#### Wiley Swain

Project Management

It is often said the apple doesn’t fall very far from the tree. It goes without saying that the influence of family, parents in particular, has a tremendous effect on an individual. However, modern culture has seen the family unit transformed over the 20^{th} century. *So, how much influence do a person’s parents have over his or her development and has that changed over time?* The hypothesize being that even given the fractured nature of today’s families, family is still paramount.

To test this hypothesis, data from the General Social Surveys was used to do a time-series study of individuals to see the effects a parent has on his or her child and also to see if the effect has waned in the latter quarter of the 20^{th} century. The individual’s income was used as dependent variable. Although this is by no means the best indicator it provides the most consistent basis for analysis. Using the same line of reasoning, the parents’ education level was chosen as a means of measuring influence. The logic being that the more education the parents have the more resources available to them to influence their children. The other independent variables will be age and education. Also, dummy variables were used for male and female to try and adjust for the inherit income difference between the sexes. Along with studying the GSS data, other research studies were examined to gain a more holistic understanding of the various influences on child development.

The importance of acquiring the necessary skill sets to compete in a global marketplace is growing day by day. Therefore, it becomes paramount to study and understand the factors that influence the development of a person’s skills and abilities. The focus was put on a child development, in particular the influence of education, because it is during these formative years that a person develops the vast majority of the skills he or she will have to use to compete in society.

##### Literature Review

Several studies have noted the education of the parents to be critical in the educational success of the child. In fact, a parent’s employment and income status has been show to be less of an influence than their educational level. Seventy-three percent of children with a parent with full-time, year-round employment that does not have a high school diploma live in low-income households while only 17% of children whose parents have some college education live in low-income households (Douglass-Hall & Chau, 2007). Statistics suggest that parental education is also growing in importance on the economic attainments of children. Over the past two decades the number of children in low-income households has increased by almost 14% if the parents do not have a high school diploma; even if the parents have full-time, year-round employment.

Of the parents, the mother has been noted by several studies to be the dominate factor on childhood educational attainment. The higher the mother’s educational level, the more was expected of the children (Davis-Kean, 2005). Some information also suggests that the importance of the maternal educational level is even greater than the income level of the family. The role of parental education plays in childhood development appears to be most important in creating a nurturing, supportive environment for the child.

Besides parental education, variables such as child health, childcare arrangements, home environment, and food security are identified as key to child development. Another factor that has to be accounted for is race because the cultural and social perceptions about education among different races can greatly affect the educational growth of a child. The warmth and stability provided to a child can often mitigate the disadvantages of being in a low-income household (Davis-Kean 2005). If children are provided a stable environment the negative effects of financial constraints can be reduced especially as the child ages.

Although the research reviewed on the subject matter has gone to great pains to quantify the pertinent variables, the data is often based on subjective analysis. For instance, the aforementioned factors such as food security and home environment are often quantified through surveys that rely on the subject to somehow assign a numeric value to a feeling or emotion. This could lead to inconsistent results especially when one considers that cultural norms can greatly influence a person’s perception of a factor. Added to all these potential problems is the fact that child development is a time-consuming process with a variety of direct and indirect factors to consider. Given all of these considerations, this data analysis attempts to review the end results of the parent-child relationship. First, we start with the income level of the adult and then identify the educational level of the parents to try and establish some form of direct correlation between the parents’ educational level and the socio-economic status of the adult offspring.

##### Data and Methods

Starting with 1977, data from every three years was taken (or the next available year) to produce the results of the study. The year 1977 was chosen as the initial data point because this was the first year income was adjusted for inflation allowing for a more consistent comparison of results. Respondent income is used as the dependent variable because income among respondents provides a more level field of comparison of socioeconomic attainment by an individual. The educational levels of both parents as well as the respondent are used as independent variables so as to analyze the effects of each on respondent income. Age of respondent is also included because the age and experiences of an individual greatly influences his or her income earning potential. Finally, a dummy variable was included for gender to adjust for the income difference between similarly situated males and females.

##### Equation

*EQUATION:*Income= B_{0} + B_{1}Education (M) + B_{2}Education (F) + B_{3}Education(R) + B_{4}Age + B_{5}Male + *e*

*Empirical results: *

The above chart plots the p-values for the years sampled. For the purposes of this study a p-value at or below .05 is considered statistically significant. The results of this study should be considered preliminary at best, but they tend to support the conclusions draw by other studies. The mother’s educational level appears to be more statistically significant than the father’s educational level. Also, by performing a linear regression on the data, the importance of both parents educational attainment seems to increase over the last thirty years.

##### Conclusion

##### Bibliography

Kirsch, I., Braun, H., & Yamamota, K. (2007). “America’s Perfect Storm: Three Forces Changing Our Nation’s Future.” Princeton, NJ: Educational Testing Service Policy Information Center, 1-27.

Cherubini, L. & Hodson, J. (August 2, 2008). “Ontario Ministry of Education Policy and Aboriginal Learners’ Epistemologies: A Fundamental Disconnect.” Canadian Journal of Educational Administration and Policy.

Juslin, R. W. & Brembreg, S. (2005). “Greater parental influence enhances educational achievement: A Systematic Review.” Swedish National Institute of Public Health, 03.

Davis-Kean, P. (2005). “The Influence of Parent Education and Family Income on Child Achievement: The Indirect Role of Parental Expectations and the Home Environment.” Journal of Family Psychology, 19(2), 294-304.

Douglas-Hall, A. & Chau, M. (November, 2007). “Parents’ Low Education Leads to Low Income, Despite Full-Time Employment.” National Center for Children in Poverty.

Yao, X., Hongbin, L., Zhang, J., & Zhou, L. (October 2005). “Parental childcare and children’s educational attainment: evidence from China.” Applied Econonmics, 37.18, 2067 (10).

Sawhill, I. & Morton, J. (2007). “Economic Mobility: Is the American Dream Alive and Well?” The Pew Charitable Trusts.

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 1906.054 | 5 | 381.211 | 49.209 | .000^{a} |

Residual | 5105.083 | 659 | 7.747 | |||

Total | 7011.137 | 664 | ||||

a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER | ||||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 1854.880 | 5 | 370.976 | 43.272 | .000^{a} |

Residual | 5641.048 | 658 | 8.573 | |||

Total | 7495.928 | 663 | ||||

a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER | ||||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 2019.985 | 5 | 403.997 | 44.680 | .000^{a} |

Residual | 6989.540 | 773 | 9.042 | |||

Total | 9009.525 | 778 | ||||

a. Predictors: (Constant), if r=1 then male, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER, HIGHEST YEAR SCHOOL COMPLETED, MOTHER | ||||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 1723.224 | 5 | 344.645 | 36.091 | .000^{a} |

Residual | 7066.579 | 740 | 9.549 | |||

Total | 8789.803 | 745 | ||||

a. Predictors: (Constant), if r=1 then male, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER | ||||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 1801.650 | 5 | 360.330 | 38.573 | .000^{a} |

Residual | 8201.802 | 878 | 9.341 | |||

Total | 10003.452 | 883 | ||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 1349.928 | 5 | 269.986 | 34.674 | .000^{a} |

Residual | 6182.467 | 794 | 7.786 | |||

Total | 7532.395 | 799 | ||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 1349.928 | 5 | 269.986 | 34.674 | .000^{a} |

Residual | 6182.467 | 794 | 7.786 | |||

Total | 7532.395 | 799 | ||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 698.654 | 5 | 139.731 | 16.401 | .000^{a} |

Residual | 11475.986 | 1347 | 8.520 | |||

Total | 12174.640 | 1352 | ||||

a. Predictors: (Constant), if male r=1, HIGHEST YEAR SCHOOL COMPLETED, MOTHER, AGE OF RESPONDENT, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, FATHER | ||||||

b. Dependent Variable: RESPONDENTS INCOME |

ANOVA^{b} |
||||||

Model | Sum of Squares | df | Mean Square | F | Sig. | |

1 | Regression | 691.176 | 4 | 172.794 | 24.791 | .000^{a} |

Residual | 9207.331 | 1321 | 6.970 | |||

Total | 9898.507 | 1325 | ||||

a. Predictors: (Constant), =1 if r is male, HIGHEST YEAR SCHOOL COMPLETED, FATHER, HIGHEST YEAR OF SCHOOL COMPLETED, HIGHEST YEAR SCHOOL COMPLETED, MOTHER | ||||||

b. Dependent Variable: RESPONDENTS INCOME |

Year |
Father’s Ed p-value |
Mother’s Ed p-value |

1977 | 0.549 | 0.338 |

1980 | 0.506 | 0.672 |

1982 | 0.862 | 0.898 |

1985 | 0.278 | 0.086 |

1987 | 0.919 | 0.166 |

1993 | 0.499 | 0.092 |

1996 | 0.449 | 0.233 |

2002 | 0.1 | 0.26 |

2004 | 0.533 | 0.538 |

Variable Name |
Description |

Education(M) | mother’s highest educational level |

Education(F) | father’s highest educational level |

Education ( R ) | respondent’s highest educational level |

Male | = 1 if R is male, 0 otherwise |

Age | respondent’s age |