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Research Factors
Research Factors
The reasons why we chose these factors for our data analysis:
The reasons why we chose these factors for our data analysis:
Marital Status: In history, females undertake the responsibility of nourishing children. Examining the marital status of women can provide insights into how family structure impacts employment. Married women might face different challenges or opportunities in the job market compared to single, divorced, or widowed women. Understanding these dynamics can help us determine if marital status significantly influences employment rates among women.
Educational Attainment: Education is a crucial determinant of employment opportunities. Higher education levels often lead to better job prospects and higher wages. By analyzing the educational attainment of women, we can assess how educational disparities contribute to unemployment rates and identify potential areas for policy intervention to improve employment outcomes.
Number of Children: Because people have very limited concentration, the presence and number of children in a household can significantly affect a woman’s ability to work. Childcare responsibilities often impact women‘s employment decisions, influencing their availability and flexibility for work. Investigating this factor can help us understand the extent to which family responsibilities affect women’s participation in the labor force.
Race and Ethnicity: Racial and ethnic background can play a critical role in employment opportunities and outcomes. Systemic biases and discrimination may lead to varying unemployment rates among different racial and ethnic groups. Analyzing this factor allows us to explore how race and ethnicity intersect with other variables to influence women’s employment experiences.
GDP per Capita: Individuals often have a strong correlation with his or her period. Economic conditions, as reflected by GDP per capita, can provide context for employment trends. Regions with higher GDP per capita may offer more job opportunities and better wages, influencing women’s employment rates. By examining this factor, we can understand how economic prosperity or lack thereof impacts women’s employment prospects across different regions.
About Our Dataset
About Our Dataset
All the tables we used were sourced from online information, particularly from the U.S. Department of Labor’s Women’s Bureau. We reshaped data from various tables and extracted relevant data through coding techniques to perform our visualizations and analysis.
Marital Status
Marital Status
The heatmap offers a visual representation of the labor force participation rates among men and women based on their marital status. The heatmap is structured with marital status categories on the vertical axis and gender on the horizontal axis. The color intensity represents the labor force participation rate, with annotations to indicate the exact values.
Married men with a spouse present have an employment rate of 94.1%, while the employment rate of married women stands at 69.8%. Men with other marital statuses have a participation rate of 89.0%, which is still significantly higher than that of women with other marital statuses, 75.2%.
Educational Attainment
Educational Attainment
This radar chart provides an analysis of the labor force participation rates of women based on their educational attainment and ethnicity. The chart is structured with different educational levels along the perimeter, and the participation rates for each racial/ethnic group are represented by different colored lines.
This radar chart provides an analysis of the labor force participation rates of women based on their educational attainment and ethnicity. The chart is structured with different educational levels along the perimeter, and the participation rates for each ethnic group are represented by different colored lines. The chart reveals that women with higher educational attainment generally exhibit higher labor force participation rates across all racial/ethnic groups.
Number of Children
Number of Children
This table presents the labor force participation rate, with the y-axis representing the percentage of the population that is working or actively seeking work. On the x-axis, gender is categorized into several groups, including married women with spouses, and other living statuses for both men and women.
Women with children under the age of three have the lowest participation rates. This is likely due to the extensive care young children require, which restricts these mothers' ability to work.
Race and Ethnicity
Race and Ethnicity
The bar graph illustrates the share of the female labor force by age and race/ethnicity, excluding total values. Women aged 25 to 54 years have the highest labor force participation rates, with Black women leading at 70.6%, followed by Asian (67.7%) and Hispanic women (67.2%). This age group represents the prime working years, reflecting high engagement in the labor market. In contrast, women aged 65 years and older have the lowest participation rates, with White women at 6.6%, Black women at 5.0%, Asian women at 5.6%, and Hispanic women at 3.2%, indicating typical retirement trends.
GDP
GDP
GDP, to some extent, reflects the sum of consumption (C), government spending (G), investment (I), and net exports (S minus E). While there is an association between GDP and employment rates, the strength of this correlation requires further verification. For instance, higher wages can indeed lead to increased consumer spending, illustrating an association. However, an increase in supply, possibly due to enhanced production efficiencies such as those brought by technologies like ChatGPT or other AI-driven robotics, can also boost GDP. This increase in productivity may contribute to GDP growth without necessarily being the strongest determinant of employment rates.
This chart traces past GDP trends, showing that in 2020, GDP decreased from approximately $61.6K to $59.5K. This decline was likely due to the COVID-19 pandemic. However, the impact of the pandemic on GDP wasn’t necessarily linked to factors such as the growth rate of women or issues related to their children. The downturn was influenced by external factors, and during this period, many people faced unemployment, which was not necessarily tied to gender or directly correlated with GDP fluctuations. At the same time, analyzing these factors alongside GDP can reveal opportunities, such as gaining insights into the overall environmental conditions. Therefore, focusing on these analyses remains an important approach.
Limitations
Limitations
The primary limitation of our entire dataset is that it provides only a very general overview. If we need to pinpoint specific reasons, it’s essential to examine the detailed performance of female unemployment across different states. This detailed analysis is necessary because various local factors can influence the data, such as climate, environmental conditions, and differing economic levels among the states. Additionally, factors such as societal obligations towards women and religious practices also vary from state to state. These elements must be considered on a per-state basis, and analyzing data through per-state tables will allow for a more precise analysis.
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