Elizabeth B. Li, Giorgio Topa, and Jacob P. Weber
Measures of labor market tightness (LMT) describe how troublesome it’s for corporations to search out staff. The labor market is taken into account tight if there are few job seekers accessible relative to job openings, implying stronger wage pressures as employers compete for scarce job seekers. Thus, good measures of LMT are necessary for predicting wage inflation and calibrating financial coverage. Lately, the hiring platform LinkedIn started producing three progressive LMT measures based mostly on its consumer information. On this publish, we use public information from the Job Openings and Labor Turnover Survey (JOLTS) and the Bureau of Labor Statistics (BLS) to judge the flexibility of those novel measures to foretell wage and value inflation relative to current LMT measures, discovering that one stands out as significantly helpful and well timed.
How Are the New LinkedIn Measures Constructed?
We research three LinkedIn measures of LMT: the Jobs per Utility Index (JPAI), the Jobs per Jobseeker Index (JPJI), as outlined by McCrory and Huang (2025), and the LinkedIn Hiring Price (LHR), as outlined by Chua and Kantenga (2024) and first launched in 2017.
The JPAI is outlined because the variety of engaging job openings in a given month divided by the variety of job purposes submitted on LinkedIn. A pretty job opening on LinkedIn is outlined as satisfying two circumstances: the job poster both paid to advertise the function or manually listed the place on LinkedIn, and thus paid the chance value of their time, and, inside a time period t, the job itemizing obtained an utility from a LinkedIn member within the labor market the place the job is positioned. Right here, an utility is both submitted straight by way of LinkedIn’s Simple Apply characteristic or inferred as a result of the member clicked on a hyperlink to use to the function outdoors of LinkedIn’s platform.
The JPJI is outlined because the variety of engaging job openings that month divided by the variety of jobseekers that submitted at the least one job utility on LinkedIn that month. A pretty job and a job utility are each outlined in the identical means as for the JPAI.
Lastly, the LHR is outlined because the variety of LinkedIn members who added a brand new employer to their profile divided by the related LinkedIn member base (for instance, members positioned within the U.S.). This quantity is listed to the common month in 2016; for instance, an index of 1.05 signifies a hiring charge that’s 5 % increased than the common month in 2016. In our evaluation we use the seasonally adjusted information for the LHR, as produced by LinkedIn.
All three month-to-month measures have the benefit of being timelier than normal measures. As an example, information for August 2026 could possibly be made accessible within the first week of September, a lot prior to JOLTS information which can be found with a couple of five-week lag. Nevertheless, these measures are usually not essentially consultant of the U.S. economic system as an entire, since they seize solely jobs posted and utilized to on LinkedIn, which raises the next query: how correlated are they with current measures of LMT, and are they helpful for predicting wage and value inflation?
Evaluating New and Present Measures
How do the LinkedIn LMT measures examine to their extra extensively recognized counterparts? As a benchmark, we use the vacancies-to-unemployment (V/U) ratio, a extensively used measure of labor market tightness outlined because the variety of job vacancies (the variety of job openings posted by employers every month as measured by JOLTS) divided by the variety of people who find themselves unemployed. We additionally benchmark the LHR with the hires charge from JOLTS. In all analyses, we limit consideration to the interval beginning in July 2019 (given restricted historic information availability for the JPJI and the JPAI) and ending in April 2026.
The left panel of the chart under reveals that the JPJI tracks V/U nicely. The correlation between the 2 measures is 0.733. The JPJI is barely increased than V/U within the early aftermath of the COVID-19 pandemic, and barely decrease from late 2022 to mid-2024. The correct panel of the chart reveals that the LHR and the JOLTS hires charge are additionally very intently linked; when beginning the info collection from the start of 2021 (excluding the massive actions that occurred on the onset of COVID-19), the correlation between the 2 (normalized) variables is 0.95.
The JPJI and V/U Monitor Carefully over Time, As Do the LHR and the JOLTS Hires Price
Sources: LinkedIn; Job Openings and Labor Turnover Survey (JOLTS); Bureau of Labor Statistics.
Notes: All variables are normalized as Z-scores over the plotted time interval (centered round their imply and divided by their normal deviation). The JPJI is the Jobs per Jobseeker Index, V/U is the vacancies-to-unemployment ratio, and LHR is the LinkedIn Hiring Price.
How Good Are the New Measures for Nowcasting and Forecasting?
We now study the LinkedIn measures’ nowcasting and forecasting properties in predicting value and wage inflation in actual time. For these workouts, we use month-to-month Shopper Worth Index (CPI) information and quarterly Employment Value Index (ECI) information, respectively. Within the nowcasting train, we assess the flexibility of LMT measures to foretell the present values of the CPI and the ECI. Nowcasting is beneficial for serving to policymakers to foretell inflation earlier than the official information are launched. In follow, we run rolling regressions of inflation as much as interval t-1 (the final interval official information can be found) on LMT measures. We then use the expected inflation in interval t (utilizing LMT measures by way of t) as our nowcast.
How can we consider the “goodness of match” of our nowcast measures? We use the foundation imply squared error (RMSE) as our metric for the way nicely the nowcast matches precise inflation. After nowcasting values for CPI and ECI inflation, we compute the RMSE for the way precisely every LMT-predicted inflation measure tracks precise inflation. We then take the common RMSE of every measure and examine how they fare towards the common RMSE of V/U as a benchmark.
The chart under reveals that the JPJI and the LHR are persistently the perfect performers. The JPJI has the bottom nowcasting error, performing higher than V/U in predicting each value and wage inflation. The LHR performs higher than V/U for CPI however worse for ECI. The JPAI and the JOLTS hires charge are persistently worse than V/U.
The JPJI Has the Greatest Nowcasting Efficiency in Predicting CPI and ECI Inflation
Month-to-month CPI inflation: Nowcasting error (V/U = 100)
Quarterly CPI inflation: Nowcasting error (V/U = 100)
Sources: LinkedIn; Job Openings and Labor Turnover Survey (JOLTS); Bureau of Labor Statistics.
Notes: Nowcast errors contain evaluating every measure’s imply root imply squared error (RMSE) relative to the vacancies-to-unemployment (V/U) ratio’s imply RMSE. Shopper Worth Index (CPI) inflation is a month-to-month measure, whereas Employment Value Index (ECI) inflation is quarterly. Hires refers back to the JOLTS hires charge whereas the Jobs per Jobseeker Index (JPJI), the Jobs per Utility Index (JPAI), and the LinkedIn Hiring Price (LHR) are from LinkedIn.
We conduct the identical train for forecasting. Right here we regress every inflation measure on one-period lagged values of our LMT measures by way of interval t, to assemble a forecast for t+1 inflation. Equally to what we did with our nowcasting train, we consider the effectiveness of the brand new LinkedIn measures by computing the RMSE for the way precisely every LMT-predicted inflation measure tracks precise inflation. We then take the common RMSE of every measure and examine how they fare towards the common RMSE of V/U.
The chart under reveals that, as with nowcasting, the JPJI is the perfect performing measure, with a decrease forecasting error than V/U in predicting each value and wage inflation. Notably, the LHR additionally performs higher than V/U in predicting each inflation measures, although nonetheless worse than the JPJI. And once more, the JPAI and the JOLTS hires charge are the worst-performing measures, although the JPAI performs higher than V/U in forecasting ECI inflation.
The JPJI Additionally Has the Greatest Forecasting Efficiency for CPI and ECI Inflation
Month-to-month CPI inflation: Forecasting error (V/U = 100)
Quarterly ECI inflation: Forecasting error (V/U) = 100
Sources: LinkedIn; Job Openings and Labor Turnover Survey (JOLTS); Bureau of Labor Statistics.
Notes: Forecast errors contain evaluating every measure’s imply root imply squared error (RMSE) relative to the vacancies-to-unemployment (V/U) ratio’s imply RMSE. Shopper Worth Index (CPI) inflation is a month-to-month measure, whereas Employment Value Index (ECI) inflation is quarterly. Hires refers back to the JOLTS hires charge whereas the Jobs per Jobseeker Index (JPJI), the Jobs per Utility Index (JPAI), and the LinkedIn Hiring Price (LHR) are from LinkedIn.
Conclusion
On this publish, we have now investigated the efficiency of three novel measures of labor market tightness produced by LinkedIn in predicting wage and value inflation. We discover that the JPJI and the LHR not solely monitor current measures of LMT nicely, but additionally have a RMSE that’s akin to or higher than current, trusted LMT measures like V/U in nowcasting and forecasting value and wage inflation. Crucially, these measures are additionally timelier than the JOLTS hires charge and conventional measures of tightness, resembling V/U.
Elizabeth B. Li is a analysis analyst within the Federal Reserve Financial institution of New York’s Analysis and Statistics Group.
Giorgio Topa is an financial analysis advisor within the Federal Reserve Financial institution of New York’s Analysis and Statistics Group.
Jacob P. Weber is a analysis economist within the Federal Reserve Financial institution of New York’s Analysis and Statistics Group.
The right way to cite this publish:
Elizabeth B. Li, Giorgio Topa, and Jacob P. Weber, “Evaluating New LinkedIn Labor Market Tightness Indicators,” Federal Reserve Financial institution of New York Liberty Avenue Economics, October 30, 2026, https://doi.org/10.59576/lse.20261030
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Disclaimer
The views expressed on this publish are these of the writer(s) and don’t essentially replicate the place of the Federal Reserve Financial institution of New York or the Federal Reserve System. Any errors or omissions are the duty of the writer(s).
