Technological Change and Job-Loss Consequences: Braxton & Taska (2023)

What this is. The paper’s core results, the two-period model and the quantitative model with their key equations, and the empirical specifications: enough to understand what was found and how, without reading all 38 pages. To replicate or extend, read the full source at the original.

TL;DR

Braxton and Taska use within-occupation changes in computer and software skill requirements from the Burning Glass Technologies vacancy database (2007-2017) to measure technological change. Merging this measure with the Current Population Survey Displaced Workers Supplement (DWS), they document that workers displaced from occupations with greater technological change (i) have larger earnings declines, (ii) are more likely to switch to a lower-paying occupation, and (iii) show earnings losses concentrated among those who do switch. A calibrated search-and-matching model with an “up-to-the-task” production function attributes 45.5 percent of average post-displacement earnings losses to technological change (occupation-specific human capital accounts for 34.5 percent; moving lower on the wage ladder for 20 percent).

Core results

Magnitudes and significance are as reported; all regressions use clustered standard errors at the occupation level (SE in parentheses). Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Workers displaced from occupations with higher tech-change exposure have larger earnings declines	Table 3, Col 1, p.295	Coefficient on $\Delta\bar{z}_o$ = -0.0354 (SE 0.0114); comparing workers 1 SD above vs below mean exposure gives >7 pp larger earnings decline
R2	Higher tech-change exposure is associated with a greater probability of occupation switching after displacement	Table 4, Col 1, p.297	Coefficient = 0.0847 (SE 0.0269); comparing workers 1 SD above vs below mean exposure gives 17 pp greater switching probability
R3	Earnings-loss associations are concentrated among occupation switchers	Table 5, Col 3, p.299	Interaction ($\Delta\bar{z}_o \times S_{i,o,t}$) = -0.0494 (SE 0.0180); coefficient for stayers = 0.000532 (SE 0.0131), not statistically significant
R4	Tech-change exposure is not associated with displacement probability	Table 6, Col 1, p.301	Coefficient = -0.00125 (SE 0.00320), t-stat = -0.39; comparing 1 SD above vs below mean exposure gives a 0.25 pp difference against a 6.7% mean displacement rate
R5	Occupation stayers with greater tech-change exposure have larger earnings gains	Table 7, Col 1, p.303	Coefficient = 0.00270 (SE 0.000705); comparing workers 1 SD above vs below mean exposure gives >0.5 pp higher earnings over 12 months
R6	Model decomposition: tech change accounts for 45.5% of post-displacement earnings decline	Figure 5, p.313; §V.E	Full model: 7.63% average earnings decline; model without tech change: 4.16%; residual after also removing occ-specific HC (experience): 1.53%
R7	The Burning Glass vacancy measure aligns with O*NET and shows meaningful occupation-level change	Figure 1, p.288; text p.287, fn.22	2007 ACS-employment-weighted correlation with O*NET computer knowledge = 0.677; nearly all occupations increased computer/software requirements from 2007 to 2017, with substantial variation
R8	High-exposure occupations are more cognitive, while low-exposure occupations are more manual	Table 1, p.289	Top ten exposure increases range 0.304-0.419; bottom ten range -0.005-0.044; 7/10 top occupations have above-average nonroutine cognitive task content, all 10 bottom occupations are below average
R9	Displaced workers have lower earnings and often change occupations	Table 2, p.292; text p.291	Among 6,742 displaced workers, current weekly earnings are on average over 10% below predisplacement earnings; 63.1% work in a different four-digit SOC occupation
R10	Earnings-loss association survives control for occupation employment growth	Table 3, Col 2, p.295	Tech-change coefficient = -0.0345 (SE 0.0122); employment-share coefficient = 0.00821 (SE 0.0108), not statistically significant
R11	Earnings result persists among full-time workers and with alternative occupation coding	Table 3, Cols 3-5, p.295	Full-time-only coefficient = -0.0315 (SE 0.0127), N=4,267; Autor-Dorn coding gives -0.0259 (SE 0.00913) in the full sample and -0.0228 (SE 0.0102) full-time only
R12	Occupation-switching result persists with employment-growth controls and alternative occupation coding	Table 4, Cols 2-3, p.297	Tech-change coefficients = 0.0801 (SE 0.0242) with employment-share control and 0.0682 (SE 0.0171) using Autor-Dorn coding
R13	Switcher interaction result persists under alternative occupation coding	Table 5, Col 4, p.299	Autor-Dorn coding: occupation-switch coefficient = -0.0395 (SE 0.0245), tech-change coefficient = 0.00234 (SE 0.0105), interaction = -0.0363 (SE 0.0158)
R14	Displacement probability is lower in occupations with higher employment growth	Table 6, Col 2, p.301	Employment-share coefficient = -0.00946 (SE 0.00363); comparing occupations 1 SD above vs below mean employment-share change gives nearly 1.9 pp lower displacement probability

Overall (paper’s conclusion). Technological change, measured by changes in computer and software skill requirements in vacancy postings, explains a large share of post-displacement earnings losses. The mechanism is occupation switching: workers whose occupation introduced new technology during their employment period no longer have the skills to match with newly created jobs there, and transition to lower-tech, lower-wage occupations. This gives a new rationale for the human-capital-decline modeling device used by Ljungqvist and Sargent (1998) and others, and suggests retraining policies may be a productive complement to unemployment insurance. The earnings losses after job loss documented by Jacobson, LaLonde and Sullivan (1993) and by Couch and Placzek (2010) are here decomposed by channel; Huckfeldt (2022) shows displacement losses concentrate among occupation switchers, and this paper provides technological change as the mechanism driving the switch; Davis and von Wachter (2011) document larger earnings losses in recessions, and the paper suggests tech-change acceleration in recessions may contribute.

Theory / model

Simple two-period model (Section I, pp. 283-285)

The paper begins with a two-period model to derive the testable predictions. Two occupations, L (low-tech) and H (high-tech), use technology levels $z_L < z_H$, where $z_L = (1-\eta)z_H$ with $\eta > 0$. An “up-to-the-task” production function defines output of a worker-firm match (p.283):

$$f(h, z) = \begin{cases} z, & \text{if } h \geq z \\ 0, & \text{otherwise} \end{cases}$$

Workers have general human capital $h \sim F(h)$ and receive share $\omega \in (0,1)$ of output as wages. In period 2, a new technology $z'_H = (1+\gamma)z_H$ is introduced in occupation H (embodied in new matches, as in Mortensen and Pissarides (1998) and Violante (2002)). Workers employed in H in period 1 who do not have skills for the new technology (i.e., $z'_H > h \geq z_H$) must move to occupation L after displacement.

Define $\pi = \frac{F(z'_H) - F(z_H)}{1 - F(z_H)}$ as the share of H-employed workers who lack the skills to use the new technology (p.284). The model delivers three predictions:

• Prediction 1: If $\pi > \frac{\gamma}{\eta + \gamma}$, workers displaced from H experience larger average earnings losses than workers displaced from L.
• Prediction 2: Workers displaced from H are more likely to switch occupations (whenever $\pi > 0$).
• Prediction 3: The larger earnings losses among H-displaced workers are concentrated among occupation switchers.

These predictions guide the empirical strategy in Section IV.

Quantitative model (Section V, pp. 303-312)

The quantitative model extends the simple model to an infinite-horizon, overlapping-generations search environment with $K = 10$ occupations. Time is discrete. Technology grows at rate $g > 0$ per year (calibrated to 1.5 percent, from the Burning Glass data). Occupation $k$ has technology intensity $c_k \in [0,1]$, so the technology level in occupation $k$ at time $j$ is $z_{k,j} = c_k z_j$. Workers live $T = 120$ quarters (30 years).

Workers are heterogeneous in general human capital $h$ and occupation-specific experience $x \in \{E, N\}$. The up-to-the-task production function is (p.308):

$$f(c_k z, h, x) = \begin{cases} A_x c_k z, & \text{if } A_x h \geq c_k z \\ 0, & \text{otherwise} \end{cases}$$

where $A_N = 1$ (inexperienced) and $A_E = 1.12$ (experienced), capturing a 12 percent productivity premium from occupation-specific human capital (following Kambourov and Manovskii (2009)).

Matching follows a constant-returns-to-scale matching function (p.308):

$$M(s, v) = \frac{sv}{\left(s^\xi + v^\xi\right)^{1/\xi}}, \quad \xi = 1.6$$

Technology at an existing match and the worker’s general human capital both evolve stochastically, depreciating at rate $\mu = 1/(1+g)$ each period with probability $\iota = 0.25$ per quarter:

$$Z(z) = z' = \begin{cases} z\mu, & \text{with pr. } \iota \\ z, & \text{with pr. } 1-\iota \end{cases} \qquad H(h) = h' = \begin{cases} h\mu, & \text{with pr. } \iota \\ h, & \text{with pr. } 1-\iota \end{cases}$$

Bellman equations (pp. 306-307). For an inexperienced, unemployed worker of age $t$ with human capital $h$, the value function satisfies:

$$U_t^N(h, 0) = b + \beta E\!\left[\hat{U}_{t+1}^N(h', 0)\right], \quad \forall t \leq T$$

with terminal condition $U_{T+1}^N(h,0)=0$.

where $b$ is the public insurance transfer (calibrated so insurance replaces 41.2 percent of lost earnings, using PSID data from 2001-2013), and $\hat{U}_{t+1}^N$ is the value of search over occupations $k \in \mathcal{K}$ and wage piece rates $\omega \in [0,1]$. For an inexperienced, employed worker at a firm using technology $z$ in occupation $k$ with piece rate $\omega$:

$$W_t^N(h, z, k, \omega) = \omega f(c_k z, h, N) + \beta E\!\Bigl\{\delta\hat{U}_{t+1}^N(h', k) + (1-\delta)\bigl[\lambda_E \hat{W}_{k,t+1}^E(h', z', k, \omega) + (1-\lambda_E)\hat{W}_{t+1}^N(h', z', k, \omega)\bigr]\Bigr\}$$

with terminal condition $W_{T+1}^N(h,z,k,\omega)=0$.

where $\delta = 0.10$ per quarter is the exogenous job-destruction rate (from Shimer (2005)) and $\lambda_E = 0.05$ is the quarterly probability of becoming experienced in the current occupation.

Method

The empirical approach exploits cross-occupation heterogeneity in the change in computer and software skill requirements between 2007 and 2017, as measured in the Burning Glass Technologies vacancy database. Following Hershbein and Kahn (2018), the tech-change measure $z_{o,t}$ for occupation $o$ in year $t$ is the share of vacancies in that occupation listing a computer or software related skill. The paper defines the change as $\Delta z_o = z_{o,2017} - z_{o,2007}$, normalized to mean zero and unit SD.

The identifying assumption (selection-on-observables) is that conditional on controls including the initial level of computer requirements in 2007, the change in employment share in the occupation, age, education, gender, tenure before layoff, and unemployment spell duration, the change in tech requirements is uncorrelated with potential outcomes. Section IV.E (Table 6) finds no statistically significant association between $\Delta z_o$ and displacement probability, consistent with the model’s structure. This check does not by itself establish the selection-on-observables assumption.

The quantitative model is calibrated to match aggregate labor market moments for the 2010-2017 period. The model is solved using value function iteration over a discretized state space, with K = 10 occupations grouped by computer and software requirements from Burning Glass (the technology intensity $c_k$ is calibrated from smoothed earnings ratios across occupation groups using CPS data).

Empirical specifications

Main earnings and switching regressions (Section IV, pp. 292-299)

The baseline specification (equation 1, p.293) is:

$$Y_{i,o,t} = \alpha + \beta\Delta\tilde{z}_o + \Gamma \mathbf{X}_{i,o,t} + \varepsilon_{i,o,t} \tag{1}$$

where $Y_{i,o,t}$ is the outcome for individual $i$ displaced from occupation $o$ in DWS wave $t$ (change in log earnings, indicator for occupation switching, etc.); $\Delta\tilde{z}_o$ is the occupation-level tech-change measure normalized to mean zero and unit SD; and $\mathbf{X}_{i,o,t}$ is a vector of controls (age, log unemployment-spell duration, pre-displacement computer requirements in 2007, tenure before layoff, years of education, gender, DWS survey year, and full-time indicators). The change in occupation employment share is included in specifications that control for it, not in the baseline columns 1 of Tables 3 and 4. Standard errors cluster at the occupation level (four-digit SOC).

Applied to the sample of 6,742 displaced workers in the DWS (waves 2010, 2012, 2014, 2016, 2018, restricted to ages 25-65, employed before and after displacement, non-top-coded earnings), specification (1) produces the earnings (Table 3, R1) and occupation-switching (Table 4, R2) results. The main estimates use CPS/DWS sampling weights. Table 3 winsorizes earnings changes at the 2.5th percentiles and clusters standard errors by displaced occupation; controls are age, log unemployment duration, pre-layoff tenure, 2007 computer requirements, education, gender, survey and displacement year indicators, and full-time status before and after displacement. There are no occupation fixed effects because exposure varies at the occupation level.

The following outcome-specific forms are written out from the paper’s text; they are not separately printed equations:

$$\Delta\ln(\text{Earn}_{i,o,t}) = \alpha + \beta\Delta\tilde{z}_o + \Gamma\mathbf{X}_{i,o,t} + \varepsilon_{i,o,t}$$

for earnings in Table 3 (N = 6,742; p.295), and

$$\text{Switch}_{i,o,t} = \alpha + \beta\Delta\tilde{z}_o + \Gamma\mathbf{X}_{i,o,t} + \varepsilon_{i,o,t}$$

for occupation switching in Table 4 (N = 6,742; p.297). Table 4 adds the change in the occupation’s employment share in column 2 and changes occupation coding to Autor-Dorn in column 3. Both tables cluster standard errors by occupation and include the controls described above.

To test whether earnings losses are concentrated among occupation switchers (Model Prediction 3), the paper estimates (equation 2, p.298):

$$\Delta\ln(\text{Earn}_{i,o,t}) = \alpha + \gamma S_{i,o,t} + \beta\Delta\tilde{z}_o + \eta\!\left(\Delta\tilde{z}_o \times S_{i,o,t}\right) + \Gamma \mathbf{X}_{i,o,t} + \varepsilon_{i,o,t} \tag{2}$$

where $S_{i,o,t}$ is a dummy equal to one if the individual switches occupations following displacement. The coefficient $\eta$ on the interaction captures whether the earnings-loss effect of tech change is concentrated among switchers. Table 5, Column 3 shows $\hat{\eta} = -0.0494$ (SE 0.0180) and $\hat{\beta} = 0.000532$ (SE 0.0131, not statistically significant). This supports the conclusion that the earnings-loss association with tech change is concentrated among switchers, with no significant association among stayers (R3). Table 5 uses the same 6,742 displaced-worker sample, adds the 2007-2017 occupation employment-share change to the controls, and clusters standard errors by occupation. Column 1 estimates the switcher difference, column 2 adds exposure, column 3 estimates equation (2), and column 4 repeats the interaction using Autor-Dorn occupation coding. The fixed controls are those listed above.

Displacement probability and occupation stayers (Sections IV.E-F, pp. 300-302)

The same specification (1) is applied to two additional outcomes: (i) an indicator for being displaced (Table 6), using the full DWS sample of 239,509 individuals (not just those who regained employment), showing that $\hat{\beta} = -0.00125$ (SE 0.00320, t = -0.39) is not significantly associated with displacement probability (R4); and (ii) the 12-month change in log earnings for occupation stayers in the CPS-ORG (Table 7, N = 150,330), showing $\hat{\beta} = 0.00270$ (SE 0.000705), a positive association between exposure and earnings gains among stayers (R5).

The following outcome-specific forms are written out from the paper’s text; they are not separately printed equations:

$$\text{Displaced}_{i,o,t} = \alpha + \beta\Delta\tilde{z}_o + \Gamma\mathbf{X}_{i,o,t} + \varepsilon_{i,o,t}$$

for the full DWS sample (Table 6, p.301; N = 239,509). Columns 3-5 include survey-year fixed effects; columns 4-5 add age, education, gender, and initial computer requirements, while column 5 uses Autor-Dorn occupation coding. Standard errors cluster by occupation. Column 2 adds occupation employment-share change to the displayed equation and produces the employment-share estimate in R14.

$$\Delta_{12}\ln(\text{Earn}_{i,o,t}) = \alpha + \beta\Delta\tilde{z}_o + \Gamma\mathbf{X}_{i,o,t} + \varepsilon_{i,o,t}$$

for CPS-ORG occupation stayers (Table 7, p.303; N = 150,330). The sample requires workers to remain in their occupation under both SOC-4 and Autor-Dorn definitions. Regressions include year and gender fixed effects, age, education, and initial computer requirements; standard errors cluster by occupation.

Model decomposition (Section V.E, p.312)

The model is used as a laboratory to decompose earnings losses. Starting from the full model (average earnings decline 7.63 percent), the paper sets the technology growth rate to zero ($g = 0$) and re-solves the model; the average decline falls to 4.16 percent, implying tech change accounts for $(7.63 - 4.16) / 7.63 = 45.5$ percent of the total decline. Removing occupation-specific human capital (setting $A_E = A_N = 1$) further reduces the decline to 1.53 percent, attributing 34.5 percent to occ-specific HC. The residual 1.53 percent (20 percent share) is attributed to moving lower on the job ladder.

Datasets used

Dataset	Role in paper	Wiki page
Burning Glass Technologies vacancy database (2007-2017)	Primary source for the tech-change measure: share of vacancies listing computer or software skills by four-digit SOC occupation per year	No page yet
CPS Displaced Workers Supplement (DWS), 2010-2018 waves	Primary displaced-worker sample: earnings and occupation before and after displacement for 6,742 workers	No page yet
O*NET (vintage 15.1, 2005-2010)	Validation of Burning Glass tech-change measure via computer-knowledge ratings by occupation (Figure 1); task content measures (Table 1)	No page yet
American Community Survey (ACS), 2007 and 2017	Employment shares by occupation as controls for demand shifts; calibration of smoothed occupation earnings	No page yet
CPS Outgoing Rotation Group (CPS-ORG)	Earnings gains for occupation stayers (Table 7, N = 150,330); nondisplaced comparison group (Table 2)	No page yet
Linked SSA earnings records and CPS Annual Social and Economic Supplement (online Appendix E)	Broader laid-off-worker sample and earnings paths after layoff	No page yet

Sample (displaced workers): ages 25-65, employed both before and at time of DWS, non-top-coded earnings both before and after, displaced 2007-2017. Quantitative model calibrated to 2010-2017 at quarterly frequency.

When to read the full paper

Read the original if you are: examining the role of technological change in earnings dynamics more broadly (the paper tests against a range of controls and alternative occupation definitions); building on the Burning Glass vacancy data for measuring skill requirements (Section III and online Appendices B-C); extending the search-and-matching model to study retraining policy (the paper references a companion paper on optimal retraining subsidies); or replicating the decomposition exercise (replication data are available at the ICPSR).

Attribution and rights

Source: peer-reviewed, American Economic Review 113(2), February 2023. This distillation was first extracted on 2026-06-25 and updated by gpt-6-luna on 2026-10-04; machine-verified against the PDF on 2026-10-04, but not independently reproduced. The AER is paywalled; no open-access or CC rights were identified. Extract-only.

Braxton, J. Carter, and Bledi Taska. “Technological Change and the Consequences of Job Loss.” American Economic Review 113, no. 2 (February 2023): 279-316. DOI: 10.1257/aer.20210182. Replication data: https://doi.org/10.3886/E181166V1