A final compositional issue could arise if the conditional stipend motivates households located in non-stipend districts but adjacent to stipend districts to send their girls to schools there. This would increase the demand for education in stipend districts but reduce it in non-stipend districts, leading to no changes in net enrollment across the province. In order to check the possibility of crossover effects between contiguous stipend and non-stipend districts, econometric models of educational outcomes were estimated on a subsample of schools and households located in treatment and comparison districts that do not have common borders.<sup>39</sup> The direction, magnitude, and significance of the findings using these samples are for the most part consistent with the results obtained with the full sample, with the exception of some models that employ school level data for which the findings are qualitatively similar but not statistically significant.

A natural issue for a program like the FSSP focused exclusively to public schools is families could have moved their daughters who were previously enrolled in private schools to public schools in order to become eligible for the stipend. As a result, the observed increase in female enrollment in the public system may be offset (partially or completely) by a fall in enrollment in private schools, hence reducing the overall effectiveness of the program. Nevertheless, the main set of results of this paper uses household-level data and defines progression and completion irrespective of the type of school in which the girl is enrolled. Therefore, the DD and RDD models using these data provide estimates of the net impacts of the FSSP that already account for the possible crowding out effect induced by
Figure A4: School attendance, children aged 14-18, by poverty Figure A3: School attendance, children aged 6-13, by poverty <!-- Start of picture text --> 2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percent<br>Percent<br>Attending Not Attending Attending Not Attending<br>Total Total<br>Richest Richest<br>Q4 Q4<br>Q3 Q3<br>Q2 Q2<br>Poorest Poorest<br>Non Poor Non Poor<br>Poor Poor<br><!-- End of picture text -->

# Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017

Figure A5: School attendance, children aged 6-13, by gender Figure A6: School attendance, children aged 14-18, by gender <!-- Start of picture text --> 2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percent Percent<br>Attending Not Attending Attending Not Attending<br>Total Total<br>Girls Girls<br>Boys Boys<br><!-- End of
the average rainfall level measured over the entire area of countries like Brazil, the Russian Federation or China would hide any and all local climatic shocks. Recent work has focused on mapping subnational agricultural GDP (Blankespoor et al., 2022) and such approaches could in theory be combined with spatially explicit data sets on hazards and climate shocks to estimate the fraction of agricultural GDP that is produced in areas vulnerable to climate. Alternatively, Koomen et al. (2022) develop spatial explicit simulation methods that may project populations vulnerable to hazards and climatic shocks under alternative Shared Socioeconomic Pathways (SSP) (scenario definitions commonly used in climate scenario analysis). Efforts to summarize such vulnerability trends to a national level likely deserve a full paper on their own, particularly if the estimates were to be projected several years into the future. Finally, Andr ́ee et al. (2019a) provide projections through 2030 for deforestation, human exposure to air pollution, and carbon footprints for countries that account for a combined 85% of the world’s population. This could capture some climate and environmental risk components, but their machine learning model relies on economic variables that have overlap with the covariates used here, showing that much of the information on these environmental aspects may already be contained in poverty, income and variables describing the structural
) claim, the cost ratio may be affected only slightly by the omission of salaries as the omission will have rather similar effects on both the denominator and numerator of the ratio.

Second, as noted by Pettersson and Sisouphanthong (2005), the cost ratios in their study are rather low. This reflects the fact that the survey required considerable time for interview and follow-up per household over the month when the interviewer-supported diary method was used. Therefore, when a simpler consumption module is used, _c_ is likely to be higher.

Next, we also calculated _c_ based on the survey costs reported in Humphreys (1979, p.396) for the Ada Baseline Survey (ABS) conducted in rural Ethiopia. Their first stage sample consists of 87 administrative localities and costs $5,547, whereas 632 observations were in the second stage costs at the cost of $5,899.<sup>16</sup> Therefore, we have an estimate of _c_ as follows: 16In addition, there were fixed costs of $3,589. Humphreys (1979) use a slightly different cost function for their analysis, but their cost model reduces to ours when we ignore the terms involving square roots.

39
contracts, there is merit in further research to better understand how best to combine the information from different indices so that farmers can rely on timely claim payments in bad years. For example, an early part-settlement based on a weather index could be combined with a final adjustment based on an area yield index (Rao 2011), with weather and satellite data used behind the scenes by insurance providers to target and monitor the crop cutting experiments that form the basis for the yield index.

Finally, designing, evaluating and regulating indexed insurance products is a highly technical area and there may be a role for a central government Technical Support Unit to provide objective, technical support to states, central government and regulators. At a minimum, such a unit could provide objective analysis of the cost of and level of basis risk in products to assist states in choosing between different programs and insurance providers. However, the unit could also have a broader analytical and research function, with a mandate to study the agricultural insurance market across India and develop best practice guidelines on areas such as product design, procurement and strategies for increasing take-up.

20
The current study confirms that even when mortality risk is higher among malnourished children, this has only a minor impact on the measured nutritional status of the child population or on that status by gender with evidence from a country with high rates of malnutrition and moderately high mortality. We show this, first, by illustrating the degree to which the nutrition results reported in various surveys form India would have changed had mortality rates differed. While our initial simulations impute results using global evidence on the relationship of nutrition and risk of mortality we also bolster these simulations with additional simulations based on the estimated hazard of mortality from survey data.

The paper is organized as follows: The next section describes the data and presents some descriptive statistics. Section II outlines the theoretical framework and the empirical strategy. Section III discusses the main results, and section IV presents some implications of the findings.

# **II. Data and Descriptive Statistics**

This analysis uses data from three waves of India‘s National Family Health Survey (NFHS; 1992/93, 1998/99 and 2005/06), a survey of representative households in states and territories covering some 99 percent of the population<sup>3</sup> and similar in structure to demographic and health surveys conducted in several other countries. The NFHS follows the pattern of a standard Demographic and Health Survey. The main sample of NFHS contains information on 45,279 children in 33,032 households from the 1992/93 round, 30,984 children in 26,056 household from the 1998/99 round, and 48,679 children in 33,968 households from the 2005/06 round.<sup>4</sup> Because the NFHS does not collect information on household income or consumption, a household wealth index was constructed from the data on household assets using the method based on principal components (see, for example, Filmer and Pritchett 2001; Rutstein and Johnson 2004).

The NFHS provides height and weight data for children under age 48
constitute at least an extra 7-8 percent GDP, taking the total target to around 33 percent. From these 22, only 9 exceeded this target during the boom (Figure 26).

Figure 26. Average annual investment rates, percent of GDP between 2004-2014 <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>Gross fixed capital formation Expenditure on education Expenditure on health<br>% GDP<br>Botswana Congo, Rep Mauritania Tanzania Zambia Sudan Angola Niger Chad Gabon Uganda Madagascar Sierra Leone Mozambique Ghana Togo Guinea Mali Cote d'Ivoire Cameroon Congo, Dem Rep Central African Republic Vietnam Indonesia Uzbekistan Guyana Syrian Arab Republic<br><!-- End of picture text --> _Notes: Average annual investment rates calculated as the gross fixed capital formation, investment on education and health share of GDP over the 2004-2014 period in resource-rich Sub-Saharan African countries. Missing expenditure on education data for Nigeria and Equatorial Guinea, and missing gross fixed capital formation for Sao Tome and Principe and Liberia, therefore these were not included in the figure. Source: authors’ calculations using IMF data for the public-sector gross fixed capital formation, World Bank data for investment on health and UNESCO for investment on education._ Despite the limited number of countries exceeding the Growth Commission target, the gains from the boom boosted the resource-rich Africa group’s average annual investment rates to surpass the rates of the non-resource rich Africa group (Figure 27). This growth has been supported by an increased investment in gross fixed capital, but at the expense of a more limited growth of investment in education and health.

37
An alternative choice of numeraire could also be an “artificial” high performer. The advantage of this approach is that it is stable across subjects and over time by construction. For example, if we were to anchor _LAYS_ to a Singapore in 2015 and another country, say the Republic of Korea, outperforms Singapore in the next round, we would then have to convert _LAYS_ from Singaporeequivalent years to Korean-equivalent years, making a comparison difficult. In contrast, an artificial top performer with a fixed score would continue to serve as the anchor in this scenario. One reasonable performance benchmark would be the “advanced” benchmark of 625 on TIMSS and PIRLS, which is constant across subjects, grade levels and assessment rounds.<sup>17</sup>

# **5. Consistency of** **_LAYS_ with other evidence**

On the surface, the _LAYS_ measure has some plausibility. It makes sense that we should value education systems (and, more broadly, societies) differently based on the amount of learning they deliver. And as we have seen, cross-country comparisons using the standard _LAYS_ approach that use mean test scores from international assessments—which are admittedly based on a somewhat arbitrary scale—are similar to those based on the share of students reaching a particular level of learning proficiency (which are not scale-dependent).

This section evaluates whether other evidence is consistent with the assumptions and implications of the _LAYS_ approach. Specifically, it explores: (1) whether students’ learning gains across years exhibit local linearity, as assumed in the _LAYS_ calculations; (2) whether observed returns to schooling are consistent with the quality adjustments implied by _LAYS_ ; (3) whether the _LAYS_ learning adjustments are consistent with other test-score-based quality adjustments in the literature; and (4) how the findings from the _LAYS_ approach, which relies on a multiplicative combination of quantity and quality, would compare with those of a linear approach that has been used on subnational data. The section concludes by comparing what these different approaches—
10 income, but the majority of the significant coefficients are positive. By contrast, higher per capita incomes are associated with a significantly lower probability of ‘catastrophic’ health expenses. It is known (Van Doorslaer, O'Donnell et al. 2005) that China is unusual by Asian standards in that ‘catastrophic’ payments in most other countries are concentrated among the better off. These results show that _even after controlling for the other covariates in our models_ , a higher income reduces the probability of experiencing ‘catastrophic’ health expenses. Like O’Donnell et al. (2005), we find some evidence that higher levels of education, holding constant per capita income, reduces the probability of ‘catastrophic’ expenses. In the H8BS, the effects of ethnicity vary somewhat across the provinces, as do the effects of having small children in the house: for the most part, however, being from China’s Han majority seems to lower one’s chances of experiencing ‘high’ and ‘catastrophic’ expenses, while having young children in the household increases them.

Turning to the insurance effects, we find evidence across all surveys in support of the hypothesis that health insurance can increase financial risk. There is, however, some heterogeneity in effects by type of scheme, and by locality.

In our basic results for the CHNS data (Table 4) we find a significant effect of health insurance on the probabilities of incurring ‘high’ and ‘catastrophic’ health expenses in the sample as a whole and in the urban subsample, but not in rural areas. The marginal effect in the urban subsample indicates that health insurance raises the probabilities of ‘high’ and ‘catastrophic’ medical expenses by 11.3 and 14.5 percent respectively. In Table 5, we find that in urban areas, it is worker insurance (presumably LIS) and ‘other’ insurance (quite likely private insurance) that are associated with an increased risk of ‘high’ and ‘catastrophic’ health expenses,
by lower idiosyncratic risk. Large bank holding companies, however, are not less risky as they tend to have lower capital ratios. These contributions on the scale and risk effects of bank size cannot distinguish between absolute and systemic size as they use data for only the US.

Several studies consider the risk and return implications of combining traditional banking with other financial activities. Among these, Stiroh (2004) considers how the impact of the activity mix of US banks, proxied by the share of interest income in total income, affects bank 4
**_<u>________________________________________________________</u>_**

Through the Interchange, United States, Canada and Mexico have developed the <u>North American Transportation Statistics Database</u> that supplies users with relevant, timely and comparable transportation data and information for North America. The 13 year-old Interchange is a forum for the exchange of information and the initiation of joint projects of the transportation and statistical federal agencies in Canada, Mexico, and the United States. North American Transportation in Figures, available through the Bureau of Transportation Statistics, provides comprehensive information on vehicle ownership, vehicle use, transportation networks, trade and traffic safety for Canada, Mexico and the United States, based on 1996 data. North American <u>Transportation Highlights is a summary report of this data that can be downloaded from the web</u> for free.

_<u>UN Economic Commission for Europe Annual Bulletin of transport statistics</u>_ The publication includes data on railway, road, inland waterway, maritime and intermodal transport in Europe and North America.

# _<u>Public Partnership in Highways Toolkit</u>_

This Highways Toolkit provides advice to policy makers in developing countries on the design and implementation of private participation in the highway sector. It includes a number of tools such as a library and interactive financial models. The toolkit provides a detailed guide to introducing private participation in highways, with practical tools for structuring the main contractual and financial terms. This toolkit was prepared by the World Bank and external consultants with funding from <u>Public Private Infrastructure Advisory Facility</u> (2002).

Visitors can access the entire toolkit online, or go directly to one of its five Sections:

- <u>Section 1: presents the rationale for private participation in the highways sector,</u> alternative options, and a guide to conducting a sector diagnostic.

- <u>Section 2: sets out the key parameters needed in the design of a
**by** the standard deviation of real-exchange-rate changes,

the standard deviation of terms of trade shocks, and the standard deviation of [real imports **+** real

# exports]/GDP)

**-** Low public institutional quality (measured **by** the inverse of theKaufman et al. governance

index and the inverse of the Gastil index on political and civil rights)

**-** Lack of financial depth (measured **by** the inverse **of** quasi-liquid Liabilities **_/GDP)_**

Having constructed the return and risk indices and estimated the level of wealth and **NFA,** we proceed to the empirical estimation of the model outlined in the previous sections.

> **15** Note that some variables (such as financial depth and governance quality) enter in both the return and risk measures. They do so because of their dual effect on the country's investment profile.

> **16** The standard deviation of all listed variables is calculated as the standard deviation of observations corresponding to the current and four preceding years for each of the sample countries.

**17**
Figure 6: Trends in Students’ average Academic Performance by Cohort: 2010–2014 <!-- Start of picture text --> 61<br>60<br>59<br>58<br>16−18 years old cohort<br>19−23 years old cohort<br>57<br>2010 2011 2012 2013 2014<br>Academic Year<br>Student Overall Weighted Mean (OWM)<br><!-- End of picture text --> _Source_ : Own calculations using the NUL students records data for the academic years 2010, 2011, 2012, 2013, and 2014. _Notes_ : OWM is the overall weighted mean.

between the FPE-treated and FPE-untreated groups (Abadie, 2005). In such a case, therefore, the FPE effect identified by the standard DID would be biased. It is, therefore, important to check whether the pre-PFE characteristics are balanced between the control and treatment groups. Table 2 presents the pre-FPE compositional differences between the control and treatment groups. Specifically, the table shows pre-FPE mean differences, by treatment group, in the main outcome variable (OWM) and the individual characteristics (APS, age, gender, and location). Column (3) shows the _p−_ value of the _t−_ test for differences in mean characteristics of the control and treatment groups pre-FPE. Looking at these _p−_ values, we can see that there are statistically significant differences between the control and treatment group with respect to their age, pre-university (i.e. COSC) academic performance, gender and location (e.g. Berea and BothaBothe district).<sup>12</sup> Undoubtedly, these characteristics are associated with the changes in student’s academic performance over time. For example, because early and current investments in human

> 12Column (5) of the table presents the normalized difference in characteristics between groups. The normalized difference between means is a scale-free measure ̄ of the difference