BUSN20019: Wages and Industry Performance in Australia Report

This report investigates the relationship between wages and industry performance in Australia, utilizing secondary data from the Australian Bureau of Statistics spanning from 2007 to 2017. The research employs quantitative methods, including descriptive statistics, correlation analysis, and linear regression, using SPSS software. The findings from the descriptive statistics reveal growth in industry income, despite increased expenses. Correlation analysis suggests a positive relationship between wages and industry performance, while regression results indicate a negative impact of wages on industry profits. Based on these results, the report recommends firms adopt a productivity wage system, while also considering minimum wage provisions. The research includes an introduction, literature review, methodology, results and discussion, conclusion, and recommendations, along with limitations and references.

Chapter: 1 Relationship between wages and industry performance in
Australia
Executive summary
As per the economic theories the wages paid to the employees should be based on their marginal
productivity. There has been many researches in the field of employee wages and the
profitability of the firms. However, only few of them have focused on relationship between
wages and industry performance. The current research is aimed to examine the relationship
between the two variable, wages and the industry performance.
Secondary data was collected from the database of the Austrian Bureau of Statistics for the time
period 2007-2017. The data analysis was conducted using the various quantitative methods. This
includes descriptive statistics, correlation analysis and the linear regression. All the analysis was
conducted using SPSS software
Findings from the descriptive statistics shows that there is significant growth in the income of the
industry however due to increase in the total expenses, the growth of the profit is low. The
correlation analysis suggested positive and significant relationship between the wages and the
industry performance. However the results from the regression shows negative and significant
impact of wages on the industry profits. On the basis of the results, it has been recommended that
the firms should introduce the productivity wage system. However the provision of the minimum
wage should be taken into consideration.

Relationship between wages and industry performance in Australia
Contents
Chapter: 1 Relationship between wages and industry performance in Australia............................1
1.1 Introduction.................................................................................................................................3
1.2 Research Method and Data collection..........................................................................................5
1.3 Results and Discussion................................................................................................................5
1.3.1 Descriptive Statistics............................................................................................................5
1.3.2 Correlation Analysis............................................................................................................9
1.3.3 Regression analysis............................................................................................................10
1.3.4 Discussion..........................................................................................................................14
1.4 Conclusion and Recommendation..............................................................................................15
1.4.1 Limitations.........................................................................................................................15
1.4.2 Recommendations.............................................................................................................15
References.............................................................................................................................................16
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Relationship between wages and industry performance in Australia
1.1 Introduction
The relationship between the wages and the performance of the firm is a widely researched area
around the world(Atkinson, Piketty, & Saez, 2011; Bell & Reenen, 2012; Reenen, Draca, &
Machin, 2011). The wage rate and the industry performance are usually negatively related, as the
wages are the cost for the company and increase in the cost leads to decline in the profit.
However, the relationship is not unilateral(Bell & Reenen, 2012; Calvin, 2017). The increase in
the wages may lead to increase in the productivity of the workers which leads to increase in the
financial performance of the firm. Since, the relationship is different in different scenario, the
current research is aimed to examine the relationship between the wages and the industry
performance in Australia.
Aims and the objectives of the research:
 To examine the relationship between the wages and the industry performance
 To examine the trend of wage growth in Australia
 To examine the trend of industry profit in Australia
Research Questions:
Research question 1: What is the relationship between the wages and the industry performance in
Australia?
Research question 2: What is the growth trend of wages and the profits in Australian Industry?
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Relationship between wages and industry performance in Australia
Literature Review
In the section the previous literature on the wages and the industry have been discussed. The
review of literature will not only provide the overview of the past studies on the similar area, but
also allows the researcher to identify the gaps in the literature.
There has been many researches in the field of employee wages and the profitability of the firms.
As per the economic theories the wages paid to the employees should be based on their marginal
productivity. However, this is true only theoretically. In most of the countries the government
has fixed the minimum wage rate for the workers. The minimum wage rate should be paid to the
employees irrespective of the marginal productivity. Government and the international
organizations have fixed the minimum wage so that the employees can maintain a minimum
standard of living(Bell & Machin, 2015; Cengiz, Dube, Lindner, & Zipperer, 2018).
There has been various research on fixing the wage rate for the employees. According to a
research by (Miller, 2014), that with the advancement in the technology and the efficient
management systems, the industries are in the favor of the productivity based pay rather than the
minimum wage rate to the employees. However (Vaughan-Whitehead, 2014) argued that
companies are still hiring employees based on the traditional system of compensation.
The previous literature in the similar area has also focused on examining the relationship
between the minimum wage and the performance of the firm. One study by (Reenen et al., 2011)
argued that most of the research in the field of wages are focused on the labor market and there is
lack of research on the relationship between the firm’s performance and wages. Authors
examined the relationship between wages and performance of the firms and concluded that there
is negative relationship between the variables and increase in wages significantly reduce the
firm’s profit. However another research by (Garin & Silverio, 2017) argued that there is still lack
of evidence that change in the firms’ financial performance have direct and significant impact on
the labor market and the wages.
Another study by (Atkinson et al., 2011; David Card, Cardoso, & Kline, 2016) suggested that
there is long run correlation between the change in the wage rate and the firm’s performance.
Authors also concluded that the relationship between the wages and productivity is much
stronger as compared to firms’ performance. Similar results were proposed by many other
scholars (Bell & Reenen, 2012; Buhai, Cottini, & WestergÂrd-Nielsen, 2008; Calvin, 2017).
The impact of rising wages on the inequalities among the firms is another important area where
the most of the recent research is focused. According to (D Card, Devicienti, & Maida, 2011;
David Card et al., 2016; Furman & Orszag, 2015) differences in the wage in the firms leads to
increase in wage dispersion in the labor market, which ultimately affect the financial
performance of the firms. Apart from these there are other researchers who supported that the
wages and the financial performance of the firms are negatively related (Bell & Reenen, 2012;
Calvin, 2017; Draca, Machin, & Reenen, 2011; Huselid, 2017; Lollo & O’Rourke, 2018). On the
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Relationship between wages and industry performance in Australia
other hand some of the scholars have argued that increase in the wages in the short run will help
the firms to earn even higher profits in the long run (Furman & Orszag, 2015; Helpman,
Itskhoki, & Redding, 2010; Miller, 2014; Nagaraju & J, 2017; Reenen et al., 2011; Vaughan-
Whitehead, 2014). There may be different factors due to which different scholars showed diverse
results. It can be the selection of the data, selection of the sample size, and selection of the
indicator for wages and profitability (Ballou & Pazer, 1985; L, Lee, & Wang, 2012) (Barreiro &
Albandoz, 2001; Teddlie & Yu, 2007; Wilumila, 2002).
On the basis of the review of the literature it can be concluded that there is ample research on the
relationship between the wages and the labor market. However, there is only limited research
which focus on the relationship between the wages and the financial performance of the industry.
So, the current research is aimed to fill some gap in the literature.
1.2 Research Method and Data collection
Research method is considered as one of the most important part of any research. The proposed
research questions and the research hypothesis are tested and answered based on the results from
the collected data. In this section, the research method used in this section has been discussed
along with the data collection and analysis procedure(Hazen, Boone, Ezell, & Jones-Farmer,
2014; Kwon, Lee, & Shin, 2014).
Since the main aim of this research is examine the relationship between the wages and the
financial performance of the industry, secondary data collected from the data base of Australian
bureau of Statistics. Time series data was collected from the time period 2007- 2017. The major
variables for which the data was collected includes the wages paid to the employees, total
income, total expenses, profit, and total assets. The total wage has been taken as the indicator of
the wages in the industry whereas the profit earned by the industry was taken as the indicator of
the profit for the firms.
For the analysis purpose, various data analysis techniques has been used. Since the data is in the
numerical form, the quantitative research methods have been used for the analysis. Various data
analysis methods includes the descriptive statistics which provides a complete overview of the
collected data. Similarly to examine the relationship between the variables, the correlation
analysis was conducted. The correlation analysis is a very useful analysis if the researcher wants
to examine the relationship between the two variables. However, the results from the correlation
should be interpreted carefully as in some cases the researchers confuse the correlation with
causation. Apart from the correlation analysis, the linear regression has also been performed, to
examine the impact of the wages on the profit of the selected industry(Cai & Hayes, 2008;
Cerrito, 2010; George, Seals, & Aban, 2014).
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Relationship between wages and industry performance in Australia
For all the analysis and the data cleaning process the statistical software SPSS have been used.
1.3 Results and Discussion
This section is devoted to the results and the discussion from the data analysis. In the initial part
of the section the findings from the descriptive statistics is presented. The following sections are
dedicated to the correlation and the regression analysis.
1.3.1 Descriptive Statistics
The descriptive statistics for the wage related variables are presented in the table below. As its
shows, the values of mean, standard deviation, maximum, minimum along with the Skewness
and kurtosis have been calculated.
Statistics
Wages Employer
Contribution
Worker's
Compensatio
n
N Valid 11 11 11
Missing 0 0 0
Mean 459709.4545 40881.8182 8284.4545
Median 473701.0000 40728.0000 8200.0000
Mode 348294.00a 32195.00a 7587.00a
Std. Deviation 69730.41201 5949.29954 445.92878
Variance 4862330359.
273
35394164.96
4
198852.473
Skewness -.232 .117 -.118
Std. Error of
Skewness
.661 .661 .661
Kurtosis -1.418 -1.452 -1.622
Std. Error of Kurtosis 1.279 1.279 1.279
Minimum 348294.00 32195.00 7587.00
Maximum 550834.00 49614.00 8805.00
a. Multiple modes exist. The smallest value is shown
This shows that the average wage paid in the industry is 459709 with standard deviation of
69730. This indicates the variability in the data set is high as shown by the high value of standard
deviation. The Median value is 473701 which is higher than the mean value. Furthermore the
minimum and maximum value are 348294 and 550834 respectively. This shows that the wages
paid has increased significantly over the period of time.
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Relationship between wages and industry performance in Australia
In case of the employer contribution, the mean value is 40881 with standard deviation of 5949.
The employer contribution is the amount contributed by the employer in provident fund for
employees. This is apart from the salary paid to the employees. This is usually contributed for
the social security of the employees after the retirement. In addition the results for the workers
contribution has also been shows. It shows that the mean value is 8284 and standard deviation is
also low at 445. This clearly indicates that contribution of the employer is much higher than the
contribution of the employees. So the cost for the companies is higher which may directly affect
their profit.
Statistics
Total income Total expenses Profit Industry value
added
Total assets
N
Valid 11 11 11 11 11
Missin
g
0 0 0 0 0
Mean 2823969.2727 2514390.1818 375568.7273 951687.0000 245336.2727
Median 2917708.0000 2574095.0000 391762.0000 1002409.0000 243057.0000
Mode 2261903.00a 1994662.00a 290498.00a 728971.00a 212845.00a
Std.
Deviation
319389.56034 291734.04674 49540.42148 133834.52808 24835.37870
Variance 102009691256.4
18
85108754026.1
64
2454253360.8
18
17911680906.4
00
616796035.4
18
Skewness -.435 -.411 -.616 -.407 -.033
Std. Error
of
Skewness
.661 .661 .661 .661 .661
Kurtosis -.997 -.979 -.612 -1.134 -1.837
Std. Error
of
Kurtosis
1.279 1.279 1.279 1.279 1.279
Minimum 2261903.00 1994662.00 290498.00 728971.00 212845.00
Maximu
m
3253572.00 2895931.00 446772.00 1134730.00 276655.00
a. Multiple modes exist. The smallest value is shown
Similarly in the above table, the descriptive results for the variables related to the financial
performance of the industry is presented. It shows that the average income of the industry in each
year is 2823969.2727 and the standard deviation of 319389.56034. The high value of the
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Relationship between wages and industry performance in Australia
standard deviation indicates that the data is scattered and also the values are far from its average
value. Furthermore, the most important variable is the profit earned by the industry. The average
profit for the time period selected is 375568.7273 with minimum and the maximum value of
290498.00 and 446772.00 respectively. The profit are less as compared to the average income of
the industry. This also implies that there is total expense is high which has led to decline in the
profit of the industry. It should also be noted that the wages paid is also one of the major
component of the total expenses. This also shows the negative relationship between the two
variables.
The histogram of the wages shows that the variable is not distributed normally as the shape of
the histogram is not bell shaped. This is may be because of the time series data. Histograms are
more suitable for the cross sectional data as compared to the time series data.
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Relationship between wages and industry performance in Australia
Similarly the histogram for the profit is shown in the figure above and the results show that the
variable is bell shaped indicating the normal distribution of the profit.
1.3.2 Correlation Analysis
In this section the results from the correlation analysis are presented along with their significance
value. The significance value helps the researcher to identify which correlation are coefficients
are significant from those which are not significant.
Correlations
Wages Total
income
Total
expenses
Profit Industry
value added
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Relationship between wages and industry performance in Australia
Wages
Pearson
Correlation
1 .994** .994** .942** .992**
Sig. (2-tailed) .000 .000 .000 .000
N 11 11 11 11 11
Total income
Pearson
Correlation
.994** 1 .998** .963** .997**
Sig. (2-tailed) .000 .000 .000 .000
N 11 11 11 11 11
Total expenses
Pearson
Correlation
.994** .998** 1 .948** .991**
Sig. (2-tailed) .000 .000 .000 .000
N 11 11 11 11 11
Profit
Pearson
Correlation
.942** .963** .948** 1 .976**
Sig. (2-tailed) .000 .000 .000 .000
N 11 11 11 11 11
Industry value
added
Pearson
Correlation
.992** .997** .991** .976** 1
Sig. (2-tailed) .000 .000 .000 .000
N 11 11 11 11 11
**. Correlation is significant at the 0.01 level (2-tailed).
As the table shows the correlation between wages and profit is 0.994 which is also statistically
significant as the 2 tailed significant value is less than 0.05. The correlation value of 0.99 is very
close to 1, which indicates that there is very strong and the positive relationship between wages
and the profit. Usually the previous results have shown negative relationship between the two
variables, however in this case it is very strong and positive.
1.3.3 Regression analysis
Finally the last part of the current analysis is the regression analysis and the findings are
presented in the following section.
Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .999a .889 .870 2140.35330 1.747
a. Predictors: (Constant), Total expenses, Industry value added , Wages
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Relationship between wages and industry performance in Australia
b. Dependent Variable: Profit
As the result shows, the R squared value is 0.89. This indicates that the profit of the industry is
mostly explained by the variables include in the regression model. Also the value of durbin
Watson test confirms that there is no serious problem of autocorrelation among the variables.
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 24510465822
.294
3 8170155274.
098
1783.444 .000b
Residual 32067785.88
8
7 4581112.270
Total 24542533608
.182
10
a. Dependent Variable: Profit
b. Predictors: (Constant), Total expenses, Industry value added , Wages
The Anova table shows the value of F statistics which is used to examine the cumulative impact
of the independent variables on the dependent variable. In this case the F statistics of 1783 is
significant with the significance value less than 0.05. In this research the 95 % significance is
used.
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 14037.396 14153.502 .992 .354
Wages -1.079 .103 -1.519 -10.527 .000
Industry value
added
1.000 .043 2.702 23.203 .000
Total expenses -.038 .024 -.221 -1.578 .159
a. Dependent Variable: Profit
The regression coefficients are presented in the table above. The first coefficient is wages, which
is negative. This clearly indicates that the increases in the wages lead to decline in the profit. The
p value for wages is 0.00 which also confirms that the impact of wages on profit is statistically
significant.
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Relationship between wages and industry performance in Australia
Furthermore another important coefficient is total expenses is the total expenses which also
shows the negative regression coefficient. However, the coefficient is not statistically significant.
The coefficient of industry value added is positive and significant.
Residuals Statisticsa
Minimum Maximum Mean Std.
Deviation
N
Predicted Value 292522.8750 446011.4063 375568.7273 49508.04563 11
Residual -2802.95410 2648.23608 .00000 1790.74805 11
Std. Predicted
Value
-1.677 1.423 .000 1.000 11
Std. Residual -1.310 1.237 .000 .837 11
a. Dependent Variable: Profit
The results from the residual statistics is shown in the table above. The residuals in this
case are very small which indicates better fit of the model.
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