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India’s financial sector has faced many challenges in recent decades, with a large, negative, and persistent credit to GDP gap since 2012. We examine how cyclical financial conditions affect GDP growth using a growth-at-risk (GaR) approach and analyze the link between bank balance sheets, credit growth, and long-term growth using bank-level panel regressions for both public and private banks. We find that on a cyclical basis, a negative shock to credit or a rise in macro vulnerability all shift the distribution of growth to the left, with lower expected growth and higher negative tail risks; over the long term, the results indicate that higher credit growth, arising from better capitalized banks with lower NPLs, is associated with higher GDP growth.

• I. Introduction

India’s financial sector has faced many challenges in recent decades, including a rapid increase in non-performing assets (referred hereafter as non-performing loans, NPLs)after the global financial crisis (GFC) and the 2018–2019 run on non-banking financial companies (NBFCs). Credit growth has been weak for sometime, with a large, negative, and persistent credit to GDP gap since 2012. Just as the balance sheets of the financial sector started to gradually improve, the COVID-19 shock hit the economy, raising concerns about a new wave of NPLs and corporate defaults. At the same time, real GDP growth averaged 6.7 percent from 2011 to 2018, before moderating to 3.7 percent in 2019 (NBFC crisis) prior to the COVID-19 crisis. As India recovers from the pandemic, strong GDP growth will need to be sustained over the near- and medium-term for India to achieve many of its development goals.

This paper examines the nexus between the financial sector in India and economic growth and analyzes the potential impact of financial sector weakness on India’s economic growth. The financial sector could affect economic growth through multiple channels, with both cyclical and long-term effects. This paper focuses on these two channels and abstracts from the question of whether the size or structure of the financial system is important for growth. 2 Specifically, this paper first examines how cyclical financial conditions affect GDP growth using a growth-at-risk (GaR) approach ( Adrian et al., 2019 ) and assesses how financial conditions and credit risks could be associated with expected GDP growth going forward. Second, the paper analyzes the relationship between bank balance sheets, credit growth, and long-term growth using bank-level panel regressions for both public and private banks accounting for about 85 percent of total banking sector assets.

This paper is related to two strands of literature on financial sector and economic growth. The first strand examines the cyclical perspective. Adrian et al. (2019 ), Prasad et al. (2019 ) and IMF (2017) apply the GaR approach to use the information content of financial indicators to forecast risks to growth. Both fast-moving asset prices and slow-moving credit aggregates are found to be useful predictors of future output growth. For example, Ang, Piazzesi, and Wei (2006) highlight the importance of the yield curve, particularly short rate, in predicting GDP growth. Goodhart and Hofmann (2008) assess the linkages between credit, money, house prices, and economic activity in 17 industrialized countries over the last three decades and find that shocks to credit have significant repercussions on economic activity. Furthermore, recessions associated with financial crises are shown to have more severe and prolonged impact on the economy than typical recessions (see, for example, Claessens, Kose, and Terrones 2011a , 2011b ). The second strand of the literature examines the link between the health of the banking sector and real GDP growth. For example, Levine (2005) found that countries with large, privately owned banks tend to channel credit to private enterprises and liquid stock exchanges and experience faster economic growth. Using balance sheet data for international banks from a range of advanced economies, Gambacorta and Shin (2016) and Muduli and Behera (2021) show that well-capitalized banks enjoy lower costs of debt financing compared to more leveraged competitors, which in turn translates into higher annual credit growth and can impact monetary policy transmission.

The GaR analysis finds that higher credit and lower NPLs are associated with higher GDP growth in the near- and medium-term. More favorable credit conditions are particularly important during periods with low growth. A negative shock to credit and leverage could shift the entire growth distribution to the left, with lower expected growth and higher negative tail risks. The results for the second section of the paper confirm that in India, at least for private banks, the level of capitalization is strongly correlated with credit growth. The relationship for public banks appears to be much weaker. Additionally, it is when those banks which are better capitalized extend more credit that India observes higher real GDP growth, but only on the condition that these banks do not have excessive NPLs.

The paper is organized as follows. Section II examines the link between cyclical financial conditions and growth. Section III analyzes the link between financial sector and long-term growth. Section IV offers some concluding remarks.

II. Cyclical Financial Conditions and Near-Term Growth

• A. Data and Stylized Facts

A quarterly database is constructed for macro-financial data for India from 2000Q1 to 2021Q3. The database covers key macro-financial variables, including GDP growth, inflation, policy rate, bond yields, sovereign spreads, stock prices, credit growth, credit to GDP gap, the NPL ratio, world growth, oil prices and exchange rates, and other macroeconomic variables. The database draws from multiple sources, including Haver Analytics, Reserve Bank of India, Central Statistics Office, International Monetary Fund, Bank of International Settlements, Ministry of Statistics and Programme Implementation, Bombay Stock Exchange, Energy Information administration/Chicago Mercantile Exchange, and Bloomberg. The detailed definition of the underlying data and sources can be found in Annex Table 1 .

The analysis focuses on the broad definition of credit that covers both bank credit and debt securities. The credit-to-GDP ratio peaked at around 106 percent in 2012 and declined to around 90 percent in 2021, while the bank-credit-to-GDP ratios currently stands at around 55 percent. Following a period of double-digit credit growth, the credit-to-GDP gap 3 turned negative from 2012 ( Figure 1 ). The decline in credit since 2012 was mostly driven by the deleveraging process of the corporate sector. Corporate credit growth slowed from a peak of close to 30 percent in 2008 to zero at its trough, with a sharper decline in corporate credit growth compared with the household segment. At the same time, the broad credit growth also experienced a sharper slowdown than bank credit growth, suggesting that the deleveraging process not only took place in the banking sector, but also in broader debt financing. The NPL ratio peaked at around 11 percent in 2017 but has since come down to around 8 percent.

Credit and Leverage

Citation: IMF Working Papers 2022, 137; 10.5089/9798400216404.001.A001

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• B. Methodology

The GaR analysis in the India economy follows closely the approach of Adrian et al. (2019 ) and Prasad et al. (2019 ). GaR provides a tractable and robust estimation of the severity and the likelihood of a sharp economic slowdown. The model uses information contained in financial prices and aggregates to identify macro-financial linkages and gauge financial vulnerabilities. Importantly, GaR captures the entire growth distribution at different future horizons—reflecting both downside and upside risks—in addition to central-scenario growth forecasts. The concept helps better understanding of the relative importance of key drivers of future growth.

The first step of GaR analysis involves aggregating the set of macrofinancial variables into economically meaningful groups (“partitions”). In this approach, five main partitions of macrofinancial variables are considered ( Table 1 ): 1) domestic prices, which capture the policy interest rate, 10 -year treasury bond yield, sovereign bond spread, and a change in stock prices; 2) credit and leverage, which includes credit growth, the credit to GDP ratio, the credit to GDP gap, and the NPL ratio; 3) macroeconomic vulnerabilities, which capture inflation, the current account balance to GDP ratio, and the short-term external debt to reserve ratio; 4) external prices, which include changes in oil prices and exchange rates; and finally 5) external macro that captures world GDP growth. These partitions are then computed using the principal component analysis (PCA) that aggregates information about common trends among these macro-financial variables.

Partition of Macro-Financial Variables

The second step of GaR uses a quantile regression approach to estimate the impact of financial conditions on different quantiles of real GDP growth in India. The following specification of the quantile regression is estimated:

where y t + h q captures the h quarter ahead GDP growth (year-on-year) for quantile q; X 1,t denotes the partition for domestic prices; X 2,t captures the partition of credit and leverage; X 3,t denotes the partition of macroeconomic vulnerabilities; X 4,t represents the partition of external prices; and X 5,t captures the partition of external macro conditions. Furthermore, ε t + h q denotes the residual, and α q , β 1 q , β 2 q a n d β 3 q are the coefficients of the regression. In the analysis, five different quantiles (or percentiles) are considered, at 10 percent, 25 percent, 50 percent, 75 percent, and 90 percent, which capture the linkages between macro-financial conditions and growth at different points of the future growth distribution. For example, the 10 percent quantile captures low growth periods (when growth rate is at the bottom 10 percentile), while the 90 percent quantile features high growth periods. Multiple forecast horizons (for example, 4 quarter ahead to 16 quarter ahead) are also considered to examine the impact of financial conditions on near-and medium-term growth.

Based on the results of the quantile regression, a t-skew distribution is then used to derive the probability density distribution of future GDP growth. The GaR framework could also be used to conduct scenario analysis, which examines the impact of shocks to the different partitions including credit and leverage, domestic prices, and macroeconomic vulnerabilities on the future growth distribution.

Macro-financial Partitions and Loadings

The relationship between different macro-financial partitions and real GDP growth in India is examined. As seen in Figure 2 , the credit to GDP ratio, the credit to GDP gap and credit growth have positive loadings on the first principal component 4 of credit and leverage indicators, while the NPL ratio has a negative loading. Therefore, an increase in the credit and leverage summary indicator would imply higher credit or more favorable credit conditions. After peaking in 2005/2006, the credit and leverage indicator has been on a downward trend since 2011/2012, coinciding with the period of negative credit to GDP gap.

Macro-Financial Partitions and Loadings

On domestic prices, 10-year treasury bill yields, policy interest rate and sovereign yields have a positive sign in the principal component, while a change in stock prices has a negative loading. An increase in the principal component of domestic prices would then imply a tightening in price-based financial conditions. In the first half of the sample, there was an inverse relationship between real GDP growth and the summary domestic price indicator, where a tightening in the price-based financial conditions is associated with a decline in growth. More recently, there has been a continued loosening of financial conditions, with the link between price-based financial conditions and economic growth less pronounced.

On macro economic vulnerabilities, short term external debt and inflation have positive loadings on the principal component, while the current account balance has a negative sign. A rise in the principal component of macroeconomic vulnerabilities would then imply higher vulnerabilities in the economy. Figure 2 shows that macroeconomic vulnerabilities peaked in 2012/13 but has been on a downward trend since then.

Scenario Analysis

A scenario analysis is conducted and considers a two standard deviation negative shock to the credit and leverage partition ( Figure 3 ). A decline in the credit and leverage partition (here, referring to the principal component) would imply a tightening of the credit conditions and a worsening in credit quality, as measured by the NPL ratio. The blue line captures the density before shock and the red line captures the one afterwards. Following the negative shock, the entire distribution of GDP growth would shift to the left. The mode of the 4 quarter ahead GDP growth would decline from 7.6 percent to 5.3 percent. Moreover, the tail risks would increase considerably, with the 5 percent GaR shifting from -5.7 percent to -12.2 percent. In other words, there was a 5 percent probability that growth could be below-5.7 percent prior to the shock. However, after the shock, there is 5 percent probability that growth could be below-12.2 percent, and the probability of growth below -5.7 percent increased to 11 percent, a much more severe tail outcome.

Growth-at-Risk: Shock to Credit and Leverage

In addition, a two standard deviation positive shock to the domestic prices partition and to the macro vulnerability partition are considered, respectively. An increase in the domestic prices partition would imply a tightening in the price-based financial conditions ( Figure 4 , left chart). The mode of the 4 quarter ahead GDP growth would decline from 7.6 percent to 7 percent, with a slight shift of the growth distribution to the left following the shock. The relatively milder impact of the domestic price shock could be potentially attributed to be weaker relationship between domestic prices and growth in recent years. On macroeconomic vulnerabilities ( Figure 4 , right chart), a two-standard deviation positive shock (higher vulnerabilities) would imply a decline in 4 quarter ahead GDP growth from 7.6 percent to 5.9 percent (mode), with the growth distribution shifted to the left, capturing higher tail risks.

Growth-at-Risk: Shocks to Domestic Prices and Macroeconomic Vulnerabilities

Term Structure of Credit and Leverage Indicators

Furthermore, the term structure of the credit and leverage indicators and the impact on GDP growth across different horizons is examined. Specifically, the 4 quarter, 8 quarter, 12 quarter and 16 quarter ahead quantile regression results are considered. In Figure 5 , the y-axis refers to the coefficient of the credit and leverage partition in the quantile regression (Equation (1)) and the x-axis refers to the different quantiles, capturing GDP growth at the 10 th , 25 th , 50 th , 75 th and 90 th percentiles. The results suggest that high credit and low NPLs have a positive and significant impact on GDP growth across all horizons. Furthermore, the impact is even larger at lower quantiles when GDP growth is lower. In other words, a favorable credit condition with higher credit and stronger credit quality is particularly important in supporting the economic recovery during periods of low growth.

Growth-at-Risk: Term Structure for Credit Indicators

As a robustness check, we also consider an alternative specification focusing on the bank credit to GDP ratio and bank credit growth, instead of the broader concept of credit. The results are found to be robust. A negative shock to the credit and leverage partition would again shift the GDP growth distribution to the left. Higher bank credit and stronger credit quality are particularly supportive to the economy when growth is relatively weak (see Annex Figures 1 and 2 ).

Investment- and Consumption-at-Risk

Having established the importance of credit and leverage variables for GDP growth, we also examine the extent to which they influence components of GDP growth, such as investment and consumption. Inequation (1), we consider y t + h q as the h quarter ahead investment and consumption growth (year-on-year) for quantile q , respectively.

Similar to the aggregate GDP growth, we consider the impact of a two standard deviation negative shock to the credit and leverage partition ( Figure 6 ) on investment and consumption. Following the negative shock, both the distributions of investment and consumption growth would shift to the left. The mode of the 4 quarter ahead investment growth would decline from 20 percent to 10.4 percent, with the 5 percent Investment-at-Risk shifting from-10.9 percent to -19.2 percent. In other words, there was a 5 percent probability that investment growth could be below-10.9 percent prior to the shock. However, after the shock, there is 5 percent probability that growth could be below-19.2 percent, a much more severe tail outcome. For consumption, the mode of the 4 quarter ahead consumption growth would decline from 12.5 percent to 6.7 percent, with the 5 percent Consumption-at-Risk shifting from -5 percent to -15.6 percent, also implying higher tail risks.

Investment-at-Risk and Consumption at Risk: Shock to Credit and Leverage

• D. Policy Discussions

The results from GaR suggest that higher credit and lower NPLs are associated with higher GDP growth in the near- and medium-term. A negative shock to credit and leverage (lower credit and higher NPL ratio) could shift the distribution of GDP, investment, and consumption growth to the left, with lower expected growth and larger downside risks.

During periods of low economic growth, policies to support credit growth and to strengthen balance sheets would be particularly important. In this regard, policy responses such as credit guarantee schemes for MSMEs, loan restructuring scheme for COVID-affected borrowers were important to support credit growth and cushion the economic impact of the pandemic.

Going forward, further efforts to make support measures even more targeted and facilitate the exit of non-viable firms may be warranted. In addition, financial regulators should continue to ensure that loans benefiting from COVID-related restructuring schemes are closely monitored and properly provisioned for, to safeguard the health of financial sector balance sheets and help support the economic recovery.

III. Financial Sector and Long-Term Growth

Several studies document that poor capitalization and weak asset quality negatively impact banks’ ability to provide credit to the economy. Using balance sheet data for international banks from a range of advanced economies, Gambacorta and Shin (2018) show that well-capitalized banks enjoy lower costs of debt financing compared to more leveraged competitors, which in turn translates into higher annual credit growth. Muduli and Behera (2021) find similar evidence in India, of a positive correlation between bank equity and credit growth, and that this plays a role in monetary policy transmission. Blattneret al. (2019) look at a macro-angle and show that less-capitalized banks cut lending in response to higher capital requirements, which potentially contribute to weaker productivity growth. This section of the paper builds on this literature by examining the role of balance sheets of Indian banks on credit growth, and ultimately overall output growth in the economy. The focus is, in particular, on the differential role of public and private banks in driving credit growth.

Data for the main bank-level variables of interest (cost of funding, growth of debt funding, credit growth, and bank capitalization) as well as bank-level control variables (non-performing assets, return on assets) are from FitchConnect. The sample is at an annual frequency from 1998–2021. Only public banks and private banks are kept in the sample, excluding such entities as non-bank financial companies, foreign banks and development banks. 5 The sample accounts for about 85 percent of total assets in the Indian financial sector in any given year of the sample. For the macro-level analysis, the data on GDP growth and various India-level or global controls are from the Reserve Bank of India via Haver and CEIC. Details of the data and sources are available in Annex Table 2 .

The main explanatory variable is bank-level capitalization which, based on existing literature for banks in advanced economies as well as in India, is an important driver of credit growth. Several definitions are considered to determine the robustness of the results. First capitalization is defined in turn as either common equity over total assets, total equity over total assets, or regulatory Tier 1 capital over total assets. The fourth measure of bank-level capitalization is the capital adequacy ratio, defined as Tier 1 regulatory capital over risk weighted assets. Figure 7 shows the path of bank-level capitalization overtime for public banks (PSBs) and private banks, as defined by the simple ratio of equity to assets and by the capital adequacy ratio. While median bank capitalization was volatile and slightly higher for private banks in the earlier years of the sample, since 2010 the gap between private and public banks has widened, though both have been trending upwards in recent years. Similarly, there has been a notable upward shift in the capital adequacy ratio since 2012, when India announced its intended adoption of the Basel III requirements (recommending a 9 percent capital adequacy ratio), aimed to be implemented in 2018–19. 6

Bank-level capitalization

As has been documented in the literature, while banks may use their capital to fund lending, given the relatively low share of capital on their balance sheets it is more likely that lending is funded through debt liabilities. This also appears to be in the case for Indian banks, as depicted in Figure 8 , that capital makes up a relatively small share of both private banks and PSBs.

Bank balance sheet composition

At the same time, there is evidence of a relationship between bank equity and bank assets (a large part of which is lending) in India, as has also been identified for other countries (Gambacorta and Shin, 2018). This is shown by estimating the simple correlation between total assets and total equity, both at the bank,;’, year, t , level:

where the model, in turn, includes the vector* of bank-level control variables (return on assets, NPLs), a set of bank-fixed effects, α t , and a set of year fixed effects, γ t . The coefficient/? indicates the correlation between bank assets and bank equity, which estimate separately for private banks and PSBs. These correlation estimates are reported graphically in Figure 9 . Indeed, the results suggest that for private banks in India there is a correlation between assets and equity close to one, even after including the full set of control variables. That is, as in Gambacorta and Shin (2018) the hypothesis of unit elasticity between the two variables can not be rejected, meaning they move closely together overtime. However, given the low share of equity in bank funding, even though equity and assets move closely together it cannot be the case that increases in equity directly result in increases in lending. Furthermore, for PSBs, this correlation is much weaker once aggregate factors that affect all bank assets simultaneously are controlled for (via time fixed effects)..suggesting an even weaker relationship.

Correlation – Total Assets and Total Equity

Having established a strong correlation between bank assets (largely comprised of lending) and bank equity—at least for private banks—the question of whether there is a direct link between the capitalization of Indian banks and their lending growth, via debt funding, is formalized. The analysis proceeds in three steps, following the literature. First, asking whether a bank’s capitalization reduces its cost of funding—this is important as it was previously established that most lending is likely stemming from debt funding. Second, investigating whether capitalization not only decreases funding costs, but whether it is actually associated with an increase in debt funding. Figure 10 , panels A and B, show these two simple correlations, and suggest that for Indian banks, there is a strong association between higher bank capitalization, lower funding costs, and greater debt funding growth. Finally, as seen in panel C, there is a strong positive correlation between lending growth and capitalization. Together, these suggest that better capitalized banks lend more, possibly through a cheaper debt funding channel. Such a result would be consistent with the existing literature on international banks. In the next section, these relationships are formalized.

Bank capitalization, funding, and lending

Finally, the paper will look at the macro-level and attempt to formalize the relationship between credit growth with real GDP growth. Because the distinction between public and private banks is made in the bank-level analysis, it is important to also understand how each contributes to aggregate credit growth in India. Figure 11 shows that throughout the period under analysis, public banks have been responsible for the largest share of credit to the economy. However, since around 2013, private bank credit growth has been much faster than public bank credit growth, suggesting private banks are becoming an increasingly important player in the Indian banking sector. Figure 12 reports the aggregate correlation between real GDP growth and credit growth for each type of bank, with both showing relatively strong positive correlations. This relationship is explored more carefully in the next sections.

Aggregate bank credit to the economy

Real GDP growth and credit growth

The methodology for the bank-level analysis follows the approach of two closely related papers, Gambacorta and Shin (2018) and Muduli and Behera (2021) . It then extends the analysis to the macro-level to analyze the impact of bank lending on real GDP growth in India. The approach will take several stages. First, it examines whether bank capitalization leads to lower debt funding costs and higher debt funding—establishing whether the channel of debt funding for lending also exists in private and public banks in India in the sample period. Then, it turns to lending, to examine whether bank capitalization matters for lending, again distinguishing between private and public banks. Finally, it looks at the aggregate and examines whether bank lending is correlated with higher real GDP growth in India. This latter step raises questions of causality—namely, whether lending boosts real GDP (for instance by increasing consumption and investment) or whether lending rises when real GDP growth is higher. Many papers have tried to tease out this relationship using data from other countries. While this paper has insufficient data to carefully establish causality (only a correlation), it will nonetheless argue that the approach suggests there is a likely channel of transmission from bank lending to real GDP growth in India.

To estimate the role of bank capitalization on funding costs, debt funding costs of bank i in period t, cost it , defined as the average cost of funding given by total interest rate paid overtotal level of debt (excluding equity and reserves), is regressed on bank capitalization (Capitalization it-1 ) , using various definitions described in the previous section. Time fixed effects and bank level controls, X it-1 , including return on assets, total assets, and the NPL ratio, are also included:

The model is estimated using the dynamic Generalized Method of Moments (GMM) estimator (Arellano and Bond, 1991), which ensures efficiency and consistency of the estimates. This is useful in this setting since the outcome variable likely depends on past realizations of itself. It is important to note that while this regression model can inform on the relationship between bank capitalization and funding costs, it cannot identify a causal relationship between these variables. Consistent with existing literature, it is expected that the results will show that lower capital levels are associated with higher prices for debt funding (i.e . higher equity reduces the cost of debt or that well capitalized banks pay less for their funding).

Having established a link between bank capitalization and the cost of funding, the analysis then estimates the impact of bank capitalization on funding levels, using a similar set-up, with the dependent variable this time the growth of debt funding, funds it :

In this case, it is expected that better capitalization and an increase in asset quality will increase the rate of debt funding.

The final step in the bank-level analysis estimates the impact of bank capitalization on credit supply, again in a similar setup as equations (3) and (4):

In this case, it is expected that better capitalization increases the growth rate of loans.

As a robustness exercise, the models in equations (3) -(5) are estimated via panel fixed effects estimation, which allow for the inclusion of both year and bank time fixed effects. The results for these exercises are reported in Annex Table 3 – 6 .

With bank-level results established, the analysis turns to addressing the question of what the relationship between banking lending, through bank balance sheets, is with the macroeconomy in India. This remains an open question because, while there is evidence that higher credit growth is often associated with higher GDP growth, in emerging markets this is sometimes the result of a boom-bust cycle which can ultimately lead to lower growth. In such a case, it may indeed be that the health of bank balance sheets is particularly important to avoid these extreme swings. Also motived by the results from the GaR model, a measure of balance sheet health is controlled for directly, defined using the NPL ratio. The following regression model is estimated to determine the relationship between real GDP growth and credit growth (at the bank-year, it , level) in India:

where the set of control variables are macro controls, including inflation, the real effective exchange rate, and world GDP growth. NPLs are defined as a dummy variable, equal to one if bank i’s NPL ratio in year t is below the sample mean. Credit growth, in turn, is defined as actual credit growth or as a dummy variable for high credit growth equals to one if bank i’s credit growth ratio in year t is above the sample mean. The credit growth variable is also winsorized at the 1 st and 99 th percentile, to account for extreme outliers. 7 Given the potential endogeneity between credit growth and GDP growth, this regression is unable to identify a causal relationship between the two variables but rather speaks to their correlation. Furthermore, given that we examine output growth at the aggregate level (GDP growth) we are estimating an average effect of credit growth over all bank characteristics – for instance, type of bank (public versus private) and size of bank. We address this averaging effect by examining split samples along various characteristics. With these caveats in mind, the next section presents the results.

The first results, based on estimating equation (4), are reported in Table 2 . The sample is split between public banks (columns 1 to 4) and private banks (column 5 to 8) and results are shown for the four different measures of bank capitalization. The results suggest that higher capitalization is associated with lower debt funding costs, especially and more so for private banks. This is consistent with what Gambacorta and Shin (2018) find for advance country banks. Muduliand Behera (2021) find a related, nuanced result for India, that (consistent with the results presented in this paper) a higher level of bank capital is associated with lowerfunding costs but for public banks it is only associated with lowerfunding costs if they have lower non-performing assets. In contrast, the results here indicate there is some negative association on average, regardless of the level of NPLs, but it is not as strong as for private banks. This could be because public banks often get public capital infusions, thus limiting the extent to which capital is indicative of risk for public banks.

Bank capitalization and cost of debt funding

Next, the analysis asks whether capitalization matters for the overall growth of debt funding. The results for estimating this, as indicated in equation (5), are reported in Table 3 , again separately analyzing public and private firms. Again, there is a similar distinction between the role of capitalization in public versus private banks. Private banks that have greater capitalization are associated with large, and significantly greater debt funding growth. For public banks, the relationship is less robust across the different measures of capitalization but there does seem to be a positive, albeit smaller, relationship.

Bank capitalization and debt funding growth

The above established that better capitalized Indian (private) banks are able to find cheaper debt funding and raise more funds relative to less well capitalized banks, and this can be a source of funds for lending. The final exercise is to examine banks’ lending practices directly. Table 4 reports results from estimating equation (6). With respect to private banks, there is some evidence of a positive relationship between capitalization and lending. For public banks, no such evidence is found. Isolating the period from 2010–21, which is both when the RBI adopted the Basel II regulations and when private banks became much more prominent in India, delivers an even stronger positive relationship between capitalization and lending, as shown in Table 5 . Together, the results suggest that credit growth in India can be supported by ensuring banks are adequately capitalized, which enables them to raise more debt funding, at cheaper rates, which is then ultimately used to support lending growth. This relationship is, however, specific to private banks and does not seem to hold for public banks, which may have different funding models and different ability to lend.

Bank capitalization and lending growth

Bank capitalization and lending growth, 2010–21

Turing to the macro-level results, reported in Table 6 , columns (3) and (4) suggest that there is a strong positive correlation between higher credit growth and real GDP growth, but only for those banks with a low NPL ratio. Furthermore, this result appears to be entirely driven by private banks (column (5)), with public banks (column (6)) showing no relationships between credit growth and real GDP growth regardless of the level of NPLs. Finally, the size of the bank (column (7)) does not appear to be related to whether credit growth is associated with higher GDP growth. 8

Real GDP Growth and Credit Growth

While the methodology used here cannot speak to the reason for the lack of relationship between public banks’ lending and growth, the reasons could be varied: public banks may have different objectives than private banks, and often engage in directed lending (also known as priority sector lending); the results could also reflect implicit guarantees that public banks have from the government. The result is also consistent with a large literature that finds publicly owned banks are generally associated with lower employment and growth (see, for instance, Carvalho, 2014 and La Porta et al., 2002 ). If real GDP growth is the overarching objective, then the results suggest private bank lending by banks with healthy balance sheets should be promoted. There may nonetheless be alternative reasons for continuing to promote public bank lending. It is also important to recall that this methodology does not speak to a causal relationship between bank lending and real GDP growth. The positive correlation may imply that private bank credit growth from banks with low NPLs spurs real growth, but it may also indicate procyclical lending by private banks (and countercyclical lending by public banks). Further analysis with micro-level data would be needed to disentangle this relationship, which is left to future research. Finally, the results presented here abstract from any lending by non-banks, which represent a large share of credit in India and may themselves also be important for real GDP growth. 9

• D. Policy Implications

Results from this panel regression analysis, as with the results from the GaR, highlight the importance of ensuring adequate credit growth and improving bank balance sheets, particularly through reducing NPLs, to boost growth. It is only those banks with low NPLs and high credit growth that are associated with higher GDP growth.

At the bank level, to ensure high credit growth, it is also imperative that banks are well capitalized. This allows them access to more and cheaper debt funding, which is in turn used to fund lending. These relationships, however, seem to exist primarily for private banks. Public banks, which may have different motivations for lending, appear to be less affected by their capital position in terms of their ability to lend.

Looking ahead, efforts to clean up bank balance sheets and boost capitalization—especially for private banks—will be critical in boosting credit growth, and thus GDP growth over the medium term.

• IV. Conclusions

This paper has examined the nexus between India’s financial sector and economic growth. It highlights the important role of financial sector on growth outcomes. Using two distinct methodologies, the results provide consistent messages. On a cyclical basis, a negative shock to credit and leverage or a rise in macro vulnerability all shift the distribution of growth to the left, with lower expected growth and higher negative tail risks, implying lower expected growth and higher downside risks. Over the long term, the results indicate that higher credit growth, arising from better capitalized banks with lower NPLs, is associated with higher GDP growth.

Together, these results point to several policy considerations. First, the results highlight the importance of ensuring adequate credit growth and improving the balance sheets of banks, particularly through reducing problem loans. During periods of low economic growth, policies to support credit growth and to strengthen balance sheets would be particularly important. Additionally, a focus on ensuring that private banks are well capitalized, either through new equity issuance or reducing cash dividends, is crucial, given the relationship between their balance sheets and credit to the economy. Finally, given the differences in results between private and public banks, efforts to better understand the drivers of this difference and address it could help promote growth.

Adrian , Tobias , Nina Boyarchenko , and Domenico Giannone , 2019 . “ Vulnerable Growth .” American Economic Review , Vol 109 ( 4 ): 1263 – 89

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Gambacorta , Leonardo and Hyun Song Shin , 2016 . “ Why bank capital matters for monetary policy ”, BIS Working Paper 558 .

Goodhart , Charles and Boris Hofmann , 2008 . “ House prices, money, credit, and the macroeconomy ,” Oxford Review of Economic Policy , Oxford University Press , Vol. 24 ( 1 ), pages 180 – 205 , spring .

International Monetary Fund , 2017 . “ Global Financial Stability Report .” Washington , October 2017.

La Porta , Rafael , Florencio Lopez-de-Silanes , and Andrei Shleifer , 2002 , “ Government ownership of banks ,” Journal of Finance 62 , pp. 265 – 302 .

Levine , Ross , 2005 . “ Finance and Growth: Theory and Evidence ,” Handbook of Economic Growth , in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth , edition 1 , volume 1 , chapter 12, pages 865 – 934 , Elsevier .

Muduli , S. and Behera , H. , 2021 . Bank capital and monetary policy transmission in India . Macroeconomics and Finance in Emerging Market Economies , pp. 1 – 25 .

Prasad , Ananthakrishnan , Selim Elekdag , Phakawa Jeasakul , Romain Lafarguette , Adrian Alter , Alan Xiaochen Feng and Changchun Wang , 2019 . “ Growth at Risk: Concept and Application in IMF Country Surveillance ,” IMF Working Papers 2019/036 , International Monetary Fund .

Seth , Gaurav , Supriya Katti and B.V. Phani , 2022 . “ Stock Price Reaction on the Announcement of Basel Implementation: Evidence from Indian Banks ” Reserve Bank of India Working Paper No. 1

• Annex I. Tables

Definitions and Data Sources of Macro-Financial Variables

Definitions and Data Sources of Panel Regression Variables

Foreign bank capitalization and funding costs, debt, and lending growth

• Annex II. Figures

Robustness Check – Growth-at-Risk: Shock to Credit and Leverage (Bank Credit)

Robustness Check-Growth-at-Risk: Term Structure for Credit Indicators (Bank Credit)

For a discussion of this broader topic, see Demirguc-Kunt and Levine (2018) and references therein.

The credit-to-GDP gap is based on BIS calculations, defined as the difference between the credit-to-GDP ratio and its long-term trend. According to the BIS, the long-term trend is computed using a one-side Hodrick-Prescott filter with lambda equal to 400,000, as credit cycles are on average longer than standard business cycles. For detailed methodology, please see Recent enhancements to the BIS statistics.

The first principal component of the credit and leverage partition (comprised of the credit -to-GDP ratio, the credit-to-GDP gap, credit growth, and the NPL ratio)captures77 percent of the variance.

We focus on domestic banks only as they have the best data coverage.

See Seth et al. (2022) for a timeline on India’s adoption of the Basel recommendations.

Results are robust to not winsorizing and available upon request.

The results for bank size are robust to defining a large bank as those with total assets in the top 25 and top 10 percent of the distribution of banks’total assets.

Results for foreign banks are presented in Annex Table 7 . While generally robust to the main results on private sector banks, the sample of foreign banks is relatively small, and thus difficult to assess with any precision the quality of the results.

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• 13 December 2023

Where science meets Indian economics: in five charts

• Andy Tay 0 &
• Jack Leeming

Andy Tay is a freelance writer in Singapore.

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This year, India overtook China to become the world’s most populous country. But it struggles to develop its economy and lags behind many other nations in terms of its investment in science and technology. How can better funding for research help its economic development?

The human factor

India has the world’s largest population, but how well does it look after all those people? The Human Development Index is a United Nations metric that quantifies a country’s human development in terms of health, lifespan, education and standard of living.

Source: https://data.worldbank.org ; United Nations Development Programme. Infographic by Mohamed Ashour

How they compare

In some key indicators of human development, India lags behind high-income countries such as the United States. For easy comparison, the scores in these radar charts are given in the percentile rankings of each country compared with all other countries.

Source: United Nations Development Programme; https://worldpopulationreview.com ; https://www.numbeo.com ; https://data.worldbank.org ; RSF Reporters without Borders. Infographic by Mohamed Ashour

Science spending

India spends less than the global average on research and development (R&D), but it has kept this spending largely consistent as its economy has grown in the past two decades. A good indication of a science-based society is the proportion of investment in research from private sources. India lags behind other countries in this metric. It is, however, the world’s largest outsourcer of programmers, and 60% of the world’s vaccines are produced in the country. And in August this year, India joined an exclusive club, becoming only the fourth country to make a soft landing on the Moon.

Source: https://data.worldbank.org ; https://sgp.fas.org ; Government of India’s Department of Science & Technology. Infographic by Mohamed Ashour

From degree to PhD

India has a smaller proportion of people with a university-degree-level qualification than many other nations, but those who do get an undergraduate degree are much more likely to complete a PhD. Indeed, India has the highest proportion of university graduates who go on to complete a doctoral degree in the world, at around 5% of graduates.

Source: https://gpseducation.oecd.org . Infographic by Mohamed Ashour

Publishing performance

India is among the world‘s most prolific publishers of research, behind only the United States and China.

Source: https://www.scimagojr.com ; https://data.worldbank.org . Infographic by Mohamed Ashour

Nature 624 , S20-S21 (2023)

doi: https://doi.org/10.1038/d41586-023-03907-5

This article is part of Nature Spotlight: India , an editorially independent supplement. Advertisers have no influence over the content.

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Current Issues in the Economy and Finance of India

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• Aswini Kumar Mishra 0 ,
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• Debasis Patnaik 2

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• Highlights innovative research on sustainable development, economics and finance in India
• Covers timely topics such as inflation dynamics, information transmission, leverage effect and volatility asymmetry, structural change and tax systems, among other
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This proceedings volume highlights important points of achieving a balanced and sustained growth path from diverse economics and finance perspectives, touching on a wide array of economic and social analyses in India. Featuring contributions presented at the 2018 International Conference on Economics and Finance (ICEF-2018) held at the Birla Institute of Technology and Science, Pilani, Goa, India, the enclosed papers explore topics such as inflation dynamics, information transmission in post-recession era, leverage effect and volatility asymmetry, structural change and economic growth and reforming tax systems, among others.

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Macroeconomic Overview of the Indian Economy

Introducing the Volume on India’s Contemporary Macroeocnomic Themes-Looking Beyond 2020

The Impossible Trinity: Where Does India Stand?

• Indian Finance
• Indian Economics
• Agricultural Economics
• Rural Livelihood Programs
• India’s Service Trade
• Public Sector Banks in India
• Private Sector Banks in India
• Health Economics in India
• Ecotourism in India
• Heritage Communism
• Indian Stock Market
• India and the ASEAN
• Rural Wage Dynamics in India
• Human Capital Formation
• Economic Globalization in India

Table of contents (16 papers)

Front matter, globalization, macroeconomic performance and monetary policy, core inflation dynamics and impact of demand and supply shocks: evidence from india.

• Priyanka Sahu, Naresh Kumar Sharma

Demand for Money in India: An ARDL Approach

• Abdul Rishad, Akhil Sharma, Sanjeev Gupta

Some Empirical Evidence on the Effects of Monetary Policy in India: A Vector Autoregressive Based Analysis

• Gyanendra Pratap Singh

Stock Indices and Inflation Through Industry Prisms

• Abha Mohan, Tomy Mathew, K. Subramanian

Fundamental Drivers of Indian Stock Market

• Rubina Barodawala, Diksha Ranawat

Financial Institutions, Instruments and Markets

Information transmission in post-recession era: evidence from india, china, hong kong and japan.

• Ashish Kumar, Swati Khanna

A Statistical Re-assessment of Capital Adequacy and Insolvency Risk in Commercial Banks of India

• Nupur Moni Das, Joyeeta Deb

Examining the Linkages Between Financial Development and Energy Consumption in India

• Seema Saini, Yadawananda Neog

Leverage Effect and Volatility Asymmetry

• Parthajit Kayal, S. Maheswaran

Regional Economic Development Issues

Structural change and economic growth in india—a state-wise analysis.

• Kshamanidhi Adabar, Trupti Mayee Sahoo

Financing MSMEs Rife with Problems—A Demand Side Analysis

• Chinmayee Sahoo, Alok Ranjan Behera

Impact of MGNREGS on Rural Labour Markets and Agriculture: A Study of Madhya Pradesh

• T. Prabhakara Reddy, V. Suresh Babu

Public Policy Economics and Analysis

Reforming tax systems: key policy considerations from south and south-west asia.

• Matthew Hammill, Jose Antonio Pedrosa-Garcia

Policy Implications for Financial Inclusion of Unskilled Labor Migrants: Insights from Goa

• Mridula Goel, Saurabh Nayak

Income and Expenditure Mismatch of Poorest of the Poor: An Analysis of Financial Requirement of Slum Dwellers

• Firdous Ahmad Malik, D. K. Yadav, Ranu Jain

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Aswini Kumar Mishra, Debasis Patnaik

Vairam Arunachalam

Bibliographic Information

Book Title : Current Issues in the Economy and Finance of India

Book Subtitle : ICEF 2018

Editors : Aswini Kumar Mishra, Vairam Arunachalam, Debasis Patnaik

Series Title : Springer Proceedings in Business and Economics

DOI : https://doi.org/10.1007/978-3-319-99555-7

Publisher : Springer Cham

eBook Packages : Economics and Finance , Economics and Finance (R0)

Copyright Information : Springer Nature Switzerland AG 2018

Hardcover ISBN : 978-3-319-99554-0 Published: 14 September 2018

Softcover ISBN : 978-3-030-07610-8 Published: 26 December 2018

eBook ISBN : 978-3-319-99555-7 Published: 13 September 2018

Series ISSN : 2198-7246

Series E-ISSN : 2198-7254

Edition Number : 1

Number of Pages : XXVII, 284

Number of Illustrations : 21 b/w illustrations, 17 illustrations in colour

Topics : Asian Economics , Regional/Spatial Science , International Economics , International Finance

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Current World Environment

An international research journal of environmental science.

ISSN:0973-4929, Online ISSN:2320-8031

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Climate Change and the Indian Economy - A Review

DOI: http://dx.doi.org/10.12944/CWE.17.1.3

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Sharma M, Singh R, Kathuria A. Climate Change and the Indian Economy - A Review. Curr World Environ 2022;17(1). DOI: http://dx.doi.org/10.12944/CWE.17.1.3

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Article Publishing History

Introduction For a developing nation like India, climate change is a harsh reality. This is mostly because the backbone of the growth of a developing country is made of conventional methods of generating energy and resources. Despite a huge advancement in technologies, such countries often find themselves in conflicting positions. Economy, development and climate change often cross each other’s paths resulting in increased risk and vulnerability. This can be understood from the precipitation requirements and rainfall. For example, owing to climate change, in many areas the groundwater level has plummeted. This is the outcome of more than ever concrete surfaces diminishing the recharge rate of aquifers. 1 Indian agriculture rests on the support of groundwater and seasonal rainfall for the most part of the year. Consequently, the interplay of climate change and development factors has resulted in an acute water shortage for at least one month every year affecting a billion people in India while around 180 million suffer from severe water scarcity throughout the year. 2 Latterly, climatic variations disguised as cyclones and floods have caused massive desolation of crops, property, and infrastructure. This has also caused negative impacts on human health, especially heat stressors. Rural dwellers continue to depend on agriculture for livelihood and food, making them explicitly vulnerable to climate variability and change. All these factors hitch socio-economic development goals. 3,4 The national policies on climate change (“National Action Plan on Climate Change” (NAPCC)) are concentrated around human development and economic - industrial development policies. Local policies have helped in reducing urban air pollution levels. It is noteworthy that India is not responsible for rising temperatures despite contributing to 17.8% of the world’s population. It accounts for only 3.2% of cumulative emissions. 5 However, a report prepared by Deloitte Economics Institute, entitled “India’s Turning Point: How climate action can drive our economic future” projects that if the current practices and policies continue then India, may lose US$6 trillion in current value by 2050 that is 6% of the GDP in 2050 only. Averagely, in the next 30 years India will lose about 3% of GDP. This figure sores even more when we reach to 2070, wherein India will lose about US$35 trillion i.e., 12.6% of the GDP. 6 Yet, aspiring developmental goals without considering climate change is futile. At the same time, the huge and urgent developmental challenges cannot be ignored. Hence, both international efforts to alleviate the degree of climate change and domestic efforts to acclimatize the global warming already locked from earlier emissions. 7 The literature review on impact of climate change on economic development is quite overwhelming. It is not only in depth but also has good coverage. Although, literature pertaining to developing countries is not in abundance. However, it has been suggested that climate change do leave an impact on the economy and a transition to low carbon economy is possible only if the measures benefit economically. 8 Through this paper, we shall be highlighting how climate change is impacting the economy of India. Such nearly backward countries are not responsible for the large-scale emissions that are jeopardizing the present and future generations. While who is responsible or who is not for current climatic adversities, is very subjective. Herein, we shall be presenting why such immediate policy changes meet reluctance and how despite this India can reach its developmental goals in the long run. Materials and Methods This paper presents a qualitative research based on data extracted and analyzed from crucial government documents like “Assessment of Climate Change over the Indian Region Report 2020” and research papers. 9 Initially, we have tried to express the climate change briefly supported via facts and figures. Appropriate figures for time series analysis of temperature and monsoon have been included for a comprehensive interpretation of the trends from available data. Further we have given context to the pillars that make up the Indian economy and how they have been suffering as a consequence of climate change. We have emphasized on agriculture, livestock, infrastructure, and low-income households. Then we have discussed the energy needs that are crucial to development and how they present a difficult situation. We have discussed how meeting energy needs in developing countries leads to climate changes. Further we have stressed on how we can aspire for growth and development, even while keeping a check on climate change with valuable suggestions. Regional Indian Climate Change The climate of India is quite diversified in nature, from the Himalayan crown to the flat beaches, a significant transition in climate is visible. The climate varies from the freezing temperatures of the Himalayan Mountains to the tropical climatic conditions of southern India. The eastern states received the maximum rainfall while the western states dried of water make up the arid deserts of Thar and Great Indian Desert. Such a vastness of climatic conditions has always benefited India. However, in recent years many reports have projected the possibility of irreversible climatic changes. The IPCC 2021 report of climate change came as a shock for many as the report solidified its case of climatic worries and warned of severe consequences. For India too, in the past, many documents and reports have repeatedly shown the changing climatic trends and their impact on the Indian dimensions. The amplitude of the “CO 2  mixing ratio” has been rising gradually for the last few years. How has the climate so far changed…? Temperature A report on the assessment of Indian climate (“Assessment of Climate Change over the Indian Region Report 2020”) 9 has shown that the annual mean, minimum and maximum temperatures for the period of 1986-2015 have shown considerable warming by 0.15 °C, 0.13 °C, and 0.15 °C respectively. A significant change in pre-monsoon temperatures has also been seen with the highest warming trend. Heat extremes have increased over pan India during the period of 1951-2015. An ascending warming trend has been seen in the recent 30 years. An increase in the warmest day and warmest night temperatures along with the coldest night temperature has been observed since 1986. For India, an earlier IPCC report has forecasted the increased number of heat waves, and hot days. Deaths due to heat stress have also risen in recent years. 10 The Indian Ocean Sea Surface Temperature has been increasing with an average increase of 1.0 °C which was higher than the global average (0.7 °C) during 1951-2015. It has been speculated that around 90% of heating/warming is due to emissions caused by human activity and this will continue in the upcoming future in case of both high and medium emissions. 11

The first graph is for largest maximum temperature for the months of March to May. The second graph is for the lowest minimum temperature for the months of December to February. The third graph is the difference between the two temperatures denoted for four major climate zones that are Bhubaneswar (blue line), Mumbai (green line), and Delhi (red line) during 1951-2015 and Chennai (black line) during 1980-2015. The calculations and graphical analysis have been done using Mann Kendall rank test with a 90% significance level. From the Figure 1, it can be observed that there is high variability in the minimum and maximum temperature in the later years (1981-2015). 12 These observations are in compliance with the theoretical data that has been published in climate assessment reports (Table 1). Below mentioned is tabular data for temperature increase for different months/seasons during a year. 13 Table 1: Temperature Trends for different Months/Seasons during the Years 1986 - 2015.

Rainfall As the temperature increases, its effect can be easily seen on the rainfall of the region. This is because warm air holds greater moisture in comparison to cold air and warm water evaporates at a faster pace. A cumulative effect of these is seen in the rain. These are causing more frequent heavy downpours which are not usually common. During the period of 1950 to 2015, there has been a threefold increase in heavy precipitation in the central Indian region. 14 While extreme precipitation has considerably risen over the subcontinent, however, an extremely contrasting observation has also been made. According to the assessment report, there has been an overall plummeting rainfall trend in the annual all-India and mean summer monsoon precipitation in the period of 1951 to 2015. This has been observed largely in the Western Ghats and Indo-Gangetic Plains. The cause for this trend is a notably increased concentration of anthropogenic (human-caused) aerosols over the northern hemisphere. Urbanization, improper land use, and increased anthropogenic aerosols are considered the main factor behind the increased localized rainfall and overall mean rainfall decrease. The time scale analysis of rainfall for the current year during the monsoon season from June to September depicts intense monsoon variability with frequent maximum peaks (Figure 2). As expected from theoretical research, the monsoon is becoming severe. India receives most of its rainfall from the monsoon. This exotic wind pattern has been responsible for a significant amount of rainfall over the Indian subcontinent. Hence, a major impact of climate change has been seen on this pattern. It has been projected that the monsoonal precipitation is going to become more severe in the future due to an increase in mixture content as a consequence of increased temperatures.

The first graph is for the monsoon season from June to September. The second graph is a comparison of the cumulative rainfall for the monsoon season for the current year (2021) and from 1961-2010. The third graph is the depreciation in monsoon rainfall for the current year. 15 Drought During the period of 1951 to 2015, the number and geographical extent of droughts have risen over the subcontinent. Drought severity is mainly observed in parts of central India and parts of Indo-Gangetic Plains. These observations are in-line with a decrease in mean summer season monsoon precipitation. However, at the same time rise in the occurrence of localized rainfall has increased the probabilities of fatal floods. Climate models have projected a rise in the extent, occurrence, and severity of droughts over pan India while flood propensity is predicted to be higher in Himalayan River basins. Continuous drought in the years 1999 and 2000 led to a steep decrease in the groundwater tables of the northwest region and the 2000-2002 droughts caused extreme crop failure which led to the worst massive starvation and affected 11 million people in Orissa. 16 Himalayan Region According to the “Assessment of Climate Change over the Indian Region report 2020” of India,9 substantial warming in the Himalayan region has been observed in the twentieth century. The warming is quite prominent in the Hindu Kush Himalayan (HKH) regions that is having the most area with non-temporal ice cover after the south, and north poles. The annual mean temperature in the HKH region has been incessantly increasing by 0.1 °C per decade during 1901-2014, which further increased at about 0.2 °C per decade during 1951-2014. At elevated regions (>4000m), the warming is quite strong, as high as 0.5 °C per decade. It has been further projected that the HKH region will keep on warming in the range of 2.6-4.6 °C by the end of the 21st century. Economy and Climate Change Positively, the Indian democracy has resulted in equity moderately greater than the global average and the dependency ratio is also relatively greater. Nonetheless, the poor living standards of people involved in agriculture and people born into socially and economically backward castes and regions limit the robustness of the wholesome economy. It is possible and predicted that climate change will rip off the existing economic standards of these people so much so that it will result in severe taxes on the economic and industrial assets of the state and central government. It has been projected that climate change can deplete India’s GDP by circa 2.6% by 2100 even while capping the global temperature rise below 2 °C. In a scenario where global temperature also keeps increasing (4 °C), this depletion is projected at 13.4%. These figures are an outcome of the changes in precipitation and temperature levels, and the impact of climate change on labor productivity. Labor productivity may as well get affected by endemic vector-borne diseases like malaria, dengue, etc. The probability of the outbreak of such diseases increases due to climate change. 17 Nevertheless, gauging the exact financial and economic costs of climate change is a herculean task and also appears complicated due to uncertainties at every step. The absolute cost of flooding, heatwaves, cyclones, water scarcity, sea-level rise, and other climate-related hazards can be determined by the level and direction of economic development, the solutions opted in infrastructure development, spatial planning in the future, and the intermingling of hazards and how they will multiply each other. On top of everything, global warming will have a major role to play in determining the economic costs. Agriculture Even after 74 years of independence, India is still mostly an agrarian economy. About 50% of the Indian population is still directly or indirectly dependent on agriculture for meeting essential needs. If the harvest is good enough, the economy also benefits. So, Indian economic development can be seen on a proportional line with agriculture. However, agriculture is itself dependent on natural forces like the monsoon, rainfall and temperature. Agriculture contributes about 50% to the Indian economy. Although this has been decreasing recently, yet even today, slight upheavals in agriculture directly impact the economy. When we discuss the impact of climate change, its impact on agriculture can’t be ignored. Even in its raw and backward form, agriculture has been supporting the backbone of the Indian Economy. In many parts of the country, farmers are dependent on the monsoon for irrigation and good harvest. There is a huge demand for another green revolution as the benefits of the first green revolution was limited to only a few parts of the country, mainly Punjab and Haryana. Admittedly, the effects of climate change will be felt chiefly on the agricultural sector and the corresponding water requirements and availability. Agriculture production in the North region depends on spring snowmelt to replenish water supplies. It has been predicted that earlier snowmelt on account of climate change can substantially reduce the water table during the growing season impacting production. The southwest monsoon is critical for agriculture as it provides for about 80% of rainfall to the country. This also acts as an important tool to determine optimal dates for plantings. Many models have projected that India will suffer from intense and longer summer monsoon and weak and short winter monsoon. At the same time, pronounced warming will contract overall rainfall. 18 Monsoon-dependent agriculture will see profound transitions. Without proper or no irrigation, landless agriculture laborers, and small farmers will face loss of livelihood and extreme food shortages. Most of these will go to cities in search of work and economic prospects. 19 Numerous people will be affected by decreased food productivity leading to malnutrition, hunger, diseases, etc. This will also increase the burden of providing assistance to these small landholders on the state and center. There will be increased demand for infrastructure following a major internal migration will occur, owing to decreased agriculture output and income, to urban areas. The need to replace the existing infrastructure (e.g., in the transportation and energy sectors, irrigation systems) due to climate change will cause greater economic costs. Livestock India has the most livestock population globally. This is primarily because of the large-scale milk production, nutrient recycling (manure), household capital, draft animals, etc. These animals are used as household capital in landless households. Many low-income rural families even use animals as means of transportation and consider livestock as a potential economic asset. However, the reproduction and production of livestock are affected by increasing temperatures. Heat stressors reduce feed and fodder intake and increase vulnerability to diseases. Feeding is affected as fodder gets expensive due to increasing agricultural - produce costs. One example of a heat stressor was the outbreak of foot and mouth disease in cattle. 52% (Andhra Pradesh) and 84% (Maharashtra) were found to be affected, owing to high temperature, rainfall, and humidity conditions. A disease called mastitis occurs in dairy animals during hot and humid weather. 20 Infrastructure A good and sound infrastructure contributes a great deal to the economy of a nation. Without proper infrastructure many economic prospects and projects are desolate. However, the increased extremes of natural calamity as an outcome of climate change have deeply affected the infrastructure. Palpably, in India, 14% of the annual maintenance and repair budget is spent on maintaining the Konkan Railway. Consequently, tracks, cuttings, and bridges are damaged each year due to uneventful weather conditions. Landslides remain a constant source of worry. During heavy rains, the developmental projects have to be stalled for more than seven days leading to extended costs. Massive destruction of on-site material also takes place. 21 In the last few decades, as flood-like situations have prominently risen, a major portion of the budget goes to disaster relief. India spent $3 billion of economic damage caused by floods in the last decade which is 10% of the global economic loss. 22 In 2020, cyclone Amphan distressed around 13 million people and caused more than $13 billion in damage in the region. 23 In such a disaster, the direct impact can be seen on low-income households which are displaced and find it difficult to accumulate assets to enhance their security. Low Salaried/Income Household Low-income households are more susceptible to economic losses due to climate change. This is because they settle in densely populated regions that lack basic infrastructure and services like paved roads, safe and piped water, decent housings, drainage, etc. it has also been found that many people live in low-lying coastal areas, steep slopes, and flood-prone regions as the cost of land is cheaper. 24 Furthermore, these people will also be directly affected by a combination of increased cereal prices, a slower economic growth rate due to climate change, and declining wages in the agricultural sector. It is feared that if the situation persists, it might increase the national poverty rate by 3.5% in 2040 contrastingly greater than what is expected in a zero-emission-warming scenario. 25,26 Energy Economy and Climate Change Energy is required to sustain not only people but everyone all around. It lights homes, runs factories and vehicles, draws water, and much more. In a way, energy needs and production are also a measure of economic progress. Hence, it won’t be wrong to conclude that energy dynamics and climate change are inseparable. Climate change has a direct consequence on the energy demands and production of a country and vice-versa. The extremism of climate change is becoming a major cause of concern for the energy sector of developing and under-developed countries. Owing to a stressed economy, lack of technological innovation, and infrastructure to sustain new technologies, these countries are forced to stick to the conventional sources of energy. These sources of energy largely depend on fossil fuel burning and hence contribute significantly to Green House Gas (GHG) emissions. The per capita demand for energy is about 1/10th of the OECD average with a constantly increasing demand - 3.2 percent per year (2000-2005). It is speculated that the energy needs of India will double by 2030 (considering the growth rate of 6.3% GDP annually). 27 In India major energy usage is for producing electricity and transportation fuels. Most of these energy needs are met by domestic coal and petroleum reserves along with imported oil. Fossil fuels contribute about 82.7%, hydropower 14.5%, and nuclear only 3.4%. The transportation sector is supported by imported fuels as the domestic production is extremely less, about 785,000 bbl/day opposed to a demand of 2.45 million bbl/day. The IEA has described this situation as a system fueled “largely by coal and combined renewables and waste, with much smaller but growing shares of gas, oil, hydro, and nuclear". 28 At the same time, the growing inequality in energy demand and supply cannot be ignored. As development paces, the demand for energy increases. However, the current production is not sufficient. Circa 401 million people live without electricity, use of fuel wood and dung is prevalent leading to greater than 400,000 premature deaths yearly, mostly of children and women. Energy poverty can be seen in India as the economy booms and the economic conditions have benefited the “haves” but not the “have-nots”. 26 Income inequalities are largely responsible for this economic disparity. Evidently, electrical vehicles are being made available for Indians, however, their soaring prices make them unappeasable for the majority of the population. To bridge this gap, India must heavily invest in providing energy to all its people. However, this can’t happen without involving fossil fuels in the picture in the short run. In such a scenario, for India the battle becomes more difficult as it can’t severe itself from the conventional means of energy generation and employment. The discontinuation of coal will affect employment of numerous and at the same time putting millions of people into darkness and shut hundreds of productions units. This will again add to the woes of economy. Results and Discussions By now we have seen the existing climatic variations and the challenges presented to the pillars of economy. We now have an idea as to how climate change has affected us in every possible way. Perhaps something unavoidable. Yet, development measures themselves possess great risk when it comes to climate change. Rainfall As evident from the above discussion, the temperatures are rising consequently of climate change. This will result in escalated evaporation of water and accumulate abundant water for precipitation, thereby leading to flood-like situations. Similarly, increase in the evaporation rate of water and tremendous change in wind pattern will lead to decreased rainfall leading to drought like situations. Hence, there will be an overall increase in storms and strong rainfall. So, areas in their direct contact will experience excessive precipitation. While areas away from them will experience water scarcity. Temperature Temperature is itself regulated by the water cycle and the atmospheric gasses. With an increase in the concentration of greenhouse gases, the temperature of earth will rise as more and more heat will get trapped in the atmosphere. All this is powered via climate change. Agriculture Both temperature and rainfall directly impact the agriculture. The reason being certain crops need certain physical condition for proper growth. Hence, climate change can make the growth of a particular crop difficult. For example, crops that need lower temperature will suffer from lower yields due to global warming (heating of the earth atmosphere). At the same time, crops needing less amount of water will get destroyed due to increased precipitation. Impact of Development on Climate Change The impact of development on climate change is very subjective and highly improbable. The reason being, the impact of development varies according to the different techniques used. However, as a summation it can be concluded that conventional mode of development like dependence on fossil fuels have degraded the climate and contributed to maximum climate change. As the time changed, and policies started adopting greener methods of development, there have been positive impact on the climate change. But the impact of development before the 20 th  century had impacted the climate in the most non-ignorable ways. It may be noted that the countries contributing to global pollution levels, global warming, and climate change are developed economies which experienced development through the 19 th  and 20 th  century. While countries who are either developing or underdeveloped contribute less to climate change parameters. Economy and Environment Go Hand In Hand India is blessed with enormous alternatives to meet its developmental needs. Stronger carbon emission targets can be met without compromising on developmental aspirations. The gradual decrease in public support for coal and improvement in electricity distribution can help to free fiscal space when public debt is increasing. This can also help in the generation of economic diversifications in the regions heavily dependent on coal for revenues and employment. Promoting clean and green electricity generation can help in diverting the burden from fossil fuels and reducing air pollution while generating more employment opportunities. Developing new mass transit systems and extending the present ones can reduce vehicular emissions while blooming employment. It will also stimulate economic growth through agglomeration economies in the future. Conservation and enhancement of wetlands and forests will support agricultural productivity, sequester CO 2  emissions, and enhance resilience power to environmental shocks. New metro systems are being developed and ambitious plans for vehicles and full electrification of railways are imperative. India has also started considering climate change in its policies for agriculture and water. Many times, the low-carbon options are more affordable than their counterparts and they also help in addressing socio-political needs urgently like the cleansing of air and access to quality jobs and services. The low-carbon alternatives will help in raising the standards of living and reduce GHG emissions simultaneously. 29,30,31,32 The Nationally Determined Contribution (NDC) report of India aims at 40% of energy generation from clean energy and a 33–35% reduction in emission intensity of GDP by 2030. India today is spending on energy-efficient lighting and renewable electricity more than ever. 33,34 India has committed to reduce its carbon emission by 1 billion by 2030 and reduce the dependence of the economy on carbon by 45% by the end of the decade at the COP26 Glasgow summit. It also aspires a net-zero carbon emission by 2070. 35 The below mentioned can be considered as a pivot point while forming climate policies.

• Solar Energy

India has been recently investing a lot in solar energy. This will help to eventually shift from fossil-fuel-based electricity generation. At the same time, it will create more employment opportunities in the short and long term. It can also help in reducing the gender gap in the economy. The people already involved in fossil fuel-based jobs can be trained for this switch, thereby protecting their employment prospects. The development of solar villages will not only help in raising the standards of all people but also cap GHG emissions.

• Waste Management

Mismanagement of waste is also leading to widespread water pollution and disturbs the ecological balance. In many areas, people are exposed to untreated waste leading to poor health and reduced life expectancy. Currently, India does not have any clear policy mandate on waste management. In recent years a lot of efforts have been given to solid waste management, but they remain lacking. The development of waste-selective management plants like waste gasification will tackle this problem. Building the infrastructure of these plants and future maintenance will open new employment opportunities for both skilled and unskilled laborers.

• Gasification

Gasification is also another field of interest when it comes to reducing climate change. At present many alternatives for petrol and diesel are present. Organic fuels like methanol and biofuels can essentially help motivate people to go green without any compromise on quality. In many countries, gasification is already used as an alternative to fossil fuels in countries like Japan. India should also join them. It will help in achieving the short-term goals of climate change. 36

• Electrical Vehicles

Electrical vehicles are the future of this world. In many countries, a lot of stress is already being given to EVs. However, these come at greater costs and are not affordable without compromise on quality. So, they should be developed as long-term goals. Special highways and express easy should be built to initiate the process.

• Afforestation

Forests are known for regulation rainfall and temperature. Restoration of the lost forest cover is essential. This will help in meeting needs and maintaining the ecological balance. A great amount of CO? will also get absorbed leading to maintained CO? levels. At the same time, precipitation and temperature will also be checked. This will improve/ maintain agricultural productivity.

• Alternatives for Pollution-Causing Substances

India should invest a great deal into its Research and development sector. Explorations and innovations for alternatives to existing pollution-causing substances will help in meeting the desired targets as soon as possible. Conclusion We have seen how climate change is affecting the pillars of Indian Economy (Agriculture, livestock, etc.) and why adopting harsh climate policies often meet reluctance (energy economy). Although India is the only G20 nation with a 2 °C compatible emissions, there is no harm for it to adopt an even more stringent approach in reducing climate change. The adoption of more carbon-efficient and resilient policies like National Clean Energy Fund and International Solar Alliance will enable it to climate-proof its future developmental endeavors. This will require the collective efforts of the government and the people. This is possible when people abide by the rules and regulations formed by the government towards reduction of climate change. At the same time, the government also boosts the motivation of the people via rewards.  Recently, the Indian government at the COP26 summit committed to a net zero carbon economy in the near future.  The words ‘climate’ and ‘economic-development’ are therefore inevitably and closely linked in India for decades to come. Funding Source No funds, grants or other support was received to assist with the preparation of this manuscript. Conflicts of Interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Acknowledgement We gratefully acknowledge Ramjas College, University of Delhi and Central University of Jammu for providing the financial support and assistance to the authors. References

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Tax structure and economic growth: a study of selected Indian states

• Yadawananda Neog   ORCID: orcid.org/0000-0002-3578-0460 1 &
• Achal Kumar Gaur 1  

Journal of Economic Structures volume  9 , Article number:  38 ( 2020 ) Cite this article

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The present study examines the long-run and short-run relationship between tax structure and state-level growth performance in India for the period 1991–2016. The analysis in this paper is based on the model of Acosta-Ormaechea and Yoo ( 2012 ), and for the verification of the relationship between taxation and economic growth the panel regression method is used. With the use of 14 Indian states data, Panel Pool mean group estimation indicates that income tax and commodity–service tax have negative effects whilst property and capital transaction tax have a significant positive effect on state economic growth. This study finds ‘U’ shape relationship between tax structure and growth performance. Based on the analysis, we conclude that for faster growth of Indian states, policymakers should give more focus on property taxes along with the reduction in income taxes.

1 Introduction

The study on the potential association between tax structure and growth performance has gathered a great deal of attention from policymakers, academicians and regulatory circles for several reasons. First, the developing and emerging economies require a large volume of tax revenues for the smooth and efficient functioning of the state at both the national and sub-national levels. Globalization has laid down the foundation for Goods and Service Tax (GST) in many developing countries (Mcnabb 2018 ). Due to competition, developing countries are also facing the difficulties to maintain existing tax revenues (Bird and Zolt 2011 ). Second, tax collection and structure of it create distortionary impacts in the economy through tax burden. Thus, the positive and negative impact of tax made the ‘tax–growth’ relationship more complex and the structure of taxation has a definite role in the development process of an economy.

In a budget constraint economy like India, investigation of tax–growth relationship enables us to formulate the suitable policy measure for the more inclusive and equitable growth process. The budget crisis is usually resolved through the cut-down of public spending or/and an increase in tax revenues (Macek 2014 ). Rapid reduction in spending or increase in taxes is harmful to long-run growth performance. Thus, the concern of the government lies with the problem of fiscal consolidation with sustainable growth performance where tax policies are vital.

Empirical evidence on the impact of tax structure on growth performance is not conclusive. India has adopted the Goods and Service Tax (GST) policy in 2017 intending to raise indirect tax collections and transform the indirect tax structure into a single market to avoid tax evasions and double taxation. GST is regarded as one of the major tax policy changes in independent India and economists are an optimist about its impact on revenue generations and growth performance. But this policy is not the only policy that shaped in independent India; other major policy changes also take place after independence. Footnote 1 Tax Reform Committee (TRC) report of 1991 regarded one of the productive and structured policy recommendations in the recent decade. At the state level, sales tax reform in the form of Value Added Tax (VAT) in 2005 becomes a fruitful policy initiative. However, the tax collections in both national and sub-national level are still low as compared to the international standards. Changes in tax policy also change in the tax structure in the economy and India witnessed these changes at both levels of governments. Recent studies proved that the changes in tax structure have decisive implication in the growth performance through work–leisure behaviour, investment decisions and overall productivity (Arnold et al. 2011 ; Gemmell et al. 2011 ; Macek 2014 ; Mdanat et al. 2018 ; Durusu-Ciftci 2018 ). In India, very few empirical studies are available which analyse the impact of these changes in tax structure on growth performance and this study will be first to investigate tax–growth nexus in India with the use of state-level data.

This analysis primarily concerned with tax structure rather than to tax levels (usually measured as a tax–GDP ratio). The main advantage of tax structure analysis is that it provides revenue-neutral tax policy changes which remove the difficulties related with the question of how aggregate tax revenue changes relates with expenditure changes (Arnold et al. 2011 ). The empirical results from linear panel regression suggest us that property and capital transection tax are positively affecting the state’s growth performance, where commodity and service tax effect negatively. However, the non-linear panel regression indicates that the positive effect is only visible for property taxes at a higher level where the negative effect of commodity and service taxes becomes positive after a threshold point. The effect of income tax is not significant in long run irrespective of panel regression models.

The structure of the paper is as follows: Sect.  2 deals with the theoretical framework and empirical literature, followed by a brief description of data and methodology in Sect.  3 . Empirical results and discussion are presented in Sect.  4 and our last Sect.  5 is for conclusions and recommendations.

2 Theoretical framework and empirical literature

Growth literature very recently acknowledges the role of taxation in the growth process of an economy. Until recently, growth models are more concerned with the steady-state process and exogenous changes. On the theoretical ground, taxation does not have any impact on growth (Myles 2000 ). Development of endogenous growth models creates the space for fiscal policy especially tax policy in determining the growth performance. Barro ( 1990 ), King and Rebello ( 1990 ) and Jones et al. ( 1993 ) were the pioneer in this regard. Tax level and tax structure have an impact on the saving behaviour of the household and investment in human capital. On the other hand, the firm also changes its investment decisions and innovations following tax policies (Johansson et al. 2008 ). These decisions and incentives in the accumulation of physical and human capital create the ‘Growth’ disparities amongst the countries and state economies.

A large body of literature available on “Tax-Growth” relationship is mostly dedicated to cross-country settings (Martin and Fardmanesh 1990 ; Karras 1999 ; Myles 2000 ; Tosun and Abizadeh 2005 ; Johansson et al. 2008 ; Vartia et al. 2008 ; Arnold 2011 ; Szarowska 2013; Macek 2014 ; Stoilova 2017 ; Safi et al. 2017 ; Durusu-Ciftci 2018 ) that investigates the effect of tax policy on economic performance. Income and corporation taxes are the major tax instruments for the governments irrespective of the level of developments of a country. The formation of tax structure with these two taxes has many implications in the growth performance. The study made by Arnold et al. ( 2011 ), Macek ( 2014 ) and Dackehag and Hansson ( 2012 ) has explored the negative relation of income and corporation tax with growth performance. Vartia et al. ( 2008 ) find the negative impact of corporation tax for OECD countries. If we consider the average and marginal tax rate, marginal tax is very influential than to average tax rate in investment decisions and labour supply. Empirical studies prove that marginal tax has a negative relation with growth, which indicate raising of marginal tax rate is associated with compromises with growth performance (Padovano and Galli 2001 ; Lee and Gordon 2005 ; Poulson and Kaplani 2008 ). Studies also established that other type of taxes also has a significant impact on growth performance, like consumption tax (Johansson et al. 2008 ; Durusu-Ciftci 2018 ), GST and Payroll (Tosun and Abizadeh 2005 ), property tax (Xing 2011 ), labour tax (Szarowska 2014 ), sales tax (Ojede and Yamarik 2012 ), excise (Reynolds 2006 ), etc.

However, looking at the single country’s perspective, we find very little evidence on the same. Stockey and Rebelo ( 1995 ) with the use of the endogenous growth model study the role of tax reforms on U.S. growth performance. They have found that tax reforms have very minor implication with economic outcomes. There are several studies exist for US economy where they empirically try to establish the link between tax and growth. Atems ( 2015 ) finds the spatial spillover effect of income taxes on the growth of 48 contiguous states. On the other hand, Ojede and Yamarik ( 2012 ) have not found any kind of impact of income taxes on growth in these states. Their panel pool mean group estimation indicates that property and sales tax has detrimental consequences in development. With the use of data for the U.S. covering the period of 1912–2006, Barro and Redlick ( 2009 ) find that average marginal income taxes were halting the economic growth. However, they have provided an interesting argument that in wartime, the tax does not have any kind of relation with growth. In search of an answer to the question that whether corporate tax rise destroys jobs in the U.S., Ljungqvist and Smolyansky ( 2016 ) use firm-level data for the period 1970–2010. The main conclusion of the paper is that a rise in corporate tax is not good for employment and income and has very little impact on economic activity. Using the error correction model, Mdanat et al. ( 2018 ) find for Jordan that income tax, corporation tax and personal tax negatively impact the growth. They suggest that irrespective of tax collection, the prime focus of the government should be social justice of the people. Dladla and Khobai ( 2018 ) also find similar results for South Africa where income taxes are coming out to be negative. For the case of Italy, Federici and Parisi ( 2015 ) used the 880 firms’ data and results show that corporation tax is bad for investments with the consideration of both effective average and marginal taxes rates.

Looking at the literature, the empirical relationship of tax structure with growth performance is still unclear for India. This study attempts to fill the gap by examining the effect of tax policy on economic performance in an emerging economy such as India at the state level. Second, with the use of panel Pool Mean Group (PMG) estimator which assumes slope homogeneity in the long run and heterogeneity in the short run, we can incorporate the dynamic behaviour of the variables which will be new to tax structure–growth study in India. Third, the tax–growth nexus may show a non-linear relationship due to the threshold effect. We consider this non-linearity in our panel regression model which will be a contribution to the existing literature.

3 Data and methodology

To study the effect of tax policy on economic performance in India, we employed three models and included each tax instruments in the models separately to avoid the problem of Multicollinearity. Following the works of Arnold et al. ( 2011 ) and Acosta-Ormaechea and Yoo ( 2012 ), the tax structure is measured by the share of individual tax to the total state tax revenues. We investigate the tax–growth relationship with the following equation.

Here, Y it is the growth rate of Per capita net state domestic product (NSDP), SGI is the state gross investment as a percentage of state domestic product, TAX is one of the tax shares (Property, Commodity & Services and Income), Tax Burden Footnote 2 is the ratio of total tax revenues to state domestic product and ϵ is the error term. Per the work of Acosta-Ormaechea and Yoo ( 2012 ), this study is more concerned with the impact of tax structure on growth rate rather than level effect. In model 1, we include property tax share, and in model 2 and model 3, we incorporate commodity & service tax and income tax, respectively. By following the approach of Arnold et al. ( 2011 ), we include total tax burden as a control variable which will reduce the biases that may occur from correlation in between tax mix and tax burden. We also included Secondary Enrollment Rate as a proxy variable for human capital in our model, but the inconsistent and insignificant results make us drop the variable from the final estimation model.

In search of a possible non-linear relationship, we introduce a separate panel regression by introducing the square of each tax share into the models.

If the coefficient of α 3 significant and carries an opposite sign to α 2 , then we can conclude that there is a non-linear relationship exist.

In this study, we included 14 Indian states for the period 1991 to 2016 and excluded North-Eastern states due to their relatively small tax revenue collections. Data have been taken from the Centre for Monitoring Indian Economy (CMIE) and Handbook of Statistics on the Indian States, published by Reserve Bank of India. The states that are included in this study are Andhra Pradesh (undivided), Footnote 3 Assam, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Karnataka, Kerala, Maharashtra, Punjab, Tamil Nadu, Orissa, Rajasthan and West Bengal. All the states are included in model 1 and model 2. For model 3, due to the data availability, we include only seven states Footnote 4 namely Andhra Pradesh, Assam, Gujarat, Karnataka, Kerala, Maharashtra, and West Bengal.

The selection of the study period is primarily driven by the argument provided by Rao and Rao ( 2006 ) that after the market-oriented economic reform of 1991, more systematic and long-term goal-oriented tax reforms were initiated in state level for India. The economic reform also brings rapid growth in India and it becomes very interesting to look at the tax–growth nexus after the economic reform. The second restriction related to the use of long data span is the availability of data for each tax head for each of the states under this study.

3.1 Unit root

Pool Mean Group (PMG) specification is very fruitful and widely used model to capture the dynamic behaviour of policy variables. This model is very powerful as it can investigate both I (0) and I (1) variables in a single autoregressive distributive lag (ARDL) model setup. A necessary condition in the ARDL model is that the model cannot deal with the I(2) variables. Thus, the investigation of stationarity becomes a compulsion. We used popular panel unit root tests like LLC (Levin et al. 2002 ), the IPS (Im et al. 2003 ), the ADF-Fisher Chi square (Maddala and Wu 1999 ) and PP-Fisher Chi square (Choi 2001 ) in this study.

3.2 Panel PMG model

The Mean Group (MG) estimator was developed by Pesaran and Smith ( 1995 ) to solve the issue of bias related to heterogeneous slopes in dynamic panels. Traditional panel models like instrumental variables’ estimator of Anderson and Hsiao ( 1981 , 1982 ) and Arellano and Bond ( 1991 ) may produce inconsistent results in a dynamic panel framework (Pesaran et al. 1999 ). MG estimator takes the average value of every cross-section and provides the long-run estimate for ARDL or PMG. On the other hand, Pooled Mean Group (PMG) estimator developed by Pesaran et al. ( 1999 ) assumes slope homogeneity in the long run but heterogeneous slopes in the short run for cross-section units. Dynamic Fixed Effect (DFE) also works like PMG and restricts cointegrating vector to be equal across all panels and restricts the speed of adjustment to be equal.

Under these assumptions, PMG is more efficient estimator than to MG and DFE estimator. The prime requirement for PMG estimator is that T should be sufficiently large to N. Panel ARDL or PMG works through maximum likelihood. Our basic PMG begins with the following equation.

Here, x it is the vector explanatory variables and y i is the lag dependent variable. X it allows the inclusion of both I (0) and I (1) variables. State fixed effect is captured through μ i . Above equation can be re-parameterized to ARDL format.

ɸ i measures the state-specific speed of adjustment and known as Error Correction Term. Β i is the vector of long-run relationships and α ij and θ ij are the vectors of short-run dynamic relationships. Pesaran et al. ( 1999 ) did not provide any statistical test for checking long-run relationship but it can be concluded form sign and magnitude of Error Correction Term (ECT). If it is negative and less than − 2, a long-run relationship can be established.

4 Results and discussion

Panel unit root test results from Table 1 suggest that in the case of Model 1 & 2, the Growth rate of Per Capita Net State Domestic Product (PC-NSDP), Property tax and commodity taxes are stationary at level. Gross investment and total tax revenue share to GDP are stationary at the 1st difference in all models and income tax share is also stationary at the same order.

5 PMG model results

We have reported MG, PMG and DFE estimation in the Tables  2 and 3 . The Hausman test indicates that the PMG model is the best model for our data than to MG model. Negative and significant error correction terms in all the models show the long-run relationship in between variable. One major issue related to the tax–growth equation is the problem of endogeneity of the variables. As growth in per capita GDP is our dependent variables, there is a possibility that tax collections behave along with business cycles. Therefore, we tested the weak/strong exogeneity of the tax variables through the correlation analysis between business cycles and tax shares. Business cycles have been calculated using the Hodrick-Prescott (HP) Filter. We have found that all the tax instruments are very weakly related to the business cycles movement and thus, we conclude that variables are not truly endogenous.

The speed of adjustment in PMG model 1, 2 and 3 are 78.9%, 78.4% and 79.6%, respectively. For the sake of completeness, we have reported MG and DFE Footnote 5 model results also. But we are more concerned with the results of PMG estimator as Hausman test suggested that PMG is a better model than to MG. The sign of the property tax is positive and significant in the long run as well as in the short run. Results are in line with the findings of Acosta-Ormaechea and Yoo ( 2012 ). Property tax generally considered a good revenue source for state and municipal governments for providing economic and social services in the city. This tax is also able to establish cost–benefit linkages and feasible decisions for the citizens. The positive impact of property taxes indicates that the revenue generation and productive utilization of these revenues exceed the distortionary effect in these states. As we expected, the tax burden is negatively associated with growth performance in both long run and short run. The relationship is showing the distortionary effect of the tax collection in the state economy. In all models, gross investment enhancing the growth in per capita SDP in the long run. Signs are readily justified as enlargement of capital formation has a positive impact on output and employment which channelized to the development outcomes (Swan 1956 , Solow 1956 ).

Commodity and service taxes are negatively related to the growth in per-capita SDP in the long run as well as in short run and findings are similar to the work of Ojede and Yamarik ( 2012 ). Footnote 6 This tax now comes under the Goods & Services taxes, but in the pre-GST period, commodity and service taxes are reducing growth in per capita NSDP. Commodity taxes are indirect taxes and state own tax revenues mostly come from indirect taxes. As indirect taxes, it has certain disadvantages like inflationary pressure in the economy and regressive to the poor section of the society. Our results also support the same hypothesis that increased commodity tax share is harmful. In India, commodity and service tax includes central sale tax, state excise duty, vehicle tax, goods & passenger tax, electricity duty and entertainment tax. Central sale tax was imposed on interstate trade of commodities which is now transformed to Inter-State GST (IGST). According to Das ( 2017 ), if the IGST rate is high to the Revenue Neutral Rate, it will harm the aggregate demand in the economy through the reduction of disposable income. Heavy vehicle and passenger tax collections are creating an abysmal environment for industrial activities. The tax burden variable is also carrying a negative sign in both long run and short run and magnitude is very similar to model 1. Income tax share has become insignificant and positive in the long run and negative insignificant in the short run.

After examining the linear relationships, we extended our analysis to the examination of a non-linear relationship with the use of PMG estimation model. The result from Tables  4 and 5 indicates the existence of a non-linear relationship between tax structure and growth performance for Indian states. The linear coefficient for property taxes has now become negative and the square of it turns out to be positive. Thus, the property taxes show a ‘U’-shaped relationship with states’ growth performance which implies that a rise in property taxes is bad for growth initially and after a threshold point, it becomes growth enhancing. The threshold point for property taxes is 1.88 which indicates that more than 80.77% observation is more than to threshold point.

In the case of commodity and service taxes, both the linear and non-linear coefficients are significant with different signs. However, the coefficient magnitudes are abnormally large and this is due to the inclusion of both linear and quadratic terms into the single equation. The small commodity and service taxes are very bad for the state economy, whereas the large amount of it shows a positive relation. The threshold point for this tax is 4.45 which implies that 79.95% observation lies above the threshold. This is a very interesting result as high commodity and service taxes could lead to high inflation in the economy and high inflation regarded as atrocious for growth. Further investigation of these findings is highly recommendable. As like linear panel regression, the income tax shows no relation in our non-linear regression model also. However, the short-run coefficient for income tax is significant and shows a negative relationship. Income tax is considered to be distortionary tax to the economy in the presence of income and substitution effect (Kotlan 2011 ). Income tax mostly impacts the savings of the households and labour supply which is regarded as an engine of growth.

6 Conclusions and recommendations

In this study, we try to find out the long-run and short-run relationship between different tax structure and economic growth in states of India. Empirical evidence from linear regression suggests that the property tax enhancing growth and commodity & service taxes reduce it. The non-linear regression validates these findings for property taxes where high property taxes are good for growth. In the case of commodity & service taxes, the results become opposite after the threshold point and affecting the growth negatively. Interestingly, we do not find any significant impact of income taxes on growth in both linear and non-linear regressions in the long run.

As far as the total tax burden is concerned, negative relation with the growth performance is verified and results are in line with Arnold et al. ( 2011 ). The negative effect of commodity and service taxes in the short run is expected to be neutralized through the implementation of GST in India. Promotion of growth performance at the state level concerning income taxes is also very crucial. Income tax has a direct effect on individuals and their saving and investment behaviour. On the other side, tax revenues should be placed in productive investments. With the spending, the government can promote inclusive growth, equality and efficiency in the economy.

The most promising path emerged through this study for long-run growth performance in Indian states is to lower the total tax burden and shifting from income and commodity taxes to property tax for revenue generations. The conclusion may be debatable on various grounds as the studied variables do not take into account institutional quality, administrative efficiency in tax collection, fiscal balance and condition of the states and existence of informal sectors. Future research can be done to incorporate these issues.

Availability of data and materials

Dataset analysed in this study is available from the corresponding author on reasonable request.

One can see the writings of Rao and Rao ( 2006 ) for brief discussion.

This is the proxy for total tax burden in the economy with certain limitations. It does not include informal economy and expenditure policies.

Telangana state was established in 2014. We merged the data of Andhra Pradesh and Telangana to achieve aggregate data for undivided Andhra Pradesh.

Data for Income tax are available for ten states, but inclusion of these states made the model inconsistent due to huge fluctuations in tax revenue collections.

Most of the coefficients of PMG and DFE are in similar range and smaller than to MG estimator. This is due to MG estimator only takes the information of each state time series to estimate long-run and short-run coefficients.

They use sale tax, where our study takes aggregate revenue for commodity and services. However, inference can be drawn as sale tax and is one of the dominant contributors in total commodity and service tax revenue in India.

Abbreviations

Net state domestic product

Goods and service tax

Foreign direct investments

• Pool mean group

Dynamic fixed effect

Auto-regressive distributed lag

The organization for economic co-operation and development

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Neog, Y., Gaur, A.K. Tax structure and economic growth: a study of selected Indian states. Economic Structures 9 , 38 (2020). https://doi.org/10.1186/s40008-020-00215-3

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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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Research Papers

100+ Indian Economy Topics For Presentation (Updated)

Published by admin on october 3, 2020 october 3, 2020.

This post covers a list of more than 100 Indian economy topics for presentation. These Indian economy-related PowerPoint presentation topics can be used for seminars, webinars, conferences, discussions, essays, speeches, oral presentations and other events. Hope these updated presentation topics will help MBA, BBA, Bcom, Mcom, MCA, PGDM, MMM, MMS and other specializations students and other management researchers.

Let’s explore Indian Economy related presentation topics updated in 2021.

Will 2021 be better for Indian Economy?

Is Indian economy recovering !

The rise of Covidnomics

Role of IT in Small scale industries in mission 2021

How Indian Economy Works?

Ban on Chinese Apps in India: Impact on Indian economy!

Is the worst over for Indian economy?

Aatmanirbhar Bharat Package: Boon or Bane for Indian Economy?

Vocal for Local Vs. Global Aspirations of India!

Impact of deceasing value of rupee on Indian economy

What has gone wrong with Indian Economy?

Globalization and Indian Economy!

Is India still a favourite among foreign investors?

Indian Economy: Before GST, After GST!

Impact of Demonetization on Indian Economy?

Indian economy & Moody’s ratings!

The Role of the Government in the Economy

World banks view on Indian Economy!

Manmohan Singh & Economic reforms!

The role of Women in Indian Economy!

Making sense of the Global & Indian Economy!

India’s Economy SlowDown: Natural or Result of wrong policies?

India’s Journey towards $5 Trillion Economy

Aatmnirbhar Reality : India’s dream towards self reliant India

Next Economic Powers of Asia: India’s Position!

India’s economy: Why the time for growth is now?

Is India really developing?

India’s dependence on China

Impact of privatisation on Indian economy!

Impact of GST on Indian Economy!

The sectoral Imbalance In India!

Is Indian ready for cashless economy?

Future of Indian Economy!

Learning from an ancient India’s economy!

FDI – Foreign Direct Investment in India: advantages and disadvantages

Aatmanirbhar Bharat : Boon or bane for Indian economy!

Depreciation of Indian Rupee: Reasons & Remedies!

How do elections affect the Indian Economy?

The Indian Industry is positive on economic growth!

India Economy 2021: It is not a recession, the economy is growing!

Is China’s loss of India’s gain?

Indian Economy: Looking beyond crises!

India’s Cashless Economy: UPI’s contribution

Impact of Atmanirnhar Baharat scheme on Indian Economy!

What makes a country a developed one?

Uniquely Indian – is there any product like that?

Can the Indian economy afford boycotting Chinese products?

Can India become a developed country : Dream Vs. Reality!

Atmanirbhar Bharat Abhiyan: A true commitment or a cosmetic gimmick?

India’s Economy: How Did We Get Here and What Can be Done?

The root cause for Indian Economy slowdown: Corruption or Wrong Policies

FDI to India: Advantages and Disadvantages

India needs a federal commission , not just a finance commission!

Role of IT in the Service Sector in mission 2021

Rise of E-commerce in India: advantages & disadvantages

Indian Service Sector – can it be peoples worth

Does India benefit from a Sino-US trade conflict?

Where does India stand in the International Forum?

What does India need to become a Superpower nation?

What happens if India survives only on service?

Standard of Indian service sector

The services sector :a key driver of India’s economic growth

Will the Indian economy recover in the year 2021?

Technology – really core strength of India!

Role of agriculture in Indian economy

Role of RBI in Indian Economy!

The need to care about the care economy

The modern face of Indian Manufacturing

Ideas to make India Economic Superpower

Impact of Make In India on Indian Economy!

The Indian service sector is a quality conscious

Is Indian economy moving towards recession?

Make in India – Key to Revival of Economy

Infrastructure in India is it at par with other developing nations

Economy Vs Environment : What’s important!

Impact of FDI on local companies and businesses!

Problem of Non Performing Assets in India

Impact of better ease of doing rank on Indian economy

Ease of doing business in India: Rankings improved, but issues remain

INR Vs. USD : Why so much of gap!

Indian economy on the eve of Independence !

India Vs the World: A brief economical comparison

Economic ideas of Gandhiji

Towards a more sophisticated Indian economy

The battle of 1991: How India’s economy was reformed!

Fundamentals of Indian Economy

Ease of doing business in India : Myths and realities

Contribution of Tourism Sector in Indian Economy

Sectors of Indian Economy

Economic and Social Development in India

Thalinomics: The Economics of a Plate of Food in India

Balance between economy & environment !

Indian Economy: Green Revolution

Sustainable Development and Climate Change: Challenges and opportunities

Chanakya’s Arthashastra & its relevance today.

Impact of National Education Policy 2019 on Indian Economy

Impact of National Health Policy on Health Sector & Indian Economy

The impact of India’s trade agreements on overall trade balance

Targeting Ease of Doing Business in India

India’s Balance of Payments (BoP) position

Future of coal: Economic & Environmental consequences!

This is all about Indian economy presentation topics updated in 2021.

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Federal Pandemic Relief and Academic Recovery

We measure the effect of district use of federal pandemic relief during the 2022-23 school year for a sample of more than 5000 districts in 29 states. We rely on several plausibly exogenous sources of variation in federal grants: differences in state Title I funding formulas, estimation error in Census local area poverty rates and differences in eligibility for federal Title I and subsidized lunch eligibility. We find that each $1000 in spending per student was associated with a .0086 SD improvement in math and a .0049 SD improvement in reading. Both are consistent with a recent meta-analysis of spending impacts by Jackson and Mackevicius (2023). As a placebo test, we find no relationship between federal dollars that were not yet spent during the 2022-23 year. We also find similar results using synthetic control group methods to compare high-poverty districts with high and low amounts of federal aid, but with similar trends in achievement through 2022. Because the federal aid was targeted at higher poverty districts, we find the federal dollars not only contributed to the recovery, but also helped narrow the gaps in achievement which had widened during the pandemic.

We thank Pete Claar at SchoolDigger, Sadie Richardson, Julia Paris, Demetra Kalogrides, Jie Min, and Jiyeon Shim for their assistance in producing the Stanford Education Data Archive (SEDA) data used in this paper. We thank staff at the U.S. Department of Education and Sarah Reber at Brookings Institution for providing data and technical details regarding the Title I program. We received data on spending of federal relief dollars from Dennis and Julie Roche at Burbio and Marguerite Roza at the Edunomics Lab at Georgetown University. Victoria Carbonari and Dean Kaplan at the Harvard Center for Education Policy Research provided research assistance. The National Center for Education Statistics (NCES) and the National Assessment Governing Board provided data on student achievement by state which we used to rescale state proficiency data. The research was supported by grants from the Carnegie Corporation of New York, Bloomberg Philanthropies, and Kenneth C. Griffin. The Bill & Melinda Gates Foundation has separately provided funding to the Stanford Education Data Archive. The opinions expressed here are ours and do not represent views of NCES, the U.S. Department of Education, or any of the funders. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

Douglas Staiger is a co-founder, consults for, and holds an equity interest in ArborMetrix, Inc., a company that sells efficiency measurement systems and consulting services to insurers and hospitals.

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