ASSOCIATION BETWEEN INFORMAL EMPLOYMENT AND COVID-19 POSITIVE CASES IN PERU

ABSTRACT

Objectives: To identify the association between the number of informal employees and the number of positive COVID-19 cases in Peru. Methods: Data from the Peruvian National Institute of Statistics and Informatics and the National COVID-19 database were used. Bivariate linear regression and multivariate logistic regression were performed to evaluate the number of informal employees, population density, and altitude in relation to the number of COVID-19 positive cases. Results: Bivariate analysis showed that the number of informal employees was significantly associated with the number of COVID-19 positive cases in both high and low altitude regions (p<0.001). In the multivariate analysis, it was found that the number of informal employees (p<0.001), population density (p=0.02), and altitude (p<0.001) were associated with the number of COVID-19 positive cases. Conclusions: Informal employees are common in low- and middle-income countries where there is no social security and they are economically dependent on daily wages. Their situation worsened due to social mobility restrictions, forcing them to continue working and, consequently, becoming quickly infected, further contributing as a contagion focus.

Keywords: Informal sector, COVID, Peru. (Source: MeSH – NLM)


RESUMEN

Objetivos: Identificar la asociación entre el número de empleados informales y el número de casos positivos de COVID-19 en Perú. Métodos: Se utilizaron datos del Instituto Nacional de Estadística e Informática del Perú y de la Base de Datos Nacional de COVID-19. Se realizó una regresión lineal bivariada y logística multivariada para evaluar el número de empleados informales, la densidad de la población y la altitud en relación con el número de casos positivos de COVID-19. Resultados: El análisis bivariado mostró que el número de empleados informales estuvo significativamente asociado con los casos positivos de COVID-19 en regiones de alta y baja altitud (p<0.001). En cuanto al análisis multivariado, se encontró que los empleados informales (p<0.001), la densidad poblacional (p=0.02) y la altitud (p<0.001) estuvieron asociados con el número de casos positivos de COVID-19. Conclusiones: Los empleados informales son comunes en países de bajos y medianos ingresos donde no hay seguridad social y dependen económicamente de salarios diarios. Su situación se vio agravada debido a las restricciones en la movilidad social, lo que los obligó a seguir trabajando y, en consecuencia, a contagiarse rápidamente, convirtiéndose en un foco de contagio.

Palabras clave: Sector informal, COVID, Perú. (Fuente: DeCS – Bireme)

INTRODUCTION

In low- and middle-income countries, almost three-quarters of non-agricultural jobs are informal ⁽¹⁾. This population, lacking social security and government protection, along with their low resources and limited job opportunities, economically depends on daily wages to cover basic needs ⁽²⁾. However, due to current social mobility restrictions and limited support, they have been forced to choose between working with the risk of infection or starving ⁽³⁾.

Depending on the type of occupation, there are certain health risks due to lack of labor regulation compared to formal workers. Associations have been found between informal employment and respiratory infections, such as a high prevalence of tuberculosis ⁽⁴⁾, which could be exacerbated by the virulence of the coronavirus.

The health emergency led countries to implement social distancing. Although the measures adopted were strict and early, public health was affected by pre- pandemic factors such as informality and social inequality, with Peru being the 7th country with the most cases and the first in global mortality. Additionally, the government took 54 days after imposing quarantine to provide economic support to the informal sector, although food baskets were not included as in neighboring countries. Thus, at the beginning of the quarantine, 59% of families were vulnerable and forced to go out into the streets, exposing themselves to coronavirus infection in violation of containment and mitigation measures ⁽⁵⁾.

Therefore, our objective was to determine the association between informal employment and positive COVID-19 cases in the Peruvian territory.

METHODS

Design and study area: Observational, analytical, and cross-sectional study using secondary sources. The authors used data from the Instituto Nacional de Estadística e Informática of 2018, a nationally representative survey ⁽⁶⁾, and the National Open Data Platform where COVID-19 cases have been reported from March 3 to November 17, 2020 ⁽⁷⁾.

Variables and instruments: The units of analysis were the Peruvian geographical regions. The dependent variable was the number of confirmed cases (thousands of positive COVID-19 cases), while the independent variables were the number of informal employees (thousands), population density (inhabitants per km²), and altitude (meters). Informal employment was defined as self-employed employers whose productive unit belongs to the informal sector; employees without social security funded by their employer, and unpaid family workers ⁽⁸⁾. Altitude was classified as low and high. The capital region, Lima, was excluded from the analysis due to being an outlier with its high population density and number of cases.

Statistical analysis: The authors analyzed the data using Stata 16 software. Bivariate and multivariate analyses were performed with linear regression, and a P-value <0.05 was considered significant.

Ethical considerations: Not relevant when working with secondary sources open to the general reader, where no survey or study participant data are recorded.

RESULTS

Twenty-five geographical regions were included after excluding Lima, 16 of which were categorized as low-altitude areas and 9 as high-altitude areas. The high-altitude regions had a lower prevalence of positive cases. In the bivariate analysis (Fig. 1), it was found that in low-altitude regions, the number of informal employees was positively associated with the number of positive cases (β=4.0x10^-2; p<0.001), while population density was not associated (β=2.2x10^-3; p=0.2). In high-altitude regions, the number of informal employees was also positively associated with the number of positive cases (β=3.0x10^-2; p<0.001); however, density was not associated (β=5.1x10^-1; p=0.08).

Fig. 1: The relationship between the number of informal workers (in thousands) and the number of positive COVID-19 cases (in thousands) until November 17, 2020, in high and low altitude regions.

In the multivariate analysis, it was evidenced that the number of informal employees (β=3.7x10^-2; p<0.001), population density (β=2.4x10^-3; p<0.001), and region altitude (β=-12.6; p<0.001) were associated with the number of positive COVID-19 cases.

DISCUSSION

Our results showed an inverse relationship between altitude and the number of positive COVID-19 cases, as previously reported in other studies ⁽⁹⁾. After stratifying the regions by altitude, the number of informal employees was positively associated with positive COVID-19 cases, suggesting that regions with higher informality will continue to be affected, especially in the face of possible waves or variants. In countries like India or South Africa, where informality rates are higher, the number of cases per 100,000 inhabitants has not been as high as in Peru.

This could be explained by the fact that these populations received early support from their governments through basic cash subsidies and food baskets, as well as personal protective equipment and a safe working environment after the quarantine ⁽¹⁰⁾.

Due to low wages, nonexistent health coverage, and poor working conditions offered by informal employment to economically dependent families, they become more vulnerable to diseases and consequently have to pay for treatments, making them poorer and more dependent on their employment ⁽²⁾. This can be demonstrated as more than 50% of sick women continue to work informally out of necessity ⁽¹⁾. Therefore, in this pandemic situation, the need forces them to go out into the streets and expose themselves to infection.

CONCLUSIONS

In conclusion, it is necessary to provide these families with financial assistance plans, such as tax reductions and financial facilities, accompanied by continuous epidemiological surveillance. In the long term, the government can offer jobs to low-income individuals and monitor working conditions to integrate them into the formal sector. This association should be considered in countries with high rates of informal employment to prevent this group from becoming a high-prevalence population and a focus of contagion in future health emergencies.

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