rafmacalaba/data-use-mentions-extended huggingface dataset:
not always) be linked back to a specific verification. The key information available in the payments table is the date of payment and values. The dataset also includes variables with the names and “document number” of payees - these are often unique national identifiers for individuals (CPF) and firms (CNPJ) but are not available for all payments or municipalities. Again, one verification event can lead to one or multiple payments, so payments are uniquely identified by the variable id_pagamento_bd and can be linked back to verification events by id_liquidacao_bd and to commitments by id_empenho_bd.

# **2.3 Coverage and descriptive statistics**

Our dataset on municipal procurement and budget execution currently covers seven of Brazil’s twenty-seven states, highlighted in Figure 1: Ceará (CE), Minas Gerais (MG), Paraíba (PB), Pernambuco (PE), Paraná (PR), Rio Grande do Sul (RS), and São Paulo (SP). These are large states that cover a substantial share of the total number of municipalities (55%), population (48%), and GDP (49%) of the country according to 2020 data. Notably, our dataset only covers states in the South, Southeast and Northeast regions - data from the states in the North and

> 16For a detailed discussion of expenditure functions see Manual do Orçamento (in Portuguese).

7
to distance varies from 0.38 to 0.49. A significant negative common trend is observed in most estimates, reflecting a variety of developments in structure and technology. A large part of the negative trend development is likely to be better captured with trade-pair specific trend variables, but for presentation, we prefer the common trend. The overall goodness of fit, between 33 and 40 percent with 639 to 1131 observations, is reasonable. A substantial portion of the variation is explained by dummy variables. The comparison of estimation of a spline by distance with an unrestricted estimation (see appendix) supports the restriction of the observations to those below a maximum distance.

# _<<B>>Further assessment of estimation results_

A different way of assessing the estimation results of transaction cost equations is to compare predicted values of transaction costs with estimates of transaction costs obtained from surveys. For this purpose, we use a survey of traders in Malawi covering the period from August 1999 to February 2000 that is documented in Fafchamps et al. (2005)<sup>11</sup> . This survey is a representative sample of 738 Malawi traders. However, the survey contains only 275 observations of maize transactions by maize traders<sup>12</sup> . In figure 1, we show the density functions of unit transaction costs and transaction costs relative to sales prices, both from this survey and from predictions on the basis of the estimated transaction cost equations. In table 2, we summarize the average and standard deviation of transaction costs by rural region and urban center, in level and share, from this survey and from predictions on the basis of the estimated transaction cost equations. The table also reports test results on the equality of the average unit transaction costs and the average shares of transaction costs of sales prices.

# **Figure 1    Comparison of Predicted and Observed Transaction Costs***

20
# Figure 4. Occupational wage inequality, average level of protection and mean income

(a) Occupational wage inequality and level of income (b) Average tariff rate and level of income <!-- Start of picture text --> 6 7 8 9 10 11<br>log(gdppp)<br>gini of wages, x1wu F itted values<br>.8<br>.6<br>.4<br>gini of wages, x1wu/Fitted values<br>.2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 6 7 8 9 10 11<br>log(gdppp)<br>unweighted average tariff rate f rom W B sourcesF itted values<br>100<br>80<br>60<br>40<br>20<br>unweighted average tariff rate from WB sources/Fitted values<br>0<br><!-- End of picture text --> (c ) Occupational wage inequality and average tariff rate <!-- Start of picture text --> 0 20 40 60 80 100<br>unweighted average tariff rate from WB sources<br>gini of wages, x1wu Fitted values<br>.8<br>.6<br>.4<br>gini of wages, x1wu/Fitted values<br>.2<br>0<br><!-- End of picture text --> (d) Occupational wage inequality (controlled for income) and average tariff rate <!-- Start of picture text --> 0 20 40 60 80 100<br>unweighted average tariff rate from WB sources<br>res Fitted values<br>.3<br>.2<br>.1<br>0<br>res/Fitted values<br>-.1
reserved space. Women who perceive this attitude to be the prevailing norm among men are 79 percent (23 percentage points) more likely to ride in the reserved space than those who do not (column 4). This correlation could arise through a channel of increased perceived risk of harassment: women who perceive this attitude to be the norm may expect men to act accordingly and harass them. However, two points suggest that this is not the only mechanism for this relationship. First, women’s second order beliefs about other _women’s_ beliefs are also strongly correlated with their choice of the reserved space, with a similar magnitude (column 3). Second, the estimates are also similar with and without conditioning on the respondent’s perceived probability of harassment in the public space (Panel A vs. B). This result echoes the result of a fixed-amenity value arising from our rider experiments (Section 7), and is consistent with the idea of a social norm that it is more appropriate for women to choose the reserved space drives demand for the reserved space.

# **8 Alternative Mechanisms and Robustness**

We now investigate journey planning, avoiding other crimes, sorting of perpetrators, and congestion as alternative mechanisms that may underlie both riders’ demand for the reserved space and affect our interpretations of treatment effects when riders are randomly assigned to ride in a given space. We find that these channels are unlikely to explain our results or our conclusions.

## **8.1 Journey Planning as a Margin of Adjustment**

In this sub-section, we leverage our rich ride-level data to explore the possibility that that riders respond to the experimental variation in opportunity cost or choice of space generated through the app by making changes in their itinerary.

We start by testing whether riders respond to our offer of a higher payment to ride in the public space by adjusting their travel plans at other margins. Specifically, we investigate 29
NO2 may also be interpreted as a new variant in big data efforts to quantify cyclical fluctuations. For example, across countries in Sub-Saharan Africa, Buell et al. (2021) present a suite of high-frequency indicators, including Google search trends and mobile payments. Similarly, across ZIP codes in the United States, Chetty et al. (2020) build a database that uses anonymized data from private companies to proxy consumer spending, business revenues and employment rates nearly in real-time. Similarly, Goolsbee and Syverson (2021) and others rely on mobility data. The advantages of air pollution data over any of these alternative sources is that satellite NO2 readings have global coverage, are available in real-time, are entirely open access, and are now reaching their maturity, with comprehensive data back over 15 years.

**Outline of the Paper.** The paper proceeds as follows: Section 2 provides background on satellite measurements of NO2. Section 3 describes the data in further detail, including summary statistics and other basic characteristics. In Section 4, we describe the methodology, including our objective procedure for identification. Section 5 showcases the baseline results, and Section 6 implements the methods to applications. Section 7 concludes.

# **2 Satellite Measurement of Nitrogen Dioxide**

**Background.** We use air pollution as a measure of economic activity. However, not all air pollution data, including that measured at ground stations, is suited to such a task. First, ground-level air pollution monitoring stations have an uneven spatial distribution. It is not the same to measure pollution in an urban intersection or in the countryside. Additionally, these stations are sparsely located, particularly in developing countries, where grid cells may contain just one station or none at all. Second, even when measured, these readings may subsequently be doctored due to political motives, such as meeting mandatory targets of energy intensity (Ghanem and Zhang, 2014).

These features are some of the primary reasons why “top-down” measurement of air pollution
<br>years of age. Women's participation rate-<br>55 percent<br>versus 54 percent for men.|
|---|---|
|Togo:|Rural- all areas outside Lome and six legally<br>designated "communes". Economically active- population<br>over 12 years of age. Women's participation rate- not<br>given.|
|Zimbabwe:|Rural- population living in towns under 2,500. During<br>1969 census urban-<br>any locality over 150 if majority of<br>males employed in non-agricultural<br>occupations.<br>Economically<br>active- population over 15. Women's<br>participation rate- 48 percent versus 80 percent for<br>men.|
trips on weekdays in October 2020 vs. 2019_

<!-- Start of picture text --> a. Bogota b. AMBA<br>45,000,000 45,000,000<br>40,000,000 40,000,000<br>35,000,000 35,000,000 -24.9%<br>30,000,000 30,000,000<br>25,000,000 -15.2% 25,000,000<br>20,000,000 20,000,000<br>15,000,000 15,000,000<br>10,000,000 10,000,000<br>5,000,000 5,000,000<br>0 0<br>2019 2020 2019 2020<br>Source: Authors based on CDR data<br><!-- End of picture text --> The overall fall in the number of trips was driven mostly by falling trip generation rates (TGRs) per person, although population out-movement also played a role. In October 2019, Bogota’s residents made on average 2.5 trips per day on weekdays and 2.2 trips and 1.8 trips on Saturdays and Sundays, respectively. In contrast, by October 2020, the TGRs had fallen by 15 percent on weekdays although only by 7 percent on Saturdays and 1-2 percent on Sundays. In AMBA, the TGRs on weekdays, Saturdays, and Sundays in 2019 were, respectively, 2.8, 2.46, and 2.21. By October 2020, they had declined by about 25 percent on weekdays and Sundays and by 14 percent on Saturdays.

As with total trips, TGRs per person fell disproportionately more in Bogota D.C. and CABA than in the overall metropolitan areas, from an initially higher level. The reduction in weekday TGRs was greater in Bogota’s northern localities – from
of this methodology by applying it to India. Two reasons motivate this choice. First is scale: accounting for almost a fifth of the world population, India encompasses states with incomes per capita comparable to Mexico and others similar to Benin. Second is the “messiness” of its urbanization process, characterized by a wide variation in the strength of local urban authorities, from high in state capitals to dismal in other cities. Scale and messiness result in an enormous diversity of places, ranging from a glamorous metropolis such as Mumbai, capital of Maharashtra, to a shabby town such as Bagula in West Bengal, to any among hundreds of thousands of villages.

With such wide spatial diversity, not surprisingly there is considerable debate as to how urbanized India actually is. The official urbanization rate for 2011 is 31.2 percent. Several studies using alternative population thresholds or satellite imageries of built-up cover have argued that many areas of India are misclassified as rural by the administrative definition. Depending on the methodology, the urbanization rates estimated by these studies range from 42.0 to 78.0 percent (Denis and Marius-Gnanou 2011; Dijkstra et al. 2018; Ministry of Finance 2017).

Conversely, a study in this special issue relies on a range of parameters to delineate urban markets and suggests that India could be even less urbanized than official figures imply. In this other study the urbanization rate ranges from 14.8 to 33.5 percent when using nighttime light data, and from 27.1 to 39.4 percent when using built-up cover data (Baragwanath et al. 2019). Shedding light on this debate is the second motivation for choosing India to illustrate our proposed methodology.

Subjective assessments are increasingly being used to complement more objective approaches across various fields in economics. For example, good-looking ratings by independent parties have been shown to be correlated with occupational sorting, earnings differentials and physical performance (Beller et al.

2
||||-0.00|||||0.00|
||||||(0.00)|||||(0.00)|
|Social investment (%|exp)||||-0.00<br>(0.00)|||||-0.00<br>(0.00)|
|Constant|0.98***|0.96***|0.80***|0.88***|0.91***|0.23***|0.21***|0.21***|0.21***|0.50**|
||(0.00)|(0.01)|(0.06)|(0.06)|(0.07)|(0.01)|(0.02)|(0.02)|(0.04)|(0.22)|
|Observations|18,850|18,875|18,840|18,840|11,866|18,850|18,875|18,271|18,271|11,516|
|Number ofgroups|4,749|4,749|4,741|4,741|3,665|4,749|4,749|4,621|4,621|3,508|
|Department FE|No|No|No|Yes|Yes|No|No|No|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Source_ : Author based on DHS 2005/2010/2015. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<
# 5.  Conclusions and Looking Ahead

Using nationally-representative, self-reported survey data elicited from men and women in Sub-Saharan Africa (Malawi, Tanzania and Ethiopia) and Southeast Asia (Cambodia), this paper highlights how intrahousehold, individual-disaggregated survey data collection on ownership and valuation of physical and financial assets can cast a brighter light on economic inequality among men and women. The data from the surveys supported by the World Bank LSMS+ program reveal significant (i) intra-household differences in asset ownership and wealth aggregates among men and women, and (ii) more pronounced estimates of overall inequality based on individual asset wealth vis-à-vis household per capita expenditures and per capita asset wealth. Land, in particular, is an important component of individual wealth inequality, and the contribution of residential versus non-residential land varies by country context. Compared to the survey approach of interviewing a single, most knowledgeable household member to report asset owners and values, self-reported information on asset ownership and valuation leads to greater estimates of wealth inequality. Additional sensitivity checks to address missing asset values and variation in asset valuation among joint asset owners yield similar results.

Overall, this paper makes an important contribution towards a more nuanced understanding of longerterm economic inequalities among men and women that are often disguised in existing survey data. The demonstrated utility of individual-disaggregated survey data on asset ownership and values further contributes to the broader momentum, including as part of the World Bank LSMS program, to improve the scope of and approach to data collection in household surveys for better capturing the extent and drivers of gender inequities in development outcomes. In the context of future large-scale household surveys in which the target variables include “wealth,” the estimated parameters informing sampling design simulations (e.g. intra-cluster correlation) are expected to be different for individual wealth vis-à-vis per capita consumption expenditures. Our findings regarding the asset classes that contribute the most to the 38
# Figure 9. Business and financial sector reforms

# A. Business regulatory environment

<!-- Start of picture text --> Score<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>DB2006 DB2013 DB2020<br><!-- End of picture text -->

# C. Financial regulatory environment

<!-- Start of picture text --> Score<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>DB2006 DB2013 DB2020<br><!-- End of picture text -->

# E. Capital account openness

<!-- Start of picture text --> Index<br>1996 2006 2016<br>1.0<br>Advanced economy average<br>0.8<br>0.6<br>EMDE average<br>0.4<br>0.2<br>0.0<br>LAC SSA MNA EAP ECA SAR<br><!-- End of picture text -->

# B. Business regulatory environment

<!-- Start of picture text --> Number of reforms Percent of population<br>Improving Deteriorating Difference<br>1000 50<br>800 40<br>600 30<br>400 20<br>200 10<br>0 0<br>-200 -10<br>DB08-14 DB14-20 2006 2013 2017<br>Reforms Internet Access (RHS)<br><!-- End of picture text -->

# D. Financial regulatory environment

<!-- Start of picture text --> Number of reforms<br>590 Improving Deteriorating Difference<br>490<br>390<br>290<br>190<br>90<br>-10
|0.49|0.70|0.73|0.68|
|Employed in Industry|0.08|0.08|0.12|0.06|0.04|0.05|0.08|0.04|0.04|0.04|
|Employed in Services|0.26|0.23|0.40|0.16|0.07|0.14|0.24|0.10|0.07|0.08|
|**Access to basic services**|||||||||||
|Improved water|0.69|0.77|0.86|0.73|0.59|0.65|0.74|0.61|0.59|0.63|
|Improved sanitation facility|0.24|0.25|0.32|0.21|0.16|0.18|0.23|0.14|0.17|0.20|
|Access to electricity|0.48|0.37|0.56|0.29|0.20|0.26|0.35|0.22|0.23|0.20|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line.

42
specifically those with low income and high costs. To be more precise, it includes households with energy costs exceeding a 'reasonable level' (the median) and whose residual equivalized income would fall below the official poverty line if they were to spend that amount. Notice that two types of households meet these conditions: 1) Households that are income poor and have relatively high energy expenditures; and 2) Households that are just above the poverty line and would fall into income poverty after considering energy expenses (households vulnerable to poverty), with relatively high energy expenditures. This definition excludes income-poor households with relatively low energy expenditures and households above the poverty line that do not fall into poverty when energy expenses are taken into account. Similarly to the M/2 and the 2M metrics, the “LIHC” also uses relative thresholds, making comparisons over time difficult unless the thresholds are anchored.

**A similar definition is the after-fuel-costs poverty (AFCP) indicator, developed by Hills (2011).** Still, it considers only one condition: household equalized income (possibly subtracting housing costs and domestic energy costs) is below the standard threshold of 60% of equivalized national income (Figure 4).<sup>29</sup> In addition to the incidence of energy poverty, the energy poverty gap can also be estimated. The depth of energy poverty is defined as the amount by which the assessed energy requirements of fuelpoor households exceed the threshold for reasonable costs.

**The energy expenditure can be the observed energy expenditure, for example, from the HBS, or it can be modeled. In the UK, the energy cost is modeled rather than based on observed spending.** This ensures that one does not overlook households with low energy bills simply because they actively limit their energy use at home, for example, by not heating their home. It is calculated by combining the fuel requirements of the household with corresponding fuel prices. This approach is data intensive, as these required costs capture
these tariff rates across all sectors implies that tariff revenue in the revised database is about

1.3% of GDP, which is consistent with collected revenues in Tanzania.<sup>25</sup> The SAM has some detail on taxes, which include direct taxes on households and enterprises, import tariffs, producer taxes, indirect (sales) taxes and factor taxes. The data for import tariffs are replaced with collected tariff rate data for the year 2006.

Given that Tanzania participates in preferential trade areas with the East Africa Customs Union and the South African Development Community, it was necessary to make further adjustments. That is, since, in principle, tariff rates should be zero within these preferential trade areas, we set tariff collections on imports from SADC and EACU (our Africa region) at zero. We then increased the tariff rates for the other regions in our model so that the overall weighted average collected tariff rate is unchanged. We used the trade flow data, disaggregated by regions and sectors of our model to weight the tariff rates. This adjustment has the impact of raising the collected tariff rates for the regions in our model where positive tariff rates apply. The resulting adjusted tariff rates are reported in Table 4.

> 25 For the year 2006, aggregate data from Tanzania show that tariff collections are 1.47 percent of GDP.

99
0.599|0.584<br>0.569|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : The ratio represents the share of workers with 20 or more years of experience in the total of employed individuals. The level of experience of workers is calculated as the age minus the number of years of completed education minus six. The sample was restricted to individuals aged 18 to 65.

57
non-PSW adjustments provides incremental improvements.

> 9 The SWIFT model for all countries can be seen in Appendix 1. The simulation results of the SWIFT model are available upon request.

> 10 Adding the non-PSW adjustment (maxentropy), compared to PSW (Lee 2006), has a negligible impact on the SWIFT based poverty projection. The SWIFT-based poverty rate with the inverse propensity score is 37.3 percent, which is the closest to the national poverty rate among all reweighting procedures tested in this experiment. Results are available upon request. Note that the standard errors of all SWIFT-based poverty projections are significantly larger than that of the rural poverty rate estimated from actual consumption data in EICV5 due to the following two reasons: (i) the sample size of the phone owner sample is smaller than the full rural sample of EICV5, and (ii) the SWIFT poverty projections inevitably add prediction errors.

13
internal models. Depending on the severity of data and risk management capacity constraints, different levels of simplification of the IASB rule may be considered.

Mexico, for example, has recently introduced a type of expected loss provisioning for credit card debt, mortgages, and non-revolving consumer credit, incorporating system-wide estimations of probability of default (PD), loss given default (LGD), and exposure at default (EAD).<sup>29</sup> The model estimates system-wide PD for a certain type of portfolio in the next 12 months, which is consistent with the timeframe specified under the IRB approach of Basel II for capital requirement calculation. The provisioning requirement based on such a model would ensure a floor on provisioning—that is, the expected losses in the next 12 months—but does not consider expected losses over the life of a portfolio, as is required in the joint IASB/FASB proposal. Nonetheless, the regulatory approach with a system-wide view may help mitigate concerns about banks‘ internal risk management capacity. In addition, this approach better utilizes all available

> 28 At the time of this writing, a range of options is under debate on the measurement of expected losses, including expected value (probability-weighted possible outcomes), most likely outcome, the maximum amount that is more likely to occur than not, and whether to use discounted or undiscounted amounts.

> 29 For a detailed methodology and estimation using information on credit card portfolios of Mexican banks, see Elizondo Flores and others (2010).

14