rafmacalaba/data-use-mentions-extended huggingface dataset:
Kenya, Liberia, Mauritius, Senegal, and South Africa are the only countries with a reserve margin greater than 15 percent. Namibia and Zimbabwe have supply shortages in excess of 20 percent of peak demand.

# 3.5 Consumption

Consumption data are available for 46 countries. Average annual consumption in SSA, as measured by the amount billed by the utilities in the respective reference years and totaled across the countries studied, was 375 kWh per capita, falling to 153 kWh per capita if South Africa is excluded (see annex 6 for more detail). Although this measure does not capture self-generation or unbilled electricity consumption—which could be considerable—this is low by any international standards, even when compared to other emerging markets. For example, India had average consumption of 780 kWh per capita in 2011 (Pargal and Banerjee 2014) and Mexico averaged 1,952 kWh per capita in 2012 (IEA 2015). Within SSA, there is significantly higher consumption per capita in Southern Africa—pulled up by South Africa—than elsewhere. Annual consumption in South Africa was 4,000 kWh per person. Even after excluding South Africa, annual consumption in the rest of Southern Africa was 428 kWh per person, compared to only 111 kWh per person in Eastern Africa, the lowest consuming region in SSA. Annual consumption per connected customer is significantly higher, at 3,875 kWh per capita excluding South Africa, with the difference reflecting the low levels of access in SSA.

Fourteen countries have systems larger than 1 GW. Together, these larger systems accounted for more than 90 percent of electricity consumption in SSA, whereas 20 countries with systems smaller than 150 MW accounted for just over 1 percent (Figure 3).

25
. However, the timing of the increase in SRB, and the group that particularly contributed to that increase in SRB, hint at the plausibility of a causal relationship.

Caution must be used when interpreting the results outlined above. A causal relationship between social protection and SRB is not fully established in these studies – an issue acknowledged by the authors of these studies as well. In the China study, for example, the instrumental variable approach used may not appropriately account for factors that affect both the instrument for program availability (population age structure) and the SRB. In the Armenia study, (a) the assumption that the trends in the male fraction of births for high educated and low educated parents were parallel in the Soviet era is not tested, and (b) other possible causes for rising SRB among low educated parents are not fully ruled out. Overall, the main difficulty with identifying a causal impact in these cases comes from the non-random assignment (or withdrawal) of social protection.

# _Female political participation_

Measures to encourage political participation among women can be fruitful as well. With more female representation in the political sphere, girls’ aspirations and parents’ valuation of girls’ capabilities can change as female politicians can serve as role models.

Beamen et al. (2009) test this hypothesis using 1991 census data and 2006-2007 household survey data from the Birbhum district in the state of West Bengal, India. In 1993, it was legally required that one-third of village council positions in India be randomly reserved for women. Since the assignment of the reservations for women was random, Beamen et al. (2009) can use simple regression models to estimate the causal impact of female political presence on gender attitudes.

32
|Constant|0.230***<br>(0.023)|0.211***<br>(0.021)|
|Observations|4,093|4,093|
|Adjusted R-squared|0.114|0.084|

Notes: Controlling by level of schooling in the estimation of height, and by height in the estimation of schooling. Standard clustered errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed with data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the ENCEL panel 1997-2000.

The potential mobility results confirm that, although an important persistence in the determination of human capital between generations exists, a vast potential for intergenerational mobility exists among the rural population of PROSPERA. That is, independence between the result observed for youth and the result observed for providers implies that such results are modifiable depending on external factors, and that they are not entirely genetically predetermined.

_B. Effects of PROSPERA: What is the contribution of PROSPERA to the health and education of the population who received the intervention during critical development moments?_ We expect that absolute intergenerational mobility is positive, reflecting an upward movement associated with greater welfare for younger generations, and that such mobility is of greater magnitude when the exposure to the program is higher in critical periods for development. Table 26
technologies and hence to different growth convergence clubs (Howitt & Mayer-Foulkes, 2005), the empirical work on innovative capacity proxied by engineering as a determinant of growth (see, for example, Maloney & Valencia, 2022), as well as the broader literature on national learning (see, for example, Stiglitz & Greenwald, 2014).

# **2 Historical Evidence on the Importance of Entrepreneurial Capital**

In the absence of clear measures of entrepreneurial capital, we explore three features of entrepreneurship at the time of the second industrial revolution that are consistent with our framework.

## **2.1 The dominance of foreign entrepreneurs in the industrialization process**

The fact that immigrants and foreign entrepreneurs play a disproportionate role in the industrialization process in Latin America despite facing an identical institutional and business climate and without enjoying especially favorable access to credit suggests that they embodied a cultural or human capital that local entrepreneurs lacked. Table 1 draws on collected data on firm ownership from detailed census data for Argentina, Mexico and the US, as well as other industrial source data from Brazil (Bresser Pereira, 1994; Birchal, 1999), Chile (Ortega, 1990), Colombia (Rinc ́on et al., 2005; EAFIT, 2013; Becerra & Restrepo, 1987), and the US, at the turn of the 20th century (see Annex 1). Column 2 presents the share of firms owned or started by locals and immigrants for select countries and regions around 1900. Column 3 presents the share of immigrants in the population. Columns 4 and 5 standardize immigrant ownership by share in the population and can be interpreted as a measure of “comparative advantage in entrepreneurship” both relative to the total population and to the male population. Since in this period, most firms were started by men and most immigrants were men, the latter, while a lower bound for immigrant CA, is probably closer to the truth.

Looking first at the US, we find that roughly 30% of firm owners
-7-

# Enmloyment Reforms

The use of salary restraint to control the size of the global wage bill evades the question of the number of civil servants. Sometimes this number is so large that even freezing wages does little to reduce expenditure. Though wage freezes may be politically difficult for governments to impose, they nonetheless appear to be more palatable than reducing overall public sector employment itself. Hence (as discussed earlier in this paper), most regimes prefer to expand employment at diminishing wage levels.

Nonetheless, employment was frozen or even reduced in some cases examined. Four countries froze employment, and in the case of 5 more the Bank urged overall employment reductions. Ten countries actually succeeded in cutting employment either through Bank programs or on their own. Employment reform methods can be located along a continuum of political difficulty. At the low difficulty end of this continuum is one of the first actions usually taken, the elimination of ghosts. This is generally accomplished through the execution of a civil service census and the computerization, mechanization, and reconciliation of personnel management records with those of the payroll (usually situated in the Ministry of Finance). The effort to eliminate ghosts was an important part of operations in 3 countries. Another measure incurring only moderate political cost is the elimination of officially sanctioned posts wihich are not currently filled. (This occurred in the Gambia). Next, temporary workers (to whom the government usually has fewer legal severance obligations) may be retrenched, as occurred in the CAR and Jamaica for example. Several countries have enforced automatic retirement for those reaching retirement age or the requisite years of service. This policy, however, has not been without problems. In Senegal, for example, where official retirement came between ages 55 and 58 or 30 years of service, many employees retained their positions by falsifying records to conceal their true age. The degree to which such automatic retirement policies have a meaningful effect depends, too, on the overall age structure of the civil service and on the pension costs generated
share of GDP in the region. Excluding these countries yields more representative estimates for the region. China dominates the East Asia and Pacific figures, while India dominates South Asia.

Because Nigeria and South Africa were excluded from the Sub-Saharan Africa estimates in Figure 6 (owing to their dominant GDP share), Figures 7 and 8 plot the two measures of TFP growth for these countries separately. In the case of Nigeria there is a clear anomaly in the GDP growth data in 2004, which boosts the TFP growth rate excluding natural resources in particular – further work on Nigerian data will clearly be required. South Africa exhibits high volatility in the TPF estimates, with modest average rates of TFP growth over the period.

**Figure 7. Total factor productivity growth in Nigeria, 1996-2014**

<!-- Start of picture text --> Nigeria: Total Factor Productivity<br>35.00%<br>30.00%<br>25.00%<br>20.00%<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>‐5.00%<br>TFP, with natural resources TFP, excluding natural resources<br><!-- End of picture text --> Note: Average TFP growth with natural resources was 3.11%, compared with 2.23% excluding natural resources

**Figure 8. Total factor productivity growth in South Africa, 1996-2014**

South Africa: Total Factor Productivity <!-- Start of picture text --> 6.00%<br>5.00%<br>4.00%<br>3.00%<br>2.00%<br>1.00%<br>0.00%<br>1996
23 perspective, with a factor effect of exp(-0.40) = 0.67. This finding highlights the persistent vulnerability of widows as a population subgroup.

However, the opposite is true for the demographic impact of the conflict. A shortage of potential partners continues to limit fertility (with a factor effect of exp(0.98-0.61) = 1.44), but less so than in the immediate post-conflict period. This may be due to the fact that a larger share of sample respondents in the 2005 RDHS belongs to younger birth cohorts which are less severely affected by gender imbalances than in the 2000 RDHS. With respect to other control variables, many trends identified in the short term analysis also hold in the long term.

# **c) The impact of conflict exposure on fertility by age group**

In order to explore the cohort-specific effects of conflict exposure on fertility, the original model is estimated by cohorts. Results for the youngest group of women are displayed in Table 7. Women in this age group surveyed in 2000 were 9-18 years old during the genocide; those surveyed in 2005 were 4-13 years old during the genocide. Very few sample women had given birth to a child before 1994 or were married at that time. Hence, CHILDDEATH, SONDEATH, DAUGHTERDEATH, and WIDOW are not used as proxies for conflict exposure in this age group. Interestingly, the only proxy for conflict exposure with a significant impact on fertility rates in the short term is the death of a sister during the genocide (SISTERDEATH). As argued above, this proxy possibly captures the effect of trauma and stigmatization as a victimized survivor of the genocide. However, this effect does not significantly impact fertility in the long term. Neither before nor after the genocide do sex ratios have a significant impact on the fertility of young women. Possibly, for many women of this age group, the matching of partners in the marriage market is not yet complete and gender imbalances among young women are not
of pollution exposure might be expected to reduce year-over-year changes in GDP following years with higher levels of pollution. This hypothesis finds support in recent work examining air pollution in Europe at the NUTS-3 level that finds a 1 _μg/m_<sup>3</sup> increase in PM2 _._ 5 levels reduces annual changes in GDP by 0.8% (Dechezleprˆetre et al., 2019) and evidence from the United States that pollution reduces GDP by 0.4% in rural counties (Avila Uribe, 2023).<sup>2</sup> This paper investigates whether increasing pollution in a given year has negative consequences for economic activity in that year using a panel of district level GDP data from India covering the years 1998-2020. Existing empirical work demonstrating that pollution exposure can impact aggregate GDP is based on evidence from Europe and the United States. However, exposure to pollution in both these locations is substantially lower than average exposure across low- and middle-income countries. On average the level of PM2 _._ 5 in the countries studied in Dechezleprˆetre et al. (2019) was 10 _μg/m_<sup>3</sup> . Average levels of exposure in India and China exceed 50 _μg/m_<sup>3</sup> . In some districts of India the annual average level of

> 1For a recent review of the literature on the variety of negative impacts of air pollution, especially those on human capital, see Aguilar-Gomez et al. (2022).

> 2NUTS-3 is the smallest administrative unit in Europe.

2
Policy Research Working Paper 6037

# **Abstract**

Fragile and conflict-affected states face daunting challenges for development. Aid has a greater importance on development in these states than in others, and therefore aid effectiveness—management and delivery of aid—bears serious consideration. Despite its significance, aid effectiveness is appreciably lower in fragile and conflict-affected states than in others. What are the key aid effectiveness challenges in these states and how can these issues be better addressed?

As important initial steps, this paper aims to identify (i) aid effectiveness challenges facing fragile and conflictaffected states and (ii) good aid effectiveness examples using the results of the Survey on Monitoring the Paris Declaration on aid effectiveness, which was designed as a mechanism to support global and country level accountability.

Both fragile and conflict-affected states (recipients) and development partners (providers) are mutually accountable for aid effectiveness; therefore, this paper focuses on both sides. While the analysis confirms the significantly lower aid effectiveness performance in fragile and conflict-affected states—especially on aid on budget, aid predictability, and use of country systems—good performance examples in several of these states are identified. The aid effectiveness performance of development partners in fragile and conflict-affected states differs significantly across different groups. Multilateral development banks and other multilateral organizations perform better on average than bilateral organizations and vertical funds. Disaggregation of development partner performance at the institutional level and the partner country level enables the analysis successfully to identify good performance examples. In using the results of this paper to improve aid effectiveness, key additional steps should include (i) considering whether the identified challenges are essential; (ii) analyzing the factors/reasons behind good performance examples; and (iii) discussing whether good performance examples can provide lessons that can be adapted and applied.

This paper is a product of the Corporate Reform and Strategy Unit, Operations Policy and Country Services. 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
# **Figure 7: The share of work going to women tends to be lower in poorer states**

<!-- Start of picture text -->
1.0<br>Female share of non-PW work<br>Female share of MGNREGS<br>0.8<br>Female share of<br>MGNREGS<br>0.6<br>0.4<br>Female share of<br>non-PW work<br>0.2<br>0.0<br>0 5 10 15 20 25 30 35 40 45 50 55 60<br>Headcount index of rural poverty 2009/10 (% below poverty line)<br>Figure 8: Higher rationing tends to come with a lower share of the work going to women<br>100<br>r=-0.58<br>Kerala<br>80<br>Tamil Nadu<br>60<br>40<br>20<br>Jammu & Kashmir<br>0<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Rationing rate (share of those who wanted work who get it)<br>Female share of person days of employment (%)<br>Female share of work<br><!-- End of picture text -->

**Figure 8: Higher rationing tends to come with a lower share of the work going to women**

24
PTA depth and trade intensity uncovered in this analysis will still likely delimit future policy choices. Policy makers can either

> 13 Baier et al. (2008) code the dummy EEA equal to 1 from 1973 onwards if one of the countries is in the EU and the other is in EFTA.

> 14 Similarly, a recent study by Kee and Nicita (2016) focusing on tariff changes finds that leaving the EU would lead to a 2 percent decrease in the UK’s exports in the short run.

> 15 Given the limited coverage of WIOD, we compute these statistics using data from COMTRADE.

20
# 1. Introduction

By one estimate, governments in low and middle income countries around the world are investing around US$ 1 trillion (or between 3.4% to 5% of GDP) in infrastructure every year (Fay et al., 2019). Still, the quality and adequacy of infrastructure services varies widely across countries. Millions of people – especially in the fast-growing cities in developing countries – are facing the challenging consequences of substandard infrastructure, and often at a significant cost. Underfunding and poor maintenance are some of the key factors resulting in unreliable electricity grids, water and sanitation systems, and overstrained transport networks. Natural hazards are often a leading cause of disruptions in these already fragile systems.

Yet, in principle, infrastructure providers have a wide range of options at their disposal to increase the quality and resilience of infrastructure services. Strengthening the physical quality of assets, incorporating redundancy in infrastructure networks, using nature-based solutions, or increasing the funding for asset management and maintenance – these are readily available and often cost-effective measures to increase the quality of infrastructure services and users. This raises the question of why these options are not always implemented and why infrastructure systems so often are prone to frequent disruptions and unable to cope with natural hazards – not rarely despite significant spending.

A government’s ability to implement resilience-building options depends on whether it has effective systems in place to implement, finance, manage, and maintain infrastructure assets. Strong institutions, clear assignment of responsibilities, and transparent and reliable financing mechanisms are all crucial to ensuring the effective provision of public services. Using a range of infrastructure spending and performance data, this study explores the role of governance in determining the efficiency of infrastructure spending.

The remainder of this study is structured as follows. Section 2 describes major empirical patterns and introduces the literature on governance and infrastructure quality. Section 3 presents empirical evidence on the importance of governance in determining the provision of high quality infrastructure. Section 4 concludes.

# 2. Governance and infrastructure quality: A review of
that the persistence is much stronger for the girls born to fathers with low education ( _IGME_ 0 estimates are: 0.65 (daughters) vs. 0.55 (sons)), but, again, the gender difference becomes negligible when the father has college or more education: ( _IGME_ 16 estimates are: 0.33 (sons) vs. 0.35 (daughters)). Please see Figure F4.IU. The evidence on absolute and relative mobility thus favors the idea that gender difference in educational attainment may primarily be a product of low education (and thus low permanent income) of parents. This also brings into focus the incorrect conclusions from the standard linear CEF that the daughters face less relative mobility throughout the parental schooling distribution. A researcher focused exclusively on the linear CEF would completely miss the gender convergence in relative mobility at the right tail of father’s schooling distribution.

# **Sources of Gender Bias**

The extended Becker-Tomes model in section (2) above highlights parental educational investment as a major mechanism through which inequality persists across generations. Thus, it is informative to look first at the estimates of the investment equation to understand the economic mechanisms at play. The estimates of _θ_ 0 and _θ_ 1 for urban India are reported in Table T3.IU, using IHDS 2005 and NSS 1995 data. We report estimates for two age groups of children for each data set to ensure robustness of the conclusions.<sup>22</sup> The evidence suggests strongly that _θ_<sup>ˆ</sup> 1<sup>_d<θ_ˆ</sup> 1<sup>_s_;theinteractionoffather’seducationwith</sup> the daughter dummy is negative across the board, and is statistically significant at the 10 percent or lower level in 7 out of 8 cases.<sup>23</sup> The estimates for the intercept shows a contrasting picture: the daughter dummy is _not_ statistically significant at the 10 percent level in 6 out of
# **4. Data sources and parameter estimation**

We apply the theoretical model to a grid of 100 x 80 km that largely encompasses the existing urban footprint of Cape Town. The grid is subdivided into 500m x 500m cells. Each cell represents a location **x** in our theoretical framework. Because the different data sets that we use are available at different spatial resolutions, we either spatially aggregated or disaggregated the information using cell areas as weights.

We make use of a variety of data sets as direct inputs into the model and in order to calibrate parameter values. More specifically, the inputs that are fed into the model consist of the total population in the city at an initial date and its decomposition into income groups ( _Ni_ ), the average income and employment rate (χ _i_ ) by income group, land use constraints ( _Lh_ ( **x** )), and the monetary and time cost of commuting (τ _m_ ) and (δ _m_ ) between cells and job centers, for 5 different modes (walking, minibus/taxi, train, bus, private car).<sup>30</sup> Exogenously chosen parameters include the size of subsidized housing plot ( _qFS_ ), the time lag of housing investment (τ), the housing stock depreciation parameter (θ), the financial depreciation rate of built capital (ρ), and the agricultural land rent ( _PA_ ). Estimated parameters include the gravity equation parameter (λ), wages at workplace ( _wic_ ), housing consumption elasticity in the utility function (β), the minimum housing consumption ( _q_ 0), the land elasticity of housing production ( _a_ ), the scale parameter of housing production (κ), and the index value of amenities _A_ ( **x** ). The disutility parameter of informal housing ( _B_ ) is calibrated to reproduce the distribution between housing types generated by the model.

Below, we describe in more detail data sources and the