rafmacalaba/data-use-mentions-extended huggingface dataset:
These findings are consistent with the finding presented in the previous section on facility selection, which shows that wealthier women are willing to travel farther to reach high‐level and high‐quality facilities.

# 6. Discussion

A growing body of evidence highlights the critical importance of quality of care for improving health outcomes in low‐ and middle‐income settings. Closing the gap between the health outcomes of the poor and wealthy will require not only increasing access for the poor, but also improving the clinical quality of the care they receive. In their analysis of data from 64 developing countries, Wagstaff, Bredenkamp, and Buisman (2014) find that progress on health service coverage has been considerably more pro‐poor than progress on health status; they hypothesize that the quality of health care is worse for the poor. Our study corroborates this hypothesis, at least for the DRC. In this paper, we demonstrate that socioeconomic differences in the quality of care are indeed substantial. Our analysis employs data collected through household surveys, direct clinical observations, and exit interviews. Although each individual source of data has its limitations in objectively measuring the quality of care, the combination of these data sources provides robust evidence on the existence of a wealth‐quality gradient.

The novel data that link health facilities to households in their catchment area as well as the content of care to price of consultations enables carefully disentangling the various mechanisms underlying the observed wealth‐quality relationship. Although spatial correlation between household wealth and quality of care accounts for more than half of the overall gradient, a statistically significant positive association between wealth and the quality of antenatal care also emerges when we compare women from the same village. This gradient appears to a large extent to be driven by differential sorting or selection into facilities. Empirically, better‐off women are more likely to leave their catchment area to seek care at more distant, higher‐level, or better facilities, where they pay higher prices for the consultations. Both findings imply that equality in quality of health
, Kline, P., and Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. _The Quarterly Journal of Economics_ , 129(4):1553–1623.

- Datta, S. and Kingdon, G. G. (2019). Gender Bias in Intra-Household Allocation of Education in India: Has It Fallen over Time? IZA Discussion Papers 12671, Institute of Labor Economics (IZA).

- Deaton, A. (1987). _The Analysis of Household Surveys: A Microeconometric Approach to Development Policy_ . Johns Hopkins University Press.

- Dhar, D., Jain, T., and Jayachandran, S. (2019). Intergenerational Transmission of Gender Attitudes: Evidence from India. _Journal of Development Studies_ , 55(12):2572–2592.

- Duflo, E. (2012). Women Empowerment and Economic Development. _Journal of Economic Literature_ , 50(4):1051–1079.

35
weights for the IHPS sample that is used for analysis. The model controls for a range of individualand household-level demographic and socioeconomic attributes that predict response (and that are also potentially associated with land ownership).<sup>19</sup> Table 1 shows a within-household success rate breaking down the number of eligible adults versus the number of individual interviews completed. Across all households, regardless of the number of adults, all eligible adults were successfully interviewed 68 percent of the time. The remaining 32 percent of households had more than one adult but failed to interview at least one of them. Given that the household head or their spouse is most likely to be the household member owning or managing assets listed by a household, part of the analysis outlined in Section 4 has a focus on members of the principal couple. Of the 2,477 households included in the individual household sample, 72 percent had a principal couple, and in 75 percent of these cases enumerators managed to interview both the husband and spouse.

**Table 1. Distribution of IHPS Households According to Number of Adults Interviewed**

||**Pan**<br>|**el**<br>|
|---|---|---|
||**Total**|**%**|
|**Households Interviewed**|**2477**||
|**All Eligible Adults Interviewed**|**1675**|**68%**|
|4 adults|115|5%|
|3 adults|225|9%|
|2 adults|1003|40%|
|1 adult|332|13%|
|**Subset of Eligible Adults Interviewed**|**802**|**32%**|
|3 out of 4|106|4%|
|2 out of 4|92|4%|
|1 out of 4|29
5 Grootaert chose the 75,000 CFAF per day poverty line as the index of extreme poverty because it corresponded to a headcount index of 10 percent in 1985, based on the set of deflators he used. This paper uses the CPI series published by the INS as the basis for its deflators. For a discussion of the construction of the deflator series for the household surveys, see Appendix B. The 75,000 CFAF/day poverty line translates into $PP 410 (purchasing power parity), slightly exceeding the poverty line of $PP 370 (1985$PP) used in the 1990 World Development Report. This calculation is based on the purchasing power parity exchange rate for 1985 for private consumption of 182.85 CFAF/$PP from the Summers and Heston data which was used in Chen, Datt, and Ravallion's (1994) estimations of the incidence of poverty in developing countries.

4
mentioning that CDRs need to be integrated with other background data such as that from a census, to capture the socioeconomic characteristics of the target population. We have provided several lessons and experiences which should be useful to others who would like to replicate similar work. We summarize these key lessons in the following paragraph.

The first and perhaps most crucial step when dealing with CDR data is to negotiate access to the data. We have learned from this project and others that although there are several avenues to access the data, including the use of third-party firms, if there is time and financial resource constraints, then working with the telecom regulators and involving all key stakeholders works best. Once the permission to access the data has been granted and a memorandum of understanding has been signed, the data need to be properly stored. For this, we recommend the use of a Hadoop-based system for both data storage and, subsequently, data processing. We have produced two main categories of outputs in this work, as follows: OD matrices and individual-based mobility characteristics. We have shown that these analysis results do match with expected and prior studied results. We investigated how individual-based mobility characteristics (radius of gyration and average number of trips) vary by wealth and found a slight correlation between wealth and shorter trips.

# **ACKNOWLEDGMENTS**

We thank NATCOM for agreeing to share the CDR data which we used in particular thank _Victor Barber Richards_ , and the Ministry of Transport for their leadership. The team would like to acknowledge the timely and valuable support, guidance and advice received from _Gayle Martin_ , Country Manager of Sierra Leone, _Nicolas Peltier_ , Practice Manager for Transport AFR and _Olivier Dupriez_ , Manager for DECAT. Special thanks colleagues, _Georges Bianco Darido_ , Lead Urban Transport Specialist and Global Lead for Urban Mobility GSG, _Holly Krambeck_ , Senior Economist, _Vivien Deparday_ , Disaster Risk Management Specialist and to _Andrew Nelson,_ graduate student at UC Berkeley. The team also received logistics support from _Konjit Negash Gebreselassie_ , Program Assistant, _Balume Alpha Abonabo_
Table 4: Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference

||||Sector of W<br>|ork<br>||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|0.093|0.059|0.064|-0.048|-0.786|
||(0.058)|(0.028)|(0.047)|(0.031)|(0.468)|
||[0.107]|[0.033]|[0.169]|[0.116]|[0.093]|
|Mean of Dependent|0.321|0.082|0.100|0.312|7.704|
|(Pre-Reform Cohorts)||||||
|First Stage F-Statistic|6.06|6.63|6.63|6.63|6.26|
|N|13,909|13,755|13,755|13,755|13,789|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the
(Benveniste, Marshall and Santibañez 2007; World Bank 2008). Provider absence rates also constitute one of the core indicators adopted in the multi-country governance measurement effort being launched by the African Economic Research Consortium with World Bank assistance (Bold et al. 2010), and they have been included in recent proposals for governance indicators in health (Lewis and Pettersson 2009; Savedoff 2009).

# **Financing and Resource Management**

The second essential element of governance in service delivery is _financing and resource management_ . This element includes the rules that define budgets and govern the transfer of resources across agencies and levels in the service delivery process; the extent to which the use of resources is monitored and reported; and the influence that the rules have on whether resources are available and are used at the point of service delivery.

As a start, governance indicators for financing and resource management can build on the PEFA (Public Expenditure and Financial Accountability) framework. PEFA was developed by a donor-financed secretariat at the World Bank, which built on the Heavily Indebted Poor Countries (HIPC) initiative’s efforts to develop indicators to monitor government expenditures and progress against HIPC commitments in countries’ Poverty Reduction Strategy Papers (PRSP). Beginning in early 2002, the formal PEFA framework was developed, piloted in 24 countries through desk exercises, subjected to consultations, and then revised.

PEFA surveys, which are now regularly conducted in about 100 countries, summarize public finance performance using a set of 28 composite indicators. These indicators rank 19
Figures in round parentheses denote standard errors. Figures in square parentheses denote probability levels. * denotes rejection of the null at the 5% level of significance.

Both estimations return an income elasticity that lies above unity. In the case of electricity demand, the elasticity of 1.4 is comparable to other international findings. For instance, the World Development Report (1994) reports electricity income elasticities varying between 1.6 and 1.7, while Fay (2001) reports power elasticity for Latin America of approximately 1.1. The telephone elasticity of approximately 1.2 is again comparable to other international evidence, with the World Development Report (1994) again reporting a 1.6 to 1.7 elasticity range for telephones, while Fay's (2001) Latin American elasticity is approximately 1.0. Given that the WDR (1994) data sample did not extend beyond the late 1980's, our estimated elasticities are thus relatively close to the study using more recent data (Fay's data sample extends to 1995).

The share of agriculture reports a negative and statistically significant association with the two per capita infrastructure measures. While Fay (2001) reports positive coefficients on YAG for Latin America, the broader panel of Fay and Yepes (2003) reports results consistent with ours. Given that a declining share of agriculture in GDP is associated with industrialization and urbanization, with a rising share of economic activity and of households more intensive in electricity and telephone use, the negative coefficient is plausible.

12
8 investing across borders: do fdi-specific regulations matter?

framework. And they associate FDI inflow with _general_ quality of institutions, not quality that is _specific_ to FDI.<sup>11</sup> Bergstrand and Egger (2009) summarize the vast literature on the theoretical and empirical foundations of gravity models of trade and FDI. I am, however, not aware of any paper that addresses the three problems mentioned above in a study of the institutional determinants of bilateral FDI. As already mentioned, I use new data on regulations that are specific to FDI and the new empirical methodology that has been applied to export flows by Helpman et al. (2008).

Central to the paper’s methodology is the exploitation of the presence of zero flows between trading partners to estimate productivity cut-offs of firms that export abroad profitably. In trade, zero flows inferred from databases like the United Nations Commodity Trade Statistics Database (COMTRADE) are flawed because unreported figures are conflated with confirmed zeros. Baranga (2009) estimates that roughly 20 percent of the sample used by Helpman et al. (2008) has been misclassified. In contrast, the database – OECD (2011) – that I use for FDI clearly distinguishes between missing and zero flows between investment partners. This greatly enhances the appropriateness of the use of the new methodology in this paper.

# 1.3 model

The model is adapted from the gravity-type trade model of Helpman et al. (2008) and Anderson & Van Wincoop (2003). The derivation of the model – for FDI – is detailed in Appendix A. A representative consumer prefers variety (v) and maximizes a Constant Elasticity of Substitution (CES) utility function U = [� x(v)<sup>ρ</sup> dv]<sup>1/ρ</sup> subject to aggregate expenditure (E). All varieties have a constant demand elasticity, σ = 1−1ρ<sup>>1.Afirmincountryiproducesoneunitofoutputwitha</sup>

> 11 One
in and the maximum amount of learning decreases. The opposite happens to the budget curve conditional on choosing the comparison school: the pupil-resource ratio decreases and the amount of learning per unit of schooling increases. Hence the budget constraint rotates out, and the maximum amount of learning increases. This leads to more participation in comparison schools and less participation in treatment schools than there would have been in absence of the transfers or in the case of a perfectly elastic supply curve.

The intention-to-treat estimator computed from the data of the randomized evaluation incorporate price increases in treatment schools and price reductions in comparison schools that are due to the transfers. When school meals are implemented on a general scale, we would expect fewer transfers because the budget constraint of a household would rotate out regardless of which school it chooses. Therefore we would expect the prices in the general setting to be higher in comparison schools and lower in treatment schools than in the randomized evaluation. Hence the estimator of school participation in the randomized experiment is a lower bound of what would happen in a setting where school meals are implemented on a general scale. Note that we do not need baseline children to transfer at all to obtain these general equilibrium e¤ects. It is su¢ cient that children who do not belong to the baseline but who would otherwise have attended, switch schools.

# 6.2 Empirical evidence on price e¤ects

The empirical evidence from the school meals program supports the case of an upward sloping supply of schooling, though there are puzzles remaining. The upward sloping supply curve implies that the price of schooling in treatment schools will be higher than before the introduction of the program and that the price will be higher than in comparison schools. This is supported by the empirical evidence: the price per day of participation was 58 percent higher in treatment schools than in comparison schools (Table 15, column 4).<sup>40</sup> Children from treatment school baselines paid 57 percent more per day of school attendance than children from comparison
Figure 1. The universe of official sovereign creditor institutions 1790 – 2015 Foreign and Colonial Offices Ministries of Development **Bilateral** Export Credit Banks **creditors** Central Banks & Treasuries 1800 1918 1945 1973 2015 League of Nations United Nations System Bank for International Settlements **Multilateral creditors** International Monetary Fund World Bank Regional Development Banks Regional Safety Nets _Sources:_ Official international lending database and Cogan et al. (2016).

The practice of official sovereign lending across borders changed profoundly with the onset of World War I. European sovereigns established a new type of official creditor institution: state-owned export banks that guaranteed private trade credits and directly extended buyer credits to foreign sovereigns, in particular to the Soviet Union that had lost access to private capital markets (Margold, 1934). In addition, the League of Nations, founded in 1920, started to cooperate with national treasuries to mobilize rescue loans for crisis countries in need, especially in Central and Eastern Europe where governments tried to stabilize their currencies after the war (Flores Zendejas and Decorzant 2016). In doing so, they were supported by increasingly active central banks, which extended record amounts of rescue loans to other central banks facing capital and gold outflows in an effort to support the interwar gold standard. This era of central bank cooperation peaked during the crisis of 1931 and quickly receded afterwards. Guided by the newly founded Bank of International Settlements (BIS), central banks mobilized significant emergency credits to Central Europe and Britain to stem the turmoil caused by the retrenchment of private flows, with limited success (Accominotti and Eichengreen 2016).

The crisis of 1931 and its aftermath mark a turning point in the development of official international lending. The crisis highlighted the volatile nature of private capital flows and led leading policy-makers and academics to acknowledge the importance of counter-cyclical official lending (Nurske, 1944). During the course of the 1930s, the United States joined European states in extending 7
The selection of TEEP provinces was purposive because it intended to cover the most depressed provinces identified in the Social Reform Agenda. TEEP allocation is rather different in the Philippines’ three macro-regions. As shown in Figure 2.1, in the northern macroregion of Luzon, TEEP was concentrated in the Cordillera Administrative Region, a mountainous region in the center of northern Luzon. In the central macro-region of Visayas, TEEP divisions were relatively evenly distributed. In the southern Mindanao macro-region, TEEP divisions were clustered, though not as clustered as in northern Luzon.

TEEP was initially designed to follow a phase-in plan with three batches at the province level. However, the plan was altered in practice due to variations in preparedness across divisions. Because understanding the implementation process of TEEP is important in choosing the appropriate strategy to identify the TEEP impacts, we collected school-level data on program implementation time and investment amounts of different components. Though the program implementation was substantially delayed,<sup>8</sup> it covered all schools in TEEP provinces. The data confirm that actual implementation did not follow the batch plan and suggest that the first and second batches were implemented almost simultaneously.<sup>9</sup> We will describe TEEP implementation in more detail in the data section.

> 8 World Bank’s TEEP Project Completion Report shows actual fund disbursements, which clearly proves substantial delays in the implementation (World Bank, 2007, page vii).

> 9 Khattri, Ling, and Jha (2010) used the lag between the first and second batches to identify the effect of school-based management on student test scores. Their analysis also includes TEEP investments such as new constructions as exogenous controlling variables. Their identification strategy is questionable given that, in reality, the initial phase plan was changed due to variations in preparedness across divisions.

7
Policy Research Working Paper 7613

# **Abstract**

Building on recent analyses that find a sizeable overall gender wage gap in Azerbaijan’s workforce, this paper uses data on young workers in their early years in the labor market to understand how gender wage gaps evolve over time, if at all. The paper uses a unique database from a survey of young people ages 15–29 years. The analysis provides evidence that new labor market entrants begin with little or no gender differences in earnings, but a wage gap gradually emerges over time closer to the childbearing years. The gender wage gap grows from virtually zero, or even a small, positive gap in favor of women, until age 20 years, to about 20 percent two years later and even more than 30 percent at age 29 years. The gap in labor supply rises from almost zero to about 20 percent during the years from 19 to 22, while the gap in hours worked falls from positive (up to six hours per week more than their male counterparts) to negative (up to five hours per week less) over the same period in the life cycle. When decomposing the gap at different deciles of the wage distribution, it appears that most of it is at the lower and upper ends of the distribution, among young adults and prime-age workers. Selection of women into employment is strong and strongly skill-based: when controlling for sample selection bias, the gender gap becomes positive.

This paper is a product of the Poverty and Equity Global 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://econ.worldbank.org. The authors may be contacted at nsingh@worldbank.org and ssattar@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
<!-- Start of picture text --> Figure 1. Growth and Poverty Reduction<br>A. Poverty Headcount B. Real GDP Growth<br>National Moderate Poverty Lines (annual percentage change)<br>60.0 12.0<br>49.1 49.1<br>10.0<br>50.0<br>8.0<br>40.0<br>33.0<br>31.5 6.0<br>30.0<br>23.9 4.0<br>20.0 2.0<br>9.8<br>0.0<br>10.0<br>-2.0<br>0.0<br>-4.0<br>Bangladesh, Peru, 2004- Thailand, 2000-<br>2000-2010 2010 2009 20002001200220032004200520062007200820092010<br>Bangladesh Peru Thailand<br>Initial period Final period<br>Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's Source: WDI, 2011.<br>SES 2000 -2009, and Bangladesh's HIES 2000 -2010.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2. Demographic Changes<br>Population Growth Population ages 15-64<br>(annual percentage change) (percent of total population)<br>2.0 75<br>1.8<br>1.6 70<br>1.4<br>1.2 65<br>1.0<br>0.8 60<br>0.6<br>0.4 55<br>0.2<br>0.0 50<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2001 2002 2003 2004 2005 2006 2007 2008