rafmacalaba/data-use-mentions-extended huggingface dataset:
of ethnic homelands from political centers as well as the inter-connectivity of ethnic groups would be if these networks existed. .

Their results are robust to the use of these instruments, thus confirming the twofold role of road networks in extending the control of political powers and reducing political instability; political authority and stability are reinforced in ethnic groups that are accessible from the political capital.

We rely on this literature, which highlights the motivation of political leaders to build roads to extend their capacity to govern, to construct our instrumental variable for proximity to the road networks. In so doing, we follow a large literature that shows that non-economic factors play determining roles in the propensity of African governments to invest in the construction of public infrastructure (Bonfatti et al., 2019; Jedwab and Storeygard, 2021; Burgess et al., 2015; Herbst, 2014). In constructing our instrumental variable, we assume that African leaders would built roads to facilitate territorial administration by easing the movement of administration officials and military troops from the capital power center to the rest of the country, and from one location to another (Herbst, 2014; Muller-Crepon et al., 2020). Following these assumptions, we use the map of African ethnolinguistic divisions produced by the American anthropologist George Peter Murdoch in 1959 (Murdock, 1959) to identify the ethnic homelands of each country in our sample. These ethnic homelands are regarded as being exogenous. Using the least-cost path algorithm, we ”construct” hypothetical roads that would link the centroids of different ethnic homelands to each country’s capital city. We proceed under the assumption that the social planner’s goal would be to minimize the construction costs of these roads if they were to be built. We assume that land altitude

> state has pursued provisions of necessary infrastructure such as roads, railways, and airports as means to promote local economic growth and, in turn, integrate ethnic minorities with the Han majority” (p. 33).

16
|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|3,687|3,687|
|Number of individuals|1,609|1,609|1,609|1,609|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results in Columns (1) and (2) are presented as marginal effects. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F- test from Stock and Yogo (2005) is 16.38. Full estimation results are reported in Table A1 (Appendix A). The adjusted _p_ -value describes a conservative (upper bound) probability that rejecting the null hypothesis will result in one or more Type I errors, as implemented in Newson (2010).

27
br>budget)|||Aid flows reported in the<br>annual budget by<br>government/aid flows actually<br>disbursed by development<br>partners(%)|Halve the gap (at least<br>**85%**reported on budget)|
|4|Strengthen capacity<br>by coordinated<br>support (technical<br>assistance)|||Amount of coordinated<br>technical cooperation / total<br>technical cooperation (%)|**50%**of technical<br>cooperation is coordinated|
|5a|Use of country PFM<br>systems|||Use of PFM/total assistance<br>(%)|A one-third (two-thirds)<br>reduction in the % of aid<br>for partner countries with<br>a score of 3.5-4.5 (5 or<br>above) on indicator 2a|
|5b|Use of country<br>procurement<br>systems|||Use of procurement / total<br>assistance (%)|No target (as no target is<br>set for indicator 2b)|
|6|Strengthen capacity<br>by avoiding parallel<br>project<br>implementation<br>units(PIUs)|||Number of parallel PIUs|Reduce by two-thirds the<br>stock of parallel PIUs (**-**<br>**67%**)|
|7|<br>Aid is more<br>predictable|||Actual aid flows recorded by<br>the government/scheduled aid<br>flows by development partners<br>(%)|Halve the proportion of<br>aid not disbursed within<br>FY for which it was<br>scheduled|
|8|Aid is untied|||OECD-DAC data|Continue
of employment, i.e., agricultural employment and self-employment in non-agricultural activities.<sup>18</sup> To further examine impacts on employment outcomes, we plot the distribution of the women’s employment index and the distribution of salaried income for each treatment group in Figures 5 and 6. These figures clearly indicate a rightward shift in the distribution of women’s employment outcomes.

**Women’s decision-making autonomy, gender attitudes, and intrahousehold dynamics.** We do not find any statistically significant impact of the intervention on women’s decision-making in the household, sharing of domestic work, freedom of movement, or gender attitudes (Table 8). For women caring for children under two years of age, there is a marginally significant increase in the likelihood that the respondent’s partner sometimes takes care of children (Table 9). However, we find no significant changes in time use, with the exception of a 20 percent increase in time spent doing paid work over the past 24 hours, compared to a control group mean of 3.4 hours (Table 10).

**Women’s psychological well-being.** Turning to women’s psychological well-being, we find a small beneficial impact of the intervention, reducing the likelihood of experiencing depression and self-reported unhappiness (significant at the 5% level) for the full sample (Table 11). We find similar effects for women in households with children under two years of age. These results are consistent with work from Bossuroy et al. (2022), finding that a multi-faceted poverty alleviation program in Niger (neighboring Burkina Faso) improved mental health by 0.13 to 0.23 standard deviations, particularly when it included a psychosocial component.

**Women’s financial outcomes.** We use an OLS specification to estimate impacts on financial outcomes since we only collected this information in the endline survey. Almost 59% of women in the control group had saved any money during the past 12 months
The weak result could be because the fuzzy definition of threshold lowers the power of this test. Column 3 adds triple interactions of _Surge*Enforcement_ and _Slump*Enforcement_ with other firm attributes (age and capital intensity) as there is a concern that the employment measure could be correlated with other firm attributes. The main results are the same. Overall, the results in Table 10 suggest that the relationship between enforcement and employment adjustment is indeed stronger among those firms that are the focus of labor inspectorates.

# **5.3 Effect on firm exit**

Table 11 provides evidence of the impact of shocks and labor regulation enforcement on firm exit, estimating Equation (5) through OLS. The baseline specification is shown in column 1; column 2 adds sectors‐year FEs and columns 3 and 4 add interactions of firm employment, age and capital‐labor ratio with shocks as additional controls.

The significant negative coefficient on _Slump_ indicates that on average, firms are more likely to exit during slumps than during normal and surge years. At ‐0.01, it is sizable compared to the baseline annual exit rate in the RUSLANA panel, which ranges between 0.04 and 0.07 during the study period. However, the estimated interaction between _Slump_ and _Enforcement_ is small (‐0.001) and statically not significant; and the same holds for the estimated interaction between _Surge_ and _Enforcement_ . Thus, enforcement is not associated with exit rates during shocks, and ignoring or including employment loss due to firm exit is immaterial to our main results.

15
<!-- Start of picture text --> Kazakhstan<br>Honduras<br>Armenia<br>Moldova<br>United Arab Emirates<br>Colombia<br>France<br>Korea, Rep.<br>Lebanon<br>Malaysia<br>Indonesia<br>Kyrgyz Republic<br>Mexico<br>Vietnam<br>Tanzania<br>Kenya<br>0 200 400 600 800 1,000 1,200<br>De minimis threshold (US$)<br><!-- End of picture text --> _Source:_ Digital Business Indicators.

_Note:_ Bangladesh, Burkina Faso, Pakistan, Senegal, and Tunisia do not have a de minimis threshold in place. Kazakhstan’s threshold is only applicable to member countries of the Eurasian Customs Union.

When comparing thresholds, it is important to keep in mind that the number of international parcels being received and thus the need to establish a de minimis threshold vary significantly across countries. Also, different conditions apply to application of the de minimis threshold in different countries—for example, in terms of product types included, the calculation of value (such as free on board [FOB], duty payable, per shipment or per day), intended use (commercial or individual use), mode of transport (air, sea, road), trading partner (such as within a regional economic community), and type of shipping (postal or express).

International organizations have begun discussions on updating customs regulations in response to the rising cross-border e-commerce. The World Customs Organization amended its Immediate Release Guidelines in 2014 and most recently in June 2018. De minimis is recognized as one of the 15 key components under the Cross-Border E-Commerce Framework of Standards. It has been suggested that the threshold be based on a cost-benefit analysis of the number of imported low-value packages, clearance times, revenue collected, cost of revenue collection, capacity of customs authority, and required trade controls
<!-- Start of picture text --> 60+ 3 3 60+ 12<br> 18-59 16 25 18-59 12 23<br> 12-17 6 6 12-17 6 7<br> 5-11 12 12 5-11 13 13<br>0-4 8 9 0-4 11 11<br>(20) (10) 0 10 20 30 (20.00) (10.00) 0.00 10.00 20.00 30.00<br>Percent of total population Percent of total population<br>Male Female Male Female<br><!-- End of picture text --> Source: World Bank 2021; UNHCR December 2023 With respect to the sector of employment, recently arrived refugees from Sudan and those who already resided in Chad are broadly comparable too. Though categorizations differ between those included in the ECOSIT IV survey and those included in the ongoing UNHCR registration process, it is evident that the vast majority is engaged in the primary occupation of agriculture, livestock rearing or fishing. Of the existing refugees, 92 percent indicated to have this as their sector of occupation, against 87 percent of the newly registered refugees.

7
**Table S1.2: average value of household food consumption per capita by household structure - all food commodities**

|Household type|PSF survey<br>Mean<br>(N)|ESPS survey<br>Mean<br>(N)|Difference<br>PSF-ESPS<br>(Std. err.)|
|---|---|---|---|
|Single person households|570,530<br>(98)|502,432<br>(540)|68,098<br>(38,759)|
|Other one cell households|289,300<br>(147)|262,873<br>(901)|26,427<br>(16,621)|
|More than one cell 'nuclear' hh.|217,181<br>(549)|193,024<br>(3,971)|24,156<br>(17,379)|
|Other households|181,343|150,434|30,909|
||(968)|(8,147)|(5,321)|
|All households|223,162|181,548|41,614|
||(1,762)|(13,559)|(6,746)|

Source: « Pauvreté et Structure Familiale » (PSF) and « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations.

Note: Summary statistics of food consumption per capita, by household types. In the first two columns, the sample size is reported between parentheses. In the third column, the standard error of the difference is reported.

**Table S1.3: average value of household total consumption per capita by household structure - all commodities**

|Household type|PSF survey<br>Mean<br
Quantifying Vulnerability to Poverty: A Proposed Measure, Applied to Indonesia Lant Pritchett (World Bank)

## Asep Suryahadi (SMERU)

Sudarno Sumarto (SMERU)

# Social Monitoring and Early Response Unit

# Jakarta

The findings and interpretations in this paper are those of the authors, and should not be attributed to the World Bank Group or to any agencies providing financial support to SMERU activities and report.

This paper is an extensive revision of the December 1999 version which adds estimates using a second panel data set (the Mini-Susenas). We thank Esther Duflo, Menno Pradhan, Martin Ravallion, and David Levine for their valuable comments and suggestions on the earlier version. We also thank Wenefrida Dwi Widyanti and Yusuf Suharso for their research assistance. We are grateful to BPS, UNDP, and UNICEF for providing access to the data.
a behavioral response involving a reduction and cessation in tobacco consumption that is manifest particularly among people who discontinue smoking and younger individuals who do not start smoking. The second channel is associated with a reduction in medical expenses associated with the averted treatment costs of tobacco-related diseases, and the third is a rise in incomes because of gains in years of employment derived from an extension in life expectancy. To assess the impact of these effects, this paper estimates the price elasticity of tobacco, simulates upper- and lowerbound scenarios, and calculates the welfare gains among various income groups.

The study is structured as follows. Section 2 reviews the literature on the health effects of tobacco, the economic costs associated with tobacco-related diseases, tobacco tax policies, and price elasticities. Section 3 presents the model, and section 4 provides an overview of the data used to estimate the impact of the tobacco tax. Section 5 examines the results. The final section concludes with a discussion on policy implications.

> 2 2016 Global Adult Tobacco Survey (GATS), Russian Federation Factsheet:

> <u>http://www.who.int/tobacco/surveillance/survey/gats/rus_factsheet_2016.pdf?ua=1</u>

> 3 The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The measure was introduced and measured by the United Nations Development Programme (UNDP).

2
give rise to long delays between the authorization of federally-financed highway spending, and the actual state-level spending itself. Their use of this lag structure is different from mine, however, in that they estimate responses to "surprises" in spending, measured as deviations in spending from what would have been predicted from past financing approvals. This strategy is appropriate in their context, given their emphasis on isolating responses to unanticipated spending shocks. In contrast, in my context I am concerned that such deviations of

> 1 In fact, restricting attention to the same set of 29 countries covered in Kraay (2012), the F-statistic from the firststage regression of changes in government spending on changes in predicted disbursements increases from 15 in the previous paper, to 28 in this paper.  Nevertheless, the point estimates of the multiplier I find  using this much larger dataset are remarkably similar to those based on World Bank-financed spending alone.  Using only World Bank project-level data to construct the instrument, I found a multiplier of 0.48 in the benchmark specification, while I obtain a multiplier much-more-precisely estimated multiplier of 0.61 using the DRS loan-level data in this paper.

4
Direct measures of market power in line with the New Empirical Industrial Organization (NEIO) include the Lerner index (Lerner, 1934) and the Boone indicator (Boone, 2008). The Lerner index is defined as the difference between output prices and marginal costs (relative to prices). Prices are calculated as total bank revenue over assets, whereas marginal costs are obtained from an estimated translog cost function with respect to output (see Demirgüç‐Kunt and Martínez Pería, 2010). Higher values of the Lerner index indicate less bank competition. The Boone indicator is estimated as the elasticity of profits to marginal costs. To obtain the elasticity, the log of profits (measured by return on assets) is regressed on the log of marginal costs. The estimated coefficient, computed from the first derivative of a trans‐log cost function, is the elasticity. The rationale behind the indicator is that higher profits are achieved by more‐efficient banks. Hence, the more negative the Boone indicator, the higher the degree of competition is because the effect of reallocation is stronger (see Schaeck and Cihák, 2012). Bank concentration ratios, the Lerner index and the Boone indicator all are from World Bank Global Financial Development Database. Finally, we employ a set of proxies for bank competition policy in order to test their effect on economic growth of financially dependent firms (see section 5 for a description and sources).

Table 1 reports some descriptive statistics (number of observations, average value, standard deviation, minimum and maximum observed values) for all the variables included in our baseline empirical specification. The variables included are: value added (valaddw1), the share of value added (valadd_share), financial dependence as measured by Rajan and Zingales (extdepRZ1), Klapper et al. (extdepKLR), Fernandez de Guevara and Maudos (extdepGM), credit‐to‐GDP (credit_GDP), the 5‐bank concentration ratio (conc5), the 3‐bank concentration ratio (conc3), the Boone indicator
destroyed by the violent attacks in 1999.

The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998/99, 1999/00 and 2000/01. We are

> 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ―Human Rights Violations Database.‖ 9 February, 2006. Website: http://www.hrdag.org/resources/timor-leste_data.shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database.

> 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001.

> 10 The questions we used are ―Was [NAME] displaced outside E. Timor in 1999?‖, and ―Was the [BUILDING] damaged in the violence of 1999?‖. 14% of the whole sample surveyed in 2001 report having been displaced, while 26% report that their house was destroyed. Within our sample of school age children, these figures are 16% and 25%, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only.

13
not),<sup>36</sup> tobacco (assuming a 50 percent rate corresponding to molassed tobacco), and gasoline (assuming the highest quality is consumed).<sup>37</sup>

# **In-kind transfers**

Receipt of in-kind benefits is based on directly identified utilization of the public education or public health care systems. The HIECS 2015 records how many household members are enrolled in the public education system (and at what levels) and whether any household members recently visited a public health care facility. The monetized value of the in-kind transfer is based on the “government cost” approach. For example, total education expenditures are divided by the total number of users (students) to get a uniform per-user cost of producing and delivering the service. This per-user cost then is defined as the value of the transfer received. This cost represents what the utilizing household would have to pay to acquire the service at the government’s cost.

We used disaggregated administrative data (by facility type) to guide our estimation of the government cost of a health care or education service acquired. For health, we used budgetary data to allocate specific expenditures to hospital and clinical care providers. For example, the public expenditures transferred to public hospitals for personnel and medical goods (including medicines) is not equivalent overall or on a per-facility basis to public expenditures for the same items for public clinic-based health care.<sup>38</sup> Offbudget, special fund expenditures for these services are known to be substantial.

Note that for both public health and education services, the estimate of the value of the benefit we allocate is limited by the information available. For example, we do not have complete information on the government’s cost of a secondary school student-year or a primary/preparatory school student-year. We assumed the former was twice as expensive (for the government) as the latter two.<sup>39</sup> With that assumption, and with the overall
) provide an unprecedentedly granular spatial distribution of wealth across these two cities and with strong performance explaining up to 76% of the variation for Lilongwe and up to 67% for Blantyre (Figure 4d).

# **Discussion**

This paper proposes and evaluates the use of a vision transformer architecture to solve multiple open problems pertaining to combining survey and satellite data to produce wealth estimates at fine spatial scales. For cross-sectional wealth predictions, careful evaluations using georeferenced census extracts from four countries show that estimates from the transformer model perform well. When paired with Landsat imagery, R<sup>2</sup> values for transformer models that incorporate geo-features outperform commonly used CNN and XGboost models for asset index prediction in all four countries, for all sample sizes considered. Across all countries, accuracy degrades rapidly when using less than 10% of the census extract for training. In Mozambique and Madagascar, estimates produced using transformer models explained approximately 20 to 30 percentage points more of the variation in wealth than estimates produced using XGboost and geo-features. Trans- former models also outperform CNNs in all four countries, by amounts up to approximately 5 percentage points in Madagascar and Mozambique. Incorporating geofeatures into the transformer architecture improves performance by 5 to 10 percentage points at small sample sizes in Mozambique, Madagascar, and Burkina Faso. Transformer models also perform well when predicting variation within cities at 0.3km scales, achieving up to 0.76 in Lilongwe and 0.67 in Blantyre. However, incorporating geofeatures at this scale reduced performance, because they are RR<sup>2</sup> constructed from lower-resolution Landsat imagery. Finally, the transformer model also generates more accurate estimates than CNNs and XGboost when predicting decadal changes in the asset wealth index in Mozambique and Malawi. Model predictions achieve values of 0.57 in Malawi and 0.42 in Mozambique at fine spatial levels, despite the lack of available geo-features. This is a large improvement over the 0.15 to 0.17 RR<sup>2<