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) suffer larger earnings declines, (ii) are more likely to switch to a lower-paying occupation, and (iii) show earnings losses concentrated entirely 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 higher-tech-change occupations suffer larger earnings declines	Table 3, Col 1, p.295	Coefficient on $\Delta\bar{z}_o$ = -0.0354 (SE 0.0114); 1 SD higher exposure implies >7 pp larger earnings decline
R2	Higher tech-change exposure raises the probability of occupation switching after displacement	Table 4, Col 1, p.297	Coefficient = 0.0847 (SE 0.0269); 1 SD higher exposure implies 17 pp greater probability of switching
R3	Earnings losses are concentrated among occupation switchers; stayers are unaffected	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), insignificant
R4	Tech-change exposure does not raise the probability of displacement	Table 6, Col 1, p.301	Coefficient = -0.00125 (SE 0.00320), t-stat = -0.39; economically negligible (0.25 pp vs mean displacement rate of 6.7%)
R5	Occupation stayers more exposed to tech change earn more over time	Table 7, Col 1, p.302	Coefficient = 0.00270 (SE 0.000705); 1 SD above mean implies >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%

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:

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

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 $\omega$:

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

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. Crucially, Section IV.E (Table 6) shows that $\Delta z_o$ is uncorrelated with the probability of displacement itself, consistent with the model’s structure and validating the approach.

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:

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

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.); $\Delta\bar{z}_o$ 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, full-time indicators, change in occupation employment share). Standard errors clustered 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.

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

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

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, insignificant), confirming that occupation stayers are unaffected by tech change while switchers bear the full cost (R3).

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), confirming tech change does not raise 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), confirming occupation stayers benefit from tech change (R5).

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

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 extracted by an LLM on 2026-06-25 and is not human-verified or 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