Abstract
Mismatches between workers’ actual and preferred hours of work are ubiquitous and have detrimental effects on well-being. Yet, the full heterogeneity of these effects and the characteristics of the most and least affected subpopulations remain largely unknown. This study collects survey data from 37 countries and estimates the full heterogeneity in the effects using a newly developed method—the sorted partial effect method. Based on the full heterogeneity, we employ classification analyses on the 10%-most and 10%-least affected groups and show that individuals most (vs. least) affected by overemployment are younger, while those most (vs. least) affected by underemployment are older. Age is the most influential factor that distinguishes the most and least affected workers when compared with other individual-level factors such as education level, household income, and the number of children. Country-level differences between the most and least affected groups imply that work hour mismatch is more tolerable for workers in relatively poorer countries than for workers in wealthier countries. These findings underscore age-tailored policy responses for alleviating the negative effects of work hour mismatch and provide insights for understanding the complex economic preferences across countries.
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Notes
Partnered refers to living with one’s spouse in marriage or spouse in common-law relationship.
Education level is categorized according to the International Standard Classification of Education (ISCED). Low education level refers to ISCED 1 or lower: primary education or lower, including primary school graduates, dropouts, and those who never attended school. Lower-middle education level refers to ISCED 2 & 3: secondary education, including junior and senior high school graduates. Upper-middle education level refers to ISCED 4 & 5: post-secondary non-tertiary education and Short-cycle tertiary education, including those who completed professional school or junior college. High education level referes to ISCED 6 or upper: 4-year university education or above, including Bachelor, Master, Doctoral or equivalent degrees.
This categorization is a general indication of the nature of an individual's employment relationships, reflecting the diversity of work arrangements and employment status that exist in the workforce. Individuals in these occupations have different levels of autonomy and independence over their work arrangement, which is expected to generate heterogeneities in the effects of work hour mismatch. Given that our research focus is the heterogenous effects in a multinational context, it may be a better approach to account for occupation instead of industry. This is because the occupation is more relevant to work hour mismatch than the industry to work in; industry structure differs by country and some industries are less prevalent in certain countries than others, resulting in limited observations for the industry categories in a given country that affect the empirical results; moreover, it would induce multicollinearity to further account for industry category in regression models that have accounted for education, occupation, and household income level. Future studies that are interested in the role played by industry should find a better way to deal with the above-mentioned issues.
To account for variation in the numbers of individuals within household, we followed Jebb et al. (2018) to use the square root equivalency scale to construct equivalized household income, which is the median value of the chosen income range divided by the square root of household size. For comparability across countries, we generated a categorical variable for equivalized household income levels based on the 20th, 40th, 60th, and 80th quantiles of the empirical distribution of equivalized household income within the subsample of each country. Those who answered ‘Do not know/Do not want to answer’ were included as an individual category.
The variable of perceived corruption in this study is constructed by subtracting the original Transparency International Index from 100, making higher values for higher levels of corruption.
The empirical analyses of study focus on observational survey data rather than experimental data in which the groups of observations for comparison have similar characteristics except for the dependent and treatment variables. Therefore, we acknowledged that the aim of this study is to estimate the partial effects of work hour mismatch on life evaluation, instead of making causal claims.
Marginal effect at the mean is calculated using the means of the covariates, the mean reflects the average or typical person on the covariate. The calculation of the average marginal effect uses the all the actual observed values for the covariates, not just the means; it calculates the marginal effect for each case and then averages them. Average marginal effect is the average value of the partial effect across the population.
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Funding
This study was funded by Environmental Restoration and Conservation Agency, S-14-1 (Shunsuke Managi), Ministry of Education, Culture, Sports, Science and Technology (JP), 26000001, (Shunsuke Managi), Postdoctoral Research Foundation of China (Chi Zhang).
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Appendix
Appendix
See Tables 3,
4, and
See Figs.
Summary descriptions on workweek a and work hour mismatch b by country. Note Subfigure a shows the box-and-whisker plot of workweek; Redpoint represents the average value; box shows the median value and two ranges of values, which contain the 25th and 75th percentiles of the observations, respectively; lowest/highest whisker represents the lower/upper extreme defined by the lower/upper percentile value deducts/pluses 1.5 times of the interquartile range. Subfigure b shows the stacked share of working hour mismatch status by each country. W Europe, Western Europe/Scandinavia; E Europe, Eastern Europe/the Balkans; N America, Northern America; L America, Latin America; E Asia, East Asia; SE Asia, Southeast Asia; C/S Asia, Central/Southern Asia; ME/W Asia, Middle East/Western Asia; AUS, Australia; SSA, Sub-Saharan Africa
4,
Projections of the 90% confidence sets of the most and least affected groups by overemployment a and underemployment b against workweek and the two-way interaction between gender and partnership status and occupation. Note The projections partially reproduce the corresponding results in Table 1 and further display how the most and least affected groups are distributed across the interactive categories of gender and partnership status and occupation at different levels of workweek. Overlapped observations were dropped. Self: self-employed; Prof: professionals, representing lawyers, medical doctors, and professors; Gov: government employee; Com: company owner; Part: part-time employee; Full: full-time employee
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Zhang, C., Piao, X. & Managi, S. Work Hour Mismatch on Life Evaluation: Full Heterogeneity and Individual- and Country-Level Characteristics of the Most and Least Affected Workers. Soc Indic Res 170, 637–674 (2023). https://doi.org/10.1007/s11205-023-03193-8
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DOI: https://doi.org/10.1007/s11205-023-03193-8