Value of Working Conditions: Maestas et al. (2023)

What this is. This is a distilled skeleton of Maestas et al. (2023). Read the original at https://doi.org/10.1257/aer.20190846 to replicate or extend.

TL;DR

Maestas et al. (2023) field the American Working Conditions Survey (AWCS), a new nationally representative survey covering 1,738 employed Americans, and use embedded stated-preference experiments to estimate how much workers are willing to pay for nine nonwage job amenities. Across all amenities, switching from the worst to the best job is equivalent to a 55 percent wage increase, confirming that nonwage job attributes are a central component of total compensation. Workers differ widely in their valuations by gender, race, education, and age: older workers and women place especially high value on physical job demands and paid time off. Incorporating both the incidence and the valuation of amenities into standard wage differentials attenuates the gender gap (24 percent) but widens the race and education gaps, and raises overall wage inequality. The paper builds on the compensating differentials framework of Rosen (1986) and extends the experimental approach of Mas and Pallais (2017) to a nationally representative sample and a broader set of amenities. Related prior work includes Wiswall and Zafar (2018) on stated-preference evidence for job attributes among students, and Pierce (2001) on compensation inequality once fringe benefits are included.

Core results

#	Result	Locator	Magnitude as reported
R1	Best vs worst amenity bundle: total WTP	Table 2 col 5, p. 2025	55.0% wage equivalent
R2	Paid time off: WTP for 10 days vs none	Table 2 col 5, p. 2025	16.4% wage equivalent
R3	Paid time off: WTP for 20 days vs none	Table 2 col 5, pp. 2025-2027	23.0% wage equivalent
R4	Physical demands: WTP for moderate vs heavy activity	Table 2 col 5, pp. 2025-2027	14.5% wage equivalent
R5	Schedule flexibility: WTP for setting own schedule	Table 2 col 5, pp. 2025-2027	8.9% wage equivalent
R6	Work arrangement: WTP for working alone (vs team-evaluated team)	Table 2 col 5, pp. 2025-2027	8.6% wage equivalent
R7	Gender log compensation gap with preference heterogeneity	Table 8 Panel A col 3, p. 2040	-0.142 log pts (vs -0.192 unadjusted); 24% reduction
R8	Race log compensation gap with preference heterogeneity	Table 8 Panel A col 3, pp. 2040-2041	-0.274 log pts (vs -0.208 unadjusted); 27% widening
R9	Education log compensation gap (HS or less vs college)	Table 8 Panel A col 3, p. 2041	-0.667 log pts (vs -0.559 unadjusted); 19% widening
R10	Overall wage inequality: 90-10 log wage gap	Table 8 Panel C col 3, p. 2042	1.769 (vs 1.664 unadjusted); +10.5 log pts

Overall (paper’s conclusion). Working conditions vary widely across demographic groups and throughout the wage distribution. Workers have measurable willingness to pay for most job amenities studied. Accounting for both the incidence of amenities and heterogeneity in valuations changes standard measures of the wage structure: the gender gap narrows, the race and education gaps widen, and overall wage inequality increases. Contrary to the conclusion of Krueger and Summers (1988), accounting for the value of working conditions widens rather than narrows interindustry wage differentials.

Theory / model

The paper builds on the competitive compensating differentials framework of Rosen (1986). In a long-run competitive equilibrium, workers sort into jobs that equate, at the margin, their willingness to pay for an amenity with the market wage-amenity trade-off required by firms. Observed wages therefore understate total compensation for workers in jobs with desirable nonwage attributes.

There is no formal general equilibrium model estimated in the paper, but the theoretical logic motivates the empirical setup: if workers trade wages for amenities, then the true compensation differential between two workers must add back the market value of the amenities each holds. The indirect utility function of individual $i$ over job alternative $j$ in choice pair $t$ is specified as (p. 2021, equation in estimation section):

V_{ijt} = \alpha + A'_{ijt}\,\beta_i + \delta_i \ln w_{ijt} + \varepsilon_{ijt} \tag{1}

where $A_{ijt}$ is the vector of nonwage job characteristics (length $R$), $w_{ijt}$ is the offered wage, and $\beta_i$ and $\delta_i$ allow heterogeneous marginal utilities across individuals. The error $\varepsilon_{ijt}$ is i.i.d. Extreme Value Type I, yielding a logit choice probability.

The identification logic is that the stated-preference experiments vary $A_{ijt}$ and $w_{ijt}$ independently and randomly across choice pairs for each respondent, so the wage-amenity trade-off is observed directly without the selection confounds that plague hedonic regressions estimated from observational job choices.

Method

Estimating equation. Under the logit assumption, the probability that individual $i$ prefers job $j$ over job $k$ in choice pair $t$ is (p. 2021):

\Pr(V_{ijt} > V_{ikt}) = \frac{\exp\!\bigl[(A'_{ijt} - A'_{ikt})\,\beta_i + \delta_i\,(\ln w_{ijt} - \ln w_{ikt})\bigr]}{1 + \exp\!\bigl[(A'_{ijt} - A'_{ikt})\,\beta_i + \delta_i\,(\ln w_{ijt} - \ln w_{ikt})\bigr]} \tag{2}

The authors estimate two versions: (i) a standard logit where $\beta_i = \beta$ and $\delta_i = \delta$ for all $i$ (so WTP does not vary across individuals except through their wage level), and (ii) a mixed logit where $\beta_i \sim N(\beta,\Sigma_\beta)$ to allow unobserved preference heterogeneity. Results are similar across both specifications; the standard logit is used for subgroup and robustness analyses.

Willingness-to-pay derivation. Individual $i$ is indifferent between not having attribute $r$ at wage $w_i$ and having it at wage $w_i - WTP_i^r$. Setting the two utility levels equal (p. 2022, eq. 1):

\delta_i \ln w_i = \beta_i^r + \delta_i \ln\!\bigl[w_i - WTP_i^r\bigr] \tag{3}

Solving for willingness to pay (p. 2022, eq. 2):

WTP_i^r = w_i\Bigl[1 - e^{(-\beta_i^r/\delta_i)}\Bigr] \tag{4}

This is reported as $100\times[1 - e^{(-\beta^r/\delta)}]$ percent of the wage for the standard logit.

Best-to-worst WTP. The total value of the best amenity bundle relative to the worst is (p. 2022, eq. 3):

WTP_i^{\text{FULL}} = w_i\Bigl[1 - e^{(-\sum_r \beta_i^r / \delta_i)}\Bigr] \tag{5}

where the sum is over the most-preferred value of each attribute. This yields 55 percent of the wage for the full sample (Table 2 col 5, p. 2025).

Log total compensation for the wage-structure analysis is (p. 2039):

\ln\!\Bigl(w_i + w_i\Bigl[1 - e^{(-\sum_r A_{ir}\,\beta_i^r / \delta_i)}\Bigr]\Bigr) \tag{6}

where $A_{ir}$ is an indicator for whether respondent $i$‘s current job has attribute $r$.

Standard errors use the delta method for the WTP estimates and are clustered by respondent throughout.

Empirical specifications

Stated-preference experiment design. The AWCS administered 10 stated-preference experiments per respondent (December 2015-February 2016). In each experiment, respondents chose between two hypothetical jobs (Job A and Job B). Job attributes were drawn from the respondent’s own current job as a baseline, with two nonwage attributes randomly selected to vary between jobs. The offered wage $w_{ijt}$ was $\theta \cdot w_i$ where $\theta \sim N(1, 0.1^2)$, truncated to $[0.75, 1.25]$, ensuring wage variation of at most 50 percent of the current wage. Respondents chose from four options: Strongly Prefer A, Prefer A, Prefer B, Strongly Prefer B, which are aggregated into a binary indicator for Job A preference (p. 2021). This design means identification of $\beta / \delta$ (the WTP ratio) comes from within-respondent random variation in both wages and attributes across the 10 choice pairs.

Subgroup WTP regressions. To document heterogeneity in valuations, the authors estimate the standard logit model separately for subgroups defined by gender (Table 4), race (Table 5), education (Table 6), and age (Table 7), computing $WTP^r$ for each amenity within each subgroup.

Wage and compensation differential regressions (Section V, Table 8). For each measure of compensation, the paper estimates separate regressions of log compensation on indicator variables for demographic group and industry (aggregated to 11 NAICS supersectors), with no constant (demeaned within supersector for industry analysis):

\ln(\text{compensation}_i) = \sum_k \phi_k D_{ik} + \text{controls} + e_i \tag{7}

where $D_{ik}$ are indicators for demographic group $k$ (female, non-White, education group, age group) and $\phi_k$ is the log compensation differential relative to the omitted group. Three versions are estimated: (i) log wage only, (ii) log total compensation holding valuations at full-sample estimates from Table 2 col 5, and (iii) log total compensation allowing valuations to differ by gender, race, education, and age. The 90th, 50th, and 10th percentile differences are computed from these regressions. Standard errors and confidence intervals use a block (by respondent) bootstrap with 500 iterations (p. 2039).

Sorting validation. To test internal validity of the stated-preference estimates, the authors use the 2018 AWCS follow-up wave ($N = 977$ matched respondents) to test whether individuals who hold a given amenity in 2015 value it more in the experiments than those without it. Those with a desired attribute in 2015 value it 4.1 percentage points more than those who transition away from it ($p < 0.01$), consistent with preference-driven sorting (Table 3, p. 2032).

Datasets used

Dataset	Role in paper	Wiki page
American Working Conditions Survey (AWCS), waves 2015 and 2016	Primary data source: incidence of 9 job attributes by demographic group (wave 1, July-October 2015); stated-preference experiments for WTP estimation (wave 2, December 2015-February 2016); N = 1,738 workers	no page yet
American Working Conditions Survey (AWCS), follow-up wave 2018	Longitudinal follow-up for sorting validation and preference-transition analysis; N = 977 matched respondents	no page yet
RAND American Life Panel (ALP)	Nationally representative probability-based panel that served as the sampling frame for all AWCS waves	no page yet
Current Population Survey (CPS)	Used to generate survey weights matching AWCS to US working population demographics	no page yet

Sample: 1,738 employed workers ages 25-71, from the RAND ALP, weighted to match the US working population via CPS. The AWCS data are available publicly at https://www.rand.org/pubs/tools/TL269.html. Replication data are archived at ICPSR (Maestas et al. 2023, DOI 10.3886/E184378V1).

When to read the full paper

Read the source if you are:

• Estimating compensating wage differentials or the value of specific job amenities (Tables 2-7 provide WTP estimates by amenity and demographic group with standard errors).
• Adjusting wage gaps (gender, race, education, interindustry) for nonwage job attributes; Table 8 and Section V detail the methodology and results.
• Designing stated-preference experiments for labor market research; Sections III-IV provide the experimental design, logit estimation, and robustness checks including attention screens, probit alternatives, and common-baseline variants.
• Studying heterogeneity in labor market preferences; Tables 4-7 present results by gender, race, education, and age with cross-group p-values.

Attribution and rights

This page is a distilled extract. The source paper is:

Maestas, Nicole, Kathleen J. Mullen, David Powell, Till von Wachter, and Jeffrey B. Wenger. 2023. “The Value of Working Conditions in the United States and Implications for the Structure of Wages.” American Economic Review 113(7): 2007-2047. https://doi.org/10.1257/aer.20190846

Copyright American Economic Association; reproduced with permission. Extract only; full text available at aeaweb.org (paywalled; AEA 3-year embargo elapses July 2026). This summary is LLM-distilled by IAR, not human-verified, and not reproduced.