rafmacalaba/data-use-mentions-extended huggingface dataset:
household income (Ravallion 2003; Deaton 2005; Prydz et al., 2021, Lakner et al., 2022). Nevertheless, growth rates in national accounts do a fairly good job of identifying the trends in changes in poverty (Mahler et al., 2021a).<sup>3</sup> Macro growth rates are even more important at times when micro data are not widely available. Collection of micro data in the last two years has been especially challenging due to the COVID19 pandemic. Only a few countries in Africa were able to collect non-traditional sources of survey data, which we discuss and use in the next section.

The procedures to estimate the impact of the pandemic on poverty in Africa are the following (see also, Mahler et al., 2022): first, to project household surveys to 2020, we need distribution of welfare (income or consumption) in 2019 for all countries. However, household surveys are not conducted annually in most low and middle-income countries. In most cases, they are conducted in 3, 5, or even 10-year intervals. To derive a 2019 distribution of welfare for a country without a household survey in 2019, we extrapolate the welfare aggregate available in the household survey from the last available year, say 2018, using per capita GDP growth rates from the World Development Indicators. We use per capita GDP growth rates to grow household income of all households within a country. Since the applied growth rate is the same across all households, the inequality observed in that country is held fixed.<sup>4</sup> This allows us to have a baseline distribution for 2019 for all countries.<sup>5</sup> Then, using the same extrapolation method, we extrapolate the 2019 country distributions forward to 2020 and 2021. Following Mahler et al. (2022), we report two different scenarios projecting poverty for the COVID-19 affected years: (i) a series incorporating the effect of the pandemic (“with COVID-19”
intervention as well as cash grants, whereas those in the control group received only cash grants. The study, conducted with 235 SME entrepreneurs, found that CBT leads to improvements in mental health outcomes in the short run. Meaningful and statistically significant improvements in mental health were achieved, with improvements persisting and increasing beyond the immediate post‐intervention period. Three months after the intervention, based on analysis of pooled data across two follow‐up rounds (at five weeks and three months post‐intervention), entrepreneurs in the treatment group experienced statistically significant reduction in the intensity and prevalence of depression and anxiety symptoms (measured by the Patient Health Questionnaire Anxiety and Depression Scale) and higher levels of well‐being (measured by the WHO‐5 Well‐Being Index) compared with the control group. The effect was marked for those experiencing mild/moderate levels of depression and anxiety, indicating the clinical value of such low touch early interventions. Overall, the study demonstrates that empirical research through Randomized Control Trials (RCTs) can be conducted in challenging, FCV settings through appropriate rapid training of local researchers and non‐specialist providers (NSPs) at a low cost, yielding scalable programmatic and policy level lessons.

2. Why Focus on the Mental Health and Well‐Being of SME Entrepreneurs?

# 2.1 Global Literature Review

It is widely recognized that SMEs are a lifeline for job creation and economic growth in developing countries, including those affected by fragility, conflict, and violence (FCV). However, SME entrepreneurs deal with unusually high levels of uncertainty and experience cognitive resource depletion, which have high psychological and physical health costs (Fernet and others 2016; Monsell 2003). Studies show that individuals with a leadership predisposition and independent work arrangements, including entrepreneurs, tend to be more vulnerable to stress, alienation, loneliness, emotional turmoil, and burnout (Rokach 2014; Akisal and others 2005). Given the co‐ morbidity of stress prevalence with physical and mental health conditions, the risk of rising health
with respect to inheritance, property ownership, marital regimes and protection from workplace discrimination. Countries with more gender egalitarian legal regimes generally have higher levels of property ownership by women. The relationship between the legislations and women’s property ownership holds across rural and urban areas and is much stronger for housing than land ownership. These results suggest that gender-equitable legislative reforms could be an important avenue to increase women’s property ownership.

This paper proceeds as follows. Section 2 discusses the importance of women’s ownership of immovable property and the pathways through which women typically acquire such ownership, focusing on the role of legal systems. Section 3 describes the DHS data and shows descriptive patterns of gender gaps in property ownership across and within countries. Section 4 uses multivariate regression techniques to explore how laws with respect to inheritance, property ownership, marital regimes and protection from workplace discrimination are associated with women’s ownership of land and housing in developing countries. Section 5 concludes.

# **2. Women and Property**

# **2.1The importance of women’s property ownership**

There is significant evidence that women’s rights to property and other assets are associated with improved well-being and agency. Many models of household behavior, such as cooperative bargaining models proposed by Manser and Brown (1980) and McElroy and Horney (1981), predict that bargaining power within the marriage depends on the husband’s and wife’s ‘outside options’, e.g. their expected utility if the union were to end. These outside options depend, among other factors, on who in the family owns the household’s property, and the rules and norms that shape the division of assets and other family resources upon divorce (Lundberg and Pollak 1996; Lundberg et al 1997; Fafchamps and Quisumbing 2005). Empirical studies suggest that a more egalitarian distribution of assets between husband and wife, or the wife’s property ownership directly, correlate with the wife’s participation in intrahousehold decision making (
income levels, urbanization rate, population density, fertility, access to finance, and the rule of law are also critical for accumulating infrastructure stocks. Similar findings are reported by Randolph et al. (1999) when analyzing the factors affecting investment in transportation and communication infrastructure in 27 low and middle-income economies for the period 1980-86. They show that economic development, urbanization rate, trade openness, government expenditure, and labor force participation rate significantly impact the per-capita infrastructure spending. Banerjee et al. (2006) find increasing importance of private finance to accumulate infrastructure stocks. Using panel data from 40 developing countries during the 1990-2000 period, it shows factors that are necessary for attracting private investment (e.g., property rights, strong and fair judicial system, strong regulatory system, better contract enforcement, and a low risk of expropriation of assets) are also instrumental for those infrastructures where the private sector can play a bigger role (e.g., power generation, telecommunication, toll roads). Dao (2008) further explains the complementarity between the private and public investment in infrastructure through a study using infrastructure data from 27 developing countries. It finds that public investment complements private investment. However, public expenditure on education is found to compete with spending on telecommunication and water resources. In low-income countries, however, physical infrastructure with the exception of cellular telephone systems, is primarily provisioned by the public sector (Guarara et al. 2017). Grants and concessional loans from development partners also played an important role in infrastructure funding in such countries. Analyzing the macroeconomic risks associated with infrastructure booms in 105 advanced and emerging economies, Walsh _et al._ (2011) find that economic growth and availability of financing (e.g., developed capital market and institutions), affect infrastructure investments.

<mark>In the case of Latin American countries (LACs), Cerra et al. (2016) f</mark> ind a large variation in infrastructure development. Some of the factors contributing to the
June 2018-October 2018: Prior Elicitation from Policymakers, Academics, and program firms.

November 2019-May 2020: Follow-up survey of Management Practices conducted by IPA Colombia.

# **B More Details of the Consulting Interventions**

One of the challenges with consulting interventions is understanding exactly what was done by consultants when working one on one with firms. We used qualitative fieldwork, the administrative agreements used to contract the consulting firms, interviews with the consulting companies, our own observations, and data collected from firms, and summarize key details of each component.

The **Commercial consulting** began with a lengthy diagnostic (10 hours). This included asking firms to provide data on their five main products and then identifying their star product, defined as the one they thought would have highest profitability in the future, and examining the market for this product. This was followed by an implementation phase of 23 hours, which largely focused on developing a plan for commercial strategy and exporting for the firms. For example, a cosmetics factory making many products identified sunblock as its star product. The consultants worked with them to go through their costs and client list, and suggested the firm was offering too many discounts that were incurring losses, leading the firm to cut down on promotions. A key part of this strategy was segmentation of clients, and deciding where to focus more of their sales efforts. For example, a firm making uniforms for businesses and schools was advised to focus more on the domestic market, but also to work with other Colombian agencies like Impulsa and Procolombia to develop the export market, and to take steps like setting up a webpage in English to help reach overseas buyers.

The implementing consultants for commercial management were a consortium of two companies. There were delays in starting the commercial strategy, with implementation starting in December 2018 for many firms, in part because they had not hired enough consultants. The consulting company was very focused on diagnostics, saying there is a myth that firms in Colombia are over-diagnosed, but firms
In sum, the prevalence of counterfeit drugs can be measured in a systematic random sampling approach, both in country and across countries. As such, this measure could form the basis for a cross-country indicator on how well given countries are regulating (and also monitoring) the drug market.

# **_Governance determinants_**

The strategy for collecting information on governance determinants in ways that are comparable across countries is much less clear. Most governance determinants are context-specific despite efforts to identify objective and comparable indicators of ownership, decentralization, formal procedures and stakeholder participation. (See Box 3.)

# **Box 3: Assessing Governance Determinants with Expert Informants**

WHO has developed an assessment tool for governance of pharmaceuticals (WHO 2006). It provides a research protocol and survey instrument with 66 questions that address a wide range of formal procedural and performance measures in the regulation, procurement, distribution and dispensing of medications. The protocol provides guidance to researchers regarding the number and types of key informants who should be interviewed. The assessment is currently underway in more than 10 countries.

In 2000, the Pan American Health Organization (PAHO) convened a committee of public health experts who identified 11 Essential Public Health Functions (EPHF) and subsequently developed a survey instrument designed to assess a country‟s ability to carry out those functions. The EPHF survey is administered to individuals who are considered knowledgeable about the health sector (PAHO 2008). The intent of the survey is to answer questions about the capacity of the public health system to fulfill its essential functions in a yes-or-no format, asking for example whether the public health surveillance system is capable of analyzing a public health threat. With this series of binary questions, the EPHF assesses the public health system‟s strengths and weaknesses relative to this expert view of what a public health system should do.

Both of these initiatives, by relying on expert responses to a series of detailed questions parallel the approaches found in a number of measurement approaches being used in broader governance studies, such
9 Four stylized facts emerge in the income elasticity estimates shown in table 2:

- First, income elasticities from time series data are typically smaller than those from cross-section data. This is because cross-section analyses produce long-run elasticities, and long-run behavior is generally more responsive to income changes than short-run behavior. Roughly speaking, long-run income elasticities of motor vehicle (especially car) ownership are greater than 1, while short-run income elasticities are iess than 1.

- Second, income elasticities from urban-level data are similar to or smaller than those from country-level data largely because there are more competing modes of transportation and greater congestion in urban areas, both of which reduce the attraction of motor vehicles. In fact, the long-run elasticities from urban-level data are closer to those from country-level data than are the short-run elasticities. Using the data sets summarized in table 1, figure 1 indicates that motor vehicle ownership increases somewhat more rapidly with income at the national level than at the urban level.

- Third, income elasticities are generally larger for automobiles than for commercial vehicles, supporting the economic hypothesis that the share of passenger cars in the motor vehicle fleet increases with income.

- Finally, income elasticities of motor vehicle use are less than unity, smaller than longrun income elasticities of motor vehicle ownership, and smaller for households

**Figure 1. Per Capita Income and Motor Vehicle Ownership in 50 Countries and 35 Cities**

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of Facebook’s 2020 Future of Business Survey were more than twice as likely as male business leaders (23% vs 11%) to spend six hours or more on unpaid work per day, i.e. caring for family members and other domestic work. In addition, they were 10 percentage points more likely to report that these unpaid activities interfered with their paid work (Facebook et al., 2020). There are well-documented pre-existing constraints which are not necessarily in the direct domain of the firm but matter for firm performance and which the pandemic has exacerbated. These include, for example, women’s disproportionate share of unpaid care workload as well as mobility limitations and lack of access to digital technology (De Paz Nieves et al., 2021).

Arguably, while gender gaps in enterprise performance are well documented, there is far less conclusive evidence on how these gaps evolve during times of economic crisis. A few studies argue that gender gaps in enterprise performance and access to finance worsened in the aftermath of the 2008 global financial crisis, mostly in the context of developed and transition economies. Ahmed et al. (2020) show that women-led firms in Europe and Central Asia were more likely than men-led firms to exit the market between 2008 and 2009, even controlling for firm characteristics. Among businesses that managed to stay in business, the short-term impacts did not differ systematically for women- vs men-led firms, but women-led businesses experienced a larger decrease in net sales in the long term, i.e. approximately two years into the crisis. Cesaroni et al. (2013) use data from a Credit Registry at the Bank of Italy to show that women-owned firms faced a more pronounced credit contraction than firms owned by men between 2007 and 2009, when liquidity was tight. Similarly, Thébaud and Sharkey (2016) show that women-led firms in the United States were more likely than men-led firms to experience difficulties in acquiring funding when lending tightened in 2009 and 2010. In other contexts, however, women-led
7 groups. We focus on the employment share of urban tradables (MFG & FIRE). We could have alternatively focused on urban non-tradables but it is not yet clear which sectors should be included. Having obtained employment data for each administrative unit (1960-2015), the next step is to use these data to obtain the labor shares for each FUA.

Censuses only take place every 10-15 years. To ensure that we have enough countries for our comparison, we select for each country-FUA the closest observation to the year 2000 (within the 1990-2015 period). Doing so, we are left with 6,865 FUAs in 74 countries. Lastly, we need data on their population size c. 2000. The GHSL database reports their population for the “epoch 2015”. However, population is almost always estimated for earlier years. In many cases, the last census indeed took place in the 2000s.

**Sampling.** The 6,865 FUAs include 3 billion people and account for 75% of world’s urban population. Kolmogorov–Smirnov tests confirm that the distribution of city sizes is not significantly different between our sample and the world. However, data on a few large countries are not available in IPUMS To increase our developed (e.g., Japan). sample’s representativeness, we divide all the countries in the world into ten deciles based on their log per capita GDP c. 2000 (PPP; source: World Bank (2021)). We then obtain the share of each decile in the world’s urban population and compare these shares to the shares in our sample. Based on the differences, we create weights over-sampling developed countries.

**Methodology.** Since we aim to identify which cities have high shares of MFG+FIRE relative to other cities of the same size and for a given level of urban economic development, we categorize the FUAs in 10 deciles. Based on a range of log population from 10.8 (50K) to 17
On one hand, the infrastructure where these activities are performed - berths, quays, storage areas, etc - is expensive to build _(see Box 1)_ and exhibits a problem of indivisibility, i.e. it is not possible to enlarge a port in a continuous way. Port infrastructures must be built with determined minimum dimensions, and in general, their full design is strongly conditioned by the physical characteristics of the coast where the port is located.

# **_Box 1._ Typical civil works unit costs for port infrastructure**

Dredging (confined space restricted by existent berth $ 7.5 / cu.m requirements) Quayside (35m wide berth) $ 54,000 / m Container yard paving and infrastructure $ 63.8 / sq.m Open storage yard $ 5s / sq.m Sheds $ 375 / sq.m _(") 1997 data, for a 14 metre draft 500-600 m long berth._ _Source:_ Drewry Shipping Consultants (1998) 6
Nonetheless, despite all this good recent work on inequality at the Bank and the Fund, the growth in research and public debate on inequality elsewhere has been so large that the Bretton Woods institutions have become relatively smaller intellectual players in the field. In and of itself, this is clearly no bad thing. Inequality truly is one of the fundamental challenges facing today’s human societies, and the greater visibility and popularity of the topic is hugely welcome. Research institutes such as the World Inequality Lab at the Paris School of Economics, which hosts the World Inequality Database and publishes an annual _World Inequality Report_ , are producing important, pathbreaking work extending our knowledge of inequality around the world. Other examples include the team at UNU-WIDER, who host the World Income Inequality Database (WIID) and have a had a long tradition of work on both income and wealth inequality, as well as the Stone Center on Socio-Economic Inequality at the City University of New York. Of course, most academic research on inequality is produced at universities around the world, whether they host one of these institutes or not.

From the perspective of the two institutions, however, two features of this particular surge in external research pose a challenge. First, the bulk of the inequality research currently conducted at the world’s leading research universities and institutes focuses on rich countries. This is not exceptional; the same is true of most other topics. Authors, journal editors and referees residing in the United States and Europe tend to be very interested in what is happening in their own countries. Second, a considerable share of the new work on inequality appears to focus on rich people. This too is perhaps unsurprising, given the rapid rise in top incomes – and top income shares – in most countries for which reliable data is available. It is also part of the key innovation that spurred much of this research which, as noted earlier, was the use of alternative data sources to supplement the inadequate information contained in most household surveys on the richest households
are likely to have had more impact in occupations in which immigrants are overrepresented.

# 7. From Ex Ante Vulnerability to Ex Post Job Losses

In this section, we examine the correlation between our different measures of ex-ante vulnerability to COVID-19, and the actual employment impacts of the pandemic on workers by immigration status. We utilize the latest available of EU-LFS in 2020, which covers the first year of the pandemic. As per the previous waves, the data covers all Western European Union countries as well as Norway and Switzerland.

Figure 9 presents the growth rates of different types of jobs over time, based on the categories of ex ante measures of risk we had identified earlier. In Panel A, we see that the growth rates of essential and non-essential jobs followed a parallel path since 2015, including the decline of around 1.5 percent 2020. In sharp contrast, in Panel B, the number of teleworkable jobs grew at an even higher rate in 2020, while the number of non-teleworkable jobs dropped sharply by 3.3 percent. Income risk combines the two previous measures and Panel C shows that employment in occupations categorized as having an income risk dropped much more sharply (-3.6%) than in occupations considered as income safe (-0,1%). Finally, employment in occupations with a health and job tenure. However, better-suited proxies, such as job vacancies in the EU, are not available at finer levels of disaggregation of occupations and sectors.

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