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Prelims

Raktim Ghosh (University of Gour Banga, India)
Bhaskar Bagchi (University of Gour Banga, India)

Economic Policy Uncertainty and the Indian Economy

ISBN: 978-1-80455-937-6, eISBN: 978-1-80455-936-9

Publication date: 30 January 2023

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Citation

Ghosh, R. and Bagchi, B. (2023), "Prelims", Economic Policy Uncertainty and the Indian Economy, Emerald Publishing Limited, Leeds, pp. i-xxii. https://doi.org/10.1108/978-1-80455-936-920221008

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Emerald Publishing Limited

Copyright © 2023 Raktim Ghosh and Bhaskar Bagchi

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ECONOMIC POLICY UNCERTAINTY AND THE INDIAN ECONOMY

Title Page

ECONOMIC POLICY UNCERTAINTY AND THE INDIAN ECONOMY

BY

RAKTIM GHOSH

University of Gour Banga, India

AND

Bhaskar Bagchi

University of Gour Banga, India

United Kingdom – North America – Japan – India – Malaysia – China

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Emerald Publishing Limited

Howard House, Wagon Lane, Bingley BD16 1WA, UK

First edition 2023

Copyright © 2023 Raktim Ghosh and Bhaskar Bagchi.

Published under an exclusive license by Emerald Publishing Limited.

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British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN: 978-1-80455-937-6 (Print)

ISBN: 978-1-80455-936-9 (Online)

ISBN: 978-1-80455-938-3 (Epub)

Dedication Page

To the Holy Feet of

Lord Venkateshwara Swamy

Dedication Page

Contents

List of Tables and Figures ix
List of Abbreviations xiii
About the Authors xv
Preface xvii
Acknowledgements xxi
1. Introduction 1
1.1. Background of the Study 1
1.2. Literature Survey 4
1.3. Research Gaps 21
1.4. Objectives of the Study 22
1.5. Research Methodology 23
1.6. Significance of the Study 32
1.7. Chapter Planning of the Study 33
2. Economic Policy Uncertainty (EPU) In The Indian Context 35
2.1. Introduction 35
3. Macroeconomic Indicators And Indian Stock Markets: An Overview 39
3.1. Introduction 39
3.2. Export 39
3.3. Import 40
3.4. Foreign Direct Investment 41
3.5. Foreign Portfolio Investment 43
3.6. T-bill (364 Days) 44
3.7. Gross Domestic Product 45
3.8. Bombay Stock Exchange 46
3.9. National Stock Exchange 47
4. Effects Of Economic Policy Uncertainty On Indian Economy And Stock Markets In Times Of COVID-19 Crisis 49
4.1. Introduction 49
4.2. Descriptive Statistics 51
4.3. Wald Test 53
4.4. Granger Causality Test 54
4.5. Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH (1,1)) Model 58
5. Impact Of The Russia–Ukraine Conflict On The Indian Economy 65
5.1. Introduction 65
5.2. Economic Waves of the Russia–Ukraine Conflict: Global Perspective 66
5.3. Effects of the Russia–Ukraine Conflict in the Light of India 67
5.4. Effects of Crude Oil Price on the Indian Stock Market and the REER 70
6. Empirical Data Analysis And Findings of The Study 83
6.1. Introduction 83
6.2. Descriptive Statistics 84
6.3. Breakpoint Unit Root Test 88
6.4. Johansen Co-integration Test 95
6.5. Wald Test 98
6.6. Vector Error Correction Model 99
6.7. Granger Causality Test 102
6.8. MRS Model 107
6.9. FIGARCH (1,1) Model 112
6.10. DCC-MGARCH (1,1) Model 116
7. Conclusions And Recommendations 123
7.1. Conclusions 123
7.2. Recommendations 125
7.3. Policy Implications 125
7.4. Limitations of the Study 126
7.5. Further Scope of Research 127
Bibliography 129
Appendices 145
Index 167

List of Tables and Figures

Tables

Table 4.1. Descriptive Statistics of the Select Variables 52
Table 4.2. Descriptive Statistics of the Select Variables 53
Table 4.3. Wald Test of the Select Variables 53
Table 4.4. Granger Causality Test of BSE Sensex and EPU 55
Table 4.5. Granger Causality Test of NIFTY 50 and EPU 55
Table 4.6. Granger Causality Test of Import and EPU 56
Table 4.7. Granger Causality Test of Export and EPU 56
Table 4.8. Granger Causality Test of FDI and EPU 57
Table 4.9. Granger Causality Test of FPI and EPU 57
Table 4.10. Granger Causality Test of T-bill and EPU 57
Table 4.11. Granger Causality Test of GDP and EPU 58
Table 4.12. FIGARCH (1,1) Results of the Variables 58
Table 5.1. Descriptive Statistics of the Select Variables 71
Table 5.2. Breakpoint Unit Root Test (Innovation Outlier Model) of the Select Variables 73
Table 5.3. Wald Test of the Select Variables 78
Table 5.4. FIGARCH (1,1) Results of the Variables 78
Table 5.5. DCC Results of the Variables 81
Table 5.6. MGARCH (1,1) Results of the Variables 81
Table 6.1. Descriptive Statistics of the Select Variables 84
Table 6.2. Descriptive Statistics of the Select Variables 85
Table 6.3. Breakpoint Unit Root Test (Innovation Outlier Model) of the Select Variables 89
Table 6.4. Johansen Co-integration Test Between BSE Sensex and EPU 96
Table 6.5. Johansen Co-integration Test Between NIFTY 50 and EPU 96
Table 6.6. Johansen Co-integration Test Between Import and EPU 96
Table 6.7. Johansen Co-integration Test Between Export and EPU 96
Table 6.8. Johansen Co-integration Test Between FDI and EPU 97
Table 6.9. Johansen Co-integration Test Between FPI and EPU 97
Table 6.10. Johansen Co-integration Test Between GDP and EPU 97
Table 6.11. Johansen Co-integration Test Between T-bill and EPU 97
Table 6.12. Wald Test of the Select Variables 98
Table 6.13. VECM Results of the Variables 101
Table 6.14. Granger Causality Test of BSE Sensex and EPU 103
Table 6.15. Granger Causality Test of NIFTY 50 and EPU 103
Table 6.16. Granger Causality Test of Import and EPU 104
Table 6.17. Granger Causality Test of Export and EPU 104
Table 6.18. Granger Causality Test of FDI and EPU 105
Table 6.19. Granger Causality Test of FPI and EPU 105
Table 6.20. Granger Causality Test of T-bill and EPU 106
Table 6.21. Granger Causality Test of GDP and EPU 106
Table 6.22. MRS Model Results of the Select Variables 107
Table 6.23. FIGARCH (1,1) Results of the Variables 113
Table 6.24. DCC Results of the Variables 117
Table 6.25. MGARCH (1,1) Results of the Variables 118

Figures

Figure 2.1. Trend of EPU of India 37
Figure 3.1. Trend of Exports of India 40
Figure 3.2. Trend of Imports of India 41
Figure 3.3. FDI Inflow into India (from 2000–2001 to 2017–2018) (US$ Billion) 42
Figure 3.4. Growth Rate of FDI Inflow into India (%) 42
Figure 3.5. FDI Inflow into India and India’s Share in Global FDI Inflows – The UNCTAD Estimates (1990–2017) 43
Figure 3.6. Trend of FPI of India 44
Figure 3.7. Trend of T-bill of India 45
Figure 3.8. Trend of GDP of India 46
Figure 3.9. Trend of BSE Sensex of India 47
Figure 3.10. Trend of NIFTY 50 of India 48
Figure 4.1. Daily New Confirmed Cases of COVID-19 in India 50
Figure 4.2. State-wise Healthcare Expenditure/Infrastructure and COVID-19 Outcome 51
Figure 4.3. GARCH Graph of Conditional Variance of BSE Sensex 60
Figure 4.4. GARCH Graph of Conditional Variance of NIFTY 50 60
Figure 4.5. GARCH Graph of Conditional Variance of Import 61
Figure 4.6. GARCH Graph of Conditional Variance of Export 61
Figure 4.7. GARCH Graph of Conditional Variance of FDI 62
Figure 4.8. GARCH Graph of Conditional Variance of FPI 62
Figure 4.9. GARCH Graph of Conditional Variance of T-bill 63
Figure 4.10. GARCH Graph of Conditional Variance of GDP 63
Figure 5.1. Commodity Price Change 67
Figure 5.2. Box Plot of Brent Crude Oil Price 71
Figure 5.3. Box Plot of BSE Sensex 72
Figure 5.4. Box Plot of NIFTY 50 72
Figure 5.5. Box Plot of REER 73
Figure 5.6. Brent Crude Oil at Level 74
Figure 5.7. Brent Crude Oil at First Difference 74
Figure 5.8. BSE Sensex at Level 75
Figure 5.9. BSE Sensex at First Difference 75
Figure 5.10. NIFTY 50 at Level 76
Figure 5.11. NIFTY 50 at First Difference 76
Figure 5.12. REER at Level 77
Figure 5.13. REER at First Difference 77
Figure 5.14. GARCH Graph of BSE Sensex 79
Figure 5.15. GARCH Graph of NIFTY 50 80
Figure 5.16. GARCH Graph of REER 80
Figure 6.1. Box Plot of BSE Sensex 85
Figure 6.2. Box Plot of NIFTY 50 86
Figure 6.3. Box Plot of Import 86
Figure 6.4. Box Plot of Export 86
Figure 6.5. Box Plot of FDI 87
Figure 6.6. Box Plot of FPI 87
Figure 6.7. Box Plot of T-bill 87
Figure 6.8. Box Plot of GDP 88
Figure 6.9. Box Plot of EPU 88
Figure 6.10. BSE Sensex at Level 89
Figure 6.11. BSE Sensex at First Difference 90
Figure 6.12. NIFTY 50 at Level 90
Figure 6.13. NIFTY 50 at First Difference 90
Figure 6.14. Import at Level 91
Figure 6.15. Import at First Difference 91
Figure 6.16. Export at Level 91
Figure 6.17. Export at First Difference 92
Figure 6.18. FDI at Level 92
Figure 6.19. FDI at First Difference 92
Figure 6.20. FPI at Level 93
Figure 6.21. FPI at First Difference 93
Figure 6.22. T-bill at Level 93
Figure 6.23. T-bill at First Difference 94
Figure 6.24. GDP at Level 94
Figure 6.25. GDP at First Difference 94
Figure 6.26. EPU at Level 95
Figure 6.27. EPU at First Difference 95
Figure 6.28. Markov Switching Smoothed Regime Probabilities of BSE Sensex 109
Figure 6.29. Markov Switching Smoothed Regime Probabilities of NIFTY 50 109
Figure 6.30. Markov Switching Smoothed Regime Probabilities of Import 110
Figure 6.31. Markov Switching Smoothed Regime Probabilities of Export 110
Figure 6.32. Markov Switching Smoothed Regime Probabilities of FDI 111
Figure 6.33. Markov Switching Smoothed Regime Probabilities of FPI 111
Figure 6.34. Markov Switching Smoothed Regime Probabilities of T-bill 112
Figure 6.35. Markov Switching Smoothed Regime Probabilities of GDP 112
Figure 6.36. FIGARCH Graph of BSE Sensex 114
Figure 6.37. FIGARCH Graph of NIFTY 50 114
Figure 6.38. FIGARCH Graph of Import 114
Figure 6.39. FIGARCH Graph of Export 115
Figure 6.40. FIGARCH Graph of FDI 115
Figure 6.41. FIGARCH Graph of FPI 115
Figure 6.42. FIGARCH Graph of T-bill 116
Figure 6.43. FIGARCH Graph of GDP 116
Figure 6.44. DCC GARCH Graph of BSE Sensex 119
Figure 6.45. DCC GARCH Graph of NIFTY 50 119
Figure 6.46. DCC GARCH Graph of Import 120
Figure 6.47. DCC GARCH Graph of Export 120
Figure 6.48. DCC GARCH Graph of FDI 120
Figure 6.49. DCC GARCH Graph of FPI 121
Figure 6.50. DCC GARCH Graph of T-bill 121
Figure 6.51. DCC GARCH Graph of GDP 121

List of Abbreviations

  • ADF – Augmented Dickey–Fuller

  • AIDS – Acquired Immune Deficiency Syndrome

  • ARIMA – Autoregressive Integrated Moving Average

  • AR – Autoregressive

  • ARCH-LM – Autoregressive Conditional Heteroscedasticity–Lagrange Multiplier

  • ARDL – Autoregressive Distributed Lag

  • BOLT – BSE Online Trading

  • BRICS – Brazil, Russia, India, China, and South Africa

  • BSE SENSEX – Bombay Stock Exchange Sensitive Index

  • BTC – Bitcoin

  • CAD – Current Account Deficit

  • CEE – Central and Eastern European

  • COVID – Coronavirus

  • DCC-MGARCH – Dynamic Conditional Correlation–Multivariate generalized Autoregressive Conditional Heteroskedasticity

  • DF – Dickey–Fuller

  • DIPP – Department of Industrial Policy and Promotion

  • DJIM – Dow Jones Islamic Market

  • EoDB – Ease of Doing Business

  • EPU – Economic Policy Uncertainty

  • EPZ – Export Processing Zone

  • EU – European Union

  • FC – Factor Cost

  • FCI – Food Corporation of India

  • FDI – Foreign Direct Investment

  • FIGARCH – Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity

  • FOB – Free on Board

  • FPI – Foreign Portfolio Investment

  • FRED – Federal Reserve Economic Data

  • G7 – Group of Seven

  • GARCH – generalized Autoregressive Conditional Heteroscedasticity

  • GDP – Gross Domestic Product

  • GEPU – Global Economic Policy Uncertainty

  • GGM – Gaussian Graphical Model

  • GMO – Genetically Modified Organism

  • GVAR – Global Vector Autoregression

  • H1N1 – Haemagglutinin Type 1 and Neuraminidase Type 1

  • HIV – Human Immunodeficiency Virus

  • ICSS – Iterated Cumulative Sum of Squares

  • IMF – International Monetary Fund

  • IOC – Indian Oil Corporation

  • KPSS – Kwiatkowski–Phillips–Schmidt–Shin

  • MLE – Maximum Likelihood Estimation

  • MPU – Monetary Policy Uncertainty

  • MRS – Markov Regime Switching

  • NATO – North Atlantic Treaty Organization

  • NIFTY 50 – National Stock Exchange 50

  • NSDL – National Securities Depository Limited

  • NSE – National Stock Exchange

  • OLS – Ordinary Least Squares

  • PP – Phillips–Perron

  • PPE – Personal Protective Equipment

  • QQR – Quantile-on-Quantile Regression

  • RBI – Reserve Bank of India

  • REER – Real Effective Exchange Rate

  • SBI – State Bank of India

  • S&P – Standard and Poor

  • SEBI – Securities and Exchange Board of India

  • SEZ – Special Economic Zone

  • SSSS – Stochastic Search Specification Selection

  • SVAR – Structural Vector Autoregressive Model

  • T-bill – Treasury Bill

  • TGARCH – Threshold Generalized Autoregressive Conditional Heteroskedasticity

  • TVP-VAR – Time-Varying Parameter Vector Autoregression

  • UK – United Kingdom

  • UNCTAD – United Nations Conference on Trade and Development

  • USA – United States of America

  • USD – United States Dollar

  • USSR – Union of Soviet Socialist Republics

  • VAR – Vector Autoregression

  • VC – Venture Capital

  • VECM – Vector Error Correction Model

  • VIX – Volatility Index

  • WHO – World Health Organization

About the Authors

Raktim Ghosh is presently pursuing his PhD research in the Department of Commerce, University of Gour Banga, Malda, West Bengal, India. He is also serving as a faculty in the Department of Commerce, Maharaja Srischandra College. He graduated from Heramba Chandra College and completed his MCom from the Department of Commerce, University of Calcutta. He also pursued MPhil in Commerce from the University of Calcutta. His research interest area includes capital market, mutual funds, stress management, macroeconomic developments, and other contemporary issues on the subject.

Bhaskar Bagchi, PhD, works as a Professor in the Department of Commerce, University of Gour Banga, Malda, West Bengal, India. He has teaching and research experience of more than 20 years and is an active reviewer of many reputed international journals. He has authored four books on finance and has several publications in some of the leading finance journals of the world. His areas of interest include corporate finance, capital markets, economic policy, and financial econometrics.

Preface

This study empirically investigates the effects of economic policy uncertainty (EPU) on the Indian economy and stock markets in times of different crises like global recession, COVID-19 pandemic, and Russia–Ukraine conflict. Simultaneously, it measures the impact of the conflict between Russia and Ukraine on the Indian economy by analysing the effect of surging crude oil price on the Indian stock market indices and the real effective exchange rate (REER) using daily data from 24 February 2022 to 29 July 2022. Moreover, it also measures and analyses the long-run and short-run relationship between the EPU index and select Indian macroeconomic variables like export of goods and services of India, import of goods and services of India, foreign direct investment (FDI) in India (net), foreign portfolio investment (net), treasury bill (T-bill) yields (364 days), and gross domestic product (GDP) along with stock market indices from India like Bombay Stock Exchange Sensitive Index (BSE Sensex) and National Stock Exchange 50 (NIFTY 50). The study furthermore examines the changeover in a relationship (if any) among the select variables during the global financial recession period (from December 2007 to June 2009), pre-recession period (from April 2003 to November 2007), post-recession along with pre-COVID-19 period (from July 2009 to February 2020) and COVID-19 period (from March 2020 to January 2022). Moreover, the causal relationship between the EPU index, select macroeconomic variables, and Indian stock market indices along with the regime-switching behaviour of the select variables during the global recession period, pre-recession period, post-recession along with pre-COVID-19 period and COVID-19 period, that is, from low-volatility regime to high-volatility regime and vice versa is also measured. The volatility spillovers among the EPU index, select macroeconomic variables, and Indian stock market indices for the study period are also studied.

Theoretically, an attempt has been made to exhibit an idea regarding EPU and the EPU index from an Indian perspective. Further, a broad-spectrum summary on macroeconomic indicators and the functioning of Indian stock markets is also summarized. Apart from using daily data to examine the effect of the Russia–Ukraine conflict on Indian stock markets and REER, in order to accomplish the other objectives of the study, monthly data of select variables like macroeconomic indicators (export of goods and services of India, import of goods and services of India, FDI in India (net), FPI (net), T-bill yields (364 days), GDP, and EPU index) have been used along with stock market indices like BSE Sensex and NIFTY 50. Various econometric tools like breakpoint unit root test (innovative outlier model), Johansen co-integration analysis, Wald test, Granger causality test, vector error correction model (VECM), Markov regime-switching (MRS) model, fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model, and dynamic conditional correlation–multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model are used.

The data of the different variables like T-bill rate (364 days) and FDI are collected from the Reserve Bank of India (RBI) database. Import and export data are collected from the database of the Ministry of Commerce and Industry, Government of India; BSE Sensex data are collected from BSE database; NIFTY 50 data are collected from NSE database. Data on FPI are collected from the database of National Securities Depository Limited (NSDL); data on the GDP are collected from the database of Federal Reserve Economic Data (FRED); and the data of EPU index are collected from India news-based policy-uncertainty database by Baker, Bloom, and Davis (www.policyuncertainty.com). The total period of the study spans from April 2003 to January 2022 covering a period of 19 years approximately. Furthermore, to study the effects of the Russia–Ukraine conflict, daily data of Brent crude oil prices, BSE Sensex, NIFTY 50, and REER are collected from 1 September 2021 to 29 July 2022. The data of Brent crude oil price are collected from the database of investing.com, and the data of REER are collected from the database of BIS Statistics Warehouse.

In conducting this study, one of the motivations is to cover different financial shocks as much as possible that have occurred over the years. So, with this objective in mind, the authors decided to choose the study period from April 2003, because the dataset on the EPU index for India is not available beyond this period. The period of the study is extended up to January 2022, so that the authors can study the effect of COVID-19 shocks as well. The study period is mainly fragmented into four divisions – pre-recession period (from April 2003 to November 2007), global recession period (from December 2007 to June 2009), post-recession along with pre-COVID-19 period (from July 2009 to February 2020), and COVID-19 period (from March 2020 to January 2022). The period of global recession has been determined as per the reports of the Business Cycle Dating Committee, National Bureau of Economic Research, USA.

BSE Sensex, NIFTY 50, import, export, FDI, FPI, T-bill, and GDP that have been selected for the study are normal in nature. It is observed that BSE Sensex, NIFTY 50, import, export, T-bill, and GDP are non-stationary at the level but stationary at first difference. However, FDI, FPI, and EPU are stationary at both level and first difference. Hence, the non-existence of a unit root is confirmed for all the stock market indices along with the macroeconomic indicators with EPU, and therefore, the variables are free from a random walk. It can be noted that there remains a long-run association among the select variables or the select variables are co-integrated. It is found that there remains a short-run association between EPU and BSE Sensex and EPU and NIFTY 50. Also, there remains a short-run association between EPU and import and EPU and T-bill. Though there is no short-run association between EPU and exports, FDI, FPI, and GDP. The VECM model provide the findings that export, FDI, FPI, T-bill, and GDP bear a negative coefficient, and BSE Sensex, NIFTY 50, and import bear a positive coefficient, which allows the researcher to conclude that the negative coefficients indicate the percentage of correction in terms of speed made within the variables following a deviation in the previous month. The positive coefficients indicate that the variables instead of returning to equilibrium continue to move away from equilibrium. Also, there is a significant long-run causality running from EPU to BSE Sensex, NIFTY 50, export, FDI, FPI, and T-bill. Granger causality suggests that there is bidirectional causality between EPU and BSE Sensex, NIFTY 50, import, export, FDI, FPI, and T-bill, although there is no significant causality between EPU and GDP. MRS model represents the possibility of the select variables to move from high-volatility regime to a low-volatility regime and vice versa along with the possibility to remain in one particular state.

The FIGARCH (1,1) model indicates the presence of the ARCH effect or volatility within all the dependent variables running from EPU. The variance in volatility is noted for all the variables except import. However, a long-memory effect is observed for BSE Sensex, NIFTY 50, FPI, and GDP, indicating the remembrance of the shock from EPU over a long time period. The DCC-MGARCH (1,1) model indicates the presence of both short-run and long-run volatility within the select variables running from EPU. Even, a long-memory effect was found for BSE Sensex and NIFTY 50 due to a steep surge in Brent crude oil price during the conflict between Russia and Ukraine. Both short-run and long-run volatility spillover are noted from crude oil prices during the period from 1 September 2021 to 29 July 2022.

It is suggested that policies should be framed by the government to curb the effects of EPU at the macrolevel. It is recommended that necessary policies should be taken up to check the underlying effects of the global financial recession and the outbreak of the COVID-19 pandemic. Impetus needs to be provided to the prospective investors for making investments in the stock market. Congenial investment conditions should be made to attract FDI and FPI which can lead to an infusion of foreign exchange within the economy. Dependency on imports should be reduced, and production in the home country, that is, India, should be escalated along with an increase in exports to maintain the foreign exchange reserve which can be the resilience to shocks. Relaxations should be provided in terms of the legal framework, providing a greater amount of relief in tax burden, setting up new business parks, and many more can be done by the government to invite new foreign investment in the form of FDI and FPI. More investments in T-bills need to be ensured. All these can lead to a greater GDP.

After China, India is the world’s second-largest importer of crude oil, over 80% of which is imported. Because of the Russia–Ukraine conflict, there is no doubt that the steep surge in oil prices increases the likelihood of inflation accelerating in India. In order to shield the economy from the negative impact of escalating crude oil prices, Indian oil companies like Indian Oil, Numaligarh Refinery, and others have bought millions of barrels of crude oil from Russia at discounted rates ignoring global backlash including Western countries. According to the report published by ‘Nomura’, the steep hike in crude oil prices, coupled with high domestic demand, is going to drastically escalate India’s import bill. Although India does not import much of its crude oil from Russia, still a neighbourhood effect of Russia–Ukraine war can be noted in case of India. Moreover, India should also adopt policies to make necessary corrections in their domestic currency to check the volatility in the REER.

Acknowledgements

At the very outset, it becomes our responsibility and also it gives us immense pleasure to express our deep sense of gratitude towards all of them who have provided us with great support. We take this opportunity to express our thankfulness to the numerous researchers in the field of economic policy and macroeconomics across the globe for their rich contributions which have helped us in shaping our understanding of the subject.

We are highly indebted to Dr Shanti Chhetry, Honourable Vice Chancellor, University of Gour Banga, West Bengal, India, for his inspiration throughout the journey. We are also grateful to Dr Apurba Chakrabarty, Registrar (A/C), and Dr Md Jahir Hossain, Finance Officer, University of Gour Banga, West Bengal, India, and other administrative officers of this university for their heartiest support and enthusiasm at every stage of this work.

We would be failing in our duties if we do not show our deepest sense of appreciation to Prof. Uttam Kumar Dutta, Professor of Commerce, School of Professional Studies, Netaji Subhas Open University, India; Prof. Dhruba Ranjan Dandapat, Professor, Department of Commerce, and Former Dean, Faculty of Commerce, Social Welfare and Business Management, University of Calcutta, India; Prof. Jadab Krishna Das, Dean, Faculty of Commerce, Social Welfare and Business Management, and Professor, Department of Commerce, University of Calcutta, India; Prof. Ashish Kumar Sana, Professor, Department of Commerce, University of Calcutta, India; Prof. Debasish Sur, Professor, Department of Commerce, The University of Burdwan, India; Dr Biswajit Paul, Assistant Professor (Stage II), Department of Commerce, University of Gour Banga, India; and Mr Dipankar Bhaumik, Associate Professor, Department of Commerce, Birpara College, India, who have always extended their support with their intellectual suggestions and inspirational talk throughout this entire research work.

We would also like to express our gratefulness to Dr Biswajit Das, University Librarian, University of Gour Banga, and to the Librarian of the British Council, Kolkata, for providing us assistance with regard to using the enriched library.

We also extend our sincere thanks to our colleagues at the Department of Commerce, University of Gour Banga, India, and Maharaja Srischandra College, India, from whom we have received valuable advice at different stages of this study.

We are grateful to the publishers, Emerald Group Publishing, Inc., for giving us an opportunity to publish this book. In this connection, we would like to mention the name of Kirsty Woods, Commissioning Editor, Emerald Group Publishing, Inc.; Madison Klopfer, Editorial Assistant, Emerald Group Publishing, Inc.; and other valuable members of the books team at Emerald Group Publishing, Inc., who have been continuously monitoring and supporting us at every step of the publication.

Last but not the least, our indebtedness to all our family members for their constant support and encouragement throughout this journey remains beyond words.

Raktim Ghosh

Bhaskar Bagchi

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