Wyoming Unemployment Insurance Wage Records Revisited


Gayle C. Edlin

Wyoming Unemployment Insurance (UI) wage record summary statistics first appeared in the May 1995 issue of Wyoming Labor Force Trends. Wage records contain information on quarterly wages for all individuals covered under UI in Wyoming by social security number (SSN) and by UI account number (an identifier of the individuals employer). In the past, data obtained from wage records has indicated such trends as: the number of people entering Wyoming's work force for the first time is declining, the average annual wage is not keeping up with inflation, and multiple job holding is on the increase. Now, wage records provide compelling evidence that workers who stay with the same employer for extended periods of time earn substantially more than other workers. Furthermore, the wages of steady workers stand a greater chance of keeping pace with the rate of inflation.

When the UI account number from wage records is merged with data from the ES-202 program (a section of the Department of Employment which deals with total UI covered employment, payroll and average weekly wages for Wyoming and its counties by industry), the data collected by ES-202 can be utilized to separate UI wage record information by industry. This opens new areas of analysis for Research & Planning (R&P). By matching SSNs and UI account numbers, we can look at employees who have been with the same employer over the twelve-quarter period from the first quarter of 1992 (92Q1) to the fourth quarter of 1994 (94Q4) and compare their wages to those of all other workers. Also, quarterly wages can be averaged over yearly periods, giving quarterly averages (QAVE) to include in analysis.

Those employees who remained with the same employer from 92Q1 to 94Q4 (hereafter referred to as "steady" workers) earned substantially higher wages than all other employees (hereafter referred to as "non-steady" workers) in each of the ten industries in every quarter for which comparable industry data were available (92Q1 to 94Q1). Furthermore, the wages of steady workers were more likely to keep up with the rate of inflation (indeed, to exceed it, in the majority of the cases) than those of non-steady workers.

Steady and non-steady workers as percentages of the entire group of UI covered employees were also examined. Agriculture, Construction and Retail Trade were the three industries with the highest percentages of non-steady workers in every quarter (92Q1 to 94Q1). Only one industry, Public Administration, was found to have the percentage of steady workers exceed the percentage of non-steady workers; this occurred in every quarter except 92Q3 and 93Q3. Public Administration was also the industry where the percentage of steady workers was closest to the percentage of non-steady workers.


Wage records available for analysis begin with 92Q11. While 95Q1 data has recently become available2, this data was not included in this analysis. Additionally, multiple job holding was not a concern in this study. Only the primary job (the job in which an individual had the highest earnings in a quarter) held by each SSN was examined. Another consideration was that for an SSN to be included in the UI wage record database for a particular year, that individual had to show earnings for at least one quarter in the year.

The entire database of UI covered employees were assigned a standard industry classification (SIC) by matching UI account numbers of the workers employers with those in the ES-202 program (ES-202 data3 differs from wage records in that the ES-202 program is organized around jobs while the wage record data shows individual people, who may hold more than one job). These four-digit SIC codes were later aggregated into the ten encompassing industries (Agriculture; Mining; Construction; Manufacturing; Transportation, Communications, & Public Utilities; Wholesale Trade; Retail Trade; Finance, Insurance, & Real Estate; Services; and Public Administration--note that Public Administration excludes federal employees).

Steady workers were isolated from all UI covered employees by comparing SSNs with each quarters earnings (in their primary jobs) and UI account number. Steady workers were defined as those who worked for the same employer (UI account) and had earnings in each of the twelve quarters from 92Q1 to 94Q4. A total of 67,924 such workers were found, which amounted to approximately one-quarter of the UI covered employees each year. The balance of the employees in the wage record database were defined to be non-steady workers. The total of non-steady workers was 211,262 for 1992; 199,529 for 1993; and 203,698 for 1994.

A small number of employees were not considered in the analysis since there were no matches found for their UI account numbers with the ES-202 data. This problem is a result of predecessor/successor businesses (organizations in which there is a change of ownership). The administrative paperwork which accompanies an alteration in business status such as this takes time and can be difficult to trace. However, only a very small percentage of UI covered employees had to be discarded due to predecessor/successor relationships. For example, only 95 such cases existed among steady workers (about one-tenth of one percent of those 67,924 originally found).

The assumption was made (for steady workers only) that the SIC of the employing business remained the same from 92Q1 to 94Q4. While a business may change its SIC code (for example, if a firm changed from retail to wholesale trade), the number of UI employees affected by such changes would, as in the case of predecessor/successor changes, be very small. Therefore, to expedite the wage record analysis, SICs were assumed unchanged among steady workers. This also explains why industry data is shown in the tables for steady workers only from 94Q2 to 94Q4. The UI accounts of steady workers were assumed to remain in the same SIC over the twelve-quarter period (the SIC was taken from 92Q1). The matching data from ES-202 is not yet available for 94Q2 to 94Q4 to provide SIC codes for non-steady workers (although the UI accounts are known).



Tables 1 and 2 show quarterly employment and wages by industry for steady and non-steady workers from 92Q1 to 94Q4 as well as quarterly average data for each year. These two tables summarize the majority of the information found by analysis of wage records for steady and non-steady workers by industry. However, closer inspection of these tables yields many interesting results.

For instance, comparing the numbers of steady and non-steady workers, reveals distinct differences among industries (see Figure 1). Public Administration is the only industry which exhibits a higher percentage of steady workers than non-steady workers by quarterly average. Indeed, one can observe from Table 1 that Public Administration is the only industry which had a higher percentage of steady workers (i.e., more than 50%) and it maintains this singular distinction in each quarter except 92Q3 and 93Q3. Not surprisingly, Retail Trade, Agriculture and Construction compose the three industries which consistently have the lowest percentages of steady workers by quarterly average and in each quarter as well. This may be due, at least in part, to the seasonal nature of these industries.

Figure 2 represents data not directly apparent in Table 1. Individuals were counted to see how many quarters of work were missed each year. Steady workers were excluded from this count, since they were employed with the same UI account for twelve consecutive quarters; thus, only non-steady workers are represented in Figure 2. Interestingly, in each year, over half of non-steady workers were employed for the full year (with different employers, differentiating them from steady workers) and the balance was fairly evenly divided as to whether they missed one, two or three quarters of work (13 - 16% in each category). Of those who missed any quarters of work each year, the average number of quarters missed was two.

Which quarter was most often the quarter of lowest employment? We can look at the number of non-steady workers in each quarter in Table 1 to obtain the answer to this question. In 1992 and 1993, the quarter of lowest employment is most often the first quarter. This remains true across the industries, with the only exceptions being TCPU (Transportation, Communications, & Public Utilities) in 1992, and Mining and Manufacturing in 1993. Industry-specific circumstances may explain these deviations.

The quarterly average data in Table 2 gives some indication of whether the industries are increasing or decreasing employment overall, since the wage records show us individuals who have their primary job in these industries. From 1992 to 1993, the quarterly average number of workers increased--at least slightly--for all industries except Wholesale Trade and Public Administration. Unfortunately, the data is not yet available to see if these trends are continuing with wage records in 1994.


The differences between the wages of steady and non-steady workers (as seen in Tables 1 and 2) are substantial. In some industries and quarters, steady workers earn more than double the wages of non-steady workers. The 1992 quarterly average wage in Agriculture, for example, is $4,866.40 for steady workers and $2,375.77 for non-steady. While the wage disparity is not as pronounced in industries such as Mining (where the 1992 quarterly average wage for steady workers is $11,032.11 and $8,186.55 for non-steady), it is clearly present across all industries, as well as in every quarter in which comparable data were available. This disparity may be due (at least in part) to higher proportions of temporary, seasonal and part-time employees among non-steady workers in certain occupations and industries.

Figure 3 is a graphical representation of the percent change in wages and the rate of inflation. In terms of keeping up with the rate of inflation, steady workers do quite well (the rate of change is calculated from 92QAVE and 93QAVE). The wages of steady workers in all industries increased at rates which exceeded the rate of inflation (3.0% for 1992 to 1993). Generally, the quarterly average wages of non-steady workers increased less dramatically than those of steady workers from 1992 to 1993, and even decreased in some industries. However, in Agriculture and Public Administration the opposite was true, and in FIRE (Finance, Insurance, & Real Estate), the percent change in wage of non-steady workers nearly matched that of steady workers (although the actual wage of non-steady workers was lower in value, as was mentioned earlier in this discussion). These exceptions are noteworthy but the reasons for their occurrence are no immediately clear.

It is interesting to note, in comparing Table 2 with Figure 3, that the changes in quarterly average wages from 1992 to 1993 appear to have no correspondence with the actual value of the wages in 1992. The Mining industry, for example, was the highest-paying industry observed in wage record analysis, yet average quarterly wages for steady workers in this industry increased only 3.87%--just slightly over the rate of inflation. Non-steady mining employees saw their wages decline by 1.77%. Retail Trade, with the lowest wages in 1992, had an increase in average quarterly wages for both steady and non-steady workers (6.32% and 0.15%, respectively). Agriculture, with the second lowest wages, also had increases in average quarterly wages for both sets of employees, but non-steady workers out-paced steady workers (7.27% and 3.70%, respectively).


In the past, much of the data available to Research & Planning for analysis was drawn from the ES-202 program. The nature of the information limited the research that could be performed to fairly basic statistical measurements such as mean, median and mode. In contrast, wage records permit us to examine the distribution of earnings across industries and localities. In short, wage record analysis opens the door to a fascinating new area of study for Research & Planning. We can study employment and wages inexpensively and at no additional burden to employers since wage records come from UI data that employers already submit. The topics discussed in this article are only a beginning. Some future areas of study might be: examining those employees who missed a quarter or more of work and back-tracking to see what industry they were in when they were last employed; investigating employees who switch from one industry to another to see if any pattern emerges over time; or evaluating the success of government and private job training programs (as was suggested in the May 1995 issue of Trends). Watch this space for future wage record analysis!

Gayle C. Edlin is a Senior Statistician with Research & Planning, and is Editor of Wyoming Labor Force Trends.

Wayne Gosar is gratefully acknowledged for preliminary analysis and technical advice.

1 "Wyoming Unemployment Insurance Wage Record Summary Statistics: A New Way to Look at Wyoming", Wyoming Labor Force Trends, (May 1995 issue).

2 "Wyoming Unemployment Insurance Wage Record Summary Statistics Update", Wyoming Labor Force Trends (August 1995 issue).

3 Annual Covered Employment and Wages, 1993 edition. (1994 edition.)

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