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Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)

An Online International Research Journal (ISSN: 2306-367X)

2017 Vol: 6 Issue: 2

2203 www.globalbizresearch.org

Demystifying Consumption – Savings Paradox:

An Exploratory Study of Indian Case

B. Venkatraja,

Assistant Professor-Economics,

Shri Dharmasthala Manjunatheshwara Institute for Management Development (SDMIMD),

Mysore- India.

E-mail: [email protected]

___________________________________________________________________________

Abstract

At the background of the onslaught of the great depression, John Maynard Keynes identifies

consumption spending and investment spending as the major driving forces of the growth.

Economists from the subsequent schools of thought raised concerns that Keynes failed to

realise that a great deal of trade-off is involved between these two activities. In order to invest

more today, we have to save more and consume less which depresses the growth than

accelerating. Identification of real factor- whether consumption spending or investment

spending as the growth driver- is currently debated and unresolved issue, which has

paramount policy implications. The present study is an attempt in addressing the ambiguity

created by the theories of Keynes. The study is driven by secondary data from India for the

post economic reforms period of 1991-92 to 2014-15 employing Keynes GDP identity with

modification. The study estimates the multiple linear regression model to elicit the factors

governing economic growth in India. Variance decomposition technique and impulse

response function have been approached to address the objectives of the study effectively.

Results demonstrate that in the given growth model of Keynes, consumption spending and

investment spending appear to be the significant growth governing factors. From the study it

seems that economic growth of India is not sensitive to the government spending and exports.

Furthermore, among consumption and investment spending, the variations in the GDP is

defined largely by the consumption spending. Hence, the government may design and

implement policies towards building up sound consumption practices.

___________________________________________________________________________

Key Words: Economic growth, consumption, savings, investment, thrift paradox, India

JEL Classification: E12, E21





1. Introduction

Subsequent to the great depression and its impact world over, John Maynard Keynes

(1936), in his book titled The General Theory of Employment, Interest, and Money advocated

the ways to overcome such economic turbulence and spelled out the factors govern economic

growth. For Keynes, consumption spending and investment spending are the major driving

forces of the growth. According to the framework of Keynesian model higher consumption

spending translates into more demand and rise in employment and income. On the investment

perspective, accelerated investment through higher savings is expected to open up more jobs,

business activities and economic prosperity. But this masks the fact that these two activities

are actually in opposition in the short run. In order to invest more today, we have to save

more and consume less. As a result, GDP in-and-of-itself reveals nothing about what grows

an economy; at best, it demonstrates how large the economy is and whether it’s growing or

shrinking (Papola, 2013). Evidences from conceptual and empirical studies support both

theories. Hence it is very demanding to investigate whether consumption spending or

investment spending drive Indian economic growth. The present study is an attempt in filling

the vacuum created by the theorists and researchers of the past. The outcome of the study may

provide right directions to the government machineries to frame effective polices either to

boost up domestic consumption spending or to promote savings and attract investment.

2. Literature Review

Baldwin and Seghezza (1996) tried to establish a link between trade liberalization and

investment-led growth. Estimating equations are derived from the model and estimated with

three stage least squares on a cross-country data sample. Results found that domestic

protection depresses investment and thereby slows growth. Foreign trade barriers also lower

domestic investment. The paper thereby infers that investment crunch owing to trade barriers

depress the economic growth. However, this result was disagreed by Herrerias and Orts

(2007). Using Johansen’s cointegration and vector autoregressive (VAR) model, Herrerias

and Orts attempted to test the relative significance of export and investment in driving the

growth of the domestic economy. It was discovered to a significant degree that exports

exogenously drive economic growth. This effectively leads to the conclusion to the study that

investment was not the growth force of China in comparison to export.

Anh, Pham Mai (2008) studied to explore whether Vietnam’s economy was driven by

export or by investment for the period 1986 -2007. The study developed the structural Vector

Auto-regression model with four variables - GDP, investment, export, and productivity. The

results indicated that neither investment nor export had significant impact on the country’s

GDP growth as they were found to be very small. Similar results were obtained by Razmi

(2008) for China economy. Razmi analysed the macroeconomic viability of investment and





export led growth in China by using a Kaleckian framework. Results show that the China’s

strategy of large investment and export promotion may have largely outlived its utility. The

study also brings out concerns over the sustainability of economic growth derived from the

current strategies. This leads to the conclusion that investment and exports would not be the

viable growth mechanisms.

Amin (2011) studied the causal relationship between consumption expenditure and

economic growth in Bangladesh. The study applied a bivariate framework using annual data

from 1976 to 2009. Johansen cointegration and ARDL cointegration were estimated and the

empirical findings reveal the presence of long run cointegration between the variables. From

the Granger causality test it was discovered that a long run unidirectional causal relationship

is running from economic growth to consumption expenditure. The study did not find any

evidence of consumption expenditure becoming a cause of economic growth.

Ahuja and Nabar (2012) studied the global growth impact of investment shocks in China.

It was found that each percentage point deceleration in China’s investment growth is

estimated to subtract between one-half and nine-tenths of a percentage point from GDP

growth in regional supply chain economies such as Taiwan Province of China, Korea, and

Malaysia. Major commodity producers with relatively large exposures to China such as Chile

and Saudi Arabia are also likely to suffer substantial growth declines in response to an

investment deceleration in China. The spillover effects from an investment slowdown in

China also register strongly across a range of macroeconomic, trade, and financial variables

among G20 trading partners as well as world commodity prices.

The study of Ansar. Et.al. (2016) also brought out the negative impact of investment on

the growth, but in this case the focus was on the infrastructure investment. The objective of

the study was to examine empirically whether the infrastructure investment lead to economic

growth or economic fragility in China. The study was done at the time when China’s growth

was staggering after decades of rapid growth coinciding with heavy infrastructure investment.

It was explored from the study that infrastructure investments in China failed to deliver

positive risk adjusted return. It was traced out that the non-performance of infrastructure

investment was owing to uncertainty surrounding costs, time, and benefits parameters.

In an empirical study, Georgiou (2012) investigates whether consumption drives economic

growth. This study was under taken covering samples from all the countries for the data from

2006 to 2011. Results point out that consumption generates economic growth. Whereas,

Kharroubi and Kohlscheen (2017) contradicts with the results of Georgiou (2012). They

investigated the impact of consumption led expansions. It was observed that GDP growth has

increasingly been led by consumption. However, consumption-led expansions tend to be

significantly weaker than when growth is driven by other components of aggregate demand





such as investment, government spending or exports. This is mainly owing to build up

imbalances. The study showed that while credit growth and rising house prices can boost

consumption in the short run, the incidence of consumption-led growth and rising debt service

ratios significantly dampen growth in the medium to long run.

The representative empirical findings, as presented in this section, are largely fragmented.

Different studies support different factors as growth driver. If some studies have considered

consumption as growth driving factor, other studies have explored the significant role

investment plays in expanding the economy. Contradicting economic theories and empirical

findings generate the research question as to what drives economic growth- whether

consumption or investment? The current study is an attempt in finding the answer to this

question. Further, no empirical literature is available in Indian scenario analysing the relative

significance of the components of GDP. To fill this vacuum, this paper analyses the relative

contribution of consumption spending and investment spending to the economic growth of

India.

3. Methodology

3.1 Data and Period of Study

The study is based on secondary data collected from authentic source. Required data on

different variables selected for the study are procured from the Reserve Bank of India (RBI)

Handbook of Statistics on Indian Economy. Post reform period i.e. 1991-92 to 2014-15 has

been included in the study. The time series data procured are on annual basis. Indian economy

started growing rapidly since 1991 along with policy focus on investment, exports and

consumption as well.

3.2 Variables

Since the objective of the study is to identify the relative significance of consumption

spending and investment in the economic growth, private consumption spending and

investment spending form the integral part of the study. As the review of literature reflects,

government spending and exports also have profound impact on the growth. Hence, they were

included in the study as control variables. Inclusion of the control variables would enable in

obtaining the realistic impact of private consumption spending and investment spending on

growth. The economic growth is measured by the Gross Domestic Product (GDP) at market

price. Selection of variables owe to Keynesian growth model as well as existing literature.

3.3 Model Specification

J.M.Keynes advocates that economic growth depends on macroeconomic factors such as

consumption spending, investment, government spending and net exports. From this Keynes’

national income identity could be derived as follows:

Y = C + I + G + (X-M) -------------- (1)





Where, Y is the gross domestic product measuring the national income, C is the

consumption spending, I is investment, G is government spending, X is export earnings, M is

import payments and X-M is the net exports.

Keynesian GDP identity is being adopted in this study with modification. The present

study measures the sensitivity of GDP to the consumption spending and investment spending.

To get their impact accurately, other components of the identity i.e. government expenditure

has also been included as control variable. With the growing exports since economic reforms,

what is the size of impact it leaves on the economic growth is to be studied. For which, only

the export values are considered rather than net exports as in the Keynesian identity. Thus the

model used for the study could be re-presented as:

Y = C + I + G + X ------------------ (2)

Where, Y is the gross domestic product (GDP) measuring the national income, C is the

consumption spending, I is investment, G is government spending and X is export earnings.

The specific functional form of the model could be:

GDP = f (C + I + G + X) ---------- (3)

3.4 Tools of Data Analysis

The study applies certain appropriate econometric tools to enable clear analysis of the

behaviour of the data. Linear multiple regression has been run to identify which are the

components have significant impact on the GDP. Upon testing the relationship between

dependent variables and GDP, variance decomposition is applied to identify accurately, how

much change in GDP is owing to the variation in consumption spending and investment

spending. Since the global economic world is volatile which may affect consumption,

investment and so GDP. To understand quantitatively the response of GDP for the shocks in

consumption spending and investment spending, impulse response function is applied.

4. Trends in Growth Components

It is evident from the raw data presented in Table-1 & Figure-1 that every growth

component and even GDP had positive growth since 1991-92. The trends in all the variables

are on the similar direction throughout. This reflects parallel movement of the GDP with its

component variables. Though there was positive growth in the economy during the first

decade of the economic reforms, the growth was moderate. The real growth was evident since

2002-03. The global economic recession had minor impact on Indian economy. From the

trends it appears that investment and exports had negative growth during 2008-09 and 2009-

10. Interestingly, domestic household consumption did not get much affected by global

recession. Indian GDP was quick to recover from the shocks of global economic turmoil.

Further, in 2014-15, when there was signs of sluggish growth owing to China slowdown, all

the variables- arguably the growth drivers were on downward trend. At the outset, we can





assume a strong correlation among GDP and other variables. Only the advanced study will

explore whether GDP is sensitive to the shocks administered by the other variables.

Table 1: Components of GDP in India from 1991-92 to 2014-15

Year PC GC GFCF X GDP

1991-92 10224.58 1831.8 3285.94 1106.37 15033.37

1992-93 10488.25 1895.03 3581.62 1160.5 15857.55

1993-94 10944.17 2007.51 3548.48 1320.41 16610.91

1994-95 11476.07 2035.29 3884.1 1492.65 17717.02

1995-96 12174.72 2194.12 4515.96 1961.28 19058.99

1996-97 13121.14 2295.94 4653.55 2084.64 20497.86

1997-98 13513.42 2554.29 5067.06 2036.1 21327.98

1998-99 14391.95 2865.72 5559.13 2318.8 22646.99

1999-00 15266.89 3203.2 5999.73 2736.17 24563.63

2000-01 15792.01 3247.27 5916.1 3232.88 25540.04

2001-02 16732.09 3323.69 6821.43 3372.21 26802.8

2002-03 17212.38 3317.53 6791.7 4083.24 27850.13

2003-04 18232.27 3409.62 7509.4 4474.5 30062.54

2004-05 19175.08 3545.18 9310.28 5690.51 32422.09

2005-06 20833.09 3860.07 10817.91 7174.24 35432.44

2006-07 22598.92 4005.79 12312.65 8634.59 38714.89

2007-08 24713.97 4389.19 14307.64 9146.28 42509.47

2008-09 26496.1 4844.59 14809.44 10481.4 44163.51

2009-10 28453.03 5517.03 15944.75 9990.3 47908.46

2010-11 30923.73 5835.45 17697.92 11950.03 52823.84

2011-12 33785.07 6235.74 19866.45 13811.29 56330.49

2012-13 35475.83 6620.33 20020.47 14498.03 58998.49

2013-14 37195.68 6873.89 19999.37 15722.22 61958.41

2014-15 18583.83 3502.08 9932.47 7915.15 30581.59

Note: 1. All variables are constant prices & measured at Rs. in billion.

2. GDP is at market price measured for 2004-04 base year prices.

Source: RBI Handbook of Statistics on Indian Economy





Figure 1: Trends in Growth Components of India

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1991-9

2

1992-9

3

1993-9

4

1994-9

5

1995-9

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1996-9

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1997-9

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9

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2013-1

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2014-1

5

GDP PC GFCFGC X

5. Factors Governing Economic Growth

As observed from the trend analysis economic growth is volatile and is sensitive. To

ascertain the factors contributing to the changes in growth, a linear multiple regression model

is estimated. Keynes’ national income identity serves as the theoretical framework in

estimating regression model. Borrowing from the Keynes’ theory, the growth model adopted

for the study as in the equation (2) and (3), the linear multiple regression model is estimated.

The regression model estimated has GDP as proxy variable to the economic growth. The

components of economic growth or factors determining the economic growth which form the

set of growth predictors are: private consumption spending (PC) and gross fixed capital

formation (GFCF) a proxy to investment spending. Government consumption spending (GC)

and exports (X) are also included to the set of predictors as control variables. By including

government spending and export earnings, the aim is to obtain the realistic role of

consumption and investment in determining the economic growth of India. The regression

equation is as follows:

GDP = bo + b1PC + b2GFCF + b3GC + b4X + e ------------ (4)

Where, GDP is the dependent variable, PC, GFCF, GC and X are independent variables,

b1, b2, b3 and b4 are coefficient values of independent variables and e is the error term. The

regression is estimated applying least square method using E-Views statistical package. The

results are presented in Table-2.

The results provide interesting insights on the relationship between the GDP and its

different components. It could be noted that exports and government spending do not have

significant impact on the economic growth of India. The result does not support the argument

of scholars who support export led growth of India. Surprisingly, the results indicate even

negative relationship between the exports and economic growth. Non- reliance of Indian

economic growth to the export market volatility was evident during the subprime lending





crisis which shook the macroeconomic fundamentals of the global economy. It is evident

that, unlike India, China’s export and economic growth was affected badly during that period.

From the results as presented in Table-2, private consumption function and investment

(GFCF) have significant positive impact on the GDP. The estimated model reflects that the

private consumption spending elasticity of GDP is greater than investment elasticity of GDP.

A 10 percent increase in the household consumption spending leads to economic growth by

the size of 13 percent. While, a 10 percent rise in investment may result in only 5.9 percent of

increase in GDP. Though investment is a significant determinant of GDP, household

consumption appears to be the major growth driving force. Thus, multiple regression model

has estimated that private consumption spending and investment spending do contribute to the

economic growth significantly.

Table 2: Regression results estimating the growth components

Variable Coefficient Std. Error t-Statistic Prob.

PC 1.313771 0.185829 7.069802 0.0000

GFCF 0.597716 0.227127 2.631639 0.0160

GC 0.317005 0.903282 0.350948 0.7293

X -0.063385 0.191222 -0.331476 0.7437

R-squared 0.998424 Mean dependent var 32725.56

Adjusted R-squared 0.998188 S.D. dependent var 14514.41

S.E. of regression 617.8927 Akaike info criterion 15.84152

Sum squared resid 7635829. Schwarz criterion 16.03786

Log likelihood -186.0982 Hannan-Quinn criter. 15.89361

Durbin-Watson stat 0.785385

Dependent Variable: GDP

Method: Least Squares

Included observations: 24

Regression results do not explain how much variability in economic growth is caused by

its own shocks and how much variation is caused by shocks in the significant variables viz.

private consumption spending and investment spending. Variance decomposition technique

brings out such analysis for over the period time. In the general linear model, the relationship

between the two variables is captured by the linear equation (5):

Y = a + bX + c -------------- (5)

Y is dependent variable or response variable, and X is independent variable or explanatory

factor.

With every unit change or shocks in X, there is a corresponding variation in Y. The variance

decomposition focuses on the ‘response variable’ i.e. Y which responds to the variations in

the independent variable i.e. X. The variance of Y for the shocks of other endogenous

variable in the model (X) can be presented as follows.

Var(Y) = E(Var[Y|X]) + Var(E[Y|X]) ---------------(6)





In the given equation (6), Var(Y) is variance of Y, E(Var[Y|X]) is explained variation of

Y directly due to changes in X and Var(E[Y|X]) reflects unexplained variation comes from

somewhere other than X. Thus, the variance decomposition brings out the variance of Y

owing to: (1) the expected variance of Y with respect to X, and (2) the variance of the

“expected variance of Y” with respect to X. In other words, the variance of Y is its expected

value plus the “variance of this expected value.”

In short, the result derived through this process enables to isolate to appreciate the fact

that the response in Y has variation; this variation is comprised of 2 components. When these

components are decomposed they are one type of variation that is explained by the changes of

X and another variance that is completely due to chance stance, i.e. unexplained. The results

derived through this process are presented in Table-3.

Table 3: Variance Decomposition of GDP

Period S.E. GDP PC GFCF

1 6352.537 100.0000 0.000000 0.000000

2 9024.580 95.58823 0.948059 3.463709

3 13210.70 88.63448 8.091226 3.274295

4 14999.06 68.87521 26.39970 4.725087

5 18554.88 55.30975 41.50042 3.189836

6 29144.06 73.85809 23.55326 2.588653

7 38116.77 79.90954 13.85125 6.239209

8 45027.64 75.74383 15.64855 8.607624

9 52256.15 61.48619 30.34262 8.171189

10 77227.11 69.46395 26.47883 4.057217

The results of variance decomposition demonstrate how much variance of GDP is due to

own shocks, how much changes is because of shocks in private consumption spending and

how much change in GDP is explained by the shocks of investment spending over the period

of time. In a time horizon, private consumption spending appears to cause the largest

variation in GDP. While shocks in investment spending has the least share in the total

variance of GDP. Variance decomposition estimation shows that nearly 26 percent variation

in GDP is owing to the shocks in private consumption spending, whereas changes in

investment spending cause only 4 percent variance in the GDP growth rate. It is significant to

note that 69 percent of variation in GDP is caused by its own shocks. Thus, from the results it

appears that the forecasting error in economic growth is significantly explained by the lagged

values of private consumption spending. The findings of variance decomposition show that

forecasting error in economic growth is not significantly explained by investment

expenditure. This supplements regression results.

The regression estimation shows the impact of the explanatory variables on growth and

the variance decomposition explains how much each variable including GDP causes the

variance in GDP. But they do not explain precisely the response of economic growth for the





shocks in the private consumption spending and investment spending in a time horizon. To

fulfil this requirement, impulse response function is administered.

Impulse response function gives accuracy on the nature of relationship between the variables

in the system. This econometric technique explains the responsiveness of the endogenous

variable in the system to shocks to each of the other endogenous variables. For each

endogenous variable in the system, a unit shock is applied to the error, and the effects over

time are noted. Impulse response function estimates accurately the percentage change in GDP

for a given percentage change in the private consumption spending and investment spending

over the long run.

Figure 2: Impulse Response of GDP to the Shocks of Consumption and Investment

Figure-2 predicts the response of the GDP to the shocks of private consumption spending

and investment spending. For each variable in the system, a unit shock is applied to the error,

and the effects over time are noted. The results presented in the Figure-2 have evidences state

that future values of GDP respond significantly and positively to the shocks of private

consumption spending after initial 5years. In the long run household consumption appears to

drive domestic demand and spur up rest of the economic activities leading to economic

prosperity and stability. Whereas for a unit shock administered to the investment expenditure,

the future values of GDP do not respond in the short run, but turns negative in the medium

term before turning to be positive in the long run. Thus private consumption spending seems

to be more effective than investment expenditure to derive higher growth.

-40,000

-20,000

0

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40,000

60,000

1 2 3 4 5 6 7 8 9 10

Response of GDP to GDP

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Response of GDP to PC

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Response of GDP to GFCF

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Response of GFCF to PC

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Response of GFCF to GFCF

Response to Cholesky One S.D. Innovations





6. Major Findings

To summarise the major findings of the study: among the different components of the

GDP, private consumption spending and investment spending have significant positive impact

on the GDP. In the given change in the economic growth of India, 26 percent is contributed

by the private consumption spending, whereas, investment spending causes only 4 percent

variability in the GDP. The GDP responds positively to the shocks of private consumption in

the long run. An investment shock generates no response from GDP initially and provides

negative response over the period of time. This enables us to infer that household

consumption is the most crucial parameter of economic growth of India. When the growth

generates from the consumption drive, that growth would be more equitable and sustainable.

The demographic composition of India primarily the large proportion of youth to the total

population stabilises the consumption. Even improving social security measures also provide

confidence to the public to spend more and save less. This consumption driven growth

outcome corroborates the results of Georgiou (2012) and contradicts with Amin (2011) and

Kharroubi and Kohlscheen (2017). Similar concussion was made by Razmi (2008) and

Herrerias and Orts (2007) with reference to China that investment and exports would not be

the viable growth mechanisms seems to be supported from Indian data as well.

7. Recommendations and Policy Implications

The findings of the study have significant policy implications. As the household

consumption spending seems to be the growth driver, the government may initiate policies

which encourage general public to spend more on consumer goods than save and invest. India

requires to rationalise the direct taxation policies to broad-base the tax net and reduce the tax

burden on a very limited part of the population. Lowering the personal income tax would go a

long way in enabling the rise in disposable personal income and thereby consumption

spending. Scientific social security policies towards unorganised sector and stringent labour

laws will ensure financial security and job security to the employees. These may enhance the

confidence of the larger society to spend more and save less. Sound fiscal and monetary

policies are essential to stabilise the macroeconomic environment of the country such as low

and stable inflation, income and employment.

8. Direction for Future Studies

The future study may intensively investigate to identify the region-based differences in

India in respect of role of consumption and savings-led investment in economic growth. A

state-wise study may provide better perspective. Since India is demographically diversified, it

would be essential to investigate whether the conclusions of this study would be applicable

throughout the country. Again, the policy initiatives need to be different in urban and rural





areas. Hence an extended study may incorporate rural – urban factors in to the model. This

would enable appropriate policy targeting to the different demographic condition.

References

Ahuja, Ashvin and Nabar, Malhar. (2012). Investment-Led Growth in China: Global Spillovers, IMF

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Amin, Sakib-Bin. (2011). Causal Relationship between Consumption Expenditure and Economic

Growth in Bangladesh, World Journal of Social Sciences, Vol. 1(2). pp.158 – 169.

Anh, Pham Mai. (2008). Can Vietnam’s Economic Growth be Explained by Investment or Export: A

VAR Analysis, Working Paper 0815, retrieved from: ttp://www.vdf.org.vn/workingpapers/vdfwp0815

Ansar, Atif; Flyvbjerg, Bent; Budzier, Alexander and Lunn, Daniel. (2016). Does infrastructure

investment lead to economic growth or economic fragility? Evidence from China, Oxford Review of

Economic Policy, Vol.32 (2). Pp.360–390.

Baldwin, E. Richard and Seghezza, Elena. (1996). Testing for Trade-Induced Investment-Led Growth,

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Kharroubi, Enisse and Kohlscheen, Emanuel. (2017). Consumption-led expansions, BIS Quarterly

Review, March, pp.25-37

Papola, John. (2013). Think Consumption Is The 'Engine' Of Our Economy? Think Again, Retrieved

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