Education in Ecuador: Examining the influence of parent’s educational achievement on the...

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Education in Ecuador:

Examining the influence of parent’s educational achievement on the educational

attainment of their children

Robert Daniel Smith

G21130524

5/3/2013



(I) Introduction:

Education is generally seen as a necessary component in the development of a

nation’s economy. After the primary stages of economic development, investments in

human capital become a driving force in productivity – the depended factor on which

income is generated. Increases in efficiency in the export sector indirectly lead to a total

increase in wages of an economy, as theorized by the Balassa-Samuelson effect, thus

making an educated and efficient workforce crucial to successful economic development.

Therefore it is important to examine data available on education because it provides

insight into an important economic resource. What determines a child’s educational

achievement?

One seemingly likely answer is the educational attainment of their parents. But

how much of an impact does the parent’s education make? Is it possible that the mother

or fathers educational attainment might have different levels of influence on their child’s

education attainment? These questions can be answered by testing to see if an

Ecuadorian’s education attainment is similar to their parents. Furthermore this can be

examined while taking other variables such as region, gender, race, and age into account.

Ecuador is a nation with problematic record on education. In 1999, 64% of

Ecuadorians completing primary education while only 29% completing secondary

education.i There are also great disparities in the educational achievement of

Ecuadorians. Rural residents and certain ethic groups receive a much lower average

education achievement; the rural average years of education is 4.9 while the national

average is 7.3.ii It appears that the government’s efforts to increase access to education

are exhibiting an urban development bias. Ecuador also struggles with high levels of



child labor – which negatively impacted educational attainment – and heavy indebtedness

(debt repayments make up 40% of Gov. Budget, 9% GDP).iii

A model measuring the effect of a subjects mother’s and father’s education, along

with their age, race, gender, and region showed that the subjects are likely to achieve

about the average educational achievement of their parents. The education of the father

carries slightly more weight than the mother’s. For instance a subject whose parents have

the average level of educational attainment (father = 5.89, mother =6.58) will have about

6 (5.987) years of education, dependent on other variables. This fact demonstrates that

the educational attainment of parent is important in predicting the educational attainment

of children. The subject’s education is less influenced by their parent’s education if they

are black, female, old, or from region 1.



(II) Data:

The data used in this project comes from a survey on Ecuadorian income and

expenditure from 2003.

There are seven total variables. The dependent variable is EYears which

represents the number of years of education a subject has. The primary explanatory

variables of interest (continuous variables) are EYEARS_M and EYEARS_F which area

father’s years of education and mother’s years of education respectively. In the sample,

mothers are more likely to have a higher level of education, but the education of the

mother and father are highly correlated. All of the subjects were more educated than

their parents on average. The only other continuous variable is the age of the subjects

(denoted by AGE). The Average age was around 37 and the standard deviation was a

little high at 16.62; there is a wide spectrum of ages is in this dataset. None of the

subjects are under 16.

The rest of the variables are categorical dummy variables. The Female variable

tests whether the likelihood of educational attainment is affected by gender. About half

of the sample was of the female gender. The Black variable represents whether the

subject is white or black; about 5% of the sample was black. The Region_1 variable



explains whether the subject was located in region 1 or region 2. 54.5% of subjects were

located in Region 1.

A histogram of the dependent variable EYEARS shows that the data is bimodal

with 6 and 12 years as being the two modes. This could be due to that fact that 6 years is

a common primary school length while 12 years is a common secondary through

primary school length. According the Ecuadorian Constitution, the students are expected

to achieve a basic level of education of around 9 years, which is the .5 less than the

mean years of education for the sampleiv

.

Histogram of EYEARS, the

distribution of this variable proved

normal at α = .05%

A histogram of maternal education shows A histogram of Paternal education shows a



6 years as the most prevalent level of

educational attainment, followed by 0.

similar distribution as the maternal

education attainment

It seems quite likely that increased availability of secondary and tertiary schooling has

led to the increase in subjects with above 6 years of education. During the 1980’s and

1990’s the primary enrollment rate increased from 68.6% to 90% and the government

created programs to increase access to and incentivize education in rural areasv.



(III) Econometric Model

The model seeks to explain the effect of parental education on the educational

attainment of their children by examining the educational attainment of Ecuadorians

and regressing it with the educational attainment of their parents. To determine if the

effect increases when both parents are highly educated, an interaction term of

EYEARS_F and EYEARS_M is created. The AGE variable will test to see if older or

younger subjects are more likely to be highly educated. The three dummy variables –

region_1, Female, Black – will test to see if any of these important demographic

factors heavily influence the educational attainment of the subjects. This will allow

for the better isolation of the parental education attainment variables. The equation

for determining educational attainment (EYEARS) is as follows:

( )()

β1 – Years of paternal education

β2 – Years of maternal education

β3 – Age in Years

β4 – (0,1) Dummy for if subject lives in Region 1

β5 – (0,1) Dummy for subject’s gender

β6 – (0,1) Dummy for Subjects race, either black or white

εi – Error term



(IV) Results:

The parameter estimates from the model illustrate which of the variables have a

profound impact on the dependent variable. Both maternal and paternal education has

a significant coefficient and a high t-stat. The Paternal education has a larger effect on

determining the educational attainment of the subject. The difference in effects

between EYEARS_M and YEARS_F is statistically significant, as the 95%

confidence interval demonstrates – there is no overlap in two variables intervals. The

interaction term between maternal and paternal education demonstrated that an

increase in both parents education can lead to a slight decrease in educational

attainment of their child. The AGE variable shows that the older the subject, the less

likely they will have attained a high level of education; however at -.0237, the effect

is not spectacular. The dummy variables reveal that region and race more heavily

effect education than gender. A white, male, from Region 2 is likely to be the most



educated type of subject, with demographics earning him an average of 1.85 more

years of education. While all statistically significant, the disparities are not as large as

they could be. Black subjects received about a year less of education that Whites,

while region made a little over a half a year difference.

F-Test

H0: either β 1 = 0 or β 2 = 0 or both

Ha: either β 1 ≠ 0 or β 2 ≠ 0 or both

p-value < .05; Reject H0, accept Ha

An F-Test of the principle explanatoryvariables shows that their parameters are

not equal to 0. This is not surprising

considering that all of the variables

individually test significant in T-Tests

The results from the F-Test and T-tests indicate that the two primary explanatory

variables adequately provide good estimation of the effect of a parent’s education on

the education of their children. Granted, there are many other possible confounding

variables that should be accounted for. The data also tested as heteroskedastic.



(V) Results Explored:

(V.i) Internal Validity

The threat of a confounding variable is a real threat to the model. This is

especially the case when considering the effect of income on education attainment.

Even though primary and secondary school are provided by the government, there are

generally additional costs associated with attending school.i It is very possible that

there is a connection between the income of a subject’s parents and the subject’s level

of attainment. A study on educational attainment and income in 1987 found that there

was a statistically significant correlation between income and years of schooling.vi

It

would be difficult, however, to retroactively attempt to measure parental income; a

long term study could shed results on incomes effect on educational attainment (as

would a model that measured family educational expenditures). If income was added

to the model used, it could better reveal parental influence on education. An

interaction term could determine if wealthy, highly educated parents are very likely to

have well educated children and if poorer, but still well educated parents are less

likely to have well educated children.

Another way the model could possibly be improved is by controlling for age

better. It is unlikely that subjects 16 years of age with have, on average, the same

educational attainment as a subject 20 years of age. Likewise, a 60 year old subject is

going to have had less access to education as well. The model showed a slight

negative relationship between age and educational attainment, which supports the

idea that older subjects did not have access to certain educational resources.



(V.ii) Bias

There is selection bias present the sample. It does not adequately represent

Ecuador, meaning that the results cannot be properly used to make inferences about

rest of the country. The sample only provided subjects from two regions and only

from urban sources. It is very possible that the variables in this model have different

impacts on different populations in Ecuador; it is especially feasible that rural areas

would be influenced differently that urban populations.

(V.iii) Omitted-variable bias

This again is related to income. It is quite possible that the model overstates the

impact of the parental educational attainment. There is generally a correlation

between education and income. If parental income is also correlated to educational

attainment of subjects, then it would make the omission of income an Omitted-

variable bias since it is correlated to the dependent variable and the independent

variables EYEARS_F and EYEARS_M.

If the scope of the study was expanded to include other regions and rural areas,

dummy variables for the regions could be created. There is not OVB with regards to

these variables in the model so long as no inference about the general population of

Ecuador is made.

(V.iv) External Validity



The external validity of this model is compromised by the inability to apply the

results to the other populations of Ecuador. Inferences on the results could be made,

but not without heavy bias.

(VI) Conclusion

Because the educational attainment of a subject is a product of the weighted

average between their parent’s years of schooling, it seems as though the parental

education is a good factor for the determination of a child’s education. The fact that

the model proved statistically significant reinforces this conclusion. Taking Age,

Gender, Region, and Race into account helped create a more accurate estimate of

parental education’s effect on children by isolating possible confounding variables. It

also seems that the father’s education makes a greater impact, granted the mothers

have a higher average of years in school. There is also a slight negative correlation

between parents who are both educated and their children’s education. This is

puzzling, but could signal a decreasing marginal return in regard to parental education

(very possibly due to the weighting of the father ’s education). Cleary the education of

children is important to educated parents; a greater study should be done to examine

the role of income and demographics in educational attainment.

i Rosa María Torres , ILLITERACY AND LITERACY EDUCATION IN ECUADOR: OPTIONS FOR POLICY AND

PRACTICE, Fronesis Institute (2005).ii Ibid i.iii Ibid i.

iv Department of Labor, Ecuador, http://www.dol.gov/ilab/media/reports/iclp/tda2001/ecuador.htm

vEducation and Development in Low income countries : how to improve efficiency and effectiveness of

Education Policies

http://www.iss.nl/fileadmin/ASSETS/iss/Documents/Research_and_projects/Education_Policies_in_Ecua

dor.pdf vi Lusia Gomez-Castellanos and George Psacharopoulos, “Earning and Education in Ecuador: Evidence from

the 1987 Household Survey”, Economics of Education Review (1990).

http://www.dol.gov/ilab/media/reports/iclp/tda2001/ecuador.htm



http://www.iss.nl/fileadmin/ASSETS/iss/Documents/Research_and_projects/Education_Policies_in_Ecuador.pdf






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