College Education Matters for Happier Marriages and Higher Salaries ----Evidence from State Level Data in the US

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1 College Education Matters for Happier Marriages and Higher Salaries ----Evidence from State Level Data in the US Anonymous Authors: SH, AL, YM Contact TF: Kevin Rader Abstract It is a general consensus in society that education provides individuals with the necessary opportunities to rise through society. Using simple and multiple linear regressions, this paper shows that there is a significant negative correlation between the divorce rate and percentage of population with bachelors degree in state within the United States (P<0.001), but no significant correlation between the divorce rate and the percentage of population with high school education (P>0.05). Meanwhile, there is a significant positive correlation between the proportion of population with bachelor s degree in a state and the state s average salary (P<0.001); however, the correlation between the average salary and the proportion of population with a high school diploma is almost insignificant (P 0.05). Comparing results based on statistics from 2006, 2000, and 1990, the paper also finds that the relationship between college education and average salary has strengthened over time. The paper therefore concludes that college education has important implications for a society s overall level of family happiness and fiscal well-being. I. Introduction The impact of college education, because of its relevance to everyone s personal development, has long been a discussion drawing the attention of all members of society. Scholars started studying college education from a very early time. C. Robert Pace (1941, 1979), James Trent and Leland Medsker (1968), and Kenneth Feldman and Theodore Newcomb (1969) are among the earliest scholars to systematically analyze college outcomes. Through interviews and subject tracking, their studies used individual level data to explore basic distinctions between those who attended college and those who did not. In the end, they all found that college did make a difference. This paper recognizes that the impact of college education may well be multi-dimensional. Consequently, this paper chooses to look into the aggregate-level effects of college education on two particular social characteristics: divorce rate and average salary. It tries to determine whether one s college experience would improve the quality and long-term sustainability of people s marriage prospects, for marriage has a significant influence on people s daily lives and social well-being. It also tries to determine whether going to college will help people fiscally in the long run, since income is the foundation of everyone s quality of life. Using state level data, this paper investigates how the proportion of population with college education in a state is associated with its divorce rate and average salary. The paper compares the effect of college education with that of high school education. It also uses data from three 1

2 different years (2006, 2000 and 1990) to see if the impact of college education has changed over time. II. Methods The data used in the analysis are retrieved from two sources, the U.S. Census website and Gallup.com, a website that offers research in economics, sociology and psychology. The U.S. Census website provides data on salaries, divorce rates, unemployment rate, poverty rate, and educational attainment in 1990, 2000, and Gallup.com provides data on the church attendance rate in Table 1: Description of Data Variable Name Description Mean (Standard deviation) Source Divorcerate06 Rate of divorce per 1,000 population in 2006 (by state) 3.89 (0.99) US Census Salary06 Annual personal income per capita in constant dollars in 2006 (by state) (5417) US Census Salary00 Annual personal income per capita in constant dollars in 2000 (by state) (4662) US Census Salary90 Annual personal income per capita in constant dollars in 1990 (by state) (3833) US Census Bachelor06 % of people with bachelor s degree or above in 2006 (by state) 26.7 (5.41) US Census Bachelor00 % of people with bachelor s degree or above in 2000 (by state) 24.1 (4.80) US Census Bachelor90 % of people with bachelor s degree or above in 1990 (by state) 20.0 (4.20) US Census HighSchool06 % of people with high school diploma or above in 2006 (by state) (3.60) US Census HighSchool00 % of people with high school diploma or above in 2000 (by state) 81.8 (4.35) US Census HighSchool90 % of people with high school diploma or above in 1990 (by state) 76.1 (5.61) US Census Church06 % of people who attend church a week or almost every week (by state) 42.1 (9.25) Gallup.com Unem05 Unemployment rate in 2005 (by state) 4.93 (1.07) US Census Poverty06 Poverty rate in (3.19) US Census The first set of models tests the relationship between divorce rate and education level. Model is a simple linear regression of divorce rate with percentage of population with college education as the explanatory variable, while Model is a step-wise regression of divorce rate with the addition of several controlling variables. Models and perform the same regressions, only with percentage of population with high school diploma replacing percentage of population with bachelor s degree. Since data for divorce rate in California, Georgia, Idaho, Iowa, Maine and Mississippi are not available, these states are not included 2

3 in the regression. The church attendance rate for Alaska is not available, so in the regression controlling for church attendance, Alaska is also excluded. In this case, it is difficult to compare how the correlation between divorce rate and education attainment has evolved over time because the divorce rate data are only available for years after The second set of models investigates the relationship between average salary in a state and the state s percentage of population with college and high school education. Models 2.1.1, and are simple linear regression models that test the correlation between percentage of college graduates and average salary, and Models 2.2.1, and test the correlation between the percentage of high school graduates and average salary, based on data in 2006, 2000 and 1990 respectively. All 50 states are included. In all regressions, a variable is considered statistically significant if P < Education Attainment and Divorce Rate III. Results Table 2: Divorce Rate and Education Attainment Model Model Model Model Percentage of Population with College Education *** (0.022) *** (0.024) Percentage of Population with High School Education * (0.042) * (0.048) Percentage of Frequent Church-goers ** (0.015) (0.018) Constant 6.824*** (0.591) 8.984*** (1.117) 11.14** (3.614) 12.19** (4.426) Observations R-squared Adjusted R-squared ***p<0.001, **p<0.05, *P<0.1 1) College Education and Divorce Rate Based on data from 2006, a scatter plot with divorce rate on the y-axis and percentage of population with college education on the x-axis demonstrates that a linear relationship exist between the two variables Bachelor DivorceRate 3

4 Model is a simple linear regression to test the correlation between the two variables, assuming education attainment as the only factor that influences divorce rate. Model shows: E(DivorceRate06)= Bachelor06 The linear regression shows, without controlling for other variables, a 1% increase in the proportion of population with college degree lowers the divorce rate by 0.11% on average. The model is statistically significant (P<0.001); it explains around 36.5% variation of divorce rates (adjusted R-squared=0.3647) and it fairly precisely predicts divorce rate (Root MSE=0.792). Model tries to control for several other variables. It uses a backward stepwise regression with divorce rate as the dependent variable and percentage of population with college education, percentage of population with regular church attendance, poverty rate and unemployment rate in the previous year. Poverty rate and unemployment rate are dropped at the 5% significance level, and the regression result is: E(DivorceRate06)= Bachelor Church06 Controlling for church attendance rates, a 1% increase in the proportion of population with college degree lowers divorce rate by 0.14% on average. The influence of percentage of college graduates in the population is highly statistically significant (P<0.001). The model can explain around 41.8% of the variation of divorce rates across states, and the model fairly precisely predicts the divorce rate (Root MSE =0.760). 2) High School Education and Divorce Rate Based on data from 2006, a scatter plot with divorce rate on the y-axis and percentage of population with high school education on the x-axis doesn t seem to show a strong linear relationship between the two variables HighSchool DivorceRate Model is a simple linear regression similar to Model to test the correlation between the two variables: E(DivorceRate06)= HighSchool06 4

5 In this case, the model is almost insignificant (P=0.051) and this model explains only 6.6% of the variation in divorce rate. Since Model shows that church attendance is negatively correlated with divorce rate, therefore, Model also controls for frequent church attendance rate. E(DivorceRate06)= HighSchool Church06 However, in this case, neither explanatory variable is significant at the 5% level, though no problem of multicollinearity is detected (correlation of HighShcool06 and Church 06 is -0.2). This set of models suggests that college education may have important influence on the happiness of marriage. States with higher percentage of college graduates have lower divorce rates, but states with higher percentage of high school graduates don t necessarily have lower divorce rates. 2. Education Attainment and Average Salary Percentage of Population with College education Percentage of Population with High School education Table 3. Average Salary and Education Attainment Model Model Model Model Model Model *** (82.35) 805.4*** (78.51) *** (80.67) 458.1** (207.1) 346.8** (146.5) 9426*** 9301*** 8825*** Constant (2244) (1926) (1650) (17700) (12000) Observations R-squared ** (90.10) 2074 (6877) Adjusted R-squared ***p<0.001, **p<0.05, *P<0.1 1) College Education and Average Salary Model uses a simple linear regression to test the relationship between percentage of college graduates and average salary based on data from 50 US states in Models and test the same relationship in 2000 and 1990 respectively. The regression results of the three models are: E(Salary06)= Bachelor06 [P<0.001, R-squared: 0.663, Root MSE:3190] E (Salary00)= Bachelor00 [P<0.001, R-squared: 0.664, Root MSE: 2740] E(Salary90)= Bachelor90 [P<0.001, R-squared: 0.612, Root MSE :2410] An interesting result is that not only is percentage of college graduates in the population significantly positively correlated with average salary in the state (P<0.001), but the correlation has become stronger in the past two decades. This is indicated by increases in the

6 values of the coefficient and slight increases in the R-squared values. This finding confirms with the general perception that as technology advances increase the demand for high-skilled workers, higher education becomes more and more important to securing high-paying jobs. 2) High School Education and Average Salary Similar to the models in the previous subsection, Models 2.2.1, and are simple linear regression models that test the relationship between percentage of high school graduates and average salary based on data from all 50 states in 2006, 2000 and 1990 respectively. The regression results are: E(Salary06)= HighSchool06 [P=0.049, R-squared:0.077, Root MSE:5240] E (Salary00)= HighSchool00 [P=0.043, R-squared:0.081, Root MSE: 4540] E(Salary90)= HighSchool90 [P=0.006, R-squared:0.145, Root MSE:3590] Compared with regression results in the last subsection, it s clear from the R-squared values that the percentage of population with a high school education explains a much smaller portion of variation in average salary. Also, the Root MSE values indicate that models in this subsection are consistently less precise in predicting a state s average salary than their counterparts in the last subsection. An interesting result to note is that, whereas the explanatory power of the percentage of college graduates on average salary has become stronger and stronger, the explanatory power of the percentage of high school graduates has significantly decreased since 1990 (as demonstrated by the R-squared value). The results suggest that college education, compared to high school education, has become more important over time. IV. Conclusion and Discussion As this paper demonstrated, the percentage of population with college education is significantly negatively correlated with the divorce rate in a state (P<0.001), whereas the percentage of population with high school education is not (P>0.05). Controlling for church attendance rates, a 1% increase in the proportion of population with college degree lowers divorce rate by 0.14%, on average. College education is also significantly positively correlated with average salary (P<0.001). The linear regression shows that, without controlling for other variables, a 1% increase in the proportion of population with college degree increases average salary by around $800 on average, and that the correlation has strengthened from 1990 to High school graduation was correlated with average salary in 1990 (P=0.006), but the correlation became barely significant after 2000 (P=0.043). Therefore, this paper concludes that college education matters for happier families and higher salaries at the state level. One weakness of this study is that not enough control variables are included in the models. For example, for the second model regressing the state-level average salaries, only educational attainment is used as an explanatory variable, which may not be sufficient. In addition, the result that states with more college graduates have higher average salaries may be attributable to industrial differences. For instance, states where the major industry is financial services may attract more people with higher education and at the same time pay higher salaries than agricultural states. It may also be true that states with higher average 6

7 salary attract more college graduates than states with lower income. However, this reasoning cannot very well explain the increasing strength of correlation between percentage of population with college education and average salary. Another potential weakness may come from the nature of the data. People who are currently enrolled in college are not counted as people with a college education, but rather simply with a high school education. However, the proportion of people currently enrolled in college may not be big enough to have a major impact on the regression results. The obvious broader implication of this study is that institutions and governmental organizations should recognize the critical importance of college education on the general well-being of the society and try to promote college education for more American. It may also imply that students and parents should be aware of the increasing importance of college education and seriously consider college decisions. Possible future studies may probe the influence of college on other social characteristics and other types of social capital, for example, levels of discrimination, trust, innovation and etc. It may also be worthwhile to investigate what factors influence the percentage of population with college education and get a better understanding of how to effectively promote college education. V. References: "College and Its Effect on Students - Early Work on the Impact of College, Nine Generalizations, Later Studies, Pascarella and Terenzini." StateUniversity.com. Web. 2 Dec < Students.html>. Newport, Frank. "Church Attendance Lowest in New England, Highest in South." Gallup.Com. 27 Apr Web. 26 Nov StataCorp. Stata ed. College Station, 2009 United States. U.S. Census Bureau. Educational Attainment by State: 1990 to U.S. Census Bureau. Web < United States. U.S. Census Bureau. Marriages and Divorces Number and Rate by State: 1990 to U.S. Census Bureau. Web. 26 Nov < United States. U.S. Census Bureau. Personal Income Per Capita in Current and Constant (2000) Dollars by State: 2000 to Web. 26 Nov < 7

8 VII. Appendix Model Using percentage of population with bachelor s degree to explain divorce rate Bachelor DivorceRate regress divorcerate06 bachelor06 Source SS df MS Number of obs = 44 F( 1, 42) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = divorcera~06 Coef. Std. Err. t P> t [95% Conf. Interval] bachelor _cons Note: Even though D.C. (at the far left) and Nevada (at the top) appear to be an outlier in the residual plot, they are not excluded because they do not significantly change the shape of the fitted line as shown in the scatter plot. Model Using percentage of population with bachelor s degree to explain divorce rate (controlling for other variables). 8

9 sw regress divorcerate06 bachelor06 church poverty06 unem05, pr(0.05) begin with full model p = >= removing poverty06 p = >= removing unem05 Source SS df MS Number of obs = 43 F( 2, 40) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = divorcera~06 Coef. Std. Err. t P> t [95% Conf. Interval] bachelor church _cons Model Using percentage of population with high school degree to explain divorce rate HighSchool DivorceRate 9

10 regress divorcerate06 highschool06 Source SS df MS Number of obs = 44 F( 1, 42) = 4.04 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = divorcera~06 Coef. Std. Err. t P> t [95% Conf. Interval] highschool _cons Model Using percentage of population with high school degree to explain divorce rate (controlling for other variables).. regress divorcerate06 highschool06 church06 Source SS df MS Number of obs = 43 F( 2, 40) = 2.17 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = divorcera~06 Coef. Std. Err. t P> t [95% Conf. Interval] highschool church _cons

11 Model Using percentage of population with bachelor s degree to explain average salary in Bachelor Salary06. regress salary06 bachelor06 Source SS df MS Number of obs = 50 F( 1, 48) = Model Prob > F = Residual R-squared = Adj R-squared = Total e Root MSE = salary06 Coef. Std. Err. t P> t [95% Conf. Interval] bachelor _cons

12 Note: The point on the far right (D.C.) seems to be an outlier in the residual plot, however, the scatter plot shows that it fits in the fitted line very well. Therefore, it is still included in the model. Model Using percentage of population with bachelor s degree to explain average salary in Bachelor00 Salary00. regress salary00 bachelor00 Source SS df MS Number of obs = 50 F( 1, 48) = Model Prob > F = Residual R-squared = Adj R-squared = Total e Root MSE = salary00 Coef. Std. Err. t P> t [95% Conf. Interval] bachelor _cons

13 Model Using percentage of population with bachelor s degree to explain average salary in Bachelor90 Salary90. regress salary90 bachelor90 Source SS df MS Number of obs = 50 F( 1, 48) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = salary90 Coef. Std. Err. t P> t [95% Conf. Interval] bachelor _cons

14 Model Using percentage of population with high school degree to explain average salary in HighSchool Salary06. regress salary06 highschool06 Source SS df MS Number of obs = 50 F( 1, 48) = 4.89 Model Prob > F = Residual e R-squared = Adj R-squared = Total e Root MSE = salary06 Coef. Std. Err. t P> t [95% Conf. Interval] highschool _cons Model Using percentage of population with high school degree to explain average salary in

15 HS00 Salary00. regress salary00 highschool00 Source SS df MS Number of obs = 50 F( 1, 48) = 5.60 Model Prob > F = Residual R-squared = Adj R-squared = Total e Root MSE = 4457 salary00 Coef. Std. Err. t P> t [95% Conf. Interval] highschool _cons Model Using percentage of population with high school degree to explain average salary in

16 HS90 Salary90. regress salary90 highschool90 Source SS df MS Number of obs = 50 F( 1, 48) = 9.52 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = salary90 Coef. Std. Err. t P> t [95% Conf. Interval] highschool _cons