rafmacalaba/data-use-mentions-extended huggingface dataset:
highest among poorer households. This suggests that efforts to improve the accuracy in reported consumption of FAFH even among those households who do not consume much in absolute terms is critically important.

# **6. Discussion**

Three conclusions can be drawn from the results presented thus far: (1) One-line is not enough to collect information on FAFH; (2) The bounding/salience arm performed the best at collection of FAFH consumption; and (3) despite the fact that the individual recall arm was less accurate than the bounding/salience arm, it did perform significantly better than the one-line option. The different methods implemented present some significant trade-offs in terms of accuracy and cost of implementation. We explore further both of these aspects.

## 6.1 Accuracy

From our analysis, we have a good understanding of the relative accuracy of each treatment arm. The next important question to answer is why certain variations outperformed others. Was it due to the respondent (individual vs informant), partial collection of FAFH information (particularly for the individual-level recall arm), bounding, or salience? While we cannot empirically determine which of the components made the largest contribution to accuracy, we explore the trade-offs of each arm as well as examine some qualitative information collected from enumerators to give some indications of successful components. An evaluation of the trade-offs (in terms of accuracy and cost) and qualitative information combined with our empirical findings will allow us to provide guidance to survey practitioners on the most effective design of an FAFH consumption module.

In terms of accuracy, the individual-level arm had the advantage of eliciting information from the best informant possible: the individual. However, it came at the cost of requiring interviewers to reach all adults in the household, which was only partially addressed by allowing phone interviews. Additionally, the arm was subject to the sources of measurement error affecting all recall surveys: informants were asked to make the cognitive effort of recalling the consumption over the last seven days, without any help in identifying the starting
# 2.4 Matching Health Data to Water Quality Data

The primary challenge to evaluate the pollution‐health relationship is the endogeneity of pollution exposure. Pollution is not randomly assigned and is often the byproduct of productive activities. In the case of nitrates in the water, it is largely a byproduct of intensive agriculture and untreated urban waste. Thus, a naïve approach which examines impacts of local pollution on local impacts will likely conflate the positive effects of increased production with the negative externalities of water pollution, and underestimate the latter’s effect. To circumvent this bias, we construct a measure of upstream pollution using the geography of river flow. Similar techniques to identify upstream‐downstream relationships have been applied in recent economics literature (Garg et al., 2018’ Do et al., 2018 and Keiser, 2018). This choice is predicated on the fact that the decision to pollute upstream is orthogonal to downstream health, while geography dictates that pollution flows downstream.

We make use of a digital elevation model from the Shuttle Radar Topography Mission (SRTM) mission to identify the direction of stream flow and to track upstream and downstream through surface waters in India. We link the districts in our sample to all other districts that are upstream from it as connected by the stream network as shown in Figure 1. Since water quality decays over time, we bound the distance between upstream and downstream district‐pairs such that the upstream district is the closest upstream district, and up to 300 km apart. For any given downstream district, we then calculate the average concentrations of nitrate‐nitrogen pollution in the upstream districts.

# 2.5 Data for Vietnam and Sub‐Saharan Africa

To test for external validity of the results from India, we also measure the impact of nitrogen pollution on health in other geographic settings: Vietnam and 33 countries in Africa.

# _Data for Vietnam_

The water quality data come from the Mekong River Commission (MRC), which collects data for four countries (Cambodia, Lao PDR, Thailand,
Another validity check for our corruption measures is how well they correlate with the macrolevel corruption measures typically used in the literature. Thus, we check for the correlation between our overall and petty corruption measures and other corruption measures including the Corruption Perception Index (Transparency International), Control of Corruption (Worldwide Governance Indicators, World Bank) and the one from ICRG. All these external corruption measures are re-scaled so that higher values imply higher corruption. Of course, this does not affect the analysis that follows.

Table A2 in Appendix A shows the correlations. By and large, there is a high correlation between our corruption measures (overall and petty corruption) defined at the country-level and the rest, significant at the 1 percent or 5 percent level. For instance, the Spearman’s rank correlation between our overall corruption measure and the Control of Corruption measure equals 0.51. The corresponding figure for the incidence of petty corruption equals 0.65 and 0.62 for the depth of petty corruption. There is one exception, however. The Pearson’s correlation between our overall corruption measure and the ICRG corruption measure is somewhat weak equaling 0.196, significant at the 10 percent level. Note that the ICRG corruption measure has a poor coverage and is available for only 88 (out of 131) countries in our sample. Control of Corruption and Corruption Perception Index are available for 131 and 120 countries, respectively.

# 2.4 _Main explanatory variable_

Our main explanatory variable is a measure of the level of regulation experienced by the private firms. The ES asked firms the percentage of their senior management’s time that is spent in dealing with business regulations. It is not possible to use this measure directly in the regressions as it is likely to be endogenous to various firm characteristics. Further, firms’ experience with corruption 11
protect data assets such as threat intelligence, managed file transfer, identity and access governance, cyber analytics, data loss prevention, and encryption.<sup>62</sup> The studies are based on surveys of selected participants, which of course reflect who is invited to take them and who actually completes them.

Furthermore, any study of costs is necessarily incomplete. Privacy law also affects firms in ways that are difficult to quantify. If a firm decides not to offer a feature or decides not to enter a jurisdiction because of privacy law, the opportunity foregone is difficult to value. Little information is available on the costs of restructuring of operations by businesses to bring them into compliance.

We conducted a survey among privacy experts to seek to obtain information about the costs of compliance for private enterprises.<sup>63</sup> The survey was circulated to members of the International Association of Privacy Professionals, and was also circulated by leading privacy expert Daniel Solove to his LinkedIn network. We also emailed the survey to privacy lawyers we identified via web searches and through LinkedIn. The survey was open for responses from June 18 to August 3, 2020. The survey was posted to the web via Qualtrics and was available only to those that had the link. Various biases introduced by a web-based survey both suggest caution in relying on its results, and we do not rely on the survey results for our conclusions in this paper.

The questionnaire asked privacy professionals to indicate whether they worked at companies that largely collect data on those companies’ own behalf, or companies that help other organizations manage their data. It tailored most of the remaining questions based on the answer to that initial query. The questions focused on the costs of complying with the privacy regimes of the three jurisdictions that are the focus of this study, the impact of those regimes on decisions by companies, and questions about cross-border data flows. To help provide consistency of responses, privacy professionals helping

> 60 _See infra_ note 69 and accompanying text.
provinces, it is assumed that Vietnam’s workers are “sufficiently immobile” (Hanson, 2005, p.4) across provinces as province-specific labor demand shocks influence the wages of workers living in that province.<sup>21</sup> Overall, the empirical framework is similar to the model employed by Chiquiar (2008) who finds responses of the “Stolper-Samuelson type” effect following the North American Free Trade Area (NAFTA), by exploiting the regional variations in exposure to international markets.<sup>22</sup> Third, the S-S results may become less pronounced if the supply of unskilled labor is elastic. Winters (2002) presents a useful way of thinking about the impacts of trade on wages for unskilled workers by considering two polar forms of labor markets in developing countries. The first is that assumed by traditional trade theory in which factor supplies are exogenously fixed and wages are perfectly flexible (Winters, 2002, p.1348-1350). In this case, the StolperSamuelson Theorem, under particular conditions, generates the powerful result that an increase in the price of the unskilled labor-intensive good in production will increase the unskilled real wage and decrease that of skilled workers. The polar opposite view of labor markets for unskilled workers is one suggested in development theory that considers factor supplies to be infinitely elastic (Lewis, 1954; Winters, 2002, p.1350-1352). In the latter case, the formal sector can draw infinite amounts of labor from the informal sector or subsistence agriculture at a subsistence wage, such that the wage for unskilled workers is unaffected by the trade shock.

> 21 It appears that Vietnam’s labor is neither “perfectly” mobile nor “perfectly” immobile. The 1999 and 2009 census indicates that interprovincial migration accelerated in the past decade as the number of people aged five and older who had migrated across provinces within the census period increased from 2.0 million individuals (or 2.9 percent of population) in the 1999 census to 3.4 million
**Figure 9: Reform-2: Tax maneuver, nobody formalizes. Percentage change in consumable income w.r.t. no-reform scenario by decile, age group, household and location types.**

<!-- Start of picture text --> 2<br>1<br>0<br>-1<br>-2<br>2<br>1<br>0<br>-1<br>-2<br>Scenario A Scenario B Scenario C<br><!-- End of picture text --> Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

Tax maneuver in combination with formalization would have a negative welfare impact for most income and socio-demographic groups. Figure 10 shows the distributional outcomes of reform 2 with the assumption that 50 percent of informal workers move to formal sector, by the population subgroups. In this case the welfare gains obtained through the reduction of SIC accruing to employees (in scenario B) are compensated by the higher SIC taxes paid on the previously untaxed informal incomes. Yet in scenarios A and C the negative effect will be lower for the bottom deciles than for the rest of population. This is because households in the bottom of the distribution rely less on labor income.

25
**|0.792***|0.827***|0.928***|1||
|IMF’s “financial markets”<br>development index|0.366***|0.733***|0.648***|0.696***|0.956***|0.777***|1|

Notes: Global Financial Development Database=GFDD. * p < 0.05, ** p < 0.01, *** p < 0.001. “Account ownership” is the percentage of survey respondents (aged 15 or above) who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months. The IMF’s “financial development” index has the following two components: “financial markets” development and “financial institutions” development”. The IMF’s “financial markets” development index captures access to, and depth and efficiency of, a country’s stock and debt markets. The IMF’s “financial institutions” development index measures how developed financial institutions are in terms of their depth (size and liquidity), access (the ability of individuals and companies to access financial services), and efficiency (the ability of institutions to provide financial services at low cost and with sustainable revenues). Data on financial development over the period 2000-18 are available from the World Bank’s Global Financial Development Database, the World Development Indicators, the Global Findex Database, and the IMF’s Financial Development Index Database.

19
final sample of 9,468 cities, which includes cities of all sizes (Table 1). More than half the cities in the sample have less than 200,000 inhabitants.

**Table 1: Distribution of Cities by City Size and Region**

|**Region**|**Less than**<br>**70k**|**70k -**<br>**100k**|**100k -**<br>**200k**|**200k -**<br>**500k**|**500k -**<br>**1m**|**1m- 10m**|**More**<br>**than 10m**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific|612|609|819|476|131|116|11|
|Europe & Central Asia|345|297|324|225|77|56|2|
|Latin America & Caribbean|267|228|255|158|60|51|3|
|Middle East & North Africa|221|137|199|113|43|40|2|
|North America|77|86|92|60|22|33|2|
|South Asia|312|362|588|390|122|84|7|
|Sub-Saharan Africa|300|290|439|217|55|52|1|

Source: GHS urban centers database One needs to keep in mind that our analysis uses cities as the unit of analysis and does not include urban agglomerations, which often include adjacent suburbs. However, to enable comparability, a density threshold of 1,500 people per km<sup>2</sup> is used
with identical observable characteristics exhibit differential responses to common shocks, such as policy changes. A crucial feature of these types of models is that individuals differ in the amount of utility they derive from being in a particular job. Such differences may arise from differences in the preferences of the worker, or the characteristics of the job. Hence, exploring the latent heterogeneity of satisfaction within sectors is important for our understanding of labor markets.

This paper undertakes such an exploration by using recent advances in stochastic random parameter (mixed) discrete choice models to provide a more complete description of the distribution of subjective welfare across employment sectors. We do so in the developing country context where the role and implications of the extensive self-employed and small firm sector – broadly termed the informal sector – have been intensely debated for decades. While the advanced country literature stresses the desirability of independence and being one’s own boss that self-employment and the small firm sector offers, the developing country literature has tended to conclude from the attendant lack of social protection, and the association with poverty more generally, that such jobs are the inferior part of a highly segmented labor market.<sup>3</sup> However, demonstrating segmentation requires showing that, at the margin, utility is not equated across sectors and the conditional wage premia commonly estimated as proxies may instead reflect preference heterogeneity and compensating differentials (e.g. for risk, independence, taxes avoided, the perceived value of benefits, or training). Hence, as with the advanced country literature on self-employment, establishing the distribution of subjective measures of satisfaction offers more potential for characterizing the informal sector.

> 3Most evidence for segmentation relies on the observation that there is a sizeable formal sector wage premium; larger firms pay workers with otherwise similar observable characteristics more (see S ̈oderbom et al., 2006); and sorting is a key determinant of differences in labor income(Fafchamps et al., 2009). Yet evidence based on longitudinal data on labor market transitions from Latin America
Fig. 32 in the appendix).

Under the simulations from a 1% SRS by PSU, shown in Table 2, column 4 and in the appendix Figures 29 and 30, we notice the unit models ( _CEBa_ ) present a slight upward bias that seems to become more pronounced in more populous municipalities (Fig. 17). Notice that some bias is acceptable since gains in MSE are achieved at the expense of bias, however in this case the presence of outliers seems to lead to increased upward bias that affects more municipalities as we move to more populous deciles (box-plots for _CEBa_ in Fig. 17). Unit-context ( _UC − CEBa_ ) models on the other hand, appear to have a downward bias. In the box-plots in Figure 17 for _UC − CEBa_ , the bias in lower deciles is downward and as we move to upper deciles the downward bias is considerably reduced.

As noted in section 3.1, the problem faced by unit-context models is omitted variable bias manifested as a lack of linearity (see Fig. 18).<sup>47</sup> Figure 19 further presents the issue. Under _CEBa_ residuals appear to follow a random pattern, however under _UC − CEBa_ households in municipalities represented by only one PSU in the sample will all have the same linear fit. This manifests itself in the figures for _UC − CEBa_ as a column of vertical dots. The problem can also be observed

> 45See Table 7 in the appendix.

> 46Similar findings are obtained under the simulations for column 1.

> 47The true model includes household size at the household level. Consequently, it is a determinant of the dependent  ̄ variable. Household size at the household level ( _xach_ ) can be broken down into _zach−xac_ , thus the omitted component, _zach_ , is also a determinant of the dependent variable. The unit-context model only includes the PSU average household size ( ̄ _xac_ ) obtained from the census as a covariate. When a given survey sample is taken, the non-included covariate

>
# **5. Real Unit Labor Costs**

This section examines trends in real unit labor costs (RULCs) over the last two decades. Our focus is on the trends in each country over time, rather than making direct comparisons of unit labor costs between countries, which is fraught with difficulties as discussed in section 3. We construct indices of RULCs denominated in both local currencies and US dollars, using the data sources and methodology described below.

Average monthly wage data for the aggregate economy is used as a proxy for labor costs. Ideally, we should use data covering all labor costs, including the nonwage costs borne by employers, but this is not available for all transition economies, including most of those which are of particular interest in this study. The monthly wage data are obtained from the database of the United Nations Economic Commission for Europe (UNECE), available on its website. Where data are missing from this database, we have used data from the ILO and the World Bank’s Jobs Gateway for Southeastern Europe. One of the difficulties with published wage data is that different sources are not always consistent, because they are compiled from different surveys (e.g. labor force surveys and establishment surveys). Therefore, when supplementing UNECE data with data from other sources, we have only used the latter when it is clear that the applicable data series are consistent; i.e. when overlapping data points have identical entries. For most of the transition economies, we have annual data on average monthly wages from 2000 to 2017, but a few countries (Montenegro, Turkmenistan and Uzbekistan) data for some years are missing.<sup>1</sup> To generate a series on real wages, we deflate the average monthly wage data with the applicable GDP deflator. For each country we generate an index of real wages in local currency.

To estimate labor productivity, we divide GDP in constant price local currency by the number of persons employed, using the data in the
|4.27|17.4|3.99|3.33|3.44|
|yy|High|5.05|4.00|4.32|18.2|4.24|3.53|3.76|
|Cit oulation|Low|5.06|3.85|4.32|16.4|4.11|3.46|3.66|
|y pp|High|4.94|3.92|4.27|19.7|4.17|3.43|3.59|
|Caital Cit|No|5.01|3.85|4.26|17.2|4.14|3.45|3.63|
|p y|Yes|4.99|3.92|4.33|19.2|4.11|3.42|3.60|
|Website has information on|No|4.90|3.76|4.16|18.5|4.04|3.37|3.50|
|how to start business|Yes|6.19|5.19|5.42|10.7|5.18|4.26|5.02|
|Kearney Country|Low|4.69|3.58|4.00|20.6|3.90|3.03|3.16|
|Globalization Index|High|6.21|4.88|5.46|9.6|5.11|4.36|5.04|
|Countr Urbanization|Low|4.40|3.58|3.93|21.5|3.65|3.12|3.10|
|y|High|5.84|4.26|4.77|13.0
often fallen in such circumstances, and Diwan (2001) has shown a tendency for the share of labor in national income to decline during financial crises (counting currency crises as well as banking crises); however, sharp macroeconomic downturns have often been associated with a relative decline in the wages of highly-skilled workers, so it is not clear that wage movements will be inequality increasing.<sup>10</sup> Interest rate levels and spreads also typically increase.

An alternative to decomposing the effects into different channels is the holistic or reductionist approach of examining directly the movements of inequality and poverty around the time of crises.

> 8 It would, for example, be useful to discover whether spending patterns of households with better access to finance before the crisis are better insulated from the crisis, but this cannot readily be detected without having a post-crisis survey as well.

> 9 In addition some specialized financial sector usage surveys also explore these areas (cf. Honohan, 2004). 10 Sanchez and Schady (2003) document this pattern in their study based on household surveys for six Latin American countries.  Their sample includes “Chile in 1972-75, and again in 1982-83, Argentina in 19881990, Brazil in 1981-83, and again in 1990-92, Colombia in 1999, and Mexico in 1995. With the exception of the Mexican tequila crisis of 1995 and the Colombian recession of 1999, all of these crises have been associated with pronounced decreases in the relative demand for the most educated workers.” Sustained downturns may, however, be associated with increases in inequality (cf. Inder, 2004).

8