Introduction

I’ve read and analyzed several studies that argue whether or not family income level directly affects academic success. And there still stands a discrepancy on whether or not there is a direct negative correlation between the two. One lingering inconsistency and gaps in previous literature is what unit of analysis should be observed to signify Academic success. Many different studies have utilized different units to display their respective stances on the relationship between family income and Academic success. This issue was brought to my attention in particular because I, myself came up in a low-income/ poverty level family, yet I am now in my fourth year here at The University of Texas at Austin, and on to track to graduate due to academic success throughout the years.

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The dependent variable of interest is academic success as I am looking at how students’ academic success levels correlate and are affected by other variables. I will signify the dependent variable of academic success with the college attendance rates by city as this will show how many individuals go on to attend college as that can be referred to a signal of academic success. The independent variable of interest is Family Income. I will signify low family income percentage by the child poverty rate as that will represent the percentage of individuals whose household income lies less than $23,624. The purpose of this study is to investigate the effect of family income on student achievement. The hypothesis for this research is about students who have higher families income are having better education than those who have lower income. The research question of this study is “ Is there a relationship between income level (child poverty rate) and student achievement (college attendance)?” The unit of analysis in my research is going to be the percentage of students that make it to college.

Literature Review

 The most recent piece of literature I reviewed, released in 2017, was written by Han Lv titled “ The effects of family income on children’s education: An empirical analysis of CHNS data”. This study analyzed the relationship between family income and concluded that Family income has significant impacts on children’s educational level, which is assumed to be elevated with the increasing income. A financially well-off family is able to give more, especially educational resources. The study stated ,“For lower-income families, parents are bustle around for life and expect little from their kids, and moreover they may put subsistence before children’s learning.” This also means those who yearn for the improvement of their life by studying hard are naive as the bar is raising and the income gap is widening. On that account, governments should provide more fair education opportunities and subsidies in order to cut down the inequality of intergenerational transmission. (Lv, Han. (2017)).

 The second most recent piece of literature I reviewed ,released in 2011, was written by Laura D. Lissington titled “This study analyzed the relationship between income level and percentile among students in assessments from the ECLS database poverty significantly affects the resources available to students. Due to this lack of resources, many students struggle to reach the same academic achievement levels of students not living in poverty. The factors affecting student achievement include income, source of income, and the mother’s education level. The study stated, “Although many poor students score below average on assessment measures, instructional techniques and strategies implemented at the classroom, school, district, and government levels can help close the achievement gap by providing students with necessary assistance in order to achieve high performance in academics. “(Tissington, D. Laura (2011)).

 The third most recent piece of literature I reviewed, released in 2009, was written by Kazim Celik titled “ The relationship between the students’ academic achievement and their socioeconomic level: cross regional comparison”. This study analyzed the relationship between family household income and achievement scores in math reading and science across various regions. The study concluded that Several of the studies state that many of the variables related with family affect students’ academic achievement. In general, the results of the study also support those studies. However, familial variables show differences in their effects on different academic achievement fields. Generally, familial variables have the highest effect on math and the least effect on reading academic achievement of 15-year-old students. The study stated ,”When a cross regional comparison is made, familial variables have the most meaningful effect on mathematics achievement of 15-year-old students living in Aegean region and the least meaningful effect on mathematics achievement of 15-year-old students living in South East Anatolian region. Familial variables do not have a meaningful effect on academic achievement of 15-year-old students in East Anatolian region”. And so, as regional developmental level decreases, effects of familial variables on academic achievement decrease, too. In East Anatolian and South Anatolian regions, the education levels and annual average income levels of the families are low, at the same time, mathematics, reading and science achievement levels of 15-year-old students living in those regions are also low. All in all, it came to the conclusion that regional affiliations didn’t alter the fact that the variable of family income did have an effect on student achievement.

 The fourth most recent (least recent) and final piece of literature I reviewed, released in 2008, was written by Lance Lochner titled “The Impact of Family Income on Child Achievement; Evidence from the Earned Income Tax Credit”. This study analyzed the relationship between family income and scholastic achievement in math and reading standardized scores. The study concluded that the IV results indicate that current income has significant effects on a child’s math and reading test scores. The study stated, “The baseline estimates imply that a $1,000 increase in income raises contemporaneous math and reading test scores by 6% of a standard deviation. Over the entire sample period (1987–1999), the median EITC payment for eligible two-child families increased by $1,670 (in year 2000$), implying an average test score increase of 10% of a standard deviation for this group” . This was one of the more strong correlations compared to the other studies as this one displayed the greatest degree of impact between the variables. (Lochner, Lance. (2008).)

 Of the literatures reviewed, they weren’t all able to come to an exact agreeing result as some suggest that the family income has greater affects than shown in alternative studies as the studies all analyzed different units of analysis. Some studies suggest a moderate effect while other studies I reviewed suggested significant effects. So moving forward my study I will conduct will fight to erase some of those discrepancies and confirm or reestablish the true effects of family income on student achievement and to what degree. I will do so by looking at a different signal for income level by looking at child poverty rates and a different signal for academic success being college attendance rates.

Empirical Model

 Linear regression is used to estimate the relationship between city College attendance rates and city child poverty rates. In this paper we estimate the following regression model:

College Attendance  =  +
 β
1ChildPoverty. + 2%SingleParents + 3Race + 

 In the model above, College attendance measures the proportion of the population that moved forward to College after graduation. Child Poverty measures the proportion of the population that had a household income of less than 23,624 dollars as a child.

 The “%SingleParents” variable Measures the amount of children with single parents. The Race coefficient measures the proportion of the city population that is either black, Asian, or other as it is broken up into three dummy variables.

 This  study uses publicly available state-level data. The data source for College Attendance is based on the data from the 2000 Decennial Census. The data is available at https://www.brookings.edu/wpcontent/uploads/2018/03/es_20180314_looneyincarceration_final.pdf.

 The dependent variable of interest is academic success as I am looking at how students’ academic success levels correlate and are affected by other variables. I will signify the dependent variable of academic success with the college attendance rates by city as this will show how many individuals go on to attend college as that can be referred to a signal of academic success. The independent variable of interest is Family Income. I will signify low family income percentage by the child poverty rate as that will represent the percentage of individuals who’s household income lies less than $23,624.

TABLE 1

City

College attendance

ChildPoverty Rate

%single Parents

% Black

%Asian

%Other Race

Waco, Texas

24.0%

35.0%

12.0%

34.0%

0.0%

22.0%

Dallas,Texas

19.0%

47.0%

27.0%

85.0%

0.0%

8.0%

Tulsa, Oklahoma

29.0%

41.0%

10.0%

80.0%

0.0%

9.0%

Orlando, Florida

24.0%

42.0%

10.0%

79.0%

0.0%

6.0%

Syracuse, New York

32.0%

32.0%

12.0%

50.0%

2.0%

6.0%

La jolla, California

85.0%

8.6%

20.0%

1.0%

11.0%

5.0%

Winfield, Illinois

65.0%

50.0%

10.0%

3.0%

3.0%

3.0%

Portland Oregon

81.0%

1.9%

16.0%

1.0%

3.0%

5.0%

Mineapolis, Minnesota

82.0%

2.8%

12.0%

2.0%

5.0%

2.0%

Apline, Utah

76.0%

3.1%

7.0%

0.0%

0.0%

2.0%

Wyckoff, New Jersey

71.0%

1.2%

8.0%

0.0%

4.0%

1.0%

Moraga, California

86.0%

3.5%

13.0%

1.0%

13.0%

5.0%

Medfield, Massachusetts

78.0%

1.3%

10.0%

1.0%

2.0%

1.0%

Grossse Ile, Michigan

70.0%

2.2%

11.0%

0.0%

3.0%

2.0%

Alamo, California

85.0%

4.1%

9.0%

0.0%

6.0%

3.0%

Rye, New York

82.0%

2.2%

10.0%

1.0%

6.0%

2.0%

Princeton Junction, New Jersey

86.0%

2.6%

7.0%

3.0%

24.0%

2.0%

Jericho, New York

89.0%

4.8%

6.0%

1%

10.0%

1.0%

Mill Valey, California

78.0%

4.1%

20.0%

1%

5.0%

4.0%

Summit,New Jersey

75.0%

3.9%

12.0%

4%

5.0%

3.0%

Logmeadow, Massachussets

78.0%

0.3%

11.0%

1%

3.0%

1.0%

Northborugh,

Massachussets

69.0%

2.2%

16.0%

1%

5.0%

1.0%

Winnetka, Illinois

88.0%

1.8%

10.0%

0%

3.0%

2.0%

Englewood, Colorado

87.0%

2.0%

16.0%

2%

5.0%

3.0%

Brimingham, Michigan

85.0%

2.7%

18.0%

1%

2.0%

1.0%

 

Variable  Obs Mean Std. Dev. Min Max

     

Collegeatt~e  25 .6896 .2307683 .19 .89

ChildPover~e  25 .12092 .1703643 .003 .5

sSinglePar~s  25 .1252 .0486587 .06 .27

Black   25 .1408 .2785217 0 .85

     

Asian   25 .048 .0524404 0 .24

OtherRace  25 .04 .0436845 .01 .2

 Table 1 above summarizes the previous dataset that shows a wide variety of college attendance rates. Attendance rates range from 19 percent to 89 percent with a mean of 68.96 percent. The dataset is a random sample taken through random survey. Child poverty rates range from .3 percent to 50 percent of the city population. The unit of analysis is percentage points. The percentage points of single parents in given cities range from 6 percent to 27percent.

Empirical Results

 The regression results in Table 2 below display that child poverty is an important determinant of College attendance rates. Higher rates of child poverty lead to lower rates of college attendance. When the rate of child poverty increases by 1 percent, college attendance decreases by 26.6 percent.

TABLE 2

Source  SS  df         MS        Number of obs   = 25

        F(5, 19)        =  50.53

Model        1.1886962  5            .237739239    Prob > F        =  0.0000

Residual   .089399804  19       .004705253    R-squared       = 0.9301

        Adj R-squared   = 0.9116

Total           1.278096  24            .053254     Root MSE        = .06859

   

Collegeatte~e  Coef.  Std. Err.           t      P>t     [95% Conf. Interval]

   

ChildPovert~e  -.2659421 .1525416    -1.74    0.097    -.5852152 .053331

sSinglePare~s  .2313353 .2993839     0.77 0.449    -.3952824 .857953

Black   -.4802612 .0905258    -5.31    0.000    -.6697339 -.2907885

Asian   .6866067 .2950486     2.33    0.031     .0690629 1.30415

OtherRace  -1.212924 .4026774    -3.01    0.007    -2.055737 -.3701106

_cons   .7759752 .0454262    17.08    0.000      .680897 .8710533

 Other important determinants of obesity rates includes race. For instance you can compare the fact that Black has a coefficient of -.48 as to where Asian has a coefficient of .69. This displays that race has a negative correlation when it comes to black people yet when it comes to Asian people there is a positive correlation.

Conclusions and Policy Implications

 Empirical results show that the rate of child poverty in a given city is an important determinant of college attendance rates for that city. Therefor reducing rates of child poverty needs to be an important focus for our country.

 Moving forward this study is significant because it can better display the negative impacts of low income/ high poverty levels on students and how it can lead to a higher chance of lack of success in academics. This can then ensure that governments should act as a designer for the entire process of educational system and the policy of fair education, to ensure the equitable distribution of educational resources in particular. Relevant authorities are supposed to further develop the urban-rural educational industry in local area, so that the rural and underdeveloped regions are able to enjoy high-quality teaching resources. Our government needs to and should step in to address this policy in order to help lower income students overcome the given setbacks coming from a low income background. With this knowledge we can put forth policies to help our lower income level citizens succeed academically, attend college, and achieve great things.

References

Lv, Han. (2017). “The effects of family income on children’s education: An empirical analysis of CHNS data”. Research on Modern Higher Education. 4. 49-54. 10.24104/rmhe/2017.04.02002.

Tissington, D. Laura (2011). “The effects of poverty on academic achievement.”

   University of West Florida, Educational Research and Reviews Vol. 6.

Celik, Kazim. (2009). “The relationship between the students’ academic achievement and their socioeconomic level: cross regional comparison”. Pamukkale University, World Conference on Educational Sciences.

Lochner, Lance. (2008).” The Impact of Family Income on Child Achievement; Evidence from the Earned Income Tax Credit”. National Bureau of Economic Research, NBER Working Paper Series.

Decennial census (2000). “Family income and disadvantage in childhood”. Economics Studies at Brookings.