Skip to main content

How Much Is Your Paycheck Really Worth? 


Geography affects your cost of living, which affects your purchasing power. Use our interactive maps, graphs, and tools to see how far your paycheck goes.

RPP Indices

So how would you compare your current job in, say, St. Louis with a new job in, say, New York City? You'd expect to pay more for most things in New York, but also to have a higher salary to compensate for the higher cost of living. But how do you accurately compare income in a high-cost region with income in low-cost region? These graphs and maps use regional price parities (RPPs) to make even comparisons across the country.

The Bureau of Economic Analysis (BEA) computes an RPP for each state and Metropolitan Statistical Area (MSA) that depicts the state's or MSA's cost of living compared with a national average of all areas, which is 100. Values above 100 reflect higher-than-average costs and values below 100 reflect lower-than-average costs. 


NOTE: Click on the bars to reveal the three expenditure categories: goods, services, and housing. Orange bars indicate states of the Eighth Federal Reserve District, home of the St. Louis Fed. For details on how these RPPs were calculated, see the BEA website.


Income

RPPs also let us compare income levels around the United States to show the real buying power of a paycheck for each state. U.S. median household income (MHI) was $60,336 in 2017. Missouri's MHI, adjusted for its own cost of living, was $56,436, compared with its unadjusted MHI of $53,578. So, Missouri's lower cost of living gives a boost to the value of a paycheck in that state.



Minimum Wage

A state's cost of living also affects the value of its minimum wage. Some states set their minimum wage higher than the federal minimum wage of $7.25. Does that mean workers in that state earn a paycheck that's worth more than the federal minimum wage would provide? Maybe yes, maybe no. To know for sure, we first need to calculate the real minimum wage for each state.

Some states have real minimum wages that are actually lower than the federal minimum wage. For example, New York has a relatively high RPP (115.8) and its minimum wage is $10.40 (in 2018), which is higher than the federal minimum wage of $7.25. But after adjusting for cost of living, New York's real minimum wage is only $6.26. In Missouri, the opposite is true: The state minimum wage is $7.85, but it's actually $9.27 when adjusted for Missouri's relatively low cost of living.


NOTE: We adjust 2018 minimum wage data for cost of living using the RPPs from 2017. The correlation between state-level RPPs from one year to the next is approximately 99.9%, so we expect the 2018 RPPs are very close to the 2017 RPPs. However, minimum wage laws are constantly changing in different states, so we use 2018 minimum wage data in this map. For more information about minimum wage laws across and within states, see Gascon (2014).


The District

A Closer Look at St. Louis

How does St. Louis measure up? We compare the RPP-adjusted measures of income for St. Louis with the national average. We also look at the measures of income for the three next-largest MSAs in the Eighth Federal Reserve District—Louisville, Memphis, and Little Rock.

The St. Louis Fed is part of the Eighth Federal Reserve District, which includes all of Arkansas and parts of Illinois, Indiana, Kentucky, Mississippi, Missouri, and Tennessee. Its headquarters is in St. Louis, and its branch offices are in Little Rock, Louisville, and Memphis.

St. Louis has relatively high living standards despite slow income growth in the District overall. In fact, the St. Louis MSA ranks in the top 8% of MSAs based on real per capita personal income (PCPI) and in the top 12% based on real median household income (MHI), according to the most recent data.

The living standards for Louisville, Memphis, and Little Rock are comparable to national averages.


NOTE: PCPI is an average while MHI is the median. PCPI has increased at a faster rate than MHI for a variety of reasons. See Coughlin, Gascon, and Kliesen (2017) for more information.


Calculators


To help interpret this calculation, see the notes on RPP and standard of living.

Consider the entire national basket of goods and services in the United States: Some of the prices for these goods and services vary, depending on the region you're in. Regional Price Parities (RPPs) are spatial price indexes that help adjust for these geographic differences.

The formal definition of an RPP is an expenditure-weighted average of the price level of goods and services for the typical consumer in one geographic region compared with all other regions in the U.S. Other similar indices exist, but we like this one for its accuracy and reliability. The RPP for the entire U.S. is 100. So areas with higher RPP values have prices that are, on average, higher; areas with lower RPP values have prices that are, on average, lower. Missouri's RPP in 2016 was 89.5, which means the prices of goods and services purchased in Missouri were 10.5% lower than the national average. New York's RPP was 115.6, which is 15.6% higher than the national average. So a "typical" consumer in Missouri pays 26.1% less than a consumer in New York. The BEA releases the RPP on a two-year lag.

RPPs are based on what the typical consumer purchases in one geographical area. While the basket of goods and services doesn't change, the choices of the items purchased may change from one area to the next. For instance, since housing is pricier in New York City, a consumer spends more income on rent and housing and less on other goods and services, whereas the opposite would be true in a place like St. Louis, where the price of rent and housing is lower. Thus, when we compare standards of living, we're comparing the different purchasing power from the cost-of-living-adjusted incomes between areas. In this way, the RPP indexes account for the idea that a consumer in New York may only rent a studio apartment while a consumer in St. Louis may be renting a two-bedroom apartment. This assumes that consumers maximize preferences given a menu of relative costs.


NOTE: Using the expenditure weights data from the BEA and calculations from Coughlin, Gascon, and Kliesen (2017), we included information about the changes in three specific expenditure categories: Goods, rent, and other services. This is useful for understanding what specific expenditures change for the average consumer. Rents are the most variable expenditure category across different areas. The expenditure weights are calculated on five-year rolling averages and change little from year to year. The data used in our calculation is from 2015. For more information on how the expenditure weights are calculated from the Bureau of Labor Statistics' CPI price survey and the Census Bureau's ACS housing survey, see the BEA website.


Wage Calculator by Occupation

Different states have different wages for the same occupation. Where can you earn the most? Choose general categories or very specific occupations to reveal the median salary in each state adjusted for that state's cost of living.


NOTE: We convert annual salaries from hourly wages as follows: Annual wage = Hourly wage x 2,080 hours. We adjust 2017 minimum wage data for cost of living using the RPPs from 2016. The correlation between state-level RPPs from one year to the next is approximately 99.9%, so we expect the 2017 RPPs are very close to the 2016 RPPs. Blank/white states indicate missing data.



Correlation of RPP and Population

The relationship between the size of a MSA and its corresponding RPP is particularly interesting. Do large population centers, such as San Francisco and New York City, actually have the highest cost and standards of living? The scatter plots below chart the RPP against the size of a MSA. The correlation between each type of RPP and the population of the MSA is positive; however, the correlation is not as strong as one might expect. The correlation is the strongest for Overall and Rents RPP; however, the correlation is weaker for Other and Goods RPP.



NOTE: The size of a MSA is displayed as the natural logarithm of its population, in thousands. A line of best fit and coefficient of determination (r-squared value) for each scatter plot is displayed. Eighth district MSAs are highlighted in orange.


References and Additional Resources

Bullard, James. "Comparing Living Standards across U.S. Metro Areas: Which Ones Fared Well?" Federal Reserve Bank of St. Louis Regional Economist, March 1, 2018; https://www.stlouisfed.org/publications/regional-economist/first-quarter-2018/comparing-living-standards.

Coughlin, Cletus C.; Gascon, Charles S. and Kliesen, Kevin L. "Living Standards in St. Louis and the Eighth Federal Reserve District: Let's Get Real." Federal Reserve Bank of St. Louis Review, Fourth Quarter 2017, pp. 377-94; https://research.stlouisfed.org/publications/review/2017/10/04/living-standards-in-st-louis-and-the-eighth-federal-reserve-district-lets-get-real/.

Gascon, Charles S. "Buying Power of Minimum Wage Varies across and within States." Federal Reserve Bank of St. Louis Regional Economist, October 2014, pp. 20-21; https://www.stlouisfed.org/publications/regional-economist/october-2014/buying-power-of-minimum-wage-varies-across-and-within-states.

Reinbold, Brian and Wen, Yi. "Income and Living Standards within the Eighth District." Federal Reserve Bank of St. Louis Regional Economist, First Quarter 2018; https://www.stlouisfed.org/publications/regional-economist/first-quarter-2018/income-living-standards.


This webpage was designed by Suvy Qin as part of a 2018 internship project in the Research Division of the Federal Reserve Bank of St. Louis. Content is based on previous research, especially Coughlin, Gascon, and Kliesen (2017). Daniel Eubanks, Charles Gascon, and Andrew Spewak provided technical assistance and helpful suggestions.