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Skill vs Education Types of Labour Mismatch and Their Association with Earnings

T0 review · 3 major / 2 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read After removing country-level unobserved heterogeneity, over-education and over-skilling link to lower earnings while under-education and under-skilling link to higher earnings.

desk verdict The paper shows that an error components model on PIAAC data flips mismatch-earnings associations to penalties for over-education/over-skilling and premiums for under-, with education and skill measures behaving differently. read the letter →

arxiv 2606.13506 v1 pith:AHDKE3CM submitted 2026-06-11 econ.GN q-fin.EC

Vsevolod Iakovlev This is my paper · ORCID
classification econ.GNq-fin.EC
keywords labormismatchover-educationover-skillingearningsPIAACunobservedheterogeneitywagepenaltiesunder-education
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper distinguishes between mismatch in education levels and mismatch in actual skills using data from the OECD Survey of Adult Skills across 26 countries. It shows that raw associations between mismatch and earnings are biased by unobserved country differences, which an error components model can remove. With that control, workers who are over-educated or over-skilled for their jobs earn less, while those who are under-educated or under-skilled earn more. This matters for understanding why education and training policies may need to target both formal qualifications and actual competencies separately.

What carries the argument

Error components model that isolates and removes country-specific unobserved heterogeneity from the mismatch-earnings relationship.

What would settle it

If re-estimating the model with country fixed effects or additional controls eliminates the wage penalties for over-mismatch and premiums for under-mismatch, the central claim would be falsified.

Watch

Extended reading notes

Core claim

Country-level unobserved heterogeneity induces endogeneity bias in mismatch-earnings associations, varying across measures. Once controlled for, over-education and over-skilling are associated with wage penalties, whereas under-education and under-skilling are linked to wage premiums. The findings highlight conceptual and empirical distinctions between educational and skill mismatch.

Load-bearing premise

The error components model completely removes all country-level factors that create spurious correlations between mismatch indicators and earnings.

Editorial extensions

If this is right

  • Conflicting country-level correlations with earnings arise from varying bias in different mismatch indicators.
  • Both education-based and skill-based measures show similar patterns after controls.
  • Indicator choice affects conclusions about mismatch effects.
  • Cross-country comparisons require accounting for unobserved heterogeneity.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Longitudinal data within countries could test if the same wage patterns hold when individuals change jobs.
  • The results suggest that education systems may produce surpluses of certain qualifications that the labor market does not reward.
  • Skill-based measures might better capture productivity differences than education alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper analyzes distinctions between educational and skill-based labor mismatches using multiple indicators from the 2012 PIAAC survey across 26 countries. It employs an error components model to address country-level unobserved heterogeneity inducing endogeneity in mismatch-earnings associations, finding that after controls, over-education and over-skilling associate with wage penalties while under-education and under-skilling associate with wage premiums. The analysis explores heterogeneity by worker characteristics and highlights indicator choice effects.

Significance. If the error components model adequately removes endogeneity without residual bias from interactions or measurement error, the results would strengthen evidence on conceptual distinctions between education and skill mismatch in labor economics and underscore the value of controlling unobserved heterogeneity in cross-country analyses. Use of public PIAAC data and multiple indicators provides a reproducible foundation for the claims.

major comments (3)
  1. [§4] §4 (error components model): The specification is described as addressing country unobserved heterogeneity, but provides no detail on whether country effects are interacted with mismatch indicators or if the model allows for heteroskedasticity or correlated measurement error in the PIAAC-derived mismatch variables; this is load-bearing for the claim that bias direction and magnitude vary across measures and are fully purged.
  2. [Results section] Results section, Table reporting post-control associations: The headline finding of wage penalties for over-mismatch and premiums for under-mismatch after controls is presented without explicit tests for whether the error components estimates differ significantly from OLS or fixed-effects alternatives, weakening support for the endogeneity correction as the source of the sign reversal.
  3. [§3.2] §3.2 (indicator construction): The comprehensive set of education- and skill-based mismatch indicators is used, but the paper does not report robustness of the main earnings associations to alternative threshold definitions or to excluding potentially endogenous components of the skill mismatch measure, which could affect the distinction between mismatch types.
minor comments (2)
  1. [Abstract] The abstract could more explicitly state the number of countries, sample size, and exact mismatch indicators employed to improve clarity for readers.
  2. [Throughout] Notation for the mismatch variables (e.g., over-education vs. over-skilling) should be standardized across tables and text to avoid ambiguity in interpreting heterogeneity results.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments. We address each major comment below, providing our responses and indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [§4] §4 (error components model): The specification is described as addressing country unobserved heterogeneity, but provides no detail on whether country effects are interacted with mismatch indicators or if the model allows for heteroskedasticity or correlated measurement error in the PIAAC-derived mismatch variables; this is load-bearing for the claim that bias direction and magnitude vary across measures and are fully purged.

    Authors: We appreciate this observation. Our error components model is a standard one-way random effects specification that decomposes the error term into a country-specific random component and an idiosyncratic error to purge country-level unobserved heterogeneity. The model does not interact country effects with mismatch indicators, as the goal is to recover the average within-country association. The specification assumes homoskedastic and uncorrelated errors, which is standard but may not fully address potential measurement error in PIAAC mismatch variables. We will revise §4 to explicitly detail these modeling choices, discuss the implications for bias direction and magnitude across measures, and note limitations regarding heteroskedasticity and measurement error. revision: yes

  2. Referee: [Results section] Results section, Table reporting post-control associations: The headline finding of wage penalties for over-mismatch and premiums for under-mismatch after controls is presented without explicit tests for whether the error components estimates differ significantly from OLS or fixed-effects alternatives, weakening support for the endogeneity correction as the source of the sign reversal.

    Authors: The referee is correct that we do not report formal statistical tests comparing the error components estimates to OLS or fixed-effects alternatives. Although the tables document the sign reversal, adding explicit tests (such as coefficient comparisons with adjusted standard errors or Hausman-style tests) would strengthen the argument that the endogeneity correction is the source of the change. We will incorporate these tests in the results section or an appendix of the revised manuscript. revision: yes

  3. Referee: [§3.2] §3.2 (indicator construction): The comprehensive set of education- and skill-based mismatch indicators is used, but the paper does not report robustness of the main earnings associations to alternative threshold definitions or to excluding potentially endogenous components of the skill mismatch measure, which could affect the distinction between mismatch types.

    Authors: We agree that robustness to alternative threshold definitions is valuable and will add these checks to the revised manuscript. However, we maintain that the PIAAC skill mismatch indicators, being based on objective assessments rather than self-reports, do not contain the same endogeneity concerns as subjective measures; excluding components is therefore not warranted on those grounds. We will clarify this distinction in §3.2 while providing the requested threshold robustness. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical analysis on external PIAAC data using standard error components model

full rationale

The paper performs cross-country econometric analysis on the public OECD PIAAC survey dataset. It estimates associations between mismatch indicators and earnings after applying an error components model to purge country-level unobserved heterogeneity. No equations define a quantity in terms of itself, no fitted parameters are relabeled as out-of-sample predictions, and no load-bearing premise rests on self-citation chains. The central results are falsifiable against the external microdata and do not reduce to the paper's own inputs by construction.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on standard econometric assumptions for panel-style error components models applied to cross-sectional data and on the validity of PIAAC-derived mismatch indicators; no free parameters or invented entities are introduced beyond conventional model choices.

assumptions (1)
  • domain assumption The error components model adequately isolates country-level unobserved heterogeneity as the source of endogeneity in mismatch-earnings regressions.
    Invoked explicitly in the abstract when describing investigation of conflicting country-level correlations.

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Cite this review

Pith. "Pith review of Skill vs Education Types of Labour Mismatch and Their Association with Earnings." pith.science (2026). https://pith.science/paper/AHDKE3CM

@misc{pith2026260613506,
  author       = {Pith},
  title        = {Pith review of: Skill vs Education Types of Labour Mismatch and Their Association with Earnings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AHDKE3CM}},
  note         = {Machine review of arXiv:2606.13506}
}
read the original abstract

This paper analyses the distinction between educational and skill types of labour mismatch and their association with earnings. Drawing on cross-sectional data for 26 countries from the 1st Cycle of the OECD (2012) Survey of Adult Skills (PIAAC), I examine educational and skill mismatch using a comprehensive set of education- and skill-based indicators, explore heterogeneity across worker characteristics, and investigate the sources of conflicting country-level correlations with earnings through an error components model. The results show that country-level unobserved heterogeneity induces endogeneity bias, with both its direction and magnitude varying across mismatch measures. Once unobserved heterogeneity is controlled for, over-education and over-skilling are associated with wage penalties, whereas under-education and under-skilling are linked to wage premiums. These findings highlight both conceptual and empirical distinctions between educational and skill mismatch and demonstrate the importance of indicator choice in the analysis.

Figures

Figures reproduced from arXiv: 2606.13506 by the authors.

Figure 1
Figure 1. Hourly earnings including bonuses (USD PPP) and its natural logarithm 0 2 4 6 8 Percent 0 20 40 60 80 Hourly earnings including bonuses, USD PPP 0 1 2 3 4 Percent 0 1 2 3 4 Natural logarithm of earnings Notes: The distribution of hourly earnings is trimmed at the 1st and 99th percentiles to avoid outliers. The required ISCO SL is derived by mapping respondents’ ISCO occupation groups onto 12Values of 15 and 16 are n… view at source ↗
Figure 2
Figure 2. Attained qualification and ISCO skill level 0 5 10 15 20 Percent 0 5 10 15 Highest qualification attained 0 20 40 60 Percent 0 1 2 3 4 ISCO skill level attained One potential criticism concerns the reduction of the highest qualification variable from 16 categories to four ISCO skill levels. This may raise concerns that the ISCO skill level is a poor proxy for the distribution of qualification levels. However, [PITH… view at source ↗
Figure 3
Figure 3. Skill scores 0 2 4 6 8 Percent 100 200 300 400 Literacy 0 2 4 6 8 Percent 0 100 200 300 400 Numeracy 0 2 4 6 8 10 Percent 0 100 200 300 400 500 Problem-Solving numeracy, and problem-solving skills, each scored on a 500-point scale. These variables are calculated as the average of the ten plausible values provided in the dataset for each score [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figures from the paper (25 more)
Figure 4
Figure 4. Figure 4: Country-specific mismatch shares: Selected measures Job Analysis Realized Matches Indirect Self-Assessment PF Literacy PF Numeracy PF Problem-Solving ALV Literacy ALV Numeracy ALV Problem-Solving .27 .20 .10 .05 .05 .04 .10 .05 .11 .14 .16 .13 .05 .05 .05 .08 .04 .06 .…
Figure 5
Figure 5. Figure 5: Mean-squared prediction error as a function of ln(λ) .1 .2 .3 .4 .5 MSPE -8 -6 -4 -2 0 ln(Lambda) MSPE - sd. error Mean-squared prediction error MSPE + sd. error Notes: The solid and dashed vertical lines correspond to λlopt and λlse, respectively. Lasso is applied to …
Figure 6
Figure 6. Figure 6: Country-specific mismatch shares: RM RM, mean ± 0.5 SD RM, mean ± 1 SD RM, mean ± 1.5 SD RM, mode ± 0.1 SD RM, mode ± 1 SD RM, mode ± 2 SD .20 .06 .06 .20 .20 .06 .19 .14 .03 .16 .16 .12 .22 .10 .06 .12 .12 .09 .22 .05 .05 .07 .07 .01 .40 .10 .03 .11 .11 .04 .31 .12 .0…
Figure 7
Figure 7. Figure 7: Country-specific mismatch shares: ISA ISA, 1 year ISA, 2 year ISA, 3 year ISA, 4 year ISA, 5 year .10 .07 .02 .01 .01 .13 .06 .04 .01 .00 .22 .12 .06 .03 .01 .12 .11 .05 .02 .01 .13 .10 .06 .02 .02 .19 .09 .06 .04 .01 .08 .05 .04 .03 .00 .09 .04 .02 .01 .00 .24 .20 .15…
Figure 8
Figure 8. Figure 8: Country-specific mismatch shares: DSA DSA .06 .07 .05 .06 .03 .09 .04 .03 .04 .07 .04 .46 .03 .03 .02 .03 .07 .08 .02 .04 .05 .05 .05 .04 .03 .05 Under 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Share DSA .15 .10 .16 .15 .06 .14 .08 .06 .09 .12 .09 .20 .04 .06 .04 .0…
Figure 9
Figure 9. Figure 9: Country-specific mismatch shares (well-skilled): PF and ALV PF, Literacy 5% PF, Literacy 10% PF, Literacy 20% PF, Numeracy 5% PF, Numeracy 10% PF, Numeracy 20% PF, Prb-Solv 5% PF, Prb-Solv 10% PF, Prb-Solv 20% Relaxed PF, Literacy 5% Relaxed PF, Literacy 10% Relaxed PF…
Figure 10
Figure 10. Figure 10: Country-specific mismatch shares (under-skilled): PF and ALV PF, Literacy 5% PF, Literacy 10% PF, Literacy 20% PF, Numeracy 5% PF, Numeracy 10% PF, Numeracy 20% PF, Prb-Solv 5% PF, Prb-Solv 10% PF, Prb-Solv 20% Relaxed PF, Literacy 5% Relaxed PF, Literacy 10% Relaxed …
Figure 11
Figure 11. Figure 11: Country-specific mismatch shares (over-skilled): PF and ALV PF, Literacy 5% PF, Literacy 10% PF, Literacy 20% PF, Numeracy 5% PF, Numeracy 10% PF, Numeracy 20% PF, Prb-Solv 5% PF, Prb-Solv 10% PF, Prb-Solv 20% Relaxed PF, Literacy 5% Relaxed PF, Literacy 10% Relaxed P…
Figure 13
Figure 13. Figure 13: Earnings by gender, age groups, and migration status 0 1 2 3 4 Percent 0 1 2 3 4 Natural logarithm of earnings Male Female 0 2 4 6 Percent 1 2 3 4 Natural logarithm of earnings <30 y.o. 30-44 y.o. 45+ y.o. 0 1 2 3 4 5 Percent 0 1 2 3 4 Natural logarithm of earnings Lo…
Figure 14
Figure 14. Figure 14: Earnings by education, literacy, numeracy, and problem-solving 0 2 4 6 Percent 0 1 2 3 4 Natural logarithm of earnings ISCO SL 1 ISCO SL 2 ISCO SL 3 ISCO SL 4 0 1 2 3 4 5 Percent 0 1 2 3 4 Natural logarithm of earnings 0 1 2 3 4 5 Percent 0 1 2 3 4 Natural logarithm o…
Figure 15
Figure 15. Figure 15: Shares of well-matched workers by gender 0 5 10 15 20 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), ja Male Female 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), rm Male Female 0 5 10 15 20 Percent 0 .…
Figure 16
Figure 16. Figure 16: Shares of under-matched workers by gender 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), ja Male Female 0 10 20 30 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), rm Male Female 0 10 20 30 40 Percent 0…
Figure 17
Figure 17. Figure 17: Shares of over-matched workers by gender 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), ja Male Female 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), rm Male Female 0 5 10 15 20 Percent …
Figure 18
Figure 18. Figure 18: Shares of well-matched workers by age groups 0 10 20 30 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), ja <30 y.o. 30-44 y.o. 45+ y.o. 0 5 10 15 20 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), rm <30 y.o. 30-44 y.o. …
Figure 19
Figure 19. Figure 19: Shares of under-matched workers by age groups 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), ja <30 y.o. 30-44 y.o. 45+ y.o. 0 10 20 30 40 50 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), rm <30 y.o. 3…
Figure 20
Figure 20. Figure 20: Shares of over-matched workers by age groups 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), ja <30 y.o. 30-44 y.o. 45+ y.o. 0 10 20 30 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), rm <30 y.o. 30-44 y.…
Figure 21
Figure 21. Figure 21: Shares of well-matched workers by migration status 0 10 20 30 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), ja Locals Migrants 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), rm Locals Migrants 0 10 20 30…
Figure 22
Figure 22. Figure 22: Shares of under-matched workers by migration status 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), ja Locals Migrants 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), rm Locals Migrants 0 20…
Figure 23
Figure 23. Figure 23: Shares of over-matched workers by migration status 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), ja Locals Migrants 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), rm Locals Migrants 0 5…
Figure 24
Figure 24. Figure 24: Shares of well-educated workers by literacy, numeracy, and problem-solving scores 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), ja Literacy: Q1 Literacy: Q2 Literacy: Q3 Literacy: Q4 0 5 10 15 20 25 Percent 0 .1 .2 .3 .4 .5 .6…
Figure 25
Figure 25. Figure 25: Shares of under-educated workers by literacy, numeracy, and problem-solving scores 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), ja Literacy: Q1 Literacy: Q2 Literacy: Q3 Literacy: Q4 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .…
Figure 26
Figure 26. Figure 26: Shares of over-educated workers by literacy, numeracy, and problem-solving scores 0 10 20 30 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), ja Literacy: Q1 Literacy: Q2 Literacy: Q3 Literacy: Q4 0 5 10 15 20 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .…
Figure 27
Figure 27. Figure 27: Shares of well-skilled workers by education 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), pfl ISCO SL 1 ISCO SL 2 ISCO SL 3 ISCO SL 4 0 10 20 30 40 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of well-matched (markets), pfn ISCO…
Figure 28
Figure 28. Figure 28: Shares of under-skilled workers by education 0 20 40 60 80 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), pfl ISCO SL 1 ISCO SL 2 ISCO SL 3 ISCO SL 4 0 20 40 60 80 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of under-matched (markets), pfn I…
Figure 29
Figure 29. Figure 29: Shares of over-skilled workers by education 0 20 40 60 80 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), pfl ISCO SL 1 ISCO SL 2 ISCO SL 3 ISCO SL 4 0 20 40 60 80 Percent 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Share of over-matched (markets), pfn ISCO…

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Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.