Search results

Financial liberalization is assumed to be the integration of a country's local financial system with international financial markets and institutions. This integration usually requires that governments liberalize the domestic financial sector and the capital account. Financial sectors were liberalized in most of the developing countries in Asia, Africa, and Latin America within the early 1990s. Among these countries, emerging economies are those who promise huge potential for growth but also pose significant political, monetary, and social risks. Brazil and India are often compared among the major emerging economies. Despite these general similarities between them, there are notable differences in various aspects of opening the balance of payments capital account in both countries. In this chapter, we have tried to analyze the long-run as well as short-run relationship between quarterly growth rate of GDP with the stock market, real market, and money market macroeconomic variables in India and Brazil during the period from the first quarter of 1996–1997 to the second quarter of 2018–2019. To estimate the cointegration relationship between growth rate of GDP and its determinants, we employ the bounds testing procedure (modified-ARDL) developed by Pesaran, Shin, and Smith (2001, Journal of Applied Econometrics, 16(3), 289–326). According to our results, stock market plays a positive role in long-term growth in India. Although during the beginning period of the neoliberal reforms, India faced strong domestic political opposition, our study shows that liberalizing the financial market has been fruitful for long-term growth. Our results in case of Brazil show that inflation has a negative and significant impact on long-run growth rate of GDP. The results further show that the share of gross fixed capital formation in GDP in Brazil has a positive and significant long-run relation with the growth rate of GDP. The empirical results further indicate that just like India, liberalization of the financial market and allowing foreign capital inflows have been beneficial for the economy of Brazil in the long run.

This chapter introduces demographic variables in empirical regression to help find whether demographic changes have an impact on economic growth. There is evidence from estimated values in this chapter to suggest that there is no impact that demographic changes in Hong Kong is affecting the economic growth. The population growth has purely a transition impact where the fertility rate was low in early 2000 up to 2015 as the size of the dependency ratio increases. Besides testing demographic variables the government emphasises better education for all people of ages for prosperous growth but in fact has a negative response on educational investment on the growth of the economy. A well-educated country individual does not suggest a higher productivity in economy growth. An important implication is that there has been no single variable as yet that has seriously impacted the economy growth, but there will be changes in the coming years and has to be attended in result to avoid a diminishing economy.

The rapid growth of Chinese tax revenues in the past decade is often considered “unnatural” relative to GDP growth. In this paper we investigate this seemingly unnatural growth by presenting different models of the relationship between the annual growth of tax receipts and GDP. The models show different results. We also analyze various factors related to the transition from a centrally planned economy to a market economy, in particular the biased GDP calculation method, changes of the economic structure, tax policy changes and reinforcement of the tax administration. If we eliminate the impact of these factors we find that the growth of Chinese tax revenues is not unnatural, but by and large in line with the growth of GDP.

World over, change of base year in the gross domestic product (GDP) is a standard practice of GDP estimation. However, unless a consistent series of GDP is released with respect to the new base for the earlier period, the existence of multiple growth rates creates problems for applied researchers, policymakers and the general public alike. Faced with such a menu of GDP series researchers often try to interpolate a consistent series of GDP. The main purpose of this paper is to analyses the nature of the data generating process of such multiple interpolated series of quarterly growth rates and tries to discern the consistency of such processes.

The present paper tries to look into the statistical implications and complications of such interpolated quarterly GDP/growth series in India in terms of three series of GDP, namely, with 1999–2000, 2004–2005 and 2011–2012 as its bases.

The analysis reveals that as a result of a change of base year, the nature of the data generating process of the old and new GDP series could undergo changes and experience different breakpoints. While all these conclusions seem to be valid for GDP growth at quarterly intervals, taking the data at annual frequency is less problematic.

The observation suggests that in most applied work, researchers may not have the luxury of only working with annual data and certain consistency checks will be necessary to check the veracity of the results based on quarterly data with those based on annual data. Second, moving forward it may be useful for the Authorities to make a transition to a chain-based linking method rather than fixed time-period-based bases as is currently done.

The analysis of Indian GDP in this paper is, perhaps, indicative of the fact that usage of quarterly GDP data is to be handled with caution and it is preferable that any serious empirical analysis uses annual GDP data whenever it is available/feasible. The comparison of GDP growth rates at different frequencies and examining the true nature of the process are quite unique in their contribution towards empirical macroeconomic research.

Considering the continuous rise in the public debt stock of developing countries (particularly Ghana) with the unstable economic growth rate for the past decades and the recent borrowing because of the impact of COVID 19, this paper aims to examine the causal relationships between public debt and economic growth over time.

The paper uses a dynamic multivariate autoregressive-distributed lag (ARDL)-based Granger-causality model to test the causal relationships between public debt and economic growth [gross domestic product (GDP)]. Annual time-series data that spanned 1978–2018 were sourced from the World Bank Development Indicator database and the IMF fiscal Affairs Department Database and WEO.

The results reveal that public debt has no causal relationship with GDP in the short-run but there is unidirectional Granger causality running from public debt to GDP in the long run. Again, investment spending has a negative bi-directional causal relationship with GDP in the short-run but they have a positive bi-directional causal relationship in the long run. Conversely, no short-run causal relationship exists between government consumption expenditure and GDP but long-run Granger causality runs from government consumption expenditure to GDP. Finally, public debt has a positive impact on the inflation rate in the short run.

The findings imply that government(s) must ensure high fiscal discipline to serve as a precursor for the effective and efficient use of recent borrowing, that is, the loans should be used for highly prioritized projects (preferably investment spending) that are well evaluated and self-sustained to add positively to the GDP.

This paper provides contemporary findings to augment extant literature on public debt and economic growth by using variables and empirical models, which prior studies could not sufficiently cover in a developing country perspective and affirms that public debt contributes to GDP only in the long run.