Purchasing power: using wage statistics with regional price parities to create a standard for comparing wages across U.S. areas.
The U.S. Bureau of Labor Statistics (BLS) Occupational Employment Statistics (OES) program produces employment and wage estimates for more than 800 occupations. OES wage data have long been the go-to source for jobseekers and employers who want to know the average (or range) of wages by occupation at the national, state, and area levels. These wages are generally expressed as actual wages, that is the actual amount that a worker is paid by their employer. Because costs for goods and services tend to fluctuate across different areas of the country, actual wages alone rarely provide a useful metric for comparing purchasing power across areas. But by incorporating regional price parities (RPPs) from the Bureau of Economic Analysis, OES can produce a price-adjusted wage that offers data users a standard for comparing wages across Metropolitan Statistical Areas (hereinafter referred to as areas). (2)
RPPs, expressed as a percentage of the overall national price level (equal to 100), measure the differences in the price levels of goods and services across areas for a given year. If an area's RPP is greater than 100, it means that goods and services are more expensive than the national average; if an area's RPP is less than 100, goods and services are less expensive than the national average. In areas where goods and services are more expensive, actual wages tend to be higher. By adjusting the actual wage based on an area's RPP, OES produces a price-adjusted wage that gives data users a comparable standard for assessing purchasing power across different areas.
In May 2014, the average actual wage in San Jose-Sunnyvale-Santa Clara, CA ($75,770), was much higher than the average wage in Durham-Chapel Hill, NC ($55,840). But when the RPPs are taken into account to adjust for average price level, the gap between the two areas shrinks, producing price-adjusted wages of $62,107 and $58,779, respectively. (See table 1.) Data users comparing wages for San Francisco-Oakland-Fremont, CA, and the Durham area will notice the following: San Francisco area wages decrease after adjusting for average price level, while wages in the Durham area increase after the adjustment.
The San Jose and San Francisco areas, as well as New York-Northern New Jersey-Long Island, NY-NJ-PA, and Washington-Arlington-Alexandria, DC-VA-MD-WV, are among the areas where wages are highest. But when the actual wages are adjusted for average price level to show purchasing power, the rankings change. (See table 1.) The San Jose, San Francisco, and Washington areas remain among the highest paying areas, but the New York area is no longer among the highest paying. Despite the fact the New York area has high actual wages across the board, the cost of living is so high that the area's price-adjusted wages are much lower. The San Jose area remains the highest paying, even with its relatively high RPP. Actual wages in the San Jose area are so high that they offset the high cost of living. The San Francisco area falls from 2 to 10 and the New York area falls to 61. Some areas with high price-adjusted wages that may surprise data users include the Durham area; Huntsville, AL; Hartford-West Hartford-East Hartford, CT; and Springfield, IL. Table 1 shows areas with the highest average price-adjusted wages in May 2014.
Purchasing power by occupation
The relationship between wages and relative prices becomes even more interesting when occupational data is included in the analysis. Workers employed in the arts, design, entertainment, sports, and media occupational group earn higher wages in Los Angeles-Long Beach-Santa Ana, CA, and the New York, San Jose, and Washington areas than in any other areas of the United States. This remains true both before and after adjusting for prices. Computer and mathematical occupations have high wages in Seattle-Tacoma-Bellevue, WA, along with the San Jose and Durham areas both before and after adjusting the wages for regional prices. However, these occupational groups are the exceptions. Out of the 220 occupational groups in the 10 areas with the highest mean wages, the 18 occupational groups listed in table 2 are the only groups that remain among the 10 highest paying areas after adjusting wages for regional prices.
Because wages for all occupations in an area are adjusted by the same RPP, relative rankings within an area remain the same after adjusting for prices. But because actual wages and RPPs differ across areas, pay rankings for specific occupations tend to fluctuate in cross-area comparisons, creating variances. For some occupations, adjusting for regional prices decreases the difference between the highest and lowest paying areas, while for others the difference increases. This holds true for two related occupations--bookkeepers and accountants.
After adjusting wages for regional prices, the difference between the highest and lowest paying areas decreases for bookkeepers and increases for accountants. In May 2014, the preadjustment difference in wages for bookkeepers was $21,470 (a high of $49,580 in San Jose and low of $28,110 in Laredo, TX) and the postadjustment the difference was $14,096 (a high of $45,544 in Columbus, OH and low of $31,448 in Jacksonville, NC.). For accountants, the preadjustment difference was $43,920 (a high of $93,160 in New York and a low of $49,240 in Danville, IL) and the postadjustment difference was $54,162 (a high of $105,859 in Dothan, AL, and a low of $51,697 in Flagstaff, AZ).
The wages for detailed occupations by area before and after adjusting wages for regional purchasing power are available at http://www.bls.gov/oes/purchasing_power.xlsx. A sample of this listing is provided in table 3.
Across most occupations, the highest actual wages are generally earned in areas with high RPPs, such as New York and San Francisco. But after adjusting wages for regional prices, occupational differences tend to have a more pronounced effect on purchasing power than geography alone. In particular, occupations with relatively low wages tend to exhibit the greatest pre and postadjustment change. For example, food-service workers and cashiers earn more in San Francisco and San Jose than most other areas of the country before adjusting for regional prices. But after adjusting wages for prices, these workers' wages are in the bottom 25 percent nationally. After price adjustments, purchasing power for fast-food workers tends to increase in areas of Illinois, Washington, and Colorado.
Figures 1 and 2 show purchasing power and mean wages for team assemblers (3) and police officers, respectively, in selected areas. The areas in figure 1 all have an RPP of less than 100. Accordingly, the price-adjusted wage for all areas is higher than the mean wage, effectively increasing the purchasing power of team assemblers in these areas. Figure 2 includes a mix of areas, some with high RPPs and others with low RPPs. Most of the areas with high wages for police and sheriff's patrol officers had mean wages for all occupations well above the U.S. average, as well as high RPPs. For police and sheriff's patrol officers, the high occupational mean wage outweighs those factors and still produces a high rate of purchasing power. Simply put, police officers are often better off in high-wage, high-RPP areas. This differs from team assemblers, as they generally do better in low-wage, low-RPP areas.
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
Employment effects on average wages before and after price adjustments
Data in tables 1 and 2 may appear to have some inconsistencies when compared. For instance, one may wonder why Boston-Cambridge-Quincy, MA-NH, and the San Francisco area are among the top-paying areas after price adjustments in table 1, but do not appear in the top paying areas for any of the 22 occupational groups in table 2. The reason for this lies in the employment composition of the areas. Boston and San Francisco have high wages because they have a higher share of workers in high-paying occupations, not because they have high wages after adjusting wages for prices. Similarly, Bridgeport-Stamford-Norwalk, CT, appears in table 2 with high price-adjusted wages for sales and related occupations not because the area has higher wages for low-paying sales occupations such as cashiers, but because the area has relatively few cashiers and a high share of financial services sales agents. Some areas have heavy concentrations of workers in particular fields. For some occupations, a correlation exists between employment concentration and wages.
Employers generally want to locate where labor costs are low and they can attract needed workers. Of course, not all employers can relocate. Service workers and employees of local government may need to stay near their customers or constituents. For employers who are able to relocate, the location quotient can be an invaluable tool for determining the best place to set up shop. The location quotient compares the employment of an occupation in an area relative to the average for the nation. Its formula is the share of employment in an area divided by the share of the U.S. employment. If an area's location quotient for an occupation is higher than 1.0, it means that employment for the occupation is more highly concentrated in the area than the national average.
The areas in figure 1 all have location quotients greater than 1.0 for team assemblers. For example, in May 2014, the location quotient for team assemblers in Flint, MI, was 3.04 (see figure 3 data), indicating the area had 3 times the national average employment concentration for that occupation. This indicates a high concentration of team assemblers in Flint, MI, and greater purchasing power relative to team assemblers in other areas of the country. Figure 3 shows the location quotients relative to purchasing power for team assemblers.
[FIGURE 3 OMITTED]
In May 2014, the overall correlation between location quotient and purchasing power for team assemblers in the 297 areas in figure 3 was 0.42. The upward sloping line in the figure suggests that as employment concentration increases, the purchasing power of wages generally increases. For team assemblers, areas with high purchasing power and low price parities tend to have lower mean wages. For other occupations, areas with a high overall mean wage generally have higher regional prices.
Not all occupations show a relationship between location quotient and purchasing power. For example, the data for police and sheriff's patrol officers suggest little to no correlation between purchasing power and location quotients. The overall correlation between the two variables was -0.17. For this occupation, there is less variation in the location quotient when compared with team assemblers. Figure 4 shows the purchasing power and location quotients for police and sheriff s patrol officers.
[FIGURE 4 OMITTED]
In summary, this article has introduced the use of RPPs to calculate the purchasing power of wages earned in different areas and explored the relationship between wages, price-adjusted wages, and employment concentrations. It might seem obvious high-priced urban areas would offer high wages, but the actual wages of an area only tell part of the story. When wages are adjusted to account for cost of living, low-wage areas often grant workers superior purchasing power. For jobseekers, the overall purchasing power of a wage in a given area is often more important than the wage itself. For employers, the concentration of labor in an area may be as important as the impact of wages on labor cost. Accordingly, some occupations may seem more attractive in different areas. The areas with highest and lowest price-adjusted wages for occupations depend on the occupation, as does the relationship between wages and employment concentrations. For some occupations, purchasing power increases with employment concentration, while for others, purchasing power may be unaffected by the concentration of employment in an area.
(1) Here, purchasing power is defined as the mean wage for an area divided by its regional price parity and multiplied by 100. Both the wage estimates and the RPP estimates are subject to sampling error, so the purchasing power estimates are subject to error as well. Because this error range has not been calculated, some of the rankings and differences in this article may not be statistically significant.
(2) Regional Price Parities are available online at the Bureau of Economic Analysis website. Not all areas that have OES data have an exact regional price area. In some cases, an adjusted wage cannot be calculated. For regions where OES uses New England County and Town Areas, OES used wage data from the closest area match available to calculate the adjusted wage.
(3) Team assemblers are defined as employees who work as part of a team having responsibility for assembling an entire product or component of a product.
Table 1. Annual mean wage, regional price parity, and purchasing power for the 10 Metropolitan Statistical Areas with the highest purchasing power, May 2014 Annual Regional mean price Purchasing Area wage parity power San Jose-Sunnyvale-Santa Clara, CA $75,770 122.0 $62,107 Durham-Chapel Hill, NC 55,840 95.0 58,779 Huntsville, AL 51,730 91.3 56,659 Hartford-West Hartford-East 55,580 100.9 55,084 Hartford, CT Boston-Cambridge-Quincy, MA-NH 60,540 111.6 54,247 Washington-Arlington-Alexandria, 64,930 120.4 53,929 DC-VA-MD-WV Springfield, IL 49,760 92.4 53,853 Trenton-Ewing, NJ 60,020 111.5 53,830 Seattle-Tacoma-Bellevue, WA 57,370 107.0 53,617 San Francisco-Oakland-Fremont, CA 64,990 121.3 53,578 Source: U.S. Bureau of Labor Statistics, U.S. Bureau of Economic Analysis. Table 2. Annual mean wage, regional price parity, and purchasing power for major occupational groups in the 10 Metropolitan Statistical Areas (MSAs) with the highest overall purchasing power, May 2014 Annual Regional mean price Purchasing Area and major occupational group wage parity power Durham-Chapel Hill, NC, $55,840 95.0 $58,779 all occupations Management occupations 131,080 95.0 137,979 Business and financial operations 76,890 95.0 80,937 occupations Computer and mathematical 89,960 95.0 94,695 occupations Education, training, and library 70,780 95.0 74,505 occupations Arts, design, entertainment, sports, 57,860 95.0 60,905 and media occupations Sales and related occupations 43,600 95.0 45,895 Office and administrative support 36,870 95.0 38,811 occupations San Jose-Sunnyvale-Santa Clara, CA, 75,770 122.0 62,107 all occupations Management occupations 160,080 122.0 131,213 Computer and mathematical 123,910 122.0 101,566 occupations Legal occupations 142,030 122.0 116,418 Arts, design, entertainment, sports, 70,940 122.0 58,148 and media occupations Office and administrative support 47,380 122.0 38,836 occupations Seattle-Tacoma-Bellevue, WA, all 57,370 107.0 53,617 occupations Computer and mathematical 104,320 107.0 97,495 occupations Trenton-Ewing, NJ, all occupations 60,020 111.5 53,830 Protective service occupations 68,310 111.5 61,265 Washington-Arlington-Alexandria, 64,930 120.4 53,929 DC-VA-MD-WV, all occupations Arts, design, entertainment, sports, 74,860 120.4 62,176 and media occupations New York-Northern New Jersey-Long 59,060 122.2 48,331 Island, NY-NJ-PA, all occupations Arts, design, entertainment, sports, 76,110 122.2 62,283 and media occupations Boulder, CO, all occupations 56,510 108.9 51,892 Sales and related occupations 49,230 108.9 45,207 Bridgeport-Stamford-Norwalk, CT, 61,650 121.5 50,741 all occupations Sales and related occupations 55,770 121.5 45,901 Boston-Cambridge-Quincy, MA-NH, 60,540 111.6 54,247 all occupations San Francisco-Oakland-Fremont, CA, 64,990 121.3 53,578 all occupations Note: The criterion for inclusion of a major occupational group under an MSA in the table required that the MSA was among the top 10 areas for purchasing power in the group. Accordingly, not all major occupational groups are included in the table and not all MSAs have a major occupational group listed. Source: U.S. Bureau of Labor Statistics, U.S. Bureau of Economic Analysis. Table 3. Mean wage, regional price parity, and purchasing power for Metropolitan Statistical Areas with the highest purchasing power for cashiers, May 2014 Regional Mean price Purchasing Area wage parity power Bremerton-Silverdale, WA $28,060 104.6 $26,826 Spokane, WA 25,120 95.9 26,194 Mount Vernon-Anacortes, WA 25,780 98.7 26,120 Olympia, WA 27,140 104.6 25,946 Bellingham, WA 25,740 99.3 25,921 Seattle-Tacoma-Bellevue, WA 27,470 107.0 25,673 Yuba City, CA 24,940 98.3 25,371 Danville, IL 19,920 79.4 25,088 Medford, OR 24,580 98.0 25,082 Yakima, WA 23,700 94.8 25,000 Source: U.S. Bureau of Labor Statistics, U.S. Bureau of Economic Analysis.
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|Publication:||Monthly Labor Review|
|Article Type:||Statistical data|
|Date:||Apr 1, 2016|
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