As mentioned in Part One of our analysis (refer to the May issue of Wyoming Labor Force TRENDS), Labor Market Information (LMI) is the most commonly used statistical instrument to measure how well an economy is performing. With a statistical instrument like this, we can answer questions such as: "Is the increase in employment due to new growth or multiple job holders?", "Are women replacing men in the work force? If so, are they monetarily compensated the same as men?" and "Does age or geographical location have any affect on how an individual is financially rewarded?" We cannot answer these questions at a national level due to time and money constraints of collecting a representative sample. However, these questions and more can be statistically tested at the state and local levels.
Research & Planning has been compiling data to statistically test these very questions for Wyoming. The data sources that have been compiled consist of Unemployment Insurance (UI) employment wage records, quarterly UI employment by industry and the Wyoming Department of Transportation driver's license records. In Part One of our analysis, we defined each data source and described the kind of information obtained from each source. With these types of data, we can test for any dynamic influences in the labor market over time, test in- and out-migration by county as well as city, and generate various statistics concerning age and gender.
In brief review, the different data sources were compiled into a study database. This database was built by taking the wage record and driver's license databases and matching on the individual's social security number (SSN). Using this matched SSN database, we then matched the employers UI identification number to the ES-202 quarterly covered employment database to capture the employer's standard industrial code (SIC) and ownership. This allowed us to categorize employees into the industries in which they work. There were 167,384 records in the sample which accounts for approximately 80 percent of covered employment in Wyoming.
Part Two of this article, like Part One, will focus on Wyoming's employment and wage distribution by age and gender. Before a comparison can be done based on gender, a few points of emphasis need to be mentioned in regards to the limitations within the data. First, the data used for this analysis is for an individual's primary job only. The primary job is the one where the most wages were earned for a given quarter, which in this case was the third quarter of 1995. Second, the wages are not grouped according to the number of hours worked. No distinction is made between full- and part-time employees. Third, wages are typically related to an employee's years of experience or tenure with a company (see Footnote 1). This breakout is not provided in the tables. Finally, there are no occupational details, education and skill levels, or training. In some industries, even though women are employed, they may work in lower paying occupations. These are a few factors, probably not all, that need to be considered when doing a gender comparison.
In Part One of this analysis, we examined: Agriculture; Mining; Construction; Manufacturing; and Transportation & Public Utilities (TPU). This month, we examine the remaining industries (see Table 1 and Table 2): Wholesale Trade; Retail Trade; Finance, Insurance, & Real Estate (FIRE); Services; and Government (see Footnote 2).
In contrast to Part One, where only two of the 39 industries employed 60 percent or more women, 18 of the 35 industries in Part Two match or exceed 60 percent female employment. The highest percentage is in Health Services (81.0%). Depository Institutions has almost the same proportion (80.8%). The industries of Social Services and Private Households are both over 75 percent female. In fact, only six of the 35 industries analyzed this month have 60 percent or more male employment. Auto Repair (82.2%) and Miscellaneous Repair Services (80.3%) have the highest percentages. Both industries in Wholesale Trade (Durable Goods and Nondurable Goods) are male-dominated.
As we mentioned earlier, women are playing a larger role in today's work force. In the past fifteen years, we have seen women in Wyoming's labor force grow from 37.3 percent (see Footnote 3) to 46.4 percent (see Table 1). Table 3 provides a better illustration of how female employment has changed in the last five years in Wyoming. This Table was created using Total Nonagricultural Wage & Salary Employment data, some of which is seen each month in TRENDS. Total Nonagricultural Wage & Salary Employment data is generated from payroll records and survey forms returned by employers. In addition to general wage and employment information, employers are also asked to note how many women their businesses employ. In Wyoming, there are approximately 3,400 surveyed businesses, which accounts for 65 percent of all covered employment.
The exact causes of female job growth in the past few years cannot be clearly defined without more extensive research. However, the following are possible reasons why women are increasing in the work force: First, females are actually replacing males in the work force. Second, females have to work to help support existing single-family incomes. Third, more women are working multiple jobs. Finally, more women are seeking careers.
This article focuses on gender as a discriminating factor with age included for descriptive purposes (see Footnote 4). The industries analyzed in Part Two are similar to those in Part One with respect to age characteristics of the work force. The minimum age is in the teens for most industries and the maximum is usually in the 70's, 80's or 90's. The mean (arithmetic average) and median (value where half the cases are less than and half the cases are greater than) ages are generally in the upper 30's or lower 40's for both females and males. One exception is in Motion Pictures where the mean age is relatively low (26). Eating & Drinking Places also had a lower mean for both sexes. In most industries the median age is less than the mean.
Many of the minimum wages are again less than a dollar. This may seem rather odd, but probably results from individuals working only one or two days the entire quarter, or working part-time. The average weekly wage was computed by taking the total wages for the quarter and dividing by 13 (there are 13 weeks per quarter). For females in all of the industries, the mean and median wage are relatively similar. The highest mean for females is in Nondepository Institutions and the highest median value is in State Government. The mean and median are generally close for males with three major exceptions: Security & Commodity Brokers, Health Services and Legal Services. The highest mean an median for males is in Security & Commodity Brokers. The maximum wage (for all industries included in Parts One and Two, and for both genders) is for a male in Business Services. The average weekly wage for all employees (see Footnote 5) is $408.47, but the difference between males and females is substantial, with males earning $528.29 and females $270.24.
Again, there are many factors which play a role in how much a person earns, most of which could not be included in this analysis due to time constraints. However, one underlying factor is evident: in every one of the industries (both those in Part One and in Part Two), the mean wage is higher for males than it is for females. There are six industries in which the average weekly wage for males was significantly higher than that for females: Depository Institutions; Nondepository Institutions; Security & Commodity Brokers; Insurance Carriers; Health Services; and Legal Services. However, employment in each of these six industries is female-dominated. The median wage is also higher for males in every industry except Miscellaneous Services.
The statistical data does not, in itself, offer a definitive answer as to why there is such a large discrepancy between industry-wide wages of males and females. Some possible reasons as to why males earn more than females are: First, perhaps more women work part-time than men. Second, twenty years ago, most women worked in the home, so the number of years in the work force, tenure with an employer or educational attainment may not yet be equivalent. Therefore, when correlating age with average weekly wage, it should be the case that as age increases so does the discrepancy in the wages. Finally, the data within our sample may contain outliers (abnormal events) that are not typical in other quarters. Future research studies, properly structured, could help to either confirm or refute these possibilities. As more women enter the work force and the industry-wide percentage of employment becomes more balanced, perhaps the wage gap will narrow further.
Gregg Detweiler is a Principal Statistician and Brett Judd is a Senior Statistician. Both specialize in Nonagricultural Wage and Salary Employment/Current Employment Statistics (CES) with Research & Planning.
Footnote 1: To get an idea of how tenure with a company and wages are related, refer to the October 1995 issue of TRENDS.
Footnote 2: Federal Government employment is not included in the sample since federal employees are covered under a separate unemployment compensation program.
Footnote 3: 1980 Census data.
Footnote 4: Subsequent articles in Wyoming Labor Force TRENDS will address age as a factor in earnings in greater detail.
Footnote 5: While AVERAGE ACROSS ALL INDUSTRIES data is presented in Tables 1 and 2, these numbers also include industries that were covered in the May issue of Wyoming Labor Force TRENDS.
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