rafmacalaba/data-use-mentions-extended huggingface dataset:
to these two questions, and classi...ed as “high capture”...rm owners with scores of the median of 8 or above –that is if on average they agree with both statements.

Then within each strata, we ranked ...rms according to January 2009 reported pro...ts (collected in the second round survey), and formed matched quadruplets of ...rms. We used wave 2 rather than baseline pro...ts for the match since 9 percent of the ...rms did not report round 1 pro...ts. Within the quadruplet one ...rm was then randomly chosen to receive the cash treatment, one to receive the in-kind treatment, and two to be control ...rms. We then randomly selected which quadruplets would receive their treatments after each round. In the end this resulted in the 793 ...rms being matched into 195 groups, of which 4 groups ranged in size from 5 to 8 ...rms and the remainder were quadruplets.

This randomization design was based on the analysis in Bruhn and McKenzie (2009) who showed the potential for signi...cant increases in power and baseline balance from matched pairs (with a single treatment group) and strati...cation compared to simple randomization. The variables used for strati...cation were motivated by the results in de Mel et al. (2009a). In particular, we strati...ed by gender and industry since the ex post heterogeneity analysis in that paper found strong di¤erences by gender, and some suggestion of di¤erences according to whether women were working in female-dominated versus mixed industries. The choice of “high capture” as a stratifying variable is motivated by the literature referenced earlier that has suggested that many individuals who succeed in raising their incomes face large demands to share it from others. In addition, there was some evidence in Sri Lanka that a reason for the
economic growth. Since the results for economic growth are largely similar to those of the inclusive growth equation, we can confirm the robustness of the impact of housing finance development on inclusive growth.

Comparing table A.8 in the appendix to table 4.2, the results remain the same independently of the estimation method. However, governance indexed by political stability produces either positive or negative (and significant) impacts on inequality, depending on the method.

# **5. Conclusion**

This paper used a new database on mortgage depth and penetration across countries and panel data econometric techniques to investigate housing finance determinants and the potential of housing finance to contribute to inclusive growth in Africa. It begins by using a benchmarking exercise with a decomposition approach to describe and present the typology of housing finance systems in Africa.

The statistical analysis mainly revealed that housing finance performance varies significantly based on the decomposition of country characteristics. In addition, our analysis emphasized that housing finance systems in Africa are generally nascent and based more on banks than on markets.

The paper’s econometric analysis leads to the following key findings.

First, stock market capitalization and urban growth are the main determinants of housing finance development in Africa. Specifically, stock market capitalization is one of the main determinants of housing finance development in Africa; while credit to the private sector is non-significant and negatively correlated to housing finance in some countries but it is significant and positively correlated to housing finance in others. Hence, the assumption that banking development is a catalyzer of housing finance should be reconsidered.

Second, countries with more highly developed housing financial systems tend to have lower levels of inequality than those with bank-based financial systems. Hence, well-developed housing finance systems are associated with inequality reduction.

Third, human capital appears to be the main factor for reducing inequality. In fact, there is a significant negative impact of human capital on inequality, suggesting that higher levels of economic development and human capital are associated with lower levels of inequality. These results are in line with
found to be between 10 and 15 percentage points larger than that for the upstream counterparts, suggesting a more severe impact of _Formosa_ on the economic well-being of fishers living downstream.

In order to investigate the reason underlying such discrepancy in the geographic distribution of earning impacts, we turn to satellite information. Recall from the initial observation in Figure 2, there seems to be a transition in fishing grounds (i.e. the bright pixel clusters) from within the contaminated zone to the “safe” region located north of Ha Tinh. The anecdotal explanation for this altered fishing pattern pertains to the southward flow of the ocean; toxic substances discharged by _Formosa_ in Ha Tinh were likely spread south along the current (hence affecting also Quang Binh, Quang Tri, and Hue), leaving the marine region 18
IBRD 47749 | MARCH 2024

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Misperception is determined by the disparity between the Community perceived norm (on a 0-100 scale) and the Aggregate Level ofAgreement with the norm within the country (on a 0-100 scale). Here we present the actual values ofthe misperception — some countries havea positive value (overestimation) andsome a negative value (underestimation) suggesting varying degrees of misperception. Darker shades ofthe map suggest an overestimation of the female caregiver norm — individuals think their community is more gender conservative or stereotypical than actual beliefs in that country. Lighter shades ofthe map suggest an underestimation of the female caregiver norm — individuals think their community is more gender progressive or counter-stereotypical than actual beliefs in that country. Source: Gender Equality at Home Data, 2020.
# 9 CONCLUSION

In this paper, we use highly disaggregated firm-to-firm transaction-level data from a large Indian state and provide one of the first estimates of firm-level elasticities of substitution across suppliers within the same product category. We provide new estimation strategies and estimates for these elasticities by leveraging regional variation in supply-side shocks induced by the Indian government’s massive lockdown policy. We find that suppliers of inputs are highly complementary even at this very granular level. We explore explanations behind this low elasticity, showing that contract specificity of inputs, the level of inventory of the buyer’s industry, and the time horizon matter. This elasticity crucially determines aggregate impacts and the transmission of shocks across the network but is especially difficult to pin down (Baqaee and Farhi, 2019). The combined advantage of having product-level unit values and quasi-experimental variation in supply-side shocks allows us to overcome previous challenges in the literature, and credibly estimate this elasticity across suppliers of a particular product.

Since inputs are complementary, adverse shocks to even a small subset of firms that are highly linked in the supply chain can negatively affect the aggregate economy by propagating through firm networks. When we conservatively shock only the productivity of firms located in _Red_ zones by 25%, we find that if suppliers of the same product were substitutes instead of complements, the fall in aggregate quarterly GDP in the state under study would be about 870 million USD lower, or about 25 USD per capita lower per quarter. Using big data computational techniques, we quantify this decline _directly_ using information on the economy-wide firm-to-firm network without relying on first-order approximations. Our methods thus provide new techniques to quantify shocks through large and complex production networks. Using data on the entire production network in the state, we measure the full connectivity of firms in the network and show that as the level of complementarity and the magnitude of the negative productivity shock increase, it is more effective to save the more connected firms, after controlling for firm size.

Our findings have implications for policymakers worldwide, who often face difficult tradeoffs
the emergence of other platforms that concentrate information on Africa and the methodology (web scraping and self-reporting) that Crunchbase uses to gather and validate their information, the coverage for Africa appears to be relatively good for these types of exercises.

For this study, we use data from 32,530 digital businesses collected by Crunchbase as of February 2023. While this paper focuses on Africa, we use information from other regions as a benchmark. To define the location, we use information on the firm’s headquarters. We classify 7,785 digital businesses as located in Africa, 12,816 in Latin America (South America and Mexico), and 11,929 in any of the frontier cities (London, Palo Alto, Seattle, and Tokyo). These frontier cities are selected based on the fact that they are locations characterized or known as technological hubs.<sup>4</sup> At the sectoral level, we focus on three sectors: Fintech, E-Commerce, and IT. The selection of these three sectors is directly related to the distribution of recent investments 4We define alternative options for the frontier based on the top Crunchbase ranking and top revenue of the dataset, but we only use them for robustness checks. The main reason for not taking these measures into consideration in the core analysis is that these measures are a combination of technology and funding and, therefore, are already highly correlated with funding. We want a benchmark more related to technology than funding, as this later variable is one of the outcomes or firm characteristics we analyze.

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the differences in practices when projects scale up, 2) demonstrating how incentives ‘misalign,’ and 3) explaining the variation in implementation between two phases of a project.

# **METHODOLOGY**

This qualitative analysis draws on a data set of almost 800 in-depth interviews and focus group discussions conducted from 2011 to 2015 in four villages in Bihar. The villages form two matched pairs—one each in Madhubani and Muzaffarpur districts— representing two Phase I villages (where JEEViKA has been operating since 2006) and two Phase II villages (where JEEViKA has been operating since 2011).

The Phase I villages were selected randomly from a larger quantitative sample in Muzaffarpur and Madhubani districts. These two villages were matched with a set of Phase II villages using propensity score matching methods (Imbens and Rubin 2015) on the basis of village-level data on literacy, caste composition, landlessness, levels of outmigration, and availability of infrastructure from the 2001 government census.<sup>2</sup> In order to improve the quality of the match, field investigators visited all the possible Phase II villages for a week, studying their geography and their economic, social and political structures to select the two best matches for each Phase I village (see Table 1).

**Table 1: Sampling**

||**Madhubani**|**Muzaffarpur**|
|---|---|---|
|**Phase I (project from 2006)**|Ramganj<sup>3</sup>|Saifpur|
|**Phase II (project from 2011)**|Nauganj|Raipur|

The quality of the match was good. All four villages are divided into segregated and caste-homogenous habitations or _tolas_ , and are very similar in caste composition. In Madhubani district, Brahmins and Bhumiars are a majority in both villages, and their tolas are located close to the main resources of the village—the temple, pond and school—while all the other
, mining can be an important mediating factor affecting both roads and conflict events. We collected information on the location of both large and artisanal mines in eastern DRC. We used data from the World Bank and S&P Global to compile a list of mining sites, including information on their name, type of commodity extracted, status, and location. We complemented this list with additional research on the company, the type of involvement (Private vs. Public vs. Joint Venture), and the country of ownership. We used IPIS Open Artisanal and Small-Scale Mining in eastern DRC data to map the artisanal mining sites.<sup>9</sup> Mapping the location of mines and donor-funded road rehabilitation projects shows that mining sites cluster near road investment projects (Figure 8). The first reason is colonial path dependency: many of today’s mines exploit deposits first brought up to stream by the Belgian colonial administration, which developed the road network in large part to facilitate mining. However, not all currently exploited deposits existed during colonial times, and many colonial-era mining roads have been overtaken by the jungle in the decades since. The second reason is that miners find a way to reach mines and transport ores if profits exceed the costs of production and transport. In many cases, artisanal miners cut new, improvised footpaths to mining sites; in the case of rich mineral deposits, more capitalintensive roadwork may follow the mining activity, entailing road improvement exclusively with the transport of mining material in mind. There is important variation here between

> 8The resulting data for the three segments is shown in Appendix Figure A3.

> 9 `https://ipisresearch.be/publication/ipis-open-data-dashboard-on-the-artisanal-and-s mall-scale-mining-sector-in-eastern-drc/`

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13 The charts illustrate the need to look at both spending and outcomes, particularly in education where for the most part higher quality entails on average higher spending per pupil. The PISA data reveal, for example, that Uruguay fares relatively badly in terms of test scores, but also spends relatively little; in fact, it ends up on our frontier. Sweden, by contrast, does much better in terms of test scores, but spends considerably more and ends up some way above our frontier. Finland does well both in terms of its test score results and its efficiency in translating resources into outcomes; it ends up on or close to the frontier depending on the year.

Figure 2 shows the least efficient units in each of the four studies. These are all units that achieve similar outcomes to other units but at a considerably higher cost. Interestingly six of the seven least efficient countries in the education rankings are among the least efficient seven in the health system rankings. For the California schools example, the majority of the least efficient school districts listed, such as Shoreline Unified and San Pasqual Valley Unified, tend to be tiny districts located in sparsely populated remote areas, a feature similar to the Pacific Unified that Freedberg and Doig (2011) noted.

Figure 3 compares our hybrid approach with the stochastic frontier model (SFM) where inefficiency is assumed to be half-normal and time-invariant (in all four examples, we have panel data) and the relationship between expenditure and output or quality is assumed to be double logarithmic. (Interestingly, in all these applications the cross-section SFM concluded that there was no cross-sample variation in inefficiency.) The charts show the hybrid frontier and the actual expenditure-output combinations along with the deterministic section of the SFM and the efficient expenditure levels emerging from the SFM. These differ from the expenditure corresponding to the deterministic section of the frontier because the SFM approach allows for statistical noise and random shocks. In each example, the deterministic section of the SFM and most of the efficient spending levels lie well below our
with country-specific factors through GMM methods that involve taking differences of data and instruments. This implies that, although they continue to use cross-country data, these papers favor within-country changes as the main source of relevant variation. Both papers conclude that opening the economy to international trade brings about significant growth improvements. Wacziarg and Welch (2003) arrive at a similar, though more nuanced, conclusion from a methodologically different standpoint. Using an event-study methodology --where an event is defined as a year of substantial trade policy liberalization-- they find that liberalizing countries tend to experience significantly higher volumes of trade, investment rates, and, most importantly, 3
, 2022). This may be explained by differences in the nature of the geospatial covariates used in Mexico, which could lead to better out-of-sample predictions, as well as differences in the country context. Perhaps, the lower out-of-sample correlations in this case could be explained by the fact that non-sampled Communes are different from the sampled Communes, as they are more remote, and they are not covered by the survey. The household level, grid cell level, and area-level geospatial models all benefit from conditioning on the same household survey data that was used for producing the census-based estimates, making the census and geospatial estimates (household and area level) more consistent with each other in sampled areas. On the other hand, for out-of-sample areas prediction is purely based on grid cell aggregated covariates that may not be as predictive of poverty as household census covariates.

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Policy Research Working Paper 8624

# **Abstract**

This paper summarizes and discusses the results of a survey on country practices in the management of sovereign assets and liabilities. Twenty-eight countries, mostly high-income economies, responded to the questionnaire. The survey responses provide detailed information about various approaches to the sovereign asset and liability management framework in terms of balance sheet production as well as objectives, priority areas, and challenges associated with integrated management. In addition to the survey results, where possible and relevant, this paper provides insight through prominent country examples. The survey results confirm that the number of countries that have developed such a framework is limited. Although most of the respondents indicated that they regularly produce an accounting balance sheet, the objective of this exercise is often limited to monitoring sovereign assets and liabilities rather than determining mismatches between them. In the cases where a sovereign asset and liability management framework is implemented, there are significant differences across countries. Most countries include state-owned enterprises in the sovereign balance sheet, but only a minority also considers central banks, in some cases only international reserves and sovereign funds. The challenges cited include institutional arrangements, uncertain or lacking mandate, coordination between institutions, data availability, and valuation of assets. Most of these challenges are related to the implementation of the approach. The development of sound practices for sovereign asset and liability management could benefit governments and facilitate the implementation of a holistic approach to manage their balance sheets and related risks, increasing their resilience to shocks.

This paper is a product of the World Bank Treasury. 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://www.worldbank.org/research. The authors may be contacted at ccangoz@worldbank.org and sboitreaud@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas
That is the approach taken here. As we will see, even this conceptually straightforward approach faces some severe data limitations, which generate considerable uncertainty about the poverty impacts of demographic change. And even without these data problems, one would rightly question the assumptions underlying this approach. Most importantly, it does not allow for possible indirect effects on living standards of the high rates of mortality and fertility found among the world’s poor. A higher fertility rate for the poor may also entail lower future real wages for unskilled labor and hence higher poverty independently of the direct demographic effect; indeed, there is evidence for precisely this effect for India (van de Walle, 1985; Datt and Ravallion, 1998, both using state-level panel data). Or higher death rates for the poor may undermine future productivity of the survivors; for example, it has been argued that the orphans resulting from the sharp rise in adult deaths due to the HIV/AIDS epidemic have had lower schooling than they would have had otherwise.<sup>1</sup> An alternative empirical approach is to regress rates of economic growth and/or poverty reduction at country level on rates of population growth and/or their demographic covariates, with controls for other factors affecting growth. Following this approach, Kelley and Schmidt (2001) and Williamson (2001) present evidence of adverse effects of high population growth rates on rates of economic growth; Williamson argues that the key demographic variable is the growth rate of the dependent population. By this view, changes in the age composition of the population affect aggregate savings and (hence) growth. Eastwood and Lipton (1999, 2001) use instead changes over time in poverty measures as their dependent variable, for a cross-section of countries, and find evidence of adverse effects of higher fertility on measures of poverty; they find that these effects are transmitted thought both growth and distribution. However, while

> 1 See Ainsworth and Filmer (2002) who also warn against such generalizations, given the diversity in
83|1.67|1.67|1.82|1.91|<br>1.87|<br>1.73|

_Note_ : The marginal value is with respect to the previous education level. The asymptotic covariance matrix of the estimated coefficient vector in quantile regression is computed using the bootstrap method. All the coefficients are statistically significant at 5 per cent level and conditioned to age, squared age, gender, region (North, Center, South, and Mexico City).

_Source_ : WB Staff estimates using third quarter of ENEU 1997 to third quarter and urban section of ENET 2002.

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0.450|0.4064|0.3367|0.3491|
||Urban|0.56|0.46|0.45 0.206|0.151|0.172|0.1082|0.0701|0.0952|
||Rural|0.94|0.91|0.89 0.698|0.620|0.634|0.5751|0.4882|0.5166|

Source: Own computations using DHS 1995, 2000, 2005

> 15 These results are not reported here but are available upon request.

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