Different firm responses to the COVID-19 pandemic shocks: machine-learning evidence on the Vietnamese labor market

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Abstract

Purpose

This paper aims to identify the disproportionate impacts of the COVID-19 pandemic on labor markets.

Design/methodology/approach

The authors conduct a large-scale survey on 16,000 firms from 82 industries in Ho Chi Minh City, Vietnam, and analyze the data set by using different machine-learning methods.

Findings

First, job loss and reduction in state-owned enterprises have been significantly larger than in other types of organizations. Second, employees of foreign direct investment enterprises suffer a significantly lower labor income than those of other groups. Third, the adverse effects of the COVID-19 pandemic on the labor market are heterogeneous across industries and geographies. Finally, firms with high revenue in 2019 are more likely to adopt preventive measures, including the reduction of labor forces. The authors also find a significant correlation between firms' revenue and labor reduction as traditional econometrics and machine-learning techniques suggest.

Originality/value

This study has two main policy implications. First, although government support through taxes has been provided, the authors highlight evidence that there may be some additional benefit from targeting firms that have characteristics associated with layoffs or other negative labor responses. Second, the authors provide information that shows which firm characteristics are associated with particular labor market responses such as layoffs, which may help target stimulus packages. Although the COVID-19 pandemic affects most industries and occupations, heterogeneous firm responses suggest that there could be several varieties of targeted policies-targeting firms that are likely to reduce labor forces or firms likely to face reduced revenue. In this paper, the authors outline several industries and firm characteristics which appear to more directly be reducing employee counts or having negative labor responses which may lead to more cost–effect stimulus.

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The economics of COVID-19: initial empirical evidence on how family firms in five European countries cope with the corona crisis

Sascha Kraus, Thomas Clauss, Matthias Breier … (2020)

Within a very short period of time, the worldwide pandemic triggered by the novel coronavirus has not only claimed numerous lives but also caused severe limitations to daily private as well as business life. Just about every company has been affected in one way or another. This first empirical study on the effects of the COVID-19 crisis on family firms allows initial conclusions to be drawn about family firm crisis management. Exploratory qualitative research design based on 27 semi-structured interviews with key informants of family firms of all sizes in five Western European countries that are in different stages of the crisis. The COVID-19 crisis represents a new type and quality of challenge for companies. These companies are applying measures that can be assigned to three different strategies to adapt to the crisis in the short term and emerge from it stronger in the long run. Our findings show how companies in all industries and of all sizes adapt their business models to changing environmental conditions within a short period of time. Finally, the findings also show that the crisis is bringing about a significant yet unintended cultural change. On the one hand, a stronger solidarity and cohesion within the company was observed, while on the other hand, the crisis has led to a tentative digitalization. To the knowledge of the authors, this is the first empirical study in the management realm on the impacts of COVID-19 on (family) firms. It provides cross-national evidence of family firms' current reactions to the crisis.

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Gender inequality during the COVID-19 pandemic: Income, expenditure, savings, and job loss

Hai-Anh Dang, Cuong Viet Nguyen (2020)

The COVID-19 outbreak has brought unprecedented disruptions to the global economies and has led to income loss and high unemployment rates. But scant, if any, evidence exists on gender gaps in economic outcomes such as income, expenditure, savings, and job loss in a multi-country setting. We investigate the impacts of COVID-19 on gender inequality in these outcomes using data from a six-country survey that covers countries in different geographical locations and at various income levels. Our findings suggest that women are 24 percent more likely to permanently lose their job than men because of the outbreak. Women also expect their labor income to fall by 50 percent more than men do. Perhaps because of these concerns, women tend to reduce their current consumption and increase savings. Factors such as the different participation rates in work industries for men and women may take an important part in explaining these gender gaps. Our estimates also point to country heterogeneity in these gender differences that is likely due to varying infection rates and shares of women in the labor force.

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Machine Learning Methods That Economists Should Know About

Susan Athey, Guido Imbens (2019)

We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.