Which Workers are Most Exposed to COVID-19 and Social Distancing Effects in a Dual Labour Market?/Que trabajadores estan mas expuestos al covid-19 y los efectos del distanciamiento social en un mercado laboral dual?/Quais trabalhadores estao mais expostos ao COVID-19 e aos efeitos do distanciamento social em um Mercado dual de trabalho?

AuthorCardenas, Jeisson


The spread of the COVID-19 virus has affected global economic activity substantially. However, its likely effects on the informal sector have not yet been quantified accurately. This seems a serious omission given that informal workers, who are already more vulnerable to income shocks, comprise the largest share of the global workforce. In 2019, for example, the International Labour Organization (ILO) estimated that 61 % of the total world employment was informal. Bonnet et al. (2019), for example, valuated that the incidence of informality in emerging and developing economies accounts for 67 % and 90 % of total employment, respectively. Such workers are particularly concentrated amongst developing countries, where the informal sector often accounts for more than half of the GDP and employment in low-income countries (Pratap & Quintin, 2006, p. 1). The relation between informal employment and lower income has been previously studied, especially for Latin American countries (Perry et al., 2007). Even if informal work is present across all occupations and income levels, a clear overlap exists between informality and low income, low productivity, and poorer households and worker conditions.

This need to focus on the relationship between COVID-19 and informality is crucial because the nature of both employment and the employment relationship varies with the degree of informality (Williams, 2014). In terms of the nature of employment, this paper demonstrates the differences in the occupations that (i) can be performed from home (remotely) during social distancing measures and (ii) are associated with proximity (e.g., face-to-face) between workers and clients. In terms of the nature of the employment contract, informal employees (i) tend to be uninsured for personal and economic shocks (e. g., unemployment insurance) without the personal wealth to cover for such contingencies and (ii) do not contribute to social protection, thus experiencing impediments to healthcare access, (1) sick and maternity leaves, and have to rely, as far as one exists, on a social safety net.

A lack of recognised qualifications and occupations, which allow access to the formal sector, along with weak social protection associate with the necessity of work amongst those in the informal sector, often in self-employment, despite the emergence of COVID-19 (Margolis, 2014). The dilemma that the lack of savings and other forms of support create for the imperative of working amid informal workers is vividly illustrated in the works of no (2020a, 2020b). The imperative of work amongst informal occupations in developing countries is far from entrepreneurial activities undertaken by choice. Therefore, it is important to understand how the formal and informal segments duality of developing economies help to explain the differences of the COVID-19 effects across countries, regions, sectors, and occupations. Repercussions also differ through time: initial diffusion of the virus, through the first stages of social distancing to the release from confinement shifts that strain to different occupations. While all these dimensions are considered in the present paper, the focus is to understand how the risks of contracting COVID-19 differ for workers within the poorer (informal) and richer (formal) segments of a dual economy and how this can be used to inform the actions of policymakers.

The first economic potential effects of COVID-19 were measured for the us economy. Del Rio-Chanona et al. (2020) identified that big drops in the GDP occurred in nonessential sectors, where work activities cannot be performed at home. Dingel and Neiman (2020) demonstrated that the consequences of social distancing vary across regions, sectors, and occupations. In particular, they presented evidence of a positive relationship between a country's GDP and its share of work-from-home (teleworkable) occupations. This result suggests that less-developed countries will have a harder time with the imposition of confinement measures because individuals and employers do not have access to the necessary organisational and it infrastructures to work in this way. Mongey et al. (2020) calculated COVID-19's effects across industries and occupations using two different measures: low work-from-home and high-proximity occupations, including those associated with intensive face-to-face interactions. These indicators revealed vulnerable populations are more likely to be affected and that the effects will be larger for developing countries (Saltiel, 2020). These results also have potential implications as to which groups should, in principle, be the first to receive the COVID-vaccine.

This paper provides evidence regarding the share of workers who are most exposed to COVID-19 and social distancing effects in Colombia, a developing country with high shares of informality. Unlike the studies of developed countries (e. g., ons, 2020; Mongey & Weinberg, 2020; Mongey et al., 2020, etc.), it focuses explicitly on characterising the informal sector (contrasting the results between the informal and formal workers) and how these interact with the occupational characteristics identified in the literature --unlike some studies of developing countries, such as Delaporte and Pena (2020) and Dingel and Neiman (2020). In addition, it examines the ability to work from home and the issues of individual proximity, but also the necessity of face-to-face working. The present work made several statistical and methodological interventions to improve the relatively poor-quality data, which often confounds statistical analyses for developing countries, particularly for the informal population. The study replaces the outdated Colombian occupational classification by recoding the raw (uncoded) occupational data to the most recent international occupation (ISCO-08) at a highly detailed occupational level (four-digit). As the use of O*NET (Occupational Information Network) to identify the characteristics of jobs by occupation in developing economies has been widely debated, the present study uses both O*NET and STEP (Skills Toward Employability and Productivity Survey) to identify the key characteristics of these four-digit occupations (that may make them more or less susceptible to transmitting the virus) to ensure the results are comparable.

Labour workforce information from the Colombian Household Survey --Gran Encuesta Integrada de Hogares (GEIH)--is utilised to identify the share of most exposed workers to COVID-19 and social distancing effects. This survey collects information of 23 000 households monthly, which allows Colombian populations' characteristics and employment structure to be fully characterised. Having this detailed occupational classification enables to directly measute a COVID-19 occupational risk proximity index (Mongey et al. 2020) without using crosswalks or groupings and, thus, provides a more precise estimate. In order to quantify the possible effects of social distancing, work-from-home occupations are identified at the regional and sectoral levels, following Dingel and Neiman (2020), as well as an alternative measure proposed by Saltiel (2020) to compare the results.

First, we found that informal workers are less compatible with work-from-home occupations and that sectors with the largest share of employment are less compatible with telework. Second, proximity affects more formal occupations, and proximity's affectation on informal employment is comparatively small when calculated using the index for face-to-face interaction. Personal and household characteristics are important to explain proximity and face-to-face. These findings suggest that the informal population is more vulnerable to the effects of social distance. This should raise policymakers' concerns since informal workers are already in a vulnerable population and, thus, the effects after confinement affect informal workers. Additionally, if measures are not or cannot be put in place to protect such workers, it may lead to an increase in informality. We found that the results from the O*NET and STEP lead to similar statistical estimates. This fact encourages the cautionary complimentary use of the O*NET as secondary data for analysis in developing countries.

This paper makes the following contributions. First, it provides evidence on the likely different effects of COVID-19 in a developing country, Colombia, which exhibits the main characteristics of a dual economy. Second, this paper uses mixed methods, including machine learning techniques, to reallocate the raw household survey data to the latest international occupational classification, rather than relying on an outdated Colombian classification or using crosswalks. Third, it contributes to the ongoing debate on the suitability of O*NET in the context of identifying the characteristics of occupations in developing countries (Lo Bello et al., 2019). Specifically, results based on the us O*NET are compared with those derived using STEP, which measures skills in low and middle-income countries. Fourth, unlike other studies, it utilises all three measures of exposure to risk which appear in the literature (teleworkability--the ability to work from home, individual proximity--whether the individual is required to work close to other workers, customers, etc. and the need for intensive face-to-face contact) and applies them to a developing rather than a developed economy. Finally, the paper provides evidence of links between COVID-19 and informal employment, as well as personal and household characteristics that can provide a basis for informing government policies for different groups, including those most likely to benefit from COVID-19 vaccines.

This document is divided into four parts. The first one presents the data. Next, it presents the methodology using labour workforce information from the Colombian Household Survey and which occupations are...

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