TRANSITION REPORT 2014 Innovation in Transition

Cross-country analysis

Economic institutions

The previous section shows that innovative firms tend to have a much more negative view of certain aspects of their business environment when compared with non-innovative firms. This raises the question of whether such perceived constraints negatively affect innovation outcomes. Do they inhibit innovation in practice? To answer this question, the impact of various aspects of the business environment is examined in more detail using cross-country regressions.

The business environment is, to a large extent, shaped by a country’s deeper economic institutions, such as the rule of law, control of corruption, the effectiveness of the government and regulatory quality. This can be captured by the average of the relevant Worldwide Governance Indicators, as discussed in Chapter 2. Together with other country-level characteristics, such as income per capita, R&D inputs, financial development and the quality of human capital, the quality of institutions is used in this section to explain the number of patents granted per worker and the innovation intensity of exports in various countries.The results of these cross-country regressions are presented in Table 3.3.

These results indicate that better institutions are associated with increases in patenting and more innovation-intensive exports. The effect of improving institutions is stronger and has greater statistical significance in countries where institutions are relatively weak. This can be seen where the average of the Worldwide Governance Indicators is interacted with (i) a dummy variable that takes the value of one when that average is above the mean for the sample (indicating strong economic institutions); or (ii) a dummy variable that takes the value of one when that average is below the mean for the sample (indicating weak economic institutions; see columns 3 to 8).

An improvement of around half a standard deviation in the quality of economic institutions in a country with below-average economic institutions (say, from the level of Ukraine to that of Albania) is associated with a 60 per cent increase in the innovation intensity of exports. An improvement of this magnitude is also associated with a 40 to 50 per cent increase in patent output. These effects are sizeable, considering that they only capture the direct impact of the quality of institutions, beyond the indirect effect that it may have through a higher level of income and of human capital in the country.

TABLE 3.3

Determinants of patent output and the innovation intensity of exports
Variables (1)
IIE
(2)
Patent intensity
(3)
IIE
(4)
Patent intensity
(5)
IIE
(6)
Patent intensity
(7)
IIE
(8)
Patent intensity

Log of GDP per capita

-0.117

1.260***

-0.006

1.062***

-0.078

1.115**

-0.229

0.876**

(0.169)

(0.385)

(0.166)

(0.335)

(0.168)

(0.430)

(0.202)

(0.442)

Log of population

0.236***

-0.012

0.181**

-0.152

0.135**

-0.149

0.177***

-0.096

(0.069)

(0.108)

(0.069)

(0.109)

(0.064)

(0.111)

(0.067)

(0.126)

Institutions (WGIs)

0.733***

0.891*

0.333

0.763*

       

(0.230)

(0.459)

(0.225)

(0.450)

       

WGIs * high WGI dummy

       

-0.165

0.795*

-0.16

0.871*

       

(0.246)

(0.465)

(0.262)

(0.487)

WGIs * low WGI dummy

       

1.083**

0.535

1.309***

0.951

       

(0.508)

(0.980)

(0.491)

(0.952)

Average years of tertiary education

-0.132

1.311**

-0.289

0.662

-0.002

0.614

0.144

0.757

(0.372)

(0.528)

(0.420)

(0.467)

(0.426)

(0.546)

(0.418)

(0.524)

Ratio of external trade to GDP

0.002

-0.001

0.003*

-0.001

0.004**

-0.001

0.005**

0.000

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.002)

(0.003)

Financial openness

-0.001

-0.086

0.054

-0.164

0.010

-0.156

0.033

-0.146

(0.071)

(0.133)

(0.071)

(0.115)

(0.070)

(0.117)

(0.071)

(0.132)

Private credit

0.002

0.008**

0.003

0.011***

0.003

0.011***

0.004*

0.011***

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

(0.002)

(0.003)

Natural resource rents

-0.029**

-0.005

-0.032**

0.009

-0.028*

0.008

-0.014

0.021

(0.012)

(0.020)

(0.014)

(0.016)

(0.014)

(0.016)

(0.013)

(0.020)

Ratio of business R&D spending to GDP

   

0.338

0.834**

0.360**

0.826***

0.382**

0.839***

   

(0.209)

(0.315)

(0.168)

(0.309)

(0.167)

(0.261)

Ratio of government R&D spending to GDP

   

-0.63

4.845***

-0.35

4.765**

-0.221

5.053***

   

(0.989)

(1.763)

(0.944)

(1.915)

(0.907)

(1.657)

Ratio of university R&D spending to GDP

   

-0.191

-1.901

0.416

-1.949

0.550

-1.767

   

(0.637)

(1.272)

(0.681)

(1.304)

(0.704)

(1.280)

EBRD dummy

0.606***

1.325***

0.522**

0.798*

0.172

0.828*

0.188

0.882**

(0.202)

(0.372)

(0.244)

(0.420)

(0.291)

(0.481)

(0.292)

(0.423)

No. of observations

113

68

100

68

100

68

97

65

R2

0.53

0.80

0.54

0.86

0.57

0.86

0.55

0.86

Source: Authors’ calculations using data from WIPO, World Bank, UNESCO, Penn World Table 8.0, Chinn and Ito (2006) and Barro and Lee (2013).
Note: The dependent variables are the log of total patents granted per 1,000 workers (based on WIPO data; “patent intensity”) and the log of the innovation intensity of exports (IIE; see Chapter 1 for a definition), both of which are averages over the period 2010-13. “WGIs” denotes the average of four Worldwide Governance Indicators (rule of law, control of corruption, effectiveness of government and regulatory quality). Financial openness is measured using the Chinn-Ito index. Private credit is the ratio of domestic private-sector credit to GDP and is obtained from the World Bank’s Financial Development and Structure Dataset. Other sources include: Penn World Table 8.0 (GDP, population and external trade); Barro and Lee, 2013 (average years of schooling); the World Bank’s World Development Indicators (natural resource rents); and UNESCO (R&D spending). Robust standard errors are indicated in parentheses. ***, ** and * denote statistical significance at the 1, 5 and 10 per cent levels respectively. Columns 1 to 6 are estimates using ordinary least squares; columns 7 and 8 are estimates using two-stage least squares, with lagged values for income per capita, openness to trade and dependence on natural resources being used as instruments for contemporaneous values.

Economic openness

The analysis above shows that innovative firms feel far more constrained by customs and trade regulations than non-innovative firms. At the same time, firms that sell their products in export markets are more likely to innovate. The results of cross-country analysis confirm that both the size of the market (measured by population and GDP per capita) and economic openness (measured by the ratio of exports and imports to GDP) are important for the innovation intensity of exports. An increase in openness to trade totalling 30 percentage points of GDP (say, from the level of Ukraine to that of Latvia) is associated with a 9 to 15 per cent increase in the innovation intensity of exports. At the same time, no strong links are found between patent output and economic openness or the size of the economy.

In addition, there is also a positive (albeit weaker) relationship between the innovation intensity of exports and the financial openness of the economy (as measured by the Chinn-Ito index, where higher values correspond to free cross-border movement of capital and lower values correspond to more restrictive regimes).15 All in all, these results suggest that a country’s ability to commercialise innovations and adopt technologies benefits from openness to trade and a large market.

These results should be viewed as indicating a general correlation between innovation and country-level characteristics, rather than a causal relationship. For instance, the causality may also run from innovation to openness to trade. Indeed, innovation can support exports, as it can help firms to become more productive and improve their competitive positions in international markets, thereby increasing the ratio of exports to GDP. In order to take some account of such reverse causality, similar regressions have been estimated using values for income per capita, openness to trade and dependence on natural resources with a lag of ten years as proxies for their contemporaneous values. The results remain broadly unchanged (see columns 7 and 8).16


Dependence on natural resources

Interestingly, an abundance of natural resources – measured by calculating natural resource rents (that is to say, revenues net of extraction costs) as a percentage of GDP – has the opposite effect to economic openness. Reliance on commodities does not appear to have an impact on the patent output of an economy, but the exports of countries that are dependent on natural resources tend to be significantly less innovation-intensive than those of other countries (see Table 3.3).

This is, of course, partially a reflection of the fact that commodity sectors inevitably account for a larger share of such countries’ exports. However, this negative relationship may also arise because the economy’s dependence on natural resources reduces the average firm’s economic incentives to innovate, as a large percentage of the value added in the economy is derived from activities that are less reliant on continuous innovation.

For instance, while constant innovation and the adoption of cutting-edge technologies is a prerequisite for maintaining a competitive position in the automotive sector, a firm’s competitive edge in terms of natural resource exports is dependent primarily on natural resource endowments.17 At the same time, the availability of natural resource rents may enable governments (as well as universities and firms) to finance research, which offsets any negative impact that natural resources may have on patent output, but does not necessarily strengthen incentives to commercialise innovations.