This paper examines the temporal interdependence between gross domestic product and health expenditure per capita for Pakistan in an augmented Solow growth model suggested by Mankiw, Romer and Weil (1992) for the period of 1973-2001. This paper is an extension of the MRW model by incorporating health capital proxied by health expenditure to the augmented Solow model. Moreover, an openness variable is also included in the model in order to capture the effect of technological changes on growth. The paper employs co-integration, ECM methodology and several diagnostic and specification tests. The empirical findings show a significant and positive relationship between GDP and Health Expenditure, both in the long- and short-run.
In this paper we try to examine the impact of education on growth in Pakistan for the time period of 1973-2001. Education, measured as gross enrollments and total expenditures, is broken down into primary, secondary and tertiary as well as by gender in each of the above categories. Time series techniques are used to determine whether education, for each category, has a causal impact on growth. The robustness of these results is then examined using the Levine-Renelt (1992) methodology. We find that secondary and higher education has a strong and robust impact on growth, whereas, at the primary level only initial female enrolments show a causal but not robust impact on growth.human capital, economic development,
Abstract. Automatic parallelization combined with tuning techniques is an alternative to manual parallelization of sequential programs to exploit the increased computational power that current multi-core systems offer. Automatic parallelization concentrates on finding any possible parallelism in the program, whereas tuning systems help identifying efficient parallel code segments and serializing inefficient ones using runtime performance metrics. In this work we study the performance gap between automatic and hand parallel OpenMP applications and try to find whether this gap can be filled by compile-time techniques or it needs dynamic or user-interactive solutions. We implement an empirical tuning framework and propose an algorithm that partitions programs into sections and tunes each code section individually. Experiments show that tuned applications perform better than original serial programs in the worst case and sometimes outperform hand-parallel applications. Our work is one of the first approaches delivering an auto-parallelization system that guarantees performance improvements for nearly all programs; hence it eliminates the need for users to "experiment" with such tools in order to obtain the shortest runtime of their applications.
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