Effects of the COVID-19 Pandemic on the Labour Market in Thailand.

AuthorChairassamee, Nattanicha
  1. Introduction

    Government and individual behavioural responses to the COVID-19 pandemic have strongly affected Thai labour markets. During the pandemic, the unemployment rate and the number of workers who were temporarily absent from work increased sharply. Similar to other countries, the Thai government implemented stimulus programmes for those vulnerable workers; however, controversy remains over who is definitely in need of government support.

    Our study uses individual data from the National Statistical Office of Thailand (NSO) between 2020 and 2021 to analyse unemployment and absence from work, based on individual and firm characteristics. We find that less-educated workers and the elderly were more likely to be absent from work. Additionally, workers in large firms were more likely to be unemployed, suggesting that the pandemic disproportionately affected large firms rather than micro, small or medium-sized enterprises. The results also vary based on industry (such as agriculture, manufacturing and services).

    Figure 1 shows that the unemployment rate in Thailand doubled, from 1.03 per cent in the prepandemic period to 2.15 per cent in July 2020. Even though we do not have official data for the monthly unemployment rate during the lockdown, the anticipated unemployment rate could be as high as 3.1 per cent (UNICEF 2020). (1) Moreover, the International Labour Organization (ILO 2020) reported that the proportion of workers who were temporarily absent from work also increased, which could be an indicator of further unemployment.

    The heterogeneous effects of the COVID-19 pandemic, particularly the effects of the lockdown, on labour markets have been studied in several countries. In developed nations such as the United States, the most vulnerable workers who were particularly affected during the pandemic were low-wageearning women (Cajner et al. 2020; Cowan 2020; Montenovo et al. 2020), young workers aged 20 to 24 (Montenovo et al. 2020), and minority and non-native workers (Borjas and Cassidy 2020; Couch, Fairlie, and Xu 2020). All these workers had a higher chance of being unemployed, switching to part-time work, decreasing their working hours and leaving the labour force.

    In Thailand, a few studies have explored the effects of the stay-at-home order on labour markets. These results are similar to those in developed countries. Women, low-educated, young (Hirunyatrakul 2020) and informal workers (Komin et al. 2020) were more likely to be unemployed during the lockdown.

    One reason could be that all these workers tended to work in occupations that either require physical interaction or are less adaptable to working from home (Lekfuangfu et al. 2020).

    As with previous studies, the first contribution of this paper is to analyse the determinants of being unemployed and temporarily absent from work based on workers' characteristics. Our study could shed light on those who are disproportionately affected by the crisis and, therefore, on those who are most in need of government support. In contrast with previous studies, this study benefits from the use of a large dataset of individual data. We have investigated the effects of the pandemic across all provinces in Thailand, which could be a more accurate representation of the Thai labour market. The second contribution is the analysis of the workplace. We show that workers who worked in different firm sizes and sectors were affected differently by the pandemic; therefore, policymakers should take empirical evidence into account when considering economic stimulus or related programmes.

    The remainder of the paper is organized as follows. The next two sections discuss the data and empirical model, respectively. The fourth section presents the key empirical findings, while the subsequent section contains policy insights and a discussion. The final section concludes.

  2. Data and Descriptive Statistics

    We use confidential individual Labour Force Survey (LFS) data obtained from the National Statistical Office. The study is limited to people aged 15 and above, according to the labour laws and regulations in Thailand. The sample covers approximately 186,927 people across seventy-seven provinces. The data used in this study were collected from January 2020 to March 2021. The descriptive statistics of all variables are reported in Table 1.

    Slightly more than 40 per cent of the sample are female, and about 43 per cent are household heads. The proportion of female workers in the agricultural sector is relatively less than that in other sectors. Most people in the sample are married, while less than 30 per cent are single. On average, there are approximately four members in each family.

    The workers in the sample are from 26 to 59 years old, the primary ages of workers in all sectors. More than 10 per cent of workers aged from 18 to 25 years old are in the manufacturing and service sectors, while less than 10 per cent of them work in the agricultural sector. At the same time, the number of older workers (aged 60 years old and above) in the agricultural sector is about three times higher than in the manufacturing and services sectors. The low proportion of young workers in the agricultural sector is a major issue in Thailand.

    On average, workers hold less than a high school diploma. Considered by sector, workers holding less than a high school diploma are concentrated in the agricultural sector (almost 90 per cent of the total workers in the sector), while the services sector has the highest proportion of college workers (approximately 20 per cent of the total workers in the sector).

    Approximately 32 per cent of those sampled were working/used to work in micro enterprises (with less than five employees), while 40 per cent of those sampled were working/used to work in small-sized firms (five to forty-nine employees). Large firms employ about 15 per cent of those sampled.

    Figure 2 shows unemployment rates by sector during the COVID-19 pandemic. The unemployment rates in all sectors increased during the lockdown in April 2020. Unsurprisingly, a high proportion of workers in the service sector were unemployed, and that rate remained higher than the pre-lockdown levels had been.

    The status of being absent from work, by sector, in Figure 3 is consistent with the unemployment rates. Implementing the lockdown in April 2020 increased the number of workers in the services sector who were temporarily absent from work to six times higher than pre-lockdown levels.

    In Figures 4A and 4B, the average working...

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