Relationship Between Gender Gap And GDP

Dataset 1 and Dataset 2

The gender gap is the difference between the salary of men and that of women. The gender gap is attributed to not only discrimination in hiring but also the different industries which women and men work among others. Gender equality has been a major case of discussion by many people across different fields globally. According to Schwab (2017), the gender biases been experienced across the different field in the economy are keeping the mass from closing the gender gap thereby causing an overwhelming of the economy.

The following research aims at finding the relationship between the gender gap and the GDP. Thus the arising research question:

• What is the a relationship between gender gap and the GDP

The research is necessitated by the fact that closing the gender gap is vital for policymaking and development. According to Revenga and Shetty (2012), gender equality is vital for enhancing economic productivity, improving the outcomes of development for future generations, and making institutional and policies more representative. Momsen (2009), states that progress is a course which expands freedom similarly for all the people both female and male. Thus, closing gender equality improves economic productivity and improves other outcomes of development.

The net impact of gender inequality on growth is quite ambiguous. In some way, gender inequality is attributed to hindering growth or support growth circumstantially. Income and wages rapidly affect and bring about changes in aggregate demand. In the long-run, benefits of gender-equal opportunities in labor, education, and health are more efficient than the pervasive gender inequality seeing today. Thus, conversion of gender equality creates opportunities for equal outcomes.

Therefore, the question that arises is whether differences in wages and income affect economic growth or not? The following research will, therefore, endeavor to determine whether gender inequality has an economic impact. Thus, this provides a guide for the researcher to determine if indeed there is a relationship between gender gap and the GDP.

1. Dataset 1 description

Dataset 1 is a dataset specifically assigned to the undersigned researcher. The dataset entails an individual sample file from 2013 to 2014 that was obtained from the Australian Taxation Office (ATO). Thus, the dataset can be described as secondary in nature.

The dataset entails four variables; gender, occ_code, Sw_amt, and Gift_amt. The characteristics of the variables are as shown in the table below:

Table 1: Variable description

Variable	Description	Values	Type
Gender	Gender (sex)	Female or Male	Dichotomous
Occ_code	Salary/wage occupation code	0 = Occupation not listed/Occupation not specified 1 = Managers 2 = Professionals 3 = Technicians and Trades Workers 4 = Community and Personal Service Workers 5 = Clerical and Administrative Workers 6 = Sales worker 7 =  Machinery operators and drivers 8 = Laborers 9 = Consultants, apprentices and type not specified or not listed	Dichotomous
Sw_amt	Salary/wage amount	All numeric	Continuous
Gift_amt	Gifts or donation deductions	All numeric	Continuous

The first 5 cases of dataset 1 are as shown below:

Table 2: first 5 cases of dataset 1

Gender	Occ_code	Sw_amt	Gift_amt
Male	3	143179	0
Female	5	28801	0
Female	5	27675	168
Female	5	77297	0
Male	0	0	0

1. Dataset 2 description

Dataset 2 was collected from online sources, which is the Organization for Economic Co-operation and Development (OECD). The sample collected cannot be termed as biased since it was obtained from a verified source. However, the use of online data source meant that the data being searched had various disadvantages. For instance, the data collected had limited time frame as it only captured data from 1975 till 2016. Moreover, there was missing data as there was no recorded wage gap index for 1996.  Collection of the data from the OECD implies that the data is secondary in nature.

The variables used in dataset 2 are wage gap and GDP. The two variables are all numerical, thus they are continuous in nature.

Descriptive Statistics

Section 2: Descriptive Statistics

1. The relationship between the Gender variable and Occupation

The relationship between the gender variable and occupation can is as seen in the figure below:.

Figure 1: Gender distribution against the occupation

Figure 1 shows that most of the occupations including the ones not listed were highly dominated by the male gender. However, occupation 4, 5, and 6 were dominated by the female gender with a representation of 65%, 72% and 65% each. It can be noted that the male gender main domination is in occupation 7 where they have a representation of 94% compared to the female gender who have a representation of 6%. The female gender has mainly dominated occupation 5 where they are represented by 75% while the male gender gets a meager representation of 25%.

1. The relationship between the Gender Variable and Salary or wage amount

The following dot plot was constructed with the aim of coming up with a graphical presentation to show the relationship between the gender variable and the salary or wage amount.

Figure 2: Salary/wage amount against gender

Figure 2 shows that most of the female genders earn less than $200,000 except for one incidence (outlier) who earns more than $200,000. On the other hand, the more of the male gender earn more than $200,000 when compared to the female gender. Additionally, the incidence (outliers) of those who earn a great amount of salary or wages in the male gender is two with one matching the maximum of the female gender while the other earning more than $800,000.

1. The relationship between the variables Gender and Salary or wage amount (numerical summary)

The table below shows the numerical statistics which shows the relationship between gender and salary or wage amount.

Table 3: Gender vs. salary or wage amount

Row Labels	Average of Sw_amt	StdDev of Sw_amt	Min of Sw_amt	Max of Sw_amt	Count of Sw_amt
Female	35,461.83	40,188.86	–	308,183.00	461.00
Male	55,679.90	68,244.44	–	839,840.00	539.00

The mean of female gender with regards to salary or wage amount is $35,461.83 with a standard deviation of $40,188.86. On the other hand, the male gender had a salary or wage amount that averaged $55,679.90 with a standard deviation of $68.244.44. From this, it is evident that the male gender earned a high salary or wage amount compared with the female gender. Conversely, the male gender had a high variation ($68,244.44 standard deviation) compared to the female gender ($40,188.86 standard deviation).

1. The relationship between the Salary or wage amount and gifts or donation deductions

Figure 3: Salary/wage amount Vs. Gifts or donation deductions

From figure 3, it can be seen that is almost impossible to tell if salary or wage amount has a relationship with gifts or donations deductions. However, incorporation of a linear trend line shows that there is a relationship. Thus, salary or wage amount has a relationship with gifts or donation deductions.

Section 3: Inferential Statistics

Use Dataset 1

1. Top 4 occupations based on median salary and proportion of the gender

The following table displays the ranks of the occupations based on the median salary. The ranks highlighted in green represent the top 4 occupations which is of interest.

Table 4: Rank of Occupations

Rank	Occupation	Median	Proportion of Female	Proportion of Male
1	2	70427	0.52	0.48
2	1	59606	0.42	0.58
3	7	59316	0.06	0.94
4	3	56628	0.12	0.88
5	5	41304	0.72	0.28
6	8	39776	0.30	0.70
7	9	33785	0.45	0.55
8	4	27334	0.64	0.36
9	6	26255	0.65	0.35
10	0	0	0.46	0.54

From the above, it is evident that the top four occupations are 2, 1, 7 and 3 with a respective median of 79427, 59606, 59316, and 56628. Consequently, it can also be deduced that the top four occupations are highly dominated by the male gender with exemption to occupation 2. The subsequent 3 occupations in the top 4 see the gap increase where 1 has a difference of 0.16, 7 has a difference of 0.88 and 7 has a difference of 0.76 in the gender proportions.

1. Significance of proportion of male machinery operators and drivers is more than 80%

Inferential Statistics

Null hypothesis > 0.8

Alternate hypothesis < 0.8

Significance level is 0.05

Solution:

σ = sqrt [ P * (1 – P) / n ]

  = 0.062

Z = (p – P) / σ

   = (0.93 – 0.8) / 0.062

   = 2.10

Using the normal distribution calculator, the p-value of 2.1 z statistics is:

P (z < 2.10) = 0.018

Since the p value is < 0.05 we choose to reject the null hypothesis. Thus, the proportions of male machinery operators and drivers is less than 80%.

1. Hypothesis test to determine whether there is a difference in salary amount between genders.

Proportion of male gender: 0.539

Proportion of female gender: 0.461

Significance level = 0.05

Solution

Null hypothesis: p1 <= p2

Alternate hypothesis: p1 > p2

p = (p1 * n1 + p2 * n2) / (n1 + n2)

p = (0.539 * 539 + 0.461 * 461 ) / (1000)

p = 0.503

SE = sqrt { p * (1 – p) * [(1/n1) + (1/n2)]}

SE = sqrt (0.503 * 0.407 * [(1/539) + (1/461)]

SE = 0.0287

z = (p1 – p2) / SE = (0.539 -0.461) / 0.0287 = 2.72

Using the normal distribution calculator, the p-value of 2.72 z statistics is:

P (z < 2.72) = 0.003

Since the p value is < 0.05 we choose to reject the null hypothesis (Higgins et al., 2003). Thus, the proportion of the male gender is more than that of the female gender.

1. Regression analysis (using dataset 2).

To answer the research question that is, is there a relationship between gender gap and the GDP, a regression analysis was carried out. The tables below show the regression results.

Table 5: Model summary

Regression Statistics
Multiple R	0.64
R Square	0.41
Adjusted R Square	0.40
Standard Error	260820.72
Observations	41

The regression model has an adjusted R square of 0.4. Thus, the variables explain 40% of the variability in the model while 60% is explained by variables, not in the model. Consequently, the regression model does represent a good fit.

Table 6: ANOVA

	df	SS	MS	F	Significance F
Regression	1	1.88178E+12	1.88178E+12	27.66204777	0.00
Residual	39	2.65307E+12	68027446646
Total	40	4.53485E+12

Table 6 shows that the regression is statistically significant since the p < 0.05 level of significance. Therefore, there is a relationship between gender gap and GDP per capita.

Table 7: Coefficients

	Coefficients	Standard Error	t Stat	P-value
Intercept	1,907,575.45	269020.97	7.09	0.00
WAGEGAP	-84,526.45	16071.28	-5.26	0.00

From table 7, it can be seen that there is a negative relationship between GDP per capita and wage gap. Thus, a unit increase in wage gap reduces the GDP per capita by $84,5226.45. Consequently, the wage gap coefficient is statistically significant since p < 0.005.

Section 4: Discussion & Conclusion

1. Discussion and Conclusions of findings

From the regression model, it can be deduced that the research question has been sufficiently answered. It was established that there was a relationship between GDP per capita and gender gap. Moreover, the relationship is also statistically significant. It was also found out that gender gap has a negative impact on GDP. As the gender gap increases in an economy, the amount of GDP per capita is bound to reduce greatly. Thus, the findings support Revenga and Shetty (2012) claim. Therefore gender equality is important in enhancing economic productivity, improving the outcomes of development for future generations, and making institutional and policies more representative.

1. Suggestions for further research

The findings obtained from the statistical analysis carried out can be further improved by carrying out further research in the future. The statistical analysis was a case study done for Australia. Thus, future researchers can opt to do research on other economies in the world either on a country basis or regionally.

References;

Momsen, J., 2009. Gender and development. Routledge.

Revenga, A. and Shetty, S., 2012. Empowering Women Is Smart Economics-Closing gender gaps benefits countries as a whole, not just women and girls. Finance and Development-English Edition, 49(1), p.40.

Schwab, K., 2017. The fourth industrial revolution. Crown Business.