rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Previous literature (Stewart 2002) identifies two dimensions of inequality: inequality among individuals or households (vertical inequality) and inequality among regional, ethnic, and other categorical groups (horizontal inequality). There is evidence that horizontal inequality poses a greater risk for violent conflict, as compared to vertical inequality (Østby 2013). Although the relationship between inequality and conflict is not clear or direct, there is reason to believe that reducing inequality may help ease conflict between groups and thereby lower the risk of violence (United Nations and World Bank 2018). Earlier research (Celiku 2018) studies how social groups coalesce—around identity, status, feelings of humiliation, and the perception they are being politically shortchanged, among others—and the conditions under which their grievances can be mobilized. This research also argues that that reducing inequality and exclusion— particularly of women and young people—is fundamental to forging sustainable peace.

The history of Iraq over the past decade is a stark example of increasing vertical and horizontal inequalities, preceding and following a period of violent conflict. Research explores ethnic inequality with the help of night lights emissions from time-varying satellite data from 1990 through 2013 (Bormann et al. 2021). It finds Iraq exhibiting typical attributes of a weak state during the 1990s. At the start of the decade, Sunni Arab regions emitted slightly fewer nightlights per capita than the excluded Shia and Kurdish areas. But over the years, ethnic power relations and vulnerability to oil marginalized the Sunnis. This was further exacerbated post-2003, when the ethno-sectarian power-sharing arrangement designed to ensure communal stability further sustained an elite pact in which political parties captured and compromised formal state institutions. This was often facilitated by the ruling coalition's direct appointments of senior civil servants and bureaucrats, leading to higher institutional corruption (Dodge and Mansour 2021).

Past research also finds that horizontal inequality may reinforce individual-level inequality and cause various negative outcomes, including limited public goods provision and armed conflict (Bormann et al. 2021
to dissatisfaction, high labor turnover, low productivity, and impaired firms’ opportunities to strengthen participation in garment GVCs (World Bank 2012).

# **_Environmental Standards_**

Finally, higher _environmental standards_ may lead to growing demand for inputs from foreign investors or international buyers whose reputation among their final clients depends on standards compliance of their suppliers. But, while higher environmental standards compliance can raise unit costs forcing local firms to save costs elsewhere or, alternatively, to raise their productivity levels, it also improves the quality of products (for example, due to reduced pesticide usage or organic production). In this regard, the expected mediating effect of environmental standards on GVC spillovers is more similar to that of product standards.

To test this hypothesis, Prakash and Potoski (2006) employed a panel of 108 countries over seven years to investigate whether international trade encourages firms to adopt ISO 14001—the most widely adopted voluntary environmental standard. For importing countries whose own firms had high levels of adoption of ISO 14001, exporting firms from other markets showed a significantly increased rate of adoption of ISO 14001, even after controlling for firms' compliance and pollution histories, as well as addressing potential endogeneity issues. These findings point to the increasing importance of lead firms influencing organizational practices in GVCs.

Most studies in this area focused on environmental standards and their impacts in agribusiness GVCs. In 2009, the International Institute for Sustainable Development explored impacts of sustainability standards on the distribution of economic benefits along GVCs (Sexsmith and Potts 2009). The authors found standards compliance and certification often lead to producer-level price premiums for agricultural products, but less frequently for certified fisheries and forest markets. Much like product standards, the costs of certification and indirect compliance costs act as a barrier to small and resource-poor groups.

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In table S1.1 we report the main features of the ESPS and PSF consumption questionnaires. In both surveys the list of commodities is restricted to what has been included in the consumption aggregates computed for this paper. The PSF survey sample is small compared to that of the ESPS (1762 households versus 13559). For this reason, and since in Senegal the large majority of households are owners of their housing (more than 80% at the time of the survey, and more than 97% in rural areas), the PSF survey does not allow to compute imputed rents for owners in rural areas. Therefore, we decided not to include housing rents in our expenditure aggregates. Also excluded are income and direct taxes, together with gifts, construction, celebration that are not completely covered in PSF. We also exclude health expenditures, since we wish to exclude them from the analysis as interpersonal differences are likely to largely correspond to difference in needs. The PSF and ESPS surveys do not differ much in the level of disaggregation of the commodities included by the consumption questionnaire: 30 food and 29 non-food items are covered by ESPS, versus 28 food and 25 non-food in PSF. There are two major differences in the way food expenses are collected. First, while in ESPS the design of the food consumption questionnaire does not differ from that of non-food, in PSF the food section is first addressed to household members in charge of preparing meals. The respondents are asked about the amount of the daily expenditure (“dépense quotidienne”, DQ) that is spent for these meals. Field interviews that have been conducted before the survey showed that the DQ mainly covers expenses for fresh food, such as vegetables, fruits, fish and meat. Other food commodities, such as rice or oil for instance, are often bought directly by the head of the household, generally in bulks. The second part of the questionnaire covers these expenses. Later, cell heads are also asked to report any expenditure related to food and meals consumed
manufacturing experience. The rationales are intuitive: plants are likely to have less technical (as well as market knowledge) in a completely new industry. Columns (1)-(3) of Table 7 show the results from estimating our main specifications considering separately product innovations in a 6-digit industry where the plant has already manufactured other products and innovations in an entirely new 6-digit industry for the plant. Both types of innovation are associated with higher survival odds but only innovation in an old 6-digit industry has a statistically significant effect. However, the t-tests suggest that the difference in marginal effects is insignificant. Moreover, in unreported results we consider a potentially larger risk, distinguishing the effects of innovations in a new versus an old 3- digit industry. The findings are qualitatively maintained: more cautious types of innovation in an old 3-digit industry increase plant survival odds significantly while that is not the case for more risky ventures of manufacturing products in new 3-digit industries. But the difference in marginal effects is again insignificant.

The categorization of product innovations into known versus new industries is somewhat crude in that it does not take into account the intensity with which the plant did or did not concentrate in activities close to the new product. Moreover, that categorization does not take into account the relative closeness between the plants‟ established past industries and those of the new product(s). Thus, we introduce a measure that accounts for both these shortcomings: we establish for each new product its proximity to the plant‟s past production activities by computing product proximity indices following Hidalgo et al. (2007) and its application by Boschma et al. (2012). The proximity indices are based on how often countries have comparative advantage in two products simultaneously. The idea is that if countries with a comparative advantage in product A also have comparative advantage in product B with high probability, this implies that products A and B demand the same production capabilities and hence are close to one another. The indices, described in detail in the Appendix,
of business assets and business investments.<sup>17</sup> _Business performance index_ assesses the total value of profits and revenues across all beneficiary owned businesses.

**_Psychosocial outcomes._** We measure _subjective well-being_ with 14 standardized items: ten items from the CESD-R-10 depression screener (0-7 days, e.g., “Over the past seven days, how often have you felt depressed?”, _reverse-coded_ ), a life satisfaction item (Cantril ladder, 10point scale), a novel measure of inner peace (10-point scale), an original measure of feeling blessed by God (1=No, definitely not to 4=Yes, definitely), and _subjective health_ (1=Poor to 5=Excellent).

_Self-efficacy_ is assessed with four items from the Generalized Self-Efficacy Scale (Schwarzer & Jerusalem, 1995) and one item from the Rosenberg Self-Esteem Scale (1=Not at all to 4=Yes, absolutely) (Rosenberg, 1979) which are summed and then standardized to the control condition. _Future expectations_ is a single item of expected socioeconomic standing in two years, using the MacArthur Scale of Subjective Social Status (Ritterman Weintraub et al., 2015). We also construct a summary _psychological outcomes composite_ across these three outcomes.

_Social outcomes_ . We measure _social standing_ with 3 items: the MacArthur Scale of Subjective Social Status plus two adapted such scales assessing how much respondents feel that they are respected in society and are a person of good moral character in relation to others (10point scale). _Social and financial support_ is assessed with two items asking how many people the respondent could ask for advice on their economic activity and if they experience an interpersonal conflict and an item on their perceived probability of being able to amass a small sum of money in emergencies (1=Not at all likely to 4=Very likely). _Social cohesion and community closeness_ is assessed with four items: how many people out of ten in the village the respondent feels they can trust (
|0.63|0.48|0.69|0.68|
|Employed in Industry|0.08|0.06|0.07|0.06|0.04|0.06|0.07|0.07|0.04|0.04|
|Employed in Services|0.26|0.15|0.18|0.12|0.10|0.14|0.17|0.17|0.09|0.10|
|**Access to basic services**|||||||||||
|Improved water|0.69|0.66|0.61|0.71|0.62|0.64|0.62|0.70|0.63|0.64|
|Improved sanitation facility|0.24|0.16|0.12|0.20|0.20|0.17|0.13|0.26|0.16|0.20|
|Access to electricity|0.48|0.30|0.31|0.27|0.21|0.28|0.31|0.31|0.26|0.20|

Source: International Urban Poverty Database. Note: GHSPOP 1km is used for the DOU and DB methods. Poverty is measured using the $2.15 poverty line.

66
**Table 5c: Effect of formalizing on attitudes in August 2010 follow-up survey**

||Trust in<br>Provincial<br>Government|Trust in<br>Municipal<br>Government|Trust in<br>National<br>Government|Trust in<br>the courts|Trust in the<br>Police|Confident in<br>police & courts<br>to settle<br>bus. dispute|Agrees<br> being good<br>citizen means<br>payingtaxes|Thinks<br>Govt. charges <br>business<br>too much tax|Feels Govt.<br> spends revenue<br>on important<br>items|
|---|---|---|---|---|---|---|---|---|---|
|Registered with the D.S.|0.634***|0.559**|0.227|-0.0387|-0.0993|0.0267|0.511***|0.517**|-0.0410|
||(0.234)|(0.234)|(0.221)|(0.188)|(0.238)|(0.203)|(0.188)|(0.238)|(0.232)|
|Lag included|Yes|Yes|Yes|Yes|Yes|No|No|No|No|
|Observations|369|369|369|369|369|369|369|369|369|
|Mean for controlgroup|0.360|0.310|0.620|0.811|0.567|0.744|0.767|0.444|0.378|

Notes: Registration with the
typically focus on a set of doctors under a common management scheme. Fewer studies relate the performance of speci fi c doctors to the incentives they face. While this may seem innocuous, recent contributions suggest that moral hazard in teams could lead to different results with institutional rather than personal incentives (Gaynor and others 2004).

Another part of the literature examines the link between competence and effort in high-income countries and shows that performance in vignettes is different from that in practice for a sample of 34 doctors in the Netherlands (Rethans and others 1991). However, given the sample size, they do not examine the complementarity (or lack thereof) between effort and competence or the impact of incentives on this gap. Closer to the results presented here on competence and effort is more recent work by Leonard and others (2005).<sup>5</sup> Leonard and others examine a similar problem in Tanzania and obtain similar results. A detailed discussion is postponed for later.

The remainder of the paper describes the health care environment in urban India, how the data were collected and how measures of competence and effort were constructed. It also presents descriptive statistics for these variables in both the public and private sectors (Section 2). Section 3 presents a simple model (and discusses some variants) that could help explain the more salient features of the descriptive statistics. Sections 4 and 5 present an empirical strategy and regression analysis of the determinants of effort. We suggest interpretations of the coefficients given the associated identi fi cation problems. Section 6 offers a short, tentative policy discussion and a discussion of further research.

# 2 The Environment, Data Collection and Description

## 2.1 The Environment

India is a low-income country and one-third of its population of 1.2 billion lives in urban centers. The capital of the country, Delhi, is a teeming metropolis with over 14 million inhabitants and covers a 1,400 sq. km area. Delhi is rich compared to the rest of the country–in
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

Schneider and Jäger (2003) identify three types of theory for the withdrawal of the state from infrastructure industries. They classify them into three main categories: (i) dynamic theories of diffusion and contagion - emphasising the inter-relationships and co-evolution of economies, (ii) structuralist and functionalist theories - stressing the inevitability of the withdrawal of the state as a consequence of structural changes in the economy and technological evolution, and (iii) actor-centred and institutionalist theories of political scope of action - opposing the notion of inevitable convergence across the countries and emphasising the feasibility of multiple and country-specific paths and models.

Although the emergence of the liberalisation paradigm for infrastructure industries may be explained in terms of pull and push factors, its macroeconomic effects are yet to be established. As Chang (2003) points out, historically in all developed economies the state and interventionist policies played an active role. Chang also states that there is no theoretical reason that a more liberalised economy will achieve higher allocative efficiency, or that higher allocative efficiency will necessarily lead to higher economic growth.

As discussed, the predictive power of theory is limited with regard to the outcome of the reform of natural monopolies (such as the network businesses of the electricity sector), as this is dependent on how the sector is structured and regulated. Therefore, in order to enhance our understanding of the reforms, it is useful to examine and compare existing experience and evidence on the performance and determinants of reform (see e.g. Jamasb, 2002; Pollitt, 1997). In particular there is much work to be done on the detail of best reform policy. Empirical studies, especially those of the type reviewed in Megginson and Netter’s survey, tend to focus on simple comparisons of pre- and postreform situations using narrow measures of financial and operating performance. As both reform and performance are multi-dimensional, it is important to examine the details of how each reform impacted on variables
to examine why this has occurred and to ask what, if any, are the implications for developing countries.

We accomplish this in two stages: First we examine how pedestrian and occupant fatalities per VKT change with income. Second, we attempt to explain _why_ this occurs by developing theoretical models of pedestrian and occupant fatalities and estimating the models using a richer set of variables. In essence, we augment per capita income by the variables it proxies—better roads, safer vehicles, improved medical services. We also examine the impact of demographic trends on pedestrian and occupant fatalities. We address these issues using panel data for developed countries: specifically, we estimate models of pedestrian and occupant fatalities per VKT using data from 1963 through 2002 for 32 industrialized countries, nearly all of which are members of the International Road Traffic Accident Database (IRTAD).<sup>2</sup> Our main results are as follows: When we examine the impact of per capita income growth on fatalities per VKT, it is clear that, for the countries in our dataset, pedestrian fatalities per VKT fell faster with income than occupant fatalities. When a constant income elasticity is assumed, the elasticity of pedestrian fatalities per VKT with respect to income is -0.64, whereas occupant fatalities per VKT exhibit no statistically significant relationship to income, although they decline significantly over time. When we examine pedestrian fatalities per capita we find that they increase for incomes up to $13,165 (1996 Intl $), but decline with income thereafter. Occupant fatalities per capita, which also increase with income up to $13,165 (1996 Intl $), show no statistically significant relationship to income at higher income levels. This suggests that the

> 2 All of the members of IRTAD are members of OECD, with the exception of Slovenia.  We have added to the IRTAD countries data for Chile and Israel.

3
<br>. Measures the size of contribution of the selected dimensions to the Three different sources of disparity amongst the equity adjusted<br>dissimilarity index of each country coverage rates of selected countries with respect to Turkey<br>Dimensions Change<br>Age Gender Composition Scale<br>Enter Range 0.1427 to 127,213.12 Education Income Equalization<br>Source: LiTS; Global FINDEX; HICS; and, SILC. Dashboard produced as part of the background note to the Financial Inclusion Strategy for Turkey and Financial Inclusion Conference held in June 2014. Authors: João Pedro Azevedo , Judy Yang<br>and Osman Kaan Inan in collaboration with Alper Ahmet Oguz. and Ilias Skamnelos. Dashboard for comments.<br>For selecting country comparators: WDI indicators are averaged between 2008 and 2013 for avilable years. FINDEX indicators for selecting comparators represent values from FINDEX 2011.<br><!-- End of picture text --> 26
**Table 4.** Attitude Indicators

||**Condom use at**|**titudes subindex**|**Sexually conse**<br>**su**|**rvative attitudes**<br>**bindex**|**Sexual abuse r**<br>**sub**|**eporting attitudes**<br>**index**|**General at**|**titudes index**|
|---|---|---|---|---|---|---|---|---|
||**One week post**<br>**intervention**|**Six months post**<br>**intervention**|**One week post**<br>**intervention**|**Six months post**<br>**intervention**|**One week post**<br>**intervention**|**Six months post**<br>**intervention**|**One week post**<br>**intervention**|**Six months post**<br>**intervention**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Treatment students|0.170***|0.100*|0.072|0.133**|0.260***|0.112**|0.240***|0.172*
# **When Face-to-Face Interactions Become an Occupational Hazard:**

# **Jobs in the Time of COVID-19**

# _Besart Avdiu and Gaurav Nayyar_

## **1. Introduction**

There is a crisis of demand brewing around the globe as social distancing becomes the norm to counter the COVID-19 outbreak. Examples abound of job cuts as authorities ask restaurants and bars to close shop. Manufacturing activity in global value chains is increasingly disrupted too. Ripple effects will likely be felt with a negative demand shock for consumer durables such as cars and the ability of factories to supply at capacity will be affected as lockdowns are imposed. So, which parts of the economy are most in the line of fire?

There is an emerging classification of “shutdown” sectors, which includes accommodation and food services, passenger transport, personal services (e.g. hairdressers), arts, recreation and entertainment, non-essential retail, and sensitive manufacturing (Vavra 2020). While transparent and quick, such measures are somewhat ad-hoc and not data driven. One way to assess vulnerability during the COVID-19 pandemic is to look at jobs that can be done at home. Dingel and Neiman (2020) use Occupational Information Network (O*NET) data from the United States to develop a home-based work (HBW) measure. The authors define a binary (dummy) variable for whether a job can be done from home or not based on whether it comprises tasks that relate to “daily work outdoors” or the “operation of vehicles, mechanized devices, or equipment”. An occupation may consist of many jobs, although the two are identical at the most granular level. Therefore, the HBW measure reflects the percentage of jobs that can be done from home by occupation. There is, however, also a different and equally relevant dimension of measuring face-to-face interactions with consumers, which we aim to capture through a face-to-face index based on Blinder (2009).

Home-based work (HBW) and face-to-face (F2F) interactions represent related
**Figure 1: Country coverage by rating groups**

<!-- Start of picture text --> A-rated issuers (AAA to A-); 17 countries<br>B-rated issuers (BBB+ to BB-); 20 countries<br>No data<br>IBRD 45611 |<br>MARCH 2021<br><!-- End of picture text -->

emissions. Instead, we are interested in the positive effects that renewable resources have on the economy. That is, instead of asking if countries are punished for deforestation through higher yields, we ask if countries are being rewarded for afforestation with lower borrowing costs.

The remainder of this paper is structured as follows. Section 2 introduces the reader to the wealth data, the rationale behind it and how it differs from other natural resource data, along with other data sets. Section 3 conveys the gist of this study graphically and discusses the confounding factors and the ingrained income bias. Section 4 revisits the problem in an econometrically more rigorous manner. Section 5 discusses limitations of the study and Section 6 concludes.

# **2 Data**

Our sample comprises _푁_ = 37 countries, with 20 A-rated countries that have an average long-term debt rating between AAA and A- and 17 B-rated countries with ratings between BBB+ and BB(see Table 1). We cover _푇_ = 120 months between January 2009 and December 2018. The countries were selected based on the availability of sovereign bond data, i.e. countries with established domestic capital markets. This limits our analysis to high and middle income countries.

## **2.1 Wealth data**

Hamilton et al. (2006) argue that short-term economic variables alone are insufficient to measure a country’s sustainable, long-term growth potential. Wealth accounting is necessary to have a complete picture.5 The World Bank (Hamilton et al., 2006; Lange et al., 2021; Lange et al., 2018; Lange et