Is There a Relationship between One’s Social Class and Pay?

Is There a Relationship between One’s Social Class and Pay?

Introduction

Social class has become a controversial issue in our societies. Scientists group the community into hierarchical categories prevalently: upper, middle, and upper classes. A person’s social class has been noted to have far reaching consequences. In United States, social classes is the primary determinant of an individual’s family life, education and health (Sam et al. 2020).

Social class is closely tied to the socioeconomic status. A person’s social economic status is determined by the income and occupation and educational attainment. Sociologist and political theorists disagree over the question of whether social stratification even exists. Economist has models which illustrate that the social classes are determined by income but sociologists put forward that social immobility is greatly influenced by the occupation.

This article seeks to unravel the relationship that lies between social class and pay through reviewing the prior knowledge on the subject and carry out a detailed a quantitative analysis of the impact various sub-variables of social mobility on earnings.

Prior knowledge

In America and Britain social stratification is well evident. Social economic status is clearly having adverse effects various sectors. Individuals with low SES experience a lot of health problems due to the economic position as opposed to the high social classes’ individuals. Many other issues that individual are been caused or spearheaded by social economic status.  The implications poked many scholars to try and reveal other consequences of social hierarchy.

Laurison carried out analysis to understand the impact of parental social classes on the pay of their children in traditional professions and technical occupations such as Engineering. The study gathered data through questionaries’ and sourced from secondary sources through sampling from the government websites (Laurison et al. 670). Regression analysis on the various sub-variables determining social mobility and the results were amazing.

The scholar discovered that pay varies between the individuals who come from privileged backgrounds and those struggling and manage to join high-status professions. Huge pay gap still exists since the individuals from low social classes earn 17% less than those who come from privilege backgrounds (Laurison et al. 670). The percentage is about 11000 dollars less yearly pay.  The conclusion was that there is reasonable social mobility but the individual from low social class face a huge social class ceiling.

Fried man and Macmillan also carried out a study to understand the class pay gap that exist in the professional occupations. They gathered a sample of 64,566 data from UK Labor Force Survey (LFS) on the social mobility. Regression analysis was used to understand the intergenerational transition as and basing on the changes on the earnings. They found out that those from working backgrounds were given little chances in the professional occupations compared to those from privileged backgrounds (Lindsey).

Lindsey discovered a huge class pay gap exists in the finance, medicine and IT; those from working-class backgrounds earn on average £6,800 less than colleagues from professional and managerial backgrounds. Getting rid of the productivity factors those from working-class backgrounds are still paid £2,242 less than more privileged colleagues (Lindsey et al. 2017).

Another paper draws empirical data from 2014 Labor Force Survey which reveal the first large-scale and class composition of Britain’s creative workforce. The analysis showed that there is considerable under-representation of those from working classes. Also, if the individuals join the creative industries they face a “class origin pay gap” compared to those from privileged backgrounds. Further analyses the under-representation which is linked to the discrimination based on gender and ethnicity (Friedman et al. 993).

Data and methods

How was data collected

This analysis retrieved data study from United Kingdom household longitudinal study carried out to  understand the social and economic changes in social and economic conditions in Britain at both household and individual levels since 2009. The study includes a sample size of 28,000 households across the United Kingdom however; the sample was narrowed down to 6,662 respondents. The data set was obtained through annual interviews and questionnaires between 2016 and 2018.  The data on social mobility was is build up through the life course approach. According to Laurison, social mobility is noted by examining the individual who at 14 had a primary earner parent, and the research uses respondents’ parents’ specific occupations to identify those who are in the same occupational group as their primary income-earning parent.

How was the data analyzed?

Data concerning earnings based on social mobility was sampled out from the study. The earnings were broken down to the previous and current monthly earnings. The data had been arranged in such a way that, the group who answered the social movement was further guided to give the data concerning the earning changes which proved very useful.  Further, the research took the natural log of weekly gross earnings, which will be useful in the analysis of the variable in the regression model.

Father and mother’s occupation was separated, arriving at pa_occ and ma_occ, respectively. The social stratification was measured through average score of occupation on both parents and the labeled class. Further, data of parental education was categorized into several classes, namely, degree, higher degree, A level, qualification, and no qualification, and the decomposed into gender, ethnicity, and origin. Finally, the research built a measure of several parents holding a degree from a university (max2).

The analysis was carried in several significant steps. Descriptive analysis of the social mobility data is carried out. Secondly, carried out binary analysis of the impact of the variable on the key dependent variables such as average monthly earnings and make a holistic comparison with the host controls. Finally, explore diagnostics through evaluating presence and correcting the multi-collinearity.

Results

Descriptive statistics of included variables

[Table 1 about here]

            Table 1 shows descriptive statistics of the variables included in the analysis.  The variables include the log of wages, monthly pay, ages, and the number of parents in university.  Monthly wages had a mean of 2185.59 and a standards deviation of 0.756 while age had a mean of 41.83 and a standard deviation of 11.75.  The number of parents in university had a mean of 0.206 and a standard deviation of 0.52. The total observations of each variable were 6,662.

Descriptive and inferential bivariate analysis

[Table 2 about here]

Table 2 shows regression of parental occupation against the monthly earnings. From the results the parental occupation explains 4.7% of the total variance. A unit change in the mean occupation of the parents decreases earnings by 172.65.when parental occupation is kept constant, the average earnings change by 2804 units.

[Table 3 about here]

Table 3 shows the regression of monthly wages against parental education.  The coefficient shows that a unit increase in parental education leads to a rise of 454.56 units in the monthly wages in the current job. The total observations were 6662.

[Table 4 about here]

Table 4 shows the regression of the monthly wages against all the variables in the model. From the categorical occupation of the parents, those in administrative showed that a unit increase in the variable lead to a decrease of 1107.2 units in the monthly wages. Further, a unit increase in skilled trades, personal services, and sales machine operators and elementary showed a reduction of monthly wages as follows; 456.4, 853.04, 1395.4, 1142.3, and 1623.4. A unit increase in the mean of the parental occupation leads to a decrease of the 51.97 units in the monthly wages in the current job.

 Further, the research found that an increase in one unit of those with higher degrees led to a decrease of 400.21 units on the monthly wages. From the class of A level, the wages decrease by 444.92 units. Also, those parents at GCSE experienced a decrease of 557.27 units and other qualifications, and no qualifications decreased the monthly wage at 607. 7 And 818.4 units, respectively.

The research also found that an increase in age increased the monthly wages by 13.52 units, while if the individual is female, the monthly wages decreased by 746.1 units. The results also showed that mixed and East Asian had positive impacts on the monthly salaries of 215.87 units and 155.41 units. Finally, from the sample, the blacks showed a negative effect of 50.85 units. The research was extended to determine the impact of the nativity on the monthly wages. If the individual was native, then the monthly wages decreased by 18.6 units. Ethnicity was noticed to influence the monthly wages—south Asian reduced wages by 234.67 units.

Diagnostics

The research tested heteroskedasticity through Breusch pagan /cook Weisberg method. Construction of hypothesis followed that the null hypothesis was constant variance versus alternative hypothesis fitted value of the current monthly wages.  The results showed all variables had a variance inflation factor of less than 2.5, and the mean VIF is 1.25.

Conclusion

Findings

Social class derived from the parental occupation, parental education showed that social mobility actually has impacted but indirectly proportional to the monthly wages. Most variables, except for age, gender, and ethnicity, had a negative relationship with the dependent variable (pay). From the prior literature, most researchers discovered positive impact of the social class on earnings; there is a significant discrepancy between the literature and the results of this research.

Strengths and Limitations

This research had significant strengths which are: the data was easy to analyze due to the clarity and simplified tabular regression results of the statistical package. Also, the data provided was consistent, precise ad highly reliable.

However, the research had several limitations ,which might have contributed to the discrepancies. First, the sample size was inadequate due to overdependence on one source, which could not provide a considerable amount of evidence.  The other limitation is that the findings only explain the social stratification that is the results mainly focus on the composition of the demographics. Finally, the size of the occupation has changed over time, especially in technical occupations, which might impact the accuracy of the results.

Implications

The negative relationship between the social mobility and pay from this research suggests that more analysis should be carried out and correlation identified, which opens the door for more studies with an increase in composition and size of the data sets. The findings do not add but create more questions regarding the prior knowledge of the social-economic status and pay gap.

References

Friedman, Sam, and Daniel Laurison. The class ceiling: Why it pays to be privileged. Policy Press, 2020.

Friedman, Sam, Daniel Laurison, and Lindsey Macmillan. “Social mobility, the class pay gap and intergenerational worklessness: New insights from the labour force survey.” (2017).

Friedman, Sam, Dave O’Brien, and Daniel Laurison. “‘Like skydiving without a parachute’: How class origin shapes occupational trajectories in British acting.” Sociology 51.5 (2017): 992-1010.

Laurison, Daniel, and Sam Friedman. “The class pay gap in higher professional and managerial occupations.” American Sociological Review 81.4 (2016): 668-695.

Tables and figures

Table1. A basic table of descriptive statistics for all included variables

Table 2: Regression of pay against parental class

Table 3:

Table 4: