metadata: authors: 0: author: Berniell,Inés
1: author: Gasparini,Leonardo Carlos
2: author: Marchionni,Mariana
3: author: Viollaz,Mariana
count: Latin America
docna: 0: docna: The Role of Work-from-Home in the Gender
            Asymmetries of COVID-19 : An Analysis for Latin America
            Based on High-Frequency Surveys
docty: Policy Research Working Paper
subtopic: Labor Markets,Gender and Development,Rural Labor Markets,Labor & Employment Law
teratopic: Social Protections and Labor,Gender,Rural Development,Law and Development
abstracts: cdata!: This paper studies factors that could
            account for the asymmetric impact of the COVID-19 pandemic
            in Latin America, by exploiting microdata from the World
            Bank&#x2019;s high-frequency phone household surveys
            conducted immediately after the onset of the pandemic. The
            paper codifies the occupation variables in these surveys,
            constructs measures of the individual&#x2019;s potential
            for work from home, and estimates fixed-effects models of
            job loss and other labor outcomes. In line with previous
            studies, the findings show that the impact of the COVID-19
            shock was (i) harder for women and (ii) strongly decreasing
            in the ability to work from home. Importantly, the analysis
            finds that the mitigating effect of working from home on the
            severity of the impact was especially relevant for women
            with children. These effects were larger in
            countries/periods in which the containment measures
            implemented by governments against the spread of the disease
            were more stringent. The paper also provides suggestive
            evidence on a plausible mechanism underlying the results:
            women with children were more likely to stay home due to
            school closures and the traditional intrahousehold
            distribution of childcare responsibilities, and thus the
            possibility of working from home was crucial for them to
            keep their jobs.
repnme: repnme: The Role of Work-from-Home in the Gender
            Asymmetries of COVID-19 : An Analysis for Latin America
            Based on High-Frequency Surveys
display_title: The Role of Work-from-Home in the
            Gender Asymmetries of COVID-19 : An Analysis for Latin
            America Based on High-Frequency Surveys
listing_relative_url: /research/2021/12/33682585/role-work-from-home-gender-asymmetries-covid-19-analysis-latin-america-based-high-frequency-surveys
topicv3: Labor Markets m1326488 855,Gender m1327621 615,Jobs m1503931 2670,Household Surveys m1504071 2761,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2021-12-17T00:00:00Z
dois: 10.1596/1813-9450-9887
historic_topic: Social Protections and Labor,Gender,Rural Development,Law and Development
origu: EFI-LCR-POV-Poverty and Equity (ELCPV)
no_of_pages: 41
author_keys: 0: author_key: NA
1: author_key: 563550
2: author_key: 993323
3: author_key: 1473282
model_extractions: Policy Research Working Paper 9887 The Role of Work-from-Home in the Gender Asymmetries of COVID-19 An Analysis for Latin America Based on High-Frequency Surveys _Inés Berniell Leonardo Gasparini Mariana Marchionni Mariana Viollaz_ Poverty and Equity Global Practice December 2021
Policy Research Working Paper 9887

# **Abstract**

This paper studies factors that could account for the asymmetric impact of the COVID-19 pandemic in Latin America, by exploiting microdata from the World Bank’s high-frequency phone household surveys conducted immediately after the onset of the pandemic. The paper codifies the occupation variables in these surveys, constructs measures of the individual’s potential for work from home, and estimates fixed-effects models of job loss and other labor outcomes. In line with previous studies, the findings show that the impact of the COVID-19 shock was (i) harder for women and (ii) strongly decreasing in the ability to work from home. Importantly, the analysis finds that the mitigating effect of working from home on the severity of the impact was especially relevant for women with children. These effects were larger in countries/periods in which the containment measures implemented by governments against the spread of the disease were more stringent. The paper also provides suggestive evidence on a plausible mechanism underlying the results: women with children were more likely to stay home due to school closures and the traditional intrahousehold distribution of childcare responsibilities, and thus the possibility of working from home was crucial for them to keep their jobs.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lgasparini@cedlas.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Role of Work-from-Home in the Gender Asymmetries of COVID-19

An Analysis for Latin America Based on High-Frequency Surveys<sup>∗</sup> Inés Berniell<sup>†</sup> Leonardo Gasparini<sup>‡</sup> Mariana Marchionni<sup>§</sup> Mariana Viollaz<sup>¶</sup> JEL Classification: D63.

Keywords: COVID-19, occupations, labor markets, employment, gender, women, workfrom-home, Latin America.

> ∗This paper was commissioned by the Latin American and Caribbean - Poverty and Equity Global Practice (ELCPV) as part of the activities developed in two projects (i.e., LAC: COVID-19 HighFrequency Monitoring - (P173987) and LAC: COVID-19 High-Frequency Monitoring II - (P175839) ) lead by Sergio Olivieri, José Cuesta, Gabriel Lara Ibarra, Carolina Mejía-Mantilla, and Javier Romero with the guidance of Ximena del Carpio and the financial support from the Latin American and the Caribbean Vice Presidency. We are very grateful to Sergio Olivieri, Carolina Mejía-Mantilla, David Newhouse, Ximena del Carpio, Eliana Carranza, William Maloney, and seminar participants at The World Bank and UNLP for helpful discussion and suggestions. We thank Carlo Lombardo, Julián Borgo, Milagros Cejas, Fabián González, and Alejo Isacch for excellent research assistance. The usual disclaimer applies.

> †CEDLAS-IIE-FCE-Universidad Nacional de La Plata. E-mail: miberniell@gmail.com

> ‡CEDLAS-IIE-FCE-Universidad Nacional de La Plata and CONICET. E-mail: lgasparini@cedlas.org

> §CEDLAS-IIE-FCE-Universidad Nacional de La Plata and CONICET. E-mail: marchionni.mariana@gmail.com

> ¶CEDLAS-IIE-FCE-Universidad Nacional de La Plata and IZA. E-mail: mviollaz@cedlas.org
# **1 Introduction**

During 2020 and 2021, an unexpected shock dramatically affected the lives and jobs of everyone in the world: the COVID-19 pandemic. Latin America was not an exception: the pandemic produced a staggering death toll and transformed most aspects of the daily lives of Latin Americans, including their jobs and incomes.

The COVID-19 pandemic reached the region in March 2020. In an attempt to contain the spread of the disease, all the governments imposed national lockdowns, school closures, travel restrictions, and social-distancing measures. These measures contributed to saving lives but at the same time they inevitably brought negative consequences in the labor market. The economies of the region experienced an unprecedented increase in job losses, unemployment and income reduction.

Not surprisingly, the impact of the crisis was highly asymmetric. In particular, the magnitude of the employment and income loss was mediated by some job characteristics; mainly the ability to work from home (WFH), and access to labor benefits that define a job as formal or informal. Workers who could continue working from home and/or had contracts that protected them from shocks were more able to cope with the crisis (e.g. De la Vega and Gasparini 2021).

One of the asymmetries that were early noticed is that of gender: women were hit harder by the crisis than men. In particular, job losses were significantly larger among women. At least two factors could account for this gap. The first one is occupational segregation: if women are employed in sectors more affected by the pandemic and work in occupations that are more difficult to perform from home, the impact would be understandably harsher.

A second factor that could contribute to explain the gender asymmetry is the presence of children at home and the associated childcare activities. Given the long school closures, someone should stay at home to look after the children and help them with the school chores. For different reasons, including deeply rooted cultural factors, women are more likely than men to play this role, and then suffer the consequences in the labor market. In that scenario, only mothers with the ability to work from home might be able to reconcile their labor market activities with their family responsibilities.

We study these issues with the help of one of the most ambitious data sets collected immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). These surveys were carried out in three waves between May and August 2020 in over 100 countries around the globe, including 13 in Latin America. Variables in the HFPS were harmonized by the World Bank, which helped foster a growing literature (Ballon et al. 2021; Cucagna and Romero 2021; Khamis et al. 2021; Kugler et al. 2021; Mejia-Mantilla et al. 2021).

We contribute to this initiative by codifying the occupation variables in the HFPS. These variables were not previously codified because the surveys include only open questions for occupations and hence the codification becomes very arduous. In this paper 2
we make that effort and codify the 9,223 different answers to the 7 occupation variables in the HFPS. In particular, we map these answers to the 41 groups in the International Standard Classification of Occupations 2008 (ISCO-08) at 2 digits.

Having a codified occupation variable is important for two reasons. The first one is apparent: it allows us to explore heterogeneities by occupation. Which were the occupations hardest hit by the pandemic? Did people move across occupations as a reaction to the shock? The increasingly influential task-based approach stresses the role of occupations in accounting for different labor behaviors and dynamics (Acemoglu and Autor 2011). Having harmonized occupation variables is also relevant for a second reason: it allows us to construct measures of potential for work from home, a crucial factor to cope with the shock generated by the pandemic and the containment measures. We compute a measure of work from home following the proposal of Dingel and Neiman (2020) based on occupational characteristics from O*NET. Given that in some occupations the possibility of teleworking depends on having internet access, which is far from universal in Latin America, we adjust our individual measures of WFH by taking into account home internet access reported in the HFPS (Garrote-Sanchez et al. 2021).

Our main analysis is based on fixed-effects models of job losses and other labor outcomes. We focus the analysis on the effect of two factors that may account for the severity of the shock: the individual’s potential for work from home (WFH) and some sociodemographic characteristics such as gender and number of children at home. We control for several factors and include fixed effects by occupation and by country. We also explore the role of the differences in the stringency of the social-distancing measures across countries and over time.

We first confirm two important results: (1) the impact of the COVID-19 shock was strongly decreasing in the possibility of working from home, and (2) women were more likely to lose their jobs than men. Importantly, we also find that the alleviating effect of WFH on the severity of the impact was especially relevant for women with children. In particular, the coefficient for the interaction between WFH and the dummy for woman is large and statistically significant in the sample of households with children but not among those without children. The result holds when we control for occupation fixed effects, implying that even among women in the same occupation, the ability for WFH was pivotal among those with children. This evidence is consistent with a plausible mechanism: given the traditional intrahousehold distribution of childcare responsibilities in Latin America, women with children were more likely to stay home due to school closures, and therefore the possibility of WFH became very relevant for them to keep their jobs.

Our results can be important to further understand the setbacks in terms of gender equality in the labor market generated by the pandemic and the containment measures, and the role of working from home in that process. Understanding these interactions can be important to speed up the return of women to the labor market. The longer it takes the re-entry, the more serious the consequences on women’s future working conditions.

3
We organize this paper as follows. Section 2 briefly reviews the literature. Section 3 introduces the data used in the analysis, with special emphasis on the treatment of the occupation variables, and discusses our measure of work from home. Section 4 presents the main results of the paper by assessing the gender asymmetries in the relationship between job losses, the ability to work from home and childcare responsibilities in a regression analysis setting. Section 5 explores potential heterogeneities in the results according to different social-distancing measures applied by countries to cope with the pandemic whereas Section 6 analyzes the effect of the potential for work from home on other outcomes such as income losses and job changes. We conclude in Section 7 with a discussion of the main findings and the policy implications.

# **2 Literature Review**

The COVID-19 pandemic was a large and unexpected shock on all the economies around the world. The reaction of academia to this unprecedented shock was swift: in a very short time, a prolific literature developed investigating the consequences of the pandemic and the policy measures that were implemented to mitigate its effects.<sup>1</sup> In particular, the number of studies that estimate the asymmetric impact of the crisis on the labor market grows by the day.

Our paper is related to five (interrelated) strands of the economic literature on the COVID-19 pandemic. First, there is a large and more general literature on the labor market effects of the shock (Adams-Prassl et al. 2020; Brodeur et al. 2020; Cajner et al. 2020; Coibion et al. 2020; Koebel et al. 2021). These papers typically find large and asymmetric short-run impacts of the pandemic and the social-distancing measures across countries and sociodemographic groups. A more specific literature looks at the issue of work from home and the ability to teleworking (Dingel and Neiman 2020; Garrote-Sanchez et al. 2021; Gottlieb et al. 2021; Saltiel 2020; Berniell and Fernandez 2021; Delaporte et al. 2021). This line of research highlights the role of WFH in alleviating the impact of the shock, and stresses the asymmetries in the ability to teleworking among socioeconomic groups. A third strand in the literature focuses on gender differences in the impact of the shock (Alon et al. 2021; Costoya et al. 2021; Copley et al. 2020; Cucagna and Romero 2021; De Paz et al. 2020). These papers typically find that female workers were among the most negatively impacted, and suggest two drivers of this asymmetry: a particularly large fall in the activities in which women were predominantly employed and an increase in caregiving responsibilities.

Since the health crisis was global, specific regional studies flourished. In particular, there is a growing literature that examines the impact of the shock in the context of Latin America (Busso et al. 2020; De La Flor et al. 2021; Delaporte et al. 2021; ECLAC 2020;

> 1Just as an example, in just 3 months since the pandemic reached Western countries, in March 2020, the prestigious series of the National Bureau of Economic Research had published 106 articles related to COVID-19 while the IZA Institute of Labor Economics had published another 60.

4
Lustig and Tommasi 2020). These studies find that Latin America was one of the regions hardest hit by the COVID-19 pandemic and the containment measures, with staggering costs in terms of lives, jobs, incomes and welfare.

Finally, there is a literature that studies the impact of the shock exploiting the World Bank’s HFPS surveys both around the globe, and specifically in Latin America (Ballon et al. 2021; Cucagna and Romero 2021; Khamis et al. 2021; Kugler et al. 2021; MejiaMantilla et al. 2021; Olivieri and Rivadeneira, 2021). The richness of these surveys allows exploring the impact of the shock in the labor market as well as in education, food security and other outcomes.

Our paper starts by making a contribution to this latter literature. By codifying the occupation variables and constructing a variable for the individual potential for WFH we are allowed to study some previously unexplored issues with HFPS data on the asymmetric impact of the shock. Our results also make a contribution to other strands of the literature. In particular, we shed some light on the role of teleworking in alleviating the costs of the crisis, and explore the mechanisms behind the gender asymmetries. Finally, our paper is also a contribution to a better understanding of the labor market impact of the pandemic and the social-distancing measures in the specific context of Latin America.

# **3 Data**

Our main source of data is the High-Frequency Phone Surveys (HFPS) conducted by the World Bank in 2020 to assess the impact of the COVID-19 pandemic. In this section we present this data set, explain how we codify the occupation variables, present basic descriptive statistics of job losses, and discuss an index of the individual’s potential for work from home.

## **3.1 The High-Frequency Phone Surveys**

One of the reactions to understand the impact of the pandemic was to collect new data, given that regular national household surveys were not well-suited to deal with this novel situation. One of the more ambitious initiatives was led by the World Bank, which implemented or supported several waves of high-frequency phone household surveys in over 100 countries, and harmonized the results. Thirteen Latin American countries participated in the survey: Argentina, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Peru. These countries represent around 60 percent of the region’s population.

The HFPS have a panel structure over three rounds conducted between May and August 2020.<sup>2</sup> The surveys collect information on multiple dimensions, such as changes

> 2The first round was conducted between May 8 and June 14, 2020, the second round from June 5 until July 16, and the third round from July 5 until August 25, 2020. In Ecuador there was a fourth round, which was collected between August 15 and 25, 2020.

5
in employment and income, access to health and education services, and coping mechanisms to deal with job loss or other shocks related to the pandemic or the measures implemented by governments to mitigate the spread of the disease. The questionnaires also inquired about household knowledge about the disease and the degree of compliance with preventive measures.<sup>3</sup> In each selected household, only one adult aged 18 and above was interviewed. The same respondent was contacted again in the following rounds. Around 1,000 surveys per country were gathered in the first round, 73 percent in the second, and 70 percent in the third.<sup>4</sup> Survey estimates for each country are representative of individuals aged 18 and above who have an active cellphone number or a landline at home.<sup>5</sup> Also, the survey is representative of households with a landline and for which at least one member has a cellphone. Tables 1 and 2 present summary statistics of the main variables in the HFPS for the 13 Latin American countries taken together. According to Mejia-Mantilla et al. (2021), compared to national household surveys, individuals in the HFPS sample are somewhat more educated, younger and more likely to reside in urban areas.

The HFPS were extremely useful in the midst of the crisis, when data from national household surveys (NHS) was not available. But, even when these larger surveys became available, the relevance of the HFPS remained high for various reasons. First, the questionnaire of the World Bank surveys was especially tailored to the crisis and includes a large number of specific questions. Second, unlike most NHS, the HFPS are multi-wave panel surveys that allow tracking short-run changes during the pandemic. Finally, the HFPS were uniform across countries (same questionnaire, same time window, etc.), so the comparability of the results and the possibility to construct aggregate regional measures are enhanced compared to the much more heterogeneous national household surveys.

# **3.2 Occupations**

The HFPS dataset includes seven questions on the occupation of a worker: one for the pre-pandemic situation and two in each of its three rounds, one for people who worked the previous week and another for those who did not work but who still have a job. These questions on occupation capture important information on the activities performed by workers, and hence are very useful for the analysis of the labor market, as stressed by the task-based approach (Acemoglu and Autor 2011). Although the HFPS have been used as the main input for several recent papers on the impact of the COVID-19 crisis, to the best of our knowledge, the occupation variables were not considered in those studies. The most likely reason for this neglect is that, in contrast to the rest of the variables in

> 3The COVID-19 monitoring global dashboard provides harmonized indicators across countries in Latin America and the world. For more information on the HFPS, see Mejía-Mantilla et al. (2021).

> 4The sample weights take into account the attrition in the second and third rounds. The weights were calibrated to incorporate population projections of the United Nations Economic Commission for Latin America and the Caribbean (ECLAC). See Mejia-Mantilla et al. (2021).

> 5The sample is based on a dual frame of cellphone and landline numbers generated through a Random Digit Dialing (RDD) process. For a detailed description of sampling and weighting see Flores Cruz (2020).

6
the HFPS, the occupation variables had not been codified before. The responses to the occupation questions are open, and hence difficult to assign to a few categories.

We find 9,223 different answers in all the occupation variables included in the Latin American HFPS. In this study we make an effort to codify these answers in a few categories. To that aim we map the open individual responses to the occupation questions in the HFPS to the 41 groups in the 2-digit International Standard Classification of Occupations version 08 (ISCO-08). The ISCO categorization, proposed by the International Labour Organization (ILO), is currently adopted by most countries in the world, including many in Latin America, to classify the data on occupations collected in surveys, census and administrative records. We select the 2-digit grouping since it provides enough heterogeneity for the analysis, and at the same time avoids problems of lack of observations (Vosters 2018).

The codifying of the occupation variables in the HFPS implies a painstaking process of assigning each answer to an ISCO group, following the guidelines of the ISCO codebook, which includes specific information of the names, characteristics and activities of each occupation. In particular, we use the Spanish version of the codebook, consistent with the official language of the 13 Latin American countries included in the analysis. We were able to match 98 percent of the answers to the HFPS surveys to an ISCO group.<sup>6</sup> The orange bars in Figure 1 show the share of respondents in each of the ten occupation groups in the 1-digit ISCO classification before the start of the pandemic. On average, for all countries, the group with the largest share is Services and Sales Workers (21 percent) followed by Professionals (19 percent), Elementary Workers (14 percent), and Craft and Related Trades Workers (12 percent). There are some groups with a share between 6 and 9 percent (Technicians and Associate Professionals; Clerical Support Workers; Skilled Agricultural, Forestry and Fishery Workers; and Plant and Machine Operators and Assemblers). Finally, the number of observations is small for Managers (3 percent) and negligible for Armed Forces Occupations (0.27 percent).

# **3.3 Comparison with National Household Surveys**

In this section we compare the structure of occupations in the HFPS before the pandemic with the one constructed with microdata drawn from national household surveys (NHS). To that aim we take advantage of the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a large project of harmonization of national household surveys that has been carried out by CEDLAS-UNLP and The World Bank since 2002. In particular, we use the NHS of the 13 Latin American countries included in the HFPS project for circa 2019.

The occupation variables are already codified in the household surveys by the national statistical offices although, unfortunately, the codification is not uniform across countries. Latin American countries use different systems of occupation codes: they use different

> 6The codes that carry out the matching are available upon request.

7
versions of the ISCO classification or even their own codes (e.g. Argentina). In order to have a unique classification we converted the occupation codes of each country to the 2-digit ISCO 08 using official crosswalks.

Once we have the occupations in the two data sets (HFPS and NHS) grouped following the same classification (2-digit ISCO 08), we proceed to compare the resulting structure of occupations. In particular, we compare the structure for the pre-pandemic occupation question in the HFPS with the structure in the 2019 NHS. Figure 1 shows the results for the 1-digit grouping. The shares of workers in each group are similar across data sets, with two noticeable exceptions. Compared to the NHS, there is an over-representation of professionals and an under-representation of workers in skilled agricultural and elementary occupations in the HFPS. Figure 2 shows that the under-representation in the latter group occurs mainly among the agricultural, forestry and fishery laborers.<sup>7</sup> These biases are consistent with the findings of Mejia-Mantilla et al. (2021) regarding the bias of the HFPS sample towards more educated and urban households: a typical limitation in phone surveys given the difficulties in reaching rural workers. Naturally, these biases should be kept in mind when looking at aggregate statistics, but they are not necessarily a significant problem when we carry out the analysis at the individual level, as in our regression analysis.

# **3.4 Job Losses**

Our main variable of interest is job losses during the COVID-19 pandemic. We focus on permanent job losses, defined as situations where the respondent was working before the pandemic but is not working at the time of the interview and does not have a job to return to.

For simplicity, in this section we construct a binary indicator for “any job loss” that equals one if the worker suffered a job loss in any of the 3 waves compared to the prepandemic situation. For those who had a job prior to the pandemic, Figure 3 shows the percentage who lost their job in waves 1, 2, or 3 of the HFPS. Job losses were very large; the share of workers who lost their job in this very short time window was around 30 percent on average in the region. There are heterogeneities across countries, from 18 percent in Argentina to 47 percent in Colombia.

We are particularly interested in the heterogeneities of the impact of the crisis across socioeconomic characteristics, such as gender. Columns 1 and 2 of Table 3 show that job losses were larger for women than for men in all countries, and the gender gaps were as high as 22 percentage points in Paraguay. Also, job losses were larger for the youth (columns 3 and 4 of Table 3) and for the unskilled (columns 5, 6 and 7 of Table 3) in all the countries. Taking into account the pre-pandemic occupation, non-salaried workers experienced larger job losses in most countries (columns 8 and 9 of Table 3).

> 7Of course, the severity of the problem varies across countries. In Colombia, for example, there is very little difference between the share of elementary workers that arises from both sources.

8
Figure 4 shows job losses by occupation classified with the 1-digit ISCO. Job losses varied from 15 and 16 percent for managers and professionals, respectively, to 37 percent for machine operators and assemblers, and up to more than 50 percent for workers in elementary jobs. Since the ISCO classification sorts occupations by job complexity, from managers to elementary workers (Vosters 2018), Figure 5 suggests that job losses were decreasing in the complexity of the job. In the following section, we work with the 41 groups of the 2 digit ISCO classification, which implies a richer analysis.

# **3.5 An Index for Work from Home**

In order to analyze whether the differential impact of the pandemic was related to the potential for remote work in the pre-pandemic situation, we construct a variable for potential for work from home. We proceed in two steps; first we construct a measure of WFH at the occupation level (Dingel and Neiman 2020), and then adjust this measure for home internet access using the answers to the HFPS (Garrote-Sanchez et al. 2021).

The first step follows the widely used methodology of Dingel and Neiman (2020). They construct a variable of WFH at the occupation level using data on occupational characteristics from O*NET for the U.S. Some of these characteristics are suggestive of the difficulties of working from home (e.g. working directly with the public, outdoor activity, exposed to the weather). When at least one of these characteristics is assessed as "important" or "very important", the occupation is classified as not compatible with WFH.

Occupations in O*NET are classified according to the Standard Occupational Classification (SOC) System. The next step in the methodology is to map the results in the SOC-8 digits classification to the 2-digit ISCO classification, the one used in this paper.<sup>8</sup> The number of groups in the latter classification is smaller, so for each ISCO category we typically have several SOC occupations. As a result of this, our variable for WFH at the 2-digit occupation level is not a binary variable but a share: the share of SOC occupations included in an ISCO category that are compatible with WFH.

Some of the occupations that can be carried out at home require access to internet, a service that is not universal in Latin America. Given this concern, we adjust the measure of WFH for home internet access taking advantage of a relevant question in wave 1 of the HFPS (Garrote-Sanchez et al. 2021). We proceed in two steps. First, for each ISCO category we calculate the share of SOC occupations that require internet to be executed ( _ShrY_ ) and the share of occupations that do not ( _ShrN_ ).<sup>9</sup> Then, the share that requires internet is adjusted by home internet access at the individual level recorded in the first

> 8We follow Bonavida-Foschiatti and Gasparini (2020) for this step.

> 9We follow Garrote-Sanchez et al. (2021) and define the share of SOC occupations within each 2-digit ISCO occupation that requires internet using information from O*NET on the importance and frequency of computer and email use.

9
wave of the HFPS ( _Internet_ ). Formally, (1) Where _i_ labels the individual and _j_ her occupation. Notice that by adjusting for home internet access the index of _WFH_ at the occupation level _j_ becomes an index defined at the individual level. Figure 5 shows the distribution of the WFH index at the individual level for each country before the pandemic. The distributions differ across countries because of the differences in their occupational structures. For instance, the occupational structure in Ecuador or Guatemala is much less compatible with remote work than that of Argentina or Chile.

# **4 Job Losses, Work from Home and Childcare**

Our main interest is to assess the relationship between job losses and the potential for working from home of the pre-pandemic occupation and whether this relationship differs by gender. Figures 6 and 7 show suggestive evidence on this regard. Figure 6 shows the correlation across countries between the share of workers who lost their jobs and the WFH index. In the first panel—wave 1—a clear negative association between WFH and job losses is observed for both women and men.<sup>10</sup> In other words, the initial impact of the crisis implied more job losses for countries where the occupational structure was less compatible with remote work, both for male and female workers. While this pattern persists for women in the following waves of the survey, for men the relationship between job losses and WFH loses strength and virtually disappears in the third wave. Figure 7 shows that a similar pattern arises from an individual-level analysis, i.e., the correlation between the probability of job loss and the individual WFH index is stronger for women, suggesting that the possibility of working from home was more decisive in preventing job losses for women than for men.

At least two mechanisms can account for the differential importance of WFH between men and women. One is related to gender differences in childcare responsibilities, which fall mostly on women as we mentioned above. Given the allocation of these responsibilities and the school closures during the pandemic, the only way for women with young children to continue working was to do it from home. Therefore the possibility of working from home would be more relevant to avoid job losses for women with young children than for men or women without children.

Another mechanism arises from the marked occupational segregation by gender that characterizes Latin American labor markets. Then, if the possibility of doing remote work was key to keep a job during the lockdown, the fact that men and women work in different occupations that differ in the potential of WFH could also explain gender differences in

> 10Moreover, the ability to work from home is larger for women than for men in all countries which is in line with evidence presented for other countries (Gottlieb et al. 2021, Saltiel 2020).

10
job losses.

We explore these hypotheses using the following regression model.

_JobLosticw_ is an indicator that takes the value 1 if individual _i_ from country _c_ lost her job in wave _w_ relative to the pre-pandemic period; _Womenicw_ indicates whether individual _i_ is a woman and _WFHic_ 0 is the WFH index adjusted by home internet access corresponding to the job that individual _i_ had before the pandemic;<sup>11</sup> the interaction between _Womenicw_ and _WFHic_ 0 allows for gender differences in the effect of WFH on job losses; vector _Xicw_<sup>_′_includeseducationattainment,ageandsquaredage,andwhether</sup> the individual was a salaried worker or self-employed before the pandemic, which allows controlling for job stability and other characteristics not related with the content of tasks, such as time flexibility;<sup>12</sup> the model also controls for household size and includes wave dummies ( _θw_ ) as well as country fixed effects ( _λc_ ).

We estimate model 2 using the sample of individuals who were employed before the pandemic. Column 1 in Table 4 reports the results.<sup>13</sup> The estimated coefficient for the WFH index is negative and significant, indicating that, even after controlling for all other variables, there is a negative association between the possibilities of doing remote work and the probability of losing a job during the pandemic. The coefficient of the dummy _Women_ is positive and statistically significant, while the interaction of this variable and _WFH_ is negative and also highly significant. This implies that while women were more likely to lose their jobs during the pandemic than men, having a job that can be done from home largely offsets that disadvantage. For instance, women with jobs incompatible with remote work (WFH = 0) were almost 11 percentage points more likely to lose their jobs during the pandemic than men with similar characteristics including similar jobs in terms of WFH. However, if we compare similar women and men in jobs fully compatible with remote work, women were only 2.8 percentage points more likely than men to lose their jobs during the pandemic. This result is in line with our previous discussion: the

> 11Our preferred specification includes this adjusted index that captures at the individual level the combined effect of both the characteristics of the occupation and the availability of technology at home on the ability to do teleworking. We also estimate models including separately the unadjusted WFH index and household internet access. All our main results hold.

> 12Unfortunately, the HFPS does not have information on hours worked. However, from other sources (e.g., SEDLAC) we know that self-employment exhibits a greater dispersion of weekly working hours than salaried work.

> 13Tables A.1 and A.2 show the results of estimating other more parsimonious specifications that gradually add controls. Models in Table A.1 explore conditional gender differences in the probability of experiencing job losses. The results indicate that the probability of job losses is 7.7 percentage points higher for women than for men with similar characteristics—i.e., same age, education, country, etc. Models in Table A.2 add the WFH index as a regressor. Given everything else, workers in jobs fully compatible with remote work (WFH = 1) are 8.5 percentage points less likely to have lost their jobs during the pandemic than workers in jobs incompatible with remote work (WFH = 0).

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possibility of working from home was more important for women than for men in helping avoid job losses.<sup>14</sup> Next, we explore the role of the presence of children at home. We therefore add to the model an indicator of the presence of children aged between 5 and 18 in the household, its interaction with the dummy _Women_ , and the number of children in the household.<sup>15</sup> Column 2 in Table 4 shows the results that indicate that women with children at home were more likely to lose their jobs than men with similar characteristics who also have children at home.

In order to understand whether any differential role of WFH by gender is explained by childcare needs at home we run model 2 separately for households with and without children and report the results in columns 3 and 4 in Table 4.<sup>16</sup> Notice that the coefficient of the interaction between WFH and gender is negative, large in absolute value and very significant only in households with children, while in households without children the coefficient is still negative but smaller in absolute value and not statistically significant. This implies that the role of WFH in avoiding female job losses occurs mainly in households with children, possibly due to childcare needs. For instance, in households with children, women with jobs incompatible with remote work were almost 14 percentage points more likely to lose their jobs than men with similar characteristics and whose jobs were also incompatible with remote work. In contrast, in households without children the difference is only 5 percentage points.

To check the robustness of the results, Table 5 reports the estimates based on a balanced sample, i.e., the sample that includes only the individuals who were interviewed in all the 3 waves of the HFPS. Likewise, Table 6 reports the results for a model that includes fixed effects by region. In both cases the results in Table 4 hold.<sup>17</sup> Finally, we evaluate whether the mechanism underlying the gender differences in the impact of WFH on job losses is related to occupational segregation by gender. For this, we estimate a variant of model 2 that includes fixed effects by pre-pandemic occupation using the 2-digit ISCO classification. Table 7 reports these results. The coefficient associated to the interaction between gender and WFH is still negative and significant in the sample of households with children, which means that even among women in the same occupation, those with children were the ones for whom the ability for WFH was pivotal. This suggests

> 14Table A.3 reports the results of estimating a variant of model 2 where instead of the adjusted WFH index we separately incorporate the job characteristics and internet access. Our main results hold, as well as when we differentiate between the different types of internet access (Wi-Fi or mobile network).

> 15Unfortunately, the HFPS does not allow for a more complete characterization of the composition and structure of the household. For instance, we do not have information for children younger than 5 nor can we identify which household members are the parents; neither their marital status nor if there are elderly people in the home.

> 16The regressions for households with and without children are similar, in particular they include the same controls, so in principle the results are comparable. As is usual when analyzing heterogeneities, the omission of an unobservable control could generate a larger bias in one of the samples, distorting the results of the comparison.

> 17It would also have been relevant to check the robustness of the results after controlling for the prepandemic economic sector, but unfortunately this variable has 60 percent missing observations in the HFPS.

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that our results are probably not driven by gender differences in occupational structure but more likely by childcare needs.

# **5 Heterogeneities According to Social-Distancing Measures**

This section explores potential heterogeneities in the previous results depending on the social-distancing measures adopted by the countries in our sample. We start by describing the indices that we use to capture government policies in this regard.

## **5.1 The Stringency Index**

Immediately after the onset of the pandemic governments around the world imposed strong measures aimed at containing the spread of the disease, including lockdowns, travel restrictions, school closures, and various social-distancing measures. The socioeconomic impact of the pandemic is expected to be affected by the stringency of these measures. Economies in which these non-pharmaceutical interventions were stricter probably experienced a stronger shock, at least in the short run, than those where the containment measures were less rigorous.

Measuring the stringency of the policy reactions to the pandemic is however a tricky task as it involves all sorts of data and aggregation issues. In this section we use the Stringency Index of the Oxford COVID-19 Government Response Tracker (OxCGRT). The OxCGRT collects information on policy measures that governments have taken to tackle COVID-19. The different policy responses cover more than 180 countries and are coded into 23 indicators, recorded on a scale to reflect the extent of government action. This initiative, explained and documented in Hale et al. (2021), has become the most widely used database of the containment measures taken around the world.<sup>18</sup> In this section we use the Stringency Index of the OxCGRT. This index records the strictness of lockdown-style policies that restrict people’s behavior. It is calculated using all ordinal containment and closure policy indicators, plus an indicator recording public information campaigns. The index is computed as a simple average of the 9 following indicators rescaled to a maximum of 100: school closing, workplace closing, cancel of public events, restrictions on gatherings, close of public transport, stay-at-home requirements, restrictions on internal movement, international travel controls, and public information campaigns.<sup>19</sup> The index and each component reflect the presence and intensity of the

> 18For instance, see Bakker and Goncalves (2021); OECD (2020); De la Vega and Gasparini (2021); Neidhöfer and Neidhöfer (2020).

> 19 The definitions of the indicators are the following. (1) _School closing_ : 0=no measures; 1=recommend closing or all schools open with alterations resulting in significant differences compared to non-COVID-19 operations; 2=require closing (only some levels or categories); 3=require closing all levels. (2) _Workplace closing_ : 0 =no measures; 1=recommend closing (or recommend work from home) or all businesses open with alterations resulting in significant differences compared to non-COVID-19 operation; 2=require closing (or work from home) for some sectors or categories of workers; 3=require closing (or work from home) for all-but-essential workplaces.(3) _Cancel public events_ : 0=no measures; 1=recommend canceling; 2=require canceling. (4) _Restrictions on gatherings_ : 0=no restrictions; 1=restrictions on very large gatherings

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measures in each country over time.

## **5.2 The Stringency Index and Job Losses by Gender**

To assess the role of the policy reactions to the pandemic we incorporate the stringency index and the interactions with WFH and gender into model 2. Table 8 reports the results, which confirm—once again—that women were more likely to lose their jobs, especially women with children (see columns 1 and 3 of Table 8). Naturally, social distancing measures have been an important driver of job losses during the pandemic (the coefficient of the stringency index is large, positive, and statistically significant), but the possibility of working from home played a key role in mitigating the impact of the shock (the coefficient of the interaction between WFH and the stringency index is negative and statistically significant). Moreover, and in line with our previous results, we find that the mitigating effect of the WFH index was more important for women than for men (the interaction between WFH, the stringency index and gender is negative and statistically significant). For instance, with the strictest social-distancing measures (Stringency = 100), the probability that a woman loses a job fully compatible with remote work (WFH = 1) was 16 percentage points lower (a 34 percent reduction) than that of a woman with a job non-compatible with remote work (WFH = 0). For men, the possibility of doing remote work only implied a decrease in the probability of job loss of 7 percentage points (a 17 percent reduction).

Finally, we evaluate the individual role of some of the components of the stringency index, such as the restriction to stay at home (columns 4 to 6 of Table 8), school closing (columns 7 to 9) and workplace closing (columns 10 to 12). In all cases the results are similar: all these social-distancing measures are associated with job losses, while the possibility of remote work was key in mitigating the impacts, especially for women with children.

# **6 The Role of WFH on Other Outcomes**

In this section we explore the role of WFH in other outcomes beyond job losses. We start by looking at income reductions for those who had a job before the pandemic and

> (the limit is above 1000 people); 2=restrictions on gatherings between 101-1000 people; 3=restrictions on gatherings between 11-100 people; 4=restrictions on gatherings of 10 people or fewer.(5) _Close public transport_ : 0=no measures; 1=recommend closing (or significantly reduce volume/route/means of transport available); 2=require closing (or prohibit most citizens from using it). (6) _Stay at home requirements_ : 0=no measures; 1=recommend not leaving house; 2=require not leaving house with exceptions for daily exercise, grocery shopping, and ’essential’ trips; 3=require not leaving house with minimal exceptions. (7) _Restrictions on internal movement_ : 0=no measures; 1=recommend not to travel between regions/cities; 2=internal movement restrictions in place. (8) _International travel controls_ : 0=no restriction; 1=screening arrivals; 2=quarantine arrivals from some or all regions; 3=ban arrivals from some regions; 4=ban on all regions or total border closure.(9) _Public information campaigns_ : 0=no COVID-19 public information campaign; 1=public officials urging caution about COVID-19; 2=coordinated public information campaign.

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continued working afterwards. Then the analysis focuses on labor market entries and job transitions.

# **6.1 Income Reductions**

Our interest now is to assess whether for workers who continued to work during the pandemic, income reductions were associated to the possibility of working from home and whether there were asymmetries by gender.<sup>20</sup> Unfortunately, we only have a discrete measure of individual income going up, down or staying the same, and we cannot construct a household income measure to assess the relative income falls across genders. However, knowing what happened to women’s earnings from work is still useful to assess possible changes in their economic autonomy as a result of the pandemic.

We estimate a variant of model 2 where the dependent variable is an indicator that takes the value 1 if individual _i_ from country _c_ suffered a fall in her income in wave _w_ relative to the pre-pandemic period. We estimate this model for the sample of individuals who were working both before and during the pandemic. Table 9 reports the results.<sup>21</sup> Once again, the estimated coefficient for the WFH variable is negative and significant, indicating that the probability of suffering an income fall was lower among workers whose pre-pandemic jobs were more compatible with working from home. As expected, the possibility of working from home seems to have played a much more important role in avoiding job losses than income reductions for those who did not lose their jobs —notice that the coefficient of the variable WFH in the first column of Table 4 is more than 50 times larger than the corresponding one in Table 9.

Regarding gender differences, the coefficient of the variable _Women_ is positive and significant, meaning that the probability of suffering income reductions for workers in jobs incompatible with remote work was higher for women than for men. On the other hand, the estimated coefficient of the interaction between gender and WFH is negative but very small and not statistically significant, implying that the possibility of working from home did not have a differential effect on incomes of men and women who continued to work during the pandemic. Additionally, the results in columns 2, 3 and 4 suggest that income reductions are not associated with the presence of children at home for these workers.

Another interesting result that emerges from Table 9 is that the estimated coefficient of the _Employee_ dummy is negative, large in absolute value, and highly significant in all the specifications, indicating that salaried workers were less exposed to income reductions than self-employed workers.

> 20We would also like to assess the effects on hours worked but unfortunately this information is not available from the HFPS.

> 21These results should be taken with caution because the dependent variable _Income fall_ presents a lot of missing data, especially for the second wave.

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# **6.2 Job Entries**

The survey reports information on 3,144 individuals—2,389 women and 755 men—who did not have a job before the pandemic. Some of them gradually entered employment during the first months of the pandemic. In wave 3, 29 percent of these individuals were working. We first describe the profile of the new workers. For that, we estimate a linear probability model where the dependent variable takes the value 1 if individual _i_ from country _c_ is employed in wave _w_ . The results are reported in the first column of Table 10.

In line with the results of previous sections, the probability of getting a job during the pandemic was lower for women than for men, especially for women with young children at home. Also, the probability of entering employment increased with age and over time, the latter captured by the wave dummies.

To assess the WFH content of the new jobs, we estimate a model where the dependent variable is the WFH index of the new job. The results are reported in the second column of Table 10. As expected, WFH is higher for women, suggesting that women who entered employment did so in jobs compatible with remote work. Also, the probability of getting a job during the pandemic was higher for individuals with college education than for those with lower educational attainment.

# **6.3 Transitions across Occupations**

Lastly, we analyze changes across occupations for those who were employed before the pandemic and remain employed during the pandemic. Notice that we cannot observe whether the worker remains in the same exact job, but the survey allow us to know whether she remains in the same occupation, defined at the 2-digit ISCO level.

The data shows very little change in occupations prior to the first interview. Only 5 percent of the 7,993 workers who continue to work in wave 1 changed their pre-pandemic occupation. However, transitions across occupations rose markedly over the following months: 24 percent and 30 percent of the workers still employed in waves 2 and 3, respectively, had changed their pre-pandemic occupation. These figures are almost the same across genders, and there are also no significant differences between households with and without children.

The same conclusions hold in a multivariate setting. Columns 1 and 2 of Table 11 show the results of estimating a linear model of the probability of changing occupation for individuals who were employed before the pandemic and continue to work during the pandemic. The results confirm that—keeping everything else constant—the probability of changing jobs increases over time, but does not vary by gender nor does it depend on the presence of children in the household.

Figure 8 illustrates the transitions in terms of the WFH index of the pre-pandemic occupation (horizontal axis) and the occupation reported in wave 3 (vertical axis). For simplicity, the range of values of the WFH index is partitioned into 10 bins (bandwidth 16
= 0.1). The figure shows the percentage of workers in each bin of the pre-pandemic and wave 3 occupations. Most workers remained in similar occupations in terms of WFH. In the case of women, 78 percent remained in the same bin —84 percent if we also consider changes to adjacent bins. The proportions are similar for men. Of those that moved from the pre-pandemic occupation, the number of cases for which the WFH index increased or decreased is similar. The latter seems counterintuitive, but it could be associated with the increase in certain essential occupations during the pandemic that are not compatible with remote work, such as delivery services.

Columns 3 and 4 of Table 11 complement this analysis by showing the results of a model of the change in the WFH index for those who changed occupation. The dependent variable is defined as the difference between the WFH index of the occupation in wave _w_ and the WFH index of the pre-pandemic occupation of individual _i_ in country _c_ . Again, keeping everything else constant, women were more prone to move into occupations more compatible with remote work. An increase in the WFH index is also associated with the presence of children at home, with a high educational level, and with having been a salaried worker in the pre-pandemic occupation. We also find a negative and significant partial correlation between the WFH content of the pre-pandemic job and that of the new job. Although this might seem counterintuitive, it reflects a somewhat mechanical mean reversion

# **7 Concluding Remarks**

The COVID-19 pandemic has been a huge unexpected shock on the lives of people around the world. Many have lost their jobs, suffered income reductions or had to change occupations to cope with the new situation. In this paper we explore these issues in Latin America by exploiting an unusual rich survey carried out immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). In particular, by codifying the occupation variables in these surveys we are able to construct a variable for potential for work from home (WFH) and analyze the role of this factor in people’s adjustments to the shock.

Our analysis, mainly based on fixed-effects models of job losses and other labor outcomes, leads to some interesting results. First, we confirm that the impact of the COVID19 shock was (i) harder for women and (ii) strongly decreasing in the ability of doing work from home. More important, we find that the mitigating effect of WFH on the severity of the impact was especially relevant for women with children. These effects were larger in those countries/periods in which the containment measures implemented by governments against the spread of the disease were more stringent. Our results are consistent with a plausible mechanism: women with children were more likely to stay home due to school closures and the traditional intrahousehold distribution of childcare responsibilities, and thus the possibility of WFH was crucial for them to keep their jobs.

Our results provide evidence consistent with a relevant policy lesson highlighted in 17
other studies (e.g. Ballon et al. 2021): stringent lockdowns and closures helped save lives but at the same time led to substantial welfare losses, a fact that should be seriously taken into account in the design of the optimal policy response to these types of shocks.

Women workers are among those most affected by the shock. Our results help to understand the setbacks generated by the pandemic and the containment measures. A better understanding of the changes and adjustments brought about by the pandemic is important to accelerate the return of women to the labor market. The longer re-entry takes, the more serious the consequences will be for the future working conditions of women.

In most Latin American families, caregiving and home-production responsibilities still rely disproportionately on women. Our results suggest that traditional gender roles may have played a key role on the impact of the pandemic on women’s labor market outcomes. Given the long school closures during the pandemic and the traditional distribution of responsibilities across genders within households, women carried most of the extra burden imposed by the pandemic, and then suffered the consequences in the labor market. By drawing attention to the asymmetric impacts of the pandemic, this paper contributes to making visible the consequences of these deeply rooted behaviors in our societies. The return of women to the labor market will need to be accompanied by policies that help modify traditional gender roles including, for instance, public childcare centers and longer and gender-balanced parental leaves.

Finally, our results are also important to highlight the critical role of the ability to work from home. The pandemic showed the relevance of expanding connectivity and promoting digital skills. Although the pandemic will eventually end, the demand for workers with technological skills likely underwent a permanent shift, increasing the need for policies that facilitate or promote the acquisition of digital skills that help improve the employability of the most vulnerable workers and their chances to move towards better jobs.

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