Encouraging higher academic performance in secondary education:

Amanda Gant 1

Secondary school performance:

Case study of students from Cusmapa, Nicaragua

I. Introduction

College isnt for me; I dont have good grades in high school. Only the smart kids go to college. These thoughts can come from students from around the world, from the US to Nicaragua. Students are not the only ones who think that grade point averages (GPAs) are important in order to go to college. Many states, countries and schools have GPA requirements for receiving grants, scholarships and admission. Since GPAs are important for getting students to college, then some energy should be spent to look at the relationship between different variables and GPAs, with the goal of understanding what can affect GPAs. This paper examines this relationship in the GPAs of high school students at the small, rural high school of Cusmapa, in the developing country of Nicaragua.

The debates around education agree that that education helps the economic development of a country. Professor Carl Dahlman attests that higher levels of education are necessary to use, adapt, and create new knowledge; Education and training, therefore, are the key enablers of the knowledge economy and key elements for increased competitiveness and improved welfare. Eric Hanusheck, who writes for the World Bank confirms that educational quality and cognitive abilities are related with more equal income distribution and economic growth. Education is relevant for the economic health of a country, and is relevant for the improvement of Nicaragua's economy, as well. Investment in schools and teachers is an accepted route of arriving at education; however, Bruce Fuller argues that there are varied educational outcomes for equal levels of educational spending. I hope to discover some variables that affect educational outcomes that may be overlooked when focusing on school inputs.

My thesis deals with Cusmapa, a small community in Nicaragua that may be representative of many rural communities in Nicaragua. I will look at the effects of out-of-school variables such as daily attendance at supplemental secondary education programs, child sponsorship, family size and distance from learning institution on a student's GPA, which is the measure of the students academic performance or achievement. I am testing the hypothesis that student background characteristics effect academic achievement of a student. School input variables are controlled for since I am studying students from one high school. In the case that some variables are found to be correlated with GPAs, I want to examine the possibilities that policies may be suggested to create conditions that are conducive to better learning results.

The rest of this paper will flow in the following way: First I will give a literature review of economic papers that deal with monitoring academic performance, explaining that my paper differs because it is a school-level study that specifically examines out-of-school variables' relationship with variance of Grade Point Average (GPA). Then I will discuss the specifics of my research, including details about Cusmapa and the survey that I administered there, and make the case for secondary education in Cusmapa. Following that I will present the results and make some conclusions, programming suggestions and suggestions for further research.

II. Literature Review: A framework for grouping variables

The human capital theory provides a popular method with which to organize independent and control variables. The theory is that if one makes investments in education, then one should see economic results; a function of monetary input determines educational achievement, and therefore economic growth. In their paper entitled The Determinants of School Achievement in Developing Countries: A Review of the Research Simmons and Alexander explain a main idea of the human capital debate, the Educational Production Function (EPF), which serves as a central framework to economically study academic achievement in other literature, as well as in my paper. The model is constructed as follows:

A = g[S, F, P, I]

A = academic achievement, S = school inputs, F = Family background characteristics, P = peer group characteristics and I = initial endowments. Simmons and Alexander's paper goes on to review 19 papers from developing countries that study the influence of the four EPF variable groups - family background characteristics, school inputs, peer group characteristics and initial endowments on variation in student achievement. The Simmons and Alexander paper is good for gauging the results of the debates and questions concerning results from education in developing countries. From the four groups of variables in the EPF, I include proxies for three: family background characteristics - mother and father's education level, marital status and number of siblings; school inputs - distance from the high school, and daily attendance of Fabretto programs; and for initial endowments - gender and year in high school.

Hanushek and Woessmann sum up the traditional approach by saying that the general objective is to sort out the causal impacts of school factors (things that can potentially be manipulated through policy) from other influences on achievement including family background...

The standard question about analyzing educational achievement has been that school inputs, instead of non-school variables, retain their importance; school input variables are seen as the main vehicle to improve school quality. However, Bruce Fuller states that if we do not define relevant family background and social class variables within non-Western cultures then the extent that school input is having is over stated. Bruce Fuller's work asks: Do schools raise achievement after taking into account pupils' family background? implying that familial background and social class variables are important and do have an effect on achievement. His paper then goes on to classify the results of 60 econometric papers from developing countries that explain the variance of students' performance with respect to different variables from their various schools as compared to the variance in students' performance explained by students' background. The aim of his paper is to determine the relative importance of background characteristics and school characteristics.

In a paper entitled The Effect of Primary-School Quality on Academic Achievement Across Twenty-nine High- and Low-Income Countries economists Heyneman and Loxely tackle more thoroughly the debate about the relationship between in-school and out-of school variables and the quality of education received. Out-of-school variables include are set to include conditions that students inherit sex, intelligence, socioeconomic status...access to libraries. In-school variables include textbooks, budget, hours of classes, etc. Like other economic papers I encountered, their work investigates the effects of in-school variables on academic achievement, and the use of in-school variables as a vehicle to improve school quality.

Literature often falls short by discounting the influence of student background characteristics, and the ability to structure achievement-promoting policies in response to results from those characteristics. As many reports examine educational performance with the goal of determining what makes a school produce good results (usually in the form of improved performance), my paper is unique because I will also take into consideration out of school variables that may be related to good academic performance.

My study differs from both Fullers and Heynemans works in that it specifically hones in on the influence of student background on academic performance. All of the students I surveyed come from the same school, thus controlling for all of the in-school variables. I therefore am able to ask the question: Do pupils family backgrounds and other variables raise achievement, after taking into account school inputs?

I believe that policy that can encourage educational achievement goes beyond changing school funding and/or organization as much of a student's life occurs outside of school. As such, the effects of other life situations on the variance of academic achievement should also be taken into consideration. To this end I study student achievement data from one high school, thus controlling for school effects, then to analyze the results within the context of the larger and more international debate.

With respect to the variable academic achievement, one should choose the proxy while keeping in mind the comparisons one wishes to make. I have chosen student GPAs to gauge academic achievement, my dependent variable; however test scores are another popular ways to gage academic achievement. If one is doing an international or nationwide test, one may decide to look at the results of a standardized test that was given in each location studied in order to have data based on equal measures; Woessmann chose the Third International Math and Science Study for his international study on school effectiveness. Heyneman and Loxely collected their data from nationwide or international surveys performed by international or governmental institutions such as the International Association for the Evaluation of Educational Achievement or The Planning and Organizational Office of the Ministry of Education of El Salvador. These data sources are representative of the majority of data that is used in the literature, however this data tends to overlook the poorer urban and especially the rural areas where such tests are not administered. On the other hand, if one is studying the academic performance of students from within one school, as I have done with my research, then GPAs can form a strong proxy for academic achievement. When I use GPA, I am comparing the relative performance of students, and assume that within one school the grading scale will be fairly comparable throughout.

Alexander and Simmons stressed that many results from papers that they compared actually contrasted, emphasizing the importance of localized studies of each educational system. For example, larger school size was related with higher grades in Chile and India, and with lower grades in Malaysia and the Congo. My work adds to the array of literature by providing specialized insight into the high school of Cusmapa, Nicaragua. This insight is especially differentiated because this is the first econometric paper based on data from Cusmapa.

III. The specific research

Cusmapa, Nicaragua is at the end of a dirt road in the mountains in Northern Nicaragua. Located 7 hours from Managua and a two and a half hour school bus ride from the nearest urban zone, where people buy necessities such as toiletries and simple processed foods, Cusmapans are very self-sufficient, producing their own coffee, tropical fruits, beans, tomatoes, cheese and tortillas. The economy is primarily agriculture mainly for subsistence and local market sales. The hilly land makes many plots sub-optimal for growing crops.

There is one high school in Cusmapa, and this school is the location of my study. All the students from Cusmapa that attend high school must attend here. For some students, it is a 3 minute walk to the school, other students walk over two hours to arrive at the school. The high school served about 300 students in the 2006 academic year. In Cusmapa, there exist resources, in the form of state-sponsored and NGO-sponsored scholarships, for motivated students to attend college. As resources are available, success in the secondary school in Cusmapa will allow more students to make plans for higher education.

Cusmapa is one of many small, rural communities that exist in Nicaragua. The results that I find here may be transferable to other rural and poor communities. %42.7 of Nicaraguans live in rural areas, %79.9 of Nicaraguan workers earn less than $2 a day, meaning that there is a lot of rural poor. Moreover, there are many young people in the country: 53% of Nicaragua's population is under 18 years old, meaning there are many students that rely on rural high schools similar to the students and high school in Cusmapa. The results of this paper could have implications not just for Cusmapa, but also for the many other small communities that exist in Nicaragua.

My Data Sources and Empirical Strategy

I planned my survey in order to solicit data to represent variables that are important variables likely to be correlated with student academic achievement. I designed a two-page survey with 35 questions that would solicit the kinds of responses to use in my analysis. I gave the survey to a pool of 227 students ages 11 to 21 over the course of two days. I worked with the principal of the school to schedule times to visit each of the grades. As the students were in class, I simply explained a little about the research project and asked them to fill out the survey and turn it in to the front of the classroom. After collecting all of the surveys, I transferred their responses to an excel spreadsheet from whence I have begun to analyze the data.

I put the questions that solicited these variables into six categories as to give structure to the survey. The categories were: About me, school questions, work questions, questions about their future plans and hopes, family background questions and questions about participation in Fabretto programs. The independent variable is academic achievement, which is denoted here by average reported GPA, on a 0-100% scale. This number was highly recognized and easily reported by the students at the school. I chose my control variables from among factors that are usually cited as effecting academic performance and which cannot really be changed by practice or policy. In my research I investigate as control variables such as: gender, parental education levels, parental literacy, and year in school. I chose test variables by imagining which variables about a child's situation could be changed or influenced through policy or individual habit changes. In my research I investigate as test variables: daily attendance at Fabretto, time walked to the high school, number of siblings a child has and child sponsorship.

My null hypothesis is that there are variables that are correlated with higher academic performance. Upon finding these significant variables from within my data set, I would hope to see if some policy suggestions based on this locally-collected data might be offered for the Cusmapa community.

Econometric Model

I use cross sectional data set with linear regression techniques, starting with my test variables, and then adding key control variables as to explain more of the effect on GPAs. I have two baseline regressions. The first regression is of test variables, which are variables that could be affected through policies or programs. This regression is:

GPA=(distance)+ (sponsorship)+ (Fabretto attendance) + (siblings)

The second regression is what I call the traditional model, one that uses some of the many factors that are popularly sited as having correlations with student achievement. This model is as follows:

GPA= (number of siblings)+ (mom's years of school)+ (dad's years of school)+ (gender)

I then created a stronger regression by choosing control variables that produce the most compelling explanatory power; paying special attention to rising adjusted R squared values and statistical significance. For example, I substituted mother's and father's years of schooling with mother's and father's literacy for the measure of parental educational level to see which one is more substantially related with GPAs.

Returning to the literature, Heyneman and Loxely managed their data in a way that served as a guide for me; they removed variables if they showed little variance or if their coefficients were less than .05. This paper compared the performance of schools as compared with other schools from the same country, using pupil performance to measure success. I am also studying student performance and, therefore a chose to apply Heyneman's and Loxely's standards as a general method of selecting my variables.

IV. HYPOTHESES:

Control Variables:

Parental Education: I collected data for two measures of parental education levels: years schooled and a literacy dummy variable. As these two were correlated, I used only the more significant of these measures of parental educational levels in the regression years of parental education. I expect that the more years a parent has received, the more likely a student would be able to perform well in school as the parent would be in a better position to share knowledge and give advice to their children. The parent that has received more education may also value education more, and thus be more likely to encourage their children to do well in high school.

Grade level: may be a relevant control variable. This variable will help get rid of GPA-biases that may exist in a given grade. A hypothetical example would be if the fourth grade teacher gives much harder exams than the other teachers, and therefore everyone in fourth grade, all else equal, may receive a lower GPA. I believe that this variable will also capture the effects of student selection, that students self-select into higher grade levels, the strongest students making it till the fifth year. Each successive grade in the Cusmapa high school has fewer and fewer students. This is due to the fact that each year students drop out. I believe that the strongest students make it to the last year of high school, and therefore think that as grade level rises, so would GPA, giving grade level a positive correlation with GPA.

Female: The literature has shown that the coefficient associated with being a female may go either way. If we take the US as an example, girls outperform boys in reading and writing, while boys do better in math and science However, in Cusmapa, I believe that the quietness of girls, honored as a characteristic of the Machista culture, may instill an apprehension to perform well and boldly in class. Therefore, I expect that being a female will be negatively correlated with GPAs.

Married: If the parents are married, then one will expect a more stable and united family correlated with higher GPAs.

Live with parents: One can expect that the variable for living with the parents will add to the ability to do well in school, thus this variable should have a positive correlation with GPA variance.

After I decided on these most substantial control variables, I added in the test variables, to check for robustness as well as for possible interrelations between the test variables. These test variables include dummy variables for international child sponsorship and daily attendance to Fabretto programs; as well as number of minutes away from the high school and number of siblings in the household.

Some existing literature can act as a guide for forming hypotheses about many of these test variables. Some literature examines the effects the out-of-school variables that I will be studying, however, just not within a different context. Studies tend to focus on the effect of out-of-school characteristics on school attendance, not academic achievement. Nonetheless, I will be able to cite a few papers to enforce my hypotheses for my test variables.

Test variables:

Distance from high school: The literature shows distance as a hindrance for school attainment in Ghana as Morwena Griffiths cites distance from school as a reason that females cannot attend school. In a paper by Gail Kelly, distance from school was mentioned as a reason for missing school, especially for women.

This variable, then, seems that it will be negatively correlated with GPAs. If children live farther from the school, then they have to walk further to get there, spending more time and energy on transportation. In Cusmapa, transportation to school usually is in the form of walking in the forests on dirt paths, up and down mountainsides, and in between farm plots and grazing land, to school. The students who attend school will have less time and energy for out-of-school studying, possibly leading to lower performance. Distance away from school may also be correlated with lower parental education levels both of which would be assumed also lead to a negative effect in the GPA as well. I expect minutes away from the high school to be negatively correlated with GPAs.

Family size: With respect to family size, a report from Knodel and Wongsith says that in Thailand more children in the family exert a negative influence on the probability that a child will attend secondary education. and that falling birth rates contribute to increasing educational attainment in Thailand. Much research is done to show that the attainment level i.e. number of years they attend school - increases if there are less children in the family, but not much literature looks at if a smaller family size is related to higher academic performance. However, from works such as Knodel and Wongsith's I expect that smaller family size interpreted in my data as a less number of siblings - leads to higher chances for educational achievement. This may be because the parents can give more energy and time per child assisting him/her with homework, and encouraging him/her to do well, when there are less children with whom to divide time and energy. Children from a small family may also have access to more material resources per capita as the children from a smaller family would have fewer siblings amongst which to share. All of this reasoning leads me to hypothesize that the number of siblings is negatively correlated with GPAs.

International Sponsorship: In Cusmapa sponsorships are given out on a first-come-basis to whoever puts their name on the list. The sponsorship consists in the provision of a canasta basica which means basic basket of goods. These goods include a months supply of oil, beans, rice, some vegetables and soy products; school supplies such as a pencil and notebooks; and bi-yearly new shoes and school uniforms. This variable may pull in two directions; the argument for a positive correlation is that these international sponsorships give a student the food, clothing and school materials necessary in order to focus on schooling instead of working in order to buy these goods necessary for school. However, the argument for negative correlation is that this type of handout may make a student feel entitled to receive things for free and might erode his perception of the need to try hard in school in order to receive economic benefits. I hypothesize that this variable will have no significant effect.

Fabretto Attendance: My hypothesis is for this variable to have a positive correlation with GPAs, as Fabretto offers re-enforcement of subjects that are taught in school. Fabretto center is free of charge to those students who choose to participate, the students apply and commit to whatever classes or activities for which they would sign up. Fabretto offers these students classes in internet use, agricultural training, a choral group that goes on trips, and a lunch feeding program. The extra activities that Fabretto offers high school students are motivating for students and Fabretto's activities build connections between the students and the greater outside world.

A common variable that can be found in the literature, access to a library would compare with my variable of attending an after school program, as Fabretto center has a library that the high school students often use and the local high school does not have a library. In Alexander and Simmons paper access to a library has had a positive coefficient. Attendance at Fabretto is expected to have a positive correlation with GPAs.V. Results

Population description information:

First I will go over basic characteristics of the population I have surveyed, and then tell you about the main findings regarding each of the variables mentioned above. I will conclude this section with other theories that are supported by my research, but that may be somewhat tangential to my investigation for improved

Of all the 227 students I surveyed, 80% of the mothers and 79% of the fathers are literate with an average of 5.39 and 6.04 years of schooling, respectively. 47.5% of the students I surveyed are female. In the appendix, chart 1 shows the make up of the population by academic year, there are fewer students in the upper grades, as each year students tend to drop out. Chart 2 shows us the professions that the students want to pursue. Many students in Cusmapa are interested in choosing a professional life the first step is doing well in high school.

A series of Regressions:

(1) regress grades siblings minutes fab1 apad

I begin my regressions by using only my test variables. I do this to get an initial idea of relative coefficient values. Grades are defined by grade point average (GPA) on a scale of 0-100%. In this regression I see that my test variables explain only about 8 percent of variation. From the beginning, international sponsorship is much less significant than the other test variables, holding with my hypothesis that there are forces that would cause sponsorship to act in conflicting directions. Attending Fabretto has a positive and significant coefficient, as predicted. The number of siblings and number of minutes that a student lives from school also follow my prediction of being negatively correlated with GPAs.

(2) regress grades momedu dadedu female siblings

In the second regression I combine the test variables with the variables that are generally cited in development economics literature as important components to education; momedu and dadedu respectively represent the number of years of school the mother received; female is a dummy variable denoting the gender of the student. Regression 2 has a higher explanatory value, explaining around 13 percent, though it has the name number of variables/degrees of freedom.

I now begin sensitivity analysis, which allows me to create better regressions with each small tweak of the variables that I make. I do not show the regression results for all of the decisions that I have made in order to come up with my final regressions, but instead will speak about the key decisions that I have made along the way of developing these decisions.

(3) regress grades momedu dadedu siblings female minutes married momlive fab1 apad

I began by using all of the variables above in one regression. This produced only a modest increase of my R squared from regression two to .1608. However, I saw that neither mother nor father's education was very relevant. I believed that this could be because mother's education is highly correlated with father's education, and thus the two variables will be capturing the same variance in GPA. Regression 4 in the regression results chart illustrates this correlation. I therefore decided to use only the more relevant of the two variables for my regressions: mother's education. When I used mother's education instead of fathers education, the R squared of the regression increased from .1560 to .1754.

I continued adding different variables from my data. The additional control variables were yr, which means which year of high school -year one through year five- the student is in; married denotes that a student's parents are married; momlive describes the situation of a student who lives with their mother. Living with the mother, or having parents that were married both had positive correlations with GPAs, in accordance with my hypothesis.

(4) regress grades momedu yr female siblings siblingsf minutes minutesf married momlive apad fab1

There was an economically large economically and statistically negative correlation between gender and GPAs: a -3.691 coefficient. This prompted me to examine interaction terms in attempts to specify gender differences in the data. I created interaction terms for all of the variables that I used in the previous regressions. I tested all of the variables in turn using the different interaction terms to determine with which variables gender induces a difference. The addition of chosen interaction terms result in an improvement from an R squared of .2521 to and R square of .2822, without a decrease in the adj. R squared. I found that the interaction terms for siblings and minutes walked to school have added the most in terms of explanatory power and increased efficiency. The variables of siblings and minutes, that had previously been insignificant in regressions, could more correctly tell their stories.

Siblings have been found to have a negative correlation with GPA for male students. For each additional sibling that a male student has, his GPA is expected to go down about 1%. However, the interaction term for female students effectively cancels any relationship between GPAs and siblings.

Each minute that a girl walks to school is related negatively - at a 2.5% confidence level - with GPA at the rate of -.6% from her GPA for every 10 minutes. To illustrate the magnitude, a girl who lives two hours away from the school has an expected GPA that is 7.2 percentage points lower than a girl who lives next door to the high school. On the other hand minutes walked to school is shown to have little correlation with GPA for males.

5.) regress grades momedu yr female siblings siblingsf minutes minutesf married momlive

Through these regressions I learn that two of my test variables - daily Fabretto attendance and international sponsorship - are not very robust or significant. In my regressions, the coefficients for sponsorship are usually negative, ranging from -2.139 to -.635, but never show better than 25% significance levels in the presence of control variables. The inconclusive results for international sponsorship match my hypothesis. On the other hand, the insignificant coefficients for Fabretto attendance go against my hypothesis that this coefficient would be positive. In the regressions, Fabretto attendance coefficients ranges from .757 to 2.328, and while all values are positive, matching my hypothesis, the coefficients are never very statistically significant in the presence of control variables, with a 38.7% significant level being the most significance it could show a far cry from the significance I was expecting. In the end, I decide to drop these two test variables in favor of a more efficient regression.

VI. Analyzing results, conclusions and policy suggestions:

Some results matched with my hypotheses, such as the negative correlation between being a female and GPAs also matched my hypothesis, as did the positive relationships between GPAs and mother's education, parental marriage, living with the mother, and the higher years that a student is in school.

Other results matched part of my hypothesis, such as my findings for the relationship between siblings and GPAs. The number of siblings showed a negative effect with boys' GPAs, but essentially no relationship with females' GPAs. I did not expect the variable to have a different relationship with boys than with girls, and even intuitively, I think that females would have been more effected by the number of siblings, as they might be the ones looked towards to mother other siblings. Explaining the difference in the coefficients of siblings for females and males requires a level of social analysis that I may not understand. I cannot assign a possible reasoning to these results.

Minutes away from school was another variable that matched my hypothesis for one gender - this time the correlation for the female students, and not the male students, matched my hypothesis. Minutes away from the high school had a negative correlation with GPA only for the girls, with no substantial effect for male students. Though I believed that a long walk to school would tire out both the females and the males, we can return to Griffiths and Kelly's results where distance also has a gendered phenomenon. Long walks may take a stronger toll on the females' bodies than on the males' leaving the females with less energy.

The variables that I found to be insignificant also add substance to my findings. In two variables, the mothers influence on GPA was felt more strongly than the father's. Mother's education and living with the mother was found to be significant, while father's education and living with the father were variables whose coefficients had less significance, and were excluded. These results illustrate the relative importance of the mother in relationship with her children's GPAs. The argument that mothers are important in the outcome of the academic performance of their children, coupled with my findings that a female will receive a 5% lower GPA than a male, all else equal, strengthens the call to focus resources on females, both young and old in Cusmapa.

That international sponsorship was insignificant matched with my hypothesis. There is no consistent relationship between this financial support and academic performance. The sustenance provided by sponsorship programs has a value to itself. However, I am most concerned with my finding that daily Fabretto attendance explains little variation in GPAs where I had hoped to find a positive correlation. Explanations for the weak results could be that the students who attend Fabretto do activities that are unrelated to directly improving their school performance, such as eat free lunch, practice in the choir and learn computer skills all important activities, but at best indirectly related to getting better grades in school.

Policy suggestions

It should be seen that gender inequality, and the importance of mothers, as shown by my data, should support the movement for female focused policies in Cusmapa. I advocate the creation of a girls' study group, where the girls would not only practice and study what they learned in school, but discuss the importance of female leadership and initiative. These groups, if correctly lead, might give girls the confidence they need to more confidently compete with their male classmates. As far as siblings have been shown to have negative relationships with males' GPAs, there is an argument to promote family planning in order to have fewer children. The policy could be to educate all members of the family on the importance that smaller families can mean for their household. Using this data about GPAs relationship to siblings could constitute one point of discussion. Family planning could also be incorporated into the girls' study group.

As the minutes to walk to school are negatively correlated with females' GPAs, more educational centers should be constructed in the countryside as to supply schooling at a shorter distance. Fortunately, in the case of Cusmapa, this is occurring right now with the support of the Fabretto Foundation. They are currently holding classes in four centers throughout the mountainsides of Cusmapa.

How to improve research:

The best the R squared from my regressions is 28.2%, meaning that many factors that must explain GPA variance were unobserved in my regressions. These unobserved variables may be initial ability to learn, attitude towards learning, and a specific proxy for hours spent studying. One way to improve the research is to search for ways to make the unobserved variables into observed variables by collecting more data. For initial ability, one may administer an IQ test in order to obtain a proxy. Though this is difficult, it is an option that would be possible in future research. Perhaps including a variable to account for hours spent studying would also add good explanatory power to the regression.

In order to further strengthen the results I collected in my survey, I would also change the way that I asked a certain question. On the section in my survey entitled My work almost no students (only 8%) reported working, and those who did report working were unsure of how many hours they worked or of their salary. However, I observe casually that many students in Cusmapa actually do work around the house and in the farms. I believe that if I reworded the question, briefly describing what constitutes work then I would have received more responses, giving me another unobserved turned observed variable to test. I was planning using work as a test variable in the regressions, but could not do so because of the inconclusive results that my form of questioning produced.

Another important variable to include is one for parental income, or at least for familial social economic status. I tried to collect parental income data, but as income is unstable in Cusmapa, if there is income at all, the reporting on this variable was not reliable. There is under-employment, self-employment, bartering and unstable salaries. Since Cusmapa is a very self-sufficient community, much of the scant income depends on producing agricultural goods for sale or barter. If I were to do this research again, I would look at not only income, but also at other indicators of social economic status - possibly focusing the value of land and of livestock. I would also add as dummy variables the presence of a sound system or a television which seem to be a marker of social status; when the family is able to save, one of the first things they buy is a stereo system and a television.

However, as to the importance of income variance in GPA variance in developing countries, we may be able to draw conclusions from other literature: Bruce Fuller says that social class explained just 3% of variance of achievement in East Indian children. Social class could be a combination of parental education, which I include in my study, as well as an income variable, which I do not have. The statement, then demonstrates that in communities made up of generally poor people, variance in income is not as important as other variables. Parent's social class has more correlation with variance of GPAs in industrialized countries.

VII. Final conclusions:

In this paper I set out to find what variables are related with GPAs in Nicaragua, under the argument that doing well in high school is important for the future of an individual student, as well as for the economy of a country. We see that in Cusmapa there are gender inequalities to deal with, and that literature is reinforced when the data shows that in Cusmapa, mother's presence and education is relevant in the GPAs of their children. However, the coefficients for the most ambitious programs in Cusmapa, the Fabretto attendance center and international sponsorships, were insignificant. These results should tarnish the value or effectiveness of these programs, as they have goals in addition to improving academic achievement. In the case of international sponsorships, the goal is material sustenance, and not specifically educational achievement. With respect to the Fabretto Center's programming, the NGO should appreciate the results of the data and use it as a guide to reevaluate the goals of its programming, and to include more female-empowering programs.

Work Cited

Fuller, Bruce. What School Factors Raise Achievement in the Third World? Review of Educational Research, Vol. 57, No. 3. (Autumn, 1987), pp. 255-292.

Stable URL: http://links.jstor.org/sici?sici=00346543%28198723%2957%3A3%3C255%3AWSFRAI%3E2.0.CO%3B2-T

Griffiths, Morwenna and Marie Parker-Jenkins. Methodological and Ethical Dilemmas in International Research: School Attendance and Gender in Ghana. Oxford Review of Education, Vol. 20, No. 4. (1994), pp. 441-459.

Stable URL: http://links.jstor.org/sici?sici=0305-4985%281994%2920%3A4%3C441%3AMAEDII%3E2.0.CO%3B2-Z

Hanushek, Eric A., and Ludger Woessmann. The role of education in economic growth. World Bank Policy Research Working Paper 4122, February 2007

Heyneman, Stephen P. and William A. Loxley. The Distribution of Primary School Quality within High- and Low-Income Countries Comparative Education Review, Vol. 27, No. 1. (Feb., 1983), pp. 108-118.

Stable URL: http://links.jstor.org/sici?sici=0010-4086%28198302%2927%3A1%3C108%3ATDOPSQ%3E2.0.CO%3B2-9

Heyneman, Stephen P. and William A. Loxley. The Effect of Primary-School Quality on Academic Achievement Across Twenty-nine High- and Low-Income Countries. The American Journal of Sociology, Vol. 88, No. 6. (May, 1983), pp. 1162-1194. Stable URL: http://links.jstor.org/sici?sici=0002-9602%28198305%2988%3A6%3C1162%3ATEOPQO%3E2.0.CO%3B2-D

Kelly, Gail P. Setting State Policy on Women's Education in the Third World: Perspectives from Comparative Research. Comparative Education, Vol. 23, No. 1, Special Number (10): Sex Differences in Education. (1987), pp. 95-102.

Stable URL: http://links.jstor.org/sici?sici=0305-0068%281987%2923%3A1%3C95%3ASSPOWE%3E2.0.CO%3B2-O Simmons, John and Leigh Alexander. The Determinants of School Achievement in Developing Countries: A Review of the Research. Economic Development and Cultural Change, Vol. 26, No. 2. (Jan., 1978), pp. 341-357. Stable URL: http://links.jstor.org/sici?sici=0013-0079%28197801%2926%3A2%3C341%3ATDOSAI%3E2.0.CO%3B2-R

Appendix

Regression Chart

12345

Constant83.30481.10579.66576.39776.88

[.000][.000][.000][000][000]

Siblings-0.395*-0.402*-0.337-0.934**-1.019***

[0.069][.093][.179][.004][.002]

Siblingsf0.873**1.039**

[.038][.015]

Minutes-0.035*-0.0230.010.013

[.007][.124][.613][.499]

Minutesf-0.058**-0.0583

[.026]0.022

Fab12.328*0.6340.467

[.052][.638][.692]

Apad-1.714-0.747-0.504

[.148][.561][.657]

Female-3.691***-3.323-5.723***-6.455***

[.001][.005][.006][.002]

Momedu0.3130.2260.361**0.349**

[.110][.278][.036][.042]

Dadedu0.2250.162

[.172][.351]

Yr1.405***1.426***

[.001][.001]

Married1.2432.204**2.505**

[.313][.043][.022]

Momlive2.173.875*3.345

[.350][.070][.107]

Obs.194166159182188

R Sq.0.0960.14670.16080.28270.2765

Adj R Sq0.0770.12550.11010.23630.2399

F stat5.037.463.176.097.56

Variables, hypothesis and results chartHyp.Result

m/fAbbreviationVariable

GPAAcademic performance

+0fab1Daily Fabretto attendance

--femaleGender

++marriedMarital status of student's parents

++yrYear in high school

++momeduYears of education received by mother

+0dadeduYears of education received by father

--/0siblingsNumber of siblings a student has

-0/-minutes

?0apadNumber of minutes to school

Cusmapa is located in the circle.

Survey Questions:

About you

1. Name

2. Age

3. What community do you live in?

4. How long is your walk to school?

5. Gender

School Questions

6. What grade are you in now?

7. What is your GPA?

8. How often do you miss school

a. Never

b. 1-2 month

c. 1 week

d. many days a week

9. When you miss school, what are the reasons:

a. Sick

b. Work*

c. Bored

d. Friends

e. Other, what: _____________

Work Questions

10. Do you have a job now?

11. What is your job?

12. If yes, how many hours per week?

13. How much money do you earn at your work?

Future Questions

14. Do you plan to go to University?

15. What job do you think youll have after finishing school?

16. How much money will you expect to make per month?

17. Do you think your socio-economic status will be equal to, the same or better than your parents status?

Parent/family questions

18. How many siblings do you have? 19. Are your parents Married?

20. Do you live with your mother?

21. Can your mother read?

22. What is the highest level of schooling she completed?

23. What does your mom do for a living?

24. How much money does she earn?

25. Do you live with your father?

26. Can your father read?

27. What is the highest level of schooling he completed?

28. What does your father do for a living?

29. How much money does he earn?

Fabretto Questions

30. Are you sponsored?

31. By Which group?

32. Do you currently participate with Fabretto Programs in the past?

33. For how many years?

34. How often do you attend the Fabretto oratorio?

a. ____days/week

35. What classes do you take?

Technology, globalization and growth presentation, 2/7/07

Fuller, What School Factors Raise Achievement in the Third World?

See map in the Appendix

I will use GPAs as a measure of academic achievement and academic performance, and will use the words achievement and performance interchangeably throughout this paper.

The role of education quality in economic growth, p. 59

Fuller

The Determinants of School Achievement in Developing Countries: A Review of the Research , p. 347

UNDP Human Development Report, 2005.

UNICEF, HYPERLINK "http://www.unicef.org/infobycountry/nicaragua.html"http://www.unicef.org/infobycountry/nicaragua.html

10 See appendix for English version of the survey.

See hypothesis chart in the appendix

Trends in Education and Equity of Women: 2004, National Center for Education Statistics

http://links.jstor.org/sici?sici=0305-4985%281994%2920%3A4%3C441%3AMAEDII%3E2.0.CO%3B2-Z

http://www.jstor.org/view/03050068/sp030065/03x1106n/0

http://www.jstor.org/view/00703370/di973869/97p0009c/0

As I continue to discuss results, I will put the regression used in bold.

Fuller, School Effects in the Third World