5. The Impact of COVID-19 on the Lao Tourism Sector: Evidence from Employment and Value Added

Open Access
2026
OriginalPaper
Chapter

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Author: Viriyasack Sisouphanthong
Published in: Navigating Through Crisis
Publisher: Springer Nature Singapore

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Abstract

The chapter delves into the devastating effects of COVID-19 on the Lao tourism sector, focusing on employment and value-added changes. It reveals that 39.5% of tourism workers reported being affected by the pandemic, compared to 23.1% in non-tourism sectors. The study highlights significant revenue losses, with the accommodation and restaurant sectors experiencing an 87.9% reduction in value-added. The analysis also shows that tourism workers faced an average monthly wage loss of 6.9 million LAK over 4.9 months, totaling 48.5 million LAK in lost income. The chapter concludes with policy recommendations aimed at supporting the recovery of the tourism sector, emphasizing the need for targeted financial assistance and crisis management plans.

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5.1 Introduction

The tourism sector, which encompasses accommodation, restaurants, and transportation, plays a significant role in bolstering a nation’s Gross Domestic Product (GDP) and employment (Khanal et al. 2014). It promotes the inflow of foreign capital, thereby contributing substantially to macroeconomic stability (Umurzakov et al. 2023). In particular, tourism plays a key role in many developing countries, such as the Lao People’s Democratic Republic (Lao PDR) (Phoummasak et al. 2014; Phommavong, 2011). As a landlocked country with a modest population of 7.9 million, Lao PDR faces challenges in exporting goods and attracting both domestic and foreign industrial investments; therefore, the tourism sector is a core driver of economic development and income distribution.

The COVID-19 pandemic has had a substantial impact on the social, political, and economic spheres of several countries. Welfens (2020) examines the macroeconomic and healthcare dimensions of the pandemic, suggesting that countries heavily reliant on the tourism sector as a significant contributor to their Gross Domestic Product (GDP) are likely to experience more pronounced constraints on output growth. Škare et al. (2021) analyze the substantial influence of the COVID-19 pandemic on the tourism sector by employing a panel structural vector autoregression model using data from 185 countries spanning the period from 1995 to 2019. They illustrate the significant disruptions the pandemic caused to global tourism. According to Fotiadis et al. (2021), a decline in tourist arrivals ranging from 30.8% to 76.3% was projected using long short-term memory neural networks and generalized additive models.

The pandemic has also had a significant impact on the socio-economic situation of several developing countries. From 2019 to 2020, the number of tourism arrivals declined sharply in various countries, including Thailand (77%), Vietnam (73%), the Republic of Korea (70%), and China (47%) (MICT, 2019; MICT, 2020). Several studies have been conducted within the countries of the sub-Mekong region. Vithayaporn (2021) performed a systematic literature analysis to examine the adverse effects of COVID-19 on Thailand’s tourism industry, including the airline and hotel sectors. Vu et al. (2022) explored the relationship between the incidence of COVID-19 infections and the performance of the tourism sector in Vietnam. The researchers found compelling evidence that the pandemic has substantially influenced various aspects of the tourism industry, including visitor numbers, business operations, revenue generation, and employment rates. According to a study conducted by Huynh et al. (2021), many tourism enterprises have been adversely affected by successive waves of infections, resulting in a significant number of firms facing imminent insolvency or permanent closure. These businesses have experienced marked decreases in both client demand and income, leading to a subsequent reduction in staff numbers.

In Lao PDR, the cumulative number of COVID-19 infections reported by the Ministry of Health stands at 207,300, accounting for 2.6% of the total population (MOH, 2024). The pandemic slowed several significant economic sectors, including agriculture, commerce, remittance inflows, and notably, tourism (Southichack et al. 2020). Travel restrictions implemented in March 2020 prohibited Lao citizens from traveling abroad and foreign nationals from entering Lao PDR. These travel restrictions, coupled with fear of infection, led to an unprecedented slump in tourist arrivals (United Nations, 2020). Major tourist hotspots, hotels, and related businesses experienced dramatic drops in visitors, resulting in substantial revenue losses and widespread job cuts. According to a study conducted by Southichack et al. (2020), there was a significant decline in tourism revenue beginning in March 2020, which persisted throughout the remainder of the year. The MICT (2021) reports that international arrivals and tourism revenue declined by 74% and 41%, respectively. Revenue losses in the accommodation sector reached 79%, in tourism attractions 81%, and in restaurants and entertainment 72%. The report also noted that 80% of tourism enterprises laid off employees, with many reducing their workforce by 50% or more. These figures underscore not only the revenue losses, but also the significant labor market disruptions facing the Lao tourism sector.

Several studies have primarily focused on revenue losses in the tourism sector; however, there is a notable lack of research examining the effects of COVID-19 on employment outcomes and value added in the tourism sector. This study aims to address this research gap by providing a quantitative assessment of COVID-19’s impact on employment and value-added in the Lao tourism sector. Therefore, the main research objective of this study is to estimate the impact of COVID-19 on the tourism sector in Lao PDR. The specific objectives include:

Identifying the channels through which the COVID-19 pandemic affected employment in the tourism sector.
Estimating the effect of working in tourism on the probability of being affected from COVID-19.
Estimating the effect of working in tourism on the income loss from COVID-19.
Measuring the overall change in the value added by the tourism sector as a result of the pandemic.

This paper is organized into five sections. The current section provides an overview of the significance of tourism and the impact of COVID-19 in Lao PDR. Section 2 details the research design and methods employed. Section 3 describes the data utilized in the analysis. Section 4 presents the findings from the quantitative analyses. Finally, Section 5 concludes with a discussion of the results and their potential policy implications.

5.2 Methodology

This study employs a quantitative methodology using secondary data. The tourism industry can be classified into two distinct economic sectors: accommodation and restaurant services, and transportation services. The analysis involves the integration of descriptive statistical techniques and econometric estimation. We separate the analysis into two parts which are (1) descriptive analysis and estimation of the effect of COVID-19 on employment and (2) measuring the change in value-added from COVID-19. The detailed estimation technique is described in following sub-sections.

5.2.1 COVID-19 Pandemic and Tourism Employment

To analyze the effect of working in the tourism sector on the probability of being affected by COVID-19, this study employs a quantitative approach, that includes descriptive statistics and econometric estimation using individual-level data from the third Lao Labor Force Survey. This survey contains detailed information on the probability of being affected and salary losses due to COVID-19. First, we explain the channels through which COVID-19 impacts employment. Second, the econometric estimation is conducted using two outcome indicators, as outlined below:

$$\begin{array}{l}P\left( {Covid\_effect = 1} \right) = f({\alpha _0} + {\alpha _1}Tourism + {\alpha _2}Female + {\alpha _3}Age + {\alpha _4}Urban\\ \qquad \qquad \qquad \qquad \qquad \qquad + \, {\alpha _5}Lao + {\alpha _6}Khmu + {\alpha _7}Hmong + {\alpha _8}Primary + {\alpha _9}Lower\_\sec \\ \qquad \qquad \qquad \qquad \qquad \qquad + \, {\alpha _{10}}Upper\_\sec + {\alpha _{11}}Vocational + {\alpha _{12}}Higher\_edu + error),\end{array}$$

$$\begin{array}{l}\ln \left( {income\_loss} \right) = {\beta _0} + {\beta _1}Tourism + {\beta _2}Female + {\beta _3}Age + {\beta _4}Urban\\ \qquad \qquad \qquad \qquad \qquad + {\beta _5}Lao + {\beta _6}Khmu + {\beta _7}Hmong + {\beta _8}Primary + {\beta _9}Lower\_\sec \\ \qquad \qquad \qquad \qquad \qquad + {\beta _{10}}Upper\_\sec + {\beta _{11}}Vocational + {\beta _{12}}Higher\_edu + error,\end{array}$$

where the measurement of variables shown above is illustrated in the table below:

The Covid_effect is a binary variable, so the econometric equation is estimated by logit estimation, while Income_loss is a continuous variable, so its equation is estimated by the ordinary least square. In the Income_loss equation, the dependent variable is in logarithm form, thus, the coefficient from estimation is transformed into percentage terms. Furthermore, the econometric equations are then disaggregated by different demographic groups such as gender, ethnicity, and rural/urban status to understand the distribution of the COVID-19 effect (Table 5.1).

Table 5.1

Variable measurement

Variable	Measurement
Covid_effect	1 = individual reports of COVID-19 effect, 0 = otherwise
Income_loss	Monthly income loss * months lost in million LAK
Tourism	1 = works in tourism sector
Female	1 = female, 0 = otherwise
Age	Years
Urban	1 = living in an urban area, 0 = otherwise
Lao	1 = Lao ethnicity, 0 = otherwise
Khmu	1 = Khmu ethnicity, 0 = otherwise
Hmong	1 = Hmong ethnicity, 0 = otherwise
Primary	1 = primary education, 0 = otherwise
Lower_sec	1 = lower secondary education, 0 = otherwise
Upper_sec	1 = upper secondary education, 0 = otherwise
Vocational	1 = vocational education, 0 = otherwise
Higher_educ	1 = higher education, 0 = otherwise

Due to limitations in data availability, information regarding the COVID-19 effects is only available in the third Labor Force Survey. Consequently, individuals were asked about their involvement in the tourism sector in January 2022. We assume that individuals remained in the same economic sector during the pandemic through January 2022. However, this period is relatively short, and individuals have a low likelihood of transitioning between different economic sectors during that time.

5.2.2 Value-Added of the Tourism Sector Due to the COVID-19 Pandemic

To evaluate the change in value-added within the tourism sector due to COVID-19, we aim to construct a scenario of the tourism sector with and without the COVID-19 pandemic. The scenario of the tourism sector with the pandemic is the actual situation, and thus, we must create a scenario of the tourism sector without the pandemic. To construct the counterfactual scenario, we use an econometric equation with time-series data, as shown below:

$$\ln \left(V{A}_t\right)=\rho +\sum_k{\rho}_k\left(\ln \left(V{A}_{t-k}\right)\right)+\sum_j{\theta}_j\left({Q}_{jt}\right)+\delta \left( Covi{d}_t\right)+ error,$$

where VA represents the value-added of the economic sector at time t; k is the lag length that will be determined later; Q represents seasonal dummy variables controlling for quarterly effects; j indicates the quarter number; and Covid is a binary variable indicating the COVID-19 pandemic period. The parameter δ provides an assessment of the change in the tourism industry from the COVID-19 pandemic. The econometric equation is known as the Autoregressive Model, which incorporates quarterly data of value-added sourced from the Lao Statistics Bureau. The table below presents the variables’ measurements (Table 5.2).

Table 5.2

Variables measurement

Variable	Measurement
VA	Value-added of the tourism sector at time t (in constant billion LAK)
Q	Seasonable variable controlling for quarter, 1 = quarter j, 0 = otherwise
Covid	1 = during COVID-19 pandemic (April 2020 to April 2022), 0 = pre-COVID-19

5.3 Data

The data used in this study is divided into cross-sectional data for objectives one to three and time-series data for the final objective. The cross-sectional data comes from the third Lao Labor Force Survey, conducted by the Lao Statistics Bureau. It is important to note that the Labor Force Survey was carried out in early 2022, after the COVID-19 pandemic. The study has a sample size of 52,623 individuals from various regions across the country. After data cleaning and considering data availability, the final sample includes 14,449 observations.

Table 5.3 shows the descriptive statistics. The mean value for the variable Covid_effect is 0.238, indicating that 23.8% of the sample was affected by COVID-19. The Income_loss variable, based on 549 observations, has a mean of 31.090 million LAK in losses due to the COVID-19 pandemic. Among the sample, 4.2% work in the tourism sector. Demographically, the average age is 36.284 years and 43.1% of total sample is female. The urban population constitutes 39.3%, while 57.7% identify as Lao ethnicity, 10.8% as Khmu ethnicity, and 7.0% as Hmong ethnicity. Educational attainment shows that 38.3% have primary education, 25.2% have lower secondary education, 18.5% have upper secondary education, 11.2% have vocational education, and 6.0% have university or higher education.

Table 5.3

Descriptive statistics of the third Lao labor force survey

Variable	Observations	Mean	Standard deviation	Minimum	Maximum
Covid_effect	14,449	0.238	0.426	0	1
Income_loss	549	31.090	161.013	0.3	2800
Tourism	14,449	0.042	0.201	0	1
Female	14,449	0.431	0.495	0	1
Age	14,449	36.284	13.229	14	93
Urban	14,449	0.393	0.488	0	1
Lao	14,449	0.577	0.494	0	1
Khmu	14,449	0.108	0.310	0	1
Hmong	14,449	0.070	0.255	0	1
Primary	14,449	0.383	0.486	0	1
Lower_sec	14,449	0.252	0.434	0	1
Upper_sec	14,449	0.185	0.388	0	1
Vocational	14,449	0.112	0.315	0	1
Higher_educ	14,449	0.060	0.237	0	1

Source Author’s calculations using data from the Labour Force Survey 2022.

The time-series data used to estimate changes in value-added includes quarterly GDP for the accommodation, restaurant, and transportation sectors. This data spans over 40 quarters, covering the period from the first quarter of 2012 to the fourth quarter of 2021. The mean value-added for the accommodation and restaurant sectors is 671.725 billion LAK, which is higher than that for the transportation sector, at 401.100 billion LAK as shown in Table 5.4.

Table 5.4

Descriptive statistics of tourism value-added

Variable	Observations	Mean	Standard deviation	Minimum	Maximum
VA_{transportation}	40	401.100	88.939	232	560
VA_{accomodation and restaurant}	40	671.725	260.712	132	1275
VA_tourism	40	1072.825	239.619	670	1679

Source Author’s calculations using data from the Lao Statistics Bureau.

5.4 Results

5.4.1 Descriptive Statistics

Figure 5.1 illustrates the share of workers who reported being affected by COVID-19 across the overall sample, the tourism sample, and the non-tourism sample. Among the total sample, 23.8% reported being impacted by the pandemic. Workers in the tourism industry reported a significantly higher effect of 39.5%, while workers in the non-tourism sector represented 23.1%. This data highlights the substantial influence of the pandemic on both sectors, with the tourism sector experiencing nearly double the proportion of impact compared to the non-tourism sector.

Fig. 5.1

Share of COVID-19 effect on employment.