Effect Of Family Income On Student Educational Level

Introduction

    Not only has economic growth resulted from reform and opening up, but it has also resulted in rising income inequality and severe class solidification. Children from low-income homes find it difficult to advance socially on their own. According to Li Yu (2006), an individual’s educational qualifications are a crucial road to changing his or her destiny and rising to the upper class. A higher education obtained through a recruiting exam is extremely beneficial to one’s development. Because of the scarcity of educational resources, not everyone can receive a good education. So, what factors have influenced an individual’s exam performance? Parents’ familial backgrounds, including as income, education, and social position, are major variables in their children’s academic success, according to Li Chunling (2003), Guo Congbin (2007), and others. Some people believe that the family one is born into matters more than it used to.

     In the late 1990s, Liang Chen and Li Zhongqing (2012) found an increase in the number of students at Peking University who were members of a functionary family. In 1997, the proportion had risen to 39.76 percent, surpassing that of professionals’ children and well surpassing that of workers’ and farmers’ children. According to Yang Dongping (2006), educational disparities are expanding, with urban individuals having an advantage in receiving education over rural populations, and so the proportion of people with bachelor’s degrees in cities is higher than in rural areas. According to Cai Hongbin (2011), educational dualism works against poor groups longing for a better life, lowering social mobility and potentially widening inequality in the long run. Before an acceptable solution is found, the entire country may fall into the middle-income trap. As a result, conversations concerning the elements that influence academic achievement may be a viable option.

  The goal of this research is to look at difference in student educational level based on family household income, in order to present empirical data in support of income reform in the foreseeable future, distribution and education. The dependent variable for the purpose of this study is family household income while the independent variable is educational levels.

Relevant theory emphasizes the importance of a family’s social and economic standing in a child’s development. According to the family investment theory, children would process more development resources (e.g., financial assistance and more family time) when their parents have a higher social and economic position, hence encouraging students to participate more actively in their academic development. Children from low-income households, on the other hand, were deprived of such resources, resulting in a bumpy life trip or even impeding their own growth.

Two methodologies from the current literatures are used to detect the model’s possible inherent flaws from an empirical standpoint. The first employs data from adopted children to successfully regulate the relationship between family income, parental education, and children’s unobservable abilities. Plug (2005), for example, used this strategy to control forceful mating and found a positive association. Parents, on the other hand, can educate different types of children in different ways and give adopted children a better living environment and more attention. Loken (2007) used Norwegian oil prices from 1970 to 1980 as figures in a natural experiment to establish dummy variables (whether or not they were affected by the prices) as an instrumental variable, and it came out that family income had no discernible impact on children’s educational levels. The reason for this is likely due to a thriving lending market in Norway, which helped to alleviate the funding crisis. Shea (2000) considered the father’s status in the labor union and industry, as well as whether or not he has a job, to be instrumental variables in his income. Evidence suggests that the effect of parental income on children’s human capital can be overlooked. Dahl and Lochner (2005) employed the US federal government’s anti-poverty strategy for low-income families as an instrumental variable of household income, and the empirical results revealed that an increase in low-income families’ income is important for their children’s school performance.

ANOVA STATISTICAL MODEL

 The statistical approach of analysis of variance, or ANOVA, divides observed variance data into multiple components for use in additional tests. For three or more groups of data, a one-way ANOVA is used to learn more about the relationship between the dependent and independent variables. The One-Way ANOVA is frequently used to test the following variables:

  • Statistical disparities between two or more groups’ means.
  • Differences in the means of two or more interventions based on statistics.
  • Differences in statistical significance between the means of two or more change scores.

The ANOVA test is preferred above other tests because it employs variances for sample means, resulting in a single value for more than two treatments, whereas t tests result in a large number of mean differences when there are more than two treatments. ANOVA also reduces the type-I error in experiments. ANOVA, or its non-parametric analogues, can be used to assess if differences in mean values between three or more groups are due to chance or are significant. When assessing multi-item scales, such as those used in market research, ANOVA is very beneficial. Major assumptions for one way anova are that the data must be normally distributed. The ANOVA also assumes homogeneity of variance, which means that the variance across the groups should be roughly equal. In addition, ANOVA assumes that the observations are unrelated to one another. If the assumption of normality is violated, or if outliers are present, the one-way ANOVA may not be the most powerful test available, and this could be the difference between identifying a true difference between population averages and not.

Data Analysis

Descriptive statistics Table 1: Household Income by Education level

Education Level StatisticsStd.Error
Less than high school graduateMean34723.087340.004
 Median37000.00 
 Variance700383589.7 
 S.D26464.761 
 Minimum6000 
 Maximum75000 
High School graduateMean44333.336049.888
 Median35000.00 
 Variance988230769.2 
 S.D31436.138 
 Minimum5000 
 Maximum130000 
Some technical, trade or business schoolMean64538.466103.777
 Median57000.00 
 Variance968658461.5 
 S.D31123.278 
 Minimum20,000 
 Maximum150,000 
    
 Mean154133.3322927.186
Some graduate or professional school(MA,MS,PhD)Median120000 
 Variance7884838095 
 S.D88796.611 
 Minimum50000 
 Maximum300,000 
Four year college/University degreeMean76500.005746.236
 Median80000.00 
 Variance858500000.00 
 S.D29300.171 
 Minimum20,000 
 Maximum130,000 

Table 1 above shows the descriptive statistics of student income by their education level including their means, variance, standard deviation, minimum and maximum values. Less than high school graduate has an average income of $34723.08, a minimum value of $6000 and a maximum value of $75,000. Similarly, high school graduate has an average income value of $44333.33 a minimum value of $5000 and a maximum value of $130,000. Lastly, all other educational level group statistics were provided and reported in the table above.

Table 2: Normality test

Tests of Normality
 Education levelKolmogorov-SmirnovaShapiro-Wilk
 StatisticdfSig.StatisticdfSig.
Household income in 2000Less than high school graduate.24513.032.86713.047
High school graduate.14727.137.91427.029
Some technical, trade, or business school, some college, or.13526.200*.92126.049
Four year college/university degree (BA, BS).13526.200*.96326.449
Some graduate or professional school (MA, MS Ph.D.).19315.137.87015.033

A major assumption of the one way Anova the statistical model implored for this study is the normality test assumption about all independent group variables. From the kolmogorov-smirnov table above, the p-values of the educational levels income is greater than 0.01 level of significance, this mean normality test is not violated and hence the one-way anova result can be reported.

Table 3: One-Way Anova Result

ANOVA
 
 Sum of SquaresdfMean SquareFSig.
Between Groups141838865041.936435459716260.48419.020.000
Within Groups190165297948.7181021864365666.164  
Total332004162990.654106   

A one-way between subjects ANOVA was conducted to compare if average household income differs across education levels. There was a significant difference in the average household income across education levels at the p<.01 level for the five educational levels [F(4, 135) = 19.02, p = 0.000].

Table 4: LSD Post hoc test of multiple Comparisons

Multiple Comparisons
Dependent Variable: Household income in 2000  LSD
(I) Education level(J) Education levelMean Difference (I-J)Std. ErrorSig.95% Confidence Interval
Lower BoundUpper Bound
Less than high school graduateHigh school graduate-9610.25614576.122.511-38521.9219301.41
Some technical, trade, or business school, some college, or-29815.385*14666.940.045-58907.19-723.58
Four year college/university degree (BA, BS)-41776.923*14666.940.005-70868.73-12685.12
Some graduate or professional school (MA, MS Ph.D.)-119410.256*16361.656.000-151863.52-86956.99
High school graduateLess than high school graduate9610.25614576.122.511-19301.4138521.92
Some technical, trade, or business school, some college, or-20205.12811864.104.092-43737.523327.26
Four year college/university degree (BA, BS)-32166.667*11864.104.008-55699.06-8634.27
Some graduate or professional school (MA, MS Ph.D.)-109800.000*13904.734.000-137379.97-82220.03
Some technical, trade, or business school, some college, orLess than high school graduate29815.385*14666.940.045723.5858907.19
High school graduate20205.12811864.104.092-3327.2643737.52
Four year college/university degree (BA, BS)-11961.53811975.506.320-35714.9011791.82
Some graduate or professional school (MA, MS Ph.D.)-89594.872*13999.908.000-117363.62-61826.12
Four year college/university degree (BA, BS)Less than high school graduate41776.923*14666.940.00512685.1270868.73
High school graduate32166.667*11864.104.0088634.2755699.06
Some technical, trade, or business school, some college, or11961.53811975.506.320-11791.8235714.90
Some graduate or professional school (MA, MS Ph.D.)-77633.333*13999.908.000-105402.08-49864.58
Some graduate or professional school (MA, MS Ph.D.)Less than high school graduate119410.256*16361.656.00086956.99151863.52
High school graduate109800.000*13904.734.00082220.03137379.97
Some technical, trade, or business school, some college, or89594.872*13999.908.00061826.12117363.62
Four year college/university degree (BA, BS)77633.333*13999.908.00049864.58105402.08

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