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Compensation Predictions for 2018 & Lessons from 2017

Location. Location. Location. At PayScale, we analyze what matters when determining pay, from education to industry to skills to management responsibilities. Consistently, at the top of the list of these factors is location. This year, we decided to map out how location affects pay across the whole of the United States – and how things changed in 2017. We also took some time to identify some compensation predictions for 2018 to help organizations get ahead of the curve when it comes to thinking about compensation strategy and structure for the coming year and beyond.

Where U.S. Workers Earn More/Less By County

This map shows how location affects pay after controlling for the effects of experience, industry, education, and management responsibilities. In blue counties, workers with the same qualifications and experience earn more than their counterparts elsewhere. Since employees who relocate bring their education and skills with them, this map shows where they stand to earn the most. The methodology is akin to the PayScale Index, where we drill down into specific metros, industries, and job families. Note that the percentages displayed are the difference between the county in question and Dakota County, Nebraska, which functions as Everytown, USA in this analysis. For example, we expect the same worker to earn 48.4% more in King County, Washington than they would in Dakota County, Nebraska.

Who’s up and who’s down?

Two clear patterns emerge for where the money flows in the US labor market: it’s in the cities, and it’s in the ground. Counties with major urban areas crop up like oases throughout the Midwest and the South. Urban areas on the coasts also do very well, from the Boston-Washington DC corridor to Silicon Valley and Seattle.

The other big winners are regions with a strong presence in the oil, gas, and mining sectors. These sectors offer good earning opportunities for a section of the labor force that many people see as out of luck: those with only a high school education. Four regions do particularly well for this reason – northern Alaska, the Bakken shale play in western North Dakota and Eastern Montana, the Permian basin in West Texas and Southeastern New Mexico and mining communities in northern Nevada.

A band of lighter yellow traces the region inland from the mid-Atlantic seaboard and continuing into the South. Appalachia and the rural South trail the rest of the United States on several important indicators for a variety of historical and demographic reasons, and what our new research highlights is that these effects are not due only to the geographic dispersion of industries or educational outcomes.

How things changed in 2017

We also examined how these locational effects changed from 2016 to 2017. Note that these changes are not closely correlated with the size of these effects. Some areas that pay relatively little (e.g. western Nebraska or the crossroads region in Louisiana) are falling farther behind while others (e.g. central Colorado to northern New Mexico and South Dakota) are improving. On the other hand, wage growth in certain resource-rich areas was anemic (see North Dakota or northern Alaska), suggesting that wages have already peaked in those hotspots.

Year-Over-Year Wage Growth By County - 2017 vs. 2016

Three states that traditionally voted blue broke red in 2016, at least partially because of Trump’s message of economic populism – Wisconsin, Michigan, and Pennsylvania. Based on this analysis, it looks like wages in these counties fared relatively well in the first year of the Trump era – counties in those states tended to experience favorable changes in 2017.

2018 Predictions: What will this year have in store?

  1. 2018 will be the year of the bonus Wage growth has been relatively flat for the last few years and we expect that pattern to persist in 2018, in part due to the proposed tax changes in the Republican tax bill. The bill proposes drastic cuts to the corporate tax rate, which its authors claim will lead to raises for employees. However, many major companies have been upfront with their plan to instead spread the wealth to shareholders. That being said, given the competitiveness of today’s market, businesses will need to offer some type of economic incentive to keep and attract top talent and we predict this will take the form of higher and more frequent bonuses.
  2. Employers will face an even tighter talent market for hot jobs The national unemployment rate for November 2017 was 4.1 percent (2.1 percent for college educated 25+ workers). Economists regularly agree the rate of “full” employment is 4 percent – the rate at which everyone who wants a job has a job. What does this mean for you? It means there are not a lack of jobs, but rather a lack of workers. The ability to fill competitive roles will require companies to get creative in their recruiting tactics and compensation strategies; especially for hot jobs in engineering, data science, and technology.
  3. Companies will get serious about addressing the gender gap PayScale is no stranger to research on the Gender Pay Gap and has regularly found a persistent pay gap in higher level positions, as well as an opportunity gap for women to fill these positions. Given today’s current social landscape, the drive to solve potential inequities will be strong in 2018. Companies will not want to be on the wrong side of history and will build off the current momentum of 2017 as they make fairness and equity key themes in their compensation strategies.
  4. The healthcare industry will no longer be the job creator it once was Given the administration’s plan to repeal and replace the Affordable Care Act and technological advances in the fields of medicine and healthcare mobile apps, we will see employment in Healthcare dip for the first time in years. Based off an analysis done by Goldman Sachs, at least a half-million jobs were created by the ACA due to increases in the number of Americans with insurance. However, numerous studies have found the proposed changes to the ACA will greatly reduce the amount of insured people – thus likely reducing their access to regular, preventative healthcare and upping their demand for emergency care. This reduced demand for preventative care will lead to job losses; particularly in rural areas where healthcare jobs are densely concentrated.
  5. Move over Machine Learning, Artificial Intelligence will be the new hot buzzword If you thought machine learning was cool, wait until you get a hold of its big brother, artificial intelligence (AI). Machine learning entered the stratosphere in a big way in recent years as a top skill for Data Scientists (the sexiest job of the 21st century). Simply put, machine learning was the ability to develop models that could automatically learn and improve with new information. Machine learning is actually a subset of artificial intelligence, which is when a computer mimics human thoughts or decisions, but in 2018 artificial intelligence will replace machine learning as the hot data science term. AI is not new, but the advancements in AI have taken great leaps and bounds recently (e.g., self-driving cars, facial recognition software, etc.) and will be further embraced by businesses when discussing their innovation goals. We can see evidence of AI overtaking machine learning in the demand for certain skillsets as the pay bump employees with AI skills experience overshadows employees with machine learning skills (13.8%+ vs. 8.6%+).

Methodology

The maps were made using 5.1 million profiles from the PayScale salary database collected from 2007 to 2017. We control for the effects of education, industry, experience, management status, and counties over time. “How Location Affects Pay” shows the county-level change in pay conditional on those other factors, using Dakota County, Nebraska as the base case as it has the median county effect. The second map, “2017 Changes in the Effect of Location on Pay” shows how these county effects changed in 2017 vs 2016. The model assumes that the county effects are spatially correlated, so rural counties with little data for certain years utilize information from adjacent counties.

Katie Bardaro
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