By Industry and Full- or Part-Time Status

by: Chris Garrard

Frequent readers of Wyoming Labor Force Trends will recall reading about our three Wage Survey rounds initiated in 1993. The 1994 installment consisted of sales, clerical and service occupations (see the December 1994 issue of Trends). The wage data discussed in this article is a product of the 1994 Wage Survey.

Our 1994 survey procedure not only allows us to segregate data by occupations, but also by industry and full- and part-time status. The table of average hourly wages by occupation and industry ("Average Wages by Occupation and Industry: Full- and Part-Time Hourly Wages") is organized according to these categories. Some explanation of the data is in order. Because of small sample sizes and Wyoming confidentiality laws, some of the average wage rates were not publishable. These were replaced with a "Confidential" in the table. Although these numbers were not disclosed, they were still used to compute the overall averages for occupations and industries. Since the 1994 survey round concentrated on sales, clerical, and service occupations, some industries are not represented as well as others by this data (agriculture, forestry, and fishing or professional and technical, for example).

Despite the lack of data in some areas, the survey results still provide some interesting information. Not only can we calculate average wages for occupations, but we can also compare salaries between industries and full- and part-time employment. This analysis shows that, on average, the Mining industry pays more for full-time work than other industries. It also reveals that full-time employment usually pays a higher wage than does part-time employment, even for the same occupation. When combined with other information, this data also helps provide a clear picture of the job market.

Wage Comparisons

Employers often request the going wages for certain occupations. The central table contains wages broken out in several different ways, so it should be quite helpful to those interested in the surveyed occupations. Prospective employees are also interested in which careers make the most money. Table 1 lists the ten best paying full- and part-time occupations. Only four occupations are common to both top-ten lists. This would imply that the relative pay among occupations differs between full- and part-time employment. In other words, if full-time pay for one occupation exceeds that of another, it doesn't mean the same relationship will exist for part-time employment.

We are also able to examine the data in other dimensions. For instance, in those occupations in which the Mining industry is represented, Mining usually has the highest wages for full-time work. Figure 1 (average hourly wages for each industry, broken out by full- and part-time status) indicates that the Mining industry, as a whole, pays the most for full-time work. Mining is definitely the highest paying industry for full-time workers at $14.64 per hour, with Wholesale Trade ($11.58/hr) coming in second, and Retail Trade ($7.61/hr) at the bottom. The situation changes with part-time work, however. Agriculture then becomes the most lucrative industry at $10.37 per hour, and Mining drops down into fourth place. Retail Trade keeps its title as the lowest paying industry.

It also appears from Figure 1 that, in general, full-time employment pays more than part-time. (This was discussed for demand occupations in the March 1995 issue of Trends.) In fact, the average full-time wage across industries is 159% of the average part-time compensation. The Agriculture, Forestry, & Fishing industry appears to be an obvious exception to this rule. It should be noted, however, that because this industry is underrepresented in this data set, it is dangerous to draw the conclusion that actual wages are higher for part-time than for full-time workers. This issue can be more readily dealt with by the 1995 survey of agriculture, forestry, construction, and operator occupations.

Potential Uses of the Data

The data provided in the central table can have many different uses. Imagine, for instance, that I am trained as an accounting clerk (OES code 55338), and I want to know where to look for a job. One of my first concerns might be where I could make the most money with my current training. If I were to utilize this data, which is summarized in Figure 2, I would discover that full-time accounting clerks make more money working in Transportation, Public Utilities, and Wholesale Trade. I would probably begin my job search in those industries in hopes of finding a higher paying job.

Young people trying to choose a career would also find this data to be helpful. Many youth have a general idea of what they are interested in, but do not have any specific plans. Wage information would help point them in the direction of a career that would provide financial independence and stability. The same concept applies to others who are thinking of retraining for a new career.

This information is also important to agencies, such as the Department of Employment, who are in the business of arranging career training and retraining for clients. There are many factors that need to be considered when developing a training program, and one of them is wages. Retraining not only needs to provide people with skills that are in demand, but also with skills that will command a sufficient salary.

Wage data increases even more in value when combined with information pertaining to growth industries and occupations. For instance, the Wyoming Industry Projections contained in the January 1995 issue of Trends indicate that Retail Trade and Services will add the most jobs between 1993 and 1996. As shown in Figure 1, however, these are two of the lowest paying industries in Wyoming. The March 1995 issue of Trends discussed demand occupations in Wyoming. Most of the part-time demand occupations rate relatively low on the pay scale, although the full-time ones fare somewhat better. These examples show how wage information can be combined with other data to give a clearer picture of the job market than would be provided otherwise .

Future Analysis

It was mentioned earlier that full-time employment seems to attract higher wages than part-time. Is this true because a lot of part-time jobs are filled by teenagers or others without as much job experience or training? Or is it simply because people looking for part-time employment are willing to settle for less money? At this point, the answers to these questions are purely speculative. Perhaps this is a topic that could be researched in the future.

Combining wage figures with data about growth industries and occupations can be very informative, but it doesn't describe the current distribution of wages. There are certainly some high-paying jobs out there, but how many? How do the majority of Wyoming workers fit into the pay scale? Research & Planning could develop a distribution of wages to help answer these questions.

Chris Garrard is a Statistician specializing in Mass Layoff Statistics (MLS) in Research & Planning.

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