rafmacalaba/data-use-mentions-extended huggingface dataset:
The findings of our study also indicate that enhancing social protection and insurance programs, especially for the elderly, could be a promising avenue towards realizing the demographic dividend in Sub-Saharan Africa. Moreover, doing so in a gender-equitable fashion could shift the inter-generational transmission of wealth, as found by Duflo (2000). We recommend that future evaluations of the effects of implementing or broadening social protection services go beyond a narrow examination of labor supply outcomes and incorporate an assessment of fertility rates to capture their broader implications. This will provide a more comprehensive view of the impact of these programs and help to guide the development of more effective and efficient policies.

Our results further show that macro-level and micro-level studies of the income-fertility relationship can yield contradictory conclusions. The negative correlation between wealth or income and fertility rates, as evidenced by extensive cross-sectional studies both within and across countries, may be confounded by factors such as education and women’s access to long-term economic security (including pensions, health insurance and inheritance rights). Future research should investigate the source of discrepancy between the cross-sectional literature and our findings, and identify whether accounting for additional variables in crosssectional data can help reconcile our contradictory conclusions.

Finally, our results emphasize the potential value in leveraging datasets from past rigorous impact evaluations of development programs to consider unintended consequences or additional effects that may have gone unnoticed in previous studies. Making impact evaluation data accessible for secondary analysis, including for pooling data from various settings, is crucial for comprehending the true, multidimensional impact of development efforts. By doing so, we can optimize the design of future development initiatives and maximize their impact on the communities they serve.

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|**Table 1: Africa Sam**|**ple summar**<br>**Benchm**|**y statistics**<br>**ark year**|||
|---|---|---|---|---|
|**1993**|**1998**<br>|**2003**<br>|**2008**|**Total**|
|**Number of surveys**<br>22|20|30|34|106|
|**Years between survey year and benchmark year**|**(%, by bench**|**mark year)**|||
|‐2<br>0.00|20.00|6.67|26.47||
|‐1<br>27.27|5.00|13.33|14.71||
|0<br>27.27|35.00|36.67|14.71||
|1<br>31.82|5.00|16.67|20.59||
|2<br>13.64|35.00|23.33|23.53||
|3<br>0.00|0.00|3.33|0.00||
|Within +/‐ 1 of benchmark<br>86.36|45.00|66.67|50.01||
|**Income vs. Consumption surveys (%, by benchm**|**ark year)**||||
|**Consumption**<br>100|100|100|100|100|
|**Income**<br>0|0|0|0|0|
|
In the seventh week, the final survey was administered to a clustered random sample of 802 households drawn from 30 slums in Delhi<sup>i</sup> . The four selected for ethnographic study were augmented with 26 slums selected at random from the Delhi government’s newest city-wide slum register.<sup>ii</sup> The qualitative data weres transcribed and entered into a QSR-Nudist database, and the quantitative data were entered into a computerized database. In this way, lessons from and constant dialogue with the qualitative findings enabled broader and more representative claims to be made on the basis of the quantitative data.

# _Literature and Context_

The scholarly literature on slums and their leadership structures has several characteristics that render it problematic for policy purposes. The first is that, by virtue of contributions from extraordinarily rich ethnographic accounts, we know more about specific aspects of life in specific slums than the general dynamics of the internal and external governance processes that underpin their creation and maintenance. From the United States (Waldinger 1996, Venkatesh 2000) to Mexico (Lomnitz 1977, Selby, Murphy and Lorenzen 1990, Chant 1991) to Guatemala (Roberts 1995) to Brazil (Pearlman 1976, Scheper-Hughes 1992) to Indonesia (Jellenik 1991) to India (Roy 2002) to Egypt (Singerman 1996)<sup>iii</sup> , the most prominent and highest quality research on urban poverty has been largely ethnographic.

Part of the reason for the quantitative shortfall is that collecting data on slum dwellers is a difficult exercise: residents move frequently and often include illegal immigrants who endeavor to live and work in the shadow of the law. Aggregate demographic trends, urbanization rates, and migration patterns are relatively well known (e.g., Misra and Misra 1998, National Research Council 2003), but detailed household level data on survival and mobility strategies in poor 6
years. In this way, we can: (i) better isolate the effect of MDB participation on subsequent bank flows (as well as loan terms), (ii) run a series of additional tests to deal with the omitted variable bias to provide a convincing estimate of the mobilization effects, (iii) test for crowding-out effects to other flows, and (iv) estimate country-level mobilization effects. To the best of our knowledge, we provide the first assessment and quantification, fully based on loan-level data, of MDB mobilization effects, a key channel to catalyze the private sector and finance investment and growth in developing countries.

# **2 Data**

Our main source of data is the Dealogic Loan Analytics database, which contains micro data at the level of tranches of all syndicated loans to 127 developing countries from 1993 to 2017. These data are widely used for studying the international syndicated loan market (see, for instance, Esty and Megginson, 2003; Carey and Nini, 2007; Giannetti and Laeven, 2011) and their coverage is comparable to flows coming from aggregate statistics: for instance, Cerutti et al. (2015) compare syndicated loan exposures with loan claims as reported by the Bank of International Settlements, finding a very good match between the two series between 1995 and 2012.

Syndicated loans are provided by a syndicate, e.g., a group of lenders, that share risks by pooling together capital. They have been used for decades and are now becoming a dominant way to tap banks, finance companies and institutional investors (Miller, 2006). Their relevance has been expanding dramatically and they have become a key source of funding for corporations in both developing countries and advanced economies (Bruche et al., 2017; Cortina et al., 2018).

In line with existing studies (e.g., Nini, 2004; Carey and Nini, 2007; Cortina et al., 2018), we exclude loans to public authorities, as they are likely driven
because countries and sectors are interconnected through trade linkages, which can magnify the effects of demand and supply shocks. For instance, in February, Chinese lockdowns led to shortages of key inputs and disrupted production worldwide. European consumers cutting back on clothing demand led to order cancellations and delayed payments for apparel suppliers across Asia (Wall Street Journal, 2020). Recently published measures of economic risk reflecting the impact of household demand shocks explicitly allow for these trade linkages.<sup>5</sup>

> 5 Osotimehin and Popov (2020) propose sectoral measures of both health and economic risk by countrysector using the World Input-Output tables to account for demand interlinkages. The economic risk is measured as the decline in employment induced by a 90% decline in the household demand for social

5
**Table 6: Antitrust, Concentration, and Informality.** The regression left hand side variable is the estimated change in an outcome measured by the SDID estimator, where synthetic control weights match pretreatment trends in the log of sales for comparability with previous results. All specifications use a random effects meta regression. Firms with multiple establishments are treated as a single firm. The Herfindahl-Hirschman Index is the sum of firms’ squared percentage market shares, and ranges from 0 and 10,000. The CR4 ratio is the market share of the four largest firms in a market, expressed as a percentage from 0 to 100. The CR1 ratio is the market share of the largest firm in a market, also expressed as a percentage. The legal informal share of employment is the percentage of workers in establishments with workers but no recorded remuneration, indicating workers are compensated out of profits, as in the case of a family business. The social security share of wage bill is employer contributions to social security as a percentage of the wage bill. Levy Algazi (2010) proposed definitions of legal and illegal informal employment that can be measured in the Economic Census. Legal informal employment is unremunerated labor, illegal informal employment is remunerated labor without employer contributions to social security. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Change in|The log of<br>the number of<br>frms|Herfndahl-<br>Hirschman<br>Index|CR4: Sales share<br>of four<br>largest frms|CR1: Sales share<br>of largest<
limited, verifiable data from official sources can help transparently target benefits to groups that have been underserved in the past adds to this literature. It reinforces the notion that,</mark> especially in crisis or conflict settings, non-traditional data sources including mobile phone data can help target support (Aiken _et al._ 2021).

<mark>Building on studies showing spatial data can be used to enforce</mark> land use regulations (Assuncao _et al._ 2019) or certify supply chains’ environmental sustainability (Heilmayr _et al._ 2020; Moffette _et al._ 2021), including establishing links to public support (Assunção et al. 2020),<sup>1</sup> <mark>we also show that, if combined with remotely sensed imagery, cadastral data can be used beyond targeting to evaluate the impact of specific programs, something that has proven to be more difficult for mobile phone data (</mark> Aiken _et al._ 2023) <mark>. In Ukraine’s case, the ability to access a reasonably complete cadaster was a distinct advantage as setting up the spatial data on which to run a farm registry during war time was impossible.</mark> A second topic is the digitization of agriculture. While digital technology, including equipment-mounted sensors that are now routinely used by large farms (Deines et al. 2021), improved productivity (Chancellor 2023) by increasing input use efficiency and reducing uncertainty (Khanna 2021), high fixed costs imply

> 1 Refer to Moffette and Gibbs (2021), Carlson _et al._ (2018), Lee _et al._ (2020), and Pacheco _et al._ (2020) for a discussion of challenges and impacts. Merkus (2024) shows that effective enforcement can be beneficial for economic growth.

3
well as the EU (European Commission 2022).

Hence, one possible way to control state expenditures is to improve public procurement practices and enhance value for money, that is procuring the same goods and services but for better prices. North Macedonia continues to lag EU countries on governance indicators. The gap to the EU is most pronounced along institutions critical for economic growth such as the rule of law, control of corruption, and government effectiveness. One critical institutional area where governance weaknesses and state capture by private interests are evident is public procurement. Thanks to the digitalization of public procurement data and the introduction of national e-procurement systems, audits, and judicial institutions, analysts and civil society whistleblowers now have access to electronic public procurement transaction data, including the details of individual government contracts. Such administrative datasets allow us to analyze the cost of noncompetitive public procurement practices and corruption risks. Moreover, they also allow for a comprehensive analysis of economic relations, that is contracts, among public and private organizations revealing hidden partners of state capture risks.

This paper presents an in-depth analysis of public procurement in North Macedonia based on contract level data between 2011 and 2022. First, it gives a descriptive overview of the country’s public procurement market. Second, it calculates and validates a set of corruption risk indicators following the risk measurement approach introduced in Fazekas & Kocsis (2020) and applied in 3
the publicly available geo-coordinates of survey locations are generally subjected to a random offset (i.e. are scrambled) in order to protect the anonymity of survey respondents. A second objective of this study therefore is to investigate whether these random offsets undermine the ability of this approach to produce accurate poverty maps.

Assessing the success of this alternative approach to producing a poverty map will depend on how success is measured. When the ability to capture the geography of poverty is used as the metric of success, our analysis suggests that estimating small area poverty using remote sensing data offers a promising alternative to building a poverty map using population census data. In our application to Malawi the two approaches are found to produce maps that show nearly identical geographies of poverty. When the objective is to identify poverty in a specific district or set of districts of the country, however, we find that poverty estimates obtained using remote sensing data can deviate substantially from the benchmark estimates obtained using population census data. In summary, the value of poverty maps built using remote sensing data alone will depend on the needs of the decision makers.

On the empirical question of whether randomly off-setting survey coordinates (to protect anonymity of survey respondents) may undermine the accuracy of a poverty map derived from remote-sensing data, our findings echo the comparison with census-based estimates. The random scrambling of the geographic coordinates does not meaningfully alter the estimated geography of poverty, yet may have meaningful impact on estimates for individual districts and may furthermore lead to an over-estimation of statistical precision. The latter observation arguably stems from the fact that the random offsets partially destroys the spatial correlation structure in the data and error term.

Our validation study confirms that for many practical purposes accurate poverty maps can be obtained in countries where population census data are not available. Given that remote sensing data are globally available and are typically updated on an ongoing basis, this also opens the door to updating poverty maps on a higher frequency – whenever new household survey data become available. On a practical note, since the
<!-- Start of picture text --> the question ―Are you allowed to decide how daily work is organized?" (self-assessment,<br>where 0 means ―I have no influence‖, and 10 means ―I have complete control‖). The data do<br>not support the hypothesis that a substantial proportion of ESS respondents which classify<br>themselves as informal self-employed are in fact employees. Their median self-assessed<br>autonomy is 10 in all countries but PT where it is 9, and mean self-assessed autonomy in all<br>countries is well above that of formal employees. Figure 2 shows that the same is true also for<br>the 25th percentile of the autonomy variable, thus excluding the possibility that even a quarter<br>of informal self-employed are in fact dependent workers.<br>Allowed to decide how daily work is organised (self-assessment, p25)<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>RO UA CZ RU HU PL SK BG LT EE LV SI SE DK FI NO GR IT PT ES IL CY IE NL AT CH DE BE FR UK<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>points in 0-10 scale<br><!-- End of picture text -->

**Figure 2 Worker autonomy: Informal self-employed vs. formal and informal employees.**

**2004-2009**

_Notes:_ The Figure displays the 25<sup>th</sup> percentile of the self-assessed autonomy for each of the three groups. _Source:_ Calculation with ESS data When both size and composition of informal workforce are taken into account, all countries considered can be arranged in 11 clusters, as shown in Table A4 (in the Annex
construct (given that infrastructure services are often provided free of charge) and that errors in estimating initial stocks (a common problem with this methodology) tend to become less significant over time. In addition, the alternative of using actual stocks of infrastructure (such as roads, electricity production, or water supply) was not feasible due to lack of data; the only complete series that we had at our disposal was that of electricity production, but it was felt that using it as a the sole indicator of quality of the overall stock of infrastructure was not warranted.

# **3.  Quality Indicators**

In assessing the impact of the public capital stock in infrastructure on private investment, it is important to account not only for the absolute amount of that stock, but also for the quality (or efficiency) with which public capital is used.<sup>10</sup> A common procedure to estimating the quality of public infrastructure capital is to calculate the index proposed by Hulten (1996). His composite measure of public capital efficiency is based on four basic indicators: mainline faults per 100 telephone calls for telecommunications; electricity generation losses as a percent of total electricity output; the percentage of paved roads in good condition; and diesel locomotive utilization as a percentage of the total rolling stock. In practice, researchers have found that these individual quality indicators tend to be highly correlated with the quantities of each type of infrastructure.<sup>11</sup> Thus, much of the variation in infrastructure quality may be well captured by variations in its quantity.

The individual quality indicators proposed by Hulten (1996) are subject to limitations. For instance, electric power losses include both “technical” losses, reflecting the quality of the power grid, and theft; in general, the breakdown between

> 10According to the World Bank (1994, p. 1), technical inefficiencies in roads, railways, power, and water in developing countries caused losses equivalent to a quarter of their annual
parsimonious. The survey asks current household members whether they have returned from abroad in the past five years, but does not ask further questions about the past migration experience.<sup>6</sup> We are however able to complement the HIES data with detailed data on return migrants from the World Bank Bangladesh Return Migrant Survey (BRMS) 2018/2019 which was targeted to households with a return migrant.<sup>7</sup> In Pakistan, information on past migration overseas collected in nationally representative surveys is also quite parsimonious. The national Labor Force Survey (LFS) includes a short module on past migration, which allows to identify

> 5National household surveys in Bangladesh, Nepal and Pakistan ask individuals whether they have returned from abroad in the past 5 years only. Therefore, individuals who returned from overseas prior to that cannot be identified from the survey data.

> 6As in Nepal, however, the survey includes an absentee module collecting basic demographic information on household members that are currently absent from the household for work overseas, together with their country of destination, year of departure and occupation.

> 7For more details about the Bangladesh Return Migrant Survey (BRMS), see Bossavie, Gorlach, et al. (2021).

7
consistent data for West African countries. Accessing better data would help producing more precise estimates. Second, the paper does not model the transport sector and remains silent on issues of market power, back-haul, and other factors behind high transport costs. While improving road conditions can reduce transport costs, it remains an open question whether such cost reductions will be passed through to final customers or would only benefit trucking companies. Further work should also be done to include the global implications of infrastructure improvements considering the rest of the world while this paper only considers countries in the West African region.

30
as a protected reserve. World Bank (2011) therefore values protected areas at the same perhectare value as what is, on average, the least productive agricultural land in a given country – hence the notion of a ‘quasi-opportunity cost.’<sup>10</sup> The assumption is that the government establishing a protected area values the newly protected land at least as much as if it were in its alternative agriculture use. As such, the quasi-opportunity cost is therefore a lower bound on the value of protected areas.

What we analyze in the following subsections is therefore easily a third-best measure of the value of biodiversity in national balance sheets. But the analysis is not without interest, and it leads to some tentative policy conclusions.

# _Protected area rents in relation to GDP_

While World Bank (2011) publishes the asset value of protected areas – it forms part of the aggregate value of natural capital and, more broadly, comprehensive wealth – here we focus on the annual land rent from protected areas (‘PA rents’) per capita.<sup>11</sup> The natural question to ask is how this is related to GDP per capita across countries, and Figure 1 provides the answer.

**Figure 1.  PA rents per capita vs. GDP per capita, 2005**

<!-- Start of picture text --> 1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000<br>GDP, $ / capita<br>Annual PA rent, $ / capita<br><!-- End of picture text --> Source: Derived from data published in World Bank (2011)

> 10 In practice, World Bank (2011) uses the
the 3 coefficients add up to zero is 0.453). In Column 3 we control for the type of transaction performed and results are virtually unchanged.

# **6. Discussion**

Our results from both the Democratic Republic of Congo and Senegal provide two pieces of evidence indicating that gender matters in agent banking transactions. First, both male and female clients exhibit assortative gender matching. Perhaps surprisingly, for female clients this preference for female agents seems largely unimpacted by variation in the availability of female agents. When we restrict our analysis to markets where at least 25% of agents are female, same gender preference does not increase, as would be expected if convenience were the driving mechanism. This suggests that gender matching for females is not mainly driven by what Barr et al. (2012) describe as ‘opportunity’, i.e. the availability of female agents. Rather, the behavior is more consistent with the notion of ‘homophily’ as customers exhibit a stable preference to transact with agents of their gender.

Second, we use the transaction amounts and account balances to explain gender homophily. We find that female clients are more likely to use female agents the larger their transactions and account balances. We thus posit that gender-based trust may be driving the observed gender homophily as female clients have a stronger tendency to use female agents for larger transactions: larger amounts are riskier, and thus when the stakes are higher, female clients rely more heavily on female agents. Table 7 shows that both men and women transact higher average amounts at agents of their gender (men USD$250 and women USD$221). When visiting agents of the opposite gender, the average female transaction declines to USD$133 (a 40% reduction) while transactions of males only show a 17% reduction (to USD$207).

The balance data provide additional support for our conjecture that gender-based trust could be an important underlying mechanism. Since agents know the customer’s balance when a transaction is made,