Author: Sohail Akram
AIT Library Call Number: AIT RSPR no.RD-10-02
Year: 2010
Academic Program: Regional and Rural Development Planning (RRDP)
Type: Research studies project report (M. Sc.) – Asian Institute of Technology, 2010
Examination Committee: Jayant Kumar Routray (Chairperson); Gopal B. Thapa, Mokbul M. Ahmad (Examination Committee Members)
Abstract: Concern has been growing recently over wide regional disparities in levels of economic development in the developing and underdeveloped countries. Core areas have experienced rapid development, while other areas have lagged behind in the process of development and have been little affected by economic and social changes . Pakistan is administratively divided into four regions (provinces), Punjab, Sind, NWFP (North West Frontier Province), and Baluchistan. These regions are highly heterogeneous in terms of size, population, level of economic development, income and poverty. Like most developing countries, Pakistan also faces the challenge of regional disparities in social and economic development . The degree of spatial variation in level of development within a country has implications in terms of development planning and pol icy formation . Therefore a periodic documentation of the levels and trends of inter regional and intra regional variation in socioeconomic development is a pre-requisite for effective intervention to achieve spatially equitable and sustainable development i n Pakistan . It is now widely recognized that the development is a multidimensional phenomenon. Assessment of spatial variation in development requires many data related to different aspects that influence the development process. The multitude of development indicators and of territorial units makes it difficult to capture the broad picture of the comparative development status of different regions. One of the major challenges for researchers and policy makers is to reduce the complexity to a manageable set of indicators that can be used to interpret reality . The methods of multivariate statistics have been very efficient in reduction of complexity of a large data set. Using multivariate statistical methods of factor and cluster analysis , this s
Scholarship Donor(s): University of Baluchistan , Quetta, Pakistan; Asian Institute of Technology Fellowship
Note: A research study submitted in partial fulfillment of the requirement for the degree of Master of Science in Regional and Rural Development Planning
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