Oil Prices and Economic Growth in Major Emerging Economies: Evidence From Asymmetric Frequency Domain Causality Tests

Oil Prices and Economic Growth in Major Emerging Economies: Evidence From Asymmetric Frequency Domain Causality Tests

Emrah I. Cevik, Sel Dibooglu, Tugba Kantarci, Hande Caliskan
Copyright: © 2020 |Pages: 23
DOI: 10.4018/978-1-7998-1093-3.ch001
OnDemand:
(Individual Chapters)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

There is a strong correlation between energy prices and economic activity. The relationship particularly holds true for crude oil as changes in oil prices are associated with changes in production costs, and economic activity also generates significant demand for energy and crude oil. This chapter examines the relationship between economic activity and crude oil prices using causality tests in the frequency domain and taking into account the difference between positive and negative changes in both oil prices and economic activity as the relationship can be asymmetric. The authors present empirical results for major emerging economies including Brazil, Russia, India, China, South Africa, and Turkey. Empirical results indicate that for most countries there is bidirectional causality between crude oil prices and economic activity whereas only negative oil price shocks seem to negatively affect economic activity.
Chapter Preview
Top

Introduction

Energy sources such as crude oil, natural gas, and coal are important inputs for many sectors and have an important role in economic activity. Even though the use and production of energy sources varies among sectors, crude oil still occupies a central role in the global economy in comparison to other energy sources. Hence, oil prices affect several macroeconomic indicators (such as output, trade balance, inflation, stock markets, and exchange rates).

The share of primary energy sources in total energy consumption in Figure 1 shows crude oil is still an important energy source today. Crude oil is not only the highest primary energy source in the world, but the share of crude oil as the primary energy source increased from 34% to 49% between 1965 and 2017. However, it is evident that crude oil consumption started to decline in the new millennium. Coal is the second highest consumed energy source after oil while natural gas is third as shown in Figure 1.

Figure 1.

The share of primary energy resources in total energy consumption

978-1-7998-1093-3.ch001.f01
Source: BP Statistical Review of World Energy

Crude oil use around the world is shown in Figure 2. It is evident that oil consumption has increased year after year, and while total oil consumption was 1,523 million tons in 1965, it reached 4,470 million tons in 2017. North America has the highest oil consumption in the new millennium, while Africa has the lowest within the sample. The use of crude oil in the Asia Pacific region has been on the rise significantly, and since the 2000s, it has become the region with the highest petroleum consumption as two of the largest economies in the world, China and India, are located in this region.

Figure 2.

Crude oil consumption by geographical region

978-1-7998-1093-3.ch001.f02
Source: BP Statistical Review of World Energy

A closer look at oil consumption in 2017 shows that the highest oil consumption was observed in the USA (19.5%) and China (13.3%). These countries are followed by India, Japan, Saudi Arabia, Russia, Brazil, South Korea, Germany, and Canada, respectively, and these countries account for 60% of total oil consumption in the world. Moreover, these results show that crude oil has an important place not only for developed countries but also for developing countries. Therefore, changes in oil prices will have a significant effect on economies of developed and developing countries alike.

Global oil prices in the international market are influenced by various factors. These include microeconomic activities as well as political and geographic reasons. Demand and supply side effects are prominent in the determination of oil prices as well. For example, oil prices are vulnerable to demand shocks such as taxes or speculation and supply shocks such as oil production costs or the decrease in oil stocks. The increase in oil prices increases input costs for an oil importing country, causing increases in budget deficits and decreases in investments while reducing production and possibly labor productivity.

Crude oil prices (White Texas Intermediate-WTI) are presented for 1973-2017 in Figure 3. Until 1978, crude oil prices were lower than $20 per barrel, and prices exhibit an upward trend thereafter. After the Iranian revolution in 1978, oil supply decreased, and in 1980, the price of oil reached $40 per barrel. In 1980, oil prices showed a downtrend again, and they were below $30 until the end of the 1990s. At the beginning of the 2000s, a significant uptrend in oil prices emerged; in 2008 the price of crude oil exceeded $ 100 and reached a historical peak. Since then, oil price volatility has increased. While prices were $61 in 2009, they increased to $95 thereafter. After 2013, prices started to decrease. and in 2017, prices were down to $50 per barrel.

Figure 3.

Crude oil prices

978-1-7998-1093-3.ch001.f03
Source: The US Energy Information Administration

Key Terms in this Chapter

Unit Roots: A unit root test tests whether a time series variable is non-stationary and possesses a unit root.

Vector Autogression (VAR): A stochastic process model used to capture the linear interdependencies among multiple time series. They generalize the univariate autoregressive model (AR model) by allowing for more than one evolving variable.

OPEC: The Organization of the Petroleum Exporting Countries (OPEC) is an intergovernmental agency of 13 nations, founded on 14 September 1960.

Granger Causality: A way to investigate causality between two variables in a time series. The method is a probabilistic account of causality; it uses empirical data to find patterns of correlation.

Structural Break: A point in a dataset where there is a divergence or change in the behavior of the data in question.

Complete Chapter List

Search this Book:
Reset