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Can India Raise Its GDP Per Capita to $5,000 by 2030?

Jayanta Kumar Mallik ( is an economist in the Reserve Bank of India, Mumbai.

The ebbs and floods of investment and growth in the Indian economy in the past two decades are rooted in the movements in steel prices. Short-cuts used in the compilation of macro statistics obscure the policy debate by creating incongruous images: real investment growth is rising, but the rate of investment is sliding; doing business is easier, but business activity is growing slower. If policies are pursued to facilitate business activity, and better methods are used to measure it, India can raise its gross domestic product per capita to $5,000 by 2030 (from $1,965 in 2017).

The author gratefully acknowledges comments received on earlier drafts from Anupam Prakash, Ashok Sahoo, Debaprasad Rath, Keshab Das, Michael Debabrata Patra, Sitikantha Pattanaik, and an anonymous referee.

The views expressed in this article are those of the author.

The decline in investment rate and the subdued growth of private investment witnessed from 2012 to 2017 remain central to the debate on the medium-term growth prospects of the Indian economy. The Economic Survey 2018–19 of the Government of India (GoI) has reignited the debate as it advocates a private investment-led growth strategy and recommends, among other things, that “policymakers need to double down on reducing domestic economic policy uncertainty” (GoI 2019: 125). A variety of narratives are available on the behaviour of investment during the past two decades. The scrutiny in this article suggests that the changes in this variable tracked the movements in steel prices, thanks to the prevailing estimation method. The debate on the medium-term growth also took a political colour in that it became entangled with the change in government in 2014. The debate whether growth was higher during Manmohan Singh’s government or Narendra Modi’s is somewhat misplaced. The pace of economic activity, including investment during 2012–17, which included two years of the Manmohan Singh government and three years of the Modi government, was faster than what the macro numbers suggest.

Viewed from a longer-term perspective, economic growth has improved in India. The compound annual growth rate of India’s gross domestic product (GDP) per capita in 2010 dollars, 5.7% from 2000 to 2017, was substantially higher than the 1.2% in the 1960–80 period or the 3.4% in the 1980–2000 period, and second only to China’s.1 The conventional wisdom is that industrial deregulation and liberalisation, as part of wider structural reforms since the 1990s, and progressive integration into the global economy improved the rate of economic growth in India from 1980 to 2000 (Mallik 1991, 1994; Ahluwalia 2000; Acharya 2002; Virmani 2003; Kelkar 2004; Panagariya 2004). What improved the growth rate from 2000 to 2017? The answer is not easy, and conventional or received explanations vary.

The reform agenda in this period covered policies as well as institutions (Virmani 2004). From 2003 to 2008, a “dream run” or a cyclical boom coincided with an exceptional phase of the global economy (Nagaraj 2013). It was facilitated by a variety of factors, including restructuring of domestic industry and benign macroeconomic conditions (Mohan and Kapur 2015). Improvement in productivity, and the role of structural transformation in facilitating the transfer of labour from agriculture to the services sector, has been highlighted (Eichengreen and Gupta 2011). In keeping with the predictions of endogenous growth models (Arrow 1962; Uzawa 1965; Romer 1986; Lucas 1988), the role of human capital in the growth has also been emphasised (Ojha and Pradhan 2006; Viswanath et al 2009; Shukla 2017). The high growth in more recent years (2014–15 to 2016–17) has been attributed to the decline in global crude oil prices that came as a “bonanza” (Nayyar 2017).

During 2000–17, overall, India’s growth remained resilient to global booms and busts, and outperformed global growth, but the policy debate is blurred by incongruous macroeconomic images. India improved upon its 142nd position on the World Bank’s Ease of Doing Business Ranking in 2014 to the 77th position in 2018 on the back of wide-ranging reforms undertaken by the GoI.2 The growth rate of GDP slowed down instead of improving.

Between 2011–12 and 2016–17, the investment rate fell 8 percentage points. Private investment was “enigmatically” subdued despite several favourable conditions. Macroeconomic stability was much higher, public investment had picked up and its quality had improved, as had the business environment. Global liquidity continued to remain benign; the Indian equity markets had done well, offering good valuations to the companies looking to raise money; and, as per some indicators, economic uncertainty had not worsened. All these should have helped spur private investment (World Bank 2018: 44). A reversal of the “India type” investment decline seemed difficult (GoI 2018: Vol 1, Chapter 3).

During 2003–08, by contrast, there was a surge in the investment rate (by 11 percentage points) and savings rate (by 8 percentage points). The surge was perceived as “inching towards East Asian levels as part of a general strengthening of the macroeconomic environment of the economy” (Balakrishnan and Babu 2007), or a “savings–investment miracle” (Acharya 2008), or “consistent trends of increasing domestic savings and investment over the decades” (Mohan and Kapur 2015). The investment rate fell between 2012 and 2017, but the growth rate of real investment improved from 1.6% in 2013–14 to 10.1% in 2016–17. The investment rate staged a turnaround in 2017–18, which had seemed difficult not long ago. This is attributed to the “secular trend of reducing economic policy uncertainty” (GoI 2019: 122).

This article probes these incongruous macroeconomic images by analysing the procedure used to estimate capital formation in India. The estimates of gross value added (GVA) in pucca construction, the main building block of gross fixed capital formation (GFCF), are prepared from limited information on “basic” construction material using fixed ratios of a “benchmark” year. Iron and steel, with two-thirds of the weight assigned to basic construction material, occupies a key position in the estimation.3 In tandem with global prices, domestic prices of iron and steel, embedded in the wholesale price index (WPI), surged 11% on average during 2003–12 (roughly twice the increase in headline WPI). It declined 2.8% during 2012–17 (against the increase of 2.3% in headline WPI) and rose 14.1% during 2017–19 (four times as rapid as the increase in headline WPI). The variations in the investment rate—surge during 2003–08, slide during 2012–17, and the turnaround in 2017–18—tracked the movements in steel prices.

Estimation of Capital Formation

The GFCF estimates in the new National Accounts Statistics (NAS) series with 2011–12 base year are prepared for four broad asset categories as per the United Nations (UN) System of National Accounts (SNA), 2008: Dwellings, Other Buildings and Structures (DOBS); Machinery and Equipment; Cultivated Biological Resources; and Intellectual Property Rights.

Of these four, DOBS accounted for 59.3% of GFCF in 2011–12. Most of that (57.3%) was in the form of pucca (accounted) construction, the remaining 2% being the share of kutcha (unaccounted) construction. The commodity flow approach is adopted at the national level for estimating the output of pucca DOBS, while the output of kutcha DOBS is estimated using the expenditure approach. This approach envisages estimation of net availability of material for construction, considering domestic production, and adjusting it for inputs in other industries, changes in stocks, and imports and exports. It covers basic construction materials, other construction materials, and factor payments such as labour cost, rent, and interest.

The basic materials, the data on which are collected every year, include cement and cement products, iron and steel, bricks and tiles, timber and round wood, bitumen and bitumen mixtures, glass and glass products, and fixtures and fittings. The new series specifies the norms on the weightage shares in total value of construction material as 48.7% of basic materials, 16.3% of other material, and 35% of factor inputs (CSO 2012, 2015). Of the 48.7% share of basic materials, iron and steel has a share of 31.4% and occupies a key position in the estimation of GVA in construction and GFCF.

Steel prices and estimates of GVA in construction and GFCF: Global and domestic prices of iron and steel have been volatile since 2003. This volatility has been critical in the estimation of GVA in construction and GFCF. In tandem with global prices, the domestic WPI of iron and steel surged during 2003–09. After falling briefly in 2009–10, steel prices rose rapidly till 2011–12, moderated in 2012–13, turned negative during 2013–17, and reversed during 2017–19 (Figure 1).


For analytical convenience, the 1994–2019 period can be divided into four sub-periods. In Period 1 (1994–03), steel prices were benign and stayed below the headline WPI. In Period 2 (2003–12), steel prices in India surged in tandem with global prices, racing ahead of the headline WPI. In Period 3 (2012–17), steel prices remained subdued. In Period 4 (2017–19), steel prices moved up again (Table 1, p 53).

The sharp increase in steel prices was associated with a similar sharp improvement in the growth of GVA in construction and GFCF during 2003–12. The growth rates in the macro estimates slackened along with the meltdown in steel prices during 2012–17 and recovered during 2017–19 with the pick-up in steel prices. The coefficients of correlation between steel prices and the macro numbers are statistically highly significant.

Since the NAS numbers for the latest four years (2015–19) are at various stages of revision, correlation coefficients for the 1994–2015 period have also been reported. The scatter plots of the changes in steel price, GVA in construction, and GFCF bear out the correspondence among these variables (Figure 2, p 54). The transmission process is unidirectional:

Global steel prices steel prices in India estimate of construction GVA estimate of GFCF

One need not econometrically establish the direction of causality; no one will say that global steel prices were influenced by Indian prices or that the steel prices in India were affected by the GVA in construction, and so on. Transmission of the changes in steel prices to the estimates of construction GVA is a straightforward arithmetic relation, and it can be simulated.

Impact of steel price on GVA in pucca construction—Non-econometric simulation: Assume the base year value of “basic material” at ₹ 300, comprising iron and steel at ₹ 200 and other basic material (cement, bricks) at ₹ 100. The total value of construction material can be derived as basic material divided by 0.75 and factor payments as 53.9% of total value of construction material, as is done in the NAS (CSO 2015: 157). In the current year, when steel price increases by 20%—all else (volume of construction and other prices) remaining the same—GVA at current prices grows by 13.3%, the deflator rises by 5.5% reflecting the change in steel price and its weight (27.64%), and GVA at constant prices grows by 7.4%. When steel price declines by 20%, the current price estimates of GVA contract by 13.3% and constant price estimates by 8.3% (Table 2). The increase (decline) in the GVA in pucca construction is a “windfall.” The transmission of changes in steel price to the GVA at current prices is propelled by the large weight of iron and steel (two-thirds of value of basic material); its weight in the deflator being small, the deflator changes at a slower pace.4

Economic growth had slowed down during the late 1990s, with industrial investments stalled for various reasons (Acharya 2002), and real GFCF declined in 2000–01 and 2002–03 (Table 1). Against this backdrop, the increase in steel price (26% in 2003–04 and 29% in 2004–05) provided the initial (statistical) boost to capital formation. Given the estimation procedure and the numbers from the simulation, the 29% increase in steel price in 2004–05, all else remaining the same, should have led to 19% increase in GVA in construction at current prices; in constant prices, the increase should have been 13%. In the actual data, the GVA in construction recorded higher growth (36% at current prices and 16% at constant prices) reflecting various factors apart from the impact of steel price. The GFCF at current prices grew 34% and at constant prices 24%, and the investment rate moved up 6 percentage points in that year.

According to NAS 2018, the output of dwellings at constant prices contracted 6.4% on average during 2002–07, with a decline of 25% recorded in 2015–16. Output of roads and bridges expanded at 10.7% in the same period, with an increase of 24% recorded in 2015–16. There was a sharp increase in the output of other components of construction (except dwellings). The increase in the output of roads and bridges is consistent with the construction activity in this segment.5 The decline in the output of dwellings is the outcome of the estimation procedure. The estimates of output of pucca construction of the household sector (comprising consumer households, non-profit institutions serving households, and unincorporated enterprises) are derived as residual after subtracting the estimates for the public and private corporate sectors (which are independently compiled by analysing budget documents and balance sheets) from the total output of construction (estimated using the commodity flow method). The decline in steel prices dampened the growth in total output of construction, and the residual (after the components that grew rapidly are netted out) showed the decline.6

Trends in GFCF across institutional categories: Traditionally, GFCF estimates are prepared separately for three institutional sectors—public sector, private corporate sector, and household sector. In the 2011–12 NAS series, these estimates are being prepared for six institutional categories—public non-financial corporations, private non-financial corporations, public financial corporations, private financial corporations, general government, and households. Comparison of data from the old and new NAS series is possible for the three broad sectors. As in pucca construction, GFCF estimates for public sector and private corporate sector are prepared using the “expenditure approach,” based on analysis of budget documents and annual reports of enterprises. These two are subtracted from the macro estimates, prepared using the commodity flow approach, to arrive at the estimates for the household sector. While the broad approach to the estimation has remained the same, there have been changes in the sourcing of information; for instance, the new series uses information on financial parameters of non-government companies from the MCA 21 database.

The current and constant price estimates of GFCF for three broad institutional categories over the 2004–18 period have been plotted in Figures 3 and 4. Since they combine two NAS series (base year 2004–05 and base year 2011–12), data for 2011–12 from both series have been retained, leaving a gap between the two. There is a perceptible difference between the current price estimates in the two series in the private corporate sector, and not in the other two. The current price estimates for public sector showed a steady growth. The estimates for private corporate sector also exhibited continued rapid growth. However, the estimates for the household sector (the residual), which displayed rapid growth during 2004–12, showed no growth during 2011–16. The constant price estimates of GFCF of the household sector declined during 2011–16, contrasting the growth in public and private corporate sectors.


The patterns in real GFCF in a few typical years are instructive. In 2013–14, GFCF grew 9.5% in the public sector and 9% in the private corporate sector, but declined 8.5% in the household sector, recipient of residual. In 2015–16, GFCF grew 19% in the public and private corporate sector each; GFCF in the household sector declined 12%. The noteworthy point is that the investment rate fell by about 5 percentage points in 2013–14 and 1.5 percentage points in 2015–16, even though real investment in the sectors where the estimates are based on analysis of accounts recorded strong growth.

In 2011–12, GFCF in public and private corporate sectors declined by 1.3% and 0.1%, respectively; GFCF in the household sector posted a growth of 32%; and the investment rate moved up by 2.5 percentage points. In 2008–09, GFCF in the private corporate sector declined by 22% in the wake of the global financial crisis, while a growth of 12% was maintained in the public sector—these were on the expected lines; and GFCF in the household sector posted a growth of 33%—it represented the contribution of steel prices to the otherwise non-existent growth in GFCF at the macro level.

Use of commodity flow method in the estimation of capital formation—A note: The commodity flow method was developed in the 1930s by Scandinavian economists (Aukrust 1992). The commodity flow tables, also called supply and use tables, are widely used to obtain gross capital formation (GCF) (UN 2003, 2009). In the Indian context, the method of estimation of savings and investments using the commodity flow approach has been examined by a number of expert groups, and all have recommended regular revision of the ratios used (GoI 1982, 1996, 2007). This method should serve the purpose when the value added to output ratios are stable, a condition that will not be satisfied in a situation of sharp movements in the relative prices. There is a case for revisiting the method.

Incongruous Images

This section draws on the insight from the preceding analysis, takes another look at the incongruous images flagged earlier, and provides an alternative perspective on the macroeconomic growth in the 2012–17 period.

Turning points in the investment rates: The movements in the investment rates—surge during 2003–08, slide during 2012–17, and the turnaround in 2017–18—had tracked the movements in steel prices (Figure 5). There were deviations in the intervening years in the wake of the global financial crisis. In 2008–09, for instance, the investment rate fell sharply even though steel prices increased.

Sliding investment rate, subdued private investment, rising growth of real investment: With the growth in nominal GCF falling short of the GDP growth, the investment rate was sliding, and growth of private investment (real GFCF of private corporate sector plus household sector) at 4.1% was low. Growth in real GFCF improved, from 1.6% in 2013–14 to 8.3% in 2016–17, but it did not generate optimism: the perceptions that the investment rate was sliding, and that private investment was subdued, were deep-seated. These are statistical illusions, due to distortions from the decline in steel price and the estimation of GFCF in household sector as residual. Combined GFCF of public and private corporate sectors (where the estimates are based on analysis of accounts) recorded a growth of 8.3% during 2012–17. This would be closer to the true story on investment activity in the Indian economy during 2012–17: it compares well with the GDP growth of 7.1%, the impact of the steel prices notwithstanding. Investment was subdued in two of the five years (2014–15 and 2016–17) and it was strong in three others; the growth in 2015–16 was of the order of 18.8%, reflecting, among other things, the 24% increase in the output of roads and bridges (Table 3, p 55).

Alternative perspective on macroeconomic growth during 2012–17: The inflated growth in GFCF reflecting the surge in steel prices had created an upward bias for GDP growth during 2003–12. The reverse worked during 2012–17—subdued growth in GFCF due to decline in steel prices created a downward bias. The GDP excluding GFCF shows a more regular pattern of growth: consistent with the improvement in the ease of doing business, a growth of about 9% in this measure was recorded during some of the recent years (2013–16), higher than in any other period in the past. Growth was subdued during the 2011–13 phase, which witnessed a worsening in the ease of doing business on account of the “policy paralysis.” The growth rate of construction GVA, too, was unusually high during 2003–08 and low during 2012–17, although the divergence between the growth of GVA and GVA excluding construction is less evident, as construction has a small share of about 8% in GVA unlike the 32% share of GFCF in GDP (Table 4; Figures 6 and 7).




The record of India’s economic growth in the 2000–17 period is an impressive improvement over the past. Variations in the growth rate within this period were, in part, rooted in the movements in steel prices, being the outcome of the estimation procedure that extrapolates macro numbers from limited current information using fixed ratios of a benchmark year. The GDP excluding GFCF showed a more regular pattern of growth. The improvement in this measure in recent years suggests that business activity, when made easier, would grow faster, all else remaining the same. This is also buttressed by the evidence of continued rapid growth of real investment in public and private corporate sectors during 2012–17.

A compilation of macro statistics using short-cuts creates incongruous images that tend to derail the policy debate on macroeconomic growth in India. With the “sliding” investment rate and the “subdued” private investment, the debate often revolves around 7% GDP growth, termed the “new Hindu rate” of growth, when the country would need to grow faster to raise per capita income and meet people’s aspirations. In 2017, India’s per capita GDP ($1,965) was less than 50% of Indonesia’s, about 25% of China’s and South Africa’s, and less than 20% of the global average. Can India raise its per capita income to, say, $5,000—the approximate average of the middle-income country group in 2017—within a foreseeable period, say, by 2030? To achieve this goal, India needs growth at 8% per annum for 12–13 years (over the level of 2017), and GDP would need to grow at about 9.2%. This is doable—GDP excluding construction grew at 9% in some of the recent years—but not inevitable: it is necessary to pursue policies that facilitate business activity and use better methods to measure it. The quality of the data is paramount for policy.


1 The comparison relates to 50 major economies accounting for 92.7% of world GDP in 2017.

2 See Panagariya (2018) for details of the reform measures undertaken by the Government of India.

3 Steel is one of the eight core industries in India. Data on wholesale prices of “iron and steel and ferro alloys” (a composite index of seven sub-groups of the “manufacture of basic metals” group) have been used in this article.

4 In the NAS (CSO 2012), the weight assigned to iron and steel in the preparation of deflators are different for different types of construction: general pucca construction excluding residential building (27.64), rural residential buildings (34.16), urban residential buildings (27.77), rural and urban non-residential buildings, and other construction works (accounted) (34.3). Each of these being smaller than the weight of iron and steel in the value of “basic material,” the windfall would accrue in any case.

5 See the document “Achievements of four years (2014–15 to 2017–18)” of the Ministry of Transport and Highways, available at

6 While sectoral data on construction is not published, it would be reasonable to assume that the construction of roads and bridges is undertaken by entities in the public sector or the private corporate sector.


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Updated On : 14th Aug, 2019


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