Economists like to think that wages are set depending upon two basic factors plus a “catchall” variable. The two basic factors are expected price level and unemployment rate. The “catchall” variable stands for all other overlooked factors affecting wage. The way in which the relationship is established by labor theory is that expected price level affects wage determination positively (since the economy has not experienced deflation effect systematically); and, unemployment does it negatively (supposedly, given that workers compete for jobs, employers take advantage of it through price-taking behavior). All other factors affecting wages are assumed to be positive.
Among those all other factors –which are believed to affect positively wage levels- is the Unemployment Insurance benefit. However, depending upon ideology, Unemployment Insurance benefits may be interpreted as affecting wage determination either positively, or affecting wages negatively. On one side, Unemployment Insurance may affect upward wages given that it sums up into the so-called reserve salary, which is the minimum amount of money that makes a person indifferent to the choice between working and not working. In other words, if a person has Unemployment Insurance for any given dollar amount, why would that person work for less that such a figure? The flip side of the coin is that, if Unemployment Insurance contributes to keep people from work, then the unemployment rate goes up due to the UI, thereby pushing down the wages. At first glance, analysts might be tempted to think that those two forces cancel off each other. There is where data becomes important in determining the real breadth of those factors without binding to any ideology.
By the way, in case you have not noticed it yet, right wing politicians tend to believe that UI pressures upwards wages thereby increasing production costs. Therefore, right wing politicians believe that such a pressure constraints hiring within the United States affecting negatively production and forcing employers to find cheap labor elsewhere overseas.
Managers play a roll either in cutting or increasing wages:
It is important to note that wage laws create downward wage rigidity, which prevents managers to lower nominal salaries. However, and despite of such a rigidity, administrators may manage to cut ‘earnings’ by lowering workloads. Therefore, looking at measures such as hourly wage, or minimum legal wage does not capture the reality of compensation. Instead, looking at ‘earnings’ might give a hint about the variance created by unemployment insurance, unemployment rate and inflation.
So, the logic goes as follows: wage levels are an outcome of unemployment rate (negatively); plus, unemployment benefits (positively); plus, expected price level (positively). In other words, wage setting gets affected by those three factors since a manager ‘virtually’ would adjust her payroll based on how easy is for her to either hire or fire an employee, and how enthusiastic she is to increase or decrease the employee workload.
Thus, the statistical model would look like the following:
Where y is the dependent variable Average weekly earnings for November 1980 to November 2014; x1 represents Unemployment Rate at its annual average; x2 represents Unemployment Insurance Rate for November’s weeks seasonally adjusted average; x3 stands for inflation rate at its annual average.
Data and method:
Thus, I took data on three variables: Average weekly earnings for the month of November starting from 1980 through 2014. These data, taken from the U.S. Bureau of Labor Statistics (BLS), were adjusted by the average inflation rate of the correspondent year. The second variable is year average inflation rate from 1980 to 2014, taken also from BLS too. I use Inflation Rate as a proxy for the “expected price level”. The third variable is the November’s Unemployment Insurance rate from 1980 to 2014, which was taken from the Unemployment Insurance Division at the U.S. Department of Labor. I chose data on November series given that this month’s Average weekly earnings has the greatest standard deviation among all other months.
Ordinary Least Square Method was used to run the multiple regression.
Data for the month of November, starting 1980 through 2014, show that Unemployment Insurance Rate could have a negative effect on average weekly earnings for Americans. Apparently, the statistical relation of the data is negative. The actual estimated coefficient for these data points out toward a figure of (+/-) $123 less for U.S. Worker’s average weekly earnings per each percent point increase in Unemployment Insurance Rate. In other words, the greater the share of people collecting Unemployment Insurance, the lower the average weekly earnings of U.S. workers. One limitation of the regression model is that it only captures the employees effect of the variable, the model is not intended to explain costs of employers. In such a case the dependent variable should be some variable capable of capturing employer’s labor costs. The statistical significance for the effect of Unemployment Insurance on November average weekly earnings data is at 95%.
Furthermore, data also show that inflation rate (proxy for “expected price level”) actually works against average weekly earnings. The estimated coefficient for the months of November is (+/-) 28 dollars less for the average paycheck. The statistical significance for the effect of Inflation Rate on November average weekly earnings data is at 95%.
Finally, the Unemployment Rate shows a positive effect on average weekly earnings indicating that, per each percent point increase in Unemployment Rate, average weekly earnings increases by an estimated figure of (+/-) 49 dollars. The statistical significance for the effect of Unemployment Rate on November average weekly earnings data is at 90%.
Regression output table: