rafmacalaba/data-use-mentions-extended huggingface dataset:
Small Area Estimation of Poverty and Wealth Using Geospatial Data: What Have We Learned So Far?<sup>1</sup> David Newhouse (World Bank Group)

> 1 JEL codes: C53, I32. Keywords: poverty, small area estimation, poverty mapping, satellite data, machine learning We thank Partha Lahiri for his encouragement to write this article, William Bell, Chris Elbers, Carolina Franco, and Josh Merfeld for helpful comments on a previous draft, participants at the 2022 Small Area Estimation conference at the University of Maryland College Park, and Haishan Fu and Keith Garrett for their support and encouragement.
10 to aid interpretation. OLS results are -0.36** (0.08); 0.14* (0.07); 0.01*** (0.005); -0.0031 (0.067),0.013* (0.0068); for models (1), (2), (3), (4) and (5) respectively. Additional controls: Ethno-linguistic fragmentation, income inequality, (log) population, size of extractive industries, democratization (current), demographic dummy variables, economic openness (current), religious make-up variables, regional dummies (see Appendix A for details of variable descriptions). _Source: The Author_ However, while the results in Table 1.01 are consistent with the model’s theoretical expectations, they remain a relatively limited test of its robustness. Firstly, if the model is correct, it should be the case that (1) these three measures of the EDD/NEDD ratio are measuring the same underlying latent variable (extensiveness of government); and 2) while the most robust control variables were utilized, it is essential to include other control variables that may affect these results. Given the small sample size, it is not possible to introduce all the potential control variables using the OECD country sub-set alone (due to the problem of model saturation/lack of degrees of freedom), and therefore it becomes essential to utilize a larger dataset.

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the causal impact of migration we require _E_ ( _Mh × εj,h|_ **_X_** _j,h_ ) = 0. That is, after conditioning on observed characteristics in **_X_** _j,h_ , the migration decision is uncorrelated with other determinants of the outcome that appear in Eq. (2). a group of individuals related by marriage and consanguinity. [...] Families typically consist of multiple households forming a network of kith and kin, related by blood or marriage but not necessarily living together.” (pp. 3189–90).

> 11“The LSMS survey of Ecuador in 2005-2006 [...] included a module on emigrants from the household, recording their current age, sex, relationship, education, and whether the emigrant left minor children under age 18 behind (there being special concern at the time, following the surge of emigrants to Spain in 1997–2003, about who was taking care of them following the emigration of a parent, often the mother).” (Bilsborrow, 2016, p. 125)

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8.7|-2.2|19.2|7.5|-14.8|5.4|10.2|**16.7**|
|6|-12.3|-3.0|14.0|4.7|-13.7|4.4|9.0|**3.1**|
|7|-15.0|-3.5|12.6|3.3|-13.0|5.0|7.2|**-3.4**|
|8|-18.0|-4.5|9.1|2.4|-12.1|4.4|6.7|**-12.0**|
|9|-21.7|-5.3|7.9|1.2|-11.1|3.9|4.5|**-20.5**|
|richest|-26.7|-7.8|5.4|0.4|-9.7|2.3|2.1|**-34.0**|
|total|-14.8|-4.0|21.4|5.2|-13.3|4.7|6.2|**5.4**|

Note: deciles are defined based on market income. Source: authors’ calculations using HBS-2017 data.

> 8 Because of the survey bias the top deciles do not fully cover the richest class in Moldova as in many other countries.

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portfolio equity assets, _EQYA_ - _EQYL_ , and the net position in non-equity related assets ( _i.e._ ''loan assets''). In turn, the net position in non-equity related assets consists of international reserves, _RA_ , and the net loan position, _LA_ - _LL_ .

For _productivity differentials_ we use labor productivity differentials weighted by trade patterns. Then, we develop the data on labor productivity of traded and non8
overdrafts.<sup>17</sup> The average size of our firms is about 100 employees, and it varies from one to over 1,700, with the median firm size of 25 employees. Thus, most of the firms in our sample are small and medium-sized enterprises. The median firm age is 12 years, while the average is almost 18. Firm age varies from one year old startups to close to 200 years old firms. In our sample, 62 percent of firms are in manufacturing (the rest are in services, retail or construction), 23 percent are exporters (classified as such if they export at least 10 percent of their total output), about 10 percent are foreign-owned, and about 5 percent of firms are considered government owned.

The Lerner index has an average of 0.25, a median of 0.23 and a standard deviation of about 0.07. The range is between 0.07, which indicates very low markups and, hence, high competition and 0.43, which implies very high markups and, therefore, low competition. Average concentration is high, with the top three banks comprising close to 60 percent of total bank assets. The lowest share of assets held by the top three banks is about 28 percent, while the highest is over 98 percent. The Herfindahl index varies between 0.05 and 0.74.

# **3. Regression Model**

Our goal is to evaluate the impact of bank competition on firms’ access to finance. To do that, we estimate the following simple model: where _Access_ is the indicator variable for whether firm i in country c at time t has a bank loan, line of credit, or overdraft; _Competition_ refers to either the Lerner index or to two measures of concentration: the share of assets held by the top three banks and the Herfindahl index. F and X represent firm-level (e.g., size, manufacturing, exporter, etc.) and country-level
Take a moment to think about the adult women Take a moment to think about all the people where you live. These could include your family where you live. These could include your family members, friends, neighbors, and others. Out of 10 members, friends, neighbors, and others. Out of such women, how many work outside their home? ten such people how many would think or speak badly about married women who, because of work, return home after 5pm in the evening?

_For women only:_ Think now for a moment about your husband/ father/ brother, and his views Does he think or speak badly about women who work outside their homes?

To examine the cultural mental models and “schemata,” respondents were asked to record open-ended questions inspired by classical accounts of how narratives shape behavior (Bruner, 1990; Crossley, 2002; Sarbin, 1986). Each respondent was asked a random set of narrative questions that explored their views of how typical or atypical they perceived themselves or their relationship with a specified male or female relative to be, how they explained their or their spouse’s motivation to work to others, how they talked to others about work related decisions, and why they thought education is important for women’s future.

The draft quantitative survey was field tested to determine how effectively the questions elicited social norms and beliefs, improve the language and instructions provided to enumerators on questions, and refine guidance to enumerators on how to elicit informative but concise narratives from respondents. The pre-test was conducted with 50 households in Amman and included households where both the husband and wife were interviewed. The purpose of interviewing both spouses within a given household was to test the feasibility of interviewing couples in a household separately.

Some variables explored during the pre-test were excluded in the final questionnaire. An implicit attitude test (IAT) was designed but excluded once it became clear that FLFP in Jordan is likely driven by explicit rather than implicit views and because the IAT proved costly to administer
cost of the intervention in 100s.

No effects were found for the index of home activities, though positive effects were found for specific activities. Of course, the data do not provide a comprehensive picture of the quality of the activities or caregiver-child interaction more generally. Effects on school readiness likely reflect changes in the home environment that are not specifically captured in the TEHCI data. The lack of effect on school readiness for mothers with high-school education may be because the home environment is better for this sub-population, mitigating the effect of CPBA participation on the home environment. The six home activities are more prevalent among mothers with high-school education in the baseline, 2014 TEHCI.

While we cannot attribute these effects exclusively to CPBA participation, it is likely that CPBA is the main source of effect. Given the low participation in CPBAs, the effect of participating in a CPBA is likely quite high. For example, the intervention increased overall school readiness of 20
., Tibshirani, R., & Friedman, J. (2009). _The elements of statistical learning: Data mining, inference, and prediction_ . Springer. https://books.google.com/books?id=eBSgoAEACAAJ

- Hebous, S., Jia, Z., Løyland, K., Thoresen, T. O., & Øvrum, A. (2023). Do audits improve future tax compliance in the absence of penalties? evidence from random audits in norway. _Journal of Economic Behavior Organization_ , _207_ , 305–326. https://doi.org/https://doi. org/10.1016/j.jebo.2023.01.001

- Hill, J. L. (2011). Bayesian nonparametric modeling for causal inference. _Journal of Computational and Graphical Statistics_ , _20_ (1), 217–240. https://doi.org/10.1198/jcgs.2010.08162

- Imbens, G. W., & Rubin, D. B. (2015). _Causal inference for statistics, social, and biomedical sciences: An introduction_ . Cambridge University Press.

- Kirchler, E. (2007). _The economic psychology of tax behaviour_ . Cambridge University Press. Kleven, H. J., & Kopczuk, W. (2011). Transfer program complexity and the take-up of social ben-

- efits. _American Economic Journal: Economic Policy_ , _3_ (1), 54–90. https://doi.org/10.1257/ pol.3.1.54

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GDP, not as a percent of total government expenditure, so this is also what we present here. Both current and capital spending are included in these values. While ASPIRE data have limitations, they are the most comprehensive set of social protection spending data for low-income countries. In line with IMF’s recent work on social sector spending (IMF 2019), we choose ASPIRE over EURSTAT, which has data only for a limited number of countries.

> 3 See “Mobilizing Resources for Education and Improving Spending Effectiveness: Establishing Realistic Targets Based on Past Trends” (Al-Samarrai, Cerdan-Infantes, and Lehe 2018) for further details.

> 4 Our main interest is to examine the relationship between social protection spending and the HCI. We exclude pensions because spending directed toward those of pension age is unlikely to have a strong impact on the human capital of a child born today. While some evidence documents that pensions received by older women may impact the human development of the recipients’ granddaughters (Duflo 2000), the context of this study may not be globally applicable. We exclude contributory programs because our interest is in examining _government_ expenditure on human capital, rather than the contributions of its citizens, which are likely highly correlated with income.

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versus relational transactions, thereby allowing one to construct broad industry-level measures of relational GVC participation for the comprehensive set of countries in the Eora Global Supply Chain Database. Due to time constraints, such analysis was not conducted in time for the 2020 World Development Report, but hopefully it will be carried out in future work.

# 4.2 Determinants of GVC Participation

In a world of relational GVCs, the effect of factor endowments on GVC participation is largely analogous to the one developed above for the broad notion of GVCs (both at the country-industry and at the firm level). A key novel aspect, however, is that as mentioned above, the relational approach to GVCs provides a straightforward explanation for why GVC activity and FDI flows go hand in hand. When tight control over foreign production processes is necessary (perhaps because the legal environment does not suffi ce to discipline the behavior of suppliers or to avoid the leakage of intellectual property), lead firms might decide to rely on integrated suppliers and assemblers in foreign countries. This results in intrafirm trade and FDI flows. For this same reason, countries that put in place policies that are FDI friendly (such as establishing credible mechanisms to reduce the risk of expropriation or offering tax breaks for new investments by foreign companies) are more likely to be able to participate in GVCs.

The stickiness of relational GVCs also makes them particularly vulnerable to supply chain disruptions. For this reason, firms participating in relational GVCs are likely to be particularly sensitive to the availability of skilled labor in the economies in which they operate. Furthermore, in relational GVCs, informational flows and communication are particularly important, which suggests that language skills (such as a good command of English as a second language) might be particularly relevant for GVC participation.

The relational nature of GVCs also considerably reinforces the role of institutional quality as a significant determinant of GVC participation. Having said this, institutional quality and relational GVCs interact in subtle ways. On the one hand, the emphasis this approach places on contractual insecurity naturally
A small N correlational study looking at public works and IT purchases in the Slovak Republic in 20072009 suggests that electronic reverse auctions decrease prices as measured by discounts (final price / original estimated price) through increasing the number of bidders (Pavel & Sičáková-Beblavá, 2013). However, conditional on the number of participating bidders, the authors find no further price effect, i.e. no confirmation of higher intensity competition. The overall price effect attributed to the use of e-auctions compared to standard open auctions is estimated to be 2.4% lower price than the originally estimated contract value. Another correlational study using a considerably larger sample from Russian sugar purchases in 2011 finds a considerably larger effect correlated with e-auction use of 28.0%-28.7% additional increase in discounts (Yakovlev et al., 2014). This corresponds to 5.8%-6.7% lower price per kilogram compared to the regional average price of sugar. While the authors do not reach a clear conclusion regarding the mechanism bringing about price savings, they suggest it is due to the number of bidders and contract characteristics such as contract size and contract duration.

Given the very limited amount of evidence available to test the impacts of e-auction and e-evaluation tools, there is a general need to expand the evidence base in terms of more advanced analytical methods, multiple countries, and other product markets. As electronic reverse auction is currently used in only few countries (OECD, 2011b) but is increasingly promoted, a more solid evidence base seems indispensable.

# 4. <u>Preferential treatment of bidder and product classes</u>

# _a) Theory_

The clearest sign that public procurement is more and more treated as a strategic rather than administrative function is that it is increasingly used to pursue broader policy objectives through special procurement rules and procedures. These socio-economic and environmental objectives either relate to specific bidder classes or types of products. In the former case, policy objectives typically concern the support of small- and medium-sized enterprises
statistics for the years with which we concern ourselves in this paper, namely 1976, 1981, 1985 and 1996. The distributions for each of these years come from the Pesquisa Nacional por Amostra de Domicilios (PNAD), run by the Brazilian Geographical and Statistical Institute (IBGE). Except where otherwise explicitly specified, we deal with distributions where the welfare concept is total household income per capita (in constant 1996 Reais, spatially deflated to adjust for regional differences in average cost-of-living), and the unit of analysis is the individual. Details of the PNAD sampling coverage and methodology, sample sizes, the definition of key income variables, spatial and temporal deflation issues, and adjustments with respect to the National Accounts baseline are discussed in Appendix 1.
characteristics from enrolled students. Follow-up self-reported data included questions to measure the intervention’s impact on attitudes, violence, and crime; exposure to risky spaces; and educational or personal expectations. We combined this self-reported information with administrative records on math, reading, and science grades; behavioral reports; and absenteeism data from both enrolled and nonenrolled students. Schools provided these data before and after the intervention.

Additionally, we collected neurophysiological evidence from a random subsample of enrolled students at the end of the intervention. By using the James-Lange theory of valence and arousal (Davidson et al., 1990; Harmon-Jones et al., 2010; Ramirez and Vamvakousis, 2012; Verma and Tiwary, 2014), and following the methodology developed by Egana-delSol (2016b), we proxied participants’ emotional regulation and stress directly from their brain activity. We used portable

> 9As we explain in detail later, there are two samples in this study. The first one is enrolled students—those who decided to participate in the ASP and were randomly assigned to treatment or control groups (1,056 students). The second sample of nonenrolled students includes those who were not registered for the ASP but were in the same schools and classrooms as treated students (1,364 students).

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While foreign takeovers look roughly similar to domestic takeovers by industry, they also mirror world industry. Table 2 shows the industrial distribution of the top 100 multinational companies (MNCs) and of Korean acquisitions. Of the 10 main industries where multinationals are prevalent, 7 were in the top 10 industries for international takeovers of Korean firms. This suggests that certain industries, especially electronics and chemicals, are more likely to have foreign ownership. Moreover, as shown in the bottom panel of Table 3, electronics and chemicals are also the top two value-added industries in Korea in 1995, implying that these are important growth industries where Korean technology is not lacking.

In sum, the industrial distribution of foreign acquisitions in Korea is similar to that of Korean domestic acquisitions and to the industrial distribution of multinational companies worldwide. Thus, the same industries that benefit from national economies of scale also benefit from international economies of scale. One difference between domestic and international acquisitions is size, as international transactions are larger. This suggests that there is a two-stage process of consolidation, first firms consolidate nationally, then they consolidate internationally. In addition, some highgrowth industries such as chemical and electronics are more prone to international consolidation. Next, we evaluate firm level data on ownership to get a better understanding of what foreigners bought in Korea following the 1997 financial crisis and the subsequent removal of barriers to foreign ownership.

# **5.2.** Explaining **foreign ownership**

In this section, we examine how firm- as opposed to industry-level characteristics affect foreign ownership. Our focus is on ownership by large foreign investors, but the available ownership data give total foreign holdings of each firm. In order to identify firms that have a single major foreign holder we make several adjustments. First, we assume foreign direct investment is present if 12
in coping strategies in different counties. The standard errors, _εhct_ , are clustered within counties.

The child-adult ratio is calculated by dividing the number of children who are under 18 years old by the total number of adults aged between 18 and 65 in the household. We use a dummy indicating whether the dwelling space of the household has a dirt floor or finished floor as an indicator for poverty, as this measure is highly correlated with other poverty indicators in Kenya and feasible to elicit in a phone interview.

Moreover, we hypothesize that female-headed households are poorer and have a closer relationship with social networks, and these cause the gender differences in coping. To formally check whether the gender differences in coping are due to these two reasons, we interact the household head gender dummy with the two hypothetical channels: the household poverty indicator, and the status of receiving transfers from families prior to the COVID-19 crisis.

A series of sensitivity analyses are performed. Firstly, Kenyan nationals might employ different coping strategies compared to refugees. Thus, we exclude the refugee sample from the analysis and cross check the results. Second, 17.72 percent of the households have reported different household head gender across the five waves, and we exclude those households whose head gender was changed.

All analyses are conducted in Stata v. 15.0 (College Station, Texas, USA) and take into account the sample design, particularly the sample weights and stratification.

# **4 Gender Differences in Coping Strategies**

## **4.1 Main Results**

As shown in the descriptive figures (Figure 1), male-headed households are more likely to cope using household internal resources but female-headed ones rely more on social networks, although the differences are not significantly different. For example, maleheaded households are more likely to sell assets to cope with a negative economic shock (Figure 1a). On average, about 10.4 percent of the male-headed households have sold any asset or livestock within the last 14 days, but only 6
10 significant, raising the question of whether the independent variable of interest, female/male dummy, may be correlated with it. To examine this question, another probit was tested with the urban/rural dummy as the dependent variable and the logarithms of per capita expenditure and household size and the female/male dummy as explanatory variables. With the exception of Angola, the female/male dummy was always statistically significant. Therefore, the sample was split into urban and rural in every country, and regressions were run separately, eliminating the urban/rural dummy as an explanatory variable.<sup>7</sup> In every case where the coefficient for female/male dummy was statistically significant, the probability of increasing the dependent variable by switching from male‐ to female‐headed households was computed. For that purpose, the values of the remaining two variables assumed the weighted averages in urban and rural areas, respectively. For ordinary least squares, which drops all households with zero expenditure on electricity and regresses the logarithm of spending on electricity on the three explanatory variables, the percent increase in spending on electricity (not logarithm) was computed, again setting the values of the two remaining variables at average values of the households in the sample.

For cross‐regional comparison of household access to electricity, presented first, access rates in 1990 and 2012 in different regions from the 2015 Global Tracking Framework report (World Bank and IEA 2015) were compared as a function of two measures of the poverty gap ($3.10 and $1.90 per day per person at purchasing power parity in 2011 international dollars) and of logarithms of per capita GDP (at the market exchange rate in 2005 U.S. dollars and at purchasing power parity in 2011 international dollars). 2012 was selected because it is the last year for which access rates are available in the Global Tracking Framework report, three other years being 1990, 2000, and 2010. 1990 was selected as an alternative because the poverty gap