Statistical Testing for wage gap♦Image created by Hiranmayee Panchangam using CanvaThe gender Pay Gap is a real scene even in the richest of the rich countries, as the article emphasizes throughout. It could not necessarily mean that it is discrimination; however, companies have their policies and agendas for profits and lifting the economies.
Nevertheless, there could be many factors that push women out of the race, like maternity leave, Periods, and physical Dexterity.
Employee choices can impact companies hugely; it depends totally on the situation. For instance, A startup cannot rely on its whole duties and trust a new mother or a pregnant lady who might already have proven her worth and her potential.
It is challenging yet necessary for startups — to work for their goals in the first few years to break even on the capital investment; as statistics say, most startups fail in the first 5 years (Howarth, 2023).
♦This image is generated with AI only for a rough view, please dm the author for data.So, unless, irrespective of gender, the workforce has together formed a social capital and sacrificed their personal space, the probability is likely that the startup will fail.
Even though there are laws favoring women, the impact for MNCs or Big Bs can be less, but in such cases, it could be more. However, the stereotypes are now being broken, and Statistics can play a huge role in recommending better solutions.
The article by (Ortiz-Ospina et al., 2019) visualizes a data map worldwide that clearly shows that according to the 2015 timed data from the data source ILOSTAT, women are overrepresented in lower-paying jobs.
To be precise, even in rich countries like Canada, France, Switzerland, and Spain, as per the labor force heat map, women have the highest occupancy than men, which leaves us to think,
♦“Are women being limited to lower-paying jobs only?”Thus, we can analyze the following using hypothesis testing by formulating assumptions that can be experimented with using sample evidence. On the preconditions that Data can be obtained, let us consider the one specific way-
According to the visualization, the heatmap claims that New Zealand 2015 shows that about 56.3 % of the low-pay earners represent women. Let us say our parameter of interest is “p,” which denotes the proportion of women in low pay earners in 2015 in New Zealand.
♦Image created using AI Agent by Hiranmayee PanchangamSince I want to determine if p = 56.3 %, it is a two-tailed test.
H0: p >= 0.56
HA: p < 0.56
If, after sample evidence and experimentation, we reject the null hypothesis and the Null Hypothesis is actually false, then it implies that the significant strength of low-pay earners in New Zealand in 2015 is not women.
In the case that we make a Type-1 error, let us denote the probability with alpha 𝜶 at some chosen significance level. We can conduct a chi-square test, Log-linear models, etc., to test it. We can do the same with all other countries too, to prove that significant differences exist.
♦As I scrolled up the ILOSTAT site for the data, I could not find the exact data that was used for the visualization. I recommend carrying this out with more data, authentic resources that have the same metadata, like income and gender for further analysis.
But since, in real world scenario, the data available for analysis is limited, the research ends here, but leaves us with obvious implications, factors and a problem statement we gotta deal with.
Moreover, to get a take on the null hypothesis, we can use regression analysis and correlation as the two parameters to compare are income and gender (Schaeffer, 2022). Further, more statistical methods can be applied when we know the distribution of the data, which narrows down the methods for us further.
CitationsSchaeffer, J. (2022, March 15). Statistics 101 for pay equity. Equity Methods. www.equitymethods.com/articles/statistics-101-for-pay-equity/
Ortiz-Ospina, E., Hasell, J., & Roser, M. (2024, March 18). Economic inequality by gender. Our World in Data. ourworldindata.org/economic-inequality-by-gender#representation-of-women-in-low-paying-jobs
The leading source of labour statistics. ILOSTAT. (n.d.). ilostat.ilo.org/ Howarth, J. (2023, November 3). Startup failure rate statistics (2024). Exploding Topics. explodingtopics.com/blog/startup-failure-stats
♦Are Women Only Present in Low-Paying Jobs? was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.