arxiv: 2605.01268 · v1 · submitted 2026-05-02 · 💰 econ.GN · cs.SI· q-fin.EC

Recognition: unknown

Remote work expands pathways to upward career mobility

Jinhua Zhao, Yunhan Zheng

Pith reviewed 2026-05-10 14:44 UTC · model grok-4.3

classification 💰 econ.GN cs.SIq-fin.EC

keywords remote workcareer mobilitywage growthjob transitionsgeographic constraintsupward mobilitycross-metropolitan mobilitylabor market

0 comments

The pith

Entering remote-eligible jobs yields higher wage growth and upward seniority mobility than on-site roles, especially for lower-income workers from low-opportunity regions.

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

Geographic constraints have traditionally limited access to high-growth career opportunities to workers in a narrow set of cities and organizations. This paper tests whether remote work changes that structure by decoupling job matches from physical location. Using 48 million U.S. job transitions linked to employer remote-eligibility data, it shows that remote-eligible roles produce larger wage gains and more upward seniority moves than comparable on-site positions. The gains are strongest for lower-income workers and those starting in regions with sparse high-skill jobs, and they coincide with more moves across metros and to smaller employers. The results indicate remote work relaxes location barriers and redistributes upward mobility across places and income groups.

Core claim

Workers entering remote-eligible jobs experience significantly higher wage growth and higher rates of upward seniority mobility than comparable workers entering fully on-site roles. These transitions are also associated with greater cross-metropolitan job mobility and moves toward smaller, less prestigious employers. Importantly, effects are largest among lower-income workers and those originating from regions with limited high-skill opportunity density. Together, the findings indicate that remote work relaxes geographic constraints in job matching, reshaping the distribution of upward mobility across places and workers.

What carries the argument

Comparison of wage growth and seniority mobility at job transitions between remote-eligible and fully on-site roles, identified via employer-level remote eligibility measures linked to 48 million U.S. transitions from 2020-2024.

Load-bearing premise

That workers who enter remote-eligible jobs are comparable to those entering on-site roles once observables are controlled for, with no substantial selection on unobservables or confounding between remote eligibility and other job quality attributes.

What would settle it

A study that finds the wage-growth and mobility differences disappear after applying methods that address selection on unobservables, such as individual fixed effects across multiple transitions or an instrument based on pre-2020 employer remote policies.

read the original abstract

Geographic constraints have long structured access to high-growth career opportunities, concentrating upward mobility within a limited set of cities and organizations. The expansion of remote work potentially alters this opportunity structure by decoupling job matching from physical proximity, yet its implications for career mobility remain unclear. Using 48 million U.S. job transitions between 2020 and 2024 linked to employer-level measures of remote eligibility, we estimate how entering remote-eligible jobs shapes career outcomes at job transitions. Workers entering remote-eligible jobs experience significantly higher wage growth and higher rates of upward seniority mobility than comparable workers entering fully on-site roles. These transitions are also associated with greater cross-metropolitan job mobility and moves toward smaller, less prestigious employers. Importantly, effects are largest among lower-income workers and those originating from regions with limited high-skill opportunity density. Together, the findings indicate that remote work relaxes geographic constraints in job matching, reshaping the distribution of upward mobility across places and workers.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Remote-eligible jobs show bigger wage and seniority gains for lower-income workers from smaller regions, but the causal case rests on thin identification.

read the letter

The paper finds that workers who move into remote-eligible roles get higher wage growth and more upward seniority moves than those entering on-site jobs, with the largest effects among lower-income people and those from places with fewer high-skill options. Cross-metropolitan moves also rise, though often toward smaller or less prestigious employers. That pattern comes from 48 million linked U.S. job transitions between 2020 and 2024 matched to employer remote-eligibility data.

Referee Report

2 major / 2 minor

Summary. The manuscript examines the impact of remote work on career mobility using 48 million U.S. job transitions from 2020 to 2024 linked to employer remote eligibility measures. It claims that entering remote-eligible jobs is associated with higher wage growth, increased upward seniority mobility, greater cross-metropolitan job mobility, and transitions to smaller, less prestigious employers. These effects are reported to be larger for lower-income workers and those from regions with limited high-skill opportunities.

Significance. If the causal interpretation holds after addressing selection concerns, this work would significantly contribute to labor economics by demonstrating how remote work can mitigate geographic barriers to upward mobility and potentially reduce inequality in career advancement. The scale of the dataset enables detailed heterogeneity analyses across income groups and regions, which is a notable empirical strength.

major comments (2)

[Empirical Strategy] Empirical Strategy section: The identification relies on comparing transitions into remote-eligible versus on-site jobs after controlling for observables, but the manuscript supplies no details on the regression specification, full set of control variables, fixed effects, matching approach, or robustness checks. This is load-bearing for the central claim, as the headline effects on wage growth and seniority mobility (and their heterogeneity by income) cannot be evaluated without evidence that selection on unobservables (e.g., ambition, network quality, or unlisted job attributes) has been addressed.
[Results] Results, heterogeneity analysis: The claim that effects are largest among lower-income workers and those from low-opportunity-density regions is central to the paper's implications for expanding pathways to mobility, yet no information is provided on whether these differences survive alternative income cutoffs, interaction specifications, or corrections for multiple testing. Without such checks, the differential effects do not necessarily follow from the associations.

minor comments (2)

[Abstract] Abstract: While the sample size is impressive, the abstract should report the economic magnitudes of the key effects (e.g., the size of the wage growth differential) to convey substantive importance without requiring readers to consult the tables.
[Data] Data section: Provide more detail on how employer-level remote eligibility was constructed and any validation or measurement error concerns, as this variable is foundational to all comparisons.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed report. The comments highlight important gaps in the presentation of our empirical approach and robustness of the heterogeneity results. We agree that these elements require expansion and will revise the manuscript accordingly. Our point-by-point responses follow.

read point-by-point responses

Referee: [Empirical Strategy] Empirical Strategy section: The identification relies on comparing transitions into remote-eligible versus on-site jobs after controlling for observables, but the manuscript supplies no details on the regression specification, full set of control variables, fixed effects, matching approach, or robustness checks. This is load-bearing for the central claim, as the headline effects on wage growth and seniority mobility (and their heterogeneity by income) cannot be evaluated without evidence that selection on unobservables (e.g., ambition, network quality, or unlisted job attributes) has been addressed.

Authors: We agree that the current draft does not provide sufficient detail on the regression specifications, controls, fixed effects, or robustness checks. In the revised version we will expand the Empirical Strategy section to include the exact regression equations, the full list of worker-level and job-level controls, metropolitan and time fixed effects, any matching procedures used, and a set of robustness specifications (including alternative samples and placebo tests). We will also add explicit discussion of the remaining selection concerns and the extent to which the controls and fixed effects address them. revision: yes
Referee: [Results] Results, heterogeneity analysis: The claim that effects are largest among lower-income workers and those from low-opportunity-density regions is central to the paper's implications for expanding pathways to mobility, yet no information is provided on whether these differences survive alternative income cutoffs, interaction specifications, or corrections for multiple testing. Without such checks, the differential effects do not necessarily follow from the associations.

Authors: We acknowledge that the heterogeneity results would be strengthened by additional checks. In the revision we will report the main heterogeneity patterns using alternative income thresholds (e.g., quartiles and continuous interactions), present formal interaction specifications, and apply multiple-testing corrections (Bonferroni and FDR). These results will appear in the main text or appendix with the original specifications for comparison. revision: yes

Circularity Check

0 steps flagged

No significant circularity; purely empirical identification from observational data

full rationale

The paper presents an empirical analysis of 48 million job transitions using regression comparisons of workers entering remote-eligible versus on-site roles, after controlling for observables. No mathematical derivations, fitted parameters, or self-citations appear in the provided text that reduce any central claim (wage growth, seniority mobility) to an input by construction. The identification strategy rests on data patterns and standard controls rather than definitional equivalences or ansatzes smuggled via prior work. This is a self-contained observational study whose results can be falsified against external benchmarks, yielding no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is empirical and relies on standard labor-economics assumptions for observational causal inference rather than new theoretical axioms or invented entities.

axioms (1)

domain assumption Conditional independence assumption: after controlling for observables, selection into remote-eligible jobs is unrelated to potential outcomes.
Required for interpreting the reported differences as effects of remote eligibility.

pith-pipeline@v0.9.0 · 5459 in / 1214 out tokens · 37333 ms · 2026-05-10T14:44:40.881353+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

32 extracted references

[1]

Kemeny, M

T. Kemeny, M. Storper, Superstar cities and left-behind places: disruptive innovation, labor demand, and interregional inequality (2020)

2020
[2]

Autor, D

D. Autor, D. Dorn, L. F. Katz, C. Patterson, J. Van Reenen, The fall of the labor share and the rise of superstar firms.The Quarterly journal of economics135(2), 645–709 (2020)

2020
[3]

Kemeny, M

T. Kemeny, M. Storper, The changing shape of spatial income disparities in the United States. Economic Geography100(1), 1–30 (2024)

2024
[4]

Superstar Cities

P. Le Gal `es, P. Pierson, “Superstar Cities” & the generation of durable inequality.Daedalus 148(3), 46–72 (2019)

2019
[5]

J. R. Abel, R. Deitz, Why are some places so much more unequal than others?Economic Policy Review25(1), 1–18 (2019)

2019
[6]

Acolin, S

A. Acolin, S. Wachter, Opportunity and housing access.Cityscape19(1), 135–150 (2017)

2017
[7]

Ishimaru, Geographic Mobility of Youth and Spatial Gaps in Local College and Labor Market Opportunities.Journal of Labor Economics43(4), 1251–1294 (2025)

S. Ishimaru, Geographic Mobility of Youth and Spatial Gaps in Local College and Labor Market Opportunities.Journal of Labor Economics43(4), 1251–1294 (2025)

2025
[8]

Bloom, R

N. Bloom, R. Han, J. Liang, Hybrid working from home improves retention without damaging performance.Nature630(8018), 920–925 (2024)

2024
[9]

C. G. Aksoy,et al.,Working from Home in 2025: Five key facts, Tech. rep., EconPol Policy Brief (2025)

2025
[10]

A. Mas, A. Pallais, Valuing alternative work arrangements.American Economic Review 107(12), 3722–3759 (2017)

2017
[11]

J. M. Barrero, N. Bloom, S. J. Davis,Why working from home will stick, Tech. rep., National Bureau of Economic Research (2021)

2021
[12]

H. He, D. Neumark, Q. Weng, Do workers value flexible jobs? A field experiment.Journal of Labor Economics39(3), 709–738 (2021). 28

2021
[13]

E. L. Glaeser, D. C. Mar ´e, Cities and skills.Journal of Labor Economics19(2), 316–342 (2001)

2001
[14]

Storper, A

M. Storper, A. J. Venables, Buzz: face-to-face contact and the urban economy.Journal of economic geography4(4), 351–370 (2004)

2004
[15]

Yang,et al., The effects of remote work on collaboration among information workers.Nature human behaviour6(1), 43–54 (2022)

L. Yang,et al., The effects of remote work on collaboration among information workers.Nature human behaviour6(1), 43–54 (2022)

2022
[16]

Gibbs, F

M. Gibbs, F. Mengel, C. Siemroth, Work from home and productivity: Evidence from personnel and analytics data on information technology professionals.Journal of Political Economy Microeconomics1(1), 7–41 (2023)

2023
[17]

C. G. Aksoy,et al., Time savings when working from home, inAEA Papers and Proceedings (American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203), vol. 113 (2023), pp. 597–603

2014
[18]

Goldin, A grand gender convergence: Its last chapter.American economic review104(4), 1091–1119 (2014)

C. Goldin, A grand gender convergence: Its last chapter.American economic review104(4), 1091–1119 (2014)

2014
[19]

D. Card, J. Heining, P. Kline, Workplace heterogeneity and the rise of West German wage inequality.The Quarterly journal of economics128(3), 967–1015 (2013)

2013
[20]

E. P. Lazear, S. Rosen, Rank-order tournaments as optimum labor contracts.Journal of political Economy89(5), 841–864 (1981)

1981
[21]

J. I. Dingel, B. Neiman, How many jobs can be done at home?Journal of public economics 189, 104235 (2020)

2020
[22]

Ozimek, The future of remote work.Available at SSRN 3638597(2020)

A. Ozimek, The future of remote work.Available at SSRN 3638597(2020)

2020
[23]

E. L. Glaeser,Agglomeration economics(University of Chicago Press) (2010)

2010
[24]

E. L. Glaeser, Learning in cities.Journal of Urban Economics46(2), 254–277 (1999)

1999
[25]

Battiston, J

D. Battiston, J. Blanes i Vidal, T. Kirchmaier, Face-to-face communication in organizations. The Review of Economic Studies88(2), 574–609 (2021). 29

2021
[26]

T. L. Idson, W. Y. Oi, Workers are more productive in large firms.American Economic Review 89(2), 104–108 (1999)

1999
[27]

reveliolabs.com/(2025), accessed: 2025-12-28

Revelio Labs, Revelio Labs Data Dictionary,https://www.data-dictionary. reveliolabs.com/(2025), accessed: 2025-12-28

2025
[28]

com/job-postings-cosmos/(2025), accessed: 2025-12-28

Revelio Labs, COSMOS: The Largest Job Postings Dataset,https://www.reveliolabs. com/job-postings-cosmos/(2025), accessed: 2025-12-28

2025
[29]

Glaeser, Cities, productivity, and quality of life.Science333(6042), 592–594 (2011)

E. Glaeser, Cities, productivity, and quality of life.Science333(6042), 592–594 (2011)

2011
[30]

Moretti,Local labor markets, Tech

E. Moretti,Local labor markets, Tech. rep., National Bureau of Economic Research (2010)

2010
[31]

J. B. Gelbach, When do covariates matter? And which ones, and how much?Journal of Labor Economics34(2), 509–543 (2016)

2016
[32]

Remote-eligible

Revelio Labs, Inc., Best-in-class workforce data, built by researchers,https: //www.reveliolabs.com/products/research/(2025),https://www.reveliolabs. com/products/research/, accessed: 2026-01-06. Competing interests:There are no competing interests to declare. Data and materials availability:The data necessary to reproduce the figures and tables reported ...

2025