Where Does the Wyoming Worker Come From?

by: Mike Evans

This article will expand on previous research examining the earnings and demographic make up of Wyoming’s labor market. By grouping data according to the state in which their Social Security Numbers (SSN’s) were issued, and determining the current distribution of these groups for each county, industry, age, and gender, we can suggest some reasons for the proportion of non-residents in each county. Out-of-state SSN’s were found to have higher Average Weekly Wages (AWW) than workers with SSN’s issued in Wyoming, with males also having higher AWW than females. A large portion of this wage differential may be due to out-of-state SSN’s having more experience than individuals issued a Wyoming SSN and differences between the industry employed. The number of individuals employed in Wyoming from out-of-state depends on the other states proximity to Wyoming, economies of other states, industry of work, and experience or age of the non-resident.

During the last several months, Research & Planning has presented various articles using wage records, driver's license demographics, and Quarterly Unemployment Insurance (QUI) files combined. For a detailed discussion on the various databases and articles, please refer to the May through August issues of Trends. These data sets combine information on the characteristics of workers and their employers giving cross-sectional demographic and economic information on workers (age, gender, place of residence, and wages), along with longitudinal information about a worker’s employer (the employer’s location, industry, and total employment size).

There were two main reasons for doing this research: First, to follow up on the May and June issues of Trends by expanding the analysis to the geographic distribution by county of employment and wages by gender for each major industry.

A second reason is to follow up on the report by Steven Butler, Tracking University of Wyoming Graduates Into the Wyoming Work-force1 which shows that many UW graduates had not found work in Wyoming several years after graduation. One possible reason UW graduates were not finding work in Wyoming was out-migration to work in their chosen careers in other states was more effective. This study looks at the opposite process--in-migration: from which states individuals SSN’s appear, along with the industries and location in which they work in Wyoming. At this time, occupation or education level of the out-of-state and in-state individuals working in Wyoming is not available. But we can use "age" as a proxy for work experience, given the close relationship between the two variables.

Study Procedures

The first three digits of an individual’s SSN refer to the state in which the individual received their Social Security Number. After combining the three demographic and economic files, each state’s representative three digit code was grouped and the combined information analyzed using statistical methods. All of the out-of-state SSN’s were compared to Wyoming SSN’s for each county, focusing on average age, number of individuals, gender as a percentage of employment and AWW (See Tables 1 - 3).

In Table 4, the earnings of the top 10 out-of-state SSN’s states are presented for each and the number of individuals employed in each industry. Every state and territory is represented in the Wyoming workforce. Table 4 shows the total for out-of-state SSN’s, Wyoming SSN’s and a grand total combining all SSN’s for Wyoming. In some situations, the totals for counties and industries do not add up, due to employers whose industry affiliation is nonclassifiable and/or have multiple worksites.

Analysis & Results

Urban areas typically have higher earnings than rural areas, and as the May issue of Trends showed, males tend to make more than females. Even in industries employing predominantly females, wages are typically higher for males.

Counties with larger metropolitan areas and counties dominated by mining employment have higher average wages (See Table 3). Campbell and Sweetwater Counties have the largest wage differentials between males and females; this is due to the large percentage of male workers and the large size of the Mining industry in these counties. Counties with low wage differentials between males and females were rural counties like Niobrara, Goshen and Hot Springs. These counties generally also had the lowest average wage for the state.

Other factors associated with higher average wages are occupation, employer size and job tenure. The large difference in earnings between gender can be due to group characteristics typically viewed as a matter of choice, as opposed to different treatment viewed directly attributable to discrimination in the labor market. This analysis does not differentiate between the two.

Tables 1 and 2 show the wage difference between individuals with Wyoming SSN’s and all other states’ SSN’s combined by county. The most striking difference is in Albany County with a difference between in-state and out-of-state SSN's of $132 in average wage because of the Public Administration Industry in the county and quite probably the education level of occupations involved. Sweetwater and Campbell Counties also have a large difference between native and non-native; however, this includes a higher ratio of employed males to females and is also industry driven. Teton County has the largest number of non-native SSN’s (4,747 or 76%) compared to Wyoming SSN’s (1,508 or 24%).

The ratio of males to females employed from out-of-state SSN’s is only slightly higher than the ratio for Wyoming SSN’s (See Tables 1 and 2), but the average age for out-of-state SSN’s is much higher, with an average age of 41, compared with 36 for Wyoming SSN’s. Using "age" as a proxy for experience, age-experience may explain the large wage difference for individuals with an out-of-state SSN and individuals with Wyoming SSN’s (see Limitations and Future Analysis). Assuming the majority of UW graduates have Wyoming SSN’s, this finding is consistent with the Butler report, "Tracking University of Wyoming Graduates Into the Wyoming Work-force", which showed graduates working in low earning Services and Retail Trade Industries, then disappearing from Wyoming's workforce.

Table 4 presents the Social Security Numbers of the top 10 states by industry, the associated average wage, the number of individuals and percentage employed in each industry. While out-of-state SSN’s show up in low earning industries like Services and Retail Trade, they also show up in higher earning industries like Mining and Transportation, Communications, Electric & Gas. Colorado had the largest number or percentage employment by SSN working in Wyoming at 4.7 percent of the records, with five of six neighboring states in the top 10. Other states in the top 10 include California at 4 percent, with one-third of these working in the Services Industry; and North Dakota, Illinois and Texas mainly working in the Mining, Retail Trade and Services Industries. It would make sense that SSN’s from states like Illinois2 and Texas3 with declining Mining Industries show up in the Campbell County Mining Industry. The total percentage of out-of-state SSN’s in Wyoming during the third quarter of 1995 was 47.7 percent, while Wyoming SSN’s made up the other 52.3 percent of the records.

Statistical Analysis

The results presented thus far highlight the importance of gender, age and location for earnings and employment. This section presents a more formal study of these relationships by presenting results using statistical analyses of employment and wages tested for statistical significance. Statistical significance means each variable (gender, county location, industry and SSN origin) influences wages 95 or 99, (depending on the level of significance) times out of 100. That is, were we to compare the age and wages of individuals, statistical significance would be achieved if in 99 out of 100 cases greater age was associated with greater earnings. Or the wage differences could be due to chance 1 time out of 100. In so doing, we should be able to isolate and identify the specific influences of gender, age, and location on one’s probability of employment and the level of pay when working.

First, the wage differentials between males and females for each county were tested for statistical significance, where all variables were significant at the 99 percent significance level, along with the wage and age differences between out-of-staters and Wyomingites, except Big Horn, Washakie and Sublette Counties which were significant at the 95 percent level. Finally, all of the industries were significant at the 99 percent level between out-of-state SSN’s and Wyoming SSN’s, except Agriculture, Forestry & Fishing and Construction which were significant at the 95 percent level. In other words, there is a 95 percent or 99 percent probability that wage differences are due to these factors, rather than due to chance.

Limitations and Future Analysis

One underlying limitation to this method is that we do not know what state an individual moved from, only where the individual was born or received their SSN. Also, counties in which agricultural or self-employment are a large proportion of employment in a county compared to covered employment (such as Teton County) will greatly affect the average wage and employment levels.

Skill level could also contribute to the higher wages of out-of-state SSN’s compared to Wyoming SSN’s, especially in the Mining, Construction, and Transportation, Communications, Electric & Gas industries, since these industries usually require unique skills and/or trades. A question yet to be answered or partially answered is: are individuals from out-of-state working in occupations that pay more?

Possibilities for further research include: analyzing each individual county for employment and wages by industry and occupation, using a longitudinal data set. Time series analysis may show how the labor market is changing, its direction, and facilitate forecasting. Another possible avenue is to see if commuting patterns within counties affect wages and/or if wages affect commuting distance.

Only the third quarter of 1995 was used for this study, representing only a snapshot in time; to improve the analysis in the future, examination of migration in the past would enable a dynamic evaluation for each county. For example, looking at the percent increase or decrease in wages from period to period by different SSN, would enable analysis of what might be driving the labor market.


This study made the connection between previous Trends issues and the UW Report showing that males tend to have higher wages than females, along with higher wages being associated with more populous counties. It also showed that labor markets do not respect political boundaries.

In theory, occupation and education level have an impact on wages. These effects may be responsible for the differences in wages between out-of-state SSN’s to Wyoming SSN’s in Albany County. In addition, age as a proxy for experience is positively associated with dramatically higher wages.

Three main factors for out-of-state individuals employed in Wyoming and wages earned are: proximity of SSN issuing state to Wyoming, economy of industries in other states, age or experience, and industry in which the individual is working, although the wage differential is also due to choice of occupation, education level, skills and residence by county.

Possible solutions to keeping Wyoming graduates here are bringing in jobs, or internships for which individuals are trained and educated and/or changing education and training programs to fit needs of employers.


1 "Tracking University of Wyoming Graduates Into the Wyoming Work-force": A report prepared by Steven Butler for the Research & Planning Section of the Employment Resources Division, State of Wyoming, 1995.

2 Illinois Department of Employment Security; Dennis Hoffman Economic Information and Analysis; Number of Coal Mines and Employment, Table I.

3Texas Labor Market Information (LMI) Department; Texas Workforce Commission Nonagricultural Wage and Salary Employment.


Mike Evans is a Senior Economist who supervises BLS programs with Research & Planning.

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