Estimation Issues

4.3.1.1 Measures of Education

An important part of examining the educational outcomes of individuals is to choose a measure of education for the study. There are a range of measures of the education of individuals in the existing literature. The most frequently used measures of education include completed years of schooling, the highest level of completed education, enrolment in a specific level of education, the age-relevant educational attainment and drop outs from school. The measure of education is generally selected according to the system of education in a country, the aims of the study and the available information. The highest level of completed education is argued to be the most suitable measure of education in the existing literature since it has the potential to reflect the cumulative process of the education of an individual (Maitra, 2003; Hisarciklilar, 2002). In that regard, Sawada and Lokshin (2001), in their study for Pakistan, emphasised that educational attainment levels are stock rather than flow variables and, thus, the current schooling outcome depends both on past and current decisions about the education of individuals. Another commonly used measure of education is ‘current enrolment at school’ (see, for example, Pal, 2004 for India, Smits and Hosgor, 2006 for Turkey). Enrolment models are criticised, however, as they only reflect current decision-making whereas the completed level of education reflects the decisions made over an extended period. On the other hand, some studies, particularly for developing or underdeveloped countries, have used two or more measures of education such as the highest grade attained, enrolment in a specific grade and leaving school in their analyses (see, for example, Chernichovsky, 1985 for rural Botswana; Glick and Sahn, 2000 for Guinea).

168

In contrast to developed countries, most of the population in developing countries has low levels of education and a small proportion of the population has secondary or higher educational qualifications. Therefore, the studies attempting to explore the determinants of educational outcomes in a developing country context usually use a low cut-off value of education such as current enrolment in a given grade (generally below the secondary school level) for a specific age group or they restrict the analysis to completion of primary education. For example, Keng (2004) used ‘ever enrolled’ or ‘whether dropping out of school before completing grade four’ as measures of education for Cambodia. Similarly, Rose and Al-Samarrai (2001) used ‘whether enrolled’ or ‘whether complete primary school’ for Ethiopia.

Another estimation issue related to the measure of education should be stated here. Since all measures of education relate to the age of the unit of analysis, determining age restrictions is an important part of investigating the educational attainment of individuals. The age restrictions related to the unit of analysis are determined according to the aims of the study and the structure of the education system in the country. For example, Maitra (2003) included the complete educational age range (6 to 24 years) of individuals in her analysis of Bangladesh. However, Tansel (2002) included individuals aged from 14 to 20 in her analysis for Turkey covering the primary, middle and high school completion ages of the individuals.

4.3.1.2 Dependent Variables and Estimation Methods

After selecting a measure of education and the dependent variable, the next step is to identify the estimation method which largely depends on the dependent variable. When the dependent variable takes the form of years of schooling, school leaving age, highest level of educational attainment or maximum education obtained, OLS is the most common method used to model educational attainment in the existing literature (see, for example, Behrman and Wolfe, 1987; Wolfe and Behrman, 1984 and 1986 for Nicaragua; Chernichovsky, 1985 for rural Botswana; Jamison and Lockheed, 1987 for Nepal; Parish and Willis, 1993 for Taiwan). However, the studies, which use the OLS estimation method, have an important limitation since this approach does not take into account the discreteness of the data. Furthermore, there are generally a high number of zero observations for those who have no educational qualifications in the sample and similar probability spikes exist at primary and secondary education levels, where continuation to the next grade might be delayed because of fees or entrance

169

examinations. The OLS estimation method is arguably not appropriate due to these issues (Holmes, 2003).

In this respect, the ordered probit model, proposed by King and Lillard (1983 and 1987) and Lillard and King (1984) for modelling educational attainment, is more appropriate than the OLS model when the dependent variable is the highest level of education (or highest grade attained). The probit model is used when the dependent variable is current enrolment at school. Glick and Sahn (2000) examined gender differences in educational attainment using different measures of education (i.e., final educational attainment, current enrolment and withdrawal from school) for Guinea, West Africa. In the first model, they used final grade attainment as the dependent variable and estimated an ordered probit model whereas in the second and third models, the determinants of school enrolment status at the time of the survey and the determinants of leaving school were estimated by using probit models. The main findings of this study include that maternal education and household income are the main determinants of girls’ educational attainment and current enrolment but these variables have no effect on boys’ education. They also found that the presence of siblings under age 5 is negatively associated with girls’ educational attainment and current enrolment and is positively related to the probability of leaving school for girls. In accordance with Glick and Sahn (2000), Kabubo-Mariara and Mwabu (2007) investigated the determinants of school enrolment using a probit model and the highest level of educational attainment for Kenya using an ordered probit model. They found that parental education, child and household characteristics and the quality and cost of schooling are the main factors behind the demand for education in Kenya.

Although there are other studies using alternative estimation methods such as ordered logit models (see, for example, Dreze and Kingdon, 2001 for India) and IV estimation methods (see, for example, Dayioglu et al., 2009 for Turkey), when the dependent variable is the highest level of educational attainment, it can be argued that a relatively small, but growing, number of studies have used an ordered probit model to investigate educational attainment for developing countries (see, for example, Tansel, 2002 for Turkey; Holmes, 2003 for Pakistan, Maitra, 2003 for Bangladesh). The main reason for using the ordered probit model is that it allows for analysing different levels of educational attainment and, hence, comparing the impacts of factors for each educational outcome.

170