rafmacalaba/data-use-mentions-extended huggingface dataset:
course of the high-frequency data collection. Conditional on working in a given week during the high frequency survey, women and men both work approximately the same number of total hours (47 hours for men and 45 hours for women). Self-employment is common, particularly among women: conditional on being employed at baseline, 70 percent of women and 48 percent of men are self-employed. Women employed at baseline earn less than men at baseline (reporting average usual weekly earnings of 95 cedis, compared to men’s average of 162 cedis), and over the course of the high-frequency survey (reporting average earnings of 108 cedis to men’s 163 cedis during weeks in which they worked).

Table 1 also indicates that health shocks that cause respondents to miss work on days/weeks in which they were planning to work are relatively common. Over the course of the survey, women who were in the labor force at baseline missed work due to an unexpected illness or caregiving duties in 5.1 percent of weeks; the figure for men was 2.5 percent of weeks. For men, all the reported days missed due to illness were for their own illness, whereas for women, 3.1 percent of weeks involved their own illness and 2.0 percent of weeks involved caregiving. Over the course of the survey, 14 percent of men and 21 percent of women employed at baseline missed at least one day of work due to unanticipated illness or caregiving.

# **2.3 Econometric Strategy: Individual-level responses to other workers’ illness**

We begin by examining individual level outcomes. For the labor supply outcome _Yijct_ (namely, whether a respondent worked at all that week, and days and hours worked) for respondent _i_ in household _j_ in city _c_ during week _t_ , we estimate a fixed effects regression: 12
2020).

These studies also explore whether social distancing measures have disproportionally impacted specific categories of workers. Findings from this research show that jobs that cannot be performed from home are at higher risk, while, jobs in workplaces classified as essential face lower risks (Béland et al., 2020a; Cajner et al., 2020; Montenovo et al., 2020). As an estimated 93% of workers around the world live in countries with some forms of workplace restrictions (ILOa, 2020), a large body of research across the world has focused on identifying vulnerable occupations, with a particular focus on jobs that cannot be performed from home and in sectors that have been severely affected by the shutdown (Diengel and Neiman, 2020; Garrote-Sanchez et al., 2020; Hatayama et al., 2020; Hicks et al., 2020; Mongey et al. 2020; Pouliakas and Branka, 2020; Saltiel, 2020). ILO (2020a) shows that while only 7.9% of workers around the world worked from home before the crisis, almost 18% are in jobs or have access to the infrastructure that could allow them to work from home in the future. This research also shows that working from home is more feasible in high-income countries (23%) than in low-income countries (13%). Focusing on Greece, Pouliakas (2020) shows that more than one-third of jobs in the Greek labor market could be performed from home.

A number of studies have tried to identify the channels driving the observed employment impacts. Using data from the United States’ Current Employment Statistics (CES), Brinca et al. (2020) try to disentangle the aggregate COVID shock in its demand and supply components. They observe that in April 2020 employment in the private sector was significantly lower than its historical average and estimated that more than 65% of this impact was due to labor supply shocks, i.e. inability of workers to perform their jobs. Kong and Prinz (2020)
.09|0.09|0.10|0.11|
|Groundnuts|0.13|0.12|0.14|0.14|0.11|0.15|
|Soybeans|0.19|0.16|0.16|0.21|0.24|0.19|
|<br>Cotton|0.04|0.02|0.04|0.05|0.06|0.04|
|Tobacco|0.01|0.02|0.02|0.01|0.01|0.01|
|Beans, sweet potato & others|0.00|0.04|0.04|0.04|0.00|0.02|
|<br>No. of parcels (cultivated)|2,314|821|453|379|444|217|

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey 21
- Hanushek, Eric A., and Ludger Woessmann. “Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation.” _Journal of Economic Growth_ 17, no.4 (2012a): 267-321.

- Jones, Benjamin F. “The Human Capital Stock: A Generalized Approach.” _American Economic Review_ 104, no.11 (2014): 3752-77.

- Jones, Benjamin F. “The Human Capital Stock: A Generalized Approach: Reply.” _American Economic Review_ 109, no. 3 (2019): 1175-95.

- Kim, Jim Yong. “The Human Capital Gap: Getting Governments to Invest in People.” _Foreign Affairs_ 97 (2018): 92

- Kolen, Michael J., and Robert L. Brennan. _Nonequivalent groups: Linear methods. Test equating, scaling, and linking_ . (2014): 103-142.

- Kraay, Aart. “The World Bank Human Capital Index: A Guide.” _The World Bank Research Observer_ 34, no. 1 (2019): 1-33

- Krueger, Alan B. and Mikael Lindahl. “Education for Growth: Why and For Whom?” _Journal of Economic Literature_ 39, no.4 (2001): 1101-1136.

- Lange, Glenn-Marie, Quentin Wodon and Kevin Carey. _The Changing Wealth of Nations 2018: Building a Sustainable Future._ The World Bank, 2018.

16
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application ‘ _Nash Gorod_ ’ and register with the mobile phone number. They can then send complaints, selecting a problem they encounter, and accompanying the message with a photo. All complaints are accumulated in the portal’s ‘editing office,’ where thirty moderators assign the complaints to authorities responsible for the specific road under question. These decisions are made within 24 hours. The portal claims that most problems are resolved on average in five days. On the issue of road maintenance, the portal adheres to the city Resolution 762-PP,<sup>8</sup> which requires the ongoing road repairs in response to public complaints to be completed within eight business days from the time of the complaint. Because of its simplicity of use and effectiveness with public service delivery, the portal has since become the largest aggregator of complaints about potholes along with complaints in other spheres of life in Moscow, such as public transportation, various municipal services, illegal businesses, abandoned vehicles, street advertisement issues, etc. Currently, the site reports over 1.5 million portal users and over 3.9 million resolved complaints since 2011.

# Data and Empirical Model

Our analysis employs data at the municipal level of the city of Moscow over the period between the two most recent mayoral election rounds, 2013 and 2018. We scrape the electoral data (votes for the registered candidates and the turnout) from the Central Electoral Commission of the Russian Federation website.<sup>9</sup> We scrape the data about pothole complaints from the two online platforms described in the section above: _Rosyama_ (7,709 total complaints) and _Nash Gorod_ (205,935 total complaints).<sup>10</sup> As we explain above, while having considerably fewer records, the former platform was important in the early years of our data sample. Both online platforms provide information about the location of the pothole (at the municipal level), the date the complaint was registered, the unique user id who has registered
see ICG (2021) and ACAPS (2020) for descriptions of how the private sector has survived following the de-banking of Yemeni firms in international financial systems and how food supply chains are currently working in the country, respectively.

> 9RDIT cannot be blocked by state surveillance or internet control and evades firewalls by operating on hundreds of thousands of rotating domains simultaneously, as opposed to surveys offered through social media that operate on a single website (e.g., RIWI 2016).

> 10Some of the questions are only asked to subsets of respondents based on a response to a previous question; and the internet survey also has a module that is randomized across respondents where onethird of respondents are posed one of three modules. Thus, the sample sizes of questions that are compared between the surveys varies. In all cases the number of observations is listed in the analysis.

> 11The results are qualitatively identical if we use all partial answers.

> 12The survey also keeps track of limited amounts of metadata of respondents who are offered to participate in the survey. This information includes the browser type (e.g., Chrome, Android, etc.), the version of the browser type, the operating system, the version of the operating system, the type of device (e.g., smart phone, desktop, etc.), and the region associated with the IP address. However, the region can depend on the internet infrastructure in each country, where, for example, all IP addresses are

10
These studies show the impact of major events in specific socioeconomic conditions. As there is an absence of regional studies focused at both macro and household levels, the contribution of this study is to provide a regional perspective to the negative impact of hurricane windstorm on a particular set of social and economic outcomes. This is relevant not only because a systematic regional perspective is currently absent in the existing literature, but also because the region shares many characteristics and a common approach to disaster and climate risk management.

The second section of this study investigates the interconnection between hazard and damage which determine the impacts found by empirical studies. In this section we discuss how windstorm hazard data are translated into damage indexes, and present three different ways of computing hurricane damage indexes. To characterize hurricane windstorm we draw on a model developed in the WBG Latin‐American and the Caribbean Disaster Risk Management (DRM) team by Pita et al. (2015) which employs a fully probabilistic hurricane windstorm model calibrated and adjusted for Central America. This model has been used to reproduce historical hurricanes windstorm characteristics with higher temporal and spatial resolution. This model allows a richer and more accurate characterization of the hurricane windstorm hazard than heretofore seen in the literature, improving the different damage indexes used and enabling better understanding of the implicit biases in the model.

Sections 3 and 4 of the study turn to the data, methodology, and results of the analysis at macro and micro (household analysis) levels, focusing on the strong negative impacts of major hurricanes in the short term on per capita GDP (gross domestic product) growth, income, and poverty. One standard deviation in the intensity of hurricane windstorm leads to a decrease in total per capita GDP growth of between 0.9 percent and 1.6 percent, and a decrease in total income and labor income by 3 percent; which in turn increases moderate and extreme poverty by 1.5 percentage points. These socioeconomic indicators are further informed by 3 indexes derived from hurricane damage modeling. Studying both the macro
35|−126.12|−116.78|−191.76|−176.84|−124.75|−122.75|
|Sampleperiod|1986|–2005|1993|–2005|1986|–2005|1986|–2005|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city fixed effects.

*, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.<sup>a</sup> Treatment Units (three cities): Maldonado, San Carlos, and Pan de Azúcar; Control Units (32 cities): Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young.

> <sup>b</sup> Treatment Unit (one city): Maldonado; same Control Units as in A.

> <sup>c</sup> Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

28
Hubler, Michael, Sebastian Voigt and Andreas Loschel (2014). Designing an Emissions Trading Scheme for China--An Up-to-Date Climate Policy Assessment, Energy Policy, Vol. 75, pp. 57-72.

Johansson A, Guillemette Y, Murtin F et al. 2013. Long-term growth scenarios. OECD economics department working papers, Paris: OECD Publishing.

Jotzo, Frank and Andreas Loschel (2014). Emissions Trading in China: Emerging Experiences and International Lessons: Guest Editorial. Energy Policy, Vol. 75, pp. 3- 8.

Li Shan Tong. 2010. China's economic prospect for the 12th five-year plan period and 2030. China J Rev Econ Res;43:2–27.

Lo, Alex Y. (2016). Challenges to the Development of Carbon Markets in China. Climate Policy 16 (1): 109–24.

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58
Table 4: Closures provide valuable information, but the value of this information is competed away

||Depend|ent varia|ble: asin|h(tons)|
|---|---|---|---|---|
||Potential|closures|Actual|closures|
||(1)|(2)|(3)|(4)|
|1_{_Near_}_|0.171|0.104|0.310|-0.022|
||(0.073)|(0.028)|(0.080)|(0.033)|
|Distance to shore (km)|0.011|0.004|0.011|0.004|
||(0.002)|(0.001)|(0.002)|(0.001)|
|Constant|3.293||3.306||
||(0.102)||(0.082)||
|Fixed efects||X||X|

Notes: All regressions have 246,914 observations. 1 _{_ Near _}_ is an indicator for whether the set occurred inside a treatment bin in which there is a significant change in juvenile catch because of the temporary spatial closures policy. In Columns 1 and 2, Near is defined relative to potential closures (mean of this indicator is .798). In Columns 3 and 4, Near is defined relative to actual closures declared by the regulator (mean of this indicator equals .391). Electronic logbook data are for all vessels from April 2017 to January 2020. Regressions in Columns 2 and 4 include vessel by season fixed effects, day-of-sample fixed effects, and two-degree grid cell by season fixed effects. Standard errors clustered at level of two-week-of-sample by two-degree grid cell.

to occur near potential closures catch 11% more tons on average (
teaching the student or having taught the student in the previous year. We collected information on the current teacher for 85 percent of the students and on the past teacher for 62 percent of these students since some teachers had left the school.<sup>10</sup> Consequently our sample drops as we include present and past teacher controls. Moreover, this change is probably not random–particularly in the case of the present teacher, it is very likely that we lose information on those who are prone to high absenteeism.

To assess learning achievement, a maximum of 20 students in grade 5 were randomly chosen from every school in 2001 and an achievement test was administered in Mathematics and English.<sup>11</sup> The same tests were administered in 2002 to the same students leading to the construction of a two-year panel of test scores. Sampled children were also asked to complete a student questionnaire in every year with information on basic assets and demographic information for the household.

Our source of variation for shocks to teacher inputs is variation in teacher absenteeism, where absence is de fi ned as a teacher being away from school during regular school hours.<sup>12</sup> Unfortunately, schools in Zambia (and in most other low-income countries) do not maintain records of teacher’s time away from school. To the extent that such records are available, they tend to under-estimate absence by 5-10 percent (Chaudhury and others 2004). Our information on absences is instead based on three different measures that we collected as part of the school survey; spot absence, selfreported absence during the last 30 days and the head-teacher’s report of teacher absence during the last 30 days. The most satisfactory measure is the head teacher’s report, whereby head teachers provided independent reports of teacher absence over the last 30 days for the entire matched teacher sample. As an indicator for teacher shocks, this is a noisy measure and as usual, measurement error implies that our estimates are likely to
the structure of the expenditures for each decile (panel B). In general, the differences between the three series are not large, but inflation is slightly higher for the bottom income decile. When compared to households in the 10th decile, the annual inflation rate experienced by poor households was higher in six out of the 10 years covered in the analysis. The years with the largest gaps are 2013, 2015 and 2019, whereas the opposite was true during the first phase of the 201819 crisis in 2018 when fast accelerating prices in transportation had a bigger burden on better-off households. The cumulative inflation in the 10-year period was 10.39% for the bottom decile and 10.16% for the top decile, a difference of 0.23 percentage points that is statistically significant at the 5% level. Contrasting the inflation rates of the bottom decile with respect to the average inflation rates or comparing the Bottom 40 vis-à-vis the Top 60 yield similar conclusions (Figure A.1 in the Annex).

Figure 3: Inflation for the 1st Decile, 10th Decile and the overall population (2011-2020) Inflation for 1st vs 10th Deciles (2011-2020) Inflation for 1<sup>st</sup> Decile vs Overall (2011-2020) (Panel A) (Panel B) <!-- Start of picture text --> 18 18<br>16 1st Decile 16 1st Decile<br>10th Decile Average<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK We also looked at the contribution of different expenditure categories to examine the sources of variation in the incidence of inflation across the population. Figure 4 shows a breakdown of the overall inflation experience of the 1st and 10th decile across
can vary substantially reaching more than 100 percent; however, this is mainly for countries with low numbers on shared prosperity. In absolute terms, the differences are less dramatic ranging from 0 to 0.7 percentage points.

Though not strictly comparable (as different sets of countries are used), the magnitude of the change in shared prosperity estimates after excluding negative numbers seems to be higher compared to the results for grouped versus microdata. For example, exclusion of negative income can increase shared prosperity by 59 percent in the case of Belgium and reduce it by 123 percent in the case of Denmark, whereas the largest change in shared prosperity using microdata did not exceed 14 percent. In absolute terms, excluding negative incomes also has also a higher impact than the choice of grouped versus microdata. Thus, the largest gap for testing the effects of negative income on shared prosperity is 0.7 percentage points compared to 0.14 percentage points for grouped versus microdata (table of figure 5).

The largest absolute differences in estimates (in percentage points) are observed in countries with the highest shares of negative numbers (figure A3) and large negative numbers. For example, in Denmark (income year 2011), 25 percent out of 24 households (0.44 percent of the 5,355 household sample) with negative incomes (per capita per day in 2005 PPP terms) are very large, ranging from ‐20 to ‐400, while the weighted national average is 39 and the maximum is 541. This suggests that the frequency of negative numbers and their magnitude can seriously distort the shared prosperity indicator.

> 13 In addition, we use the opportunity that EUSILC data have both, negative and zero incomes, and compare impacts of dropping these observations. Results are shown in the annex. As one may expect, dropping negative observations has stronger impact than dropping observations with zero incomes on average.

15
previously were fatal become non‐fatal, and the survivors of these conditions are recorded as living with any subsequent disability. This makes it challenging to use this particular measure as an indicator of health that leads to improvements in aggregate productivity.

- The second is that data on YLD are extremely heavily imputed and extrapolated to compensate for the scarcity of the primary sources measuring the relevant non‐fatal health conditions. While such extrapolation is unavoidable if some estimate is required (no matter how imprecise)<sup>20</sup> , it makes it difficult to use this measure in a policy advocacy index since the links from the imputed health indicators to primary country data sources recognizeable by policymakers in the country are complex.  In fact, in many cases, imputed estimates of specific health conditions have no directly‐measured counterparts in the country itself.<sup>21</sup> In this case, the policy advocacy role of the HCI can be undermined by relying on heavily‐imputed data, since it risks “papering over” data gaps, reducing the incentives to fill these gaps with new data collection work.

- The third is that using imputed data makes it difficult to track and interpret changes over time in the index.  To oversimplify, if health conditions are imputed using a measure of living standards such as GDP per capita, then changes in GDP per capita (i.e. growth) will lead to changes in imputed health indicators that may have little to do with actual health outcomes or policy interventions to improve them.

While these limitations of more comprehensive but imputed indicators of non‐fatal health outcomes are real, the limitations of the data on ASR and stunting used in the HCI are real as well. Measurement of ASR requires data on death rates by age. While these are readily available in countries with strong vital registries, in roughly the poorest quarter of countries such data are missing or incomplete. In these countries, the UN Population Division instead estimates death rates by age by linking together