Economic Activities and Oil Price Shocks in Indian Outlook: Direction of Causality and Testing Cointegration

Abstract

The current study intends to find out the linkages between crude oil prices and economic activity in the context of Indian economy. The macroeconomic variables such as gross domestic product (GDP), unemployment, industrial output, inflation, exchange rate and stock market prices have been used as a proxy to economic activity. We have analysed the sample data of 30 years, that is, from year 1991 to 2020. To inspect the short-run relationship between oil prices and the above-mentioned macroeconomic variables, Granger causality test has been applied after removing the presence of unit root through differencing the series. To investigate the long-run relationship, vector error correction model (VECM) has been applied after testing cointegration through the Johansen method of cointegration. The findings of the study show that oil prices have short-run causality with all the variables, that is, GDP, unemployment, industrial output, inflation, exchange rate and stock market prices, while they have a long association with inflation, industrial production and unemployment. Further we find a negative relationship between oil prices and unemployment, industrial output, inflation and exchange rate and a positive relationship with GDP and stock prices.

Keywords

Macroeconomic activity oil prices Granger causality Johansen cointegration test vector error correction model

Introduction

Due to its outstanding significance in meeting world’s energy needs, crude oil has always been one of the key indices of economic activity worldwide. Many studies by different scholars have established an association between oil prices and different macroeconomic variables in the economy (Ahmed & Wadud, 2011; Herrera et al., 2019; Iwayemi & Fowowe, 2011). Different studies suggest different causes for oil price fluctuations, and those fluctuations are responsible for stimulating and slowing down economic development in the country. It has also been observed in the past that any major economic or non-economic event is followed by a rise in the prices of oil. A substantial rise in oil prices just after recession is evident in the history of American recessions (Sill, 2007). The increase in crude oil prices may be because of an increase in demand and fear of disruption in the supply of oil in such conditions. Small shocks to oil supply or demand result in a high elasticity of prices in oil (Arezki et al., 2017). Speculation may play a significant role in increasing oil prices (Krugman, 2008). There may be numerous explanations for oil price volatility, as the literature indicates, but historical examples demonstrate the connection between oil prices and economic activity (Trang et al., 2017; Wei & Guo, 2016).

The value of oil and the influence of its price fluctuations can be observed in the economic and social facets of human life. The prevalent view among economists, therefore, is that there is a direct connection between the economic advancement of a country and changes in oil prices. The effect of oil price volatility on economic activity in different types of economies has been verified by a wide body of studies. These effects are likely to differ between developed and developing economies, as well as between oil-importing and oil exporting countries. The effect of oil prices on real economic activity encompasses both the supply and demand sides in an economy. The effect on the supply side can be seen because oil is one of the significant inputs in the process of production and transportation. Thus, higher oil prices raise the cost of production and transportation, forcing businesses to decrease their production. Oil price adjustments, on the other hand, often have demand-side impacts on consumption and investment. Consumption is indirectly influenced via its positive relationship with disposable income. The price increases are contributing to a reduction in buying power. The current article explores the effect of fluctuations in oil prices on the economic activity of India (developing country).

Considering all of the above-mentioned economic issues, this research focusses on the linkages between oil prices and macroeconomic variables. The study progresses as follows: The second section discusses literature related to the proposed research followed by a rationale of interrelation between oil and macroeconomic variables. The third section provides objectives of the study. In the fourth section, the details related to data and methodology are provided. The fifth section describes empirical results which include the definition of variables, econometric modelling and discussion thereof. The sixth section concludes by providing the threat to validity, its implications, future research directions and the managerial implications of the proposed study.

Review of Literature

Various researchers have worked in the direction of establishing the impact of increase and decrease in oil prices on macroeconomic variables. Cunado and De Gracia (2005) examined the connection between oil prices and economic movement and consumer price indices for six Asian counties for the time period 1975–2002. Their study suggests a significant short-run association between oil prices and macroeconomic variables taken into consideration. Lescaroux and Mignon (2008) investigated both short-term and long-term association between crude oil prices and certain macroeconomic and financial variables (taking gross domestic product [GDP], household consumption expenditure, Consumer Price Index [CPI], unemployment rate and share prices) of oil-exporting and -importing countries (OPEC), oil-exporting and -importing countries. Rafiq et al. (2009) surveyed the influence of oil price fluctuations on joblessness and investment in the Thai economy (using the granger causality test and autoregressive function). Iwayemi and Fowowe (2011) studied the connection between oil and other macroeconomic variables for the period between 1970 and 2006 for the oil exporters of Africa (Algeria, Egypt, Libya and Nigeria) using vector autoregression and granger causality test. The long-term co-trending and co-movement in the price of gold, the real exchange rate for the US dollar, stock price and the oil price of crude oil in the world economy were examined by Samanta and Zadeh (2012). Bhunia (2013) studied the cointegration relationship between the Indian economy’s oil price and financial variables, using the Johansen cointegration analysis and the Granger causality method. Jain and Ghosh (2013) tested cointegration and Granger causality among global oil prices, Indian rupee–US dollar exchange rate and precious metal (gold, platinum and silver) prices for the period 2009–2011. Ozturk (2015) analysed the impact of oil price shocks on industrial production, money supply and imports in Turkey using Granger causality analysis. Ratti and Vespignani (2016) examined the relationship between oil prices, global industrial production, central bank interest rate and monetary aggregate using the Global Factor-Augmented Error Correction Model (GFAECM). Kumar (2017) made an attempt to explore the dynamic impact of variation in crude oil prices in global market on Indian stock market volatility of crude oil. Troster et al. (2018) studied the causal relationship between oil values, renewable energy consumption and economic movement in the USA (for the period 1989–2016) using Granger causality. Their results confirmed unidirectional causation from oscillations in oil prices to economic progression. Rao and Goyal (2018) explore the effect of fluctuations in the price of non-energy commodities and oil on production, inflation and real effective exchange rate (REER) in India and significant imports of commodities and oil. Troster et al. (2018) studied the causal relationship between oil values, renewable energy consumption and economic movement in the USA (for a period of 1989 to 2016) using Granger causality. Their results confirmed unidirectional causation from oscillations in oil prices to economic progression. Oteng-Abayie et al. (2018) analysed the macroeconomic determinants of crude oil demand by considering the case of Ghana. Asaleye et al. (2019) studied the impact of change in oil price on employment in the Nigerian context. Sharma et al. (2019) analysed whether oil prices are an important indicator of 31 macroeconomic variables of Indonesia, using monthly data ranging from 1986 to 2018. Using auto-regressive distributed lag (ARDL) method, Phoong and Phoong (2019) conducted a similar research on Malaysian economy. Mukhtarov et al. (2020) explored the effects of oil prices on Azerbaijan’s economic growth, export, inflation and exchange rate using Johansen cointegration.

From the conclusive summary of related literature, a wide gap can be observed in studies on the offered topic in the Indian context. The transmission channels on the association of crude oil prices and other macroeconomic variables in the Indian context are very few. This gap can be used to check the validity and affirmation of relationship and the impact of oil prices with other selected macroeconomic variables in the Indian context. Therefore, the present study intends to make some conclusive contributions to the relationship between crude oil prices and industrial value-added, inflation, foreign exchange rate (between Indian rupee and US dollar) and stock prices (S&P BSE SENSEX).

Framework of the Study

The relationship between fluctuations in oil prices and the direction of economic activities has been established in the current study. In this section, we have displayed a simple framework to identify different macroeconomic variables to represent economic activities in the country. As a standard, the price of crude oil refers to the spot price of a barrel. The current analysis uses Brent crude oil prices, since it usually remains the benchmark for other crude oil prices. The indicators used for economic activity include GDP, industrial production, inflation, exchange rate, stock prices and unemployment. Please note that all the figures drawn in this section are based on the data taken for the period from 1991 to 2020.

Oil Prices and Unemployment

Unemployment is an indicator of the economy’s stability. Unemployment rate, which is the number of unemployed people separated by the number of people in the work force, is the most frequent indicator of unemployment. The National Sample Survey Office (NSSO) describes the status of a person who is searching or available for jobs, and also a person who is not in a job, neither seeking a job nor available for work, that is, ‘Unemployed’, as unemployment.

The negative and meaningful relationship between oil prices and industrial production suggests shocks in oil prices are reducing developing countries’ industrial production. Such shocks affect industrial efficiency by raising the cost of both imports and exports, and by decreasing demand for consumption and investment. High oil price is also associated with unemployment in the economy. The statistical relationship between the unemployment rate of a country and its economy’s growth rate was proposed by Okun’s rule (Yale professor and economist Arthur Okun). One iteration of Okun’s legislation specified rather clearly that gross national product (GNP) increases by 3% when unemployment declines by 1%.

The increased prices of goods and services decrease the real income of the consumer, compelling him/her to cut down his expenditure. This disturbs the current equilibrium of demand and supply of commodities and services in the economy due to a reduction in demand. Less demand followed by less productivity affects the selling prices of goods and services, the rate of employment in the country, the pattern of consumption, the real wage rate, interest rates, the investment part and the rate of economic inflation (Loungani, 1986). Figure 1 presents percentage change in oil price and unemployment rate of India for a period of 30 years (1991–2020).

Figure 1.

Source: The authors.

Oil Prices and Gross Domestic Product

Gross domestic product is termed as the monetary value of all finished products and services produced over a particular time within a country. GDP offers a country’s economic snapshot and is used to measure the size of an economy and the pace of growth. After the oil price fluctuations in the 1970s, the link between oil prices and global GDP growth has been focussed. There are strong economic reasons to predict the inverse relationship between GDP and the price of oil. The Economic Survey 2018 reports that every US$10 per barrel increase in the price of oil reduces growth by 0.2–0.3 percentage points. Figure 2 presents the percentage change in oil price and GDP of India for a period of 30 years (1991–2020). It also explains the inverse relationship between the two variables.

Figure 2.

Source: The authors.

Oil Prices and Industrial Production

As the major contributors to GDP, most of the developed economies have an industrial sector; hence, we have a good reason to establish a connection between industrialization and economic growth. Industrial production in the country can be seen as supply side. The availability of industrial goods can be depressed by rises in oil prices because they raise the cost of manufacturing and transporting industrial produces. High oil prices will change the supply curve of products and services for which oil is an input in economic terminology. Index of industrial production has been used as a measure of industrial output in the current study. The Industrial Production Index (IIP) is an index that shows output growth rates over a stipulated period of time in various industry groups within the economy. Figure 3 presents an inverse association between the percentage change in oil prices and industry value-added.

Figure 3.

Source: The authors.

Oil Prices and Inflation

Inflation may be defined as a general rise in prices and a decline in the buying value of money. The increasing prices of oil also affect the supply curve of various intermediate and finished goods (for which, one of the inputs is oil). Fluctuations in oil price are usually followed by an upsurge in inflation and shrinking economic growth (Hooker, 2002). In the economy, price levels (inflation/deflation or stable prices) play an important role in determining purchasing power and the creation of capital in the economy. Saghaian (2010) also confirmed the strong link between oil prices and prices of different commodities. Finally, the burden of this increased price goes to the final consumer leading to decreased purchasing power. In the present study, we have used CPI as a measure of inflation. CPI is a systematic calculation used for calculating price increases in an economy; it is also called a basket of goods and services and is indicative of consumption expenditure. Figure 4 shows the percentage change in inflation and oil price over a period of 30 years. As per the literature available and Figure 4, we can see that a positive link exists between oil prices and inflation.

Figure 4.

Source: The authors.

Oil Prices and Exchange Rate

Exchange rate is also a major factor that contributes to a country’s economic growth. Economic growth can be depressed by strong exchange rate and enhanced by weak exchange rate. This occurs because exchange rate is directly related to the rise or decrease in the economy’s exports/imports. The oil price shocks and exchange rate changes are also associated in any economic scenario. Exchange rate shock has a momentous adverse influence on crude oil (Brahmasrene et al., 2014). This association may exist because an increase in the price of imported goods and their transportation costs increases the demand for foreign currency (the increase is price again associated with oil price). The decline in the price of oil leads to a higher rupee value compared with US dollars. Therefore, the exchange rate between rupee and US dollars is associated with fluctuations in the oil price. Figure 5 showing percentage change in exchange rate and crude oil prices displays the inverse relationship between the variables.

Figure 5.

Source: The authors.

Oil Prices and Stock Prices

The term ‘stock price’ refers to the current price traded on the market to buy a share of the stock. Stock price is the product of the interaction between stock exchange traders, buyers and dealers. The stock market would, generally speaking, represent the economic conditions of the economy. If an economy is increasing, production will grow and improved profitability should be experienced by most businesses. This increased profitability would also lead to a rise in share prices (i.e., stock prices) and, on the other hand, a drop in stock prices would occur if profitability goes down. The depreciation of rupees has reverberating effects on the Indian economy and even on the stock market. Indian stock markets are under considerable pressure because of the rise in crude oil prices. Many Indian companies are reliant on healthy crude oil prices. This includes businesses in the sectors of tyres, lubricants, shoes, packaging and airlines. The productivity of these companies is adversely affected by the higher costs of output. This may negatively affect stock prices in the short term. At the other hand, the country’s oil exploration companies could profit from an upsurge in oil prices. Figure 6 presents the percentage change in stock prices and oil prices over a period of 30 years.

Figure 6.

Source: The authors.

Objectives of the Study

The present study intends to find out the linkages between crude oil prices and economic activities in the Indian context. Variables such as GDP, industrial value-added, inflation, foreign exchange rates, stock prices and unemployment have been considered to represent economic activity in the country. The relationship between oil price and each macroeconomic variable has been calculated individually to find out whether is a significant association exits between oil price and that variable. The time series data of 30 years (i.e., 1991–2020) of all the mentioned variables have been taken to check the short-run and long-run association between oil price and each variable.

Data Description and Methodology

The Data

The data collected for this study contain the time series values of 30 years (annual data) starting from 1991 to 2020. The data on macroeconomic variables for current study have been collected from various sources (The World Bank, CEIC database, Reserve bank of India, S&P BSE SENSEX). The data on unemployment, GDP, industrial output and inflation have been taken from the official website of the World Bank (World Bank data sources). The data on crude oil prices have been obtained from CEIC Indian premium database. The data on exchange rate between Indian rupee and US dollars have been taken from official website of Reserve Bank of India. And the S&P BSE stock price data have been obtained from S&P BSE SENSEX official website. For numerical illustration purpose, we have coded the variables as follows: crude oil prices in US dollars (Z), unemployment rate in percentage(V1), GDP (V2), industrial output (V3), inflation (V4), exchange rate (V5) and stock market prices (V6). Our study considers the long duration of about 30 years from 1991 to 2020 to fulfil the purpose of testing the short-run as well as the long-run association among the variables considered. Owing to liberalization, privatization and globalization in 1991, during this time, the macroeconomic activity also took a pace in shaping the viewpoint so the aforesaid during was considered relevant to conduct the study.

Though the data for current study come from different sources, for better analysis and symmetric numerical analysis, all the values have been converted into their natural logarithm. The association between dependent and independent variables can be easily captured by comparability of trend (increase or decrease) in the indices of time series considered for the study.

Methodology (Econometric Methods)

To determine the long-term relationships between macroeconomic variables and the oil price fluctuation, we have used Johansen cointegration test (long-run analysis) (Engle & Granger, 1987; Nkoro & Uko, 2016). If the cointegration is found among the variables, it means the variables have long-run association among them. Johansen cointegration test was used to find the number of associations and also as a tool to approximate those relationships (Poh & Tan, 1997). To find out the short-run relationship, we have used Granger causality test. If there is cointegration among variables, then we apply Granger causality test to check the short-run association among the variables (Granger, 1986). Vector autoregressive techniques provide better non-stationarity tunings and establish long-term associations between factors (Crane & Nourzad, 1998; Schmidt, 2000).

The confirmation of causality concludes the short-run relation among variables. The Granger causality test assumes that for the estimation of these variables, the time series data of the given variables provide the relevant details. Diebold (1998), in his study, explained the concept of predictive causality. He exclaimed that for the prediction of y, y has all the relevant information. Prediction of y also requires the historical values of related variables. The test to verify the causality between two variables includes estimating the following pair of regressions:

X_{t} = \sum_{i = 0}^{n} α_{i} Y_{t - 1} + \sum_{j = 1}^{n} β_{j} X_{t - j} + u_{1 t}

(1)

Y_{t} = \sum_{i = 0}^{n} λ_{i} Y_{t - 1} + \sum_{j = 1}^{n} δ_{j} X_{t - j} + u_{2 t}

(2)

where u₁ and u₂ (the perturbations) are uncorrelated. Equation (1) states that X is dependent on the past values of X and Y and Equation (2) postulates the same for Y. Unidirectional causality from X to Y is shown if the assessed coefficient on Y (=1) lagged values is statistically different from zero as a group (i.e., Σα ≠ 0) and the estimated coefficient set on the lagged X (=2) is not significantly different from zero (i.e., Σδ = 0). On the other hand, unidirectional causality from Y to X can be seen when the set of lagged Y coefficient in Equation (1) is not statistically different from 0 (i.e., Σα = 0) and the set of lagged X coefficient in Equation 2 is different from zero (i.e., Σδ ≠ 0). Bilateral causality is assumed when the sets of X and Y variables in each of the above regression equations are statistically significantly different from zero.

Empirical Results

Unit of Variables (with Reference to Indian Context)

The following variables are considered to develop the empirical model:

Crude oil prices in US dollars (Z): in the current study, spot price of a barrel has been given in in US dollars.

Unemployment rate in percentage (V1): unemployed percentage of people >15 years (% of total population).

Gross domestic product (GDP) (V2): GDP at factor cost in US$ million.

Industrial output (V3): industrial output growth rate (base year 2011–2012).

Inflation (V4): CPI index presenting 299 items (base year 2011–2012).

Exchange rate (V5): rate of exchange between Indian rupee and US dollars.

Stock market prices (V6): S&P BSE SENSEX annual prices.

Descriptive Statistics

In this section, we have provided the data analysis results of the proposed work. Table 1 depicts the descriptive statistics and Table 2 displays the coefficients of correlation among the macroeconomic variables. Table 3 presents the results of the unit root test at a level, while Table 4 presents the unit root test results at second differencing. Figures 7–13 show the trend plots of time-series’ at the level (without differencing). Both unit root test results and figures designate that the taken series are not stationary at level. The unit test results in Table 2 have been obtained at second differencing of all the series, which indicates that the taken series are integrated at order 2.

Table 1.
Descriptive Statistics.

Variable Source: Minimum Maximum Mean Std. Deviation

Z 97.170 12.280 109.450 48.505 31.179

V1 0.539 5.281 5.820 5.588 0.127

V2 2714.895 270.105 2985.000 1214.358 901.065

V3 4.734 26.402 31.136 28.287 1.590

V4 11.379 2.491 13.870 7.151 3.300

V5 46.726 24.474 71.200 48.411 13.226

V6 39344.890 1908.850 41253.740 14251.851 12186.447

Source: The authors.

Note: Valid N (list-wise) = 30 and N = 30.

Table 2.
Correlation Statistics.

V1 V2 Z V3 V4 V5 V6

V1 1

V2 −0.094 1

Z −0.186 0.659 1

V3 −0.170 0.187 0.606 1

V4 −0.127 −0.313 0.035 0.145 1

V5 0.055 0.910 0.559 0.029 −0.498 1

V6 −0.13746 0.9800 0.5993 0.185 −0.334 0.871 1

Source: The authors.

Table 3.
Unit Root Test Results (at level).

Variable ADF PP KPSS

Test Statistic p-Value Test Statistic p-Value Test Statistic p-Value

Variable ADF PP KPSS

Test Statistic p-Value Test Statistic p-Value Test Statistic p-Value

Z −1.399 0.803 −6.499 0.712 0.843 0.01

V1 −2.917 0.221 −14.082 0.210 0.147 0.01

V2 −2.278 0.465 −6.894 0.686 1.055 0.01

V3 −3.550 0.055 −6.725 0.697 0.273 0.01

V4 −1.939 0.596 −10.359 0.456 0.221 0.01

V5 −2.541 0.365 −11.736 0.365 0.955 0.01

V6 −2.580 0.350 −15.68 0.104 1.038 0.01

Source: The authors.

Table 4.
Unit Root Test Results (second differencing).

Variable ADF PP KPSS

Test Statistic p-Value Z-alpha p-Value Test Statistic p-Value

Z −4.256 0.014 −27.136 0.01 0.060 0.1

V1 −3.943 0.025 −27.136 0.01 0.198 0.1

V2 −4.509 0.01 −33.263 0.01 0.071 0.1

V3 −3.591 0.051 −46.163 0.01 0.188 0.1

V4 −5.588 0.01 −39.263 0.01 0.078 0.1

V5 −4.443 0.01 −34.483 0.01 0.163 0.1

V6 −5.568 0.01 −35.101 0.01 0.075 0.1

Source: The authors.

Figure 7.
Source: The authors.

Figure 8.
Source: The authors.

Figure 9.
Source: The authors.

Figure 10.
Source: The authors.

Figure 11.
Source: The authors.

Figure 12.
Source: The authors.

Figure 13.
Source: The authors.

The Empirical Model

Cointegration Test (Long-run Analysis)

The results of the cointegration test have been reported in Table 5. As per the results from Table 5, there can be three cointegration equations. Since cointegration exists in the variables, VECM was applied to confirm the short- and long-run causalities among the variables. The results of VECM have been reported in Table A2. As per the results of VECM, there exists a significant long-run relationship (1% level of significance) between crude oil prices and inflation (CPI). The long-run association between industrial production and oil prices (significant at 5%) also can be exacted based on results. A long-run association exits between oil prices and unemployment . As per the results of the Granger causality test, the short-run association among variables has already been confirmed.

Table 5.
Test Results (Johansen cointegration).

Cointegration Equations Eigen Values (lambda) Trace Statistic Maximal Eigenvalue Statistic

Test Statistic Critical Value (5%) Test Statistic Critical Value (5%)

Cointegration Equations Eigen Values (lambda) Trace Statistic Maximal Eigenvalue Statistic

Test Statistic Critical Value (5%) Test Statistic Critical Value (5%)

r = 0 9.852086e-01 270.09 131.70 105.34 46.45

r ≤ 1 9.324850e-01 164.74 102.14 67.39 40.30

r ≤ 2 8.370784e-01 97.36 76.07 45.36 34.40

r ≤ 3 6.264420e-01 52.00 53.12 24.62 28.14

r ≤ 4 4.329765e-01 27.38 34.91 14.18 22.00

r ≤ 5 2.818419e-01 13.20 19.96 8.28 15.67

r ≤ 6 1.786294e-01 4.92 9.24 4.92 9.24

Source: The authors.

Measuring Causality (Short-run Analysis)

Table 6 shows the results of the Granger causality test. All variables with oil prices are considered to have bidirectional causality. From the results of Granger causality, the bidirectional causality between crude oil prices and unemployment can be noticed. Though unemployment cannot be a cause for change in oil prices due to the form of data set here, we can see the causality flowing from unemployment to oil prices. Barsky and Kilian (2004) have also suggested that oil prices can have a reverse association with macroeconomic and financial variables. Further, the path of causality varies from oil prices to GDP; it is also possible to consider the direction of causality from GDP to crude oil prices (in yearly lag 2). Again, there is the bidirectional causality between the oil prices and the added value of the industry. This can be assumed because the increase in the price of oil can cause inflationary push which may increase industry value-added, while the increasing demand for oil due to increased industrial production and no information on increased supply can cause an increase in oil prices. There is a bidirectional relationship between inflation and crude oil prices though the direction of causality from inflation to oil prices is weak. Based on the findings, bidirectional causality varies from the price of crude oil to the exchange rate, and a poor causality can again be observed from the exchange rate to the price of crude oil (at yearly lag 2). Other results infer the existence of a bidirectional causality between the price of crude oil and the stock prices (S&P BSE SENSEX). The causality from stock prices to oil prices is identified at lag order 2. The result confirms a very weak causality running from stock prices to crude oil prices. Hence, in the Indian context, the short-term association between oil prices and macroeconomic activities can be identified.

Table 6.
Results (Granger causality).

Direction of Causality F-statistic Years of Lags Probability Existence of Causality

Z ↓ V1 0.031 1 0.862 Yes

V1 ↓ Z 1.326 1 0.261 Yes

Z ↓ V2 2.720 1 0.112 Yes

V2 ↓ Z 2.262 2 0.130 Yes

Z ↓ V3 0.941 1 0.342 Yes

V3 ↓ Z 0.743 1 0.397 Yes

Z ↓ V4 0.333 1 0.569 Yes

V4 ↓ Z 0.202 1 0.657 Yes

Z ↓ V5 2.504 1 0.127 Yes

V5 ↓ Z 1.594 2 0.228 Yes

Z ↓ V6 1.177 1 0.289 Yes

V6 ↓ Z 1.413 2 0.267 Yes

Source: The authors.

Conclusion and Future Research Directions

The current study contributes to the existing literature by investigating and establishing the connection between oil price variations and the macroeconomic variables considered in the Indian context. The readings of the Granger causality test have successfully concluded the bidirectional short-run causation between oil price and macroeconomic variables. Thus, the results also contribute to the existing literature about the long-run association in oil prices and macroeconomic variables in different countries. The role of oil and gas as commodities in global trade, the position of oil prices in the macro economy and the influence of oil and gas geopolitics are seen as very important. India produces a very small quantity of crude oil and the balance has to be administered through imports from the oil-producing countries. Thus, increasing the volume of imports affects the exchange rate. While reviving economic growth, it can be inferred that cheaper crude reduces India’s foreign currency outflow and inflationary forces. The lower rates lessen stress on inflation and interest rates and revive economic growth in the economy. The finding of the study includes bidirectional causality between oil prices and other macroeconomic variables considered for the study, while a long association exists with inflation, industrial production and unemployment. Figure A1 indicates the potential existence of co-trending and co-movements among the variables; however, three of our time series are moving simultaneously, and a little deviation can be noticed about other series. As per the trend plot, the co-movement of oil is closer to GDP, unemployment and stock price, whilst it is having matching trends with other series (series in their natural logs). The VECM results also disclose the long-term relationship among other variables as well. There is ample space of prospective work in the arena of impact and association of other oil and petroleum products for reviewing their distribution and promotion, infrastructure development and oil field production, petroleum products taxation, volatility in oil prices with policy references for maintaining sufficient level of consumption and conservation.

There is a potential scope for finding out the relationship between oil and some other macroeconomic variables (the variable other than the current research). Further research can be conducted covering the areas of taxes, duties, government revenues arising from crude oil and other petroleum products.

Threats to Validity

The current research is relevant only in the Indian context and for the current data set. Our study is limited to the impact of Z on V1, V2, …, V6 and assumed the other variables as constant. It is possible that across different countries, the relation among variables varies depending on the form, property and length of the data used for the analysis. The research study is limited to the economic effects of crude oil prices, and it does not cover the areas of taxes, duties, government revenues arising from crude oil and other petroleum products. The direction and magnitude of relationship among variables can be diverse in developed, developing and under developing economies due to different phases of economic activities. India is a developing economy where this relationship between oil price and other macroeconomic variables can be different due to its own process of economic change and fiscal and monetary policies.

Managerial Implications

The present study is useful for policymakers to effectively prepare and execute various policies in different sectors if there is a fluctuation in the economy’s oil prices. Various policy fields include pricing policies, employment policies and international trade policies as indirectly affected by fluctuations in oil prices. In addition, our study describes that fund managers and individual investors should consider the volatility of the oil price when making investment decisions. Oil prices in industrial sectors make raw materials, intermediate goods and final products expensive than earlier; hence, the results of this research article can also be helpful to industrial managers in framing policies to reduce this cost and revise prices as needed. Our study will help corporate managers and decision-makers in government to understand the impact of change in oil price on related variables which in turn will allow them to make strategic decisions for future.

Appendix A

Table A1.
Definition of Variables.

Variable Coding Source Definition

Crude oil prices Z https://www.ceicdata.com/en/indicator/india/crude-oil-production The price of crude oil refers to the spot price of a barrel as the crude oil standard. The current study uses crude oil prices of Brent, since it typically remains the benchmark for other crude oils.

Unemployment rate V1 World bank data source The unemployment rate reflects the proportion of the jobless population. It is a lagging measure, which means it normally rises or falls in the wake of shifting economic conditions. The current study talks about change in unemployment due to change in crude oil prices.

Gross domestic product V2 World bank data source Gross domestic product is the overall monetary value of the goods and services produced within a country’s geographical borders over a given period of time, usually 1 year. The rate of GDP growth is a significant indicator of a country’s economic success.

Industrial output V3 World bank data source Industrial Production Index (IIP)has been used as a measure of industrial output in the current study. IIP is an index that shows output growth rates over a stipulated period of time in various industry groups within the economy.

Inflation V4 World bank data source Inflation may be defined as a general rise in prices and a decline in the buying value of money. In present study, we have used consumer price index as a measure of inflation. A systematic calculation used for calculating price increases in an economy’s consumer price index is called a basket of goods and services, and it is indicative of consumption expenditure.

Exchange rate V5 Official website of RBI An exchange rate may be termed as the rate at which one currency will be exchanged for another. The current study employs the exchange rate between Indian rupee and US dollars.

Stock market prices V6 S&P BSE SENSEX official website The term stock price refers to the current price traded on the market for a share stock. The stock price is the product of the interaction between stock exchange traders, buyers and dealers.

Source: The authors.

Table A2.
Results of Vector Error Correction Model.

V1 V2 Z V3 V4 V5 V6

ECM1 0.7845(0.5635) 3.2531(1.5510). 20.2060(7.2113)* 0.1434(0.7006) 17.8368(3.3672)** 3.6161(1.9041) 8.0958(10.7294)

ECM2 0.9278(0.5499) 2.2871(1.5135) 1.8564(7.0371) 1.6042(0.6836). 9.0795(3.2859)* 3.1531(1.8581) 2.8689(10.4703)

ECM3 0.0151(0.0458) 0.2741(0.1259). 1.4476(0.5855)* 0.0620(0.0569) 0.8521(0.2734)* 0.2758(0.1546) 0.1354(0.8712)

Intercept 5.7e-05(0.0024) 0.0016(0.0066) 0.0169(0.0306) 0.0015(0.0030) 0.0133(0.0143) 0.0020(0.0081) 0.0043(0.0455)

ddiffun 1 0.0741(0.7051) 1.7690(1.9406) 4.5030(9.0227) 1.3391(0.8765) 3.3442(4.2131) 3.6624(2.3824) 4.3905(13.4247)

ddiffg 1 0.3378(0.2478) 0.1703(0.6819) 0.9147(3.1705) 0.4824(0.3080) 9.3453(1.4804)*** 1.0857(0.8372) 2.1190(4.7173)

ddiffcr 1 0.0162(0.0488) 0.2044(0.1344) 1.0200(0.6250) 0.0670(0.0607) 1.4295(0.2919)** 0.2352(0.1650) 0.4198(0.9300)

ddiffi 1 0.1048(0.6537) 2.3638(1.7993) 21.2170(8.3658)* 1.3207(0.8127) 20.3013(3.9063)** 2.5387(2.2089) 10.3198(12.4472)

ddiffin 1 0.0201(0.0456) 0.1112(0.1255) 0.6360(0.5836) 0.0869(0.0567) 0.4835(0.2725) 0.2247(0.1541 0.0873(0.8683)

ddiffex 1 0.4740(0.3328) 0.1334(0.9161) 7.8490(4.2592) 0.5571(0.4138) 0.5965(1.9888) 1.1314(1.1246) 0.9627(6.3371)

ddiffb 1 0.0279(0.0520) 0.2645(0.1431) 1.0352(0.6652) 0.0678(0.0646) 1.2514(0.3106)** 0.3043(0.1757) 0.8294(0.9898)

ddiffun 2 0.2062(0.5002) 1.2466(1.3767) 15.5816(6.4008). 0.4478(0.6218) 20.8536(2.9888)*** 0.4889(1.6901) 1.6316(9.5235)

ddiffg 2 0.2783(0.2064) 0.0391(0.5681) 1.3812(2.6416) 0.1600(0.2566) 4.8475(1.2335)** 0.0826(0.6975) 3.2024(3.9303)

ddiffcr 2 0.0203(0.0356) 0.1581(0.0980) 0.1886(0.4554) 0.0753(0.0442) 0.0605(0.2127) 0.2112(0.1203) 0.2286(0.6776)

ddiffi 2 0.2865(0.4709) 1.6864(1.2961) 8.9209(6.0259) 0.6793(0.5854) 8.3627(2.8137)* 1.7471(1.5911) 10.1477(8.9657)

ddiffin 2 0.0089(0.0207) 0.0008(0.0570) 0.3169(0.2648) 0.0211(0.0257) 0.1052(0.1237) 0.0539(0.0699) 0.0088(0.3940)

ddiffex 2 0.1044(0.1715) 0.0937(0.4719 1.6841(2.1941) 0.1881(0.2132) 4.7862(1.0245)** 0.3623(0.5793) 1.3455(3.2645)

ddiffb 2 0.0513(0.0417) 0.1168(0.1148) 0.0455(0.5337) 0.1326(0.0519)* 0.5400(0.2492). 0.1912(0.1409) 0.4051(0.7941)

Source: The authors.

Figure A1.
Source: The authors.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Preeti Sharma

Avinash K. Shrivastava

References

1.
Ahmed H. J. A. , & Wadud I. M. (2011). Role of oil price shocks on macroeconomic activities: An SVAR approach to the Malaysian economy and monetary responses. Energy Policy , 39(12), 8062–8069.

2.
Arezki M. R. , Jakab Z. , Laxton M. D. , Matsumoto M. A. , Nurbekyan A. , Wang H. , & Yao J. (2017). Oil prices and the global economy . International Monetary Fund.

3.
Asaleye A. J. , Aremu C. O. , Lawal A. I. , Inegbedion H. , Popoola O. , Adewara S. O. , & Obasaju B. O. (2019). Oil price shock and macroeconomic performance in Nigeria: Implication on employment. International Journal of Energy Economics and Policy , 9(5), 451–457.

4.
Barsky R. B. , & Kilian L. (2004). Oil and the macroeconomy since the 1970s. Journal of Economic Perspectives , 18(4), 115–134.

5.
Bhunia A. (2013). Cointegration and causal relationship among crude price, domestic gold price and financial variables: An evidence of BSE and NSE. Journal of Contemporary Issues in Business Research , 2(1), 1–10.

6.
Brahmasrene T. , Huang J. C. , & Sissoko Y. (2014). Crude oil prices and exchange rates: Causality, variance decomposition and impulse response. Energy Economics , 44, 407–412.

7.
Crane S. E. , & Nourzad F. (1998). Improving local manufacturing employment forecasts using cointegration analysis. Growth and Change , 29(2), 175–195.

8.
Cunado J. , & De Gracia F. P. (2005). Oil prices, economic activity and inflation: Evidence for some Asian countries. The Quarterly Review of Economics and Finance , 45(1), 65–83.

9.
Diebold F. X. (1998). Elements of forecasting . South-Western College Pub.

10.
Engle R. F. , & Granger C. W. J. (1987). Cointegration and error correction: Representation, estimation, and testing. Econometrica , 55, 251–276.

11.
Granger C. W. J. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of Economics and Statistics , 48(3), 213–228.

12.
Herrera A. M. , Karaki M. B. , & Rangaraju S. K. (2019). Oil price shocks and US economic activity. Energy Policy , 129, 89–99.

13.
Hooker M. A. (2002). Are oil shocks inflationary? Asymmetric and nonlinear specifications versus changes in regime. Journal of Money, Credit and Banking , 34(2), 540–561.

14.
Iwayemi A. , & Fowowe B. (2011a). Oil and the macroeconomy: Empirical evidence from oil-exporting African countries. OPEC Energy Review , 35(3), 227–269.

15.
Iwayemi A. , & Fowowe B. (2011b). Impact of oil price shocks on selected macroeconomic variables in Nigeria. Energy Policy , 39(2), 603–612.

16.
Jain A. , & Ghosh S. (2013). Dynamics of global oil prices, exchange rate and precious metal prices in India. Resources Policy , 38(1), 88–93.

17.
Krugman P. (2008). Fuels on the hill. New York Times . https://www.nytimes.com/2008/06/27/opinion/27krugman.html

18.
Kumar R. (2017). Examining the dynamic and non-linear linkages between crude oil price and Indian stock market volatility. Global Business Review , 18(2), 388–401.

19.
Lescaroux F. , & Mignon V. (2008). On the influence of oil prices on economic activity and other macroeconomic and financial variables. OPEC Energy Review , 32(4), 343–380.

20.
Loungani P. (1986). Oil price shocks and the dispersion hypothesis. The Review of Economics and Statistics , 58, 536–539.

21.
Mukhtarov S. , Aliyev S. , & Zeynalov J. (2020). The effects of oil prices on macroeconomic variables: Evidence from Azerbaijan. International Journal of Energy Economics and Policy , 10(1), 72.

22.
Nkoro E. , & Uko A. K. (2016). Autoregressive Distributed Lag (ARDL) cointegration technique: Application and interpretation. Journal of Statistical and Econometric Methods , 5(4), 63–91.

23.
Oteng-Abayie E. F. , Ayinbilla P. A. , & Eshun M. E. (2018). Macroeconomic determinants of crude oil demand in Ghana. Global Business Review , 19(4), 873–888.

24.
Ozturk F. (2015). Oil price shocks-macro economy relationship in Turkey. Asian Economic and Financial Review , 5(5), 846–857.

25.
Phoong S. W. , & Phoong S. Y. (2019). An ARDL approach on crude oil price and macroeconomic variables. Journal of Business and Economic Review , 4(1), 68–73.

26.
Poh C. W. , & Tan R. (1997). Performance of Johansen's cointegration test. In Kendall J. D. (Ed.), East Asian economic issues (Vol. III, pp. 402–414). World Scientific.

27.
Rafiq S. , Salim R. , & Bloch H. (2009). Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy. Resources Policy , 34(3), 121–132.

28.
Rao D. T. , & Goyal S. (2018). Global commodity and oil price movements, macroeconomic performance and challenges for an emerging economy: The Indian experience. Global Business Review , 19(3), 650–674.

29.
Ratti R. A. , & Vespignani J. L. (2016). Oil prices and global factor macroeconomic variables. Energy Economics , 59, 198–212.

30.
Saghaian S. H. (2010). The impact of the oil sector on commodity prices: Correlation or causation? Journal of Agricultural and Applied Economics , 42(1379-2016-113630), 477–485.

31.
Samanta S. K. , & Zadeh A. H. (2012). Co-movements of oil, gold, the US dollar, and stocks. Modern Economy , 3(1), 111–117.

32.
Schmidt M. B. (2000). The dynamic behavior of wages and prices: Cointegration tests within a large macroeconomic system. Southern Economic Journal , 67(1), 123–138.

33.
Sharma S. S. , Phan D. H. B. , & Iyke B. (2019). Do oil prices predict Indonesian macroeconomy? Economic Modelling , 82, 2–12.

34.
Sill K. (2007). The macroeconomics of oil shocks. Federal Reserve Bank of Philadelphia, Business Review , 1(1), 21–31.

35.
Trang N. T. N. , Tho T. N. , & Hong D. T. T. (2017). The impact of oil price on the growth, inflation, unemployment and budget deficit of Vietnam. International Journal of Energy Economics and Policy , 7(3), 42–49.

36.
Troster V. , Shahbaz M. , & Uddin G. S. (2018). Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis. Energy Economics , 70, 440–452.

37.
Wei Y. , & Guo X. (2016). An empirical analysis of the relationship between oil prices and the Chinese macro-economy. Energy Economics , 56, 88–100.

Variable	Source:	Minimum	Maximum	Mean	Std. Deviation
Z	97.170	12.280	109.450	48.505	31.179
V1	0.539	5.281	5.820	5.588	0.127
V2	2714.895	270.105	2985.000	1214.358	901.065
V3	4.734	26.402	31.136	28.287	1.590
V4	11.379	2.491	13.870	7.151	3.300
V5	46.726	24.474	71.200	48.411	13.226
V6	39344.890	1908.850	41253.740	14251.851	12186.447

	V1	V2	Z	V3	V4	V5	V6
V1	1
V2	−0.094	1
Z	−0.186	0.659	1
V3	−0.170	0.187	0.606	1
V4	−0.127	−0.313	0.035	0.145	1
V5	0.055	0.910	0.559	0.029	−0.498	1
V6	−0.13746	0.9800	0.5993	0.185	−0.334	0.871	1

Variable	ADF		PP		KPSS
Variable	Test Statistic	p-Value	Test Statistic	p-Value	Test Statistic	p-Value
Variable	ADF		PP		KPSS
Variable	Test Statistic	p-Value	Test Statistic	p-Value	Test Statistic	p-Value
Z	−1.399	0.803	−6.499	0.712	0.843	0.01
V1	−2.917	0.221	−14.082	0.210	0.147	0.01
V2	−2.278	0.465	−6.894	0.686	1.055	0.01
V3	−3.550	0.055	−6.725	0.697	0.273	0.01
V4	−1.939	0.596	−10.359	0.456	0.221	0.01
V5	−2.541	0.365	−11.736	0.365	0.955	0.01
V6	−2.580	0.350	−15.68	0.104	1.038	0.01

Cointegration Equations	Eigen Values (lambda)	Trace Statistic		Maximal Eigenvalue Statistic
Cointegration Equations	Eigen Values (lambda)	Test Statistic	Critical Value (5%)	Test Statistic	Critical Value (5%)
Cointegration Equations	Eigen Values (lambda)	Trace Statistic		Maximal Eigenvalue Statistic
Cointegration Equations	Eigen Values (lambda)	Test Statistic	Critical Value (5%)	Test Statistic	Critical Value (5%)
r = 0	9.852086e-01	270.09	131.70	105.34	46.45
r ≤ 1	9.324850e-01	164.74	102.14	67.39	40.30
r ≤ 2	8.370784e-01	97.36	76.07	45.36	34.40
r ≤ 3	6.264420e-01	52.00	53.12	24.62	28.14
r ≤ 4	4.329765e-01	27.38	34.91	14.18	22.00
r ≤ 5	2.818419e-01	13.20	19.96	8.28	15.67
r ≤ 6	1.786294e-01	4.92	9.24	4.92	9.24

Direction of Causality	F-statistic	Years of Lags	Probability	Existence of Causality
Z ↓ V1	0.031	1	0.862	Yes
V1 ↓ Z	1.326	1	0.261	Yes
Z ↓ V2	2.720	1	0.112	Yes
V2 ↓ Z	2.262	2	0.130	Yes
Z ↓ V3	0.941	1	0.342	Yes
V3 ↓ Z	0.743	1	0.397	Yes
Z ↓ V4	0.333	1	0.569	Yes
V4 ↓ Z	0.202	1	0.657	Yes
Z ↓ V5	2.504	1	0.127	Yes
V5 ↓ Z	1.594	2	0.228	Yes
Z ↓ V6	1.177	1	0.289	Yes
V6 ↓ Z	1.413	2	0.267	Yes

Variable	Coding	Source	Definition
Crude oil prices	Z	https://www.ceicdata.com/en/indicator/india/crude-oil-production	The price of crude oil refers to the spot price of a barrel as the crude oil standard. The current study uses crude oil prices of Brent, since it typically remains the benchmark for other crude oils.
Unemployment rate	V1	World bank data source	The unemployment rate reflects the proportion of the jobless population. It is a lagging measure, which means it normally rises or falls in the wake of shifting economic conditions. The current study talks about change in unemployment due to change in crude oil prices.
Gross domestic product	V2	World bank data source	Gross domestic product is the overall monetary value of the goods and services produced within a country’s geographical borders over a given period of time, usually 1 year. The rate of GDP growth is a significant indicator of a country’s economic success.
Industrial output	V3	World bank data source	Industrial Production Index (IIP)has been used as a measure of industrial output in the current study. IIP is an index that shows output growth rates over a stipulated period of time in various industry groups within the economy.
Inflation	V4	World bank data source	Inflation may be defined as a general rise in prices and a decline in the buying value of money. In present study, we have used consumer price index as a measure of inflation. A systematic calculation used for calculating price increases in an economy’s consumer price index is called a basket of goods and services, and it is indicative of consumption expenditure.
Exchange rate	V5	Official website of RBI	An exchange rate may be termed as the rate at which one currency will be exchanged for another. The current study employs the exchange rate between Indian rupee and US dollars.
Stock market prices	V6	S&P BSE SENSEX official website	The term stock price refers to the current price traded on the market for a share stock. The stock price is the product of the interaction between stock exchange traders, buyers and dealers.

	V1	V2	Z	V3	V4	V5	V6
ECM1	0.7845(0.5635)	3.2531(1.5510).	20.2060(7.2113)*	0.1434(0.7006)	17.8368(3.3672)**	3.6161(1.9041)	8.0958(10.7294)
ECM2	0.9278(0.5499)	2.2871(1.5135)	1.8564(7.0371)	1.6042(0.6836).	9.0795(3.2859)*	3.1531(1.8581)	2.8689(10.4703)
ECM3	0.0151(0.0458)	0.2741(0.1259).	1.4476(0.5855)*	0.0620(0.0569)	0.8521(0.2734)*	0.2758(0.1546)	0.1354(0.8712)
Intercept	5.7e-05(0.0024)	0.0016(0.0066)	0.0169(0.0306)	0.0015(0.0030)	0.0133(0.0143)	0.0020(0.0081)	0.0043(0.0455)
ddiffun 1	0.0741(0.7051)	1.7690(1.9406)	4.5030(9.0227)	1.3391(0.8765)	3.3442(4.2131)	3.6624(2.3824)	4.3905(13.4247)
ddiffg 1	0.3378(0.2478)	0.1703(0.6819)	0.9147(3.1705)	0.4824(0.3080)	9.3453(1.4804)***	1.0857(0.8372)	2.1190(4.7173)
ddiffcr 1	0.0162(0.0488)	0.2044(0.1344)	1.0200(0.6250)	0.0670(0.0607)	1.4295(0.2919)**	0.2352(0.1650)	0.4198(0.9300)
ddiffi 1	0.1048(0.6537)	2.3638(1.7993)	21.2170(8.3658)*	1.3207(0.8127)	20.3013(3.9063)**	2.5387(2.2089)	10.3198(12.4472)
ddiffin 1	0.0201(0.0456)	0.1112(0.1255)	0.6360(0.5836)	0.0869(0.0567)	0.4835(0.2725)	0.2247(0.1541	0.0873(0.8683)
ddiffex 1	0.4740(0.3328)	0.1334(0.9161)	7.8490(4.2592)	0.5571(0.4138)	0.5965(1.9888)	1.1314(1.1246)	0.9627(6.3371)
ddiffb 1	0.0279(0.0520)	0.2645(0.1431)	1.0352(0.6652)	0.0678(0.0646)	1.2514(0.3106)**	0.3043(0.1757)	0.8294(0.9898)
ddiffun 2	0.2062(0.5002)	1.2466(1.3767)	15.5816(6.4008).	0.4478(0.6218)	20.8536(2.9888)***	0.4889(1.6901)	1.6316(9.5235)
ddiffg 2	0.2783(0.2064)	0.0391(0.5681)	1.3812(2.6416)	0.1600(0.2566)	4.8475(1.2335)**	0.0826(0.6975)	3.2024(3.9303)
ddiffcr 2	0.0203(0.0356)	0.1581(0.0980)	0.1886(0.4554)	0.0753(0.0442)	0.0605(0.2127)	0.2112(0.1203)	0.2286(0.6776)
ddiffi 2	0.2865(0.4709)	1.6864(1.2961)	8.9209(6.0259)	0.6793(0.5854)	8.3627(2.8137)*	1.7471(1.5911)	10.1477(8.9657)
ddiffin 2	0.0089(0.0207)	0.0008(0.0570)	0.3169(0.2648)	0.0211(0.0257)	0.1052(0.1237)	0.0539(0.0699)	0.0088(0.3940)
ddiffex 2	0.1044(0.1715)	0.0937(0.4719	1.6841(2.1941)	0.1881(0.2132)	4.7862(1.0245)**	0.3623(0.5793)	1.3455(3.2645)
ddiffb 2	0.0513(0.0417)	0.1168(0.1148)	0.0455(0.5337)	0.1326(0.0519)*	0.5400(0.2492).	0.1912(0.1409)	0.4051(0.7941)