pros and cons of the infrastructure are vigorously debated but little is known
conclusive about its relationship to economic growth. Different theoretical
premises, supported by different empirical examples, imply opposite
predictions. Lots of works have been done around this area of research. Here we
mention work already done.
Esfahani and Ramirez (2003) analyzed
structural model of infrastructure and output growth. They used growth rate of
GDP per captia as a dependant variable and population growth rate, log of
initial telephone per captia used as independent variables. Cross country
estimation of the model indicate that the contribution of infrastructure
services to GDP is substantial and, in general, exceeds the cost of provision
of those services.
Malik, (2009) described the infrastructure of South Asia and
East Asia. In her model, she used GDP per Capita as a dependent variable and
inflation, GDP ratio and political stability as independent variables. Applying
the fixed-effect model, the study found a positive and significant impact of
private participation in the energy and telecom sectors on GDP per capita and
(2010) in their research examined an infrastructure experiment in Mexico
to evaluate the impact of street pavement on housing values and household
outcomes. The data for this study is pre- and
post-intervention rounds of a dedicated household survey.The baseline survey
was held in February-March 2006.
Sahoo and Dash (2010) investigated the role of
infrastructure in promoting economic growth in China. They took the secondary data
and data source are World Bank and international financial corporation. They
used GDP as a dependent variable and investment in private sector, public
sector, labor force and human capital as independent variables.
Ishaq and Mushtaq (2011) described public investment on
rural infrastructure not only increases agricultural productivity but also
reduces poverty. They used TFP (total factor productivity) as dependant
variable and AGRI (aggregate expenditure on the crops), livestock and RHE
(expenditures on rural health and education) as independent variables. The
results showed that public investment on physical infrastructure and social
infrastructure has contributed significantly and positively to TFP. The study
suggested that more resources should be diverted towards the development of
physical and social infrastructure that will improve the agricultural
productivity as well as reduce the rural poverty.
Faridi et al. (2011) described that Transport and
communication sector having significant influence on economic growth. They took
GDP as dependant variable labor
transportation and commutation as independent variables. To check the
effect of transportation and communication on economic development they used
Solow growth model.
Haider et al. (2012) interpreted the impact of
infrastructure on economic growth of Pakistan. They used GDP as a dependent
variable and GFCF (gross fixed capital
formation) and TGE (total generation of electricity) used as independent
variables, time series data has been collected from 1972 to 2009. Then they
applied Ordinary Least Square (OLS) to find short-run relation between
variables and found that infrastructure is positively and significantly
contributing in Pakistan. The study suggested that government and policy makers
should focus for the development of infrastructure.
Sohail et al. (2012) analyzed the impact of natural disaster
on economic growth in Pakistan. They used GDP as a dependent variable and
growth in agriculture production and growth in industrial production as
independent variables. By using time series data from 1975 to 2010, ADF test
was used to test the stationary of the series and then OLS method was applied
to estimate the impact of natural disasters.
Soneta et al.(2012) explained that infrastructure is basic
physical and organizational structures needed for the operation of society and
facilities necessary for an economy to function. They used manufacture growth
as a dependent variable and log of transportation and communication, log of
electricity and gas distribution as independent variables.