Paper: Maestas, Mullen, Powell, von Wachter & Wenger (2023)

"The Value of Working Conditions in the United States and Implications for the Structure of Wages", (2023), AER.
[THIS PAPER SEEMS PROBLEMATIC FOR WHAT WE WERE INTERESTED IN SINCE IT INCLUDES IMPACT ON SOCIETY AS ONE OF THE VARIABLES IN THE LIST OF ATTRIBUTES THEY ANALYZE. THIS MIGHT BE UNFORTUNATE. MAYBE WE COULD STILL DO A GOOD PAPER IF WE FOCUS MORE ON THE SOCIAL ASPECT, E.G. BY ELICITING PEOPLE'S PERCEPTIONS OF WHAT IS A 'GOOD' AND A 'BAD' JOB, AND PEOPLE'S PERCEPTIONS OF OTHER PEOPLE'S PERCEPTIONS. THEN, SEE HOW THIS AFFECTS WTP? idk]
== Summary ==
=== 0. Introduction ===
It has been difficult to assess how differences in nonwage work characteristics and valuations of these shape actual compensation differentials in the labor market.They define compensation as wages + valuation of (desirable) working conditions. (1) there is no representative sample of workers with data on a range of job attributes, and (2) it is difficult to estimate WTP for job amenities based on observational data.(2) because characteristics can be correlated with unobserved wage determinants. The article makes three primary contributions:
# Addressing the lack of data in the US, they field the 'American Working Conditions Survey' (AWCS) in 2015 to a representative sample. This allows them to estimate the incidence of job amenities. 
# They estimate the WTP for each amenity using stated-preference experiments. They then fit a model of job choice to the data to estimate the trade-offs that individuals with heterogeneous preferences are willing to make between amenities and wages.
# They use WTP estimates from (2) to adjust typical estimates of wage differentials by: gender, race, age, and industry, by adding the wage value of individuals' amenities to their wages. They asses if this accounting for amenities changes the extent of wage inequality in the US. [HOW DO YOU DO THIS WHEN YOU DON'T KNOW HOW HETEROGENEITY IN VALUATIONS IS CORRELATED TO HAVING CERTAIN AMENITIES?]
Findings:
# The incidence of job amenities varies substantially across demographic groups and the wage distribution. Complex picture, see section II for more detail.
# Workers have nonnegligible WTP for most amenities included in the stated-preference experiments. Most valued: paid time off, less demand physical activity, schedule flexibility, and individual-level performance evaluation. In their experiments, a switch from the worst to the best job is equivalent to a 55% wage increase [this must be an average value over the sample?]. 
# They perform the exercise of total compensation twice: once holding valuations constant at the estimated valuations for the full sample, and second, allowing valuations to differ based on: gender, race, education, and age. In the first case, they see the gender wage gap narrowing slightly, and education, industry and overall wage differentials widen. In the second case, these patterns become stronger. 
They validate the hypothetical choices in two ways: (1) show that those who have an amenity on a current job value it more, and (2) link stated preferences to observed job transitions. 
=== I. Data on Working Conditions and Wages from the American Working Conditions Survey ===
==== I.A. The AWCS ====
The AWCS is a longitudinal survey, fielded by the RAND American Life Panel, which is a nationally representative, probability-based panel. Initial wave from Jul-Oct 2015 (harmonized with the 6th EU Working Conditions Survey), second wave that contained the stated-preference experiments administered Dec-Feb 2016, and final follow-up wave in 2018. ALP has ~5000 respondents, and the survey a 60.7% response rate, leading to 3004 respondents to the stated preference module. Selecting respondents who are (1) currently working, (2) aged 25-71, (3) reporting hourly wage between $1 and $500, and (4) completed the entire survey, leaves a final analysis sample of 1738. Weigh to match demographics in the CPS. Table 1 presents summary states by sex, race, education, age groups, and wage quintile. 
==== I.B. Main Dimensions of Working Conditions Used in Analysis ====
Their goal is was to examine a core set of job characteristics in the current labor market. They particularly wanted amenities that are intrinsic job characteristics, instead of resulting from a specific job-worker match (e.g. like commuting distance). 
* Schedule Flexibility: mentions findings from Mas & Pallais (2017) and Wiswall & Zafar (2018).
* Telecommuting: aka, ability to work from home. 
* Physical Job Demands: 
* Pace of Work: less research on importance of pace and stress
* Autonomy at Work: 
* Paid Time Off: 
* Working in Teams: as well as being evaluated on the basis of team versus individual performance
* Job Training: 
* Meaningful Work: [not the same as positive impact? e.g. building oil rigs is meaningful, but maybe 'not right'. The opposite of meaningful to me feel more like bullshit jobs]. [see references here]
=== II. Heterogeneity in Working Conditions in the United States ===
[Isn't the question of what are better amenities depending on the analysis of WTP for the amenity within that group? Or, are all amenities here positive? E.g. you might like some physical activity, dislike a relaxed pace, or (not) want to telecommute?]
Give some descriptives on how amenities are distributed by gender, race, education, age, and wage quintile.
=== III. Estimating WTP for Job Amenities ===
* Give 10 job choice scenarios to each respondent
* Each scenario has two job options, defined by varying the set of job attributes, hours, and pay
* To test for inattention, ask two 'trick questions' that the introductory text specified a prescribed answer to, and label respondents as 'attentive' if they answer at least one of these questions correctly
==== III.A+B. Creation of Hypothetical Job Profiles Based on Current Working Conditions, and Random Variation ====
* For each respondent, define a 'baseline' job around which attributes vary, based on the respondent's current job which they collect survey information on pre-experiment. For 2/10 experiments, used a common baseline for all respondents to test sensitivity to it.
* Starting from the baseline job, create two alternatives by randomly varying two non-wage attributes. Hence, for any job pair, 8/10 attributes were identical to the baseline job, and 2/10 varied.
* Offered wage always varied between job A and B, between 0.75 and 1.25 of the respondent's wage
* Respondents choose from: {"strongly prefer A", "prefer A", "prefer B", "strongly prefer B"}Shouldn't there be some indifference option?
[NOTE: THEY ONLY VARY JOB PROFILES ON 'positive impact on their community or society' BETWEEN "Frequently" AND "Occasionally". SO NO EVALUATION OF WTP FOR AVOIDING NEGATIVE OR ZERO IMPACT. THIS WILL BE ENTIRELY MISSED AND MIGHT ELICIT THE STRONGEST WTP REACTIONS.]
==== III.C. Estimation ====
Use typical setup with iid extreme value type I epsilon, leading to the usual closed-form expression for the probability that and individual chooses a job with a given vector of attributes and wage over another job with another vector of attributes and wage. Then, WTP comes from an expression of indifference between a job with attribute r and a job without it at a different wage. 
=== IV. Main Estimates of Willingness to Pay for Job Amenities ===
From table 2 which contains the main results, WTP for 'frequent opportunities to serve' compared to 'occasional opportunities to serve', is 3-4% of wages depending on the specification. This is the lowest WTP out of all the amenities they test.
==== IV.B. Preference Heterogeneity and Sorting ====
# Test if those who have a job with an amenity value it more. Find substantial evidence of this, those who select into jobs with amenities value it on average 6.1% more than those who have a job without that amenity.
# Study the relation between stated preferences in the 2015 experiment and job transitions observed from jobs held in 2018.
==== IV.C. Heterogeneity in WTP ====
Find various differences in tables 4-7 by gender, race, education, and age. Notably, WTP for 'opportunity to serve' remains the smallest and most constant.
=== V. Implications of Incidence and Valuation of Job Amenities for the Wage Distribution ===
Assess how pay differences change between gender race, education, and age groups when the 'value'=WTP of amenities are accounted for—table 8. Overall, by gender wage differences decrease, but by race, education and age they increase.
They make a note as well about a literature that aims to explain industry wage differentials, and if these could be explained by amenities. Instead, they find that wage differentials increase after accounting for amenities. Same for overall wage differentials in the population.
=== VI. Conclusion ===
== What we could add compared to this ==
* Look more at the correlates/determinants of WTP across population. E.g. young people, rich people, or a set of conditions that has to be satisfied before this type of concern comes into play.
* Re-run the survey experiment on people affiliated with the EA community? See how they react.
* Run on university students, and on a representative sample, and see how it differs. Can then test if this has something to do with age/generations (if the representative sample has enough youngish people), or not. This would test the popular narrative that young people want different/meaningful things in their jobs.
* Go more deeply into norms, e.g. by varying information on other people's perceptions of the prosocial impact of your job.
* See p. 2028, comparison with WTP for scheduling discretion and WFH from Mas & Pallais (2017), 20% and 8% resp. compared to 8.9% and 4.2% in this paper. Does this have something to do with the range of alternatives presented, the baseline scenario, or smth? It would be nice if there's a way to also understand these differences, and whether they can be explained by methods used. E.g. run survey with smaller and larger set of alternatives, or see how answers change based on relevant scenario's appearing among the first or last.
* With university students, could validate with jobs taken after uni. With other survey participants this would either require a second wave survey, or if we can field the experiment as part of a regular survey (I'm thinkin abt the recent Jager et al. paper in Germany), we might simply get this data?