rafmacalaba/data-use-mentions-extended huggingface dataset:
effects on poverty and income distribution are positive, but very small. This highlights two points, one related to the structure of the Brazilian economy, and other to an aspect of poverty.

We noted that the Brazilian economy is not very oriented towards external trade. The domestic market is far bigger and more important for the general economy than the external market, as researchers have long understood. This makes Brazil naturally less sensitive to tariff structure changes, as well as to changes in export demands.

But we should also note that approaching poverty by the household dimension, instead of by the personal dimension, and tracking the changes in the labor market from individual workers to households is an important modeling issue. In the PNAD 2001 data we used, the income of the head of the family accounts for about 65 percent of aggregated household income in Brazil. Using head-of-household income as a proxy for household income may poorly predict the effect of policy changes, as convincingly argued by Bourguignon et al (2003). If spending (and welfare) is in any sense a household phenomenon, this is the appropriate method. Even though there may be a somewhat higher computational cost associated with this procedure, it seems worthwhile.

The role played by the agriculture sector in the analysis should also be stressed. As seen before, agriculture still accounts for a large share of employment for the poorest in Brazil. Despite the steady decline over time of agricultural employment as a share of total employment we should not overlook the importance of agricultural policies for poverty alleviation in the country.

Finally, it should be noted that this study assesses only the static impact of trade liberalization scenarios. The research methodology here used fails to capture many other effects generally associated with external trade liberalization, such as endogenous technology 22
of resulting groups and reasonable cell size. We further collapse categories as needed.<sup>19</sup> After this exercise we have 43 groups all of which have 10 or more informal firms.

Similarly, we group firms in the EcC. We use firm size, firm age, invested capital, whether the firm hired a wage worker, and quartile of value added per worker. Similar to ELMPS, after obtaining the number of informal firms for each combination of categories from these variables, we impose the constraint that no category can have fewer than 10 informal firms.<sup>20</sup> After this exercise we have 76 groups all of which have 10 or more informal firms.

# **3.6. Cluster Analysis**

As an additional approach to identifying profiles of firms with varying potential for formalization, we undertake cluster analysis. Cluster analysis is a data reduction and classification method, designed to create a small number of clusters (groups) where we have maximized similarity within the groups and difference between the groups (Everitt, Landau, Leese, & Stahl, 2011). We use k-medians clustering and Gower’s dissimilarity coefficient (since it is amenable to a mix of binary and continuous data) to create 10 clusters in each of the ELMPS (pooling 2012 and 2018) and EcC. For ELMPS, we use the variables in model 3 for the cluster analysis. For EcC, we use the variables in model 2 for the cluster analysis.

After obtaining the clusters, we explore the characteristics of each of them. Based on the characteristics of these clusters, we identify the delineating features for each clusters and name clusters accordingly. We present descriptive statistics on categorical variables showing the ratio of each category within the cluster relative to the category overall in the sample.<sup>21</sup> In addition, we standardize the continuous variables in each of the clusters relative to the mean and standard deviation of that variable in the sample.

# **
that events before five years old may have a very large long-term impact on adult outcomes: child and family characteristics measured at school entry do as much to explain future outcomes as factors that economists have more traditionally focused on, such as years of education. In a study based on data for Ecuador, Paxson and Schady (2007) for instance found that health measures such as height for age and weight for age are positively related to language development, a measure of cognitive ability. Perhaps more dramatically, Helmers and Patnam (2010) found, in a study of Andhra Pradesh, India, that child health at age one influences significantly cognitive abilities at age five.<sup>7</sup> The persistent effect of health in childhood on health in adulthood appears to operate to a significant extent through its impact on educational attainment in childhood. Indeed, the available evidence suggests that protein-energy malnutrition during the early stages of a child's life can lead to permanent impairment of central nervous system functions. Folate and iodine deficiency _in utero_ , as documented earlier, and iron deficiency during infancy, may also cause permanent neurological damage. An adult’s physical work capacity is thus determined by his entire nutritional history. Consequently, health represents a key inter- and intra-generational mechanism through which poverty is transmitted: people born into poorer families experience poorer childhood health, lower benefits from investments in human capital, and poorer health in early adulthood, all of which are associated with lower

> 6See Currie (2009) and Almond and Currie (2010) for an overview of the literature on the role of health in the intergenerational transmission of socioeconomic status—with a focus, however, on industrial countries. 7They also find that child health at age one is influenced by parental care during pregnancy, consistent with our previous discussion.

8
outcomes of interest are the shares of total household income spent for a given type of commodity. To compute the denominator, I follow Dai et al. (2011) who have estimated the distribution of household income using the same data from the fourth wave of the IFLS. Income is thus obtained summing up five components: labor income; income from agricultural business; income from nonagricultural business; household nonlabor income (scholarships, pensions, other transfers); household assets income. Moreover, to correct for attrition, a two step Heckman procedure is applied, in which the probability of response is predicted by dummy variable for whether the respondent is the head of the household. Levels of (log) income and predicted income are reported in table 8 suggesting, as in table 4 that migrant households are on average poorer than nonmigrant households and households with migrant mothers poorer than households with migrant fathers.

Table 8 further shows descriptive statistics for the shares of income allocated to the various types of commodities, distinguishing, in particular, between common goods, that is, food, education, health, and durables and exclusively adult goods, which include 18
of GBF and SBF.<sup>4</sup> The index varies from 1 to 5, in which 1 represents the use of the most basic technology, and 5 is the adoption of frontier technologies. In the example of Business Administration, a firm that adopts handwritten processes and computers with standard software will have a GBF index of 2 in the extensive margin. As the firm uses two technologies, the survey asks about the most used one. If the most used technology is handwritten processes, the GBF index at the intensive margin is 1. In contrast, if standard software, the intensive margin equals 2. Likewise, if a firm in the Wearing Apparel industry uses laser cutting, a frontier technology, as the most used technology, then it will have an SBF index of 5 at both extensive and intensive margins.

# **2.2 Identifying female-managed businesses**

The FAT survey provides two variables that relate to gender participation in the management of a firm. The first variable indicates whether there are any females among the firm owners, while the second variable identifies whether the top manager is female. Although both variables are highly and significantly correlated (a coefficient of 0.5), the female ownership variable is substantially less precise, as the share of female ownership is not collected in several countries.<sup>5</sup> In contrast, the indicator of FTM shows less measurement errors and more precisely capture if females are involved in the management of firms.

The survey indicates that only 20% of firms are managed by women (Table 1), which is consistent with other findings in the literature. For instance, the World Bank Enterprise Survey reports that, for a representative sample of over 130,000 firms, about 16% are femalemanaged firms. Throughout the analysis, unless otherwise stated, we will refer to female establishments as those managed by female top managers.

The country income level is positively associated with female participation in top managerial positions. Table 1 shows
# **Reacting Quickly and Protecting Jobs: The Short-Term Impacts of the COVID-19 Lockdown on the Greek Labor Market**<sup>1</sup>

Gordon Betcherman School of International Development and Global Studies, University of Ottawa Nicholas Giannakopoulos Department of Economics, University of Patras Ioannis Laliotis Department of Economics, City, University London Ioanna Pantelaiou Department of International and European Economic Studies, Athens University of Economics and Business and World Bank Mauro Testaverde World Bank Giannis Tzimas Department of Electrical and Computer Engineering, University of Peloponnese

**Keywords:** Greece, COVID-19 pandemic, labor market impacts **JEL codes:** J21, J60, J68

> 1 Corresponding author: Mauro Testaverde, <u>mtestaverde@worldbank.org. The authors would like to</u> thank Athina Raftopoulou for insights and feedback while developing this paper. The authors would also like to acknowledge the contribution of Spyros Ntouroukis in collecting and organizing online job posting data.
**Table 1: The distribution of various charges in the data set**

||Mean|10thpercentile|90thpercentile|
|---|---|---|---|
|monthlyconsumption|166.3 kWh|24.7kWh|374.5kWh|
|monthly consumption<br>per capita|39.7 kWh|4.4 kWh|87.6 kWh|
|energycharges|₹1729|₹181|₹4086|
|fixed charges|₹348|₹180|₹480|
|electricityduty|₹138|₹20|₹310|
|tariff subsidy|₹-15.1|₹ -66.3|₹0|

Notes: Total number of observations in the sample are 7,615. The distribution was weighted by sampling probabilities.

**Table 2: Comparing the consumption distribution of sampled households to billing data set**

|Percentiles|Admin billing data<br>(all HH)|Admin billing data<br>(surveyed HH)|
|---|---|---|
|1%|1|1|
|5%|15|14|
|10%|30|28|
|25%|62|58|
|50%|115|119|
|75%|240|263|
|90%|452|436|
|95%|645|562|
|99%|1283|762|
|Mean Value|208|189|

Notes: This table compares the distribution of consumption for all households in the two district of Rajasthan in the administrative data to the consumption distribution obtained from the survey using sampling probabilities. The sample period for both data sets is restricted to January and February 2017.

**Table 3: Socioeconomic profile of the households**

|Variable|Proportion of households|
|---|---|
|Proportion ofgeneral caste|29%|
|Pukka Wall|
**Figure 1:  Regional Distribution of Business Services Exports**

<!-- Start of picture text --> 600<br>1990 1995 2000 2001 531<br>515<br>500<br>400 375<br>300 266<br>200<br>130<br>114<br>100 58 91<br>11 14 21 19 3 4 14 17 6 11 16 15<br>0<br>Africa and the South Asia LAC East Asia and OECD<br>Middle East the Pacific<br>In Billion US Dollars<br><!-- End of picture text -->

# **Figure 2**

<!-- Start of picture text --> Average Growth Rate of Exports of Business Services for<br>selected countries, during 1995-2000<br>Ind ia 43%<br>Israel 28%<br>D o minica 20%<br>B raz il 20%<br>M aurit ius 19%<br>N icarag ua 15%<br>Est o nia 14%<br>N ig eria 14%<br>R o mania 13%<br>A ust ralia 11%<br>C hina 11%<br>U nit ed St at es 11%<br>C anad a 11%<br>B arb ad o s 8%<br>Jamaica 7%<br>Peru 6%<br>Euro A rea 6%<br>A rg ent ina 5%<br>Ghana 4%<br>Jap an 1%<br><!-- End of picture text --> Source: IMF Balance of Payments Statistics; Note: -The “Business Services” category includes Total Services minus Transportation, Travel and Government Services. Alternatively, Business Services consist of: Communication, Construction, Insurance, Financial, Computer & info, Other business, Personal
where _dj_ is an indicator function that equals 1 if the household is poor and equals −1 if the household is non-poor in period _j, j_ = _2017, 2022_ , and 1 2 .<sup>5</sup> As can be noted, the application of (2) requires that ε _i,_ 2017 and ε _i,_ 2022 have a bivariate normal distribution with ff ρρ� = dd dd ρρ� zero mean, (the partial) correlation coefficient ρ, and standard deviations σε,2017 and σε,2022, respectively. This assumption is standard for most analysis of household consumption data. Furthermore, we exploit a logarithmic transformation to normalize the dependent variable. We are also implicitly assuming that no cohort-specific effects exist; neither do we explicitly assume individual level heterogeneity. While these could result in more restrictive analysis, we are here forced by the limited availability of data for Bhutan. A final crucial step in this framework is represented by the estimation of the correlation coefficient . Given equation (1) and the aforementioned assumptions, and further assuming that the number of cohorts (C) constructed from the survey data is large enough, ρρ� the partial correlation coefficient ρ is estimated from a modified version of equation (1) where all the variables are aggregated at the cohort level: where the error term εcj includes now a cohort fixed effect τc and the error νcj.<sup>6</sup> All predictions from model (1) are obtained by applying the estimated parameters from the 2017 and 2022 survey rounds to 2022 data. Following Appendix 1 in Dang and Lanjouw (2023), standard errors are obtained adjusting for sampling errors and model errors � according to V 2) , assuming that these two errors are uncorrelated with each other. The variance for the sampling errors ss mm has been estimated using the bootstrap VV(ΦΦ<sup>�</sup> = ΣΣ<sup>�</sup> + ΣΣ<sup>�</sup
7 2006) use a panel of professional educators to assess the minimum spending necessary to deliver instructional programs for schools of varying size and demographic composition and conclude that California‟s schools underspend. They conclude that only 15 to 28 of the 984 school districts examined were spending at the level adequate to reach California‟s content and performance standards in all major subjects. The authors estimated that an additional $24.14 to $32.01 billion would have been necessary in the 2004/2005 school year to ensure the opportunity of all students to reach the state‟s content and performance standards. This approach begs the question of why some school districts appear to achieve much better results with similar levels of spending per pupil, and why some spend considerably more than other and yet achieve no better results.

We sourced school districts‟ educational outcomes and expenditure data from the California Department of Education‟s website.<sup>5</sup> Each year, the California Department of Education collects and reports information about student test scores in California Standards Tests (CST), current expense of education, enrolment, number of dropouts, number of graduates, and enrolment of English learners of roughly 980 local educational agencies (LEA) or school districts in California. For the purpose of our analysis, we focus on 313 of the school districts that control both elementary and high school levels (i.e., unified school districts) within its boundary and have data on CST scaled scores for math and reading in grade 5 and grade 7, current expenditures, enrolment, and poverty estimates consistently available between 2003/2004 and 2008/2009 academic years.<sup>6</sup> Californian students attending grade 2 to grade 11 take the CST in mathematics, reading, science, history, social science, and so on. Some students with disabilities take the California Alternative Performance Assessment (CAPA) and their test scores are excluded from our sample. However, students in grade 8 and beyond tend to take
will at least reduce the effort that is needed to undertake household chores. This would still be an important improvement of household’s living conditions.

While ultimate poverty impacts on, for example, income or educational investments might be small compared to productivity gains associated with bigger infrastructure interventions, these effects are still considerable from the poor’s perspective, particularly having in mind the low investment costs of the intervention at 30 USD per kit.

Our results hence substantiate the Tier‐1‐threshold of modern energy access in the SE4All Global Tracking Framework. The Pico‐PV kits can in fact meet the need for basic energy services in poor areas where energy consumption is still at a very low level. Yet, comparing our findings to more advanced regions and larger interventions, such as grid extension, it also becomes evident that Pico‐PV kits cannot satisfy the whole portfolio of energy demand (Lenz et al. 2016). Hence, in many not so remote areas Pico‐PV kits can be considered as either a complement to a grid connection for backup purposes or as a bridging technology toward a grid connection at a later point in time. For very poor areas in the periphery of a country as studied in this paper, in contrast, Pico‐PV is in many cases the only option to obtain modern energy because, first, these regions are beyond the reach of the electricity grid for many years to come and, second, other off‐grid solutions such as larger solar home systems are too expensive. We therefore argue that households in such remote areas are the major _._ target group of Tier 1 energy systems within the SE4All initiative What is crucial for the acceptance of this new technology is the proper functioning and
well as policies and laws.

More fundamentally, measurement also reflects how disability is understood and defined, whether implicitly or explicitly. Because disability can be defined in a variety of ways, it can also be measured in many ways. Different conceptual models have been developed to define disability (Davis 2017; Goodley 2016). For instance, the medical model treats disability as a problem of the individual requiring medical treatment and rehabilitation. An individual with an impairment is considered disabled irrespective of whether it limits functioning and activities. On the other hand, the social model considers disability as socially created. Disability is not about the individual, but about the social environment in which the person lives. For instance, a person with a physical impairment may be unable to work because workplaces lack ramps and elevators and, thus, are inaccessible. Since the 2000s, disability has often been defined through interactional models as a biopsychosocial phenomenon, one that results from an individual with a health condition interacting with environmental factors. This understanding is for instance consistent with the commonly used International Classification of Functioning, Disability, and Health (ICF) (WHO 2001). In the ICF, functioning refers to body functions and structures, activities and participation, while disability is an umbrella term for impairment (e.g. blindness),

> 1 The data collection options above could be blended. There could be a short questionnaire (e.g. WGSS or the WGSS-enhanced) as part of a core questionnaire and then special modules added from time to time to capture for instance environmental barriers and other determinants of inequalities. For instance, this is the approach used by ILO (WG-ILO 2020) in its recommendations for Labor Force Surveys.

4
and machinery) in estimated production functions; examples include Fulginiti and Perrin (1993, 1998), Block (1994), Craig, Pardey, and Roseboom (1997), Wiebe et al. (2003), Cermeño, Maddala, and Trueblood (2003), Lio and Liu (2008), and Cermeño and Vázquez (2009).

Increasingly, agricultural productivity studies have also drawn on non-parametric or semiparametric approaches. These are related to index number theory, and frequently employ data envelope analysis (DEA) programming methods to estimate either input or output distance

> 4 Ball et al. (2008, 2004) construct panel data sets of agricultural capital stocks for various OECD countries starting in the early 1970s. The Groningen Growth and Development Centre (GGDC) houses the EU KLEMS database on output and input growth at the industry level for 25 European Union member states, as well as the United States and Japan from 1970 to 2007 (www.euklems.net).

> 5 Two-sector models of structural adjustment require comparable measures of capital in and out of agriculture. The lack of a consistent measure has prompted some researchers to exclude direct measures of agricultural capital from their analyses. See, for example, Restuccia, Yang, and Zhu (2008), Bah and Brada (2009), and references therein. In an examination of sectoral differences in TFP, Caselli (2005) used assumptions on factor shares to derive the sectoral (agricultural vs. nonagricultural) capital stocks necessary to compute TFP measures comparable across sectors.

3
turn depends on its market power. However, neither cost shocks nor market shares are observed directly at the firm level.<sup>3</sup> Our identification strategy goes around this by relating firm-level export prices (on which we have data) to bilateral exchange rate shocks.

Consider a firm faced with a series of bilateral exchange rate shocks, one on each of its destination markets. If it passed through the entirety of each shock onto consumer prices, its producer price in the home currency would remain the same irrespective of destinations; there would be no PTM. However, with incomplete pass-through, a fraction of each shock is absorbed by the firm into its producer price; as a result, the uniqueness of the producer price breaks down, giving rise instead to a series of differentiated ones by destination. In other words, the firm prices to market. Like in the case of a symmetric cost shock, the reaction of a firm’s producer price to an exchange rate shock tells us something about the elasticity of demand it perceives on its destination market—i.e., about its unobserved market power. Specifically, a firm with strong market power in a given destination (hence a low perceived price elasticity of demand) will absorb a large fraction of the bilateral exchange rate shock, and conversely. As destination markets vary in terms of their trade policy, we can then infer the effect of trade policy on market structure by observing the pricing behavior of firms exporting there, without directly needing to observe their market shares or any other measure of market power.

We start by discussing the impact of trade policy instruments on PTM in the destination country using a class of heterogeneous firms models featuring variable and endogenous markups.

> 2See for example Krugman (1986), Feenstra _et al._ (1996), Atkeson and Burstein (2008), Berman _et al._ (2012), Auer and Schoenle (2016), or Amiti _et al._ (2014).

> 3Market