Automation and Rent Dissipation

Acemoglu and Restrepo examine the effects of automation in economies where labor market distortions cause some workers to earn rents—wages above their opportunity cost or outside option. The central question is how the interplay between automation and these distortions shapes wages, inequality, and productivity. The paper makes three contributions: a theoretical framework identifying a rent dissipation mechanism, reduced-form empirical evidence using US data from 1980 to 2016, and a general equilibrium quantification of automation’s aggregate effects.

The theoretical framework extends the task model of Acemoglu and Restrepo (2022) to incorporate task-specific wage wedges. In this setup, a firm employing labor of type g in task x pays a wage equal to the base wage multiplied by an exogenous wedge capturing rents from efficiency wages, bargaining, licensing, regulations, or norms. Because these wedges artificially inflate labor costs in high-rent tasks, firms have a stronger incentive to automate precisely those tasks—automation saves more in labor costs where rents are highest. Proposition 3 establishes that endogenous adoption decisions are tilted toward high-rent tasks: the rent distribution in automated tasks first-order stochastically dominates the rent distribution across all tasks. This targeting generates the rent dissipation mechanism. The equilibrium is inefficient on both the intensive margin (too little employment in high-rent tasks) and the extensive margin (excessive automation of high-rent tasks that a social planner would prefer to keep labor-intensive).

The rent dissipation mechanism has three consequences identified theoretically. First, it amplifies average wage losses for exposed groups beyond what displacement alone would produce, pushing displaced workers toward lower-paying jobs. Second, it compresses within-group wage dispersion by concentrating losses at higher percentiles of the within-group distribution, generating a U-shaped pattern of wage changes: workers at low percentiles earn no rents and experience only base-wage adjustments, while workers between the 70th and 95th percentiles face the steepest declines due to loss of high-rent jobs. Third, it is inefficient: because the tasks targeted by automation are not those where wages reflect scarcity or skill but rather distortionary rents, a planner would have preferred more labor allocated to these tasks, and rent dissipation offsets part or all of the cost-saving productivity gains from automation.

The empirical analysis covers 500 detailed demographic groups defined by education (five levels), gender, five age groups, five race/ethnicity groups, and nativity. Task displacement is measured as a weighted sum of industry-level automation exposure using three proxies: adjusted industrial robot penetration, specialized software services, and dedicated machinery in value added. Workers in the middle and lower-middle of the wage distribution lost 15–20% of their tasks to automation between 1980 and 2016, while post-college workers saw few tasks automated.

A 10 percentage point increase in task displacement is associated with a 24% decline in group-level relative wages (β = −2.36, s.e. = 0.13), falling to 19% after controlling for gender, education, sectoral demand, and rent shifters (β = −1.90, s.e. = 0.29). The U-shaped pattern in within-group wage changes is clearly visible: wages decline by 25–30% per 10 percentage point task displacement at the 70th–90th percentiles, compared to only 16% at the 5th–40th percentiles. Decomposing the average wage effect, the base-wage component is β = −1.53 (s.e. = 0.33) and the rent-dissipation component is β = −0.37 (s.e. = 0.11), implying a rent dissipation rate of approximately 37%. Across multiple proxies for rents—inter-industry/occupation wage differentials, wage losses after job displacement, and quit rates—the average estimated rent dissipation rate is approximately 35%. Rent dissipation accounts for one-fifth of the overall relative wage decline experienced by groups exposed to automation.

In the general equilibrium quantification (with elasticity of substitution λ = 0.5, average cost savings π = 30%, and average rent in automated tasks of 35%), automation accounts for 52% of the rise in between-group wage inequality since 1980: 42 percentage points via baseline displacement effects on labor demand, and 10 percentage points via rent dissipation. Cost savings from automation increased TFP by approximately 3% between 1980 and 2016, but inefficient rent dissipation offsets 60–90% of these gains, leaving net TFP gains of only 0.3–1.3% and net aggregate consumption gains of only 0.45–1.95% over the 36-year period.

Q: What is the rent dissipation mechanism, and why does it arise? A: Rent dissipation arises because labor market wedges make high-rent tasks artificially costly to staff with workers, giving firms a stronger incentive to automate precisely those tasks. When automation displaces workers from high-rent jobs, workers lose the premium above their opportunity cost that those jobs paid, amplifying wage losses beyond what displacement alone would cause. The mechanism is endogenous: firms do not randomly automate tasks but disproportionately target tasks where rents are highest, since doing so saves the most in labor costs. Proposition 3 formalizes this as first-order stochastic dominance of the rent distribution in automated tasks over the rent distribution in all tasks.

Q: Why is rent dissipation inefficient? A: In a distorted economy, high-rent tasks already feature too little employment at the equilibrium—firms under-hire in these tasks because the wage wedge makes labor artificially expensive. A social planner would want to allocate more labor to these tasks, not less. When automation further removes labor from high-rent tasks, it moves the economy further from the efficient allocation, dissipating rents that reflect distortions rather than true scarcity. The TFP formula shows that this inefficient targeting offsets part or all of the cost-saving gains from automation, and can even reduce aggregate productivity if the cost savings are small relative to the rent losses.

Q: What is the U-shaped pattern of within-group wage changes, and what does it indicate? A: The U-shaped pattern means that wage declines due to automation are smallest at the bottom percentiles of a group’s within-group wage distribution, largest in the 70th–95th percentile range, and then smaller again at the very top. Workers at low percentiles earn no rents, so they experience only the base-wage adjustment from reduced labor demand. Workers in the middle-upper range of the distribution hold the high-rent jobs that are disproportionately automated, so they lose both the base-wage component and the rent component of their wages. This pattern is directly visible in US data 1980–2016, with declines of 25–30% per 10 percentage point task displacement at the 70th–90th percentiles versus 16% at the 5th–40th percentiles.

Q: How is task displacement measured, and which groups are most exposed? A: Task displacement is measured as a weighted sum of industry-level automation exposure, accounting for each demographic group’s specialization in routine tasks within industries. Three proxies are used: the adjusted penetration of industrial robots, the increase in specialized software services, and the increase in dedicated machinery in value added. Workers in the middle and lower-middle of the wage distribution—broadly corresponding to non-college workers—lost 15–20% of their tasks to automation between 1980 and 2016. Post-college degree workers saw few tasks automated.

Q: How large is the rent dissipation rate, and how robust is this estimate? A: The baseline estimate from the U-shaped within-group wage change decomposition implies a rent dissipation rate (μ_Ag/μ_g − 1) of approximately 37% (β = −0.37, s.e. = 0.11). Using inter-industry and occupation wage differentials as a proxy for rents, the estimate is 39% (β = −0.39, s.e. = 0.11). Using wage losses after job displacement, the estimate is 20% (β = −0.20, s.e. = 0.04). After purging compensating differentials from the wage differential proxy the estimate remains 37%; after purging from the displacement-loss proxy it falls to 19%. Quit-rate evidence is consistent with rent dissipation: automation shifts workers toward higher-quit-rate jobs, which are lower-rent jobs. The average across proxies is approximately 35%.

Q: How much of between-group wage inequality since 1980 does automation explain, and what share is due to rent dissipation specifically? A: Automation accounts for 52% of the rise in between-group wage inequality in the US since 1980. Of this 52 percentage points, 42 percentage points are attributable to the baseline displacement effect working through reduced labor demand for exposed groups. The remaining 10 percentage points are attributable to rent dissipation—automation pushing exposed groups away from high-rent tasks into lower-paying employment. Rent dissipation thus accounts for roughly one-fifth (10/52) of automation’s total contribution to between-group inequality.

Q: How large are the productivity gains from automation, and how much does rent dissipation offset them? A: Cost savings from automation increased TFP by approximately 3% between 1980 and 2016. However, inefficient rent dissipation offsets 60–90% of these gains, because automation disproportionately targets high-rent tasks rather than tasks where the efficiency case is strongest. The net TFP increase attributable to automation is only 0.3–1.3% over the 36-year period, and the corresponding net increase in aggregate consumption is only 0.45–1.95%.

Q: How does automation affect within-group versus between-group inequality, and why is this notable? A: Automation increases between-group inequality by reducing relative wages of exposed groups (largely non-college workers) relative to unexposed groups, accounting for 52% of the rise in between-group inequality since 1980. At the same time, automation reduces within-group wage dispersion for exposed groups by compressing wages at higher percentiles. This contrasts with the standard view that inequality is fractal—rising at all levels of aggregation due to skill-biased demand—and helps explain why within-group inequality has risen steadily for college workers since the 1980s while remaining flat and then declining for non-college workers since the 1990s.

Q: What do the propagation matrix and rent-impact matrix represent in the general equilibrium analysis? A: The propagation matrix encodes how task reallocation due to automation in one demographic group creates competition for marginal tasks across other groups, transmitting the wage effects of automation to groups not directly displaced. The rent-impact matrix encodes how this task reallocation changes the rent composition of employment across groups. Both matrices are estimated from US data on task shares and group-level wage elasticities and are used to translate partial-equilibrium estimates of task displacement and rent dissipation into general equilibrium effects on wages and productivity for all demographic groups simultaneously.

Q: What are the policy implications of inefficient rent dissipation? A: Because rent dissipation is inefficient, the social value of automation is lower than what firms and consumers are willing to pay—firms capture all the labor cost savings but do not internalize the welfare cost of destroying high-rent jobs that the distorted equilibrium already under-supplies. Second-best interventions should address the underlying distortions generating rents rather than trying to slow automation directly. The paper suggests that strengthening labor market institutions supporting worker rents in non-automatable tasks could partially counteract the adverse distributional consequences of automation.

Q: How does this paper relate to Bound and Johnson (1992) and Borjas and Ramey (1995)? A: Bound and Johnson (1992) decompose changes in the US wage structure between 1979 and 1988 into technology, supply, and rent components (modeled as exogenous industry wedges), finding that 10–20% of between-group wage changes reflect rent losses. Borjas and Ramey (1995) estimate that trade increased the college premium by 1.3–2.6 log points between 1976 and 1990, with 15–33% due to loss of rents from trade-exposed jobs. Both are comparable to this paper’s finding that rent dissipation accounts for one-fifth of the wage effect of automation, though Bound and Johnson’s estimates include all factors affecting rents while this paper isolates automation specifically.

Worker rents: Wages above a worker’s opportunity cost or outside option, arising from efficiency wages, bargaining, licensing, regulations, or norms. Modeled as task-specific multiplicative wedges (μ_gx ≥ 1) that force firms to pay more than the base wage for labor in particular tasks. Explicitly excludes compensating differentials and skill premia.

Rent dissipation: The loss of above-opportunity-cost wages experienced by workers displaced from high-rent tasks into lower-paying employment. Occurs because automation endogenously targets high-rent tasks where labor is most expensive, and pushes workers into tasks where rents are lower. Quantified as the ratio of average rents in automated tasks to average rents across all tasks, minus one (approximately 35% in US data 1980–2016).

Task displacement: The share of tasks performed by a demographic group that are automated away, measured as a weighted sum of industry-level automation exposure accounting for the group’s specialization in routine tasks. Distinct from employment loss because it captures reallocation of tasks from labor to capital within the production function.

U-shaped within-group wage change profile: The pattern whereby automation generates the largest wage declines at intermediate-to-upper percentiles (70th–95th) of an exposed group’s within-group wage distribution, with smaller declines at the bottom, because high-percentile workers disproportionately hold high-rent jobs targeted by automation. Predicted theoretically and confirmed empirically in US data 1980–2016.

Propagation matrix: A matrix estimated from US data on task shares and group-level wage elasticities that encodes how automation of tasks performed by one demographic group creates competition for marginal tasks with other groups, transmitting wage effects across the demographic distribution in general equilibrium.

Inefficient automation targeting: The mechanism by which labor market distortions cause firms to automate high-rent tasks that a social planner would prefer to keep labor-intensive, since the distorted equilibrium already features too little employment in those tasks. Results in rent dissipation offsetting 60–90% of automation’s direct TFP gains from cost savings.

Rent-impact matrix: A matrix that encodes how task reallocation due to automation changes the rent composition of employment across demographic groups, used alongside the propagation matrix to compute general equilibrium effects of automation on wages and productivity accounting for distortions.