The short answer: I apply UWLS via {fixest} and then avg_predictions(). Outcomes are CATEs in the form of log risk ratios. Regressors are study variables. Looks something like the code attached. (wlsmra() is a custom function that builds on fixest::feols().)
For those curious, the first applicant, on average, receives 35% (95%CI = [−42%, −26%]) fewer positive responses, while the latter applicant receives 18% (95%CI = [−27%, −7%]) fewer positive responses compared to a plausible control group in that market.
I can virtually "place" profiles in the labour market, such as a Middle Eastern jobseeker looking for a Manager role in Europe over the past 3 years, and compare their hiring penalty with that of a Black applicant seeking a sales role in the US during the same period, all else equal.
I've been tinkering with a -finally finished- metadataset on hiring discrimination, representing roughly 1.4 million fictitious applications, and the power of contemporary meta-regression methods combined with tools such as {marginaleffects} really struck me.
Quite funny conclusion given Boren’s “intelligence is what the test tests” (sic) take on intelligence in 1923, implicitly referring to the common underlying g factor. Appears these language model intelligence tests behave similarly.
Glad to have been part of this stimulating workshop. Thanks to @vatsalecon.bsky.social, @sanchariroy.bsky.social, Javier García-Brazales, Sonia Oreffice, and others at @exeter.ac.uk for hosting.
A draft paper on our research into the cyclicality of hiring discrimination should be available soon.
I expected exactly what you expected. Not sure whether I used this in any of my code before, but seems like normal behaviour to me.
Flemish professors working on US-linked projects recently received a questionnaire from the US embassy in Belgium surveying them on university diversity policies. Guess we're going to have to charge a 300% ‘nosy-survey’ tariff now. #econsky #academicsky www.vrt.be/vrtnws/en/20...
Read the full article here: wol.iza.org/articles/hir.... This is joint work with Stijn Baert and Brecht Neyt.
We also plead that policymaking supports research that
- evaluates under-researched types of discrimination;
- provides broader insights into discrimination mechanisms;
- pilot tests the efficacy of policy interventions.
- ethnic discrimination varies by origin and is highest for MENA candidates;
- the past decades have seen little reduction in hiring discrimination;
- research increasingly focuses on identifying the mechanisms that may explain discrimination, but no conclusive view has emerged.
Other insights are:
- hiring discrimination based on gender appears mainly in occupations dominated by a specific gender;
- age discrimination in hiring is substantial and under-researched in many countries;
- age discrimination is lower in the US than in Europe;
This insight is incompatible with policies focusing exclusively on one or a few vulnerable groups. Broad diversity policies, prioritising targeting those most disadvantaged, are needed.
Our key insight is that many groups face hiring penalties. These penalties are highest for people with a disability, less physically attractive people, older people, and people of a minority race, ethnicity or national origin, respectively.
Figure 1 from the IZA World of Labor policy article displaying hiring discrimination estimates for the publication period 2005 until 2020. The graph shows differences in employer callbacks for various vulnerable groups compared to majority reference groups. The x-axis represents the percentage difference in callbacks. Negative values indicate discrimination (fewer callbacks for the vulnerable group). Each group is represented by a square (point estimate) and a ribbon (95% confidence interval). People with disabilities are discriminated against most, followed by less physically attractive people, older people, lesbian women and gay men, and people of a minority race, ethnicity, or national origin.
Last week, @iza.org World of Labor published our policy article on hiring discrimination across vulnerable groups. In this article, we summarise insights from meta-research on the issue.
New IZA World of Labor article by @louislippens.be et al. on hiring discrimination across vulnerable groups: wol.iza.org/articles/hir...
I've learned not to overinterpret them (by eye-balling). You should be best off using wviechtb.github.io/metafor/refe..., conducting sensitivity analyses with wviechtb.github.io/metafor/refe..., or immediately correcting for various forms of publication bias.
We are also looking for a postdoc (90% research, 10% teaching). Application deadline approaching fast (21 January 2025).
We have at least one open PhD position in our Labour Economics and Welfare research area (90% research, 10% teaching). Feel free to reach out for more details. Apply until 18 February 2025. #EconSky
Actually didn't know of this package yet. Just tested out the functions. Seems extremely useful.
I’ve been recently experiencing issues with modelsummary() not displaying tables in the Viewer pane, too. Must be RStudio then.
Thanks, Lukas!
👋🏻
WFH creates opportunities for people with a physical disability, who might otherwise not have been able to work full-time.
Prof. Attila Lindner briefly presented this at #EALE2024 along with some key insights that, at the margin, minimum wages are primarily passed through to consumers and improve firm efficiency. Also interesting to see meta-science increasingly permeating economics.
Black Americans' employment rate exceeded (narrowly) white Americans' rate in Dec 2023, only the 2nd time in 52 years of data
This graph shows the ratio of Black to white Americans' rates, with Black Americans' rate stably about 5 to 10 percent lower than whites' rate, until recently.
When a minimum wage increases: "Racial inequality decreases because firms disproportionately reduce callbacks to lower-quality white applicants who benefited from discrimination under lower minimum wages."
research.upjohn.org/up_workingpa...
Really exciting and interesting work on labour market competition and its diminishing effects on wage inequality.
New article in Nature Human Behavior co-led by Aaron Nichols! In a Registered Report, we ran a field study to test whether images of organizational diversity impact the quality and quantity of applicants from majority and minority groups. www.nature.com/articles/s41...
Good piece by @ryanlcooper.com on the importance of full employment - both for ensuring people are able to find jobs, and for helping improve the quality of those jobs.