rafmacalaba/data-use-mentions-extended huggingface dataset:
also in the whole region to maximize the gains.

> 8This estimation is commonly used to quantify maintenance costs in cost-benefit analysis. It is higher than an estimation of the average combined cost of routine and periodic maintenance for a typical Sub-Saharan African country. The median value from “Study of Road Infrastructure Costs: Analysis of Unit Costs and Cost Overruns of Road Infrastructure Projects in Africa” published by the African Development Bank in May 2014 is given by 7,500 USD/km for routine maintenance, 160,644 USD/km for periodic maintenance, and 23564 for the combined annual cost. The values are adjusted at 2% annual inflation from 2006 to 2017 since data in US$ are from 2006.

26
Figure 2: Public debt in LICs (A) Change in creditor composition of public and publicly guaranteed external debt, 2007-16 <!-- Start of picture text --> Non-Paris<br>Club bilateral<br>Commercial<br>Bonds<br>Multilateral<br>Paris Club<br>bilateral<br>-6 -4 -2 0 2 4<br>Percent of GDP<br><!-- End of picture text -->

## (B) Share of non-concessional debt

<!-- Start of picture text --> Percent of government debt<br>80 Median<br>70<br>Interquartile range<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

# (C) Interest payments

(D) Share of LICs in debt distress or at high risk of distress <!-- Start of picture text --> Percent of revenue<br>12 Median<br>10 Interquartile range<br>8<br>6<br>4<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> Percent of countries<br>40<br>High In debt distress<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. GDP-weighted average across 32 low-income countries. “Bilateral” includes public and publicly
# **INTRODUCTION**

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Inclusive financial systems—allowing broad access to financial services, without price or nonprice barriers to their use—are especially likely to benefit poor people and other disadvantaged groups. Without inclusive financial systems, poor people must rely on their own limited savings to invest in their education or become entrepreneurs—and small enterprises must rely on their limited earnings to pursue promising growth opportunities. This can contribute to persistent income inequality and slower economic growth.<sup>1</sup> Until now little had been known about the global reach of the financial sector—the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This report presents the first round of the Global Findex database, a new set of indicators that measure how adults in 148 economies save, borrow, make payments, and manage risk. The indicators are constructed with survey data from interviews with more than 150,000 nationally representative and randomly selected adults age 15 and above in those 148 economies during the 2011 calendar year.<sup>2</sup> The Global Findex data show sharp disparities in the use of financial services between high-income and developing economies and across individual characteristics. The share of adults in high-income economies with an account at a formal financial institution is more than twice that in developing economies. And around the world, men and more educated, wealthier, and older adults make greater use of formal financial services.

Novel cross-country data on self-reported reasons for not having a formal account make it possible to identify barriers to financial inclusion. Moreover, the ability to disaggregate data by individual characteristics allows researchers and policy makers to identify population groups
- Brotman, L.M., S. Dawson-McClure, D. Kamboukos, K.Y. Huang, E.J. Calzada, K. Goldfeld, and E. Petkova. 2016. "Effects of ParentCorps in prekindergarten on child mental health and academic performance: Follow-up of a randomized clinical trial through 8 years of age." _JAMA Pediatrics_ 170 (12): 1149-1155.

- Campbell, F., G. Conti, J.J. Heckman, S.H. Moon, R. Pinto, E. Pungello, and Y. Pan. 2014. "Early childhood investments substantially boost adult health." _Science_ 343 (6178): 1478-1485.

- Carneiro, P., and R. Ginja. 2014. "Long-term impacts of compensatory preschool on health and behavior: Evidence from Head Start." _American Economic Journal: Economic Policy_ 6 (4): 135-73.

Cascio, E.U. 2015. _The promises and pitfalls of universal early education._ IZA World of Labor.

- Cascio, E.U., and D.W. Schanzenbach. 2014. _Proposal 1: Expanding preschool access for disadvantaged children._ Policies to address poverty in America, p.19.

- Cascio, E.U., and D.W., and Schanzenbach. 2013. "The impacts of expanding access to high-quality preschool education ." _National Bureau of Economic Research_ (No. w19735).

- Chetty, R., J.N. Friedman, N. Hilger, E. Saez, D.W. Schanzenbach, and D. Yagan. 2011. "How does your kindergarten classroom affect your earnings? Evidence from Project STAR." _The Quarterly Journal of Economics_ 126 (4): 1593-1660.

- Clements, D.H., J., Sarama, C.B. Wolfe, and M.E. Spitler. 2013. "Longitudinal evaluation of a scale-up model for teaching mathematics with trajectories and technologies: Persistence of effects
# _Differences-in-differences Estimates Compared to Simple Differences_

In addition to reporting the numbers and standard errors behind the figures, Tables 1–3 juxtapose two simple estimates of the MVP’s effects. The second-to-last column of each table shows the difference between the indicator values at three years and the baseline values, using the information presented in MVP (2010c). This before-versus-after estimate of the MVP’s effects is shown as the “simple difference within Millennium Village.” The last column of each table shows “differences-in-differences” estimates of the MVP’s effects, based on the MVP data along with DHS data. These estimates show the change in each indicator between the MV evaluation years, minus the change in each indicator in rural areas of the surrounding region between the same years. The values for rural areas of the surrounding region for the MV starting year (2005 in Kenya, 2006 in Ghana and Nigeria) are linearly interpolated using the two DHS values. For Ghana and Nigeria, the 2009 values are linearly extrapolated from 2003-2008 trends. Figure 10 summarizes these results, graphically comparing the before-versus-after (or “simple difference”) estimates compared to the differences-in-differences estimates for all three countries and all nine indicators (with the exception of the two indicators not reported in MVP [2010c] for Sauri, Kenya).

There are two clear patterns in this comparison. First, in many cases the intervention sites perform somewhat better than the surrounding area. Second, the differences-in-differences estimates—which take account of trends outside the intervention sites—are frequently about half as large as the simple before-and-after differences in Kenya and Ghana. In a few cases for those two countries the simple difference shows an important improvement in the indicator while the differences-in-difference shows no relative improvement or even a relative decline. This pattern differs in Nigeria, where while in some cases the differences-in-differences are smaller than the simple differences, the reduction due to removing trends in the surrounding area is less than half of the magnitude of the simple difference. An exception to this pattern in
- 21 -

that some firms are about 2,000 km away from the nearest border point. On average it costs about US$35 per ton.

# **<u>Table 2. Summary statistics: Firm registry data</u>**

|Variable|Abb.|Obs|Mean<br>|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Cost analysis:|||||||
|Total production or operating costs|_C_|41,410|<br>342,164|3,939,146|1.00|<br>257,000,000|
|(1,000 tenge)|||||||
|Average wage (total payroll expenses|_Wl_|41,410|<br>1,451|6,412|0.02|<br>833,377|
|divided by the average number of<br>employees during the year) (1,000 tenge)|||||||
|Energy costs divided by total asset value|_We_|41,410|<br>0.9|99.1|0.00|<br>19,744|
|Fuel costs divided by total asset value|_Wf_|41,410|<br>0.3|6.2|0.00|<br>566|
|Financing costs divided by total asset|_Wi_|41,410|<br>6.7|551.9|0.00|<br>62,446|
|value|||||||
|Other costs divided by total asset value|_Wm_|41,410|<br>16.1|839.3|0.00|<br>146,091|
adoption of cutting-edge technologies in the Food Processing sector. FTM are 5 percent less likely of adoption computer testing for input testing. In addition, we observe large differences in adopting power equipment controlled by computers or robotics for packaging.

Given a large number of technologies across several business functions, we summarize the overall technological sophistication using four different technology indexes. Table 5 provide the descriptive statistics of technology indexes and the differences of these indexes between male- and female-managed businesses. The unconditional differences in technology indexes are positive, but when countries are controlled the coefficients become negative, but the differences are insignificant. This may be because in some countries a large share of male-managed businesses rely on less sophisticated of technologies. When region, size, and sector are controlled, we find female-managed businesses tend to adopt similar level of GBF technologies, but less sophisticated SBF technologies.

Table 5: Difference in technology indexes between male- and female-managed firms managers

||Ma|le|Fem|ale|D|iference||
|---|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Uncond.|Cond.|Cond.|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|GBF - EXT|2.34|0.83|2.39|0.85|0.06*|-0.04|-0.03|
|GBF - INT|1.77|0.62|1.86|0.68|0.09***|-0.03|-0.02|
|SBF - EXT|2.09|0.98|2.18|1.04|0.08|-0.04|-0.09**
- [4] Bell, C., and van Dillen (2012), ‘How does India’s Rural Roads Program Affect the Grassroots? Findings from a Survey in Upland Orissa’, Policy Research Paper No. 6167, World Bank, August.

- [5] Binswanger, H., Khandker, S., and Rosenzweig, M. (1993), ‘How Infrastructure and Financial Institutions Affect Agricultural Output and Investment in India’, _Journal of Development Economics_ , 41(2): 337-366.

- [6] Escobal, J., and Ponce, C. (2002), ‘The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor’, GRADE, Working Paper 40-1, Lima.

- [7] Fan, S., Hazell, P., and Thorat, S. (2000), ‘Government Spending, Growth and Poverty in Rural India’, _American Journal of Agricultural Economics_ , 82(4): 10381051.

- [8] Foster, A., and Rosenzweig, M. (2004), ‘Agricultural Productivity, Growth, Rural Economic Diversity and Economic Reforms: India, 1970-2000’, _Economic Development and Cultural Change_ , 52(3): 509-42.

- [9] Jacoby, H.G., and Minten, B. (2009), ‘On Measuring the Benefits of Lower Transport Costs’, _Journal of Development Economics_ , 89(1): 28-38.

- [10] Khandker, S.R., Bakht, Z., and Koolwal, G. B. (2009), ‘The Poverty Impact of Rural Roads: Evidence from Bangladesh’, _Economic Development and Cultural Change_ , 57: 685-722.

- [11] Murty, M.N., and Goldar, B.N. (2007), ‘Estimation of National Parameters for Project Appraisal in India’, mimeo, Institute of Economic Growth, Delhi, April.

- [12] Spriggs, J
to their equilibrium size, the depth of participation -- measured by the fraction of all institutions in which the firm participates or by the degree of participation with each individual institution -- increases as well.

16 assumption that the government can perfectly exclude firms that do not voluntarily pay the full costs of participation undoubtedly is too restrictive. Hence larger businesses that have more permanent work sites are easier for the govemment to detect. So participation -- as measured by tax compliance and public registry -- will be greater for such firms.

10
households and raise the quality of education in low-cost private schools. To these ends, it employs a variety of instruments such as providing vouchers to poor households in disadvantaged urban neighborhoods to attend selected low-cost private schools and supporting low-cost private schools with monthly per-student subsidies conditional on school quality standards, teacher training courses on subject content and pedagogical and classroom management techniques, and the insertion of competitively-hired subject specialist trainers in secondary schools for fixed terms.<sup>4</sup> The FAS program is PEF’s largest program. In the fiscal year 2009−10, PEF spent roughly 2.4 billion rupees (US$28.7 million, in current dollars) on the program—this amount accounts for 93% of total expenditures by the organization in that year. It was initiated in November 2005 on a pilot basis in 54 schools in seven districts in Punjab. Since then, PEF has rapidly expanded program coverage in phases in terms of additional districts as well as more schools within districts. The program has proceeded through six phases (the pilot phase is considered by PEF to be phase 1). Phase-6 program schools entered the program in April 2010. As of June 2010, the program covers 798 thousand students in 1,779 private schools in 29 out of the 36 districts in Punjab. As a result of explicitly targeting the program at high illiteracy districts over phases 3−5, the majority of program students and schools (70% and 68%, respectively) are in seven districts located in the southern part of the province (see Figure 1).

Table 1 provides summary statistics based on current administrative data on program schools separately by phase of entry. The sample consists of 1,776 primary (classes 1–5), middle (classes 1–8), and secondary schools (classes 1–10, 6–10).<sup>5</sup> Looking at the aggregate sample (pooled across phases), the mean program school size is 455 students
household consumption tends to be more concentrated than income. Of course, this characteristic could introduce some bias and underestimate the extremes of the distribution; however, there is no other option in the absence of household income data.

Once household consumption is defined and thus consumable income is available, the rest of the income aggregates established in the CEQ methodology are calculated according to the tax structure, social protection, and social delivery in Grenada. Our point of departure is disposable income, which is reported by the survey and is assumed to equal household consumption. Then, to identify the distribution of gross income and, after that recreate the distribution of the market income, it is necessary to work backward, following the steps shown in Figure 4: _Net market income (NMI):_ This concept (a pretax income) is obtained by adding the amount of personal income tax ( _PIT_ ) to disposable income ( _DI_ ): N NN= DDNN+ PPNNPP

> 9 Through the NSO, Grenada implemented SLCHBSs in 2007–08 and 2017–18. The latter (used in this paper) collected information on household characteristics (housing, demographics, education, health status, social protection, and shocks and coping strategies). In addition, this survey focused on household consumption expenditures on food and nonfood items and other transactions such as financial payments, investment acquisitions, fees, and voluntary contributions.

> 10 This consumption aggregate is constructed from the following components: (1) expenses and received gifts of food and beverage items, (2) expenses of nonfood articles, (3) value of consumption of durable goods (such as vehicles), and (4) imputed rent of own housing. Household consumption was used as the basis for the official measurement of poverty and inequality in Grenada (World Bank 2021), following the guidelines for measuring welfare aggregates (Deaton and Zaidi 2002).

> 11 Other studies that use consumption as primary income are Lara Ibarra et al. (2019) (Arab Republic of Egypt)
The associations between the industry output share of Suharto family firms in 1996–97 and individual industry outcomes in the post-Suharto era (2000–09) did not change dramatically. Only the correlations with output and the profit elasticity have significantly weakened in the post-Suharto period. Correlations between the broad measure of connectedness and individual industry outcomes are also quite stable, with only a change in the correlation with the export propensity. In sum, at first glance, Suharto’s fall seems to have sparked mild improvements in competition industries with family connections to him relative to industries that were not connected.<sup>18</sup>

# **5.2 Impact of Political Turnover on Competition**

We now assess more rigorously the impact of the disruption of political connections on competition.<sup>19</sup> We use an event study analysis which exploits the collapse of the Suharto regime as a quasi-natural experiment by which the value of political connections was reduced. Our empirical specification is: where is one of the outcome variables in five-digit industry _j_ at time . These variables are the competition index jjii _Z_ and the specific indicators from which it is derived, notably entry and exit rates; the YY tt price–cost margin; profit elasticity; the Herfindahl-Hirschman index and the market share of the four largest companies; the number of firms; and prices. The key variables of interest are the interaction terms 18 See Table C1.2 in the Appendix for descriptive statistics on competition indicators during and after the Suharto era. Overall, competition seems to have improved somewhat in the post-Suharto era. The number of firms went up. Prices fell and concentration rates went down slightly, but entry rates decreased. Assessing to what extent these developments can be attributed to the fall of Suharto is difficult given that there are many confounders, such as globalization, technological progress, changing demographics, structural change etc.

19 As a prelude to that analysis, the online appendix C2 examines the relationship between changes in connectedness and changes in
A variety of household-level variables are also included. The probability of enrollment increases with per capita household income. Enrollment is significantly higher among people living in a household where the head of household had completed at least lower secondary education. Enrollment increases with grade completed above this level.

Rather than including community-level variables _per se_ , the enrollment equation includes fixed effects measured at the province level. This gets round two problems associated with the VLSS community-level data. First, they were collected only in rural and peri-urban areas. Second, like all community-level data, they may well miss out some potentially important community-level influences on the enrollment decision. For example, Behrman and Knowles (1999) find the premium to be a factor influencing enrollment by students in VHI. It is known that the premium for voluntary enrollment varies geographically in Vietnam (World Bank et al. 2001), but the VLSS provides no information on this variation. Similarly, Jowett (2003) finds that informal risk-sharing networks influence the uptake of VHI, but the VLSS provides no information on their existence. The province-level fixed effects vary considerably, the null hypothesis of common effects being decisively rejected.

_Table 3: Enrollment propensity score estimation results_

|Covariate|Mean|Std Dev.|Coef.|z-statistic|
|---|---|---|---|---|
|Primary school|0.234|0.423|0.0441|0.84|
|Lower secondary school|0.472|0.499|0.2442|5.00|
|Upper seconday school|0.089|0.284|0.3209|5.42|
|Post-secondary school|0.094|0.292|0.7058|12.21|
|Age in years|25.373|19.905|0.0008|0.96|
|Currently in school|0.254|0.435|0.4098|12.89|
# 1. Introduction

In the late-1990s to early-2000s, Sub-Saharan Africa experienced a surge in primary school enrollments linked to a movement to make schooling at that level free—a reform dubbed Free Primary Education (FPE). FPE has been linked to a number of positive impacts: more children enrolling in and completing schooling, and positive impacts on health, fertility, and other noneducation outcomes. But the rapid scale-up, sometimes within education systems that were not prepared for the personnel and infrastructure demands that the surge in students would require, has led many to comment on the potential harm to the quality of the average education delivered after scale-up. In particular, there were many reports of overcrowding (i.e., very high student-teacher ratios) and a lowering of teacher qualifications to address that overcrowding in the short run after the implementation of FPE in many countries.

This study explores the narrow question of whether the impacts of this rapid increase in the number of teachers associated with FPE can be felt “now” (at the time of the survey), years after the reform, in classrooms in terms of lower student learning outcomes and worse teacher “quality.” While the question has an interesting historical dimension, what makes it even more important and relevant is that at least two rapid scale-up efforts are currently being advocated for— universal access to early childhood education, and universal free secondary education. Understanding the consequences of the FPE effort can shed light on how to think about these new efforts.<sup>1</sup> The data used to analyze this question are from six Sub-Saharan African countries (Kenya, Madagascar, Mozambique, Tanzania, Togo, and Uganda) and include detailed student and teacher data collected as a part of the Service Delivery Indicators project. The analysis compares the learning outcomes of students at the time of the survey (which is, depending on the country, between 5 (Togo) and 16 (Uganda) years after FPE was implemented) whose teachers were hired just before versus just
health (Figures 7c and 7d) and a mixed story in adequate food with some countries experiencing a decrease in the access to adequate food (Figure 5b). Whether there is indeed a “causal” relationship as opposed to a simple correlation between improved access to adequate food, care and environment and health and the observed reduced stunting and increased average height‐for‐age of children, is a question that needs to be addressed with more complex models using detailed panel data on children through time.

As the first step towards exploring potential synergies in relation to child nutrition, the analysis below investigates in more detail the extent to which children have simultaneous access to adequate levels in any one, or two, or all three of the clusters of underlying determinants of nutrition. So far the access to adequate environment was treated separately from the access to adequate health services. In the remainder of this report environment and health are combined into one cluster mainly for the purpose of simplifying the presentation. One should bear in mind, that combining health and environment into one aggregate cluster creates a very strict set of standards 33