rafmacalaba/data-use-mentions-extended huggingface dataset:
by 6 percent.

Macroeconomic conditions strongly influence the loan quality of banks in Brazil. Using dynamic panel econometric techniques covering the operations of 78 Brazilian banks, Vazquez, Tabak, and Souto (2012) show that a 2-percentage point drop in yearly GDP growth is associated with a longterm increase in NPLs of 3.3 percentage points. This parameter was estimated based on quarterly data between 2003q1 and 2009q1 and focused on lending granted with non-earmarked resources. The average NPL ratio during the authors’ 7-year window period (3.6 percent) is broadly similar to what was observed before the COVID-19 pandemic (3.2 percent). Additionally, both periods exclude crisis episodes, reducing comparability issues of the parameter. This is particularly relevant, as NPLs are usually lower and less volatile during normal times than during banking crises (Ari and others 2019).

Based on those estimated coefficients, we estimate that under a low probability, high impact scenario where ecosystem services collapse, the banking system could experience a long-term increase in corporate NPLs in the order of 9 percentage points. This exercise assumes that the full impact on GDP growth occurs by the end of the projection period, that is, in 2030. Assuming a less abrupt impact of biodiversity loss on GDP growth, where half of the GDP growth drop

> 27 This comparison covers 223 countries and territories, including Sub-Saharan Africa (44), Rest of South Asia (5), Rest of Southeast Asia (7), Central America (32), South America (11), Central Asia (21), Middle East and North Africa (17), Other Europe (4), Rest of East Asia (3), Oceania (24), and the EU (29).

> 28 As indicated in the methodology section, these estimates are a lower bound.

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taxpayers and the tax authorities. The taxpayers perceive it as an increase in the authorities’ effort to recover their money; therefore, the relative return of avoidance decreases.

The problem with advocating for individual-specific interventions such as auditing or doorto-door visitations is that those are costly programs. There has been some disagreement in the literature regarding such interventions. On one hand, Slemrod and Yitzhaki, 2002 argue that due to the potentially significant costs of auditing, the optimization of audit numbers and their effective distribution emerge as a pivotal policy dilemma.

On the other hand, many studies have found high returns to investing in tax auditing. Tax authorities’ official statements seem to corroborate this understanding. The Congressional Budget Office CBO, 2020 estimated that modest investments in the IRS would generate somewhere between $60 billion and $100 billion in additional revenue over a decade. Sarin and Summers, 2020 suggest that the methodology used seriously underestimates the potential revenue. Moreover, Boning et al., 2023 find that the direct revenues collected during an audit exceed the costs of the audit by a factor of more than 2:1. The returns to audits of high-income taxpayers are substantially greater than the returns to audits of low-income taxpayers. If sticky behavioral responses are considered, the return of auditing is much higher. H. Kleven et al., 2011 conducted a seminal study in Denmark, which showed that audited taxpayers increased their reported liabilities by 55% of the audit adjustment in the subsequent year. This suggests that audits not only recover immediate lost revenue, but can also have a positive influence on future compliance. Advani et al., 2023 uses microdata from the UK to find a persistent impact of audits on increasing reported tax liabilities for five years after the audit. Moreover, effects are longerlasting for more stable sources of income, and only individuals found to have made errors respond to audits.

Similarly to our setting, Hebous et al., 2023 show that dynamic effects seem to be present despite the absence of penalties. For those found non-compliant
0_._106)|(0_._118)|–|(0_._015)|(1_._992)|

_Notes_ : Each cell reports the main coefficient from a regression of price gaps on (log) distance under alternative specifications, which are described in section 5.1. All regressions use the adjusted price gap between trading pairs and include time-product fixed effects multiplied by (1 _− ρ_ ˆ _od_<sup>_k_)</sup><sup>_/ρ_ˆ</sup><sup>_k_</sup> _od_<sup>.Standard errors</sup> in parentheses. _∗∗∗_ significant at 1%, _∗∗_ at 5%, and _∗_ at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **6 Comparison to previous literature**

To put our results in perspective, we start by comparing them to those in Atkin and Donaldson (2015), from whom we borrow the methodology to recover trade costs. Their baseline _β_<sup>ˆ</sup> is equal to 0.037 for Ethiopia, 0.056 for Nigeria, and 0.011 for the U.S. Our estimates are slightly lower. In terms of _ad valorem_ equivalents, their estimates imply transport costs of 25%, 15%, and 5% for Ethiopia, Nigeria, and the U.S., respectively.<sup>11</sup>

> 11To make their results comparable, we use the distance between the most and least remote locations that we use to compute the results in Figure 3.

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# **1. Introduction**

Subjective welfare measures, such as satisfaction with particular welfare dimensions, have become increasingly prevalent in the past decade.<sup>1</sup> Importantly, subjective welfare metrics have advantages over more traditional measures of welfare such as monetary poverty (e.g., Ravallion 2013), and sharp changes in peoples’ subjective perceptions of their welfare have been demonstrated to be particularly important to many instances of political change that have occurred across the world (e.g., Ianchovichina 2017; etc.). Furthermore, given their simplicity compared to more traditional consumption and employment-based metrics of deprivations, subjective metrics are often used to track changes in consumption and employment during times of crises.<sup>2</sup> Despite the importance of subjective welfare metrics and the greater ease in fielding subjective welfare modules during crises, there are well-known problems with using such measures. Most importantly, answers to subjective welfare questions depend on respondent-specific scales that are potentially not comparable across individuals, are potentially subject to frame-of-reference effects, and suffer from potential measurement error in ways that could differ from more traditional welfare metrics (e.g., Ravallion 2013).<sup>3</sup> However, even with these potential sources of biases that make the levels of subjective welfare metrics difficult to interpret, one common applied microeconomic technique to help better interpret subjective welfare metrics is to focus on sharp changes in response to arguably exogenous shocks.

This paper focuses on potential difficulties in interpreting changes in subjective mea-

> 1For example, a number of Sustainable Development Goals can be tracked using subjective welfare metrics.

> 2For example, traditional consumption modules typically capture the quantity and value of consumption of potentially hundreds of food and non-food items over reference periods varying from the past week to the past year. Such expansive modules are unable to be implemented via mobile phone. However, the Food Insecurity Experience Scale (FIES), which is a subjective measure of food access, is composed
However, since our dataset consists exclusively of women already engaging in transactional sex, the most that our analysis can show is evidence of transfers providing an insurance function for women already in the market. To provide some evidence on the decision to enter transactional sex, we conducted a short survey in April and May 2010. As it had been several years since we had conducted the diaries (and many women had moved, left sex work, or died), we surveyed a separate sample of 99 women. These women are currently active in the peer groups, and are similar in age and in experience in transactional sex to the women who participated in the diaries. It is important to note that these women were not randomly selected and all of them participate in the peer groups.<sup>37</sup> In the survey, we asked women questions about why they entered sex work originally, why they continue in sex work, and why they see regular clients. We also asked questions about whether they expect help from regular clients, and whom they most rely upon in times of need. To avoid influencing responses, we asked each question in two ways: (1) by asking the questions in an open-ended way and coding all responses that were given, and (2) by reading the woman a list of all possible responses and checking those to which she responded yes. The unprompted questions were always read first.

The results are presented in Table 7. The top of the Table reports responses to questions about motivations for entering sex work. As expected, the income premium to sex work is cited by the vast majority of women: 79 percent of women report this as a reason even when not prompted, and 100 percent do when prompted. Also of interest, however, is that between 78 and 99 percent of women report that they entered because they encountered an adverse shock and needed the income to cope with it. Smaller but significant percentages of women report love or looking for a husband as
|<br>119,440.0|99.1|99.2|96.9|97.9|93.5|
|35|Noneletrical<br>Machinery|<br>1,459.4|<br>1,827.7|<br>2,188.7|<br>187,895.7|105.8|110.1|110.5|108.0|108.3|
|36|Electrical<br>Equipment|1,511.8|1,661.3|1,914.5|147,943.6|99.4|96.3|99.0|97.9|102.4|
|37|<br>Transport Equipment|<br>1,601.2|<br>1,719.0|<br>1,595.9|<br>201,346.1|124.4|113.3|124.4|121.7|123.3|
|38|Instruments|305.5|452.3|623.6|51,797.6|115.4|120.1|125.5|112.3|125.1|
|39|Misc. Manufactures|390.8|445.6|386.2|26,891.4|80.5|80.2|81.7|78.2|85.1|

1/ Value added per worker in the industry expressed as a ratio to value added per worker for all United States manufacturing.

Source: All data conpiled from United States Department of Commerce, Census of Manufactures, vol. 1, Sumnary and Subject Statistics, (Washington: Department of Commerce, various years).
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31 method worked similarly well, particularly with the full asset model. Different poverty measures did not affect performance of the prediction models.

We next applied the poverty prediction methodology to two case studies, Russia and Kenya, where there are grounds for questioning official cost of living adjustments that underlie temporal poverty comparisons. Our SAE approach of predicting poverty on the basis of an asset model dispenses with the need to introduce inflation adjustments. Applying this method to the Russian data for 1994, 1998 and 2003 indicates that, indeed, there may be some basis to recent suggestions in the literature that the official inflation indices overstate the rise in prices between 1994 and 1998, the height of the financial crisis. In Kenya, official inflation indices have been found to differ markedly from those calculated directly from household survey data. The SAE approach was found to lend support to poverty trends estimated on the basis of the inflation indices derived from the survey data as opposed to the official series.

Finally, a preliminary meta analysis of our results confirms that the key determinant of success in producing reliable estimates based on these prediction methods is explanatory power in the basic consumption model. In this, it is especially the nonstaple food and non-food expenditures and full asset models that perform well. While expenditure sub-component models may be appealing, they are not likely to be available in most settings where there are concerns about data non-comparability. More practical are models based on non-consumption assets, as such information is likely to be available, and comparable, even across otherwise non-comparable data. Consumption sub-component models also still require appropriate price deflators, which can be hard to come by.
institutional strategy for environmentally and socially sustainable development. Section III of this paper introduces the Chinese context of public governance, especially environmental governance. The design and implementation of stakeholder dialogue strategies, community environmental roundtables in particular, are presented in section IV, where a number of cases are presented to illustrate the practices and the impacts. A survey of participants on the impacts of roundtable dialogues is reported in section V. Sections VI and VII offer discussions about the dialogue strategy and a conclusion to the paper.

2
preferences from our model.<sup>16</sup> Finally, we are only concerned with home ownership and defer scraping of rental data to further research. The relationship between property prices and rental prices has been discussed in depth elsewhere (e.g., Campbell et al. 2009; Engsted & Pederson 2015; Gallin 2008).

We define a basic house as a _formally supplied 50 square meters (sqm) one-bedroom, one-bathroom apartment located in an urban area within a given country, assumed to provide basic municipal or on-site services including water, sanitation, road access, and an energy source._ While we presented summary statistics for both houses and apartments, the _BHP_ deliberately only includes apartments. We constrain the _BHP_ to apartments for comparability purposes and to avoid potential distortions that can be attributed to the different reporting of size (plot size versus usable surface size) in houses. In addition, by reporting the _BHP_ exclusively for apartments, we also account for the ongoing debate regarding the need to increase the housing density in emerging economies, particularly in cities that experience an influx of migration and growth, through densification of existing settlements or the building of multi-story, complex buildings. This is particularly relevant for Africa, where cities are 20 percent more fragmented compared to cities in Asia, more expensive and less accessible for most (Lall et al. 2017). In some African countries, the densification, which is not served by the market, takes place in the informal realm. In many countries, single family homes built on a plot of land are turned into mini‐compounds where a main house is surrounded by ‘backyard shacks’ that are rented. This phenomenon of _backyarding_ is particularly well documented for South Africa where _backyarding_ increased from 1.1. million in 2011 to about 1.8 million in 2016 providing many families an informal way of overcoming the limitation of housing supply in urban areas (e.g., Brueckner et al. 2018).<sup>17</
# **6 and Estimation**

Several components of our model are directly observed in the data. These include the cost _Cit_<sup>_d_ofmigrationtodestination</sup><sup>_d_,earnings</sup><sup>_w_</sup> _it_<sup>_l_andprofits</sup><sup>_πit_byindividualcharacteristics</sup> at location _l_ , saving rates _ρ_<sup>_l_</sup> , and earnings expected prior to emigration, _w_ ̃ _it_<sup>_d_.Furthermore,</sup> the survey contains information on the reason for return migration, including whether a work contract has been terminated. We use this information to compute the corresponding probabilities _δit_<sup>_d_withineachgroupofmigrants.AppendixB,andinparticularTablesA4</sup> and A5 provide further details. In the Bangladesh Informal Firms Survey 2010, we observe the share _q_ of businesses that are inherited. Finally, _rA_ and _rL_ are, respectively, the interest rates earned on savings, or paid on migrant loans; these are obtained from Mallick (2012) and Berg, Emran, and Shilpi (2013).

We estimate the remaining structural parameters of the model with by method of simulated moments, minimizing the distance between informative moments computed for a population of agents simulated from the model, and the counterpart of these moments observed in the data. In total, we jointly estimate 52 parameters pertaining to the demand in each destination country for workers of a given education level (parameters determining the share _λ_<sup>_d_</sup> _et_<sup>ofwould-bemigrantswhoobtainaworkvisa),agents’destination-education-</sup> specific disutility from migration _ηe_<sup>_d_andtheirinitialstockofassets</sup><sup>_Ae,_0,theshare</sup><sup>_pL_</sup> _e_<sup>of</sup> would-be migrants who can finance migration on credit, as well as the cost _Ce_<sup>_I_ofsetting</sup> up a business. The latter three sets of parameters
overall, where the MVP site decline is greatest. First, in the DHS data, the percentage of children under 5 reporting fever during the last two weeks—a very rough indicator of malaria prevalence<sup>11</sup> —dropped from 40.6 to 23.7 percent nationally and from 48.5 to 24.8 percent in rural areas of Nyanza, where Sauri is

> 10 The gross attendance ratio is difficult to interpret, and a high ratio is not unambiguously positive. Ratios above 1 (such as those for Kenya and Ghana) indicate the presence of underage and/or overage children—that is, primary school attendees outside the age range 6 to 11—and an increase in grade repetition can increase the ratio.

> 11 For example, Ashraf, Fink, and Weil (2010) use changes in DHS data on fever occurrence as a rough proxy for changes in malaria morbidity in Zambia.

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_ <mark>,</mark> _<mark>96</mark>_ <mark>(5), 1802-1820.</mark>

- World Bank (2022). From the Last Mile to the Next Mile – 2022 Vietnam Poverty & Equity Assessment. Washington, DC: World Bank.

- World Bank (2023), World Development Indicators Data, Accessed on June 10, 2023. Available at: <u>https://data.worldbank.org/indicator/DT.ODA.ALLD.CD?locations=VN</u>

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regulations and successfully implement the necessary reforms, while others do not? Is it because certain government structures, such as business reform committees (Box 1) – the focus on this paper – help in this regard or do other factors matter more? Do business regulatory committees matter for successful implementation of business regulatory reforms, and if yes, which of the components or practices play a key role for countries to learn from each other? Answering these questions adds to our knowledge of institutional capacities and processes for the development of “fit for purpose” regulations.

Drawing on new global data from the World Bank _Business Reform Committees_ project _,_ this paper aims to contribute to the literature on government structures and their operating models, specifically in their effort to implement coherent and high-quality business regulatory reforms. The project developed and tested a methodology to collect data on the availability, mandate, and characteristics of business reform committees, with the aim of learning more about how business regulatory reforms happen in practice and to provide data and knowledge to help governments strengthen their institutions for better business regulations, in ways consistent with the UN SDG 16’s call for “effective, accountable, and inclusive institutions at all levels”. Drawing on this new global dataset that covers 160 economies, we present an analysis of the characteristics of reform committees under which business reform policies are undertaken across different regions and income groups.

The remainder of the paper is organized as follows. Section 2 discusses the project’s contribution to the literature and evidence. Section 3 documents the process of designing the methodology, data collection, and data validation of the _Reform Committee_ project. Section 4 presents the results from the data collected for 160 economies. Section 5 presents four country case studies. Section 6 discusses implications for future research. Section 7 concludes.

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COVID-19 pandemic between December 2020 and August 2021. The objectives were to capture key economic variables during the pandemic that would have otherwise been harder to collect through physical door-to-door surveys. We compared economic indicators from these surveys with regular labor force surveys conducted in these countries around the same time in order to assess whether these surveys accurately captured and tracked indicators. We tracked three key indicators, namely, the employment-to-population ratio, the formal share of employment, and the share of self-employment.</mark> <mark>Examining the demographic representation of the online survey data, we find that the raw or unweighted online-survey data are not nationally representative. They overrepresent men, younger individuals, and the highly educated. To attempt to overcome these biases, we estimated a propensity score model to estimate weights to make the internet survey data more representative. For this, we estimated a model to predict the selection of observations in the online survey compared to a nationally representative survey from the Global Monitoring Database (GMD) using a variety of independent variables. From this model, we further predicted inverse probability weights for adjustments. The means of variables included in the propensity score model seem to be relatively close to the GMD means, in comparison to the non-model variables whose online survey means deviated widely from the GMD mean. For example, the deviation for model variables was 32.0 percent in Brazil, but 56.8 percent for the non-model variables. In Indonesia, while the deviation for model variables was 15.3 percent, the deviation was 582.0 percent for non-model variables.</mark> <mark>Applying these estimated weights, we compared the economic indicators from the online survey with those derived from nationally representative comparative labor force surveys available during the pandemic. The results indicate that there is substantial deviation of the online survey</mark> 22