Effects of Public debt on Economic Growth (Part -1) - Public Finance | Public Finance - B Com PDF Download

Introduction

The impact of public debt on economic growth has remained a key issue in the academia. Over the past decade and especially after the financial crisis in 2008, the level of public debt is expanding in international, national and sub-national level. Heavy dependence on public debt could retard investment and economic growth. The ‘debt overhang’ hypothesis mentions that if the anticipated external debt of a country is more than it’s repayment ability, then the increased cost of servicing debt can impede investment (Krugman, 1988). If a major chunk of foreign capital is used for interest payments, then a meagre amount will remain to finance for investment that could constrain growth. This is regarded as the crowding-out effect of public debt (Diaz-Alejandro, 1981).

However, another school of thought states that, if public debt is used in productive activities, then the economy may expand without creating any macroeconomic instability (Burnside and Dollar, 2000). As far as public debt is concerned, broadly it could be divided into types, one is external debt and the other is domestic debt. The two types of debt may have distinct impact on economic growth. The rationale behind dependence on domestic debt is that it saves the home country from the adverse external shocks and foreign exchange risk, and helps in the progress of domestic financial markets (Barajas and Salazar, 1999, 2000). But, Beaugrand et al. (2002) are of the view that the cost of domestic debt is more than the cost of external debt.

Most of the recent literature on public debt and growth nexus centered on non-linear (inverted Ushape) relationship between the two and estimation of the threshold limit of public debt share to GDP (See Smyth and Hsing, 1995; Blavvy, 2006; Reinhart and Rogoff, 2010; Reinhart et al., 2012; Kumar and Woo, 2010; Cecchetti et al., 2011; Checherita-Westphal and Rother, 2012; Furceri and Zdzienicka, 2012; Herndon et al., 2013; Chen et al., 2016). However, for a developing country like India and its underdeveloped states, our hunch is that the optimum level of public debt has not yet been reached. The average public debt to gross domestic product ratio across the major 14 states for different periods from 1980-81 to 2013-14, varies from 19.1% to 35.3%. Therefore, a positive relationship between public debt and economic growth is expected. 

Earlier studies mainly focused on the impact of external debt in economic growth and therefore neglected the role of domestic debt. Further, the analysis is limited to cross-country analysis or time series analysis. Hence, this study will explore on the impact of domestic debt on economic growth along with other control variables by using a panel data set of 14 major states in India.

The present study tries to assess the impact of public debt on economic growth using a production function approach where other relevant inputs like credit and electricity are also taken as explanatory variables. Inclusion of the other relevant variables also helps in removing omitted variable bias. Using Dynamic OLS (DOLS) and Fully Modified OLS (FMOLS) the study gets positive and statistically significant impact of public debt, institutional credit and commercial electricity consumption on economic growth. For causality analysis Dumitrescu-Hurlin panel causality test is also employed.

The remainder of this study is as follows. Section 2 gives a brief overview of literature. Section 3 outlines the theoretical framework and data used in the study. Econometric methodology applied in the analysis is presented in Section 4. Section 5 outlines the results and finally section 6 provides the conclusion and policy implication of the study.

Brief overview of Literature

Public Debt and Economic Growth

Recent studies dealing with nexus between external debt and growth found that, the relationship between the two could be non-linear (inverted-U type shape). This means there could be threshold limit up to which debt can induce growth and thereafter higher debt can reduce growth. The threshold limit of external debt is estimated to be 38.4 percent of GDP by Smyth and Hsing (1995); 21 percent by Blavvy (2006); 85 percent by Cecchetti et al. (2011); 90-100 percent by ChecheritaWestphal and Rother (2012); and 90 percent by Chen et al. (2016). On the other hand the study by Reinhart and Rogoff (2010) concludes that in advanced and emerging market economies (EMEs), debt to GDP ratio of about 90 percent is growth reducing. If the ratio is below 60 percent then it can retard economic growth in only EMEs. Subsequently, Herndon et al. (2013) try to replicate the study by Reinhart and Rogoff (2010). By making some correction they find that the relationship between debt ratio and economic growth is similar in the two situations. So far as causality between public debt and economic growth is concerned, Panizza and Presbitero (2014) do not find any causality between the two, whereas Puente-Ajovín and Sanso-Navarro get bi-directional causality between public debt and economic growth. Lof and Malinen (2014) get support of one way causality from growth to debt.

Credit and Economic Growth

It is well known that financial deepening results in higher growth through different channels like more credit with financial liberalization promotes investment and innovation resulting in more efficient investment and thereby growth.  In the literature various studies are done to explore the relationship between financial development and economic growth.3 One strand of studies including Goldsmith (1969), focuses to measure the strength of the relationship between the financial development and economic performance. Others try to identify the channels through which the two are related. Pioneering work of McKinnon (1973) and Shaw (1973) reflects that financial liberalization positively affects saving and therefore more investment culminating in higher economic growth. Later on various papers using the endogenous growth models take financial development as a physical capital generating technological progress and increasing the efficiency in investment (See Bencivenga and Smith, 1991; Greenwood and Jovanovic, 1990). But not all economists are convinced with the role of credit in generating growth. Robinson (1952) states that ‘where enterprise leads, finance follows’ which means economic growth creates the condition of financial arrangement and thereby financial development. Lucas (1988) is of the view that the role of financial development in growth is ‘over stressed’. 

Early papers find a positive relationship between financial development and economic growth using cross-country analysis (See King and Levine, 1993; Demirguc-Kunt and Maksimovic, 1996; Levine and Zervos, 1998; among others). But these studies do not deal with causality analysis and do not pays any attention to time series properties of the data. Ram (1999) gets evidence of weak negative relationship between financial development and economic growth and opines that crosscountry studies have various limitations like heterogeneity issue in slope coefficients of various countries. Moreover, the results based on these type of analysis is sensitive to the sample of countries, computation method, frequency of data, functional form of the relationship etc. So the reliability of cross-country analysis is questioned by Khan and Senhadji (2003), Chua and Thai (2004), and Hassan et al. (2011) etc.  Most of the time series studies get evidence of causality between credit and economic growth with no consensus on the direction of causality (See Demetriades and Hussein, 1996;; Luintel and Khan, 1999; Bell & Rousseau, 2001; Calderon and Liu, 2003; Bhattacharya and Sivasubramanian, 2003 and Liang and Teng, 2006 among others).

Few studies get no evidence of causality between the two (see Eng and Habibullah, 2011 and Mukhopadhyay et al., 2011).

To overcome the limitations of time series and cross-section analysis, various studies use panel data to assess the nexus between the financial development and economic growth (See Levine et al., 2000; Christopoulos and Tsionas, 2004; Beck and Levine, 2004; Hassan et al., 2011, Gaffeo and Garalova, 2014. etc.). Recent study by Kar et al. (2011) could not get any clear cut relationship between financial development and growth as the results are country specific in Middle East and North African (MENA) countries. However, two studies by Arestis et al. (2014) and Valickova et al. (2014) find evidence of positive relationship between financial development and economic growth by employing meta-analysis.

Electricity Consumption and Economic Growth

In the production process of an economy energy plays an important role. Among various energy variables electricity is most flexible form and a key infrastructural input for development. There are a number of studies exploring the relationship between consumption of electricity and economic growth. In studying the relationship between the two, the prime focus was on the causality issue since the publication of Kraft and Kraft (1978). The causality analysis is important in making policy decision because if unidirectional causality is found from growth to electricity consumption, then conservation policies can be implemented without hurting economic growth. In contrast to this if unidirectional causality runs from electricity consumption to growth, then any strategy to reduce electricity consumption may reduce growth. The causality from growth to electricity could be justified on the ground that demand for electricity will increase with upsurge in population, rapid urbanization and industrialization, and rise in standard of living. Similarly, the causality from consumption to electricity could be thought up as electricity is a major infrastructure and an input for production. Causality from economic growth to electricity is supported by many studies (see Kraft and Kraft, 1978; Ghosh, 2002; Narayan and Smyth, 2005; Mozumder and Marathe, 2007; and Jamil and Ahmad, 2010; Ciarreta and Zarraga, 2010; and Shahbaz and Feridun, 2012 etc.). On the other hand, the reverse causality from electricity consumption to growth is revealed by other empirical papers (see Hsiao, 1981; Stern, 1993; Aqeel and Butt, 2001; Shiu and Lam, 2004; Altinay and Karagol, 2005; Lee and Chang, 2005; Narayan and Singh, 2007; Yuan et al., 2007; Tang, 2008, Odhiambo, 2009; and Chandran et al., 2010 etc.). Some other find bidirectional causality between electricity consumption and growth (See Yang, 2000; Jumbe, 2004; Zachariadis and Pashourtidou, 2007; Lean and Smyth, 2010; Tang et al., 2013; Osman et al., 2016 among others). Some studies report no causality between the two (e.g. Ozturk and Acaravci, 2011; Yoo and Kwak, 2010; and Wolde-Rufael, 2006; etc.). Thus, the relationship between electricity consumption and economic growth is ambiguous.

Theoretical Framework and Data

In the growth literature, various schools of thoughts propose distinct relationships between public debt and economic growth. In the Classical sense, Ricardian Equivalence states that if a government borrows today, then, it has to repay this borrowing, in future by raising taxes above the normal level and the impact of debt on growth will be neutralized (Ricardo, 1817). In the Keynesian framework, foreign aid or foreign investment is required to fill the saving-investment gap (Todaro and Smith, 2003). Solow (1957) maintained that in the short-run fiscal policy can have some impact on level of per-capita income but in the long-run the impact is neutral. In a neoclassical set up Diamond (1965) formally brought the public debt as a variable explaining growth. He opines that internal debt reduces the available capital stock due to substitution of public debt for physical capital. As per the endogenous growth models, both fiscal and monetary policies play a crucial part in determining potential economic growth. Public debt can result in technical progress and thereby can influence growth (Villanueva, 1972). But Saint-Paul (1992) using the endogenous growth caveat states that higher debt is always associated with lower growth. The recently developed new growth theories explore the relationship of public debt and growth nexus by bringing utilization and governance aspects of public debt (See Zak and Knack, 2001 and Acemoglu and Robinson, 2006).

As per our objective, the study relies on recently developed panel data analysis as it has many advantages over the pure cross-sectional or time-series analysis as noted by Osman et al. (2016). The study employs annual data for the period 1980-81 to 2013-14 for 14 major non-special category states in India. The key variables in the study include Gross State Domestic Product (GSDP) proxy used for real income, real public debt, real institutional credit to private sector, and commercial consumption of electricity (in Gigawatt). All the variables are transformed into natural logarithms prior to estimation and respectively denoted as LRY, LRD, LRC, and LCCE. As State wise data on private investment is not available for India, commercial bank credit to private sector is taken as a proxy for private investment. Nominal values of public debt and credit to private sector by commercial banks are deflated by the GSDP deflators of the respective states to obtain real values of the concerned variable. All variables except electricity are in Rs. crore at 2004-05 prices. Real GSDP data is collected from National Account Statistics published by Central Statistical Organization. Public debt and Credit variables are collected from State Finance: A Study of Budgets, published by the Reserve Bank of India and electricity data is taken from EPWRF and indiastat database. Institutional credit to private sector is a proxy for financial development and electricity consumption is a proxy for energy use.

Table 1. Descriptive Statistics

Note: Variables are in Natural Log.

Table 2. Correlation Matrix

Note: Figures are correlation, t-statistics and prob. respectively.

Table 1 depicts the descriptive statistics of all the logarithmic transformed variables. The standard deviation of all the variables are revealing that the data of all the series are dispersed around the mean. It permits us to move forward and use the data for further estimation.

Table 2 presents the correlation matrix. The correlation coefficient between all variables are very high and they are intertwined which provides us the clue to estimate their relationship.