Globalization and the Public Provision of Higher Education: Empirical Evidence from Selected Countries

This research study was designed to examine the relationship between globalization as measured by the KOF index, its related forces (economic, political, cultural and technological) and the public provision of higher education. This study is important since globalization is increasingly being associated with changes in critical aspects of higher education. The public provision of education was measured by government expenditure and educational outcomes; that is participation, gender equity and attainment. The study utilized a non-experimental quantitative research design. Data collected from secondary sources for 139 selected countries was analyzed. The countries were geographically distributed and included both developed and developing countries. The choice of countries for inclusion in the study was based on data availability. The data, which was sourced from international organizations such as the United Nations and the World Bank, were examined for different time periods using five year averages. The period covered was 1970 to 2009. The relationship between globalization and the higher education variables was examined using cross sectional regression analysis while controlling for economic, political and demographic factors. The major findings of the study are as follows. For the two spending models, only one revealed a significant relationship between globalization and education with the R2 s ranging from .222 to .448 over the period. This relationship was however negative indicating that as globalization increased, spending on higher education declined. However, for the education outcomes models, this relationship was not significant. For the sub-indices of globalization, only the political dimension showed significance as shown in the spending model. Political globalization was significant for six periods with R2 s ranging from .31 to .52. The study concluded that the results are mixed for both the spending and outcome models. It also found no robust effects of globalization on government education provision. This finding is not surprising given the existing literature which sees mixed results on the social impact of globalization.

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.