This paper seeks to evaluate the effects of wage rates and sales for Wal-Mart business using regression analysis. The data would also major on the sales-to-employee ratio. The study is carried out using quantitative research methods with findings and conclusions made on the same. The results show that the corporation employs less staff to sell its products thereby reducing instead of increasing the retail employment.
Regression analysis is used to provide the relationship between the dependent variables and independent variables with a goal to obtain estimates of parameters that are unknown, the Beta parameter denoting a change in an independent variable and its effects on the values the dependent variable takes (Cameron & Trivedi, 2013). The aim of this paper is to evaluate the impact of sales and wage in Wal-Mart’s business using a regression analysis. This would require an analysis of the corporation’s retail spending from consumers.
Wal-Mart is an American multinational retail chain that was founded in 1962 and has its headquarters in Bentonville, Arkansas. The corporation runs its warehouse and discount department stores under different names in different countries. It also owns and operates the Sam’s Club chain of retail warehouses (Stojanović, Soldatović & Milićević, 2014). As of 2014, Fortune Global 500 listed Wal-Mart as the largest company by revenue in the world and the biggest private employer with 2.2 million workers of which 1.4 million are employed in the United States.
Moreover, in 2014 Wal-Mart posted revenue amounting to $485.651 billion with an operating income of $27.147 billion. Its net income for the same year was $16.363 billion and a total equity of $85.937 billion. The corporation’s total assets amounted to $203.706 billion in 2014. Wal-Mart deals in general merchandise, such as jewelry and shoes, family apparel and household products, fabrics, toys, electronics, health and beauty products, and crafts. It also operates a photo processing center, tire and lube express, and a pharmacy department.
Description of Literature
Research methods refer to the specific activities that are designed to produce data, for instance, observation, focus groups, interviews, and questionnaires. When carrying out a research, the researcher should incorporate a research methodology, which provides their understanding and attitude toward the research as well as the strategy they choose to answer the research questions (Seber & Lee, 2012). This provides the reader with a proof that the research comes from a credible source and has been collected and analyzed in an appropriate manner. This is arrived at through asking valid and fair questions, which relate directly to the information needs of the research objective.
Research methods are either quantitative or qualitative. Quantitative research methods seek to test produce generalizable results from pre-determined hypotheses by either refuting or confirming the hypotheses. Moreover, quantitative research methods are used to measure impact with regard to humanitarian indicators with conclusions being made about the key sector needs, the greatest area of impact, and the effect. In quantitative research, data collection and analysis using representative samples is used since the data is numeric thereby providing an accurate picture of the impact of the phenomena being studied. In this kind of research, larger sample sizes are used to provide the best representative picture. Quantitative research thus provides verifiable data, numerical estimates, comparable data, data which requires no analytical judgments with regard to the presentation of information as well as an opportunity for data analysis that is relatively uncomplicated.
Qualitative research, on the other hand, is an exploratory study used when the research is not sure of the research expectations, a definition of issues, unclear understanding of how and why a phenomenon affects the population under study. Therefore, qualitative data is based on empirical investigation and evidence thereby assisting in exploring information from both individual and group perspectives thereby generating case studies and summaries instead of numeric data (Seber & Lee, 2012).
Qualitative research relies on data gathered through textual observations with a portrayal of intentions, perceptions, and attitudes. Since, qualitative research aims at exploring relationships and perceptions held by the study group, smaller sample sizes are used to produce a less complex, time-saving and an analysis that is not multi-layered. Moreover, the population’s characteristics must be fully known with random sampling being used to provide such characteristics. Therefore, qualitative studies produce detailed and rich information about a population with data collection being carried with limited resources requiring a limited number of respondents.
Quantitative research methods would be used for this research as they would provide a broad comprehension of the situation and thus provide socio-demographic characteristics of the population under study. They would also provide a basis for comparison for correlations and relations between the sales and wage relations through precise and accurate data (Montgomery, Peck & Vining, 2012).
The objective of the research would be to seek a precise measurement that quantifies and confirms the hypothesis through objectively reliable and verifiable data. Data would be counted and measured with regard to amount, measurement in categorical and numerical values. The appropriate methods used are sampling surveys and quick counting estimates.
Studies have been carried out by Dube and Wertheim (2005) on the retail employment and losses in payrolls in places Wal-Mart operates its business in relation to the places where it does not operate. This study used spatial regression and time series analysis with variables that sought to control the impact of the selection effect and possible endogenous site. They found that the company reduced its county-level retail earnings by 1.3% as wage bill reduced. This shows that total wage bill and job losses go down as a result of the company’s efficiency gains since the company sales specific retail commodities and thus the evaluation of its price effects and wage should not rely on simple comparisons of price and wage reductions (Stojanović et al., 2014).
Research Methodology and Analysis
In order to study the regression analysis of Wal-Mart with regards to sales and wage rate, baseline information was gathered to measure the changes in activity for the two years to 2014. A comparison of the company’s activity through trends, product lines’ performance was made from the company’s financial statements (Yuan, Ekici, Lu & Monteiro, 2007). A variety of data was used to make the comparisons with survey data obtained from the neighborhoods Wal-Mart runs its business through telephone calls to consumers and staff in 20 different localities. The data provided the baseline data on the number of hours worked, the wages paid and the sales revenue produced by such activity. The survey took place between the months of August and October 2015 and was done in three phases to provide the best outcome with regard to feedback and to maintain an accuracy of the data.
The basic linear regression for sales is denoted by y with the x variable being the number of years, which is shown by y = b (constant) + ax (Cameron & Trivedi, 2013). The regression analysis of the years 2013 and 2014 would thus be used to predict the sales and employment data for 2015. The A value would be the same although the B intercept would vary. The A value would amount to 16868.2 and thus the regression formula would be represented by 16868.2x + 379455.8 = y thereby presenting a sales forecast of $480 million for the company.
The researcher managed to contact 120 respondents with 84 of them sending responses as the rest were uncooperative. The employees were paid an average hourly wage of $10.47, which was way above the 2013 wage rate by $1.03. In the two years, the number of employees increased by 2% from 2013 to 2014. From the financial statements of the sampled area, the sales revenue amounted to $15 million. The sales tax data for the two years was used to prevent the problem of confounding trends with economic downturns and thus provide accurate information on the regression. The sales showed a 7% growth on an annual basis with $3 million on taxes. The sales figures show that the company is improving significantly.
The results of the regression analysis show a measure of the percentage of sales in the area of survey, which is reminiscent of the company’s business total sales. The dependent variable for the regression is denoted by the natural log of taxable sales shown in millions of dollars. This would be represented by sales taxes/1,000,000 * 100. The independent variables would be the dummy variables for the period of two years from 2013 to 2014 and the area of study specific intercept as well as a variable that signifies the year under study. Moreover, the coefficients of the two years show that sales were highest in 2014. The coefficient of the first year, on the other hand, shows that the sales grew at an average rate of 7%, which shows a 92 percent variation in sales across the area under study.
This shows an interaction of variables between the area under study and the years, which means that each area is allowed to have own growth rate over time. The coefficient of 7% for the year is interpretable as the annual growth rate for the area under study and thus represents a break in trend for the area under the study. The dummy shows a break in trend for the growth in sales in the area under study of 8.9 percent. The negative coefficient of the area under study’s break in trend was not significantly different from 0 although the magnitude with regard to trend growth rate was larger.
Summary and Conclusions
The study provides a proof that the company has replaced employment and sales from its own in the areas under study. It also shows that the company’s business is stable and has no chances of going bust any time in the future. It shows that the strong long-term effects of the Wal-Mart business. However, data shows that there was a substantial turnover from employees but that may be attributable to opening up of new stores in the area which would require experienced staff to run the businesses (Yuan et al., 2007). As such, there are no geographical data to back the estimates in the number of stores opening up on a daily or monthly basis. Some results showed inconsistent and small effects for the reduction in sales that was experienced in some months.
The retail employment levels in the areas under study rose modestly particularly due to the company’s own employees although there were negative effects of retail employment trends. This was in consistent with earlier studies that in the United States, Wal-Mart workers replace an approximately 1.4 workers who are not employed by Wal-Mart. From the results, the distributions were similar with an estimate of determinants showing the changes in sales volume and employment rates.