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India's Changing Innovation System: Achievements, Challenges, and Opportunities for Cooperation: Report of a Symposium (2007)
Chapter: iii research paper: india's knowledge economy in the global context, iii research paper.
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India’s Knowledge Economy in the Global Context 1
Carl J. Dahlman
Georgetown University
INTRODUCTION
The rise of India as an emerging economic power is increasingly in the global headlines. This is due in part to its large population and impressive growth rates, not just in the past three years, but the past decade and a half. However, it is also due to India’s increasing scientific and technological capability.
This paper assesses India’s knowledge economy in the global context. To put the analysis in context, the second section quickly summarizes some of the key global trends. The third provides an overview of the Indian economy and its recent economic performance. The fourth presents India’s rising economic power and briefly summarizes some of its advantages and challenges. The fifth section benchmarks India’s position in the global knowledge economy using a four-part framework that includes the economic and institutional regime, education and training, the information infrastructure and its use, and the innovation system. It summarizes some of the key challenges and policy issues in the first three of these. The innovation system is analyzed in more detail in the sixth section. That analysis includes a quick overview of the innovation system as well as some of the key issues that need to be addressed. The seventh section summarizes some of the key opportunities for greater U.S.–India collaboration. The final section provides a very brief summary and conclusions.
KEY GLOBAL TRENDS
India’s rise needs to be seen in the broader context of some of the broader global trends affecting growth and competitiveness.
One of these is the increased importance of knowledge. The world is in the midst of what could be considered a knowledge revolution. It is not that knowledge has not always been important for growth and competitiveness, but that there has been a speeding up in the rate of creation and dissemination of knowledge.
A second key trend is an increase in globalization. The share of goods and services that are traded as a percentage of global GDP has increased from 38 percent in 1990 to 48 percent in 2004. This is the result of greater trade liberalization worldwide. However, it is also the result of reductions in transportation and communications costs that result from rapid advances in technology.
A third and related trend is that knowledge markets have become global. Products and services are increasingly designed and developed for global markets in order to recoup the research and development (R&D) investments. In addition, R&D itself is becoming increasingly globalized. This is not just an increase in joint authorship of technical papers by teams from different countries, or joint patenting. An increasing amount of R&D is now being done by multinationals in countries other than their respective home countries, and not just among developed countries. India and China in particular are also benefiting from this trend as they are becoming hosts to many R&D centers set up by multinational companies, as well.
In addition, thanks to the reduction in communications costs, there is an increasing trend to source many knowledge-intensive services in lower-cost developing countries. This is part of what is driving global offshoring of knowledge-intensive services, such as back office functions, as well as engineering design, and even contract innovation services. 2
The result of these trends is that innovation and high-level skills are becoming the most important determinants of competitiveness. Thus countries such as India need to develop more explicit strategies to take advantage of the rapid creation and dissemination of knowledge and to develop their own stronger innovation capabilities.
THE INDIAN ECONOMY
The Indian economy has had a very impressive performance ( Table 1 ). Between 1990 and 2000, it grew at an average annual rate of 6.0 percent. Between
TABLE 1 Growth of output overall and by sector (average annual % growth)
2000 and 2004, it grew at an average rate of 6.2 percent. In the past three years, it has grown at slightly over 8 percent. The sector that has been growing the fastest has been services.
Compared to China, the structure of the economy has not changed as rapidly. Twenty-five years ago the per capita income of these two giant economies was very similar. However, China has had a much faster rate or growth for a longer period of time and more rapid structural change ( Table 2 ). To some extent, India has not followed the traditional pattern of a large increase in the share of industrial value added and then a shift to services. There has been a faster and earlier shift to services, driven in part by a rapid growth of high-value knowledge-intensive services (such as information technology [IT], banking, consulting, and real estate), although they account for only a very small share of India’s very large labor force.
Another difference between India and other developing countries is that it is much less integrated into the global system through trade ( Table 3 ). The contrast with China is again very stark as the share of trade of goods and services in the Chinese economy is more than twice that of India.
INDIA AS A RISING ECONOMIC POWER
India is a rising economic power, but one that has not yet integrated very much with the global economy. It has many strengths, but it also will be facing many challenges in the increasingly globalized, competitive, and fast changing global economy.
Figure 1 presents the current and projected size through 2015 of the world’s 15 largest economies in terms of purchasing power parity (PPP) comparisons. 3 Using PPP exchange rates, India already is the fourth largest economy in the word. Moreover, using average growth rates for the period 1991–2003 to project future size, India surpasses Japan by the end of next year to become the third largest economy in the world. During the period projected, China (currently, the second largest economy), will become the largest economy, surpassing the United States by about 2013. However, it should be emphasized that past performance is not necessarily a good predictor of future performance—just of potential, as future reality is usually different than projected trend. Nevertheless, this projection based on PPP exchange rates is helpful to emphasize that India has great potential, but also faces competition, particularly from China. It is therefore useful to quickly take stock of India’s strengths and challenges.
TABLE 2 Structure of output, 1990 vs. 2004
India’s key strengths are its large domestic market, its young and growing population, a strong private sector with experience in market institutions, and a well-developed legal and financial system. In addition, from the perspective of the knowledge economy, another source of strength is a large critical mass of highly trained English-speaking engineers, business people, scientists, and other professionals, who have been the dynamo behind the growth of the high-value service sector.
However, India is still a poor developing country. Its per capita income in 2004 was just $674 and with a billion people, it accounted for 17 percent of the world’s population. Its share of global GDP is less than 2 percent (using nominal exchange rates), and just 1 percent of world trade. Moreover, 80 percent of its population lives on less that $2 a day, and 71 percent is rural, with about 60 percent of the total labor force still engaged in agriculture.
TABLE 3 Integration with global economy (% of GDP)
FIGURE 1 Current economic size and projection through 2015 for 15 largest economies.
SOURCE: Author’s projections based on data in the WDI database. World Bank, World Development Indicators 2006 , Washington, D.C.: World Bank, 2006.
One of India’s key challenges is its rapidly growing and young population. India’s population is expected to continue to grow at a rate of 1.7 percent per year until 2020 and to overtake China as the most populous country in the world. Part of the challenge is that India’s population has low average educational attainment. Years of school for the adult population averages less then 5 years, compared to nearly 8 years in China now, and 12 in developed countries. In addition, illiteracy is 52 percent among women and 27 percent among men.
Another challenges is poor infrastructure—power supply, roads, ports, and airports. This increases the cost of doing business. In addition, India is noted for an excessively bureaucratic and regulated environment which also increases the cost of doing business.
All these challenges constrain the ability of the Indian economy to react to changing opportunities. Low education reduces the flexibility to respond to new challenges. Poor infrastructure and high costs of doing business constrain domestic and foreign investment. The high costs of getting goods in or out of India also constrain India’s ability to compete internationally and to attract export-oriented foreign investment except for business that can be done digitally rather than requiring physical shipments.
Figure 2 presents alternative projections of India’s per-worker income to 2020. The projections assume that the growth of capital, labor, and education in
FIGURE 2 India’s choice set in determining its future growth path: Real GDP Per Capita—Alternate projections, 2001-2020.
NOTE: The projections assume that capital, labor, and human capital (the educational complement to labor) grow at their 1991–2000 respective annual rates of growth. What is varied is the rate of total factor productivity growth. The TFP numbers are taken from the historical experience noted for each of the projections.
SOURCE: Carl Dahlman and Anuja Utz, India and the Knowledge Economy: Leveraging Strengths and Opportunities , Washington, D.C.: The World Bank, 2005.
India continue their trend lines. The only parameter that is changed is the rate of growth of total factor productivity (TFP)—the efficiency with which these basic factors are utilized. 4 The projections show that the real per-worker income in India could be between 46 to 167 percent higher in 2020 than in 2001, depending on how effectively knowledge is used. As noted, these projections are based on
the historical trends in the growth of inputs and of TFP. To a very large extent, these depend on policy measures that are under the control of India’s policy makers, business, and the broader Indian society. The point of this projection is to emphasize that India’s performance to a very large extent depends on its policy choices—what is holding India back is itself.
There is a tremendous window of opportunity for India to leverage its strengths to improve it competitiveness and increase the well-being of its population. However, it is important to seize these opportunities and to move quickly to action. The next section will examine India’s position in the context of the global knowledge economy as a way to identify some of the key policy issues that need to be addressed to make India’s recent rapid growth sustainable.
INDIA IN THE GLOBAL KNOWLEDGE ECONOMY
The World Bank Institute has developed a useful benchmarking tool that helps to rank countries in terms of their readiness to use knowledge for development. 5 The methodology consists of examining a country’s rank ordering in four pillars based on a series of 20 indicators in each pillar. The four pillars are:
an economic and institutional regime that provides incentives for the efficient use of existing and new knowledge and the flourishing of entrepreneurship;
an educated and skilled population that can create, share, and use knowledge well;
a dynamic information infrastructure that can facilitate the effective communication, dissemination, and processing of information;
an efficient innovation system of firms, research centers, universities, consultants, and other organizations that can tap into the growing stock of global knowledge, assimilate and adapt it to local needs, and create new knowledge.
Broad Assessment of India’s Position
A simple summary measure called the Knowledge Economy Index has been developed for quick comparative benchmarking. It is an amalgamated index consisting of the average ranking of three of the most indicative indicators for each of the four sectors. 6 This index is tracked over time. It permits the comparison of a country’s current ranking to that in 1995. This is done in Figure 3 for India plus five other countries: Brazil, Russia, China, Korea, and Mexico 7 plus some other standard reference countries.
Figure 3 shows that India is placed roughly in the sixth decile of a rank-ordering distribution from the most advanced countries. It also shows that India’s relative position has slipped relative to where it was in 1995. Figure 4 shows the contribution of each of the four pillars to India’s relative ranking. India has improved its relative position on the innovation indicators and slightly on the information and communications technology (ICT) indicators. On the economic and institutional regime and education, it has slipped back. (See Annex Table A-1 for the ranking on each of the pillars.) 8
The rest of this section summarizes very briefly some of the key issues in the economic and institutional regime, education and training, and information and communication technology. The following section looks at the issues in innovation in more detail. 9
FIGURE 3 Changes to Knowledge Economy Index, 1995–2003.
NOTE: The horizontal axis represents the relative position of the country or a region in 1995. The vertical axis represents the position in the most recent year (generally 2000– 2004). The graph is split by a 45 degree line. Those countries or regions that are plotted below the line indicate a regression in their performance between the two periods. The countries or regions that are marked above the line signify improvement between the two periods, while those countries that are plotted on the line indicate stagnation. The KAM methodology allows the user to check performance in the aggregate Knowledge Economy Index (KEI), as well as the individual pillars: Economic Incentive Regime, Education, Innovation, and ICT (Information Communications Technologies).
SOURCE: World Bank Institute, KAM 2006, < http://www.worldbank.org/kam >.
Key Issues in the Economic and Institutional Regime
The economic and institutional regime is an important aspect of a country’s ability to take advantage of knowledge. It includes the overall regime of policies and institutions that give an economy the incentives to improve efficiency and the flexibility to redeploy capital and labor to their most productive use. It also includes the rule of law and government effectiveness. As was seen from the summary variables in the KAM basic scorecard, this is the second weakest of the four pillars of the knowledge economy in India, and one in which India has actually lost relative standing with respect to the rest of the world. Based on a more detailed analysis, including surveys of foreign and Indian businessmen, some of the key issues that have to be improved in the economic and institutional regime include: 10
FIGURE 4 KEI: Major world regions and largest country in each, 1995 vs. most recent.
NOTE: Each bar chart represents the most recent aggregate KEI score for a selected region or country, split into the four KE pillars. Each color band represents the relative weight of a particular pillar to the overall country’s or region’s knowledge readiness, measured by the KEI. The first line for each country is its position in the most recent year for which data are available (generally 2002–2005). The second line is for 1995. (See Annex Table A-1 for the actual ranking for each of the pillars. See Annex Figure A-1 for a comparison of the basic scorecard rankings for India with China and the United States.)
reducing the bureaucracy for the entry and exit of firms,
updating physical infrastructure,
easing restrictions on the hiring and firing of labor,
reducing tariff and nontariff barriers to trade,
encouraging foreign direct investment and increasing e-linkages with the rest of the economy,
strengthening intellectual property rights and their enforcement, and
improving e-governance and encouraging ICT use to increase government’s transparency and accountability.
Key Issues in Education and Training
Educated and skilled persons underlie the ability of an economy to take advantage of knowledge and to create new knowledge to improve economic
performance and welfare. Key elements of education and training for the knowledge economy include the level and quality of educational attainment as well as the relevance for the needs of a rapidly changing economy such as India. This is also a pillar in which India has slipped compared to its relative global ranking in 1995. Some of the key issues that India needs to address in education and training include:
expanding quality basic and secondary education to empower India’s rapidly growing young population;
raising the quality and supply of higher education institutions, not just the Indian Institutes of Technology and the Indian Institutes of Management;
embracing the contribution of private providers of education and training by relaxing bureaucratic hurdles and putting in place better accreditation systems;
increasing university–industry partnerships to ensure consistency between education, research, and the needs of the economy;
establishing partnerships between Indian and foreign universities to provide internationally recognized credentials;
using ICT to meet the double goals of expanding access and improving the quality of education;
investing in flexible, cost-effective job training programs that are able to adapt quickly to new and changing skill demands.
Key Issues in ICT
Advances in information processing, storage, and dissemination are making it possible to improve efficiency of virtually all information-intensive activities and to reduce transaction costs of many economic activities. Some of the key elements to make effective use of the potential of this new information infrastructure are the regulatory regime for the information and telecommunications industries and the skills to use the technologies, software, and applications. Some of the key issues that need to be improved in India include:
boosting ICT penetration and reducing/rationalizing tariffs on hardware and software imports;
massively enhancing ICT literacy and skills;
increasing the use of ICT as a competitive tool to improve efficiency of production and marketing (supply chain management, logistics, etc.);
moving up the value chain in IT by developing high-value products through R&D, improving the quality of products and services, marketing of products and services, and further positioning the “India” brand name;
launching suitable incentives to promote IT applications for the domestic economy, including local language content and application;
strengthening partnerships between government agencies, research/ academic institutions, private companies, and nongovernmental organizations (NGOs) to ramp up ICT infrastructure and applications;
developing/scaling up, through joint public–private partnerships, ICT applications, community radio, smart cards, Internet, satellite communications, etc.
STRENGTHENING INDIA’S INNOVATION SYSTEM
This section starts by placing India in the international context using the KAM innovation pillar as well as other data. The next subsection develops a brief framework for analyzing a developing country’s innovation system. This framework is then used to assess India’s innovation system. The last section then presents a matrix of key issues that need to be addressed to improve India’s innovation system.
Broad Assessment of India’s Position in Innovation
Figure 5 places India’s innovation system in the global context using the KAM innovation system pillars. This is based on one measure of R&D input (scientists and engineers) and two measures of output (scientific and technical publications, and patents in the United States). By this narrow measure linked primarily to formal R&D, India is in the top 13th percentile of the global distribution of countries. 11 Furthermore, it has improved its position relative to the rest of the world.
Clearly, because of India’s large critical mass of scientists and engineers engaged in R&D, India is a major player in global R&D. However, it is instructive to compare India’s share of the world in scientists and engineers, scientific and technical publications, and patents with its share of population and GDP measured in nominal as well as PPP exchange rates ( Figure 6 ). From this figure, it can be seen that, as expected, India’s share of scientists and engineers in R&D is much lower than its share of population or GDP in PPP terms, although it is slightly higher than its GDP share in nominal terms. Its share of scientific and technical publications is smaller than its share of GDP in nominal terms. Its share of all patents in the United States is extremely small (only 0.2 percent—too small to be in the figure). One quick conclusion from this comparison is that India is stronger in its basic scientific inputs that in its outputs of basic scientific and technical knowledge, since its share of publications is smaller than its share of personnel engaged in R&D. It is even weaker in turning that scientific output into commercially relevant knowledge, as suggested by its much smaller share of
FIGURE 5 Global context of India’s innovation system.
NOTE: This figure is based on the absolute size of India’s innovative effort. If this were to be scaled by population (i.e., scientists and engineers in R&D per million population, scientific and technical publications per million population, patents in the United States per million population), India’s relative position would fall to the 67th percentile of the country distribution.
patents in the United States. However, a developing-country’s innovation system should be analyzed in a broader context, as developed below.
Components of a Developing County’s Innovation System
A country’s innovation system consists of the institutions and agents that create, adapt, acquire, disseminate, and use knowledge. It also includes the policies and instruments that affect the efficiency with which this is done. In developing countries, innovation should not be interpreted only as application of knowledge that is new at the level of the world frontier, but as product, process, organization, or business knowledge that is new to the local context. Therefore, in developing countries the innovation system should include not only domestic research and development and its commercialization and application. It should
FIGURE 6 Key indicators of India’s share in the world.S&T Publications
SOURCE: Calculated from World Bank, World Development Indicators 2006 , Washington, D.C.: World Bank, 2006.
also include the policies, institutions, mechanisms, and agents that affect the extent to which the country taps into and makes effective use of global knowledge that is new to the country.
The innovation system of a developing country such as India can be thought of as consisting of four parts. One is formal R&D that is carried out in India. This is the most visible and most easily measured. A second is the informal innovation in India. This may happen as the result of insights or experience by individuals or groups working in large of small enterprises or informal production. It can also be the result of decades of indigenous informal experimentation or accumulation of knowledge. This is not so visible and there is very little systematic quantification of this type of innovative effort. A third is formal acquisition of foreign knowledge. This includes the knowledge first brought in through direct foreign investment or technology transfer. The fourth is the informal acquisition, adaptation, and use of knowledge acquired through the import of capital goods, component products, and services that are new to the economy. It also includes knowledge obtained by copying, reverse engineering, or otherwise imitating what has already been done by others abroad. Other informal mechanisms include foreign study, travel, or work experience, as well as technical literature. Increasingly,
it also includes all kinds of knowledge that can be acquired through the Internet including detailed manuals, designs, and data sets. 12
Assessment of India’s Innovation System
Table 4 compares some of the key indicators of India’s broadly defined innovation system with that of the other BRICKM economies. China is the most relevant country for comparison because it is the closest in size and level of development. Figure 7 presents the main variables for India and China in graphical scorecard mode. 13
Formal R&D
In India, the formal R&D effort is quite small. Total expenditures are only 0.8 percent of GDP and have been at that level for 15 years. The bulk of that effort (around 70–80 percent) is carried out by the public sector (federal and state), and most of that is mission-oriented R&D in defense, aerospace, and oceans. Only about 20 percent of that, or roughly 0.16 percent of GDP, is more applied work in agriculture, medicine, and industry. 14
R&D spending by the private sector is only 16–20 percent of the total, or about 0.12 percent GDP. It is highly concentrated in a few large enterprises. The sectors that do the most R&D are pharmaceuticals, auto parts, electronics, and software.
A special feature is increasing R&D being done by multinational companies (MNCs) As of the end of 2004, there were nearly 200 R&D centers, including ABB, Astra Zeneca, Bell Labs Boeing, Bosch, Dell, Cummins, Dupont, Ericsson, Google, Honda, IBM, GE, GM Honda, Hyundai, Microsoft, Monsanto, Motorola, Nestle, Nokia, Oracle, Pfizer, Philips, Roche, Samsung, Sharp, Siemens, Unilever, and Whirlpool. 15 MNCs are attracted to set up R&D centers in India because of the lower salaries for Indian scientists and engineers, which are one-fourth to one-fifth that of comparable engineers in the United States.
TABLE 4 Innovation comparisons with BRICKMs
FIGURE 7 India-China comparison on selected indicators of innovation system.
Informal Innovation
Informal innovation efforts are quite large. This consists not only of the experimentation and learning by doing that is done in the formal and informal sectors. There is very likely a grassroots innovation effort. Several NGOs have sprung up to support such grassroots innovation. They include Honeybee network, the Society for Research and Initiatives for Sustainable Development (SRISTI), and the Grassroots Innovation Augmentation Network (GIAN). In addition, the government has set up the National Innovation Foundation (NIF) to help document and finance grassroots innovations. The NIF has created a database of over 50,000 grassroots innovations. These consist of improvements in simple agricultural instruments, and agricultural techniques as well as indigenous knowledge. However, despite all these efforts, it has been difficult to develop appropriate funding and mechanisms to support the improvement, scale-up, and broad dissemination of grassroots innovations because of very high transaction costs and limited resources. 16
Formal Acquisition of Foreign Knowledge
In India this has been small until relatively recently. For a long time, India has had a very strongly autarkic technology policy. There has been a gradual
opening up of various parts of the economy to foreign investment. Now most sectors are open. The same is true for technology licensing, although there are still controls on the maximum royalty rates that can be charged. Until relatively recently, foreign investment into India was not allowed in many sectors, and was strictly regulated and kept to minority shares in joint ventures in others. There has been significant liberalization over the past 15 years, but India has not received as much foreign investment as the BRICKM countries. As can be seen from Table 4 , gross foreign investment inflows as a share of GDP between 1994 and 2003 were the lowest among the six countries. Purchases of foreign technology have also been the lowest among the six countries, both in absolute terms and even more on a per capita basis. In addition, part of the reluctance of foreigners to invest in India, even after the sectors have been opened up, is the high degree of red tape, corruption, and bureaucracy as well as very poor physical infrastructure services. Some also worry about poor intellectual property rights enforcement.
Informal Acquisition of Foreign Knowledge
This is perhaps the most important source of domestic innovation in developing countries (except those that are very dependent on foreign investment such as Singapore and Hong Kong). As can also be seen in Table 4 , India is again the least open economy of the six BRICKM countries as measured by degree of integration into the world economy through imports and exports of manufactured products. The share of manifested trade is only 13.5 percent of GDP in India compared to around 50 percent in China, Korea, and Mexico. Brazil and Russia are also less integrated with the global economy. However, these countries are outliers as the rest of the countries of the world are much more integrated into the global system (refer back to Table 3 for the share of merchandise trade and services in India compared to the average for other low-income countries, as well as lower and upper middle income countries, developed countries, and the world).
From Figure 7 , comparing the key variables on the innovation system between India and China, it can be seen that China is ahead of India in virtually all the indicators, except the availability of venture capital, as well as some qualitative assessments on firm-level technology absorption and value chain reference where the persons surveyed have put India ahead.
However, in terms of the four-part framework laid out above, the following summary assessment can be made. It is hard to compare the domestic informal efforts, and so, that will be left aside. On acquiring knowledge from abroad informally, China is considerably ahead of India because it is much more integrated into the global system through trade and foreign education, and has a higher level of average educational attainment that facilitates the rapid assimilation of foreign knowledge. On acquiring foreign knowledge formally, China is also ahead because it has had a much more open policy for a longer period of time and has attracted much higher volumes of foreign investment as part of an explicit strategy
to use foreign investment to produce new goods and services new to the Indian market, but also for exporting to the global market. Finally, in formal R&D effort, whereas China’s spending as a share of GDP was comparable to India’s in 1998, by 2005 it had been increased to 1.4 percent of GDP. China also plans to increase it further to 2.0 percent by 2010. In fact, in PPP terms, China in 2006 is probably already the second spender on R&D in the world, ahead of Japan and second only to the United States. Essentially, while China has been very effective at tapping global knowledge informally and informally leveraging these sources of innovation to improve its growth and welfare, it has now decided to do more to innovate on its own account, hence its major drive to increase formal R&D spending. Thus, it will be an even more formidable player on the global stage.
Key Areas for Strengthening India’s Innovation System
Given the foregoing analysis, there is much that India needs to do to strengthen its innovation system. Time is of the essence given the trends and the increasing competitive demands of the global system, and the strategies of other countries—China in particular.
Table 5 summarizes in matrix form the main assessments made in the preceding section and proposes some areas for policy reform. The list is quite extensive. Furthermore, some of the proposed reforms get into areas where there may be considerable opposition and internal debate in India from various groups. Some of this is based on concerns about national sovereignty and ideology. Others are based on the concerns of groups with vested interests who want to maintain their position vis a vis new entrants, domestic as well as foreign. Thus, in a large complex democracy such as India, there will necessarily be a lot of debate. This process will take time. It is hoped that the analysis presented here can contribute to that debate and that concrete policies and investments will soon emerge.
OPPORTUNITIES FOR U.S.–INDIA COLLABORATION
There are many fertile areas for greater U.S.–India collaboration. These include trade, foreign investment, research, and education, and they are likely to increase as India advances in its reforms.
In trade, there is scope for increased exports and imports from each country to the other. Currently, trade levels are quite low, but the products and services produced by each country are very complementary so there is great potential to increase trade in both goods and services, particularly as India further liberalizes its trade regime.
There is also great scope for increased U.S. foreign investment in India as well as for more Indian investment in the United States. U.S. firms are already the largest investors in India, particularly in ICT service-related areas as well as in R&D centers. There is also much scope for increased strategic technological
TABLE 5 Summary of assessment and of areas in need of improvement
alliances between firms from the two countries. Some of the sectors in which there is strong potential for greater collaboration include pharmaceuticals, engineering goods, automobiles and auto parts, telecommunications equipment and services, and software.
There is also potential for greater collaboration between the United States and India in joint research on energy, environment, and space and in fact, several major agreements have recently been initiated between the two countries. Furthermore, given India’s needs and experience and its large public research institute infrastructure, there is scope for joint work on major public good initiatives in health and preventive medicine as well as in agriculture and sustainable livelihoods.
In addition, there are many opportunities in higher education, including joint degrees, joint ventures, wholly owned subsidiaries or franchises. Furthermore, these are not just from the United States into India, but also from India to the United States. For example, NIT has set up many training facilities and developed specialized corporate training activities in the United States.
In sum, India has made great progress but faces daunting challenges. India has many strengths, particularly a young and growing population, experience and institutions of a market economy, a critical mass of entrepreneurs and highly skilled professionals, and a large public research infrastructure. It has the potential to leverage its strengths to improve its competitiveness and welfare. It faces many internal challenges as well as a much more demanding and competitive international environment.
This paper has presented a quick overview of the broad range of issues where India needs to deepen its economic reforms and make additional investments. It has assessed in a little more detail some of the key issues in its innovation system, and identified specific areas that need improvement.
There is also tremendous potential for increased U.S.–India cooperation across many areas. This conference is an opportunity to begin to develop this mutually beneficial cooperation. Hopefully this is just part of a series of events that will help to push the reforms and investment forward. Greater mutual understanding will spur greater public–public, public–private, and private–private cooperation, which will strengthen the mutually beneficial and strategic relationships between these two countries.
FIGURE A-1 Basic scorecard.
TABLE A-1 KAM ranking: How India compares with world regions and BRICKMs
As part of its review of Comparative National Innovation Policies: Best Practice for the 21st Century , the Board on Science, Technology, and Economic Policy convened a major symposium in Washington to examine the policy changes that have contributed to India's enhanced innovative capacity. This major event, organized in cooperation with the Confederation of Indian Industry, was particularly timely given President Bush's March 2006 visit to India and the Joint Statement issued with the Indian government calling for strategic cooperation in innovation and the development of advanced technologies. The conference, which brought together leading figures from the public and private sectors from both India and the United States, identified accomplishments and existing challenges in the Indian innovation system and reviewed synergies and opportunities for enhanced cooperation between the Indian and U.S. innovation systems. This report on the conference contains three elements: a summary of the key symposium presentations, an introductory chapter analyzing the policy issues raised at the symposium, and a research paper providing a detailed examination of India's knowledge economy, placing it in terms of overall global trends and analyzing its challenges and opportunities.
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- Original Research
- Published: 26 May 2022
Nexus Between Indian Financial Markets and Macro-economic Shocks: A VAR Approach
- Prabhas Kumar Rath ORCID: orcid.org/0000-0002-7141-1320 1
Asia-Pacific Financial Markets ( 2022 ) Cite this article
This paper studies the nexus between asset returns volatility in six major segments of Indian financial markets (viz. money, equity, gsec, forex, equity and banking stocks) and macro-economic shocks (viz. GDP, Inflation, Current Account Deficit, market capitalisation to GDP ratio, US Treasury Yield and Foreign Portfolio Investment). The period of study is from April 2002 to March 2021, a period covering four instances of significant economic and financial market stress. Findings of the study are generally aligned to economic theory, except for the case of gsec market. Besides, macro-variables were found to be exerting greater impact when they are in their weaker/unstable state and the behaviour of US treasury yield and FPI flows were found be more significant factors during stress periods and recovery immediately thereafter. Therefore, there is a need to focus on maintaining macroeconomic stability as a policy to foster financial market stability. Besides, there is a need to monitor a customized and dynamic list of macroeconomic variables in respect of each of the financial market segments to decide on the timing, type and quantum of policy and regulatory responses from time to time. This study contributed towards financial markets public policy, particularly during periods of uncertainties.
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1 Background
The fundamental function of financial markets is to price and re-price the financial assets based on all publicly available information, on a real-time basis. It is through this role of being able to price the risk weighted returns associated with various financial assets that financial markets facilitate a more efficient allocation of financial resources to alternative economic activities. At a macro level, given the available investible funds, information on real sector activities in the economy, to which financial markets allocate resources, forms the very basis for pricing of instruments in the financial markets. Hence, both pricing and price driven allocation of resources, by financial markets, themselves are derived from the developments in the real sector of the economy. Therefore, naturally there is a strong nexus between the financial markets and real sector activities. Accordingly, systemic macroeconomic real sector factors like prices, output and position on external account of the economy, etc. can play an important role on the overall outlook of financial markets.
As far as the response of asset prices to new information is concerned, accordingly to the efficient market hypothesis, (Fama, 1971 ), asset prices should incorporate new information on a real time basis. Ross ( 1989 ) also argues that the volatility of prices should capture new information in an efficient market within an arbitrage-free economy. Therefore, other things remaining same, arrival of new public news is supposed to increase price volatility (Foster & Viswanathan, 1993 and Pasquariello & Vega, 2007 ).
Additionally, the way financial markets could respond to such macroeconomic factors would also depend on the depth of the financial markets themselves. For instance, a large and liquid financial market segment is expected to absorb the news of a macro development better than a relatively shallow and illiquid market segment. In a globalized world, cross-border flow of information and investments are also expected to play an important role in shaping the outlook of the financial markets. In addition, there are also certain exogenous developments, like change in government policy or strike of a catastrophe or onset of a crisis like situations, which can have sudden and significant influence on the financial markets, that too without much advance notice.
Theoretically, any development in real economy having impact on the credit, liquid or market risk or a combination of them in an asset class and therefore on the expected returns from the asset class, would lead to revision in pricing of such assets. Under uncertainty prices of such assets could become even more volatile leading to further uncertainty and at times herd behaviour, creating a self-fulfilling loop of volatility.
An analysis by IMF (2020) Footnote 1 infers that “if the level of macroprudential regulation is low, an increase in global risk aversion (proxied by the Chicago Board Option Exchange Volatility Index (VIX)) or an outflow of foreign capital considerably reduces economic growth in emerging markets. For example, a 60 percent spike in the VIX—about half of what we experienced in the first quarter of 2020 as a result of the COVID-19 pandemic—or a capital outflow equal to 2 percent of GDP in a quarter can push a typical emerging market into a recession”. Accordingly, given that macro-economic shocks transmit to real economies via the financial markets, monitoring and manging of financial market volatility becomes very crucial, more so during periods of stress and uncertainty.
In this backdrop, this paper seeks to empirically test the response of financial market volatility in India to a set of macro-economic variables in terms of their direction, extent and duration of impact. This paper seeks to add to the literature by:
Including all six major segments of Indian financial markets, unlike majority of earlier studies on the subject focusing on the nexus between macroeconomic variables vs. one or two segments of financial market;
also considering new macro-economic variables representing global macro-economic shocks and depth of Indian financial market; and
considering a long study period covering multiple macro-economic shocks viz. dotcom bubble bust in late 1990s to global financial crisis during 2007–08 to Eurozone debt crisis during 2009–13 to US Fed tape tantrum during 2013 to Covid 19 led shocks most recently.
2 Literature Review
2.1 macro-economic factors and money market.
As far money market rates are concerned, they are predominantly driven by prevailing liquidity in the financial system. Liquidity as such depends on multiple factors such as structural factors, (viz. GDP growth, inflation, capital flows, forex market intervention, credit to deposit ratio), frictional factors (viz., seasonal demand for cash vs. cash balances of government maintained with the Central Bank) and most importantly the policy induced factors viz. change in policy rates, advance tax payments and Open Market Operations (OMO).
In this light, a study on the effect of structural and frictional liquidity shocks on call money rates and the pattern of volatility (Singh, 2020 ) suggests that among the key exogenous liquidity shocks impacting call money rates in India, there is strong evidence of currency demand, forex inflows and movements in government’s cash balances with the RBI as principal drivers. Given the significant currency-GDP ratio in India, movements in currency demand result in sudden changes in money market liquidity. A key structural driver of liquidity demand in money markets is also the credit to deposit growth of the banking system. Forex inflows, particularly led by portfolio inflows, are more volatile in nature as these are strongly influenced by foreign investors’ expectations and risk-taking behaviour. As far government’s cash balance is concerned, while tax revenues are relatively predictable, expenditures are uneven, causing unanticipated liquidity demand/supply and hence higher volatility in money market rates. On the other hand, Ramchander et al. (2003) find that yield variability in money market is fundamentally linked to the release of macroeconomic news that conveys important information on inflation.
2.2 Macro-economic Factors and Forex Market
Exchange rate stability is one of the crucial factors for macroeconomic stability. Typically, exchange rate volatility arises due to macro-economic fundamentals like growth, trade, price level, interest rate, foreign exchange reserve etc. and short-term speculation. For instance, exchange rate volatility in India, post the taper talk reached its peak in August 2013 when the exchange rate depreciated by 10% in just one month. This kind of volatility in exchange rate to a significant extent also due to the then prevailing high current deficit and weaker macroeconomic health of India. RBI had to intervene swiftly to stabilize the situation.
In the empirical literature, the findings of researchers on the impact of macro-economic factors on exchange rate is mixed. In the short run particularly, market participants do not in fact use a commonly agreed model for evaluating the outlook of the foreign exchange market and do not all share the same expectations at any point of time” (Frankel et. al., 1996 ). Macroeconomic fundamentals are barely useful in predicting the rate movement in the short-run, particularly after the introduction of on-line trading systems that made the tick-by-tick (high frequency) data available (Sarno & Taylor, 2001 ). More than macroeconomic fundamentals, the dealers consider other variables that are micro in nature (Lyons, 1995 ). The micro variables are bid-ask spreads, trading volume, own volatility, nonsynchronous trading, information (both private and public), inventory cost, etc. Moreover, the macro models to forecast exchange rate lost its allure post the seminal conclusion by the work of Meese and Rogoff ( 1983 ) that “forecasts based on monetary approach to exchange rate determination could not out-perform the random walk forecasts”. Many studies thereafter corroborated that, fundamentals cannot provide best forecasts for the exchange rate movement (see Mark Nelson, 1995 ; Mark & Sul, 2001 ; Cheng et al., 2002 ; and Chinn & Meese, 1995 , Evans & Lyons, 1999 ).
On the other hand, there are also several macro models in the international economics literature dealing with exchange rate determination (Gandolfo, 2001 ) which shows that exchange rates are driven by a gamut of economic, political, and psychological factors that are highly correlated and interactive in a very complex way (Alagidede & Ibrahim, 2017 ; Huang et al., 2004 ; Yu et al., 2010 ). In the context of India, Mishra and Yadav ( 2012 ) found that money supply and inflation rate have the most notable effect on exchange rate. Saha and Biswas ( 2014 ) found that export, interest rate, foreign exchange reserve and economic growth have appreciating effect whereas import and inflation have depreciating effect on exchange rate. Another study by Khushboo and Syeedun ( 2019 ) found that foreign exchange reserve, money supply and interest rate have a significant influence on exchange rate in India while current account deficit have a non- significant influence on exchange rate.
2.3 Macro-economic Factors and Stock Market
As far as the empirical literature on macroeconomic development and stock market volatility is concerned, the findings here too are mixed. Schwert ( 1989 ) finds that macroeconomic variables play significant role in prediction of stock market volatility and their impact has been more during the period of depression. From the theoretical perspective, the dividend discount model (DDM) and arbitrage pricing theory (APT) provide the theoretical framework through which the behaviour of macroeconomic fundamentals can be linked to the stock market volatility (see Chen et al., 2007 ). These models emphasize that any expected or unexpected arrival of new information and policy decisions regarding macroeconomic variables such as gross domestic product (GDP), money supply, inflation, interest rates, exchange rates and foreign institutional investments (FIIs) will change the equity prices and further the volatility of stocks via change in the future cash flows and expected dividends. Intuitively, the essence of the theoretical link between the macroeconomic fundamentals and equity market volatility is that any change or shock in the macroeconomic variables will raise the source of systematic and idiosyncratic risk of the market portfolio, irrespective of how well the portfolio is diversified (Chowdhury and Rahman, 2004 ). Diebold and Yilmaz ( 2008 ) empirically investigate the issue taking sample of 45 markets including developed and emerging and suggest a significant positive relationship between volatility of stock returns and GDP volatility. On the other hand, using the VAR framework, Morelli ( 2002 ) empirically tested the issue in the UK stock market and documented no significant explanatory power of macroeconomic volatility in determining the stock market volatility.
The literature on the relationship between macroeconomic factors and stock returns volatility in India largely emphasizes on the long run causal links and long run co-movements of the variables. For instance, Darrat and Mukherjee ( 1986 ), and Mukherjee and Naka ( 1995 ) examine the long run relationships and co-movements of the macroeconomic fundamentals and stock returns. The study has demonstrated the absence of the long run co-movements among the variables. However, Naka et al. ( 1998 ) find a long run relationship among the variables. Panda and Kamiah ( 2001 ) further estimate the causal and dynamic linkages among the monetary policy variables and volatility and conclude that macroeconomic factors cause the volatility in the market. More recently, Manel et al. ( 2021 ) investigated the dynamic connectedness between stock indices and the effect of economic policy uncertainty (EPU) in eight countries where COVID-19 was most widespread (China, Italy, France, Germany, Spain, Russia, the US, and the UK) and found that the direction of the EPU effect on net connectedness changed during the pandemic onset, indicating that information spillovers from a given market may signal either good or bad news for other markets, depending on the prevailing economic situation.
2.4 Macro-economic Factors and Bond Market
Empirical literature on macro economy and bond market nexus supports that “macroeconomic news is most important for Govt. bond markets” (Macqueen and Roley, 1993 ). In the context of 10-year US Treasury Bonds, macroeconomic news has a strong impact on the dynamics of bond market volatility. News on employment situation and inflation are especially influential at the intermediate and long end of the yield curve, while monetary policy seems to affect the short-term volatility (de Goeij and Marquering 2006 ). Das ( 2002 ) and Piazzesi ( 2003 ) show that the Federal Open Market Committee (FOMC) release on its target rate can explain the jump behaviour of interest rates. Brenner et al. (2009) studied the impact of the release of surprise U.S. macroeconomic information on U.S. stock, Treasury, and corporate bond markets volatilities and co-movements of their returns by applying several extensions of the parsimonious multivariate GARCH-DCC model of Engle (2002). This study found that both the process of price formation in each of these financial markets and co-movement of their returns appear to be driven by fundamentals. Inflation rate, terms of trade and the exchange rate of domestic currency influence government bond yields. Inflation rate has a positive effect on yield. Haque et al. ( 1996 ) find that the prices of government bonds in developing countries are affected by the ratio of reserves to total imports, the ratio of the balance of payments to GDP, economic growth and inflation. Therefore, macroeconomic factors exhibit a significant relationship with volatility in all the bond markets, more specifically in the emerging bond markets. Thenmozhi ( 2012 ) found that past lags explain bond volatility in India, Brazil, USA, UK and Japan, which reasserts that the assumptions of random walk hypothesis does not hold true and bond markets are predictable in the long run.
With regard to various risks and consequent yield volatility associated with corporate bonds, Gemmill and Keswani ( 2011 ) found that corporate bond yield spreads are mostly caused by default losses. Liquidity risk, however, is important to the corporate credit risk and expected corporate bond returns, more particularly during stress periods (Lin et. al., 2011 ). Acharya and Pedersen ( 2005 ) found that the expected returns of bonds depend on the expected liquidity, the covariance of the returns and market liquidity. A broad overview of the literature on factors affecting yields on corporate bonds, therefore, include the Treasury market variables (e.g., Longstaff and Schwartz ( 1995 )), liquidity (Longstaff et al., 2005 ), equity market variables (e.g., Collin-Dufresne et al. ( 2001 ), and macro-economic variables (e.g., Jean and Kleiman ( 1997 ), Greg and Stein ( 2002 ) and the monetary policy stance shaped by a multitude of macro factors (Smolyansky and Suarez ( 2021 )).
2.5 Macro-economic Factors and Banking Sector
In literature studies on the relationship between macroeconomic factors and individual bank risk are relatively rare (e.g., Buch et al., 2007 ; Wedow, 2006 , Baele et al., 2004 ). Most of the existing theoretical and empirical research rather focus the relationship between macro-economic variables and stock market returns. The major empirical findings with regard to the macro factors impacting returns on bank stock in particular, inter alia, comprise of variables such as GDP, inflation rate, interest rate and exchange rate (Acaravci & Çalim, 2013 ; Jara-Bertin et al., 2014 ; Menicucci & Paolucci, 2016 ; Pasiouras & Kosmidou, 2007 ). The study by Paul and Mallik ( 2003 ) found that, as per Australian experience, the interest rate has a negative effect, whereas GDP growth has a positive effect on bank and finance stock prices and Inflation has no significant effect on stock prices. Another study by Lucey, Lucey et al. ( 2008 ) investigate the relationship between macroeconomic surprises and returns of stock exchanges in developed countries viz., Canada, France, Germany, Hong Kong, Italy, Singapore and UK. Applying GARCH model on the monthly data of 1999–2007, this study found that unexpected news of macroeconomic factors had significant impact on the returns of Stock Exchanges. Al-Homaidi et al., ( 2018 ) find that macroeconomic factor such as GDP, inflation rate, interest rate and exchange rate negatively impact on Indian commercial banks profitability. Joaqui-Barandica et al. ( 2021 ) identified three main macroeconomic factors underlying banking profitability: the financial burden of households and economic activity; household income and net worth and, in the case of ROA and ROE, corporate indebtedness.
3 Variables and Data
In order to capture the effect of selected macro-economic developments or shocks on financial market volatility in India, the current study uses one representative indicators for each of the six segments of Indian financial markets, namely, MIBOR to represent money market, USD INR to represent foreign exchange market, 10-year Gsec yield to represent GSEC market, CRISIL Corporate Bond Composite Index to represent corporate bond market, NSE 500 to represent broader equity market and NSE Bank Nifty to represent the banking sector in India. The macroeconomic variables considered for assessing their impact on the six financial market segments of India include the ratio of Equity Market Capitalisation to Gross Domestic Product (EMCGDP) to represent financial market depth; Gross Domestic Growth rate (GDPG), Consumer Price Index (CPI) and Current Account Deficit (CAD) to represent the macro fundamentals of the economy; Foreign Portfolio Investment (FPI) in India and the US Treasury Bill rate (USTB) to represent respectively the domestic and global economic sentiments.
The period of study is from April 2002 to March 2021, with 218 number of monthly observations. Further the whole period has been divided into sub sample periods based on structural breaks through Chow break point and the NBER business cycle as well as the consideration of having adequate data sample size for undertaking empirical estimations for each sub sample periods. The study considers five sub periods along with the whole time period such as Sample Period I (Apr 2002–Nov 2007), Sample Period II (Dec 2007–Dec 2013), Sample Period III (Jul 2009–Dec 2013), Sample Period IV (Jan 2014–Feb 2020), and Sample Period V (Jan 2014–Mar 2021). All data have been sourced from respective secondary sources, including Bloomberg.
4 Methodology
The study has converted all raw data into natural log returns using the formula:
where P t refers to today’s price, and P t-1 refers to yesterday’s price. Furthermore, in order to transform the GSEC and MIBOR yields into monthly returns, such that the time frame of all sectors, and market would be matched, the following calculation is used:
where Rf represents the risk-free rates, while c and t characterize specific country and time of the return respectively, while the denominator (12) is the average number of months annually in the respective segment.
The precondition to applying any econometrics model including VAR is to ensure the stationarity of the variables. The study performs the stationarity of the variables through Augmented Dickey-Fuller (ADF), and Zivot Andrews test. We found that all data are stationary at the level and have applied Vector Auto Regression (VAR) to capture the dependencies among the variables and segments under considerations. Further to run VAR, we need to first select the lag length. In this study we have used the following:
4.1 Vector Auto Regression (VAR)
The simple univariate Autoregressive (AR) model is represented by
Here, the present value of variable y is dependent on its initial lag, where a1 is parameter coefficient, and the lag is written as subscript. It is called autoregressive of order one as it contains only one lagged value or AR (1). However, the order can easily be increased by adding more lags, that is, AR(p). Here, et is the error term which is assumed to be normally distributed with mean zero and variance is equal to σ2. A VAR is in a sense, a systems regression model, where there are multiple dependent variables. Simplest case is a bivariate VAR, which can be written as equations iv and v , where U(it) is an independent and identically distributed term with E(Uit) = 0, i = 1,2; and E (U1t U2t) = 0.
The symmetric covariance matrices of standard VAR models show the relation correlation between endogenous variables. The premise behind VAR is that each of the time arrangements in the framework impacts one another; that is, we can foresee the arrangement with past estimations of itself alongside different arrangements in the framework.
4.2 Impulse Response function
VAR models are often tough to interpret. One solution is to construct the impulse responses and variance decompositions. Impulse response analysis is an important step in econometric analysis, which utilizes vector autoregressive models. Their fundamental reason is to describe the development of a model’s variables in response to a shock in one or more variables. This element helps tracing the transmission of a single shock in an otherwise noisy system of equations, therefore making it a very useful tool in the calculation of economic policies. A common method to recognize the shocks of a VAR model is by using orthogonal impulse response (OIR). The objective here is to decompose the variance–covariance matrix, therefore Σ = PP′, where P is a lower triangular matrix with positive diagonal elements, that is mostly obtained by a cholesky decomposition.
4.3 Variance Decomposition
Variance decompositions suggests a somewhat different technique of examining VAR dynamics. They give the proportion of the movements in the dependent variables that are due to their “own” shocks, versus shocks from the other variables. This would be done by determining how much of the s-step ahead forecast error variance for each variable is explained innovation to each explanatory variable (s = 1, 2…). The variance decomposition gives information about the relative importance of each shock to the variables in the VAR.
5 Discussion of Empirical Results
5.1 diagnostic tests.
For the post diagnostic tests, we check the autocorrelation through Portmanteau test, Heteroscedasticity through ARCH test and normality through Jarque–Bera test. All the tests confirmed that models under considerations are good fit and none of the equations are violating the diagnostic tests (Tables 1 , 2 , 3 and 4 ).
Pursuant to the diagnostic tests, estimations have been done to see the impact of selected macro-economic shocks for each of the financial market indicators for each different sample periods, not only to see the impact of shocks but also to see how they behave in different sample periods, representing different scenarios.
5.2 Impulse Response Plots
The impact of macro-economic shocks on financial market indicators have been estimated using the change in macro-variables on the return on financial market indicators. The impacts have been captured by computing the Impulse Response Plot (IRP), based separately on the data sample for each sample periods considered in this study. The impulse response function indicates the transmission effect of innovations in one variable to the shock of another variable.
Illustratively, the impulse response plot for the full sample period may be referred to in Figs. 1 , 2 , 3 , 4 , 5 and 6 below:
Impulse Plot for MIBOR–Full Sample Period (2002–21)
Impulse Plot for USD-INR–Full Sample Period (2002–21)
Impulse plot for LTY–full sample period (2002–21)
Impulse plot for COB–full sample period (2002–21)
Impulse plot for NSE500–full sample period (2002–21)
Impulse plot for NSEBN–full sample period (2002–21)
It was found that the response of MIBOR to macro-economic shocks are in accordance with theory e.g., negative to Inflation, CAD and US Treasury Yields. On the other hand, the response of MIBOR is positive to GDP growth signifying higher demand for money. Response of MIBOR to FPI flows becomes positive and then turn negative, before converging to steady state. This kind of trend seems to be a response to use of Market Stabilisation Scheme (MSS) by the Reserve Bank of India (RBI) to sterilize the enhanced foreign liquidity coming through FPIs. An increase in financial market depth, represented by market cap to GDP ratio is also a positive for MIBOR. Further, the response of MIBOR dies down in three to five months of the impulses coming from the macro-economic shocks.
As far as the response of USD INR rate to macro-economic shocks is concerned, the response will come from the impact of such shocks on the expectations regarding the underlying net demand for USD. With an increase in inflation and CAD, for instance, the demand for USD will go up, as both these shocks means net increase in demand for foreign goods and services, and therefore, more domestic demand for USD to service imports. On the other hand, an increase in USD treasury yield would mean net outflow or lower net inflow of USD through FPIs, thereby adversely impacting the net supply of USD. Therefore, as expected, the response of USD INR rate to shocks in inflation, current account deficits and USD treasury yields is positive, indicating weakening of rupee and potential depreciation of Indian currency. On the other hand, the response is negative to shocks in GDP growth rate and market cap to GDP, indicating strengthening of forex inflows and consequent exchange rate appreciations. The positive response of USD-INR exchange rate to FPI flows i.e., appreciation of rupee following capital inflows is also theoretically intuitive, as with FPI flows the exchange rate can potentially appreciate. Further, the response of USD-INR dies down in three to five months of the impulses coming from the macro-economic shocks.
The response of Gsec Yield to GDP growth, Inflation and CAD, after becoming negative then turns to have a general positive bias before returning to steady state. This kind of a behavior seems to be reflecting the dominant role of monetary policy and over-all political economy in India in shaping the yields on Gsec. As expected, however, the response of Gsec yield to US Treasury Yields is positive. On the other hand, the response of Gsec yield to FPI flows and market cap to GDP have been negative, as a higher of these variables reflect positive trends and increasing likely interest of investors in equity markets and consequent addition to liquidity in the system. Another important pattern observed is that the response of Gsec yield to macro shocks persists for longer period, ranging from 10 months to more than 20 months, before the response dies down to respective impulses coming from the macro-economic shocks.
In case of corporate bonds, their interest rate risks are driven by their underlying financial strength. The better are these companies' balance sheets, cash balances, and underlying business trends, the less likely they are to default (miss a payment of principal or interest). They, therefore, tend to react differently to macro-economic shocks than a Gsec per se. In our analysis, the response of corporate bond is negative to GDP growth and US Treasury yield shocks, while they are positive to Inflation and CAD. On the other hand, the response of corporate bonds, similar to Gsec yield, have been negative to FPI flows and market cap to GDP. The response of corporate bonds dies down in two to five months of the impulses coming from the macro-economic shocks. The response of corporate bonds to macro variables are more aligned to theory as compared to their Gsec counterpart.
The response of NSE 500 to macro-economic shocks are in accordance with theory e.g., positive to GDP growth, negative to Inflation, CAD and US Treasury Yields. On the other hand, the response of NSE 500 to FPI flows and market cap to GDP has been negative, implying, respectively, the immediate profit bookings mentality of domestic investors and stickiness of market capitalization to GDP in India. The similar pattern is also visible in NIFTY Bank Index, except response to market cap to GDP, which is positive in this case, implying banking stocks reacting positively to increased stock market depth. The response of equity market dies down in three to five months of the impulses coming from the macro-economic shocks.
Another important observation is regarding the time it takes in various sub-periods, to reach back to the steady state, post the macro-economic shocks. For the full sample period, the response of financial market indicators, except for Gsec yields, normally takes dies down between 2 to 5 days. However, when seen for different sub—periods, it took longer times in each of those sub-periods to reach steady states in financial market variables following a shock in the macro-economic indicators. Another general observation has been that, in the sample periods, which also includes the time period of general market revival following the crises e.g., GFC (2009 to 2013) and COVID (2014 to 2021), the response functions themselves have been more volatile, reflecting shaky sentiments in such periods.
Moreover, it may be highlighted that he response of broader equity index to an impulse from FPI flows, an indicator signifying investors’ confidence, has been negative for the entire sample period. However, it is observed from the impulse response for different sample periods that, during the sample period from 2009 to 2013 and sample period from 2014 to 2021, the response of equity market to FPI follows have been positive. This may be explained by the fact that, as economy recovers from crises, FPI inflows signifies an immediate positive sentiment about the market, leading to overall bullishness and recovery in the equity market.
5.3 Variance Decomposition Analysis (VDA)
When we forecast for N periods, the forecast error variance decomposition indicates how much a variable’s own past movements explain its own variation and to what extent other variables, included in the analysis, explain its variation. Put simply, it shows as to how much of own shocks and how much other variables shocks are impacting one particular variable. Generally, own shock becomes predominant in this analysis.
5.4 VDA of MIBOR
In the case of MIBOR the variance decomposition analysis (VDA) for the entire sample period from 2002 to 2021 revealed that, apart from its own lags, which explains 97% of its variations, FPI flows explains the rest 3% of variations in MIBOR. During sample period I, the lags of MIBOR explain at least 70% of its variation, followed by FPI flows upto 9%, CAD about 6% and CPI about 5%. During sample period II, its own lags explain at least 57% of variations in MIBOR, followed by upto 11% by Market Capitalization to GDP, upto 10% by CAD and upto 6% each by FPI, CPI and GDP Growth. In sample period III, at least 37% of variations in MIBOR is explained by its own lags, followed by CAD upto 24%, GDP growth upto 15%, US treasury yields upto 11% and FPI flows upto 9%. This is the sample period which represents the Eurozone crisis and taper tantrum by US Fed at a time of higher CAD, slowing GDP growth and overall fiscal imbalance domestically in India. In sample period IV, at least 57% of variations in MIBOR is explained by its own lags, followed by CAD upto 15%, CPI upto 8% and FPI flows upto 7%. In sample period V, at least 63% of variations in MIBOR is explained by its own lags, followed by CAD upto 18% and FPI flows upto 7%. This period also includes the post Covid period of general negative shock to global economy, indicating potential decline in exports.
5.5 VDA of USD-INR Rate
In the case of USD-INR, apart from its own lags which explain at least 98% of its variations, only CPI explains the balance for the full sample period. During sample period I, the lags of USD-INR explain at least 54% its variations, followed by upto 19% by CPI, upto 11% by market capitalization to GDP ratio and upto 6% each by CAD, FPI and USTB. During sample period II, at least 60% of variations in USD-INR is explained by its own lags, followed by USTB upto 12%, EMCGDP 9%, FPI flows upto 7%, CAD upto 5% and CPI and GDP growth rate upto 4% each. During sample period III, at least 73% variations in USD-INR is explained by itself, followed by upto 13% by USTB, upto 4% by EMCGDP, upto 3% each by CPI, FPI and GDP growth and upto 2% by CAD. This is the sample period which represents the Eurozone crisis and taper tantrum on global fronts, combined with higher CAD, slowing GDP growth and overall fiscal imbalance domestically in India. In sample period IV, at least 53% of variations in USD-INR is explained by its own lags, followed by CPI upto 12%, CAD and USTB upto 9%, EMCGDP upto 7%, FPI upto 6% and GDP growth upto 5%. In sample period V, at least 76% of variations in USD-INR is explained by its own lags, followed by CPI upto 10% and FPI upto 7%, CAD upto 3%, EMCGDP and GDP growth upto 2% and upto 1% by USTB. This period also includes the post Covid period of general negative shock to global economy.
5.6 VDA of LTY
The VDA results of LTY i.e10 year Gsec yield for the full sample period shows that, for LTY, its own lags explain at least 90% of its variations, followed by upto 0.5% by EMCGDP, upto 2% by FPI and 1% by GDP Growth and CAD. During sample period I, the lags of LTY explain at least 67% its variations, followed by upto 6% each by EMCGDP, CPI, CAD; upto 5% each by FPI, GDP Growth and USTB. During sample period II, at least 71% variations in LTY is explained by itself, followed by upto 14% by EMCGDP, upto 5% by CAD, upto 4% by USTB, upto 3% by CPI and upto 2% each by FPI and GDP growth. During sample period III, the variation in LTY is explained by its own lags upto 57% followed by upto 15% by CAD, upto 9% by USTB, upto 5% each by EMCGDP, GDP Growth and FPI and upto 4% by CPI. This is the sample period which represents the Eurozone crisis and taper tantrum on global fronts, combined with higher CAD, slowing GDP growth and overall fiscal imbalance domestically in India. In sample period IV, at least 54% of variations in LTY is explained by its own lags, followed by FPI upto 12%, GDP Growth upto 11%, EMCGDP upto 9%, CPI upto 6%, CAD upto 5% and USTB upto 4%. In sample period V, at least 54% of variations in LTY is explained by its own lags, followed by FPI flow upto 16%, GDP growth upto 10%, USTB upto 8%, EMCGDP upto 6%, CAD upto 4% and CPI upto 3%. This period includes the post Covid period of general negative shock to global economy. As can be seen, sentiment indicators like FPI and USTB and economic revival represented by GDP growth had major influence on Gsec yield or LTY during this period.
5.7 VDA of COB
For corporate bond, its own lags explain at least 94% of its variations, followed by upto 2% each by CPI and USTB and 1% by FPI during the full sample period. During the sample period I, the lags of corporate bond explain at least 78% its variations, followed by upto 10% by USTB, upto 6% by CAD; upto 4% by FPI and upto 1% by GDP Growth, CPI and EMCGDP. During sample period II, at least 67% variations in corporate bond is explained by itself, followed by upto 11% by CPI, upto 9% by EMCGDP, upto 5% by USTB, upto 3% each by FPI and CAD and upto 2% by GDP growth. During sample period III, the variation in corporate bond is explained by its own lags upto 46% followed by upto 23% by CPI, upto 17% by USTB, upto 6% by GDP growth, upto 5% by EMCGDP and upto 2% each by FPI and CAD. This is the sample period which represents the Eurozone crisis and taper tantrum on global fronts, combined with higher CAD, slowing GDP growth and overall fiscal imbalance domestically in India. In sample period IV, at least 58% of variations in corporate bond is explained by its own lags, followed by GDP growth upto 10%, EMCGDP and CAD each upto 8%, FPI upto 6% and CPI and USTB upto 5% each. In sample period V, at least 48% of variations in corporate bond is explained by its own lags, followed by USTB upto 13%, GDP growth upto 12%, FPI upto 10%, CAD upto 9%, EMCGDP upto 6%, CPI upto 4%. This period includes the post Covid period of general negative shock to global economy. As can be seen, sentiment indicators like FPI and USTB and economic revival represented by GDP growth had major influence on corporate bonds during this period.
5.8 VDA of NIFTY500
VDA of NIFTY500 for the full sample period shows that, for NSE500, its own lags explain at least 97% of its variations, followed by upto 1% each by EMCGDP and CPI. In sample period I, the lags of NSE500 explain at least 48% its variations, followed by upto 18% by EMCGDP, upto 10% by CAD; upto 7% each by GDP growth and CPI, upto 5% each by CAD and USTB. During sample period II, at least 70% variations in NSE500 is explained by itself, followed by upto 14% by USTB, upto 5% each by FPI and CPI, upto 4% by EMCGDP, upto 2% by CAD and upto 1% by GDP growth. This period includes the period of GFC and hence a significant impact of USTB. During sample period III, the variation in NSE500 is explained by its own lags at least 37% followed by upto 35% by USTB, upto 14% by FPI, upto 11% by CPI and upto 3% each by EMCGDP, GDP growth and CAD. This is the sample period which represents the Eurozone crisis and taper tantrum on global fronts, combined with higher CAD, slowing GDP growth and overall fiscal imbalance domestically in India. In sample period IV, at least 59% of variations in NSE500 is explained by its own lags, followed by CPI upto 12%, GDP growth upto 10%, CAD upto 7%, EMCGDP upto 6%, FPI upto 4% and USTB upto 3%. In sample period V, at least 67% of variations in NSE500 is explained by its own lags, followed by GDP growth upto 9%, CAD upto 7%, CPI upto 6%, FPI and EMCGDP each upto 4% and USTB upto 3%. This period includes the post Covid period of general negative shock to global economy.
5.9 VDA of NSEBN
VDA of NSEBN for the full sample period shows that, for NSEBN, its own lags explain at least 97% of its variations, followed by upto 1% each by GDP growth and USTB. In sample period I, the lags of NSEBN explain at least 53% its variations, followed by upto 16% by CPI, upto 13% by EMCGDP, upto 7% by CAD; upto 5% by GDP growth, upto 4% by USTB and upto 2% by FPI. During sample period II, at least 64% variations in NSEBN is explained by itself, followed by upto 20% by USTB, upto 6% each by CPI, upto 4% by EMCGDP, upto 3% by FPI, and upto 2% each by CAD and GDP growth. This period includes the period of GFC and hence a significant impact of USTB. During sample period III, the variation in NSEBN is explained by its own lags at least 36% followed by upto 33% by USTB, upto 11% by EMCGDP, upto 8% by CPI, upto 6% by FPI and upto 4% each by growth and upto 2% by CAD. This is the sample period which represents the Eurozone crisis and taper tantrum on global fronts, combined with higher CAD, slowing GDP growth, high inflation and overall fiscal imbalance domestically in India. In sample period IV, at least 49% of variations in NSE500 is explained by its own lags, followed by CPI upto 19%, CAD upto 15%, EMCGDP upto 7%, GDP growth upto 6%, USTB upto 4% and FPI upto 2%. In sample period V, at least 56% of variations in NSEBN500 is explained by its own lags, followed by upto 11% by CAD, upto 10% by GDP growth, upto 9% by EMCGDP, upto 8% by CPI and upto 3% each by FPI and USTB. This period includes the post Covid period of general negative shock to global economy.
6 Summary of Findings
The direction and extent of response by financial market variables are generally aligned to economic theory. Corporate bonds, however, tend to react differently to macro-economic shocks than a government bond. It takes at least 3–5 months for the impact of macro-economic shocks on financial markets to die down. In case of Gsec market, though, such impacts are more persistent and less aligned to economic theory, indicating a possible strong influence of monetary policy actions and larger political economy in shaping Gsec yields. It is also observed that as economy recovers from crises, the response of financial markets to macro-economic developments becomes even more volatile, reflecting shaky sentiments during such periods. During such periods, FPI inflows seems to signify an immediate positive sentiment about the market, leading to overall bullishness and recovery in the equity market.
FPI flow, current account deficit (CAD), inflation (CPI) and economic growth are predominant macro factors influencing money market and exchange rates. During uncertain times, USTB also significantly shapes the trajectory of MIBOR and exchange rates. CAD and CPI have significant impact on Gsec yield. A higher market capitalization to GDP ratio, representing higher financial market depth, is a big positive for Gsec yields. USTB and FPI impact Gsec yields more during uncertain periods. USTB has a significant impact on corporate bonds, more particularly in uncertain times, reflecting the fact that global sentiment has a role to play in assessing the corporate sector performance in India.
In times of global uncertainty, USTB and FPI flows have significant impact in forecasting equity market returns. Both these indicators represent market sentiments. Secondly, domestic macro-economic variables, in their weaker state, tend to influence the equity market more. CPI has a significant impact on bank stocks and corporate bonds, given that CPI is the harbinger of interest rate expectations and consequent business and profitability of banks.
7 Policy Lessons
The type and extant of the impact of macro-economic factors varies across financial market segments. The nexus depends on how macro-indicators impact valuation of expected returns from different asset classes. Secondly, macro-variables, when they are in their weaker state, tend to exert greater impact. For example, news on CAD, when CAD is already high, is likely to impact the expected return of the asset class more. Therefore, there is a need to focus on maintaining macroeconomic stability as a policy to foster financial market stability. Thirdly, sentiments play a significant role in financial markets, particularly during periods of global uncertainty. Therefore, the behaviour of indicators like US treasury yield and FPI flows needs to be watched more closely during stress periods to assess financial markets volatility and to decide on the timing, type and quantum of response domestically. Fourthly, a deeper equity market, by providing for an alternative platform for fund raising, is positive for bond market volatility. Finally, given that nexus between macro factors and financial markets is subject to change with time and circumstance in the short-run as well as economic and market structures in the long run, there is an unavoidable need to monitor a customized and dynamic list of macroeconomic variables in respect of each of the financial market segments for early detection of trend reversals so as to decide on the timing, type and quantum of policy and regulatory responses from time to time.
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Macroeconomic assessment of India’s development and mitigation pathways
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Although a rapidly growing economy, India faces many challenges, including in meeting the Sustainable Development Goals of the United Nations. Moreover, post-2020 climate actions outlined in India’s Nationally Determined Contribution (NDC) under the Paris Agreement envision development along low-carbon emission pathways. With coal providing almost three-quarters of Indian electricity, achieving such targets will have wide-ranging implications for economic activity. Assessing such implications is the focus of our research. To do so, we use a hybrid modelling architecture that combines the strengths of the AIM/Enduse bottom-up model of energy systems and the IMACLIM top-down economy-wide model. This hybrid architecture rests upon an original dataset that brings together national accounting, energy balance and energy price data. We analyse four scenarios ranging to mid-century: business-as-usual (BAU), 2°C, sustainable 2°C and 1.5°C. Our 2°C pathway proves compatible with economic growth close to the 6% yearly rate of BAU from 2012 to 2050, at the cost of reduced household consumption but with significant positive impact on foreign debt accumulation. The latter impact stems from improvement of the trade balance, whose current large deficit is the primary cause of high fossil fuel imports. Further mitigation effort backing our 1.5°C scenario shows slightly higher annual GDP growth, thereby revealing potential synergies between deep environmental performance and economic growth. Structural change assumptions common to our scenarios significantly transform the activity shares of sectors. The envisioned transition will require appropriate policies, notably to manage the conflicting interests of entrenched players in traditional sectors like coal and oil, and the emerging players of the low-carbon economy.
Low carbon pathways are compatible with Indian growth despite their high investment costs
Moving away from fossil fuel-based energy systems would result in foreign exchange savings to the tune of $1 trillion from 2012 to 2050 for oil imports.
Achieving deep decarbonization in India requires higher mobilized capital in renewables and energy efficiency enhancements.
Phasing out fossil fuels would, however, require careful balancing of interests between conventional and emerging sector players through just transitions.
- energy-economy model
- sustainable development
- climate policy
Since economic liberalization in 1991, India’s GDP has been growing at an annual rate of 7% to 8%. Part of this growth stems from structural change, which saw the Indian economy turn from agriculture in the 1970s, to services and industry, which contributed 53% and 31% of GDP respectively in 2017 (Economic Survey, Citation 2018 ). This drive is expected to continue, with governmental policies like Make in India, Smart Cities Mission and Housing for All providing impetus to the manufacturing sector and infrastructure development. Services should also benefit from public programmes like Digital India and Start-up India.
Despite this robust growth trend, India faces many socio-economic challenges resonating with the Sustainable Development Goals (SDGs) of the United Nations. Nearly 300 million people are still living in poverty (MoSPI, Citation 2018 ) and without access to electricity (NEP, Citation 2017 ). About 50% of rural households lack basic socio-economic services (SECC, Citation 2015 ). Per capita energy consumption is only one third of the global average (IEA, Citation 2015 ), which betrays low levels of energy services. The SDGs must, however, be balanced with national targets for greenhouse-gas emissions abatement. In its nationally determined contribution (NDC) submitted under the Paris Agreement, India has committed to reducing the emission intensity of its GDP 33% to 35% below its 2005 level by 2030, and to scaling up its non-fossil share of power capacity to 40% (MoEFCC, Citation 2015 ). This commitment should be seen in the context of coal currently contributing to nearly three quarters of power generation, and fossil fuels more generally meeting three quarters of total energy demand. Moreover, Indian energy demand is expected to grow exponentially following rapid urbanization, industrialization and the rising purchasing power of the population. By mid-century, India is projected to be among the world’s largest national energy consumers (IEA, Citation 2018 ). Decarbonizing energy supply will require substantial investment costs, whereas it should improve the trade balance, in a context where oil imports amount to 80% of the current trade deficit (ETEnergyWorld, Citation 2018 ). Our research aims to analyse the balance of such losses and gains, that is, the ultimate macroeconomic impacts of low-carbon development pathways for India.
Numerous studies have investigated the implications of decarbonization strategies on the energy system and economic development of India (Dubash, Khosla, Rao, & Sharma, Citation 2015 ; Parikh & Parikh, Citation 2011 ; Shukla, Dhar, & Mahapatra, Citation 2008 ; Shukla & Chaturvedi, Citation 2012 ; van Ruijven et al., Citation 2012 ). Gambhir, Napp, Emmott, and Anandarajah ( Citation 2014 ) investigate the financial and other potential benefits of decarbonization using the TIMES bottom-up model of energy systems. They compare Indian mitigation costs with global average costs to determine potential revenues from the sale of international carbon credits. Byravan et al. ( Citation 2017 ) also implement the TIMES model to compare the GHG emissions, primary energy demand, investment costs and energy imports requirement of a business-as-usual (BAU) versus a sustainable development scenario. Multiregional studies like Fragkos and Kouvaritakis ( Citation 2018 ), Van Soest et al. ( Citation 2017 ) and Vandyck, Keramidas, Saveyn, Kitous, and Vrontisi ( Citation 2016 ) underline the large emission gap between the NDC and 2°C pathways for India using a global energy system model. Vishwanathan, Garg, and Tiwari ( Citation 2018 ; Vishwanathan, Garg, Tiwari, & Shukla, Citation 2018 ) apply the AIM/Enduse bottom-up model to determine the challenges and opportunities involved in limiting global warming to 2°C and below. Chaturvedi, Koti, and Chordia ( Citation 2018 ) implement the GCAM integrated assessment model to analyse 216 scenarios combining key technical uncertainties characterizing mitigation strategies. However, all these technology-rich studies lack economy-wide coverage, that is, they overlook feedbacks of energy constraints on economic activity and hence energy demand.
Top-down approaches provide such coverage. Van Soest et al. ( Citation 2016 ) and Saveyn, Paroussos, and Ciscar ( Citation 2012 ) use the multiregional Computable General Equilibrium (CGE) model GEM-E3 to discuss the economic implications of energy efficiency measures and the penetration of carbon-free technologies in a 2°C scenario. Another recent study by Mittal, Liu, Fujimori, and Shukla ( Citation 2018 ) assesses the mitigation costs of achieving global temperature stabilization well below 2°C and 1.5°C, using the AIM CGE model. However, both models are global and represent India as one region among many, in a standard CGE framework of perfect markets ill-suited to the country’s specificities. They also lack the technology-rich information of bottom-up approaches to frame their outlooks on India’s energy futures.
This underlines the need for hybrid models that combine the strengths of top-down (TD) and bottom-up (BU) approaches (Hourcade, Jaccard, Bataille, & Ghersi, Citation 2006 ). Pradhan and Ghosh ( Citation 2012 ) make some attempt in this direction by building an original social accounting matrix and combining a CGE model with a global climate model to analyse the impact of carbon taxes and emissions trading on GDP growth. However, they fail to take account of energy flow statistics at any stage of their modelling endeavour. Shukla et al. ( Citation 2008 ), Fragkos et al. ( Citation 2018 ) or Vishwanathan, Fragkos, Fragkiadakis, Paroussos, and Garg ( Citation 2019 ) deploy soft-coupling strategies between BU models and TD models, but limit them to the one-way feeding of BU information into their TD models and do not consider feedbacks.
Our research attempts at further bridging the gap between BU and TD assessments of Indian development pathways. We develop a hybrid architecture that couples the AIM/End-use model of Indian energy systems and the IMACLIM model of the Indian economy and considers the feedback loops between the two tools. Additionally, IMACLIM-IND calibrates upon an original dataset reconciling national accounting, energy flow and energy price data. We apply the AIM/Enduse and IMACLIM architecture to the exploration of four scenarios: BAU, 2°C, sustainable 2°C, 1.5°C, to determine the implications of mitigation strategies on the energy systems and the economy of India. The second section of our paper outlines the methodology and data backing our analysis. The third section describes the architecture of our scenarios. The fourth section presents and discusses scenario results while the fifth section concludes.
Published online:
Figure 1. Iteration process.
A precondition to the relevance of such coupling was the construction of an original dataset hybridizing extensive energy/economy data from various sources (see Appendix A ). The resulting dataset (Gupta, Ghersi, & Garg, Citation 2018a ) has the advantage of acknowledging the heterogeneity of energy prices faced by different economic agents, as recorded by energy statistics. IMACLIM reflects this heterogeneity by considering agent-specific sales margins (Gupta, Ghersi, & Garg, Citation 2018b ). Both data hybridization and the heterogeneity of energy prices have non-marginal impacts on policy evaluation (Combet, Ghersi, Lefèvre, & Le Treut, Citation 2014 ; Le Treut, Citation 2017 ).
Table 1. IMACLIM-IND sectors.
The AIM/Enduse BU model provides a techno-economic perspective at the national level along with sectoral granularity. It is a linear cost-optimization model based on technology selection. The total cost of the Indian energy system is minimized under constraints of service demand, energy resource availability and material and other system constraints (Kainuma, Matsuoka, & Morita, Citation 2011 ). AIM/Enduse outputs cover energy demands, energy efficiency, capital intensity and technology substitution across sectors. Vishwanathan, Garg, Tiwari et al. ( Citation 2018 ) and Vishwanathan et al. ( Citation 2017 ) provide a detailed description of the assumptions and parameters backing the Indian AIM/Enduse model. The model has been calibrated to energy-economy data up to 2015 and runs in annual time steps to 2050. It is updated with new technologies including smart grids, electric vehicles, Carbon Capture, Utilization and Storage (CCUS), battery storage, improved coal technologies like Integrated Gasification Combined Cycle (IGCC), Pulverized Coal (PC) or Ultra Super Critical Coal (USCC) and advanced renewable technologies like solar with storage.
The IMACLIM model is a multi-sectoral dynamic recursive model Footnote 1 that pictures economic growth as proceeding from exogenous increases of labour supply and labour productivity. It is specifically designed to accommodate exogenous BU information on energy supply, demand and trade (Ghersi, Citation 2015 ), thereby renouncing micro-foundation of the producers’ and consumer’s energy supply and consumption behaviours in favour of forced technical coefficients. IMACLIM-IND extends the process to the capital intensity of important non-energy sectors, building on the annualized investment costs per unit output reported by AIM/Enduse for the iron & steel, cement, chemical & petrochemical, textile and aluminium sectors. Considering our implementation in single time steps from 2012 to 2030 and 2050, IMACLIM-IND renounces the ad hoc calibration of the standard accumulation rule and simplifies capital accumulation by assuming that the capital stock grows proportionally to investment flows, which are an exogenous share of GDP. Footnote 2 The consequence is that capital stock grows broadly in pace with efficient labour endowment (the dominant GDP driver). The rental price of capital adjusts to clear capital markets, considering substitution possibilities with labour in those sectors not informed by AIM/Enduse for their capital intensities.
IMACLIM-IND has two other specific features with important bearing on its results and their interpretation. The first is a flexible trade balance, to allow assessment of the impact of low-carbon pathways on trade, considering the weight of energy imports at the 2012 base year (10.0% of GDP). The standard model of a fixed (balanced) trade via flexible terms-of-trade effectively translates trade variations into general activity. We rather strive to estimate how our scenarios affect the current large trade deficit without forcing any exogenous trade balance outcome. Trade flexibility requires some assumption regarding the terms-of-trade. IMACLIM-IND adjusts them to force the purchasing power of the average wage to increase at the same pace as labour productivity. This is the condition for a stable unemployment rate (at its 2012 level) following a ‘wage curve’ specification acknowledging the observed correlation between the unemployment rate and the real average wage (Blanchflower & Oswald, Citation 2005 ). The policy interpretation of this specification is that of the Government of India taking measures to control the Indian exchange rate with a view to stabilize unemployment.
A second specific feature of IMACLIM-IND is its choice of macroeconomic closure. Rather than considering some exogenous savings rate and closing on investment (neoclassical closure of the standard CGE model), IMACLIM-IND considers a fixed investment effort (‘Johansen closure’ following Sen, Citation 1963 ) and closes on the households’ saving rate – taking account of the foreign saving capacity induced by the flexible trade balance. This specification means to reflect the significant level of intervention of the government of India in economic affairs: the government controls the country’s investment trajectory by adjusting its net transfers to households, either in the form of fiscal (public income) or social (public expenditure) reforms.
The consequence of both features is that the interpretation of IMACLIM-IND results differs from that of standard models. Notwithstanding the absence of a welfare index (which flows from the forcing of BU-sourced energy consumptions), the fixed investment trajectory induces stability of GDP via stability of capital accumulation across mitigation scenarios. Household consumption adjustments, which in effect finance this stability of GDP, are more relevant indicators of economic performance. However, the flexible trade balance also matters as it implies differentiated accumulation of foreign debt across scenarios. Consequently, we systematically report these two indicators when commenting upon our scenario results (see Section 4). Gupta et al. ( Citation 2018b ) provide a complete online description of the model.
Table 2. Scenario assumptions.
Business as usual (bau) scenario.
Our BAU scenario reflects current energy-economy system dynamics under constraint of the public policies of the National Action Plan on Climate Change (NAPCC) (PMCoCC, Citation 2008 ), the draft National Electricity Plan (NEP) ( Citation 2017 ), the Indian NDC (MoEFCC, Citation 2015 ) and the Perform Achieve Trade (PAT) scheme – a market-based mechanism for energy intensive industries to trade energy-saving certificates. NAPCC includes eight national missions led by various ministries in the areas of Solar Energy, Sustainable Habitat, Sustainable Agriculture, Enhanced Energy Efficiency, Water, Green India, Sustaining the Himalayan Ecosystem and Strategic Knowledge for Climate Change. These national plans set the broad objectives for all 32 States/Union Territories of India to prepare their respective State Action Plans on Climate Change (SAPCC).
Under its NDC, India has committed to two major quantitative objectives, namely, reducing national emission intensity to 33–35% below its 2005 level in 2030, and raising the contribution of non-fossil fuels to 40% of total power capacity by 2030 (MoEFCC, Citation 2015 ). Under the NAPCC, the Government of India (GoI) sets additional goals that include creating smart grids, improving the power system, building sustainable infrastructure and buildings, and generating on-grid and off-grid renewable power from sources like solar, wind, small hydro and bioenergy. All these goals are met endogenously in AIM/Enduse by way of capacity and technology-share constraints. These include increasing the modal share of railways and metros, the building of dedicated freight corridors and the penetration of electric vehicles (EVs). Households turn to energy-efficient technologies like solar cooking stoves. For details on implementation of policies sector by sector see Appendix B .
Low-carbon scenarios
The design of the 2DEG and 2DSUS scenarios builds on policies that allow containing Indian CO 2 emissions within a 115–147 billion tons (Bt) CO 2 budget between 2011 and 2050 (CD-Links, Citation 2019 ; Tavoni et al., Citation 2014 ), while in the BAU scenario it goes up to 165 Bt CO 2 . This is line with global models (van den Berg et al., Citation 2019 ), which set a range of 90–125 Gt-CO 2 . Though our analysis is limited to CO 2 emissions outside those from afforestation, reforestation and land-use change, other emissions are important for India, particularly CH 4 emissions from agriculture and livestock, which employ the majority of the Indian population. India also aims to increase its carbon sinks through afforestation. In fact, the Indian forested area has increased over recent years due to the national policies of sustainable forest management and afforestation. The 2DEG scenario does not put any constraint on coal use, which leads to coal remaining the mainstay of the Indian energy system. The 2DSUS scenario on the other hand assumes complete phase-out of coal in power generation by 2050.
The 1.5DEG scenario envisions further measures still, which cap the 2011–2050 carbon budget below the 115 Bt CO 2 estimated as India’s share of a global 1.5°C-compatible budget (CD-Links, Citation 2019 ; van den Berg et al., Citation 2019 ). Using the carbon constraint option in AIM/Enduse model, the model endogenously picks up more efficient coal and gas technologies, renewables and micro-grids based on cost optimization and technology availability. Energy intensive sectors like aluminium, steel or cement see their activities reduce thanks to developments in material sciences that could change the profile of end-use materials as we know them today. They pick up transformative technologies like switching to pulverized coal injection and top recovery turbine in the iron & steel sector. This implies increased capital intensities, which AIM duly reports and which we use to shape the capital intensity trajectories of some non-energy sectors in IMACLIM. The 1.5DEG scenario is based on the premise that technology and behavioural lock-ins are avoided and that carbon-saving technical change happens from the very beginning of our time horizon.
Converging AIM/Enduse and IMACLIM-IND without any assumption on market instruments inducing the modelled transformations amounts to considering a command-and-control implementation of scenario constraints. The optimization framework of AIM additionally implies that the policy maker has perfect information regarding the merit order of energy supply and end-use technologies.
Figure 2. Indian CO 2 emissions, GtCO 2 .
Figure 3. Final energy consumption of productive sectors.
Figure 4. Final energy consumption of households.
Figure 5. Energy inputs into power generation.
Table 3. Macroeconomic results of four scenarios, 2030.
Table 4. macroeconomic results of four scenarios, 2050..
The scenarios affect the share of household consumption in GDP in ways that nuance these GDP results. They decrease this share by 4.4–5.0 percentage points in 2030 and by 1.6–1.9 percentage points in 2050. Footnote 4 This is the direct consequence of the increased trade balance i.e. decreased foreign savings under the assumption of Johansen closure on consumption (see Section 2). Indeed, mitigation has a strong impact on the trade balance, whose deficit recedes by up to 5 percentage points in 2030 and 1.8 percentage points in 2050, compared to BAU. In 2050, the weight of energy imports shifts from 7.3% of GDP, to 5.4% in the 2DEG scenario, 5% in the 1.5DEG and 4.7% in the 2DSUS scenario. This is an obvious consequence of the decline of fossil fuel energy uses like that of oil fuels in the transport sector, of natural gas in industry and of high-grade coal in steel production. Crude oil and other refined fuel imports decline from a 26% share of total imports in the base year to a 15% share in 1.5DEG in 2050. This amounts to foreign exchange savings of 620 billion USD over 2012–2030 from a reduction in just oil imports in the 2DEG scenario compared to BAU, and savings close to 1 trillion USD from 2012 to 2050. Through this impact on energy trade, mitigation positively affects the foreign debt of India by constraining it below or only slightly above current levels at 2030 and 2050 horizons. The contrast is high with the BAU foreign debt, which reaches close to 200% of GDP by 2030 and remains at that problematically high level in 2050.
Figure 6. (a) Share of non-energy sectors in gross value-added. (b) Share of energy sectors in gross value-added.
Table 5. Energy supply investment at projected horizons.
The objective of our study was to assess the macroeconomic implications of low carbon development pathways in India. We used a novel methodology of converging bottom-up (AIM/Enduse) and top-down (IMACLIM-IND) models for this purpose. Economy-wide and energy systems implications for India have hardly been assessed by linking national bottom-up and top-down models. Our work makes an important contribution to the existing literature on Indian pathways. We now derive policy-relevant insights from our results which could be useful for decision makers.
Our macroeconomic analysis of India’s pathways to 2030 and 2050 across four scenarios BAU, 2°C, 2°C sustainable and 1.5°C delivers the following results. We find the impact on economic growth of tightening decarbonization targets to be slightly positive, under condition of a maintained investment effort. We also find that decarbonization has a strong bearing on India’s foreign debt via reduced energy trade deficits. Even a stringent 1.5DEG scenario with India’s carbon budget cut by two-thirds compared to BAU results in a slightly higher GDP and a foreign debt contained at 102% of GDP in 2030 and 122% of GDP in 2050. This partly reflects the balance of mitigation costs and energy savings as depicted by our AIM/Enduse model of Indian energy systems. It also stems from the specific Indian energy-economy context, where fossil fuel imports mobilize a substantial share of GDP. Shifting away from fossil fuel based energy systems results in foreign exchange savings of 1 trillion USD from just oil imports over 2012 to 2050. Low-carbon scenarios would thus provide the co-benefit of energy security, as reliance on energy imports reduces thanks to the combined penetrations of domestic non-fossil fuel energy sources and energy efficiency technological innovations. The trade-off here is meeting the higher capital cost to reach the energy intensity targets and avoid locking in capital in inefficient technologies early on. The investment requirements in low carbon scenarios increase compared to the BAU scenario as a result of the shift towards clean technologies. Our results indicate that an energy supply investment of 131 billion USD/year would be required from 2012 to 2050 to achieve low carbon energy systems.
Low-carbon scenarios also raise the share of energy sectors in gross value-added. Further structural transformation in energy systems for low carbon growth is required with renewables constituting a major share of energy consumption, reduced energy demand from industries, commercial sector and households, and employment of clean coal technologies. The nature of those adjustments to the AIM model that allow striking a 1.5°C scenario at little macro-economic cost demonstrates that policymakers can focus on improving energy efficiency and reducing end-use demand. The energy sector transformation might engender conflicts between the entrenched players in the fossil fuel sector and the emerging non-fossil fuel based technology businesses. Policymakers need to balance the interests of both the parties by providing necessary support to both.
In conclusion, low carbon growth is contingent on the availability of transformative technologies and the necessary capital for deploying them. In a developing country like India, where compelling development needs have to be balanced with mitigation targets, international finance may play a vital role in achieving low-carbon development.
a The Renewable Energy sector groups solar, wind, nuclear and hydrogen power-generation options. Its only use is as Electricity sector input.
AMRUT: Atal Mission for Rejuvenation and Urban Transformation.
a For air conditioners, refrigerators, distribution transformers, tubular fluorescent lights, ceiling fans, TVs, refrigerators, washing machines, gas stoves, water pump sets (Garg, Dhar, Kankal, & Mohan, Citation 2017 ).
b Aluminium, cement, chlor-alkali, fertilizer, iron & steel, paper & pulp, thermal power plant, textile, railways, electricity DISCOMS (distribution companies) and refineries (in all 737 DCs under PAT)
c 770 million LED bulbs to domestic consumers (UJALA programme); 80 million beneficiaries (Ujjwala scheme).
Related Research Data
The IMACLIM model benefits from support of the Chair Long-term Modelling for Sustainable Development (Ponts Paristech-Mines Paristech) funded by Ademe, Grt-Gaz, Schneider Electric, EDF, RTE, Total and the French ministry of Environment. We acknowledge the valuable comments of 3 anonymous reviewers and Dr Depledge. All shortcomings of this paper remain of course our own.
No potential conflict of interest was reported by the authors.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
1 Coupling IMACLIM to AIM/Enduse therefore implies mixing intertemporal optimization of energy investment decisions with simulation of non-energy investment decisions. We consider this a minor, acceptable inconsistency because of the uncertainty surrounding the optimization parameters of AIM/Enduse and conversely, the possibility to describe the exogenous investment decision of IMACLIM as resulting from intertemporal optimization by selecting appropriate parameters. The latter is particularly true in the case of our IMACLIM-IND implementation in single time-steps to 2030 and 2050 horizons.
2 Calibrating the accumulation rule would require settling on ad hoc assumptions regarding the base-year capital stock, the depreciation rate and the pace of investment increase between the base year and projection horizon, to produce ‘sensible’ capital stock estimates (estimates broadly in line with real GDP growth) at projection years. Our way of bypassing the accumulation rule implies that IMACLIM-IND performs comparative statics rather than dynamic recursive analyses, although not at one single year but between two distant years.
3 All our mitigation scenarios thus develop under constraint of global climate action leading to above-2°C warming with high probability. Considering global action increasing in parallel to Indian action would induce considering depressed fossil energy import prices, which would further reduce the costs (or increase the benefits) of the scenarios, although marginally increasing Indian emissions.
4 Additionally, IMACLIM-IND has no way to mark the higher costs of the more efficient equipment ascribed by AIM/Enduse to more ambitious scenarios. This means that identical consumption levels in scenarios with higher mitigation ambitions may mask lower welfare.
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Appendix A Data hybridization
The data hybridization process outlined below is the first step towards building an original Energy-Environment-Economy (EEE) modelling capacity for determining Indian mitigation pathways. The goal is to reconcile the energy balance and national accounting statistics to produce a dual accounting of energy flows, in volume and money metrics, using agent specific pricing of homogeneous energy goods. This is one of the salient improvements over standard computable general equilibrium techniques where all agents are assumed to buy homogenous energy goods at same net-of-tax price. The process is based on two guiding principles for maintaining consistency of data. First, both physical and money values should follow the conservation principle that is resources and uses must be balanced. Second, the physical and money flows are linked by a unique system of prices implying that the money values can be obtained by multiplying the volumes by the corresponding price. Further, there are two rules guiding the methodology: one, the economic size is always preserved while correcting the statistical gaps; second, the purchasing price heterogeneities faced by different sectors and households is taken into account.
Reorganizing the original energy balance data (in kilo tons of oil equivalent, Ktoe) and energy prices (in Lakh rupees/ktoe) into the sectoral distribution matching the input-output table (IOT) from national accounting. This not only involves reallocation of physical energy flows of energy balance to production sectors and households, but also entails re-interpretation of the flows in national accounting terms. In other words, it involves sorting out the flows that indeed correspond to economic transaction between national accounting agents. For instance, attributing the autoproduction of electricity to the accounting sectors; considering only the commercial flows especially in case of energy industry own use in energy balance; adjusting the data on international bunkers since energy balance reports data based on geography while IOT reports data based on national accounting rules.
Multiplying the volumes with corresponding prices to obtain energy expenses at the same level of disaggregation as IOT.
Plugging of the matrix of energy expenditures into original IOT and adjusting the other values of the table such that accounting approach is not disturbed and the total value added of domestic production remains same. This is done by: first, adjusting difference in uses and corresponding resources for energy sectors to the non-energy expenses on pro rata basis; second, by adjusting the difference in original and recomputed expenditures for the non-energy sectors to the most aggregated non-energy good which is ‘other services’ mostly.
Each of these steps must be adapted to the specifics of the energy systems of the region chosen for analysis. It is the purpose of this note to describe how we adapted them in the case of India.
We constructed the commodity × commodity Input Output (IO) table for 65 commodities using the supply and use tables for the year 2012–13 recently released by the Central Statistical Office (CSO), the government organization responsible for coordinating statistical activities in the country. The IO table was constructed by manipulating the supply use matrix, with 140 products and 66 sectors (CSO, Citation 2016 ), based on industry technology assumption. The data on energy volumes was taken from IEA and AIM/Enduse model. Several government reports and company websites were referred for the data on heterogeneous prices for energy goods.
The decision regarding energy and non-energy sectors was taken based on the specific features of Indian energy sectors and Indian economy. For instance, we take cement and aluminium manufacturing sectors since these are the two most energy intensive sectors in the Indian economy. Government of India (GOI) has specified these sectors as the focus areas for meeting the energy efficiency targets for instance in the policy named Perform Achieve Trade (PAT) scheme. The further decision to add the renewable sector was based on the policy framework being pursued by GOI wherein the target has been set to achieve 175 GW renewable energy capacity by the year 2022. We have taken the 22 products (see Table 1 ) for the hybridization procedure.
The coal expenses going into electricity sector in original IO were just 25% of those obtained by multiplying available price and volume estimates (hereafter the ‘volume × price’ approach). The official documentation on IO reveals that the coal expenses have been calculated using the inputs of electricity distribution companies like state electricity boards, departmental commercial undertakings of central and state governments and private electricity companies. On the other hand, the volumes in energy balance have been computed using the coal controller’s reports which give the numbers for the output of coal companies going into electricity generation sector. Literature shows that thermal efficiency of coal plants in India is 30% on average (Colin, Citation 2015 ). This accounts for possible explanation for the mismatch in coal expenses. The remaining differences can be attributed to the fact that several companies like Adani, Tata, Reliance and BHEL generate electricity as a secondary output. Further coal companies like Neyveli Lignite corporation (NLC) are also generating electricity. Due to the above factors, we take the expenses obtained from volume × price rather than those from national accounting IO table.
Another source of difference in IO and volume × price coal expenses is the phenomenon of captive coal mining (introduced in the year 1993) implying that coal is being produced by sectors like power, iron and steel and cement for their own use. The purpose of the government in allowing private companies into coal mining is to boost the thermal power generation in order to meet the increasing power demand. Though the percentage of captive coal (12%) is not significant compared to total coal produced, it is expected to gain significance in future (Coal Controller’s Organisation, Citation 2015 ). In order to treat the goods properly, the costs of captive coal mining must be transferred to the coal sector, which is actually the sum of coal mining activities regardless of which sector undertakes the activity. The process involves following steps: (1) the coal expense of captive mines operators is increased via a price × volume approach using the appropriate coal cost net of profit as the price; (2) all cost elements of the coal mining ‘sector’ (activity) are increased homothetically in order to rebalance rise in sales; (3) the costs of the captive mine operator for each item are reduced such as to exactly compensate the cost increase in the coal mining activity. The broad idea is to transfer the costs of the captive coal mining to the general coal mining activity, and to treat captive coal expenses as any other coal expenses. The question of an increase of the share of captive mining in coal expenses can be taken care while modelling pathways by assuming a decrease of the average profit rate of coal mining.
Next is the trading issue that is natural gas being bought by the refined petroleum sector to be sold to consumers. The refined petroleum products expenses going into chemical and electricity sector from original IO is 2 and 1.5 times respectively of the expenses obtained by volume × price approach. On the other hand, natural gas expenses (original IO) into electricity sector is just 0.3% of the expenses from other approach. Natural gas expenses into chemical sector (original IO) are 37% of those obtained from volume × price approach. Refined petroleum products sector appears to play a role of trader, buying a huge amount of natural gas and selling it back to other businesses without consuming it. This implies that the switch from an industry × industry to a commodity × commodity matrix is not complete, there remains some natural gas sales covered by the refined pet products ‘sector’ of the commodity × commodity matrix. In such a case IO values can be misleading, hence we decide to use volume × price data.
Bulk of the total energy consumption by households in India is for cooking purpose. Biomass such as firewood, cowdung and agricultural residues, which are normally collected by the households themselves (Pachauri, Citation 2007 ) is the most commonly used fuel for this purpose. The data on household expenditure on biomass is obtained from the National Sample Survey Office (NSSO), which conducts regular socio-economic survey. Though we have an estimate of the monthly per capita expense on firewood and cow-dung and percentage of people using these fuels, it is hard to get an estimate for the non-market consumption that is number of people collecting the biomass themselves. We compare the household expenses on forestry products specified in IO table (1.3% of total household expenditure) with the firewood and cowdung (1.83% and 0.16% of total household expenditure) consumption from NSSO data. The two numbers seem compatible considering the fact that some proportion of biomass is non-marketable. Hence, we decide to take IOT household expense on forestry sector for representing household expenditure on biomass in the final hybrid matrix.
Another noteworthy issue was the fact that there are significant amount of non-energy uses of some petroleum products like petroleum coke, lubricants, naphtha and other non-specified oil products in India as opposed to the situation in developed economies. While bitumen non-energy uses were adjusted in the construction sector, petroleum coke was adjusted in cement sector since it is one of the largest consumers of petroleum coke in India. Remaining petroleum coke and other non-energy uses of petroleum products were distributed on pro-rata basis of the respected unaccounted share in volume × price compared to IO expense across all sectors.
Considering the increasing prominence of renewables in the Indian energy policies and the specific tariffs and incentives for this sector, it was added as a separate sector in the matrix. The costs into renewables sector were assumed proportional to the electricity costs after deducting the fossil fuel costs. This assumption is taken for lack of more data on the cost structure. The uses of renewables were calculated based on the feed-in tariff provided by the government and the volumes from the energy balance data.
Appendix B Sensitivity analysis
We conduct sensitivity analysis of our key macroeconomic results to check their variations with changes in exogenous parameters shaping foreign trade flows and household consumption.
Price elasticities of imports and exports have little impact on economic activity measured by real GDP ( Tables B1 and B2 ). The reason is our choice of Johansen closure, which warrants maintained investment effort trajectories (as GDP shares) in all scenarios. Domestic savings thus mechanically compensate the fluctuations of foreign savings mirroring those of the trade balance. Household consumption therefore fluctuates exactly opposite to the trade balance, which improves under lower elasticities and deteriorates under higher elasticities, considering the appreciation of terms-of-trade in all scenarios at both horizons for our central parameterization. The slight GDP adjustments only reflect relative variations of the investment price index and the GDP price index. The trade balance fluctuations induce significant fluctuations of the foreign debt, however not differentiated enough to question our qualitative result of the 2DEG scenario significantly improving the Indian economy’s external position.
Table B1. Sensitivity to price-elasticities of exports (11 goods).
Table b2. sensitivity to price-elasticities of imports (11 goods)..
Conversely, not only the foreign debt but also GDP appear sensitive to variations of the income elasticities that shape household demand of 7 out of the 22 goods of our model, despite the Johansen closure ( Table B3 ). This is because these income-elasticity changes induce structural change via significant shifts of households’ consumption budgets. The ‘Other services’ sector, which captures close to 100% of the budget allocated to non-energy goods without income-elastic specification, has a comparatively high labour efficiency. Structural change in its favour via lower income-elasticities of the 7 income-elastic goods significantly improves real GDP, and conversely.
The impact on the foreign debt via that on the cumulated trade deficits is massive. In the case of the BAU, higher income-elasticities i.e. a lesser structural change in favour of labour-extensive services leads India towards an unsustainable foreign debt above 300% (204.9% + 104.1%) of its GDP in 2050. This could not but induce macroeconomic shocks outside the scope of our modelling tool. The 2DEG scenario mitigates this risk but still see the debt overcome 220% (133.0%+91.0%) of GDP in 2050 in case of lesser structural change.
Table B3. Sensitivity to income-elasticities of household consumptions (7 goods).
Appendix c scenario implementation, table c1. scenario policies, corresponding aim/enduse drivers/constraints, results and insights., appendix d soft-linking convergence process.
In the Tables D1 – D5 below we provide the values of energy-economy variables in the pre-iterations and post-iterations stages of IMACLIM-IND and AIM/Enduse coupling process. The rationale behind the coupling of bottom-up and top-down models is investigated in Hourcade et al. ( Citation 2006 ) and Ghersi ( Citation 2015 ). This approach benefits from the strengths (and avoids the weaknesses) of both models.
Table D1. Aggregate energy consumption mix of productive sectors pre and post iterations.
Table d2. macroeconomic results pre and post iterations., table d3. power generation mix pre and post iterations., table d4. household energy consumption mix pre and post iterations., table d5. shares of industrial sectors in total output pre and post iterations., reprints and permissions.
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Impact of Macroeconomic Variables on Economic Performance: An Empirical Study of India and Sri Lanka
35 Pages Posted: 11 May 2011
Gagan Deep Sharma
Guru Gobind Singh Indraprastha (GGSIP) University
Sanjeet Singh
UCRD, Chandigarh University; Chandigarh University
Gurvinder Singh
BBSB Engineering College
Date Written: May 9, 2011
Macroeconomic variables (e.g. economic output, unemployment and employment, and inflation) play a vital role in the economic performance of any country. For the past three decades, evidence of key macroeconomic variables helping predict the time series of stock returns has accumulated in direct contradiction to the conclusions drawn by the Efficient Market Theory. The majority of research concentrates on the financial markets of the developed countries, which are efficient enough and do not suffer from the inefficiency problems found in less developed countries. Considering this matter, the subject of financial markets in developing countries still needs lengthy analysis and more research attention. This research studies the pattern of CPI, WPI, GDP, GNI and Rate of interest in India and Sri Lanka for the year 2002-2009 while also analyzing the impact of macro-economic variable on GDP growth in India vis-à-vis Sri Lanka. The econometrics tools (e.g. unit root test, Granger Causality Test, cointegration test, vector auto regression, Variance decomposition, and Variance Decomposition Analysis) have been used for the analysis purpose.
Keywords: macroeconomic variables, efficient market, stock returns, developing countries, VAR, unit root test
JEL Classification: G14, G15
Suggested Citation: Suggested Citation
Gagan Deep Sharma (Contact Author)
Guru gobind singh indraprastha (ggsip) university ( email ).
Sector 16 C, Dwarka Delhi, Delhi 110078 India
UCRD, Chandigarh University ( email )
National Highway 95 Punjab Mohali, IN Punjab 140413 India
Chandigarh University ( email )
Gharuan Mohali Mohali, Punjab 140413 India
BBSB Engineering College ( email )
Near Jyoti Saroop Gurudwara Fatehgarh Sahib, Punjab 140407 India
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For each news article A containing w number of words, we compute an article-wise net sentiment score for each article in our dataset as follows:
where W+ and W- denote the pre-defined positive and negative list of words based on the Loughran-McDonald lexicon. For instance, the sentiment score for typical headlines appearing in business news dailies, such as those shown below, would be:
The article-wise sentiment score is then aggregated for all articles each day of the month to arrive at a monthly sentiment index (normalized using the total number of articles (N) per month):
At this stage, we de-seasonalize the index to filter out any deterministic seasonal pattern that may have crept in the data, and then detrend the index using a standard Hodrick-Prescott (HP) filter to arrive at the final index. An index value greater than zero signifies net positive public sentiment and vice-versa. The final Net Sentiment Score (NSS) Index is plotted in Chart 2 .
III.3. Internet search intensity-based Uncertainty Index
The third index is computed based on internet search intensity of 70 keywords pertaining to fiscal, monetary and trade policies via Google Trends for the period January 2004 to February 2019. Google Trends provides search volume intensity for a given word which is a number in the range of 0 to 100, indicating relative search volume of a given word relative to the total search volume. The maximum value 100 corresponds to a particular time where the relative search volume of a given keyword was maximum in the entire sample. Increased interest in any particular topic results in increased internet searches, thus resulting in a higher index value. This forms the underlying principle for using Google Trends to construct an uncertainty index. While the topic (or keyword) may not necessarily carry any negative or uncertainty connotation by itself, increased ‘uncertainty’ in the economy (often caused due to unexpected events or policy announcements) may prompt people to search for information related to the concerned topic on the internet. To compute an uncertainty index using this approach, one must first select the list of keywords for which internet search intensity data is to be obtained and then combine the search volumes to compute the index.
We select a set of 70 keywords ( Appendix B ) representing different types of policies and compute their search volume intensity (SVI). As our study is focused on computing uncertainty index for India, SVI for selected keywords pertains to internet searches made within India only. It is important to note here that comparing SVI between keywords is not meaningful due to their relative scaling with itself. A keyword with higher SVI relative to another keyword at time t may have a lower actual search volume compared with a frequently used word, since SVI are compared with maximum search volume of itself.
To solve for this limitation and obtain a single uncertainty index, SVIs of all keywords were consolidated following Castelnuovo and Tran (2017). Since SVI does not contain the absolute magnitude of search volume, we compute SVI for each selected word relative to the SVI of a benchmark word (economy in our case). The SVI of the benchmark word (k b ) computed independently is denoted by SVI* b . Since Google Trends permits inputting only five words in every instance, the fifth word is kept as a benchmark word for the purpose of consolidation; thus, only the first four words are changed and fifth remains as a benchmark in a set j. The SVI of word i in a set of words j is denoted as SVI ij and of benchmark word k b in set j as SVI bj. It is important to note that SVI bj would vary from one set to another. Notwithstanding that it pertains to the same benchmark word. This is because of the relative nature of the SVI, which makes the highest searched term in the new set of words automatically valued to have a maximum of 100. Thus, to overcome this and construct the relative SVI for each word, using SVI bj , we compute the search intensity of each word which is finally used to construct the index as a ratio. A list of keywords and computational details for the index are provided in the Appendix B and C . In the last step, Google Uncertainty Index 5 (GUI) for India is computed by summing the relative SVI of all the keywords ( Chart 3 ):
III.4. A Combined Uncertainty Index for India – INDIA-UI Index
A reasonably good measure of uncertainty should be able to capture major domestic and global events that are expected to impact the economy. The newspaper-based index captures the views of market commentators and journalists on economic events and shocks. The sentiments-based index, on the other hand, suggests the nature of the overall sentiment expressed in such articles - positive or negative. As mentioned earlier, negative public sentiments and higher uncertainty coincide with each other. Lastly, when economic or financial shocks occur, people try to ‘search’ for more information to get clarity about the shock and its likely impact. This behavior is captured by the Google Trends-based index. We note that both the EPU and the GUI peak around all major domestic/global events indicating their ability to capture major events that are expected to lead to heightened uncertainty in the economy. However, no such pattern is discernible in the sentiments-based index, although it does remain negative during 2010-13, a phase of economic slowdown in India, turning sharply positive around the parliamentary elections in 2014, remaining positive almost till the end of 2016. Using a standard PCA approach, we combine the indices to arrive at our final uncertainty index, namely the ‘India Uncertainty Index’ or in short, the INDIA-UI Index ( Chart 4 ). Both domestic and global politico-economic events are captured by the index. It can also be seen that uncertainty in the Indian economy generally increases and remains high during the recession. This broad countercyclical trend – observable across other developing and developed countries – is recognized as one of the pervasive characteristics of uncertainty (Bloom, 2014).
IV. Uncertainty and Indian Macroeconomy - Empirical Analysis
In this section, we devote our attention to analyse the dynamic impact of uncertainty, as captured by INDIA-UI, on the Indian macroeconomy. Uncertainty, notwithstanding its source, may impact financial markets as well as the real economy leading to economy-wide adverse effects, such as heightened risk, volatility along with sharp decreases in investment, hiring and output.
Chart 5 reports the sample scatter plots of INDIA-UI (shown on the x-axis in each plot) along with India-VIX index, risk premium 6 , monthly returns on NSE Nifty index, financial conditions index 7 , USD-INR exchange rate and net foreign institutional investment (net FII) for India. The VIX index and risk premium, both conventional risk measures based on financial markets, show a strong positive correlation with uncertainty. Not surprisingly, financial conditions seem to deteriorate during times of higher uncertainty. The stock market (NSE Nifty) returns are lower and it seems that the Indian Rupee (INR) also tends to face depreciation pressures against the US Dollar (USD) when uncertainty increases, although the observed correlation is not very strong. These relationships, though, are broadly in sync with the empirical literature on the impact of uncertainty on financial markets.
Further, INDIA-UI (x-axis) is plotted alongside macroeconomic variables (all real variables taken in year-on-year (YoY) percentage change terms), namely, gross fixed capital formation (GFCF) as a measure of overall private investment; gross final private consumption as a measure of total private consumption; total bank credit as a measure of lending activity; and, gross domestic product (GDP) as a measure for overall economic activity ( Chart 6 ). Similar to other countries, uncertainty correlates negatively with economic activity in India. Therefore, on an average, higher the uncertainty, lower is the private consumption, bank credit, private investment and output. This underlines the strong procyclical nature of uncertainty.
As a next step, we employ the Granger causality test to understand the predictive relationship i.e., lead-lag relationship between uncertainty and indicators of risk, investment and output discussed above. The feasibility of using uncertainty index to forecast other economic indicators can also be determined from this exercise. Test results have been provided in Appendix Table A1 . The test fails to reject the null that uncertainty does not granger cause financial market indicators. On the contrary, the causality (in granger sense) runs from financial markets to uncertainty. This may suggest that financial markets are ahead of newspapers and economic agents in picking up early signs of any impending risk and/or uncertainty in the economy. Broadly in line with the literature, uncertainty granger causes real macroeconomic indicators, such as investment (GFCF), consumption (PFCE) and real GDP growth.
IV.1. Dynamic Impact of Uncertainty Shocks
To understand the macroeconomic impact of uncertainty shocks on Indian financial markets and the broader real economy, we estimate a model based on the local projections (LP) framework proposed by Jordá (2005) as follows:
where h = (1, 2…, H) and Y t is a set of endogenous variables. In its basic form, local projection framework consists of sequential regressions of endogenous variables shifted several steps ahead. In other words, this involves directly regressing the variable of interest on the shock, controlling for other variables (Nakamura and Steinsson, 2018). To construct impulse response functions (IRFs) from this approach, separate regression for each forecast horizon (h) must be estimated:
In contrast, standard vector autoregressions (VARs) are based on a global approximation of the data generating process (DGP) and thus uses the estimated dynamics of the entire system to ‘iterate’ the response of the concerned variable to a shock. In such a scenario, estimated IRFs from a VAR could be biased if the underlying VAR is mis-specified. The LP method has been shown to be robust to model mis-specification error and can be easily estimated using ordinary least squares (OLS) technique.
We set up two models – one focused on private investment and another on overall economic activity – and estimate impulse responses (IRs) using the LP method to summarize the dynamic impact of uncertainty shocks on financial and macroeconomic variables. The model, variable and sample details are provided in Table 2 . To ensure that the shocks are well-identified and uncertainty shocks are orthogonal to other stochastic elements in the econometric model, we rely on a Cholesky Decomposition with the same ordering as variables mentioned in Table 2 . This lets us gauge whether uncertainty shocks foreshadow weaker macroeconomic performance in terms of increased risk, lower investment and output growth in the economy.
Quantifying the impact of uncertainty shock on investment activity, Chart 7 shows the model-implied responses of risk premia, weighted average lending rate (WALR) and private investment activity (GFCF) to a one standard deviation shock to uncertainty 8 . The results of the model suggest that there is an instantaneous increase in risk premia following an uncertainty shock. Similarly, investment activity also falls by around 2.0 per cent after an uncertainty shock. The increase in risk premia tends to dissipate quickly but the decline in investment activity is prolonged up to four quarters after which the impact is found to be statistically insignificant. The impulse response of investment to lending rates turn statistically significant from second quarter onwards implying the dampening and sustained impact of monetary policy on investment that works mainly through the lending rate channel 9 . The broad results are found to be similar when other financial market variables, such as change in NSE Nifty Index or India VIX Index, are used in place of risk premia. The overall results, in line with earlier studies (Anand and Tulin, 2014), seem to suggest that uncertainty is negatively associated with investment activity in India.
As far as the second model is concerned, Chart 8 shows the response of real GDP growth for India to uncertainty shocks. Like investment, overall economic activity (real GDP) also witnesses a sharp fall in response to an uncertainty shock triggering almost a 0.8 per cent loss in growth. The impact is, however, sustained only till the third quarter. Ghosh et al. (2017) find that uncertainty shocks lead to an increase in inflationary expectations in the economy, captured in the form of survey-based expectations. If inflationary expectations remain high for a sustained period, it is likely to result into an increase in actual inflation. Arguably, in our case, uncertainty shocks also seem to cause an increase in actual inflation, albeit after a prolonged gap of more than a year i.e., in the 5th and the 6th quarter. Thus, we may conclude that, in addition to impacting investment activity, uncertainty also negatively impacts overall economic activity in India.
V. Conclusion and Way Forward
The importance of uncertainty in the evolution of financial markets and macroeconomic conditions of a country has been highlighted in various studies. In this paper, we aimed to develop alternative uncertainty indices for India. We constructed three uncertainty indices based on newspaper articles, sentiment analysis of news articles and internet search intensity, respectively. To capture overall uncertainty in the economy, a single index was constructed using a principal components approach. This index captures views of news media as well as economic agents and can capture both domestic and international events.
The validity of uncertainty index for India is assessed in terms of its impact on financial markets as well as the real economy. As the theory suggests, uncertainty is positively correlated with risk measures while it shows a negative relationship with measures of economic activity in India. Based on granger causality test, financial markets appear to factor-in uncertainty in advance, while uncertainty seems to ‘lead’ the changes in investments and consumption. Using a robust local projections-based econometric framework, we assess the impact of uncertainty shocks on financial markets and the real economy. Results suggest that financial markets, private investment, inflation and overall economic activity are negatively impacted by heightened uncertainty. The findings are in line with results obtained in other country-specific studies. From a policy perspective, our results suggest that policymakers can use the information on uncertainty for devising policy framework and institutional arrangements that foster sound and predictable policies.
The creation of a novel dataset and automated algorithms to compute uncertainty indices undertaken for this study should pave way for further research on uncertainty within and outside the Reserve Bank. For instance, it is now possible to create specific indicators on uncertainty related to Trade Policy, Fiscal Policy, Monetary Policy, Regulatory Policy etc., using our dataset and algorithms. It may also be possible to compute state-level indicators of uncertainty, which can be used to study the impact of uncertainty on investment flows and medium-term economic activity at the state-level in India. Lastly, such uncertainty indices can also help strengthen policy simulation exercises to study the impact of low/high uncertainty scenarios and improve near-term projection of macroeconomic variables which exhibit high degree of sensitivity to uncertainty.
@ Nalin Priyaranjan ( [email protected] ) and Bhanu Pratap ( [email protected] ) are Managers in the Department of Economic and Policy Research (DEPR), Reserve Bank of India (RBI).
* The authors are grateful to Sitikantha Pattanaik and Harendra Behera for their constant guidance and support. Authors also express their gratitude to Dr. Pushpa Trivedi (IIT, Bombay), Indranil Gayen (RBI) and other participants at the DEPR Study Circle Seminar for providing insightful comments on the paper. Suggestions from Anagha Deodhar (ICICI Securities) on an earlier draft of this paper are also thankfully acknowledged. Views expressed in this paper are those of the authors and not of the institution to which they belong.
1 Bloom, Baker and Davis (2016).
2 “How does Policy Uncertainty affect Investment?”, Chapter 6, Economic Survey 2018-19.
3 The newspaper archives were accessed from ProQuest database.
4 See Appendix B for the complete list of keywords.
5 The index is de-seasonalized using X-13 ARIMA and de-trended using standard HP filter to deal with any deterministic seasonality or trend that may have appeared in the index.
6 Calculated as the spread between 5-year AAA-rated corporate bond yield and 5-year government bond yield.
7 Citi Bank Financial Conditions Index (Bloomberg).
8 For this paper, we have used localirfs add-in program (EViews) provided by Ocakverdi (2016). Complete model-implied impulse responses have been provided in the appendix.
9 See Appendix Chart A1 .
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Appendix A: Results
Appendix B: Keyword Lists for Newspaper-based Index
EPU index is constructed following Baker et al. (2016) approach by searching for certain keywords in each newspaper article. Each news article was then classified by them as signaling uncertainty if it contained at least one keyword each from sets Economic (E), Policy (P) and Uncertainty (U) given below:
Internet search intensity (via Google Trends) based uncertainty index for India (GUI) is constructed using internet search intensity of different set of keywords pertaining to Trade, Monetary and Fiscal policies ( Table B2 ). Irrelevant topics are removed by ‘-’ operator.
Appendix C: Computation of Internet search intensity-based Index
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