Abstract
Based on the nationally representative micro-level dataset, the present paper investigates the prevalence of education-job mismatches and their effects on individual and aggregate earnings in Kazakhstan. The analysis shows that the incidence of education-job mismatches varies across both individual and employment characteristics, and, in particular, across industries. We find that relative to being well-matched, vertical and horizontal mismatches have negative effects on individual earnings, while the penalty for being both vertically and horizontally mismatched is the greatest. Importantly, the study develops a theoretical model explaining the link between productivity and mismatch and quantifies the aggregate productivity gains that would result from reducing the levels of education-job mismatches in the labor market. The counterfactual analysis suggests that the country’s output will increase on average by 11.3% if the level of education-job mismatch is reduced across all industries.
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Notes
The terms “education-job mismatch” and “educational mismatch” are used interchangeably throughout the paper.
This study except for the part on the effects of educational mismatch on aggregate output is part of the PhD thesis.
In the current paper we use the terms “vertical mismatch” and “overeducation” interchangeably. The rationale for excluding two other categories of responses on the vertical mismatch question is provided in Sect. 4.
Source: The Bureau of National Statistics (BNS).
Source: The Bureau of National Statistics (BNS).
Source: World Bank, World Development Indicators databank.
Source: World Bank, World Development Indicators databank.
We think of \(\tau _{jM}\) as representing a friction due to a particular type of educational mismatch that affects the cost of choosing a particular industry of employment. As such, \(\tau _{jM}\) can reflect productivity changes experienced by different types of mismatched workers in different industries. For example,a librarian might be less productive as an applications developer than as a database administrator.
The derivation of the result can be found in Hsieh et al. (2019).
Some other studies that used an augmented Roy model include Hsieh et al. (2019) and Abdulla (2019). While these papers focus on the role of gender and racial differences in employment in explaining observed gaps in earnings, the focus of the current study is on the impact of education-job mismatch on productivity.
Efficiency units are the total amount of human capital available for production, which is a product of the number of workers who possess human capital and their skill level.
We have included Table 11 in the Appendix which illustrates the sample size before and after dropping missing income observations from the analysis across survey years as the main variable that affects the sample size is earnings. Starting from 2016, income isn’t reported by many observations. Since the study investigates individuals who are strongly attached to the labor market and those with non-zero earnings, we exclude individuals with missing income and zero values.
Tenge is the national currency of Kazakhstan, which was introduced in 1993.
The skill intensity of the industries is determined using the average education level of workers across industries. The industry groups are defined according to the average education level of workers as follows: lower-skill industries with the average years of education less than or equal to 13 years; higher-skill industries with the average years of education above 13 years. The industry groups defined by skill level are given in Table 10.
While higher educational attainment is generally associated with better labor market outcomes, some studies find OLS estimates of the returns to education to be biased due to endogeneity and use the instrumental variable approach to address the issue. However, due to data availability constraints, we could not implement the IV method in the present study.
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Serikbayeva, B., Abdulla, K. Education-Job Mismatch: Implications for Individual Earnings and Aggregate Output. Soc Indic Res 163, 723–752 (2022). https://doi.org/10.1007/s11205-022-02912-x
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DOI: https://doi.org/10.1007/s11205-022-02912-x