Revisiting the Pandemic: Surveys on the Impact of COVID-19 on Small Businesses and Workers THAILAND THAILAND Revisiting the Pandemic: Surveys on the Impact of COVID-19 on Small Businesses and Workers Thailand Report Three Rounds of Surveys (May 2020 - January 2021) Nalitra Thaiprasert Supanika Leurcharasmee Matthew Chatsuwan Woraphon Yamaka Thomas Parks MAY 2021 Copyright © The Asia Foundation 2021 Table of contents ACKNOWLEDGEMENTS 1 INTRODUCTION 2 RESEARCH METHODOLOGY 3 IMPACT ON THE THAI WORKFORCE 11 IMPACT ON MICRO AND SMALL BUSINESSES 25 CONCLUSIONS AND POLICY RECOMMENDATIONS 41 ANNEX 44 Acknowledgements The Asia Foundation gratefully acknowledges the contributions that many individuals, organizations, and funders have made in carrying out this study on the impact of COVID-19 on small businesses and workers, which covers six countries in Southeast Asia: Cambodia, the Lao Peoples’ Democratic Republic (Lao P.D.R.), Malaysia, Myanmar, Thailand, and Timor-Leste. In most cases, the Foundation collaborated with local partners in designing and carrying out the country surveys, conducting interviews for the case studies, and analyzing the data. For this report, which presents research we conducted in Thailand, the Foundation would like to thank our Thai partners: the School of Development Economics of the National Institute of Development Administration (NIDA), and the survey firm, MI Advisory (Thailand). We would also like to thank our research assistant, Nathapong Tuntichiranon, who helped us to analyze the data, and produced the excellent figures; Jirakom Sirisrisakulchai and Paravee Maneejuk from Chiang Mai University’s Faculty of Economics, who reviewed the econometric models; Athima Bhukdeewuth, who prepared the cover design and the layout; and our technical editor, Ann Bishop. This study would not have been possible either without the generous support of our funders. In Thailand, this was the United States Congressional Appropriation. The findings, interpretations, and conclusions of this study do not necessarily represent the views of The Asia Foundation or our funders. 1 Introduction The COVID-19 pandemic is leading to a dramatic roll- As large amounts of public money have been mobilized back of economic progress across Southeast Asia. to help address the unprecedented COVID-19 crisis, While the region has managed to contain the spread of governments urgently need ground-level data on how the virus better than most others, the economic impact businesses and workers are being affected, and how on the region has been devastating. As a result of its they are coping. This information is essential so that heavy dependence on the tourism sector, Thailand governments can target their programs to achieve has one of the worst affected economies in Southeast maximum benefit. To address governments’ need for Asia. Since international travel stopped almost entirely accurate data on how COVID-19 is impacting MSMEs, in March 2020, Thailand’s tourism and business travel vulnerable workers, and the informal economy in sectors have experienced unprecedented contraction. heavily affected sectors, and how they are coping, Many travel industry micro and small enterprises The Asia Foundation (the Foundation) has conducted (MSMEs) have closed permanently as they could a series of surveys and case studies in Thailand, and not survive the economic contractions brought on by five other Southeast Asian countries: Cambodia, COVID-19 lockdowns and travel restrictions. With each the Lao Peoples’ Democratic Republic (Lao P.D.R.), passing month, tens of thousands more Thai workers Myanmar, Malaysia, and Timor-Leste. These surveys have become at risk of sliding into poverty, including and cases studies, which have been conducted many in the middle class. As the pandemic drags on, with the Foundation’s local research partners, were temporary job losses have become permanent, and carried out via telephone calls and internet platforms. household incomes have plummeted. To determine the key survey questions for all six countries, the Foundation’s offices in each country Governments across Southeast Asia have responded engaged with national government policy-makers and with an array of new programs to help the people and other relevant officials. The Foundation’s local research businesses most affected by the pandemic. Thailand partners then tailored the questions for the surveys has supplemented its existing social protection and case studies to make them locally relevant. The schemes by introducing new programs for informal local partners also carried out the surveys and case workers, and temporarily reduced the expenses studies, analyzed the data, and collaborated with the of people who have lost their jobs and income. For Foundation in writing up the results. businesses, the Thai government has introduced new subsidized loan programs, tax breaks, debt repayment This research aimed to identify the MSMEs and holidays, and incentives for keeping employees on workers that are the most affected by the COVID-19 the payroll. These crucial programs are essential for crisis so that policy makers and development agencies economic recovery, and the prevention of large-scale are informed about the situation on the ground, and increases in poverty and inequality. can make informed decisions on how best to keep the country’s path to recovery on a stable trajectory. 2 Research Methodology To assess the impact of COVID-19 on Thai workers May 2020 (first period), August 2020 (second period) and MSMEs, between May 2020 and January 2021, and November 2020 (third period). The surveys with The Asia Foundation’s local partners conducted three the Thai MSMEs were conducted in June 2020 rounds of surveys with a sample of Thai workers and (first period), September 2020 (second period), and three rounds of surveys with a sample of MSMEs. December 2020/January 2021 (third period). The surveys with Thai workers were conducted in Thai Workforce Micro & Small Businesses Report Released First period May 2020 June 2020 September 2020 Second period August 2020 September 2020 N/A Third period November 2020 December 2020 - January 2021 May 2021 The Survivability Model For workers, the information collected from each included their geographic region, age, gender, The research team developed a model to identify the education level, occupation, income, debt, and most important factors that determine the extent of government assistance received. To measure their COVID-19’s impact on individual workers and MSMEs. survivability, workers were asked the question “If This “Survivability Model” is an econometric analysis1 Thailand faces COVID-19 for another year, how that was used to analyze this study’s survey data by long do you think you can last, given your income, focusing on the factors that supported or hindered savings, and all the food that you have now?” the survivability of the Thai workers and MSMEs In answering this question, each worker was given in each of the three time periods listed above. The a range of choices from one day to one year, and term “survivability” applies to how long an individual this answer was used to determine a respondent’s worker or MSME owner believes their resources survivability. Note, although the term “workforce will last if the pandemic continues. In other words, survey” was used, a small number of respondents the survivability of a worker or MSME is an estimate were retired or not working. Also note, this is an of how much longer they could keep going, under economic model, and the term “survivability” does present conditions, before they run out of money and not concern health or mortality resulting from the have to cut back on their consumption. pandemic. 1. The Cox proportional-hazards regression model is used to predict the end of an event, such as death, bankruptcy, or consumption failure. The LASSO (Least Absolute Shrinkage and Selection Operator) method developed by Tibshirani (1997) is used to perform variable selection. The LASSO estimation allows a large number of variables to be included in the model and can enhance the prediction accuracy and interpretability of the resulting statistical model. Specifically, the LASSO model estimates values of parameters and simultaneously forces out unrelated regressors from the regression model. Thus, the best set of factors affecting the survival probability of individuals can be obtained. Note that the statistical inference on the parameter’s estimation is not required because the LASSO model has already selected the best set of regressors that achieved the lowest level of prediction errors. Source: Tibshirani, R. (1997). “The LASSO method for variable selection in the Cox model.” Statistics in Medicine, 16, 385–395. The description of the Cox model used for this study appears in Leurcharusmee, S., Yamaka, W., Maneejuk, P., Thaiprasert, N. & Tuntichiranon, N. (In Progress). “Economic Survival Duration of Thai Workers During COVID-19.” A Working Paper. Chiang Mai University; and in Maneejuk, P., Leurcharusmee, S., Yamaka, W., Thaiprasert, N. & Tuntichiranon, N. (In progress). “A Survival Analysis of Thai Micro and Small Enterprises: Does the COVID-19 Pandemic Matter?” A Working Paper. Chiang Mai University. 3 The analysis of workers’ and MSMEs’ survey data The “survivability” of an individual worker or produced a series of charts that show the estimated MSME is their own estimate of how much longer “survivability” of the workers and MSMEs surveyed. they can keep going, under present conditions,