Page 256 - ES 2020-21_Volume-1-2 [28-01-21]

Innovation: Trending Up but needs thrust, especially from the Private Sector 239

Box 1: Literature on Innovation, R&D and Growth

The importance of technological progress in economic growth began with the Solow model (Solow
1956), which highlighted that output per worker mainly depends on savings, population growth and
technological progress. This model was empirically extended by Barro (1991); Barro and Sala-i-
Martin (1991, 1992), and Mankiw, Romer and Weil (1992), identifying technological progress as the
key determinant of long-term economic growth.

While the Solow model treats technological progress as exogenous, the new growth theory endogenises
technological progress and suggests several determinants of the same. These include human capital
(Lucas, 1988); search for new ideas by profit-oriented researchers (Romer, 1990); infrastructure
(Aschauer 1989); and improving quality of existing products (Grossman and Helpman 1991; Aghion
and Howitt 1992). Endogenous growth has also been explained using the Shumpeterian model of
creative destruction, where innovative products brought to the market by entrants lead to replacement/
destruction of the old ones produced by the incumbents (Aghion, Akcigit, & Howitt, 2013).

The relation between innovation and research sector received attention with endogenous growth
models (Romer, 1990 and Aghion & Howitt, 1992). Some postulated that R&D activities could make
long run growth possible (Jones, 1995) and R&D effects on aggregate production functions were
tested (Sveikauskas, 2007). Research showed that small enterprise R&D activities brought large
returns to the national economy through new technologies (Comin, 2004). More recently, studies
have focused on patenting and economic growth (Westmore, 2013; Acharya and Subramanian,
2009, Acharya et al. 2013). Studies have also established a relationship between entrepreneurship
innovation and economic growth (Galindo & Méndez, 2014). An increase of 10 per cent in R&D
investment has been associated with productivity gains ranging from 1.1 per cent to 1.4 per cent
(Donselaar and Koopmans, 2016).

Figure 1: Positive Correlation between GDP per capita (2019) and Past Innovation

A) Innovation (2016) B) Innovation (2014)

GDP per capita and Global Innovation Index GR 5 GDP per capita and Global Innovation Index US
Log 10 (GDP per capita 2019, PPP Current Int $) 4.5 4 3.5 India 66 CH FR JP UK Log 10 (GDP per capita 2019, PPP Current Int $) 4.5 4 3.5 India 76 BR CH JP CA UK
5
US
GR
CA
FR
IT
IT
BR

3
120 100 80 60 40 20 0 3 120 100 80 60 40 20 0
Rank - Global Innovation Index 2016 Rank - Global Innovation Index 2014
Source: The World Bank and Global Innovation Index database
Note: Highest possible rank is 1. Figure shows India’s innovation rank. US = USA, CH = China, JP = Japan, GR =
Germany, UK = United Kingdom, FR = France, IT = Italy, BR = Brazil and CA = Canada.

8.2 The positive correlation between past innovation performance and current GDP per capita
can be examined empirically. Figure 1 shows the positive correlation between past innovation
performance (three-years ago in 2016 and five years ago in 2014) with GDP per capita in PPP