Minimum Wage Increase Effect on Unemployment Rate

Introduction

Reflectively, there are several factors affecting the rate of unemployment. These factors include minimum wage, wage differential, and other market dynamics. In relation to this research, the scope of the paper will concentrate on establishing the relationship between the unemployment rate and minimum wage. The research paper will also provide possible reasons for the positive or negative relationship which will be established. The paper will concentrate on the labor market of the developed countries where a good percentage of the employees are paid the minimum wage as stipulated by the government labor regulatory agencies.

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Theoretical and empirical perspective

Theoretical perspective

Over the years, the minimum wage has been on the rise for several reasons in the developed and the developing labor market. As a result of the increasing minimum wage, organizations have to respond by increasing the salaries in line with the new minimum wage. However, increasing the minimum wage translates into increased cost of labor in companies that have the primary goal of optimal returns from the least possible costs. In response to the increasing minimum wage, a rational agent managing the labor function in a company will have to strike a balance between the laborers and capital to minimize the effects of increasing wages. One of the possible responses to higher wages would be streamlining the labor force by retrenchment to remain with a lean labor as one of the primary factors of production (McConnell and Macpherson 24). From this perspective, it is apparent that the minimum wage increment would negatively affect the unemployment rate as companies respond to the dynamics of increased cost of labor.

Human Capital Theory

Fringe benefits and wage earnings are identified as the main components of compensation summation. However, fringe benefits are apportioned a larger share in the total compensation matrix due to the fact that their influence was experiencing a consistent growth over the last decade in the labor market. These fringe benefits are classified as social security, unemployment compensation and employee’s compensation for every unit of labor given as indicated in the human capital theory. For instance, the wage differentials for different age groups studied average at 5. Since fringe benefits are rarely affected by age, the existing wage differential is negligible. In classification, these fringe benefits assume the form of insurance benefits, paid leave, and legally acquired benefits to a worker for every unit of labor delivered against the revenue realized. Besides these, retirement benefits and savings are included in the summation of the fringe benefits accrued by a worker. An increase in the minimum wage leads to an increase in the benefits. As a result, the unemployment rate may rise as a result of retrenchment as a strategy for managing the rising cost of labor (McConnell and Macpherson 33).

Labor Market Discrimination Theory

Type and form of fringe benefits are never universal. Rather, they are influenced by the type of industry in which labor operates, ration and occupational groups as indicated in the labor market discrimination theory. This is due to the fact that governments and other agencies have introduced laws and regulations aimed at pushing for higher and reliable compensation. In most instances, the blue collar employees have a larger share of the legalities, construed benefits than their counterparts in white collar jobs (McConnell and Macpherson 39).

Job Characteristics Theory

In a bid to extrapolate this relationship, the Job Characteristics/Compensating Wage Differentials theory is a certain reason for the experienced growth over the sample space. Reflectively, the variables interacting within the parameters of this theory are leisure and income within the normal indifference curve. Consequently, the resulting interaction becomes flexible to different bundles of budget constraints that might be present at each level of computation. Further, this theory asserts that indifference curve is a product of various fringe benefits and wage rates that interact simultaneously to yield same utility level for each worker. When all other factors are held constant, higher swing of the indifference curve indicates higher levels of utility. Irrespective of the inclination of the indifference curve, it is apparent that levels of tax advantage determine the resultant fringe benefit accrued as shown in the survey. Specifically, to support this notion, the benefits accrued from pension plans are taxable upon confirmation of receivership by an employee. Besides, the principle, dividends and interest which are part of the summation of pensions, are best achieved through pretax accumulation of the fringe benefits. On average jobs that demand higher skills attract more wages than those that demand low skills (McConnell and Macpherson 41).

Incentive pay theory

The need for intrinsic substitution as a component of the decision science aimed at managing the fringe benefits are peculiar in labor economics. In such case, the foregone alternative would be forfeiting leisure related savings for health and pension needs which are characterized as basic for every worker. The adoption of this thought is influenced by the fact that basic needs are more critical than the secondary needs in the matrix of fringe benefits. Besides, the long term effects of purchasing the basic needs are greater than those opting to acquire secondary needs upfront. Tax advantages to employers, scale of economies, and efficiency are major factors that led to the growth of fringe benefits. Therefore, as fringe benefits increase, the workers’ utility increased in the same ratio. In drawing the curve, the initial assumptions consist in the fact that the market operates within a normal profit margin in total employment and product market as part of the overall compensation effect per worker. Generally, substantial changes for each cluster of wages and benefits are negligible within the ‘employer’s isoprofit curve’. The same relationship functions in the Wage-Fringe optimum. As performance and pay interest in the labor market, there is a proportional relationship between performance and pay for each unit of labor given to a firm (principal) against the compensation offered as explained in the incentive pay theory (McConnell and Macpherson 24).

The unbalance relationship between pay and performance may result in the principal – agent problem which might culminate in under utilization of labor units since the agent (employee) may opt to increase leisure through reduced efforts at work. In order to avoid this unwanted scenario, the theory proposes different forms of incentive compensation, such as royalties, profits, and bonus plans. In most cases, employers control these incentives and limit them as a fraction of the total revenue after factoring the cost of production and each labor unit. When implementing these incentive plans, it is important to concentrate on personal performance bonuses as opposed to team bonuses, which promote a joyride attitude among workers since the process has no specific measure for distributing incentives.

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Empirical evidence

Through secondary research, Gitis (2013) established that there is an interesting relationship between increase in the minimum wage and the rate of unemployment. In a research which involved establishing the impacts of increasing minimum wage to $7.5 per hours in the year 2013 by 19 states, the researcher noted that the minimum wage policy resulted in increased rates of unemployment recorded. Gitis (2013) observed, “a $1 increase in the minimum wage was associated with a 1.48 percentage point increase in the unemployment rate, a 0.18 percentage point decrease in the net job growth rate, a 4.67 percentage point increase in the teenage unemployment rate, and a 4.01 percentage point decrease in the teenage net job growth rate” (Gitis par 6). Further, the researcher established that the high minimum wage as a policy of different states in the US resulted in increased the reported unemployment by more than 500,000 workers and reduction of the growth of job by more than 80,000 units.

The above evidence is a clear indication that increasing the minimum wage will create negative imbalances in the job markets. The strategy adopted by several states in the US to increase the minimum wage to $7.5 per hour translated into increased unemployment since most of the blue collar workers’ pay-per-hour was below the proposed increment in the minimum wage. The impact has been highest in the youthful unskilled workforce since majority in this group were earning less that the proposed $7.5 pay-per-hours. Apparently, “in high minimum wage states, the net job growth rate for teenagers was actually negative in 2013, with a mean annual average rate of -0.5 percent” (Gitis par 9). The findings of the research by Gitis (2013) suggests that the increase in minimum wage to $7.5 pay-per-hour by several states in the US were negative for the US labor market since it elevated unemployment rates and actual number of persons who were unemployed.

Data analysis

The data on minimum wage and unemployment rate in the US will be used to test the relationship between these two variables. The table presented below shows data that will be used for the analysis. The data will cover a period from 1955 and 2013. The data are presented in the table below.

Year Unemployment rate % Current dollars Constant (1996) dollars
1955 4.37 0.75 4.39
1956 4.13 1 5.77
1957 4.30 1 5.58
1958 6.84 1 5.43
1959 5.45 1 5.39
1960 5.54 1 5.3
1961 6.69 1.15 6.03
1962 5.57 1.15 5.97
1963 5.64 1.25 6.41
1964 5.16 1.25 6.33
1965 4.51 1.25 6.23
1966 3.79 1.25 6.05
1967 3.84 1.6 6.58
1968 3.56 1.6 7.21
1969 3.49 1.6 6.84
1970 4.98 1.6 6.47
1971 5.95 1.6 6.2
1972 5.60 1.6 6.01
1973 4.86 1.6 5.65
1974 5.64 2 6.37
1975 8.48 2.1 6.12
1976 7.70 2.3 6.34
1977 7.05 2.3 5.95
1978 6.07 2.65 6.38
1979 5.85 2.9 6.27
1980 7.18 3.1 5.9
1981 7.62 3.35 5.78
1982 9.71 3.35 5.78
1983 9.60 3.35 5.28
1984 7.51 3.35 5.06
1985 7.19 3.35 4.88
1986 7.00 3.35 4.8
1987 6.18 3.35 4.63
1988 5.49 3.35 4.44
1989 5.26 3.35 4.24
1990 5.62 3.8 4.56
1991 6.85 4.25 4.9
1992 7.49 4.25 4.75
1993 6.91 4.25 4.61
1994 6.10 4.25 4.5
1995 5.59 4.25 4.38
1996 5.41 4.75 4.75
1997 4.94 5.15 5.03
1998 4.50 5.15 4.96
1999 4.22 5.15 4.85
2000 3.97 5.15 4.69
2001 4.74 5.15 4.56
2002 5.78 5.15 4.49
2003 5.99 5.15 4.39
2004 5.54 5.15 4.28
2005 5.08 5.15 4.14
2006 4.61 5.15 4.04
2007 4.62 5.85 4.41
2008 5.80 6.55 4.77
2009 9.28 7.25 5.3
2010 9.63 7.25 5.22
2011 8.93 7.25 5.06
2012 8.08 7.25 4.97
2013 7.35 7.25 4.87

(Source of data – United States Department of Labor 1)

Descriptive statistics

Unemployment rate % Current dollars
Mean 6.013559322 3.418644068
Standard Error 0.205966775 0.253755827
Median 5.641666667 3.35
Mode 5.541666667 5.15
Standard Deviation 1.582060815 1.949135492
Sample Variance 2.502916423 3.799129164
Kurtosis -0.09054546 -0.837468603
Skewness 0.669363495 0.431136876
Range 6.216666667 6.5
Minimum 3.491666667 0.75
Maximum 9.708333333 7.25
Sum 354.8 201.7
Count 59 59
Largest (1) 9.708333333 7.25
Smallest (1) 3.491666667 0.75
Confidence Level (95.0%) 0.412287294 0.507947476

From the table presented above, the mean and median for unemployment rate are 6.0136 and 5.641 respectively. The maximum and minimum values of salaries yield a range of 6.217. The standard deviation, a measure of dispersion, for the unemployment rate is 1.582. This implies that the average value can either increase or drop by 1.582. The value of skewness (0.669), and Kurtosis (-0.0905). Indicates that the observations are not normally distributed. In a normal distribution, the value of skewness and Kurtosis should be zero. The mean and median for unemployment rate are 3.418 and 3.35 respectively. The maximum and minimum values yield a range of 6.5. The standard deviation is 1.949. The value of skewness (0.431) and Kurtosis (-0.837). The values show that the observations are not normally distributed (Gujarati 23).

Scatter plots

Scatter diagram seeks to establish if there exists a linear relationship between the two variables plotted. This can be observed by looking at the trend of the scatter plots. The independent variable is plotted on the x – axis while the dependent variable is on the y – axis.

Unemployment rate per minimum wage in current pice
Unemployment rate per minimum wage in current pices

The scatter plot does not seem to provide a clear relationship between the unemployment rate and minimum wage in current prices and unemployment rates.

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Minimum Wage Increase Effect on Unemployment Rate
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Unemployment rate per minimum wage in constant pices
Unemployment rate per minimum wage in constant pices

The diagram above does not provide a clear pattern. Therefore, it may be difficult to use this criterion to ascertain the relationship between the unemployment rate and minimum wage in constant prices (Gujarati 23).

Correlation coefficient

The table presented below shows the correlation coefficient.

Unemployment rate % Current dollars Constant (1996) dollars
Unemployment rate % 1
Current dollars 0.358416496 1
Constant (1996) dollars -0.064982131 -0.626143112 1

The table above shows that there is a weak positive relationship between the unemployment rate and minimum wage in current dollars. The value of the coefficient is 0.3584. Also, there is a weak relationship between the unemployment rate and minimum wage in constant dollars, that is -0.0649.

Regression results

When coming up with the regression model, it is necessary to separate between dependent and independent variables. This section carries out a simple regression analysis to establish the relationship between the unemployment rate and minimum wage (Gujarati 23).

Simplified regression equation Y = b0 + b1X

  • Y = Unemployment rate
  • X = Minimum wage
  • The theoretical expectations are b0 can take any value and b1 < 0.

Unemployment rate and minimum wage

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.358416496
R Square 0.128462385
Adjusted R Square 0.113172251
Standard Error 1.489850911
Observations 59
ANOVA
df SS MS F Significance F
Regression 1 18.64878 18.64878 8.401652 0.005313194
Residual 57 126.5204 2.219656
Total 58 145.1692
Coefficients Standard Error t Stat P-value Lower 95%
Intercept 5.019017567 0.394144 12.73396 2.61E-18 4.229757267
X Variable 1 0.290917023 0.100366 2.89856 0.005313 0.089937497

The regression equation in this case is Y = 5.019 + 0.2909X. The positive coefficient of X shows that the two variable move in the same direction. Thus, an increase in minimum wage by one unit will result in a 0.2909 increase in unemployment.

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Test of significance

A two tailed t-test will be used to test the significance of the explanatory variable. The test will be carried out at 5% level of significance. The hypothesis is stated below.

  • Null hypothesis: Ho: Xi = 0
  • Alternative hypothesis: Ho: Xi ≠ 0

The null hypothesis implies that the variable is not a significant determinant of movements in stock prices. The null hypothesis will be rejected if the value of t-calculated is greater than the value of t-tabulated or if the p-value is less than α=0.05. The t-statistics for minimum wage is 2.89856, while the p-value is 0.005313. Since the p-value is less than α=0.05, then reject the null hypothesis and conclude that minimum wage is a significant determinant of the unemployment rate (Gujarati 23).

F – Test of the regression models

The overall significance of the regression model can be analyzed using an F – test. The test will be carried out 5% significance level.

  • Null hypothesis H0: β0 = β1
  • Alternative hypothesis H1: βj ≠ 0, for at least one value of j

The null hypothesis implies that the overall regression line is not significant. The alternative hypothesis implies that overall regression line is significant. From the regression results, the value of f-calculated is 8.4016 while the value of significance f is 0.0053. Therefore, value of f-significance is less than α = 0.05. This shows that the overall regression line is significant. The coefficient of determination (R2) is 12.85%. It indicates that the regression model is weak because the independent variable explains only 12.85% of the variations in stock movement. This regression analysis shows that there exists a positive relationship between the unemployment rate and minimum wage. Thus, implies that if the government of a nation increases the minimum wage, then the unemployment level will increase. Therefore, there is a positive relationship between the unemployment rate and minimum wage (Gujarati 23). The analysis also shows that minimum wage is a significant determinant of unemployment rate and that the overall regression line is significant as indicated by the t-test and f-test. However, the coefficient of determination is small. This implies that there is a weak relationship between this variable. From an economic point of view, it means that minimum wage weakly affects unemployment rate. The regression results above are consistent with the empirical evidence discussed in the previous section.

Conclusion

The paper carried out an analysis of the relationship between unemployment rates minimum wage payments. The analysis focused on the labor market of the US economy because a good percentage of the employees are paid the minimum wage as stipulated by the government labor regulatory agencies. The empirical evidence carried out shows that increasing minimum wage caused undesirable effects in the economy. The same result was observed in the analysis section. The results show that increasing minimum wage increases unemployment rate. An increase in the unemployment rate is undesirable in the economy. Besides, the analysis showed that the relationship between these two variables is weak.

Works Cited

Gitis, Ben. How Minimum Wage Increased Unemployment and Reduced Job Creation in 2013, Web.

Gujarati, Damodar. Econometrics by Example. USA: Palgrave Macmillan, 2014. Print.

McConnell, Campbell, and David Macpherson. Contemporary Labor Economics. London, UK: McGraw-Hill Education, 2013. Print.

United States Department of Labor. Databases, Tables & Calculators by Subject. 2014. Web.

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