rafmacalaba/data-use-mentions-extended huggingface dataset:
br>Workplace mobility (% deviation from baseline) Workplace mobility (% deviation from baseline)<br>20 20<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80<br>-80<br>Workplace mobility (% deviation from baseline) Workplace mobility (% deviation from baseline)<br><!-- End of picture text --> Notes: Workplace mobility is Google measure of work-related mobility index. See Annex II for country-specific sources.

> 11 <u>https://www.nytimes.com/2020/04/19/world/asia/tokyo-japan-coronavirus.html?smid=em-share</u>

8
# iii. Using census data to establish economic and nonmonetary indicators

To study the effect of the FAIS on nonmonetary dimensions of welfare, the analysis relies on population censuses and inter-census surveys for 2005, 2010, and 2015. The relevant indicators include (a) the share of the population with access to basic services in their dwellings (electricity, drinking water, sewerage), (b) share of the population with adequate quality floors and bathrooms in their dwellings, (c) share of the population with access to health care services, (d) share of the population ages 15 or more that have completed primary education, and (e) illiteracy rates among the population age 15 or more and among children ages 5–14.

Municipality-level data were compiled to measure the stock of income earner assets and the rate at which these assets are utilized. This involved measures of physical capital, labor, and productivity. The information on these factors was taken from the economic censuses in 2004, 2009, and 2014. Data on assets, employment, and value added were used as proxies for municipality-level physical capital, skilled and unskilled labor, and productivity.

# **5. Identification strategy**

To estimate the effect of the FAIS on welfare, the analysis uses a reduced-form specification. This includes three sets of controls. The first set considers the production factors and technology available at the local level that account for the generation of private income. In particular, the analysis controls according to the stock of physical capital, labor (by skill categories), and a measure of total factor productivity. The second set includes the fiscal resources to account generally for public income. The third set exploits the panel structure of the data to absorb any unobserved heterogeneity across municipalities and over time to control for general income shocks. The reduce form specification is thus defined as follows: shocks. The reduce form specification is thus defined as follows: L m 0 1L F m 2L m 3 m 3 m m (1) mm
Combes and Gobillon, 2015). If these productivity gains are passed on to consumers in the form of lower prices that affect also ad valorem trucking rates, the causality may actually run from agglomeration to transport costs and not the other way round. We deal with this issue directly by purging our measure of ad valorem trucking costs of the effect multi-factor productivity. We also use us industry price indices to construct external instruments for our trucking rate series.

The remainder of the is structured as follows. Section 2 the theoretical paper presents background that informs our empirical strategy. Section 3 briefly documents the evolutions of geographic concentration and of transport costs in Canada, including a detailed discussion of the construction of the transport cost variable. Section 4 describes our empirical strategy and discusses the identification issues. Section 5 presents our results and provides a large number of robustness checks and iv estimates. Finally, Section 6 concludes. Technical details are relegated to an extensive set of appendices and a Supplementary Online appendix.

> 1See Holmes and Stevens (2004) for an exhaustive survey of location patterns in North America. They do not report results using continuous measures. Ellison, Glaeser, and Kerr (2010) use a ‘lumpy (county-level) approximation’ of the Duranton and Overman (2005) measure and apply it to us manufacturing data.

2
, asking participants to indicate whether they were 18-29, 30-39, 40-49, 50-59, or 60+.

**Education.** Participants were asked to indicate the highest level of education they had completed – No Education, Primary Education, Secondary Education, or Tertiary Education.

**Health worker.** Participants were asked to indicate whether they were employed as a health worker or not.

## **_Potential Study-Level Moderators_**

Based on discussion with public health and behavioral science experts and a review of the literature, we explore the following potential moderators: the month of study launch, the GDP per capita of the study country, the vaccination rate in the country at the time of the study, and trust and vaccine perception measures from the Wellcome Global Monitor Survey (2018). The Wellcome Global Monitor survey is a survey of attitudes toward science and health based on a survey with over 140,000 people in more than 140 countries. Since these data from the Wellcome Global Monitor survey are measured at the individual level, we aggregate these measures to the country level by taking their mean value. This approach is consistent with practices in cross-cultural and health research (e.g., Gelfand et al., 2011; Weinberg et al., 2021), including research estimating generalized trust at the country level (e.g., Marozzi, 2014; Reeskens & Hooghe, 2008; Robbins, 2012). See Appendix D for more details on the empirical rationale for country-level aggregation of the Wellcome Global Monitor survey indicators. The Wellcome Global Monitor 2018 data were not available for Belize, Djibouti, Jamaica, Papua New Guinea, and West Bank and Gaza.

10
Available studies show that in the context of strong investments by the Turkish government, the majority of households are able to enroll children in school and receive treatment in case of illness, although important coverage gaps still remain.<sup>9</sup> About 62% of school-age refugee children are enrolled in school. At 96%, enrollment rates are higher for the primary level (first 4 years of instruction). Enrollment at the secondary level is substantially lower (55% for middle and 24% for high school, see Regional Refugee & Resilience Plan / 3RP, 2019-20). On health care, households sought and received medical treatment for 71% of sick children and 34% of adults (WFP, 2019).

Sources of livelihood are constrained for refugees. Based on the results of a livelihoods survey conducted by Turkish Red Crescent and WFP among ESSN applicants in 19 provinces of Turkey, 84% of refugee households had at least one member who was working. However, the vast majority were working informally with low wages and unreliable access to work: 20% worked in “unskilled services” (occupations requiring physical effort or manual labor, such as cleaners, paper collectors, porters, or street vendors), followed by textile (19%), construction (12%), artisanship (10%) and agriculture (8%, WFP and TRC, 2019b). The informal nature of refugee work created additional competition for jobs in the informal sector, which accounted for 35% of the Turkish economy in 2019.<sup>10</sup> Labor market integration is hampered by language, skills and validation of degrees obtained abroad– 80% of refugees had only basic command of the Turkish language, while 11% were illiterate, 52% were literate or had up to primary school education, and 37% had a high school degree or higher (WFP and TRC, 2019b). Although Turkey introduced legislation to allow refugees to apply to work permits and work formally if they
income transfers, we would actually need to impose that all variations sum up to one.

To this end, we scaled up or down all our estimates, by subtracting the average net inflow if positive, or adding it if it turns out to be negative. One possible interpretation of this ex-post rescaling is in terms of relative competitiveness, since flows are not only affected by local conditions, but also by conditions in competing destinations.

# _6.2 Results overview_

Our rescaled estimates of changes in net foreign currency inflows, relative to the 2011 GDP level, are displayed in Table A8 of the Appendix. These variations follows a rather non-linear path. Limited increases of temperature are beneficial but higher levels are detrimental in China, the Republic of Korea, Italy and Turkey. Vice versa, initial negative impacts turn positive at +5°C in Mongolia, Estonia, Lithuania, Slovak Republic, Slovenia, Bulgaria, Belarus, Romania and Kazakhstan.

Benefits are concentrated in a few countries. For example, at +3°C only 26 countries get an increase in tourism revenue, whereas as many as 97 countries experience a relative loss. Benefitted countries include North European and North American countries, Japan and the Russian Federation, which are all rich nations: tourism impacts have adverse distributional consequences.

Furthermore, the dispersion of income flows gets larger as temperature rises. The standard deviation of the distribution of net revenue inflows increases progressively from about 1.48 billions US$ at +1°C up to around 5.36 billions US$ at +5°C.

> 2 We estimated per capita expenditure data on the basis of IMF data on tourism revenue (IMF, 2014).

15
13.3|3.5||0.16|
|Uruguay|0.8|1.7|+|2.03|
|Venezuela|12.8|13.2|+|1.37|
|Median|7.0|5.3|||

aCentral government transfers only; data exclude shared taxes.

bComputed as percentage change in t;ansfers between 19741984 relative to percentage change in GDP.

C1 975 data.

_OURCE: International Monetary Fund, Government Finance Statistics Yearbook, 1984 and 1986 (Washington, DC: IMF, 1984 and 1986).
effects of the minimum wage in the context of a developing economy, in which workers in are more likely to rely on informal employment to a greater degree.

Our work distinguishes between two sources of employment effects: employment destruction coming from firm exits (the extensive margin), and employment cuts in surviving firms (the intensive margin). The paper assesses their respective contributions to aggregate effects. Because the data allow us to track workers over time, we also study the labor-market trajectories of workers in firms that were eliminated after the minimum-wage increase, revealing potential reallocation effects. By complementing the administrative data with data from the nationally representative survey, we provide evidence on whether the destruction of formal firms and formal jobs was accompanied by a rise in informal employment and/or a rise in unemployment.

We find that raising the minimum wage substantially increases the destruction of formal firms and reduces the number of formal firms in the economy. The magnitude of the effect is sizable and robust to alternative specifications, sample restrictions, and treatment measures. Firm destruction, however, is primarily concentrated among firms that are small and had such low productivity levels that they were only marginally above subsistence before the minimum-wage increase. This suggests that minimum-wage increases can contribute to productivity gains in the formal economy by driving out less-productive firms. As expected from simple economic theory, the findings show that market conditions matter: the rise in firm destruction attributable to the minimum wage is stronger in industries with small profit margins, higher labor shares, and stronger market competition.

The paper also finds negative effects on formal employment overall and shows that firm destruction (the extensive margin) is the main driver, as opposed to employment cuts in continuing firms (intensive margin). In theory, the elimination of low-performing firms can help workers upgrade to higher-quality jobs in higher-performing firms; however, we find that, a year after the rises in the minimum wage, only a minority of workers from firms that exited had found in other, formal employment. Using complementary data from surveys
non-commodity price volatility.<sup>2</sup> As far as we know, this paper is the first to compare the volatility of individual primary commodity prices not with aggregate indexes but rather with disaggregated monthly data.

Second, our paper contributes to the literature on trends in commodity prices relative to manufactured products (e.g., Harvey et al. 2010). Our paper instead focuses on the differences in the second moments of commodity prices compared to those of noncommodity prices.

Third, this paper also contributes to the literature on the so-called “resource curse” that has focused on the adverse effect of resource endowments on economic growth (e.g., Lederman and Maloney 2007; Van der Ploeg 2011; Frankel 2012). If commodity prices are intrinsically more volatile than the prices of manufactured goods, a higher natural resource endowments could result in higher macroeconomic volatility.

The rest of this paper is organized as follows. Section II discusses the monthly data from the United States international trade records over the period from 2002 to 2011 covering more than 18 thousand goods. Section III presents the main results. Section IV provides an array of robustness tests. Section V concludes.

# **II. DATA**

> 2 More recently, Deaton and Laroque (2003) have focused on the longer-run determinants of commodity prices. They developed a Lewis model where commodity supply is infinitely elastic in the long run and the rate of growth of supply responds to the excess of the current price over the long-run supply price. They find that commodity prices are stationary around its supply price and are driven in the short run by fluctuations in world income.

5
2013). This comprehensive dataset was shared by the Socioeconomic High-resolution RuralUrban Geographic Platform for India (SHRUG), which aggregated the firm-level data to broader geographic units by matching Economic Census data with the 2011 Population Census of India including demographic data at the town and village level. This aggregation facilitates integration at the village level, resulting in a “SHRID” level dataset. A SHRID describes a geographical unit that can be mapped consistently across multiple rounds of the Indian economic censuses. In the majority of cases, a SHRID is a village or town. This dataset includes the number of private firms and their employees, which is used to calculate the average size of private firms in about 505,000 spatial units as of 2013 (Asher et al. 2021).

**Temperature.** The temperature data consist of the average daily maximum temperature in the South Asia region (Copernicus Climate Change Service 2019). The data are then aggregated to the annual level to compute the 5 year annual average maximum temperature. The 5-year annual average maximum temperature is calculated for the period between 2014 and 2018, the latest available flood data and approximate DHS survey year used in the calculation of the RWI. The temperature data are then matched to RWI grids. The merged RWI and temperature dataset contains about 606,000 spatial units covering Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. For firms, the 5-year period ranges between 2009 and 2013. The temperature data are then matched to the SHRID-level firm data.

**Flood.** The flood data we use were compiled by the Dartmouth Flood Observatory. The dataset is a comprehensive collection of all flood events that occurred in the world between 2000 and 2018 (Tellman et al. 2021). Floods are identified through a combination of news reports, government data, instrumental observations, and remote sensing technologies, including satellite imagery. Floods of Severity Level 1 and higher are included in the analysis, where a Level 1 flood represents "significant damage
The same insights that apply to the time series are relevant for the cross section: TFP needs to be de...ned using the prices perceived by households, and if the economy is open then other terms become relevant. Thus, tax rates, terms of trade, and foreign asset holdings also matter for cross-country welfare comparisons. As before, we can reduce the measurement complications enormously by using absorption rather than GDP as the de...nition of output in our householdcentric TFP measure.

These results show that we can perform interesting welfare comparisons using readily-available national income accounts data. We illustrate our methods using data for several industrialized countries for which high-quality data are available: Canada, France, Italy, Japan, Spain, the United Kingdom and the United States. We show the importance of ...scal considerations in constructing measures of welfare change over time. For example, if we assume that government spending is wasteful and taxes are lump-sum, the UK has the largest welfare gain among our group of countries over our sample period, 1985-2005, while Spain lags far behind due to its low TFP growth rate. Indeed, the US, a much richer country, has faster welfare growth than Spain under these assumptions. Allowing for distortionary taxation and assuming that government expenditures are chosen optimally, Spain has the highest welfare growth among all countries, with the UK a shade behind, and the US much further back.

However these welfare growth rates are country-speci...c indexes, and cannot be used to compare welfare across countries. We next apply our methodology to cross country-comparisons and show how these relative welfare levels evolve over time. In our benchmark case of optimal government spending and distortionary taxation, the US is the welfare leader throughout our sample period. At the start of our sample, we ...nd that France and the UK are closest to the US in terms of welfare, with France having a slight advantage over the UK. By the end of the sample, France and most of
information for each social protection program listed in the GDSPRC that was announced before June 1, 2021 from official documents (including IMF Article IV revisions and related documents from other international organizations), government websites, and news sources. We quantified the expenditures of social protection programs in 154 countries. This set of countries forms the core sample for the empirical analysis in this paper (see <u>Appendix Table A.1</u> for the list of countries).

We also used several auxiliary data sources. Information on countries’ pre-pandemic social protection landscape comes from the ASPIRE database (World Bank 2021a) and OECD’s Social Spending database (OECD 2021b). Data on employment, inactivity, and growth outcomes comes from <u>ILO (2021), the IMF (2021), and the World Bank (World Bank 2021b, 2022b). Poverty</u> projections are from Lakner et al. (2020). Information on the size of the informal sector is from the Informal Economy Database produced by the World Bank (Elgin et al. 2021).

An essential feature of our analysis is the distinction between social programs focused on income protection and programs focused on job protection. <u>Table 1</u> shows a correspondence between social protection areas, categories of social protection, as defined in GDSPRC, and the policy focus (on either income protection or job protection). All categories included in the social assistance policy areas focus on income protection; the categories of social insurance and labor market policy areas are split between those focusing on income protection and those focusing on job protection.

# **4. The nature of the social protection response and the impact of the pandemic**

Countries across the world implemented substantial economic measures in response to the economic shock of the pandemic. The size of the economic mitigation measures (also called the “stimulus budget”) averaged about 5.6 percent of GDP (Table 2). In high-income countries, the
observe the share searching in each period which theory predicts should vary with macro-shocks. We assume that this fraction is proportional to, or at least highly correlated with, the standard measure of search, the unemployment rate. The probability p, is therefore proxied by **Pt,** the number of the self-employed moving into salaried work divided by the number of unemployed. Even though we thereby avoid the need for time series data on the search intensity, we still require information about the sample average of this variable in order to rescale the estimated coefficient appropriately when conducting the macroeconomic simulations in Section 4. To this end, we use the survey response from the National Micro-Enterprise Survey (ENAMIN) which in 1992 re-interviewed roughly 11,000 of those in the 1991:4 ENEU who 13
Weighted revenues reported in SMS surveys for women in the control group show no significant differences in any bi-weekly period. Had the treatment caused negative effects for women in the control group, we would expect to see persistent declines in revenues for women in the control group over time as revenues grow for treated women. The high-frequency SMS data provide reassurance that the treatment did not lead to substantial general equilibrium effects that negatively impacted women in the control group.

The final channel through which negative spillovers may have occurred is on the extensive margin: women in the control group who did not have a business at baseline may have delayed their entry into entrepreneurship because they knew that they could participate in SBS at the end of the study. Although we cannot fully rule out this channel for negative spillovers, it is worth noting that a third of women in the control group who did not have a business at baseline report having a business at midline, and half open a business by endline.

# **6 Discussion and Conclusion**

Our study shows that skills are a relevant barrier to economic inclusion among a self-selected sample of rural women. A program that teaches entrepreneurship and business skills is effective at increasing the number of women entrepreneurs, increasing the number of businesses that each woman runs, and improving the profitability and resilience of their businesses relative to the control group. Despite positive impacts on a range of entrepreneurship outcomes, we find no evidence that the program reduces poverty over the period of our study.

Our results show that skills-based programs for women can be successful in increasing women’s incomes in rural areas and helping women cope with negative shocks, two core objectives of rural development efforts. We find that treated women’s revenues remain stable throughout the first COVID-19 lockdown in Uganda, while revenues among control women drop significantly. Although our results suggest that entrepreneurship can significantly improve the resilience of low-income women in rural areas, it is worth noting that there are limitations to the external validity of our results. The COVID-19 lockdown affected the entire
rise in exports on poverty and inequality using a rich municipality-level data set from a developing country. Mexico is an interesting case given its lackluster performance in economic growth and poverty reduction, despite a significant trade expansion (Hanson 2010). Mexico joined the forerunner of the World Trade Organization, the General Agreement on Tariffs and Trade, in 1986 and the North American Free Trade Agreement (NAFTA) in 1994, leading to substantial tariff reductions globally and regionally, greater export orientation, and diversification away from oil. Exports as a share of gross domestic product (GDP) rose by 20 percentage points beginning in 1994, following reforms to open the economy to trade and to liberalize domestic markets. Most of Mexico’s exports— 2