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India's Potential Economic Growth

This paper provides estimates of the potential growth rate for India by adopting alternative approaches of statistical trend filtering techniques and a production function. The Hodrick-Prescott filtering technique leads to estimated potential GDP growth of about 7 per cent. The warranted growth rate hovers around 8 per cent for the more recent period. The multivariate production function framework yields a potential growth of 6.6 per cent, which could be an underestimate given the data limitations. From the policy perspective, changes in policy instruments are linked to measures of output and the inflation gap within the framework of a policy reaction function. Empirical results showed that policy actions have significant association with output gap and inflation gap.

India’s Potential Economic Growth Measurement Issues and Policy Implications

This paper provides estimates of the potential growth rate for India by adopting alternative approaches of statistical trend filtering techniques and a production function. The Hodrick-Prescott filtering technique leads to estimated potential GDP growth of about 7 per cent. The warranted growth rate hovers around 8 per cent for the more recent period. The multivariate production function framework yields a potential growth of 6.6 per cent, which could be an underestimate given the data limitations. From the policy perspective, changes in policy instruments are linked to measures of output and the inflation gap within the framework of a policy reaction function. Empirical results showed that policy actions have significant association with output gap and inflation gap.


conomists held a sceptical view of India’s long-run potential growth until the 1990s. In some quarters, it was viewed that the average growth rate of the Indian economy during the 1990s was not significantly different from the 1980s, despite a plethora of reform and liberalisation measures, which were taken in 1991-92 in the wake of balance of payment crisis [Balakrishnan 2004; Virmani 2004; Acharya 2002]. Real GDP growth increased by 0.5 percentage points to an average of 6.1 per cent growth between 1992-93 and 1999-2000 compared to 5.6 per cent during the 1980s. The buoyant economic condition during 2003-04 to 2006-07, however, has led economists to take a renewed interest in India’s long-run growth potential. The euphoria over India’s strong growth potential is being increasingly shared by various international agencies. Real GDP (at factor cost at constant 1999-2000 prices) grew at an average of 8.1 per cent from 2003-04 to 2005-06, higher by 2 percentage points than in the 1990s and by 2.5 percentage points than in the 1980s. This is mainly attributable to India’s macroeconomic performance during the past decade, which has in many respects, been remarkable and awakened the world to India’s potential as a global actor. India’s emergence with a sustained high growth performance along with China as a major growth driver not only in Asia but also in the global economy is widely recognised. According to the International Monetary Fund (IMF), India’s share in world GDP was around 5.8 per cent (at PPP adjusted exchange rates) in 2004, i e, a nearly 10 per cent contribution to world growth. India has secured a position in the top 10 among all countries since the 1980s [World Bank 2006]. More importantly, the global community is appreciating India and its progress better now than ever before. All these developments, therefore, necessitate forming an opinion on India’s long-run growth trajectory and prospects for future growth. A moot question is whether the strong growth performance in the more recent period reflects a clear shift to a significantly higher growth trajectory from a long-run perspective.

Against the above backdrop, the main objective of this paper is to estimate potential growth using the methodologies feasible in the Indian context and then examine their implications for policy. The study is organised into six sections. Section I provides a theoretical perspective on the concept of potential growth.

Section II outlines the methodological approach adopted in the study. A brief review of literature in the Indian context is presented in Section III. The empirical results of the study are presented in Section IV. The policy implications of the study are discussed in Section V followed by the conclusion in Section VI.

I Concept of Potential Growth

Theoretically, the concept of potential output is used to describe the “full-employment” GDP or the level of real GDP attainable when the economy is operating at a higher level of resource use. In other words, measurement of potential growth requires estimation of an economywide indicator of productive capacity. A more precise definition, however, requires elaboration as it is viewed differently by different analysts. For instance, fiscal analysts may be interested in separating the structural and cyclical components of the government’s fiscal deficit, which requires the concept of output that links output to fiscal policy. Similarly, other analysts may be interested in knowing the potential level of output, which could have been produced if a different set of policies under a different set of social institutions prevailed. The relevant concept of potential output for monetary policy could be the one, which is directly linked with the dynamics of wage and price inflation. Although potential output measures the productive capacity of the economy, it is not a technical ceiling on output that cannot be exceeded. In fact, for monetary policy purposes, it is a measure of sustainable output, in which the intensity of resource use is neither adding to nor subtracting from inflationary pressure. If actual output exceeds its potential level, then constraints on capacity may begin to bind, restraining further growth and perhaps contributing to inflationary pressure. If output tends to fall below potential, then resources could be lying idle and inflation could trend downward.

The fact remains that assessing current economic conditions, gauging inflationary pressures, and projecting long-term economic growth are central to any economic policymaking process. Measures of potential GDP were initially devised to guide decisions regarding monetary and fiscal policy generally for a one- to twoyear horizon. If the growth rate in the economy were estimated to be below potential indicating that labour or capital were not fully employed, then monetary or fiscal policy could be used to fill the output gap without incurring the risk of significantly higher inflation. The concept of potential output was seen as a tool to help policymakers manage aggregate demand and thus maintain steady economic growth. In this context, an accurate measurement of potential growth of an economy can help policymakers steer their policy measures appropriately to exploit the existing potential in the economy. The end-use of potential GDP is not limited to short-term fiscal and monetary policy but it can serve well for gauging the economy’s productive capacity over the short-medium term horizon. However, a lot depends on the reliability and accuracy of estimation. If carefully estimated, potential GDP can provide a reasonable sense of the economy’s potential for growth.

II Methodology

Since potential output cannot be observed directly, various methodologies have been developed to estimate it, which are nevertheless subject to certain limitations. A spectrum of viewpoints exists in the literature in favour as well against the usage of potential output for policymaking purposes. The objection is mainly with regard to its accuracy as it may vary a lot in a shorter time horizon and thus may be difficult to estimate. Despite this, attempts have been made to estimate potential output and use the estimates in the policymaking process [Denis et al 2006; The US Congressional Budget Office 2001, 2004].

The empirical estimation of potential output involves an unsettled issue related to appropriate methodology. The methods popularly used for estimating potential growth include: (i) statistical filtering technique, viz, linear trend, the Hodrick-Prescott filter, the Bandpass filter and the Kalman filter; (ii) labour productivity growth accounting using a production function; (iii) simultaneous econometric models (macroeconomic models); (iv) multivariate time series models such as structural vector auto-regression models (SVAR); and (v) the concept of warranted growth rate due to Harrod (1939) also provides an alternative framework for measuring potential growth.

Butler (1996) attempted to develop a hybrid method of measuring potential output that combines economic structure with a time series filter termed as extended multivariate filter (EMV). The EMV exploits theoretical relationships that are combined to identify the demand and supply side influences on output. Therefore, both theoretic as well as atheoretic approaches exist for the estimation of potential growth rates.

Although approaches based on theoretical linkages of the economic system are always desirable to draw inferences about the productive capacity at the macrolevel, they involve processing a larger set of macroeconomic data on various aspects of the economy, which may not be available, especially for developing economies. However, various methods used to compute potential output do not benchmark their trends to inflation or any independent measure of capacity and therefore cannot be interpreted as estimating the level of maximum sustainable output. In other words, studies may provide estimates of trend levels of output rather than the potential level of output [CBO 2004]. In fact, several studies have debated the suitability of different methods for the estimation of potential output [CBO 2001, 2004; Giorno et al 1995; Cerra and Sexana 2000]. Cerra and Sexana (2000) underscored the uncertainty with the estimation of potential output.

The gist of the debate leaves the impression that the use of a particular method and its degree of accuracy depends on the availability of data. Furthermore, given the fact that estimates of potential output are subject to considerable uncertainty, it is important to use several alternative estimation techniques. In this study, potential growth for India has been measured using four alternative approaches: linear trend, Hodrick-Prescott filtering, Harrod’s warranted growth and a production function. A review of the selected methodologies is as under.

Linear Trend

Traditionally, potential growth was measured by estimation of a log-linear trend of real GDP.

Hodrick-Prescott Filtering

The univariate Hodrick-Prescott (HP) filtering has been most commonly used by economists for estimating potential GDP. It derives a long-term “trend” output, based on the optimisation of a weighted average of the gap between actual and trend output, and the rate of change in trend output, or its smoothness over the sample period [Arora and Bhundia 2003]. According to the HP filtering technique, the trend Y* for t = 1, 2, ..., T is estimated to minimise:

1T λ T–1* ** **

Min — Σ (lnYt– lnYt )2 +— Σ [(lnYt+1–lnYt ) – (lnYt – lnYt–1)]2 T t=1 T t=2

…(1) where T is the number of observations, Y and Y* represent actual and trend output respectively while the parameter λ determines smoothness of the trend. A low value of λ will produce a trend that follows actual output more closely, whereas a high value of λ reduces the sensitivity of the trend to short-term fluctuations in actual output and, in the limit, the trend tends to the mean growth rate for the whole sample. The key advantage of the HP filtering technique, compared to a linear trend approach, is that it allows a continuous time evolutionary process of potential growth, which is consistent with changes and shifts in the macroeconomic environment. However, a major limitation of this method is that it suffers from sample end-point biases. Despite its limitation, the HP-filter is widely used because of its simplicity and little requirement of data.

Warranted Growth

Harrod (1939) is credited with the concept of the warranted growth rate. The warranted growth rate is the growth rate at which all saving is absorbed into investment. To be more precise, it is a situation in which the growing volume of ex ante investment is in equilibrium with ex ante saving. The warranted rate of growth is also interpreted as full capacity or full employment rate of growth. Therefore, the warranted growth rate is the one that induces just enough of an upward shift in aggregate demand so that the added capacity represented by net ex post investment at each successive higher level of income gets utilised [Peterson 1967]. Harrod postulated that warranted growth is unique, determined by a constant saving rate and capita-output ratio:


Gw = — …(2)

k which is derived from equality between saving and investment:

S = I S = sY I = ΔK = ΔkY

where Gw, Y, S, s, I, k and K are warranted rate of growth, real GDP, aggregate savings, savings rate, aggregate investment, incremental capital-output ratio (ICOR) and capital stock, respectively. The constant saving and capital-output ratio may hold for a mature developed economy, but a developing economy, both the saving rate and capital-output ratio may undergo changes over time. Thus, warranted growth could be estimated with a varying saving rate and marginal productivity of capital to gauge potential growth.1 From an empirical perspective, warranted growth can be measured variedly using alternative measures of saving rate such as gross domestic saving rate (SG), net domestic saving rate (SN), incremental capital-output ratios related to gross domestic capital formation (ΔKG/ΔY), net domestic capital formation (ΔKN/ΔY) at constant market prices respectively. Alternatively, capital-stock output ratios related to net capital stock (KN) to net domestic product at constant market prices can also be used for deriving warranted growth rate:

Gw1 = sG/(ΔKG/ΔYG) …(3)

Gw2 = sN/(ΔKN/ΔYN) …(4)

Gw3 = sN/(KN/YN) …(5)

where the symbol ‘Δ’ denotes annual increment of the variable, SG and SN are gross domestic saving rate and net domestic saving rate in relation to gross domestic product and net domestic product at current market price, respectively. In order to connect with potential growth, the saving rate and capital-output ratios can be smoothed using HP filtering. Thus, unlike the univariate HP filtering of actual growth rate, the above approach has the advantage of deriving potential growth based on potential saving rate and capital output ratios – the two principal components of growth process.

Production Function

As alluded to earlier, statistical approaches could be useful for deriving the aggregative picture of a country’s growth potential. However, they may be construed as a “black box” presentation without an identification of proximate sources of growth, which are important for policy. In this context, a multivariate production function, such as the popular Cobb-Douglas function, is used by various international agencies including the IMF (2006), the OECD [Giorno et al 1995], the European Commission [Denis et al 2006], and countries including the US [CBO 2001, 2004], UK, France [Baghli et al 2006], and [Bank of Japan 2003]. For policy purposes, a production function could be useful in various ways. Illustratively, the underlying objective of a policy stance of maintaining a stable interest rate regime is to boost investment activities and enhance the pace of capacity building while enabling the economy to shift to a higher long-run growth path. Accordingly, potential growth should relate to accumulation of physical capital stock, which is a major input in the production process. In the context of developing and emerging market economies, higher growth is not only necessary but it has to be sustainable in the long-run, accompanied by appropriate employment growth. It is recognised that a “jobless’ growth trajectory may not be sustainable in the end, since aggregate demand for output may not keep pace with capacity expansion, leading to substantial idle capacity and resources. Thus, growth accounting needs to reflect on the association between labour input and potential output. Another important perspective from endogenous growth theory owing to Romer (1986) is that economic policies and structural and institutional development could play an important role in augmenting factor productivity. It is recognised that developing economies may experience increasing returns to scale in production arising from sustained improvements in research and development, and physical and social infrastructure. Seminal studies on the subject suggest that structural factors pertaining to, inter alia, education (primary enrolment), government spending on the social sector, energy infrastructure and trade openness could have a significant relationship with factor productivity and thus, a country’s growth potential [Barro 1991; Clark 1997; Bosworth et al 2006; Rodrick and Subramanian 2004; Reddy 2005, 2006b; Mohan 2005a,b]. Through betterment of health, education and skills, human development creates human capabilities that can then lead to productivity enhancement and acceleration in economic growth [Mohan 2005a]. According to these studies, the increase in primary school enrolment augurs well for improving the quality of human capital. Similarly, energy consumption, being a crucial input, serves as a proxy for a country’s physical infrastructure development. In an open economy, trade integration amongst the countries that is driven more by technological developments than by public policy has the overall effect of rewarding those with high and increasing productivity [Reddy 2005]. Trade openness may contribute to factor productivity by allowing greater access of imports, embodied with technological improvements and a range of better intermediate inputs [Arora and Bhundia 2003]. Moreover, trade openness could also be a prodding factor for local firms to become more competitive and improve cost effectiveness.

In the Indian context, estimation of a multivariate production function has to contend with various data limitations, especially with regard to time series data on economywide employment and key structural parameters. Nevertheless, based on the insights from the above studies, it is possible to estimate a limited version of a multivariate production function in the following form:

LYt = α + β1LKt + β2 LNt + β3LEt + β4LSt + β5LXt

where Y, K, N, E, S and X are real GDP (at factor cost at constant price), fixed capital stock at constant prices, workforce, energy consumption, school level education (primary enrolment), and trade openness (ratio of exports and imports to GDP at current market price), respectively. The symbol “L” denotes the natural logarithm transformation of variables and the parameter “α” is the intercept term.

III Review of Indian Literature

There is no consensus with regard to methodology used for estimating potential output. Therefore, it is not uncommon to find varying estimates provided by various studies based on different methodologies. In the Indian context, although a few attempts have been made to estimate potential growth rates and output gaps, such attempts are yet to be institutionalised. Some of the attempts have made projections for long-term horizons while others have focused on estimating potential growth for short-to medium-term horizons. For instance, using the latest demographic projections and a model of capital accumulation and productivity growth, Goldman Sachs, in its report on the BRIC (Brazil, Russia, India and China) economies estimated that India has the potential to show the fastest growth over the next 30 to 50 years. Growth could be higher than 5 per cent over the next 30 years and close to 5 per cent as late as 2050 if development proceeds successfully. Similarly, Deutsche Bank places the potential growth rate of the Indian economy at about 5.5 per cent during the 2006-20 period.

Consistent and moderately high GDP growth rates during most years since 1980 have encouraged optimistic projections about India’s future growth potential. The argument is that just as India transited from the “Hindu rate of growth” of around 3.5 per cent during the first three decades of planned development to a higher growth trajectory of close to 6 per cent over the last 25 years, it can now move on to a new growth trajectory on a sustainable basis. One of the forces associated with the projections of high growth potential is the demographic advantage that India currently enjoys relative to the developed countries and also countries such as China. Donde and Saggar (1999) estimated the potential output to be 6.3 per cent using the univariate approach. However, they felt that with current structural changes and ongoing reforms, policy shocks could take the potential growth rate of the Indian economy to the 8-10 per cent mark in near future. Dhal (1999a,b) using a dynamic input-output framework placed potential growth at 6.5 per cent while a potential growth rate of 8-10 per cent was estimated based on the production function and warranted growth approaches. Rodrik and Subramanian (2004) have estimated the potential output growth to be more than 7 per cent. Virmani (2006) discussed reforms taking the economy above its current potential growth rate (trend rate) of around 6.5 per cent.

A draft approach paper to the Eleventh Five-Year Plan released by the Planning Commission suggests that the economy can grow between 8 and 9 per cent a year on a sustained basis provided appropriate policies are put in place. Otherwise, in a business as usual scenario, i e, the growth expected to be achieved without significant new policy initiatives is around 7 per cent per annum [Government of India 2006]. In a report on ‘Measuring the Global Output Gap’ released in February 2006, HSBC Global Research finds that India is running above potential to an extent of 2.6 per cent and places potential growth rate at 5.6 per cent. Similarly, the OECD report on economic outlook [OECD 2006] reveals that the Indian economy has experienced extremely rapid growth in demand over the three years prior to 2005 which to a certain extent is cyclical. While GDP growth picked up to 8.5 per cent over this period, supply has not been able to match demand, despite impressive increases in investment. This, to some extent, points towards the fact that the Indian economy is operating above its potential. The IMF staff projects growth at 6.5 per cent annually for India in the medium-term but remains optimistic in the sense that growth could reach 8 per cent in a reform scenario where successful fiscal adjustment creates space for needed infrastructure and social spending and structural reforms accelerate [IMF 2006].

IV Estimation of Potential Growth

Linear Trend

The estimation of a log-linear trend of real GDP showed that the trend growth rate could vary across sample periods (Table 1). Based on the coefficients of the time trend in a linear regression model, the trend growth of real GDP turned out to be 4.4 per cent, 5.8 per cent and 6.2 per cent during 1951-2006, 1981-2006 and 1991-2006 respectively. Though the linear regression model showed a high coefficient of determination in terms of adjusted R2 and highly statistically significant coefficients of time trends, the models were not statistically sound as the Durbin Watson’s (DW) statistic was much lower than the adjusted R2. When the linear trend model was allowed for a first order autocorrelation correction error term, the trend coefficient turned statistically insignificant for the sample 1951-2006. However, for the periods 1981-2006 and 1991-2006, the autocorrelation correction of the linear trend model showed an improvement in the DW statistic without affecting the statistical significance of the coefficients of the intercept and time trend. Based on the time trend coefficient, the trend growth turned out to be 5.9 per cent and 6.3 per cent during 1981-2006 and 1991-2006 respectively.

Hodrick-Prescott Filter

According to the HP filtering method, potential output growth for India was estimated at around 7.4 per cent during 2005-06. It was evident that the acceleration in growth was witnessed during 2002-03 to 2005-06, with potential growth increasing by 1 percentage point from 6.3 per cent in 2002-03 to 7.4 per cent in 2005-06. From these estimates, it is clear that for the last five years (2000-01 to 2005-06) the average potential growth rate turned out to be 6.7 per cent and the three-year moving average of the trend growth rate turns out to be 7.1 per cent (Table 2, Annex I). A flaw associated with the HP filtering technique is its sensitivity to λ, the smoothness parameter (Annex II). Sectoral growth trends: The disaggregated analysis of GDP growth using the HP filtering technique makes it clear that services sector has shown a higher growth potential of 8.6 per cent

Table 1: Linear Trend to Real GDP

(Ln GDP = Constant + b Trend)

1951-2006 1981-2006 1991-2006
Intercept 12.186 11.631 11.446
(523.43) (467.12) (257.65)
Trend 0.043 0.056 0.060
R2 (59.23) 0.98 (97.53) 1.00 (65.13) 1.00
DW 0.12 0.75 0.85
With first order autocorrelated error, AR(1)
Intercept 8.336 11.585 11.390
(0.59) (154.26) (81.03)
Trend 0.086 0.057 0.061
(0.94) (34.32) (21.74)
AR(1) 0.985 0.667 0.581
R2 (31.20) 0.998 (3.62) 0.998 (2.63) 0.997
DW 2.485 1.646 1.658
Table 2: Potential and Actual GDP Growth*

(Per cent)

Potential Growth Three-Year Moving Actual Growth Average

2000-01 6.2 6.1 4.4 2001-02 6.3 6.2 5.8 2002-03 6.5 6.3 3.8 2003-04 6.8 6.5 8.5 2004-05 7.1 6.8 7.5 2005-06 7.4 7.1 8.4

* Based on the HP filtering technique for the period 1981-2006.


compared to agriculture (2.5 per cent) and the industrial sector

(6.9 per cent) in recent years (Annex III). In fact, there has been a systematic upward shift in the growth potential of the services sector. The high and secular growth potential offered by the sector is what makes investment in the services sector a promising proposition. A service-led growth process, as argued often in the Indian context, may not be a distant possibility with the kind of demographic and skill-based advantage that India possesses in services. Empirical results suggest that in the initial years of the planning period, the growth potential in the industrial sector was well above agriculture and services perhaps on account of the emphasis on heavy industrialisation. Thereafter, deceleration in industrial growth potential during the period 1965-80 confirms

-10.0Potential growth rate (per cent)1961-621964-651967-681970-711973-741976-771979-801982-831985-861988-891991-921994-951997-981998-992000-012003-04 QE 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 1982Growth rate (per cent)

Per cent







0.0 -2.0 -4.0

Chart 1: Output Gap



Public (Potential)

Private (Potential)

Public (Actual)

Private (Actual)

Chart 3: Warranted Growth of GDP in India

pace. Its potential to grow can be placed at about 7.3 per cent based on the average potential growth derived for the period 2002-03 and 2004-05 while the corresponding figure of potential growth for the public sector stood at 3.5 per cent. This, to some extent, confirms the reduced role of the public sector in economic activities in recent years. For the more recent period, it is found that the private sector seems to be operating above its potential while the public sector is operating below its potential.

Warranted Growth

Within the framework of warranted growth, empirical results clearly showed an upward trend in India’s potential GDP growth over the years. Until the end of the 1980s, warranted GDP growth ranged between 4.4 and 5.5 per cent. However, during the 1990s, a perceptible shift could be seen in warranted growth, which was further pronounced during 2001-04. During 2004-05, warranted growth ranged between 7.1 per cent and 8.2 per cent (Chart 3, Annex V). The warranted growth on a higher trajectory reflects partly the decreasing trend in the ICOR since the 1980s. Lowering of the ICOR signifies greater efficiency in capital usage. Further, there has been an increasing trend in saving rates, which seems to have accelerated at a rapid pace particularly since the mid-1980s.

Production Function Approach

For the production function exercise, in the first instance, a two-variable function using available data on fixed capital stock and workforce was estimated for the period 1980-2004. The sector has witnessed a declining trend so has its potential growth rate (Chart 2). In sharp contrast, the private sector has been sharing not only a larger slice in GDP, but is also growing at a rapid India has not required much of so far [Chanda 2005]. Sustained growth in the industrial sector, along with high growth potential in the services sector can entrench the overall growth process. The agriculture sector seems to be losing its sheen gradually as evident from the dwindling potential growth over the years, perhaps pointing towards a lack of investment and the need for raising productivity in this sector.

As far as sectoral distribution of the potential growth rate based on disaggregated public-private sector output is concerned, it is found that the public and private sectors have assumed a contrasting trend of secular nature (Annex IV). To be more specific, analysis of data with disaggregated public-private sector output available from 1961 onwards shows that actual growth in the public

Output Gap in the industrial sector, however, would further require institutional, infrastructural, and legislative reforms in the economy on Chart 2: Potential Growth in Public and Private Sector a large scale, which the enclave-type service sector growth in

Potential Growth Actual Growth




5.0 0.0 -5.0







2000-0120022002-032004 2004-05

1990s based on unrealistic anticipated demand. For the subsequent period 2001-06, the growth potential of the industrial sector surged to 6.9 per cent. Accelerating and sustaining growth potential the period of industrial retrogression on account of various reasons which are well known [Shetty 1978].

Since the onset of the 1980s, however, the industrial sector’s growth potential appears to be reasonably promising. It increased from 5.5 per cent during 1981-85 to 6.4 per cent during the next 10 years. However, during the latter half of the 1990s, there was a moderate deceleration in the growth potential of the industrial sector. This probably reflected the tendency among entrepreneurs for less capacity addition in view of existing surplus capacity in certain segments of industry which was created in the early empirical production function, after correcting for autocorrelation, suggested that both fixed capital stock and workforce had a significant effect on output. The impact of capital stock on output was about two and half times larger than that of the workforce. The likelihood ratio test revealed that the sum of capital and employment coefficients could significantly depart from unity elasticity of substitution, implying the possibility of increasing returns to scale in production.

The multivariate production function, which included additional variables pertaining to educational attainment, energy consumption, and trade openness, besides capital and labour, showed a statistically significant effect of structural factors in the production process. Compared with the two-variable function, the multivariate production function revealed that the impact of physical capital and labour could be influenced by structural variables through factor productivity. The parameters of the multivariate production function bring to the fore some useful insights. First, physical capital stock had a dominant effect on output, followed by labour, energy consumption, educational attainment, and trade openness. Second, the sum of coefficients of capital and labour was equal to unity elasticity of substitution, implying the possibility of constant returns to scale in production in the absence of structural parameters. In the presence of structural parameters, however, the sum of the coefficients of all five variables significantly exceeded unity elasticity of substitution, implying the presence of increasing returns to scale in production. In other words, structural variables such as educational attainment, energy infrastructure and trade openness had the impact of enhancing factor productivity to result in increasing returns to scale in production in the Indian context. Third, given the parameters of the production function and the long-term growth trajectory of factor inputs including the structural variables, it is possible to attain long-run potential growth at about 6.5 per cent as shown in Table 5.

At this level of growth, the production function is more or less consistent with a time series approach such as the HP filter, as reflected in the correlation between the estimated output gaps based on the HP filter and production function (Table 6).2 It is evident that the HP filter measure of the output gap has greater correlation with the output gap measured from the multivariate production function than the two-variable production function. Thus, a multivariate production function could be a suitable framework for estimating the aggregate potential as well as identifying the major sources of output growth. However, it is to be noted that given the data limitation and lack of information about a host of other structural parameters in the Indian context, it may be possible that the production function could be subject to some underestimation of growth potential.

Key Findings

From the above empirical analyses, it can be summarised that potential growth can range from 6 to 8 per cent, based on alternative approaches (Table 7). First, traditional trend methods such as the linear trend may yield lower potential growth than other approaches. Second, the HP filter has the advantage of tracking sustained increases in potential growth, albeit at a lower pace, depending upon the trend smoothing parameter. Third, a multivariate production function could be consistent with the time series approach but despite data limitations, it can provide useful insights about the sources of growth due to key factors of production and structural parameters determining factor productivity growth. Fourth, the warranted growth framework yields the highest potential growth, outperforming the time trend and production approaches.

V Policy Implications

From the policy perspective, measures of output gap are useful for the empirical analysis of the Phillips curve and monetary policy reaction function. Within the framework of the policy reaction function, changes in policy instruments are linked to

Table 3: Estimation of Production Function (Two-Variable)

Variable/Parameter Coefficient ‘t’ Stat
Constant -3.97 -13.54
Fixed capital stock (LK) 1.05 24.17
Workforce 0.41 3.44
Auto correlation : AR(1) Adjusted R2 0.41 0.99 4.81
Durbin Watson statistic 1.44

Test of unity elasticity of substitution: Likelihood ratio (LR) test yielded a χ2 statistic at 30.8, which was highly significant, implying that the null hypothesis of unity elasticity substitution could be rejected.

Table 4: Estimation of Production Function (Multivariate)

Variable/Parameter Coefficient ‘t’ Stat
Constant -1.61 -1.37
Fixed capital stock (LK) 0.76 6.76
Workforce (LN) 0.24 3.40
Energy consumption (LE) 0.16 2.01
Primary enrolment (LE) 0.14 2.52
Trade openness (LX) 0.10 3.44
Autocorrelation: AR(1) Adjusted R2 0.41 0.99 4.81
Durbin Watson statistic 1.53

Test of unity elasticity of substitution: Likelihood ratio (LR) test yielded a χ2 statistic at 132.9, which was highly significant, implying that the null hypothesis of unity elasticity substitution could be rejected.

Table 5: Production Function: Sources of Potential Growth

Factors of Production Factor Input-Contribution Contribution Production Function Long-term to Potential to Potential Parameters Growth@ Growth Growth (Per Cent)

Capital 0.76 5.80 4.42 67.24 Labour 0.24 2.00 0.48 7.30 Energy 0.16 5.50 0.87 13.30 Education 0.14 2.50 0.34 5.24 Trade openness 0.10 4.50 0.46 6.92 Total potential

growth 6.58 100.0

Note: @ The growth trajectory of factor inputs and structural factors was derived from a log-linear model of the trend during the sample period 1980-2004.

Table 6: Correlation among Alternative Measures of Output Gap

CLY 1.00 0.98 0.97 0.51 0.62
CLY1 0.98 1.00 1.00 0.54 0.63
CLY2 0.97 1.00 1.00 0.54 0.62
GAP 0.51 0.54 0.54 1.00 0.67
GAP1 0.62 0.63 0.62 0.67 1.00

Note: CLY, CLY1, CLY2, GAP and GAP1 are the output gap measured from HP filter (λ=100), HP filter (λ=10) output gap, HP filter (Ravn-Uhlig frequency), two-variable production function, and multivariable production function, respectively.

measures of output gap (Ygap) and inflation gap, i e, actual inflation (π) less threshold/target inflation (πT). In most developed economies, the short-term interest rate is widely used as a policy instrument. Thus, the policy reaction function based on the Taylor rule serves as a useful framework for monetary analysis. In developing economies including India, policy actions are based on an array of instruments such as interest rate and direct instruments of control on monetary aggregates such as cash reserve requirement (CRR) and statutory liquidity requirement (SLR). In this context, an appropriate approach of characterising monetary policy actions assumes critical importance. In this study, the empirical analysis uses a composite index of policy actions (MPI), defined as a geometric mean of the index of the bank rate, CRR, and SLR. The base year for the policy index is 19992000, in line with the base year of real GDP for facilitating statistical analysis. For the period of the 1990s, the repo rate has emerged as an important indicator of policy actions. Accordingly, the composite index is allowed to include an index of the repo rate for the 1990s.

It is to be noted that for a meaningful exercise, it is appropriate to use a weighted average of policy instruments for gauging policy response to growth and inflation. However, assigning weights to each instrument entails a complex process. Thus, on a cautionary note, it is stressed that the empirical exercise has the limitation of using an unweighted index of policy instruments. The main objective is to show that for a given index of policy instruments, different measures of output and inflation gaps can lead to differential assessments of policy response. In other words, an appropriate measure of potential growth assumes critical importance for policy. The reaction function is formulated as follows:

ΔMPI = c + α Ygap + β (π -πT )

where ΔMPI represents changes in the natural logarithm transformed composite index of policy instruments. The parameters α and β characterise the response of policy to output gap and inflation gap, respectively. Alternatively, these parameters also reflect the weights assigned to growth and price stability, the principal objectives of policy.

Generally, the above equation can be estimated on an ex post basis, with available information about output gap and inflation gap for the current period. However, given the fact that information about the output gap is available with a time lag, the equation can be estimated with an alternative lag structure of output gap. For annual data, it is plausible to account for a period lag of output gap. Moreover, as mentioned above, the output gap can be measured in different ways. Based on the HP trend, three alternative measures of output gaps are derived for the smoothing parameter (λ) taking values of 10 and 100 and a Ravn and Uhlig (2002) frequency specification using a fourth order lag structure of output.

For estimating the inflation gap, this study uses the “informal, indicative, self-imposed ceiling” of 5 per cent inflation over the medium- to long-term [Reddy 2006a], while earlier studies showed threshold inflation in the range of 5 to 6 per cent in the Indian context [Vasudevan et al 1999; Kannan and Joshi 2000; RBI 2001]. The empirical results, however, remained largely unchanged for the alternative threshold inflation of 5 per cent and 6 per cent. The empirical models of the policy reaction function based on alternative measures of output gap in the Indian context brings to the fore useful and differential perspectives on the underlying welfare objective of policy with regard to growth and inflation during the full sample period 1950-2005 and the reform period in particular, 1992-2005 (Table 8).

First, during the full sample 1950-2005, it is evident that output gap based on the HP trend (λ=100) and inflation gap have a highly statistically significant association with changes in policy actions (Model 1). The coefficient of the output gap at 0.88 is higher by 69 per cent than the inflation gap coefficient (0.52). A change in the output gap measure due to the HP trend (λ=10) does not produce a significantly different result (Model 2). However, the output gap due to Ravn and Uhlig (2002) leads to a strengthening

Table 7: Summary of Alternative Measures of Potential Growth for India

Approach Potential Growth

  • (i) Linear trend
  • (a) Sample: 1980-2005 5.8
  • (b) Sample: 1991-2005 6.2
  • (ii) Hodrick-Prescott filter
  • (a) As at end-March 2006 7.4
  • (b) Average 2003-2006 7.0
  • (c) Past decade (1996-2006) 6.5
  • (iii) Production function

    (subject to medium-term trends in key inputs and structural parameters) 6.6

    (iv) Warranted Growth

  • (a) Using net domestic saving rate and ICOR (the ratio of net domestic capital formation to net domestic product at constant prices) 7.8
  • (b) Net domestic saving and net fixed capital stock-output ratio 8.2
  • Table 8: Policy Response to Output Gap and Inflation Gap

    DMPI = c + a Ygap + b (p -pT )

    Independent Variables: Coefficients (‘t’ statistic) C Ygap (p -pT) R2 DW

    1951-2005 Model 1 -0.001 0.882 0.523 0.17 1.95 (-0.11) (2.23) (2.30) Model 2 -0.001 0.882 0.523 0.17 1.95 (-0.11) (2.23) (2.30) Model 3 -0.001 0.986 0.509 0.17 1.95

    (0.09) (2.36) (2.27) 1992-2005 Model 4 -0.064 1.894 1.651 0.41 2.29 (-3.95) (2.49) (2.76) Model 5 -0.064 1.894 1.651 0.41 2.29 (-3.95) (2.49) (2.76)

    Model 6 -0.064 2.675 1.634 0.39 2.31 (-3.81) (2.43) (2.54)

    C Ygap(1) (p -pT) R2 DW

    1952-2005 Model 7 0.001 0.492 0.442 0.13 1.89

  • (0.06) (1.47) (2.26) Model 8 0.001 0.492 0.442 0.13 1.89
  • (0.06) (1.47) (2.26) Model 9 0.001 0.481 0.438 0.12 1.88
  • (0.06) (1.38) (2.27) 1992-2005 Model 10 -0.061 0.782 1.498 0.27 2.1 (-3.11) (0.57) (2.10) Model 11 -0.061 0.782 1.498 0.27 2.1 (-3.11) (0.57) (2.10)

    Model 12 -0.060 0.417 1.374 0.25 2.07 (-3.10) (0.19) (1.73)

    Figures in bracket are t statistics. Note: Critical values for 5 per cent and 10 per cent level of significance are approximately 2.0 and 1.8.

    of the output coefficient at 0.99 while the inflation effect (0.51) output may not capture the emerging growth pattern adequately remained almost unchanged (Model 3). as these are based on past data on the economy. There are at

    Second, during the reform period, there was a surge in the least two factors that complicate the interpretation of potential coefficients of output gap and inflation gap compared to the full GDP in the Indian context. On the one hand, a very broad sample period coefficients. A critical finding was that though structural change process took place in the early 1990s, which

    the output coefficient remained higher than the inflation coefficient, the gap between the two had substantially narrowed during the reform period compared to the full sample period (Model 4). The output coefficient at 1.89 was only higher by

    14.5 per cent than the inflation coefficient at 1.65. A similar result was evident for the HP measure of output gap (λ=10). However, for the Ravn and Uhlig measure of output gap (Model 6), the coefficients of output and inflations gaps remained significantly different from each other during the 1990s, though the difference between the coefficients (63 per cent) was lower than that for the full sample period (94 per cent).

    Third, the empirical models based on a period lag of output gap in the place of contemporaneous output gap yielded significantly different results. In terms of size, the coefficients of output and inflation gaps were almost close to each other. However, the output coefficient was not statistically significant whereas the inflation coefficient was highly statistically significant (Models 7-12).

    The empirical findings are in line with policy response to growth and price stability in the presence of uncertainty. Moreover, it could suggest that policy may be responsive to the latest developments in economy. Thus, a key insight is that alternative measures of output gap had differential associations with policy actions. In general, the empirical models provided evidence of significant association of policy actions with price stability and growth, though there is some evidence of a moderation of response to the output gap during the reform period.

    VI Conclusion

    The present attempt may not be conclusive in itself to estimate

    Potential Growth Rate (Hodrick-Prescott Filter)

    Annex I: Year-wise Potential and Actual Growth Rate

    Year Sample: 1951-2006 Sample:1981-2006
    λ=100 λ =10 Ravn-Uhlig λ=100 Actual Growth
    1981 4.2 3.9 3.8 5.8 7.2
    1982 4.4 4.3 4.3 5.7 6.0
    1983 4.6 4.6 4.7 5.6 2.9
    1984 4.8 4.9 5.0 5.5 7.7
    1985 5.0 5.1 5.1 5.4 4.0
    1986 5.1 5.2 5.2 5.4 4.9
    1987 5.3 5.4 5.4 5.4 4.3
    1988 5.4 5.7 5.8 5.5 3.8
    1989 5.6 5.9 6.1 5.5 10.5
    1990 5.6 5.8 5.9 5.6 6.7
    1991 5.7 5.5 5.5 5.6 5.6
    1992 5.7 5.3 5.1 5.6 1.3
    1993 5.8 5.5 5.3 5.7 5.1
    1994 5.8 5.8 5.8 5.8 5.9
    1995 5.9 6.2 6.3 5.8 7.3
    1996 6.0 6.4 6.6 5.9 7.3
    1997 6.0 6.4 6.5 6.0 7.8
    1998 6.0 6.2 6.2 6.0 4.8
    1999 6.0 5.9 5.9 6.0 6.5
    2000 6.1 5.7 5.6 6.1 6.1
    2001 6.2 5.6 5.4 6.2 4.4
    2002 6.3 5.7 5.5 6.3 5.8
    2003 6.5 6.1 5.9 6.5 3.8
    2004 6.8 6.7 6.7 6.8 8.5
    2005 7.1 7.4 7.5 7.1 7.5
    2006 7.4 8.2 8.3 7.4 8.4

    Annex II: Sensitivity of Potential Growth Estimation to the Choice of Smoothness Parameter in HP Filter

    9 8 7

    potential GDP growth. The empirical estimation based on a sound

    economic basis is constrained by data availability. The univariate

    filtering technique, most popular in such cases, provides a potential

    growth rate of around 7 per cent. The exercise for estimating growth


    Per cent




    based on the concept of warranted growth yields expansion of 2 around 8 per cent for the Indian economy. Sectoral analysis for the 1 public and private sectors reveals that the private sector has been 0 growing above its potential while the public sector is operating below its potential. Not surprisingly, the growth potential in the



    private sector is found to be far higher than that in the public sector, which confirms the reduced role accorded to the public sector, particularly, since the early 1990s. The structural production function approach could yield potential growth of 6.5 per cent, more or less similar to time series approaches. However, given the data limitations and lack of information pertaining to various structural parameters, this may be an underestimate of potential growth. A key finding from the production function was the possibility of increasing returns to scale in production arising from physical and social infrastructure. Output gaps (based on filtering technique) and inflation gaps were used to explain policy reactions since 1951, which concludes that gaps in the output and inflation rate significantly explain the changes in policy actions.

    As the Indian economy has been passing through a phase of structural reforms, the currently available estimates of potential

    l=100 l =10


    Annex III: Average Potential Growth Based on Growth Series

    1951-55 3.1 5.3 3.6
    1955-60 2.5 6.7 4.6
    1960-65 2.0 6.7 5.0
    1965-1970 2.4 4.9 4.5
    1970-75 2.6 3.9 4.3
    1975-80 2.6 2.6 4.6
    1980-85 3.0 5.5 5.8 5.4 6.0 5.8
    1985-90 3.3 6.4 6.7 3.4 6.5 6.8
    1990-95 3.4 6.4 7.2 3.2 6.3 7.2
    1995-2000 2.8 6.1 7.9 2.7 6.1 7.9
    2000-06 2.5 6.9 8.6 2.5 6.9 8.6

    Based on Hodrick-Prescott Filter.

    Annex IV: Potential and Actual Growth in Public and Private Sector*

    Year Potential Growth Actual Growth
    Public Private Public Private
    1961-62 12.5 2.2 11.9 2.2
    1962-63 11.7 2.3 16.7 0.5
    1963-64 10.9 2.4 9.8 4.5
    1964-65 10.1 2.5 8.9 7.4
    1965-66 9.4 2.5 9.5 -5.4
    1966-67 8.8 2.6 6.2 0.2
    1967-68 8.3 2.7 6.6 8.4
    1968-69 7.9 2.8 8.4 1.7
    1969-70 7.6 2.8 8.0 6.3
    1970-71 7.4 2.7 9.0 4.3
    1971-72 7.2 2.7 5.9 0.1
    1972-73 7.1 2.7 6.4 -1.6
    1973-74 7.0 2.8 10.2 3.4
    1974-75 7.0 2.9 2.3 0.9
    1975-76 6.9 3.0 8.8 9.0
    1976-77 6.9 3.0 10.5 -0.8
    1977-78 6.9 3.1 5.1 8.1
    1978-79 7.0 3.2 7.3 5.1
    1979-80 7.0 3.3 4.3 -7.6
    1980-81 7.1 3.5 7.9 7.0
    1981-82 7.1 3.7 5.0 6.3
    1982-83 7.2 3.9 10.0 1.1
    1983-84 7.2 4.1 6.7 8.0
    1984-85 7.2 4.3 7.6 3.3
    1985-86 7.2 4.5 8.6 3.2
    1986-87 7.1 4.8 8.6 2.9
    1987-88 6.9 5.0 6.5 2.9
    1988-89 6.7 5.2 7.1 11.7
    1989-90 6.5 5.4 8.3 6.2
    1990-91 6.4 5.5 3.0 6.5
    1991-92 6.2 5.6 6.2 -0.4
    1992-93 6.1 5.7 2.6 6.0
    1993-94 6.1 5.8 5.1 6.2
    1994-95 6.1 5.9 7.2 7.3
    1995-96 6.1 6.0 6.0 7.8
    1996-97 6.1 6.0 4.5 9.0
    1997-98 6.0 6.1 11.9 2.5
    1998-99 5.8 6.1 7.3 6.2
    1999-00 5.5 6.3 5.7 6.2
    2000-01 5.1 6.4 0.7 5.7
    2001-02 4.6 6.6 6.9 5.4
    2002-03 4.1 6.9 6.9 3.0
    2003-04 3.5 7.3 1.8 10.9
    2004-05 2.9 7.7 1.0 8.9

    Annex V: Estimates of India’s Warranted GDP Growth

    Notes: Potential growth rates are based on the HP filtering technique applied on growth series.

    * Data used in the exercise is 1993-94 base.

    Year GW1 GW2 GW3

    1982 3.7 4.3 3.2 1983 3.9 4.5 3.3 1984 4.1 4.7 3.5 1985 4.3 4.9 3.7 1986 4.5 5.1 3.8 1987 4.6 5.3 4.0 1988 4.8 5.4 4.2 1989 4.9 5.5 4.4 1990 4.9 5.6 4.6 1991 5.0 5.6 4.8 1992 5.1 5.6 5.0 1993 5.2 5.7 5.1 1994 5.3 5.7 5.3 1995 5.4 5.8 5.4 1996 5.5 5.8 5.6 1997 5.7 5.8 5.7 1998 5.8 5.9 5.9 1999 6.0 5.9 6.1 2000 6.2 6.0 6.4 2001 6.4 6.1 6.7 2002 6.7 6.2 7.0 2003 7.1 6.5 7.4 2004 7.4 6.8 7.8 2005 7.8 7.1 8.2

    brought significant changes to the external sector (opening the domestic market, free trade agreements and the deregulation of foreign investment), as well as the resizing of the public sector and improvements in the regulatory system. Although these structural changes were expected to have a positive impact on potential GDP, some of their effects may not have materialised yet. On the other hand, all the methodologies used observed GDP data to estimate potential GDP and any underperformance on account of factors such as a poor monsoon, etc, could have a downward impact on the estimates of potential GDP growth rates. However, the empirical analysis suggests that a trend growth rate of 7 to 8 per cent is sustainable provided the agriculture sector records a stable performance.

    There are several developments that augur well for sustaining a higher growth rate in the medium- to long-term. First, the longterm growth potential of the Indian economy is expected to be aided by favourable demographics which can enhance the potential to raise savings and employment substantially. Second, the agriculture sector can provide a permanent boost to growth, provided required investment comes in to get rid of productivity stagnation on account of various constraints. Third, the manufacturing sector in India supported by domestic as well export demand is coming of age with large overseas acquisitions witnessed in the recent period. Fourth, investment in the infrastructure sector which is underway would help sustain the growth process. Fifth, the services sector is estimated to have the potential for creating 40 million jobs and generating additional $ 200 billion annual income by 2020 as per the draft approach paper to the Eleventh Plan. Sixth, there has been a consistent increase in gross domestic investment and domestic saving rates, which would provide support to the economy. Seventh, the downward trend in the capital-output ratio reflecting higher productivity growth augurs well for sustained high growth in the future [Reddy 2006b]. Eighth, the strength of the external sector would supplement the growth process. Finally, the development of India’s economy provides a large internal market that can be leveraged favourably. The percentage of middle- to high-income Indian households is expected to rise continuously. Since consumer spending is the largest component of demand, it provides a continuous boost to the GDP growth process, which bodes well for its sustainability.




    [The views expressed in the paper are those of the authors only and not of the institution to which they belong.]

    1 Harrod assumed the long-run propensity to save as constant which keeps the

    marginal propensity to save constant and always equal to the average pro

    pensity to save. However, in reality both may vary. In fact, in his commentary

    on Harrod’s assumptions, Kaldor (1955) criticises the assumption of a

    fixed savings rate and argues that it ought to vary endogenously. 2 High correlation is despite the fact that revised data (base 1999-2000)

    has been used for output gaps based on the HP filtering technique while

    for the production function approach, data used is as per base year 1993

    94 as data for capital stock is still not available for the new base year.


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