Remote Work and City Structure
What this paper finds — and why it matters
Layer 1: Overview
Monte, Porcher, and Rossi-Hansberg ask why remote work surged abruptly and permanently after COVID-19 despite information-technology advances raising it only marginally between 1980 and 2019, why the change was so heterogeneous across cities, and what the welfare consequences are. Their answer is a coordination mechanism: working downtown (the CBD) yields productive interactions with other in-office workers but entails commuting/congestion costs, while remote work avoids those costs but forgoes agglomeration benefits. Because workers do not internalize the spillovers they confer, a worker prefers the office only if others commute too — generating, in a dynamic discrete-choice model with idiosyncratic preferences and fixed switching costs, the possibility of MULTIPLE stationary equilibria with different permanent commuter shares. A temporary shock (the pandemic) that drives commuters near zero can then select the low-commuting equilibrium permanently.
The model is a dynamic monocentric city (disk-shaped, radially symmetric CBD, absentee landlords, Cobb-Douglas utility, Gumbel idiosyncratic shocks). Multiplicity arises (Proposition 4.3) when agglomeration forces are strong enough — the net strength delta + xi exceeds a threshold above theta + gamma/(2mu) — AND remote-work productivity relative to office productivity z/A lies in an intermediate “cone of multiplicity” (neither too low nor too high). The authors quantify city-specific parameters for U.S. CBSAs using pre-2019 data (Census/ACS 1980-2023, NLSY79 panel of 4,147 individuals 1998-2022, SafeGraph cell-phone mobility, Zillow ZHVI zip-code house prices). Estimation: transition elasticity s = 0.30 (elasticity of transitions into remote work = 3.09), fixed switching cost F = 1.78 (equivalent to giving up 83% of a year’s earnings); agglomeration externality delta with mean 0.067 (SD 0.022, 619 CBSAs); the amenity-vs-congestion difference xi - theta is statistically insignificant and set to zero.
Stylized facts. Predicted remote-work share (controlling for composition) rose in the ACS from under 1% (1980) to 2.6% (2019), jumped to 12% (2020), peaked at 15% (2021), and fell to 11% (2023); NLSY shows a parallel path (1.4% in 1998 to 3.7% in 2018, 9.2% in 2020, 7.8% in 2022). The remote-work wage premium rose steadily but did NOT jump post-2018: ACS discount of 44.5% in 1980 became a 6.5% premium by 2022; NLSY discount fell from 18.5% (2000) to 3.1% (2022). A stable premium alongside a sudden quantity jump argues against pure productivity/preference shocks.
Mobility/housing facts. All cities dropped to ~20% of pre-pandemic CBD trips in spring 2020 (about a 75% drop, unrelated to city size). Recoveries diverged: the 25 largest CBSAs (employment > 1.5M) stabilized at ~60% of January-2020 trips, while the 663 smallest (< 150K) returned fully to pre-pandemic levels by early 2021. New York and San Francisco stabilized near 40%; Madison, WI recovered fully. House-price distance gradients flattened ~0.01 everywhere by January 2021; the flattening persisted and stabilized around 0.095 by end-2024 in large cities but reversed in small ones.
Results and welfare. Of 278 estimated CBSAs, 208 were inside their cone of multiplicity pre-pandemic; larger cities are systematically more likely to be inside (probit on log employment significant). The cone indicator predicts trip shortfalls (R-squared 0.144 alone, retaining significance with controls) and gradient flattening. Welfare: comparing high- vs low-commuting stationary equilibria for the 208 cone cities, the loss from switching is positive but modest — mean 2.3%, median 2.2%, range 1.2% to 4.0% (Table 3). Average wages fall sharply (15-35%) but option-value and commuting-cost savings offset most of it; net strength delta - gamma/(2mu) predicts the loss with R-squared 0.85. Cities with trips at 60% or less of pre-pandemic levels have an average welfare loss of 2.7%.
Layer 2: Deep Dive
What is the core economic mechanism, and how does it generate multiple equilibria?
Office work confers productivity spillovers and CBD amenity value that rise with the mass of in-office workers (L-tilde-c), but workers do not internalize these external benefits. So each worker prefers the office only if enough others commute. In a dynamic setting with idiosyncratic Gumbel preference shocks and fixed switching costs F, this coordination can produce multiple stationary equilibria: a high-commuting and a low-commuting one (with an unstable equilibrium E2 between them). Multiplicity requires (Prop 4.3) static agglomeration forces (delta + xi) above a threshold eta_min > theta + gamma/(2mu), AND relative remote productivity z/A in an intermediate interval Z — the ‘cone of multiplicity.’ If z/A is too low, the high-commuting equilibrium is unique; if too high, only the remote equilibrium survives.
What is the identification/quantification strategy and its main threats?
To avoid taking a stand on which equilibrium generated the data, the authors rely ENTIRELY on pre-2019 data (when every city was plausibly in the high-commuting equilibrium) and on model relationships that hold in any equilibrium. Four steps: (1) transition elasticity s and cost F from NLSY79 transition probabilities via a CCP/log-linear regression (eq. 21), using past wage ratios as an instrument for future ratios to address measurement error / forward-looking expectations (IV eta0 = -0.47, eta1 = 3.09); (2) agglomeration externality delta_j from commuter-wage changes instrumented by 1980 occupational composition interacted with economy-wide occupation-specific commuter-share changes (shift-share IV, eq. 26-28), with five industry groups; (3) remote/office productivity z_j, A_j from occupation-level remote-work premia (NLSY, 22 occupation groups) reweighted by city occupation shares; (4) transport-cost elasticity gamma_j from CBSA-specific housing rent-distance gradients (ACS block-group rents 2015-2019). Main threats: selection of workers into remote work on unobservables (addressed by NLSY individual fixed effects), endogeneity of commuter shares to local productivity shocks (addressed by the shift-share IV), and the assumption that all cities were in the high-commuting equilibrium in 2019; tau_j is calibrated to match each city’s 2019 Lc/L.
How do the authors rule out competing explanations (pure productivity/preference shocks, congestion, establishment size, occupational shift)?
National productivity/preference shocks: would be expected to leave some lasting imprint even in small cities, but small CBSAs reverted fully, and at least 34% of jobs remain teleworkable even in fully-reverting cities (Dingel-Neiman teleworkable share ranges 25-55% across CBSAs), so low telework capacity cannot explain reversion; cities with permanent 40%+ trip declines have only a modestly higher 43% teleworkable share. The wage premium shows no differential evolution across high- vs low-teleworkable occupations over the pandemic. Congestion: if congestion drove the shift, large cities should show lower CBD propensity pre-pandemic, but the opposite holds (30.6% of trips to CBD in large vs 15.6% in small CBSAs in late 2019). Establishment concentration: employment is LESS concentrated in smaller cities, so big-employer return-to-office decisions cannot explain reversion. Occupational shift: teleworkable employment share rose only ~5% post-pandemic, and rose MORE in smaller CBSAs (7.9%) than larger (5.8%) by end-2023, the wrong direction to explain the heterogeneity.
What heterogeneity across cities is documented and how does it map to the theory?
Large cities (high agglomeration, high net strength delta - gamma/(2mu), which rises with size: doubling size raises net strength ~0.004 off a mean 0.049) are disproportionately inside the cone of multiplicity (208 of 278 estimated cities in-cone; probit on log employment positive and significant). These cities show permanent CBD-trip declines (stabilizing ~60% for the 25 largest) and persistent gradient flattening (~0.095 by 2024). Small cities are mostly outside the cone, with unique equilibria, and revert fully. The cone indicator is also positively associated with delta_j and z_j/A_j and negatively with gamma_j, as the theory predicts.
What robustness checks are run?
Estimates of s and F are similar using restricted-use county-geocoded NLSY and under an alternative city-partition definition (two days/week remote). Main results are robust to lower delta_j and higher gamma_j calibrations (Appendix A.17). A CES production function in remote/in-person labor yields very large substitution elasticities, motivating the linear specification. An endogenous-housing-supply model yields a nearly identical rent gradient (because commuters were a high share of employment pre-2020). Office-trip-only versions of the mobility figures (workplace visits) show similar patterns. The cone indicator retains significance in Table 2 after adding teleworkable share, pre-pandemic CBD-trip share, industry value-added shares, and total employment; results hold for an alternative binary ‘returned to office’ indicator 1back(5,20). Multiple DYNAMIC equilibria were not found in numerical exercises (Appendix B.6).
How does this paper differ from closely related prior work?
Unlike Davis, Ghent & Gregory (2024) (remote productivity via adoption externalities), Parkhomenko & Delventhal (2024) (amenity value of remote work), and Duranton & Handbury (2023) (exogenous changes in who may work remotely), this paper does NOT rely on exogenous productivity or amenity/preference shocks to explain the large persistent jump. Instead a temporary commuter shock SELECTS among pre-existing multiple equilibria. Liu & Su (2023) document a falling urban wage premium for remote-amenable occupations (consistent with weaker agglomeration). The paper’s documented divergence of residential rent-distance gradients between large and small cities is, to the authors’ knowledge, a new fact, interpreted structurally. Owens, Rossi-Hansberg & Sarte (2020) similarly use coordination/residential externalities (Detroit neighborhoods).
What are the policy implications and their scope conditions?
Because the coordination failure operates partly OUTSIDE firm boundaries, individual firms’ return-to-office mandates may be insufficient to restore the high-commuting equilibrium. City-level interventions — taxing remote work or subsidizing commuting — could in principle move a city back, since the only active externality in the quantification is a positive agglomeration externality (implying too little commuting relative to the efficient benchmark in all equilibria). However, the authors stress these welfare effects and the effectiveness of policy remain open questions; their welfare numbers depend on estimation details and the abstraction from a system-of-cities with migration.
What are the main caveats and abstractions?
The model treats each city as a CLOSED economy: no inter-city migration, trade, or investment links, though the authors note large cities show a small differential population drop (Appendix A.9), attributed to low migration elasticities. Remote work is ‘partial’ with a FIXED fraction mu = 3/5 of days at home, not chosen. Occupational heterogeneity is abstracted from (justified by rare occupation transitions). The amenity (xi) vs congestion (theta) externalities are not separately identified and set to zero (difference insignificant). Spillovers are not internalized by firms in the model. The welfare ranking (high-commuting preferred) is intuited from the single positive externality rather than formally proven.
Why is there a discrepancy between the abstract’s welfare figures and per-city numbers?
The abstract and revised Table 3 report a mean welfare loss of 2.3% (median 2.2%, range 1.2%-4.0%) across the 208 cone cities, and state cities with permanently low commuting (60% or less of pre-pandemic trips) experience average losses of 2.3% (2.7% in the text). The introduction additionally quotes specific city losses (about 3.7% for Los Angeles and San Jose, 3.2% for New York, 2.8% for San Francisco, 2% for Phoenix); these are the largest cities and lie within or near the upper part of the distribution, consistent with welfare loss rising in net agglomeration strength (R-squared 0.85 of loss on delta - gamma/(2mu)).