The Impact of Infrastructure on Growth in Developing Countries: Dynamic Panel Data Analysis

The Impact of Infrastructure on Growth in Developing Countries: Dynamic Panel Data Analysis

Derya Yılmaz (Uludag University, Turkey) and Işın Çetin (Uludag University, Turkey)
DOI: 10.4018/978-1-5225-2361-1.ch003
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Infrastructure and growth nexus has been debated in the literature since 1980s. This debate has a vital importance for the sake of developing countries. These countries need to grow faster in order to catch-up their advanced counterparts. Thus, it is important to detect the effect of infrastructure on growth. Bearing in mind this fact, we develop a standard growth regression in this present chapter using per capita GDP growth rate as a dependent variable. Infrastructure is added to the model as an index constructed from the indicators of infrastructure: total electric generating capacity, total telephone lines and the length of road network. We also employ set of instrumental variables comprising 29 developing countries between 1990 and 2014. In order to estimate our dynamic panel data we prefer GMM estimators. According to our empirical analysis, we can claim that infrastructure has a positive and significant impact on growth. But this impact is smaller than the earlier studies predict.
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Infrastructure consists of physical assets like roads, power plants, fiber cables or sewer systems as well as services like communication network services, power distribution services. According to World Bank, “infrastructure helps to determine the success of manufacturing and agricultural activities. Investment in infrastructure also improves lives and help to reduce poverty”. The role of infrastructure on economic growth has been discussed in the literature since the seminal study of Aschauer (1989). Several criticisms emerged afterwards, but none of them totally refused the role of infrastructure on growth. After this asserting result of Aschauer (1989), it is generally accepted that the differences in the development levels could be attributed to the differences in infrastructure stocks. However, in the 1990s, most of the developing countries faced with crisis and forced to consolidate their fiscal budgets. Most developing countries postponed and even cancelled their infrastructure investment. The infrastructure gap between advanced and developing countries have been retched up as a result. After 2000s, most governments try to compensate the infrastructure investment gaps. Financial crisis erupted in August 2007 had also effects on developing countries. However, the crisis was a financial crisis that had repercussions on real economy. For this reason, governments expanded their fiscal expenditures generously. They introduced fiscal stimulus packages sequentially. Bearing in mind the growth effects of infrastructure, an average developing country devoted 40 percent of the fiscal stimulus packages to infrastructure investment, while advanced countries devoted 21 percent.1

Infrastructure was considered as a public good as it generates externalities and it is difficult to exclude the non-payers from using infrastructure. However, in recent decades, private sectors have begun to play an important role in infrastructure services. Infrastructure used to be provided by the governments in developing countries but with the widespread of the privatization trend, public-private partnerships and build-operate-own models gain importance.

Infrastructure, as World Bank (1994) classified, is analyzed in four broad categories: transportation, power (or energy), telecommunication and water and sanitation. In this study, we identify it in three broad categories: transportation, power and telecommunications. We exclude the water and sanitation infrastructure as the data is scarce. However, using these indicators in growth regression may lead to econometrical problems because these indicators are highly correlated. For this reason, in this study we compute an infrastructure index obtained from indicators of these three dimensions of infrastructure. We use Principal Component Analysis (PCA) while computing this index.2

In order to see the effect of infrastructure on growth, we develop a model of growth regression. The studies conducted to estimate this effect face with endogeneity problems as the relation may go in reverse. Economic growth could also necessitate infrastructure advancement in demand side. Furthermore, as countries grow and get richer, they could be able to devote more resources to infrastructure investment. Thus, they could build large stocks of infrastructures. This problem is important for empirical analysis as it could lead to spurious correlation. We estimate the model with using GMM estimators in order to consider endogeneity problem.

We construct a macroeconomic panel data set between 1990 and 2015 and comprising 29 countries. We get middle income countries as classified by the World Bank. But we exclude the small states – with a population less than 1 million- and the countries that do not have continuous data. As we are working with panel data comprising different countries, heterogeneity could be observed among these countries. We try to parametrize this heterogeneity by employing instrumental variables. We also use fixed effect estimators in line with the literature.

Before estimating the model we also utilize panel unit root test to detect the stationary of the data. We use the unit root test developed by Im, Pesaran and Shin. This is one of the different features of our study. Moreover, in this study we work with an updated data set –extended over 2014- which captures the effect of fiscal stimulus packages in the aftermath of the financial crisis.

Key Terms in this Chapter

Dynamic Panel Data: Multi-dimensional data frequently involving measurements over time. Panel data contain observations of multiple phenomena obtained over multiple time periods for the same firms or individuals.

Principal Component Analysis (PCA): It is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components.

Infrastructure: The basic physical and organizational structures and facilities (e.g. buildings, roads, and power supplies) needed for the operation of a society or enterprise. According to World Bank, there are four broad categories of infrastructure: transportation, telecommunication, power (or energy) and water and sanitation.

GMM: the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the distribution function of the data may not be known, and therefore maximum likelihood estimation is not applicable. The method requires that a certain number of moment conditions were specified for the model. These moment conditions are functions of the model parameters and the data, such that their expectation is zero at the true values of the parameters. The GMM method then minimizes a certain norm of the sample averages of the moment conditions.

Economic Growth: It is an increase in the capacity of an economy to produce goods and services, compared from one period of time to another. Economic growth could be measured nominally or really with eliminates the effect of inflation.

Developing Country: A country having a standard of living or level of industrial production well below that possible with financial or technical aid; a country that is not yet highly industrialized.

Panel Unit Root Tests: Testing for unit roots in time series studies is common practice among applied researchers and has become an integral part of econometric courses. We use in this study, the unit root test proposed by Im, Pesaran and Shin (1997) AU123: The in-text citation "Im, Pesaran and Shin (1997)" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. (IPS) test. IPS test allow for a heterogeneous coefficient of y it-1 and proposed an alternative testing procedure based on averaging individual unit root test statistics. IPS suggested an average of the Augmented DF (ADF) tests when u it is serially correlated with different serial correlation properties across cross-sectional units.

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