Environmental Factors in Determining Childhood Success

Jennifer Mo Advisor: Professor Raquel Bernal MMSS Senior Thesis 2005-2006

Acknowledgements I would like to thank my advisor Professor Raquel Bernal for her infinite wisdom and patience, and also for always keeping me on track. I greatly appreciate the time and effort she has placed on this project for me. Thanks to Jiuping Chen and Jon Huntley for helping me organize my data. Also, I would like to thank the many faculty and staff members of the MMSS program who have provided endless support for me throughout these past three years. I know that I could not have gotten to where I am now without them, and I will be forever grateful.

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Abstract While much of the success a child has can be attributed to family characteristics, great

amounts of variation are still left unexplained. This paper attempts to look at different

absolute and relative community variables, taken while a child is 3 years of age, and then

looks ahead to child test scores a number of years later in order to locate variables which

are predictive of testing success or failure. Results show that a number of community

variables are highly significant, including both absolute and relative variables. Crime

rate, differences in income from the community norm, and racial variables are important

predictors, though race has a very counterintuitive result. A few possible reasons for this

are explored, though results are inconclusive. Further investigation could shed some light

on this result.

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Table of Contents

1. Introduction ………………………………………………………………………….. 5

2. Background Literature ……………………………………………………………… 7

3. Data …………………………………………………………………………………. 12

3.1 Dependent variables ……………………………………………………… 13

3.2 Independent Variables …………………………………………………… 15

3.3 County-level Variables …………………………………………………… 23

4. Method ……………………………………………………………………………… 26

5. Results I …………………………………………………………………………….. 27

5.1 Family Variables …………………………………………………………. 27

5.2 Income …………………………………………………………………….. 30

5.3 Education …………………………………………………………………. 31

5.4 Racial Effects……………………………………………………………… 33

5.5 Absolute County Variables ……………………………………………… 33

6. Results II …………………………………………………………………………… 35

7. Results III …………………………………………………………………………... 38

8. Results IV …………………………………………………………………………… 40

9. Conclusion ………………………………………………………………………….. 42

Bibliography …………………………………………………………………………... 45

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1. Introduction

A great deal of research has been done in looking at what causes labor market

success, earnings being of particular interest. Growing inequality in earnings over the

years raises the urgency for policy reform and makes it all the more important to find the

root of the earnings question. There is a consensus in the field that a lot of these causal

factors are long engrained before the time of entering the labor market. In fact, many of

these determining factors are formed in early childhood.

Much of testing success in children can be attributed to parenting and genetics. It

is difficult to pinpoint the exact formula that will lead to successful children, but many

variables have been shown to be significant predictors. For example, a child will perform

better if a parent was present during the first years of his life, due to the increased

attention and guidance during that very impressionable time. Other important indicators

include parents’ educations, family income, and the age of the mother at the birth of the

child.

Most of the existing research focuses on which family and socioeconomic factors

are the crux to labor market success. However, these factors are not adequate at

determining variation in wages. There must be information outside of family-attributed

characteristics that can be used to predict success or failure in the market.

A child constantly interacts with his immediate environment. This may include

simple things like experiencing the warmth outside during the summer, the other children

at daycare and the park, and the smiling waitress at the local diner that help create a

positive learning environment . It may also be more substantial such as having strong

positive role models in good teachers and neighbors. Additionally, a child’s parents are

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also effected by their surroundings. Parents in poorer areas have fewer resources

available to them when they are in need, high unemployment may increase fears of losing

one’s own job, and living in a crime-ridden neighborhood may add substantial stress to

everyday life. All of these factors cause a noticeable negative change in the behavior of

parents. Such negative effects may pass on additional stress to the child, leading to lower

test scores.

So as the adage goes, no man is an island. A parent cannot protect her child from

everything, many do not have the resources to even try. It is impossible for a child to be

unaffected by the environment he lives in, if only indirectly through his parents’ own

reactions to their community. This paper attempts to look at what environmental aspects

matter in helping or hindering childhood testing success. Many ones are explored in this

paper including crime rate, unemployment rate, marriage rate, death rate, divorce rate,

and median income. These are absolute variables, but relative ones are also considered.

Relative variables are those that depend on characteristics of the participant. The

difference between a parent’s income and the median in a county, the difference between

a parent’s education level and the mean in a county, as well as the percentage of a child’s

own race present in a county are included in analysis. Using these as explanatory

variables, this paper attempts to find out once environmental impacts are isolated, which

of these variables have substantial predictive power and why.

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2. Background Literature

Much has already been explored in determinants of adulthood labor market

success. It is now believed that adulthood success is very closely related to childhood

success. By the age of 16-18, most components that determine labor market success have

already been set and children’s early achievement is highly correlated with future

success. In fact, children of mere 4 years in age have test scores that are highly predictive

of adult educational attainment.1 The conclusion is then that success is cultivated early

and action should be targeted to that time period to make a substantial difference.

The only question is what particulars matter to a young child’s testing success.

Blau (1999) performs a study on the effects of daycare and finds that a child that is a full-

time daycare participant is unaffected in the first year, but will have his test scores fall by

1.8% per year of daycare after that. Coupled with the previous information, this suggests

that the first year of development is too early to serve any meaningful impressions, while

lasting impacts may have already set in by age 4.

Alwin and Thornton (1984) further explore this in a paper about earlier versus

later experiences and its impact on childhood educational attainment, and for the most

part they find that earlier socioeconomic variables tend to have a stronger relationship to

success than later ones do, these results were consistent with the previous findings. The

one exception to this was family size which seemed to affect children at both younger

and older ages. The size of the family at birth as well as the growth of family had

negative affects on the number of years of schooling a child obtained.

1 Blau, David. “The Effects of Income on Child Development”

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Haveman, Spaulding and Wolfe (1991) look at family characteristics and the

impact on high school graduation. They use high school graduation rate as a measure of

interest because most people in poverty do not have a high school diploma or equivalent

Findings were that living in poverty or welfare early in life has a strong negative effect,

though the effect in adolescence is small. Having a mother who works during the child’s

adolescence is beneficial, while the effect is much smaller for younger children. This

suggests that the opportunity cost of going to work is high while children are young and a

mother’s absence is more detrimental. The most significant variable he found was

location moves during a young age which was found to have a very strong negative

impact, this effect is also strong and negative if it occurs during adolescence.

Many other variables have already been shown to be substantial predictors.

Mclanahan, Sandefur and Wojtkiewicz (1992) find that families without both biological

parents do not have the same level of financial and emotional stability and also have

increased risks of these problems in later generations. This is true regardless of whether

the child is in the care of two adults, and is consistent across all races and ethnicity

groups. Individuals at an adolescent age are still affected by changes around them, but

only to a certain degree. Income at this age group does not have a large affect on high

school graduation rate, perhaps this shows that only large disruptions can make a

difference at a late stage (i.e. during adolescence).

Mother-only families also contain a large number of problems, frequently

experiencing both social and economic instability. McLanahan and Booth (1989) find

that economically, single mothers make only a third of what married fathers do, having

both a lower wage and also working fewer hours on average. In fact, around one out of

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two single mothers were living in poverty in 1985 compared with only one in ten married

couples with children. It also found that child support for single mothers only makes up

10% of a white single mother’s total income and only 3.5% for a black single mother’s.

Mother-only families also move more, and are likely to increase their working hours

substantially after a divorce. Large changes like this will affect the child’s welfare

significantly. Divorce in particular also leads to inconsistent disciplining methods and

everyday routines, this effect goes beyond that of a single-mother who has not

experienced divorce. However, this effect tends to let up by 18 months after a divorce.

Despite all the disadvantages that single mothers feel, the study finds that there is no

evidence to support the view that single mothers have lower educational expectations of

their children. This seems to show that parents, regardless of their economic situation,

still have the best hopes for their children.

The single-mother effect on education has an obvious implication on income as

well. Krein (1986) looks at the relationship between growing up with one parent and

earnings. She finds that living in a single-parent family has a negative effect on earnings,

but that the effect was eliminated once education was taken into account. There is then no

support for an additional income effect beyond that of having less schooling. This effect

also varies between age and length. Longer periods of time spent living in a single-parent

home has a more detrimental effect, and children in preschool were most affected by

single parenting.

Some work has been done about the effect of environmental attributes on a child’s

success. Mayer and Jencks (1989) use their own models to find that there are significant

background effects on earnings, wages, and welfare participation. Poverty, race, and

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community variables were highly correlated to the labor market success of children. It is

unclear how large a correlation this would have been if more family attributes had been

used as explanatory variables.

Poverty however is very disproportionate towards minorities and single-mothers.

Corcoran and Chaudry (1997) conduct a study that finds for those children experiencing

long-term poverty, 90% of this group in 1992 was black. Children who live in long-term

poverty were also more likely to live in extremely poor neighborhoods. Short-term

poverty, on the other hand, appears to have little effect on children’s futures. Poverty has

grown ever since 1979, hit especially hard during recessions, while rebounding little

during economic booms.

Brooks-Gunn, Duncan, Klevbanov and Sealand (1993) worked on a similar

project and looked at the effects of living in an affluent neighborhood on childhood

success. They found that there were positive effects of living in a good neighborhood on

IQ, teenage birth rates, and school drop-out rates. These effects were still present after the

socioeconomic statuses were controlled for. They also found that a good neighborhood

tends to benefit white teenagers more than black ones. However, she found little evidence

of any effect of living in a poor neighborhood.

Something else to consider is there may also be more of a racial disparity in

income than is commonly believed. Jencks, Perman and Rainwater (1988) created an

index of job desirability (IJD) that includes 13 nonmonetary job attributes along with

earnings to determine a better scale for the desirability of a job. Nonmonetary job

attributes consist of such things as flexibility of job hours, training available, vacation

time, hours worked and job security. Together, these 13 other attributes are weighted

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twice as heavily as earnings; the weightings were based on a survey of how people rated

their job. Using this index, the study finds that inequality in the labor market is highly

underestimated with the measure of inequality doubling under the new index. Being a

white male with favorable socioeconomic status and a large amount of labor-market

experience also is worth between two and five times as much under the new measure than

when only considering salary.

All of this taken together shows that a lot of labor market success can be

attributed to early childhood experiences. At that time, socioeconomic detriments such as

living in poverty or in a single-parent family has a large impact. Because poverty is on

the rise, and inequality is perhaps much larger than commonly believed, it is increasingly

important to find what is causing testing failure in children, whether it is partly due to

discrimination, and if it can be corrected for.

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3. Data

The primary data source for this paper comes from the Bureau of Labor Statistics’

National Longitudinal Surveys (NLS). National Longitudinal Surveys of Youth 1979 and

Children of the National Longitudinal Surveys of Youth datasets were used. This

extensive data set follows 12,686 men and women who were between ages 14 and 22 in

1979 as they made important educational, financial and social decisions in their lives.

Surveys were administered annually between 1979 and 1994 and biannually starting in

1996 going to 2000. Data is collected over a variety of topics pertaining to many social

issues. Income, demographic information, educational attainment, family dynamics, drug

and alcohol participation are all available information. For the purpose of stronger

analysis, the survey is disproportionately composed of socially and economically

disadvantaged groups such as minorities and single-parent families. This allows for more

extensive data in studying social structures of those who are most in need. It is the only

dataset of its kind, a time series set both rich in number of years observed and number of

participants surveyed.

The child dataset follows children of the NLSY data set, a total of 11,205

individuals participated in the survey as of 2002. It is often more incomplete and noisier

than the mother dataset, so information was gathered from the mother dataset whenever

possible.

In addition to the regular NLS datasets, additional geocode variables were used.

This addendum provides the state and county of residence for both the participants in the

NLSY survey and their children between the years 1979 and 2000. It also provided some

additional information about the characteristics of the county of residence.

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Additional countywide information was taken from the United States Census.

Census data, which is available for every decade, was taken at years 1980, 1990 and

2000. A large majority of the children participants were applicable to this time frame.

Those who had children before this time frame did not have available data during their

early childhoods, and those children who were born after this time did not have data for

later test scores. These children were not included in analysis.

3.1 Dependent Variables

Table 1: Dependent Variables Variable Obs Mean Std. Dev. Min Max Math Score 6025 99.8913 12.1813 65 135 Reading Score 6008 103.3940 12.9287 65 135

The variables used to measure childhood success come from the Peabody

Individual Achievement Test (PIAT). This test is a frequently-used multiple-choice test

that measures academic achievement and can be given to students from kindergarten up

to 12th grade. It is commonly used by psychologists to determine learning disabilities, as

well by guidance counselors at determining the skill levels of gifted children. Three

different subjects are available in the NLS data set: math, reading recognition and reading

comprehension. Of these, only the math and reading recognition scores are used because

reading comprehension is generally a noisy and unpredictable variable. The variable is

standardized by age, and a score is given for each section between 65 and 135. Children

included from the dataset range in age from 5 to 10 years and have an average age of 7

years. This means that all participants in the analysis take the exam a number of years

after their most impressionable years. This will allow for more accuracy in measuring

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community effects. Because most children are still too young to work, it is difficult to

find accurate results using labor market variables, the data would be too sparse and

additionally, income is a difficult variable to work with. But because testing is a good

proxy for future wage success, the PIAT is a good variable to use. Both reading and math

variables were available, but as results were similar, only reading results will be

presented. In the future, as the NLS dataset grows, test scores taken at an older age can be

used also.

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3.2 Independent Variables

In order to isolate environmental impacts, it is necessary to control for everything

else. With a few exceptions, variables were taken at the time when the child was age 3.

Children are impressionable and it is generally accepted that early experiences make a

great impact on the future. In order to test this, children’s experiences from age 3 were

used and then compared to later PIAT scores.

Table 2: Independent Variables Variable Obs Mean Std. Dev. Min Max Mother’s Education Level 9408 12.2515 2.5554 0 20Father No High School 1540 0.2377 0.4258 0 1Father College 1540 0.0903 0.2866 0 1Father Advanced Degree 1540 0.0143 0.1187 0 1AFQT 10617 36.0560 27.5533 1 99Birth Order 11203 1.9419 1.1141 1 10Age of Mother at Birth 11203 24.8502 5.5455 10 42Mother Working 11205 0.4396 0.4964 0 1Mother in Army 11205 0.0104 0.1012 0 1Mother in School 11205 0.0179 0.1327 0 1Father Present in Household 7924 0.7403 0.4385 0 1Number of Siblings 11205 1.8823 1.3759 0 9Age of Child at Test 6688 91.5899 8.2153 65 129Hispanic 11205 0.1916 0.3936 0 1Black 11205 0.2770 0.4475 0 1Other 11205 0.5314 0.4990 0 1Minority 11205 0.4686 0.4990 0 1Household Income 8037 50602.06 101291.60 88.2 1665481Mother Married 11205 0.5996 0.4900 0 1Mother Never Married 11205 0.1738 0.3789 0 1State of Residence 9807 27.6379 16.6530 1 56Urban 8640 0.7869 0.4115 0 2

A description of the independent variables used follows:

A child’s ability will be highly correlated to the ability of his parents, and this will

correspond closely to the child’s test scores. Because ability cannot be directly measured,

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this study uses the combination of the next three variables to capture the ability of a

child’s parents: Mother’s Education Level, Father’s Education Level, and AFQT score.

Mother’s Education Level. Educational attainment is still not a given in the United

States, and can be used as a proxy for the ability of a mother. There is still a lot of

variation within educational attainment, with only 23% of women having a bachelor’s

degree and 83% holding high school degrees of all women 25 and older in 1999. In 1980,

it was slightly less than 20% and 70% respectively2. These changes are small; female

educational attainment has stunted in growth in recent decades. It is then likely

unnecessary to make an adjustment to absolute number of years of schooling to take

account of growth over time.

This variable measures the number of years of schooling completed by the mother

at the time of survey. The mothers themselves were generally young. Many of them were

not old enough to have completed college or participate in graduate work at the time their

child was 3 years old. Because this variable is used as a proxy for ability, it is taken at the

time of the test, which is usually around 7-8 years after birth. This gives a longer time

period to ensure that young age is not a prevalent factor.

A child’s ability is also related to his father’s. However, father’s education level is

very sparse in the data. Level of education data is available biannually only from the

years 1994 to 2000. Therefore, the years of the data used here do not coincide with

Mother’s Education Level. Fathers are generally older than mothers, they also do not

become pregnant and do not generally take paternity leave. It is possible that a father may

need to drop out of school in order to support a child financially. Though, if this is the

case, few of these fathers will actually return to school. Overall then, there is probably 2 U.S. Census Bureau, “The Graduates: Educational attainment 1999”

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not as much change over the years in education as for the mothers. There are only 1540

observations, less than 15% of the children surveyed. Because the fathers themselves

were not surveyed, it is difficult to say how accurate the data is. I would suspect there is

an upward bias in this variable. Well-educated women tend to be married to well-

educated men and have easy access to this type of information. Those women who do not

have this information available likely are involved with men of more questionable

education statuses. This variable is very important but was included in only one

regression because of the many problems mentioned. This variable is not the number of

years of schooling a participant has at time of survey, it is discrete and varies from 1

signifying no high school degree to 9 of holding a PhD. The following three dummy

variables are used to describe father education:

Father No High School. This variable is 1 if the father does not have a high school

diploma and 0 otherwise.

Father College. This variable is 1 if the father has a bachelor’s or associate degree and 0

otherwise.

Father Advanced Degree. This variable is 1 if the father has a master’s degree, a PhD, a

M.D., or a J.D. and 0 otherwise

Note that the base group left out is high school graduates and participants who

had some college experience.

AFQT. The Armed Forces Qualification Test is administered by the Department of

Defense and has been previously used in studies to represent ability as well as learned

skills. This variable represents the percentile scoring of a mother participating and ranges

from 1 to 99.

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Birth Order. This variable tells the birth order of the child and is important because it has

been shown that earlier children will perform significantly better at testing than later

ones. This could be that parents do not put in the same amount of care and effort for later

children, it could also be that they have less time to spend on an additional child due to

having to split up their time with more children.

Age of Mother at Birth. Children usually have higher test scores when their mothers are

older at the time of birth. Older mothers are generally better off financially and are more

mature and able to take care of children. Especially young mothers are still coping with

growing up themselves and may not be prepared to take care of a child of their own.

Older women also are more likely to have planned pregnancies. As a mother ages,

however, there may be health risks associated with having children that may be

detrimental to the child’s health, increasing the chances of birth defects. The mother may

also be less physically able to care for the child at older ages. The oldest mother in the

dataset was 42 at time of birth, so this mentioned effect will be negligible. It is therefore

likely that a strictly increasing relationship exists between test scores and age of mother

at birth.

The employment status of the mother is separated into the following three dummy

variables: Mother Working, Mother in Army and Mother in School. As mentioned

before, a child will test better if a parent is at home during his early years. This is

sometimes a father and that number has increased in recent times, but a majority of stay-

at-home parents is still comprised of mothers.

Mother Working. This variable is 1 if the mother is working when the child is 3 years old

and 0 otherwise.

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Mother in Army. This variable is 1 if the mother is currently enlisted in the army at the

time the child is 3 years old and 0 otherwise. Those mothers that are enrolled in the army

may have different stresses and time commitment than regular working mothers.

Mother in School. This variable is 1 if the mother is currently enrolled in school when

the child is 3 years old and 0 otherwise. Schooling also takes the mother away from the

child. A mother’s schedule may also be more hectic and difficult to balance because she

will in addition to going to class have to commit time to studying at home, taking away

time spent with her child.

Father Present in Household. This variable is 1 if the biological father is present in the

household when the child is 3 years old and 0 otherwise. A paternal presence is important

to the development of a child, and as mentioned before, it cannot even be replaced by

someone like a stepfather.

Number of Siblings. More children in a household lead to having fewer resources to give

to each individual child, ranging from parental attention to funds. There are of course

benefits to having multiple children, such as giving the children more of a chance to

interact socially with others, and perhaps learning more responsibility. Overall, the

former effect is probably more powerful.

Taken together, Number of Siblings and Father Present in Household construct a

picture of the family composition; it provides the number of caretakers and possible

income earners, as well as number of dependents in the household.

Age of Child at Test. Despite being standardized by grade, PIAT scores do tend to rise

over the years. This is true especially for those children who have taken the exam

multiple times, and have some experience with it. In addition, a somewhat common

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situation in the dataset is that a child is held back a grade, in which he would take the

same exam again. This variable corrects for this phenomenon. Children who skip grades

are also taken into account. This variable is measured in months to the exact exam date,

as a few months can make a big different for children at that age.

Race is commonly used in these types of studies. It is represented by the

following:

Hispanic. This variable is 1 if the child’s mother is Hispanic and 0 otherwise. It is noted

that Hispanic is technically not a race, but will serve the same purpose in this paper.

Mother’s race is used to represent child’s race, and was observed by the interviewer.

Black. This variable is 1 if the child’s mother is black and 0 otherwise.

Other. This variable is 1 if the child’s mother is not black or Hispanic and 0 otherwise.

Of course there are other minority groups other than Hispanic and black, but these are the

prominent ones of interest, as they are both large groups and make up a disproportionate

part of the economically disadvantaged.

Minority. This variable is 1 if the child’s mother is Hispanic or Black, it is the sum of

variables Hispanic and Black.

Note that only the first two variables Hispanic and Black are used in the

regression. The racial mixture is fairly balanced with 2147 Hispanic participants, 3104

black participants and 5954 other. This variable is distinct and exhaustive.

Household Income. This variable is comprised of all forms of income including but not

limited to unemployment, child support, food stamps, welfare, educational scholarships,

parental support if applicable and income from other household members. A household

with a higher income will be able to provide better schooling, nutrition, and medical

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assistance for a child. This income is taken at age 3 of the child and transformed to 2005

dollars using the Consumer Price Index. Keep in mind that income has been on the rise

throughout time, even when taking inflation into account, though in recent years median

income has remained the same or even decreased.

Marriage status is determined by the following two dummy variables:

Mother Married. This variable is a variable that is 1 if the mother was married at the time

the child was 3 and 0 otherwise. Married families are generally more stable, both

emotionally and financially.

Mother Never Married. This variable is 1 if the mother had never been married at the

time the child was 3 and 0 otherwise. Mothers who have never been married are often

single mothers or are in less committed relationships, this leads to numerous negative

social and financial effects on a child.

The base case includes all other choices: divorced, widowed and separated.

Though divorce, death, and separation have very extreme effects on children and

mothers, large effects are generally temporary. Children of divorced and separated

parents also may have a higher chance of having a relationship with both parents, as well

as some more consistency in financial support from the father.

State of Residence. This variable tells the ID number of the state that the mother of the

child resided in at the time the child was 3 years old. This assumes that the child lived

with the mother at that time.

Urban. This variable is 1 if the child’s mother lived in an urban area at the time the child

was 3 years of age and is 0 otherwise. Better school districts, as well as more affluent

neighborhoods are generally located around the city. A National Center for Education

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Statistics report (2004) shows that rural schools receive much less funding per pupil than

urban schools. There may still be a large discrepancy between richer suburbs and inner-

city neighborhoods.

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3.3 County-level Variables

Note that all county-level variables are taken at either the time when the child was 3 years

of age, or the closet time available. If the applicable year fell between two years of data,

the first year was used. This method was used for all previously mentioned variables as

well.

Table 3: County-specific Independent Variables Variable Obs Mean Std. Dev. Min Max County Hispanic 9798 0.0716 0.0966 0 0.4842County Black 9804 0.1394 0.1439 0 0.8846County Other 9804 0.7882 0.1554 0 1County Same Race 9798 0.5603 0.3444 0 1County Minority Race 9798 0.1384 0.1757 0 0.8846County Black Race 9798 0.0741 0.1447 0 0.8846County Hispanic Race 9798 0.0349 0.0897 0 0.4842Diversity 9798 0.4111 0.2854 0 1County Median Income 9798 43559.52 11461.48 0 91922.56Difference in Income 7791 7845.20 101328.40 -78436.32 1643679County Female Education 9798 11.0850 0.7361 7.3995 13.1466Difference in Education 8723 1.2506 2.4772 -11.373 11.1077Female Employment Participation 11141 0.0409 0.1496 0 0.7289County Unemployment Rate 9506 75.4158 32.5921 12 237County Crime Rate 9610 5721.33 2738.16 0 40687County Death Rate 9670 86.8459 20.2533 30 170County Divorce Rate 9653 53.0100 20.3750 0 202County Marriage Rate 9662 103.5682 74.2600 2 3466

The following three variables form the racial composition of the county the

child’s mother lived in:

County Hispanic. This is the percentage of Hispanic residents in the county.

County Black. This is the percentage of Black residents in the county.

County Other. This is the percentage of residents of other races living in the county.

County Same Race. This variable is the percentage of residents of the same race as the

child in their particular county at the decade closest to the time when the child was 3

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years old. This means that this variable may be off by upwards to 5 years. Most counties

do not experience extreme demographical changes in a decade, so this was not corrected

for. However, it can be corrected in the future. One possible method is that years in

between decades could be linearly interpolated.

County Minority Race. This variable is an interaction variable of the dummy variable

Minority with the percentage of minorities in a county. This variable is to see if the same

race effect from before is specific to minorities.

County Black Race. This variable is an interaction variable of dummy variable Black

with County Same Race.

County Hispanic Race. This variable is an interaction variable of dummy variable

Hispanic with County Same Race.

Diversity. This variable was created to determine the level of diversity in a county at age

3. This variable varies from 0 to 1 and is found by subtracting the absolute difference

between Other and Minority from 1. The variable is 1 at the most diverse, this is when

there is a 50/50 mix of minorities and other. At the other end of the spectrum, the variable

is 0 when one group, either minorities or other makes up 100% of the county.

County Median Income. This variable is the county median income with inflation taken

into account in 2005 dollars.

Difference in Income. This variable takes the household income of the child’s family and

subtracts from it the median income in that county at age 3. If the child’s household

income is high, this number will be positive, and likewise if it is low, it will be negative.

County Female Education. This variable was constructed solely from the 2000 year

census, because it was the only year in which education was broken down by gender.

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Because female educational attainment has been relatively stable over the past few

decades, there should be few problems with using only one year of data.

Difference in Education. This variable takes the child’s mother’s number of years of

education variable Mother’s Education Level and subtracts the previous variable County

Female Education.

Female Employment Participation. This variable tells the percentage of females in a

county over the age of 16 who participate in the labor market.

County Unemployment Rate. This variable tells the unemployment rate in a county with

one implied decimal place.

County Crime Rate. This variable tells the known number of crimes per 100,000 people

in population.

County Death Rate. This variable tells the number of deaths per 1000 people. The

thought here is that death rate may reflect certain environmental aspects. For example, a

high death rate may imply that the living standard is low, or that good health services are

not very accessible.

County Divorce rate. This variable tells the number of divorces per 1000 people.

County Marriage Rate. This variable tells the number of marriages per 1000 people. A

county with a large marriage rate might imply that people in that county value families

more, and might take care of their children accordingly. It may also imply that the

government is pushing for more marriages.

25

4. Method

This analysis was done using ordinary least squares (OLS) regressions where

PIAT test scores were used as the dependent variable dependent on the various family

and community variables listed before.

Because there is no way to mathematically differentiate one county level variable

from another for a single individual, only one county level variable was tested at a time.

However, this applies only to absolute variables such as crime rate. Because each relative

variable is a function of another variable that is unique to the participant, more than one

of these variables can be included at the same time, such as the variable Difference in

Income.

Many of the explanatory variables are in fact endogenous, which will lead to

biases in coefficients. For example, mothers who have higher AFQT scores are more

likely to have high ability, and are therefore more likely to work. Upon further

inspection, there is a .19 level of correlation between the two. Because of this correlation,

it is difficult to isolate exactly the impact on child scores between the two variables, that

is, to attribute scores to either AFQT or Mother Working.

This problem can be fixed, proxies for variables can be found such that they are

independent to one another. If instrumental variables exist, this can eradicate all of the

symptoms of this problem. Nonetheless, this is beyond the scope of this paper. Because

this paper focuses on community variables rather than family and socioeconomic ones,

endogeneity among family variables is not a big concern.

26

5. Results I

Table 4 Reading Score Coefficient Standard Error Age of Child at Test 0.0097 0.0248 Birth Order -1.3260 0.2595 *** Household Income 0.0000872 0.0000 *** Age of Mother at Birth 0.0562 0.0592 AFQT 0.1242 0.0101 *** Mother Working 0.2720 0.4038 Mother in School -0.4050 1.6197 Mother in Army (dropped) Father Present in Household 0.1011 0.7573 Mother Married 0.2105 0.8549 Mother Never Married -1.2650 0.7069 * Number of Siblings -0.3393 0.1949 * Mother’s Education Level 0.1344 0.3655 Hispanic -7.0864 1.9002 *** Black -5.9234 1.9264 *** Difference in Income -0.0000854 0.0000 *** Difference in Education 0.3831 0.3516 County Same Race -8.1036 1.9629 *** County Minority Race 6.9494 2.8262 *** County Crime Rate -0.0002 0.0001 ** Constant 101.5274 4.5251 *** Number of obs 4012 * 90% Confidence Level R-squared 0.174 ** 95% Confidence Level Adj R-squared 0.17 *** 99% Confidence Level

5.1 Family Variables

From the regression results, of the family and socioeconomic variables, Birth

Order, Household Income, AFQT, Mother Never Married, Number of Siblings, Hispanic

and Black are all significant. Because the average test score for both reading and math

sections of the PIAT is around 100, a coefficient can reasonably be interpreted as a

percentage change to a child’s test score due to a marginal change in that explanatory

variable.

Age of Child at Test is positive, confirming that older children have an advantage

on any given test, this is probably especially true for younger children, when

27

development can change greatly within a few months. The variable is measured in

months, so a child that is 6 months older than another has an advantage of about .06%, a

small amount.

As expected, birth order is negative and significant; later children tend to do

worse. Each additional child will be expected to have a score of about 1.3% lower than

the previous child.

Household Income has a positive effect of .0000872. This number may seem

small but is per dollar, so an extra ten thousand dollars will have a positive effect of about

.87% while an extra hundred thousand, which is not unheard of, will lead to a positive

change of 8.7%.

Age of Mother at Birth is insignificant as an explanatory variable, though the sign

is positive as expected. It is advantageous to have an older mother, compared to very

young. This is probably because the dataset includes a large number of teenage mothers.

Of the 11,205 children in the data set, 2107 were birthed to women under the age of 20,

457 were birthed to women 16 or younger.

AFQT score is positive with a coefficient of .1242. In this data set, the average

woman scores in the 36th percentile. If a woman were to score in the 80th percentile, not a

very unrealistic figure, her child would have a score advantage of about 5.5% over the

average, showing the large impact of ability.

Employment status did not turn out to be a large factor. Previous studies have

shown that a mother who works during the developmental age of children will have a

detrimental effect on childhood scores, so the positive coefficient of Mother Working is

surprising. This is a problem associated with endogeneity. For Mother in School, a child

28

is expected to have a score of .4% lower than a child with a mother at home, this effect is

not large. Being a student during the developmental age of children has a negative effect

on scores, and this is consistent with our beliefs. It is worse to have a mother in school

than working, which is also expected, because students generally have a large amount of

stress, as well as an unpredictable schedule and work to do at home. Mother in Army was

dropped because of the children participants who had full data, none of them had mothers

in the army at age 3. It is probably a very unlikely occurrence to have a mother that is

enlisted in the army so soon after the birth of a child.

Father Present in Household is positive; the effect is small at a coefficient of

.1011. This small effect could be because most fathers who live in the household are also

married to the mother of the child. There is probably then a strong correlation between

this variable and mother’s marital status. Some of the advantage of living with a father

will also be part of the Mother Married variable, the exact benefits from marriage and a

father present cannot be separated.

Marriage status is somewhat significant as a predictor with married parents

having an advantage of .2% over the base. Children of mothers who had never married

had a disadvantage of 1.3%, and this effect is significant. Note that in this variable, there

is no constraint that the mother must be married to the biological father of the child,

making it a little different from the previous variable. Marriage, as previously mentioned,

leads to more stability in a household.

Number of Siblings is negative; a child with one more sibling is expected to

perform .34% worse. This effect is significant and consistent with previous beliefs.

29

Mother’s Educational Level is positive, as expected, but insignificant. The actual

coefficient is .1344, which means a child with a mother that has graduated from college

has an advantage of 2.2% over a child with a mother of no education whatsoever. In this

example, the difference in schooling is 16 years; you generally would not expect such a

large difference in years of schooling.

There are two race variables used, Hispanic and Black. These impacts are relative

to the base case Other. A Hispanic child, all things equal, has a lower score on average of

7.09% compared to a similar Other child. A black child, has a lower score of 5.92% to

that of a similar Other child. These effects are large and could be due to discrimination or

cultural differences. A Hispanic child has a larger negative effect compared to a black

child, this could be because many Hispanic children are immigrants and so also have the

additional stress of learning the language and culture of the United States.

The analysis of countywide variables follows.

5.2 Income

From the results, the coefficient on the Household income variable is positive and

significant at a 1% level of significance. This result is intuitive, if a family has more

money, it has more resources that would allow for better test scores. These include being

able to live in a better school district, being able to hire a tutor if a child’s grades are

lagging, or indirectly through being less burdened by the many stressful byproducts of

being poor.

The coefficient on the difference in income is negative, this result is less obvious. It

means that having more income than the median in your area is not beneficial. This effect

30

is because a child may be influenced heavily by his environment. His friends are those

around the neighborhood, his study habits and beliefs may reflect those of the

neighborhood. And as a child grows, the effect of his environment will become much

stronger as the influence of his parents starts to wane.

An interesting result of the model is that the coefficients of the variables Household

Income and Difference in Income are nearly identical in magnitude. This means that the

additional gain of having a richer family is almost exactly cancelled out by the loss

associated with living in a worse neighborhood than a family can afford. A richer family

living in a poorer neighborhood’s child will have no advantage, all other things held

constant, to that of a poorer child in the same neighborhood. In some sense, this is saying

that if parents do not use their income to provide for better educational opportunities for

their child, then there is no benefit to having the extra income. This has a nice intuitive

result. Many parents will work hard to live in a good school district and a safe

neighborhood, and this belief appears to have some merit from these results. Parents who

highly value education will spend more in educational investment, and in turn have

higher returns than those parents who do not.

5.3 Education

In the results, the coefficient for AFQT is positive and significant at all levels of

significance. It is in fact the most significant variable in this model. This result is

consistent with previous conjectures that ability is in part determined genetically. If a

parent is more able, then their child is more able.

31

The coefficient for mother’s highest level of education is positive, small and

insignificant. This suggests that it is better for a child to have a parent with a higher

education, but it is not a very good proxy for ability. Despite the wide variation in

number of years of schooling, it appears that the AFQT is a better proxy for variation of

test scores in this model.

The coefficient for the difference in education levels is positive, 0.3821 in

magnitude but insignificant at even a 10% significance level. This coefficient is larger

than Mother’s Educational Level. It is interesting that this coefficient is positive while the

coefficient for the corresponding community income variable is negative. Perhaps there

are more benefits to having a mother of high education than through passed-on ability. A

highly-educated mother is also probably more likely to value education more and is more

likely to push her child in educational pursuits. She may also provide additional teaching

outside of the classroom.

Nonetheless, it appears that AFQT is a better measure for ability, with the other

education variables adding little. This may be because education can be affected by a lot

of different aspects, such as how highly someone values education, if someone can afford

to pursue higher levels of education, or if someone has time to pursue additional

schooling if that person has a child or a family to take care of. The last effect may be

especially prevalent as many mothers have children before the age of 22, typically the

age of most college graduates, and a majority before the age of 26, almost the earliest

someone would be able to obtain a PhD.

32

5.4 Racial Effects

The coefficient on the variable County Same Race is -8.1, while the coefficient on

the variable County Minority Race is +6.949. Both these variables are significant at every

level of significance. This seems to imply that there are very different effects on

minorities and on whites. Whites do not seem to benefit from more whites in their

community, while minorities are just the opposite.

This is not easily explained. Perhaps there is something in the dataset that applies

more to whites than minorities. A possibility is that rural areas are prominently white, and

their school systems are not as well-funded as those in urban areas. That would lead to

the appearance that being a white child in a prominently white school is detrimental.

Another possibility is that some areas, for example at a county or state level, which have

a larger percentage of white residents have some kind of idiosyncratic characteristic that

is affecting residents. These possibilities are explored further in the next section.

5.5 Absolute County Variables

Absolute countywide variables were individually tested. Of these, the variable with

the highest t statistic was crime rate. Crime rate is highly significant, and is not small in

magnitude, as this number can range upwards to tens of thousands. Every other absolute

county variable was insignificant, even at a 10% level of significance. Crime rate may

have a more direct effect than other variables. High crime rates are stressful to children

and their families, because it may cause them to live in a state of stress and fear.

Unemployment is stressful to those directly affected, but cannot directly harm a child or

33

his family otherwise. Likewise, divorce has a strong negative impact on children, but

only if it occurs in his own home.

34

6. Results II

Table 5 Reading Score Coefficient Robust Standard Error Age of Child at Test 0.0135 0.0247 Birth Order -1.3156 0.3140 *** Household Income 0.0001 0.0000 *** Age of Mother at Birth 0.0679 0.0677 AFQT 0.1211 0.0127 *** Mother Working 0.4060 0.3868 Mother in School -0.2050 1.4921 Mother in Army (dropped) Father Present in Household 0.0105 0.9230 Mother Married -0.0105 0.8911 Mother Never Married -1.3486 0.5577 ** Number of Siblings -0.3392 0.1993 * Mother’s Educational Level 0.2798 0.4261 Hispanic -8.3221 2.9685 *** Black -6.7438 2.8732 ** Difference in Income -0.0001 0.0000 *** Difference in Education 0.2420 0.4208 County Black Race 7.4793 3.8791 * County Hispanic Race 11.2204 5.5577 ** County Same Race -9.5171 3.5010 *** County Crime Rate -0.0002 0.0001 ** Urban 0.5512 0.6200 Constant 100.7574 3.8855 *** Number of obs 3879 * 90% Confidence Level R-squared 0.1746 ** 95% Confidence Level Root MSE 11.824 *** 99% Confidence Level

In order to take account for the possibility of idiosyncratic statewide

characteristics, the regression was run again, this time clustered by State of Residence. It

would be more accurate to cluster by county, but not enough counties were represented,

with many having only one residing family. The variable Urban was also added for the

reasons presented before. County Minority Race was replaced by County Black Race and

County Hispanic Race to see if the overall effect was specific for one race or for

minorities in general.

35

The results have changed slightly. Mother Married now has a negative coefficient,

though still has a much smaller effect than Mother Never Married. This is a very unlikely

possibility, but is insignificant, and may be an error due to the OLS regression. Father

Present in Household has also decreased from before, which makes sense because the two

variables are correlated.

The coefficient on Urban is .5512, meaning that overall, children in urban areas

have an advantage over children in rural areas. However, it is possible that individually,

children living in inner-city areas are still worse off than those living in urban counties

where schools are much more homogeneous. Unfortunately, that information was not

available, so there was no way to isolate inequality within urban areas.

The coefficient on County Same Race is still very negative and significant. The

coefficients on the minority variables County Hispanic Race and County Black Race are

both positive and significant. Black participants feel an overall marginal effect of -2.04

while Hispanic participants will feel an overall marginal effect of 1.70. This appears to

say that black and white children do not benefit from being in the majority, they also do

not benefit from living around more of their own race. Hispanics on the other hand,

benefit from living around more Hispanics. This could, once again, be due to a language

and cultural gap, as Hispanics are at this time still assimilating to this country, and may

not be able to benefit from other cultures. Another reason for this counterintuitive result

is that extremely white areas are generally in less industrial states such as Alaska, Idaho

and Kansas, where schooling is worse. On the other hand, areas that are filled heavily

with minorities are typically places with disproportionately poor school systems. Contrast

this with good schools that are generally in the city and where races are more diverse.

36

This may, in turn, explain the overall negative effect. This is taken account of with the

clustering, but the grouping may be too broad of a scope to be effective.

37

7. Results III

Table 6 Reading Score Coefficient Robust Standard Errors Age of Child at Test 0.0127 0.0244 Birth Order -1.2993 0.3190 *** Household Income 0.000094 0.0000 *** Age of Mother at Birth 0.0586 0.0683 AFQT 0.1219 0.0129 *** Mother Working 0.3856 0.3818 Mother in School -0.3856 1.5040 Mother in Army (dropped) Father Present in Household 0.0800 0.9162 Mother Married -0.0344 0.8793 Mother Never Married -1.3050 0.5653 ** Number of Siblings -0.3713 0.1980 * Mother’s Educational Level 0.2103 0.3950 Hispanic -0.5657 0.7488 Black 0.1234 0.7933 Difference in Income -0.000092 0.0000 *** Difference in education 0.3143 0.3849 Diversity 2.5783 1.1080 ** County Crime Rate -0.0002 0.0001 ** Urban 0.7537 0.6234 Constant 92.34969 3.6735 *** Number of obs 3879 * 90% Confidence Level R-squared 0.1727 ** 95% Confidence Level Root MSE 11.8340 *** 99% Confidence Level

Here, a diversity variable was added. There are two interesting findings in this

regression. The first one is that the coefficients on variables Black and Hispanic has

decreased substantially. In fact, the coefficient on Black is now positive, a result that is so

strange, that I can only assume that it is wrong. This huge change in coefficients brings to

light that there is a possible problem with using OLS, and also leads to questioning if the

method used is appropriate if the results are so undesirable.

Diversity is positive and significant, which is consistent with the previous

regression results. An increase of .1 in diversity (percentage of the majority would go

down 5% and the percentage of minority would go up 5%) would lead to a positive

38

increase of around .26%. This implies that increased diversity is beneficial to a child’s

scores, an agreeable conclusion. However, this result is plagued with the same

considerations as before. It is possible that better schools actively strive for more

diversification in their schools, because better schools generally recognize the value of

having students from a variety of different backgrounds. Only schools that are better off

will have the extra funding and resources to promoting diversity. On the other side,

diversity is a low priority to struggling schools. Perhaps these better schools realize that

racial diversification is in fact better for the children in their schools, but maybe it only

appears that diversification is better because these better schools actively strive for

diversity. With this regression, there is no way to tell which of these effects is true. It

could also turn out to be a combination of both. In the future, good schools with a diverse

populous can be compared with good schools with a generally homogenous student body

to find the value of diversity. At this point, at least there is no evidence against the

benefits of diversity.

39

8. Results IV

Table 7 Reading Score Coefficient Robust Standard Error Age of Child at Test 0.0328 0.0491 Birth Order -1.2750 0.5428 ** Household Income 0.0000 0.0001 Age of Mother at Birth 0.2602 0.2260 AFQT 0.1597 0.0235 *** Mother Working 0.1890 0.9766 Mother in School 2.9785 2.8537 Mother in Army (dropped) Father Present in Household -2.2548 1.4847 Mother Married 0.8306 1.9848 Mother Never Married 0.0427 1.3624 Number of Siblings -0.3316 0.4249 Mother’s Education Level 0.6472 0.9504 Hispanic -6.4836 7.6461 Black -4.8147 7.6599 Father No High School -2.6423 1.3435 ** Father College 3.6672 0.8786 *** Father Advanced Degree 7.2895 3.4463 ** Difference in Income 0.0000 0.0001 Difference in Education -0.4652 0.9053 County Black Race -0.6406 11.7308 County Hispanic Race 15.5709 13.5250 County Same Race -6.7046 8.0666 County Crime Rate3 0.0001 0.0002 Urban 0.6008 2.0103 Constant 91.6063 8.3847 *** Number of obs 574 * 90% Confidence Level R-squared 0.2093 ** 95% Confidence Level Root MSE 11.532 *** 99% Confidence Level

This regression added in father education variables. After adding the new

variables, Household Income, Hispanic, Black, Difference in Income, County Black

Race, County Hispanic Race and County Same Race are all no longer significant. This

could mean that father education is a good explanatory variable for the variation in

children’s scores, but is nonetheless difficult to say because of the lacking nature of the

data.

40

It is difficult to believe that income and race have no bearing once father

education is taken account of. All of these variables have been well-documented as being

important in determining testing success. Father education itself is not a great variable,

because of its broad discrete nature and is skewed in that only a very small number of

fathers have higher levels of education.

41

9. Conclusions

After running OLS regressions, I find results that are mostly consistent with

previous literature. Typical family and socioeconomic variables were shown to be

important; these include household income, marital status, parents’ ability, birth order,

and number of siblings. The coefficient on the working mother variable turned out to be

positive, despite literature proving otherwise. This is an unfortunate effect of running a

regular OLS regression when endogeneity is a problem.

On a county level, the difference in income, racial composition of a county

relative to one’s own race, and crime rate were all found to be significant.

The difference in income has a nice result that shows there are benefits to

investing in early education, and solely the value of not having to worry about money

does not have a measurable impact. Whether the return on income spent on education at

such a young age is worthwhile is difficult to tell.

Racial composition had a strange result that having more of one’s race at age 3 is

a deterrent for white and black children. It is positive for Hispanic children, perhaps for

cultural reasons. Racial homogeneity often helps minorities fit in with people who share

similar backgrounds and interests. Children do not have a good sense of race, but perhaps

parents feel like they are in a closer, more sociable community if there are more members

of their own race. This sense of community may in turn positively affect children. Also,

children may feel isolated if they are different from the majority of other people in their

community. There may be diminishing returns to having more members of one’s own

race in the community; a different regression would have to be preformed to confirm this.

The implication from homogeneity being a deterrent makes a case for diversity, which

42

was tested. The diversity variable was positive, but it is difficult to make real assertions

of the benefits of diversity.

Crime rate has a negative effect as expected, and is unique from the other absolute

variables in that it is highly significant; the others were not. This shows that there might

be something specific about crime rate that is particularly detrimental to childhood

testing, and is perhaps something that should be targeted in policy-making.

The results of this paper are interesting and have not been explored before. While

other papers have discussed environmental impacts, few have controlled extensively for

family-specific variables, and none have taken a look at relative community variables,

which also showed themselves to be significant.

A lot can be done to continue studies of the effects of community variables. To

truly understand which community variables matter, it will eventually be necessary to

refine the study for better accuracy. A county is a large area with much variation in it,

which leaves much to be desired in representing a child’s environment. Census data is

available for every town in America. Analysis could even be performed on a

neighborhood basis, because towns often have “good” and “bad” parts to them. There are

some difficulties with this because people might be unwilling to participate because they

will feel that it is too invasive. The Bureau of Labor Statistics currently manages the NLS

dataset and it would not be difficult to make the project even more specific. There is

already a confidentiality clause to protect participants, and this would create a great

research opportunity.

Other variables are also interesting that can be looked into in further research. The

impact of religion can be looked into. For example, what impact more churches in an area

43

has or what impact is there if more people in a neighborhood participate in your own

religion are possible questions.

Diversity is still an interesting variable, and it could be useful to pursue whether

diversity is beneficial to childhood testing or not, though a different dataset would

probably be needed. This could explore diversity in different ways beyond race such as in

religious or political aspects.

An additional study could focus more on community effects for different genders.

While females are still at an income disadvantage, in recent times, increased media

attention has been placed on the supposed “boy crisis.” Boys now account for 80% of

classroom discipline problems, make up 80% of high school dropouts and form 70% of

children who have been diagnosed with learning disabilities. In addition, a third of men

age 22 – 34 are still living at home; this is an increase of over a hundred percent

compared to 20 years ago3. As this problem grows, it will be important to study the

specific barriers to childhood and adult labor market success for both men and women.

3 Sax, Leonard, “The Trouble with Boys”

44

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