Reshaping Thailand’s Labor Market Structure: The Unified Forces of Technology and Trade
Improvements in technology can have substantial impact on the labor market both directly and indirectly via changes in global trade patterns. This paper studies the potential impact of computerization and reshoring/relocating of operations by firms on Thailand’s labor market. Specifically, the analysis is built upon Frey and Osborne’s (2017) approach and incorporates additional measures of trade-base tasks. This is so that the revised machine-learning model can account for both the impact of technology and change in global trade patterns. Our results revealed that occupations that are mostly affected are service and sales workers, and agricultural and fishery workers. In the worst-case scenario, approximately one-third of existing jobs (12.14 million jobs) could be at risk. However, in real situations, new types of jobs may be created, workers may voluntarily adjust, or other factors could drive some overseas operations back to Thailand. Therefore, the potential outlook for Thailand’s labor market may not be as severe as the model has predicted.
Labor Income Inequality in Thailand: the Roles of Education, Occupation and Employment History
Thailand’s income inequality has reportedly declined since the mid-1990s. This paper examines possible mechanisms underlying the dynamic patterns of the country’s labor income inequality. Using the Thai labor force survey between 1988 and 2017, we document that the country’s reduction in income inequality is likely driven by the fact the earnings at the bottom part of the distribution have become more similar. The median wage gap between college and non-college workers, however, still gets larger over time. Our key explanation is the changes in education-occupation composition. Recently college graduates are no longer concentrated in high skill jobs. A larger share of secondary educated workers works in low-skill jobs instead of the middle-skill ones. Using panel administrative data from the Thai Social Security Office, we find that wage disparity can also be explained by employment history. The high wage earners earn more since they enter the market, and the gap gets wider as the workers age. Additionally, the top of the group can command higher wages by working at a large firm or switching to a new job. These findings highlight the fact that to tackle the income inequality issue, the country needs to understand the underlying mechanisms behinds its dynamics.
Alternative Boomerang Kids, Intergenerational Co-residence, and Maternal Labor Supply
This study investigates the boomerang phenomenon among adult children in Thailand. We estimate the effect of having children on co-residence between parents and adult children using Socio-Economic Survey panel data. We find that adult children who have moved out tend to move back in with their parents after having children to save time and money on childcare. The presence of young children increases the likelihood of intergenerational co-residence by over 30%. This study is the first to provide empirical evidence of boomerang kids in an Asian context, which is distinctive compared with Western countries. The relationship between intergenerational co-residence and the maternal labor supply is also examined using the instrumental variable approach based on the cross-sectional Labor Force Survey, which has data covering over 30 years. Our results show that co-residence increases the female labor supply by 21% and also extends women’s working hours by 10 hours.
Parenthood Penalty and Gender Wage Gap: Recent Evidence from Thailand
This study first examines the evolution of gender wage gap in Thailand, using cross-sectional data from the Labor Force Survey (LFS) for 1985–2017. We find that education, occupation, and industry significantly contribute to gender wage gap convergence in Thailand. Furthermore, for females, the wage gap between mothers and non-mothers has increased over time, while for males, the changes are relatively small. Thereafter, we examine the gender wage gap associated with marriage and parental status, using panel data from the Socio-Economic Survey (SES) for 2005– 2012, and find wage penalty for both motherhood and fatherhood in Thailand.
On Worker and Firm Heterogeneity in Wages and Employment Mobility: Evidence from Danish Register Data
In this paper, we develop a model of wage dynamics and employment mobility with unrestricted interactions between worker and firm unobserved characteristics in both wages and employment mobility. We adopt the finite mixture approach of Bonhomme et al. (2017). The model is estimated on Danish matched employer-employee data for the period 1985-2013. The estimation includes gender, education, age, tenure and time controls. We find significant sorting on wages and it is stable over the period. Sorting is established early in careers, increasing during the first decade after which it declines steadily. Job-to-job mobility displays a “mean-reverting” pattern that maintains correlations between worker and firm types to a stationary level. Counterfactuals demonstrate that sorting is primarily driven by two channels: First, a “preference” channel whereby higher wage workers are more likely to accept jobs in higher wage firms. Second, a job finding channel where the job destination distribution out of non-employment is stochastically increasing in the wage type of the worker.
Labour Supply of Married Women in Thailand: 1985-2016
This study investigates the labour supply behaviour of married Thai women with reference to their own and their spouse’s wages. By utilising data of the national Labour Force Survey in Thailand from 1985 to 2016, the wage imputation technique and the instrumental variables approach are applied to correct sample selection and to alleviate endogeneity, common issues that cause bias in estimating female labour supply. By controlling for spousal education and number of children, the main findings indicate an inverse relationship between married women’s labour supply and wages, contrary to the results found in most developed countries. The estimated own wage elasticity ranges from -1.70 to -2.40 and cross elasticity ranges from -0.16 to -0.17, indicating that the impact of own wage on labour supplied is much larger than spouse’s wage. The results from disaggregation classified according to different socioeconomic backgrounds also show the negative elasticities between own and spouses’ wage across all subgroups, except for those with university degrees and higher income.
Minimum Wage and Lives of the Poor: Evidence from Thailand
Studying how the poor respond to the minimum wage policy in Thailand, I find that a notable increase in the minimum wage has no significant impact on employment among the poor even though wage plays a vital but heterogeneous role in determining employment. Also, this policy can significantly boost expenditure among the poor residing in provinces where the minimum wage is adjusted dramatically. Surprisingly, food does not account for the largest share of consumption as the income of the poor rises. The results are still robust to additional controls and redefinition of the poverty.
Uncovering Productivity Puzzles in Thailand: Lessons from Microdata
The Asian financial crisis in 1997 has an impact on Thailand’s productivity both in the short run and in the long run. The post-crisis productivity growth rate dropped to merely 1% per year in comparison to the pre-crisis level at 2% per year. Thus, a better understanding about the factors determining Thailand’s aggregate productivity is a key to raising Thailand’s output in the long run. Recent literature has identified resource misallocation as an important factor to explain the difference in the productivity levels between developed and developing economies. This paper uses the plant-level data to estimate the allocative efficiency and to identify the source of resource misallocation in the Thai manufacturing sector. The results suggest that the size-dependent policies could contribute to the factor misallocation and that market concentration, foreign investment, and financial deepening could help alleviate the misallocation problem at the sector level. However, R&D activities intensifies resource misallocation that calls for well-defined policies to promote knowledge spillover within industry and to reduce the frontier-laggard gap. Dynamic resource reallocation helps shore up TFP growth over the business cycle that emphasizing a set of policy to reinforce the mechanism of creative destruction.
Predicting the Present Revisited: The Case of Thailand
Google is currently the most-used search engine in the world. There are approximately 3.5 billion searches being conducted on Google each day. With real-time processing, Google Trends data can be used in a prediction technique called nowcasting (or “predicting the present”) – using the current period’s real-time information to estimate the current period’s indicators of interest. In this paper, we showed how Google Trends can be used for nowcasting Thailand’s various economic indicators. The sectors being analyzed are (i) the labor market sector (unemployment rate and unemployment registration), (ii) the real sector (automobile sales), and (iii) the financial sector (SET index). The results revealed that incorporating the Google Trends data into the prediction models improved the Adjusted R-Squared and improved the predication accuracies under various measures.
The Impact of Immigration on Wages, Internal Migration and Welfare
This paper studies the impact of immigration on wages, internal migration and welfare. Using U.S. Census data, I estimate a spatial equilibrium model where labor differs by skill level, gender and nativity. Workers are heterogeneous in city preferences. Cities vary in productivity levels, housing prices and amenities. I use the estimated model to assess the distributional consequences of several immigration policies. The results show that a skill selective immigration policy leads to welfare gains for low skill workers, but welfare losses for high skill workers. The negative impacts are more substantial among the incumbent high skill immigrants. Internal migration mitigates the initial negative impacts, particularity in cities where high skill workers are relatively mobile. However, the negative impacts on some workers intensify. This is because an out-migration of workers of a given type may raise the local wages for workers of that type, while reducing the local wages of workers with complementary characteristics. Overall, there are substantial variations in the welfare effects of immigration across and within cities. Further, I also use the model to assess a non-selective immigration policy and deportation of unauthorized immigrants in specific areas.