INNOVATION AND ECONOMIC GROWTH: CROSS-COUNTRY ANALYSIS USING SCIENCE & TECHNOLOGY INDICATORS

Transcription

1 INNOVATION AND ECONOMIC GROWTH: CROSS-COUNTRY ANALYSIS USING SCIENCE & TECHNOLOGY INDICATORS A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy By Leonel José Prieto, M.S.F. Washington, DC April, 2017

2 Copyright 2017 by Leonel José Prieto, M.S.F. All Rights Reserved ii

3 INNOVATION AND ECONOMIC GROWTH: CROSS-COUNTRY ANALYSIS USING SCIENCE & TECHNOLOGY INDICATORS Leonel José Prieto, M.S.F Thesis Advisor: Eliane Catilina, Ph.D. Abstract This paper constructs a cross-country model to evaluate how the differences in performance in Science and Technology indicators as proxies of Innovation correlate with different levels of economic growth. Performance in science and technology indicators are considered as inputs and economic growth and similar indicators are considered as output. The results show that innovation has a positive effect on GDP growth. Moreover, the indicator with the strongest impact on economic growth is R&D expenditures as percentage of GDP. iii

4 Table of Contents 1. Introduction & Background 1 2. Literature Review 4 3. Conceptual Framework and Methodology Data Description Regression Results Policy Analysis Conclusion Reference 26 iv

5 1. Introduction & Background Can innovation be one of the drivers of economic growth? Innovation can be helpful in increasing productivity. Applying technology could lead to a more efficient use of productive resources. Moreover, this could generate a virtuous circle in which R&D expenses, innovation, productivity, and per capita income could mutually reinforce each other and could lead countries to achieve constant economic growth. Changing production patterns are simultaneously one of the most obvious displays and one of the factors of economic growth. As many authors have affirmed, growth does not happen evenly between one sector and another or even within the same sectors. Instead, it arises in conjunction with new technologies. In line with the literature, countries and companies that invest in technology and innovation are more able to introduce new technological advances. Likewise, those countries and firms that innovate show a greater labor productivity than those that do not. Innovation can be defined as the introduction of new ways of doing things. This definition includes aspects such as new organizational structures or new products and even new processes. Innovation takes the form of successful adaptation of technologies to country-specific conditions, as well as wide diffusion across firms and sectors. This process is essential for productive

6 transformation. In fact, some of the countries around the world that have been most successful in terms of catching up on economic growth, such as Finland, Israel and South Korea, are world leaders in research and development (R&D). Properly quantifying innovation and its results is not easy. This happens because innovation s outputs can take the form of many different intangibles, such as organizational changes. Therefore, it is required to rely on indirect indicators of innovation. These indicators usually include expenses in research and development (R&D) and the public and private resources put into innovative firms or programs. For innovating agents, innovation translates into opportunities to produce more efficiently, increase production capacity, or create new markets. Consequently, innovation becomes valuable as companies develop technological skills, innovative models and internally shared knowledge. In this context specifically, national systems of innovation can occupy an intermediate position between market forces and the State, in conducting innovation activities in general. Such systems lie between a pure market view of the operation of enterprises and a planning or State-led model. The topic of innovation and economic growth has widely been discussed. This thesis seeks to dig deeper into this relationship and past research, by focusing on specific science and technology 2

7 indicators to explore the relationship between performance in these proxies for innovation and economic growth. In addition, it uses the most updated data available. Likewise, the analysis will allow to identify not only how innovation and growth are connected but also to specifically which elements of innovation are most closely related to higher growth rates. 3

8 2. Literature Review My research contributes to existing literature that further analyzes the relationship between forms of innovation and economic growth. The scope of these broad elements can be further focused, in order to analyze better the relationship between these two areas. The OECD (2003) defined investment in innovation as the sum of R&D expenditure, expenditure for higher education and investment in software. Relevant indicators of economic impact are associated with growth but also with productivity. Economists have shown that R&D expenses can result in better production technology. Also, these R&D investments could raise productivity as well as the rates of return on investment. Griliches (1986, 1990), Mansfield (1988), Goto and Suzuki (1989), Meliciani (2000), Timmer (2003), and Gonzalez and Gascon (2004) have provided theoretical arguments and evidence to support these conclusions. R&D investment is one of the most crucial elements in promoting scientific and technological progress. Thus, it is also important to consider its efficiency. Any country that uses the resources inefficiently could bear a penalty in the form of slow economic and productivity growth. Besides, if R&D capital is not 4

9 used efficiently, additional investment may not be supportive in fostering progress. Nevertheless, other elements need to be considered as well, such as imitation and technology acquisition. According to Katz (1986) and Bell and Pavitt (1993), the roles of imitation and technology acquisition are more important than direct R&D and innovation as preconditions for learning and catching up. Thus, it is important to consider innovation efforts beyond R&D. There are also other issues related to R&D that have been discussed in the economic literature; for example: issues related to patenting, patent quality, and business strategies. Griliches (1990), Ginarte and Park (1997) and Penin (2005) examined the economic aspects of patents. King (2003) analyzed patent quality and examination procedures. Consequently, the present research takes a holistic approach of innovation that considers different elements, including R&D investments and trademarks. Empirical analysis and measurement of the productivity effects of innovation is a difficult job in economics. One way to approach this question, is analyzing the information at the firm level. From this perspective, several studies (Griffith et al., 2004; Griffith et al., 2006; Mairesse et al., 2005; Mairesse and Mohnen, 2010; OECD, 2009) have focused on showing the positive links between R&D, innovation, and productivity in firms operating in industrialized countries. 5

10 In addition, Hall and Mairesse (2006), and Mairesse and Mohnen (2010) have noted that firm heterogeneity is relevant in explaining innovation activities and the effects they generate on the performance of these companies. Also, they state the importance of controlling for this in empirical estimations. In reference to the impact of R&D on innovation outputs, these analyses have consistently shown a positive association. In this sense, firms that invest more in R&D are more likely to discover or reach innovations, controlling for characteristics of the firm, such as its size, type of innovation strategies, organizational design, or affiliation with groups. Other economists have noted that the correlation between product innovation and productivity is often higher for larger firms (Griffith et al., 2006; OECD, 2009). Additionally, studies have also shown (OECD, 2009) that, in the majority of countries, the productivity impact of product innovation is larger in the manufacturing sector than in the services sector. Another way to approach this issue is by using countries as the units of analysis. In other words, implementing a cross-country approach to answer the research question. Several cross-country studies (Hall and Jones, 1999; Rouvinen, 2002) have focused on the relationship between R&D spending, innovation, productivity, and per capita income. In addition, Frantzen (2000), and Griffith, Redding and Reenen (2002) have looked at the relationship between 6

11 countries own R&D and productivity growth using international panel data. However, opportunities remain for additional research in areas such as: using the most updated information, combining a different selection of indicators, having a regional focus (i.e.: Europe or Latin America), or having an economic development focus in the country composition of the sample (i.e.: developed economies and emerging markets). Another important element to consider is methodology. Different authors (Griliches, 1979; Griliches and Pakes, 1980) have contributed to developing a model of the relationship between innovation and its factors in a knowledge production function, including the contribution of innovation to productivity. The knowledge production function approach (Griliches, 1979) supposes that the production of new knowledge depends on current and past investment or expenses in new knowledge (i.e., current and past R&D expenditures). Also, it assumes that this depends on other factors such as knowledge flows from outside the firm. Taking advantage of innovation surveys and a broader set of available indicators, Crepon et al., (1998), were the first to integrate empirically these relationships in a recursive model (the CDM model), allowing for the estimation of innovation inputs (R&D investment) in an investment function. Building on the CDM approach, a new wave of studies based on innovation surveys emerged 7

12 (Janz et al., 2004; Loof & Heshmati, 2002; Loof et al., 2003; Mohnen et. al, 2006; Van Leeuwen & Klomp, 2006). Using different indicators of economic performance such as firms labor productivity, multifactor productivity, sales, profit margins, and market value, studies in this vein have shown that technological innovation can lead to superior firm economic performance. Crespi and Zuniga (2011) realized a similar research in which they analyzed the determinants of technological innovation and its impact on firm labor productivity. The economic literature on the systematic measurement of production efficiency has offered two major approaches. One is data envelopment analysis (DEA). It comprises the use of linear programming techniques to trace the efficiency frontier. Second is the stochastic frontier analysis (SFA) approach, which applies econometric techniques to estimate various production/cost frontiers. Both SFA and DEA have been widely applied to a variety of issues in economics and the managerial sciences. Aigner, Lovell, and Schmidt (1977), Battese and Coelli (1992, 1995), Coelli (1995), Kumbhakar and Lovell (2000), and Ahn, Lee, and Schmidt (2001) contributed to the theoretical development and empirical application of SFA at both the firm and industry levels. In addition, Kotsemir (2013) has completed a comprehensive overview of empirical papers on cross-country analysis of efficiency of national innovation systems using DEA technique. 8

13 More recently, Fried, Schmidt, and Yaisawarng (1999) and Fried, Lovell, Schmidt, and Yaisawarng (2002) have proposed additional methods that involve the use of DEA, SFA, and the Tobit model to screen out external factors from the producer s performance and achieve a more precise efficiency measure. Some theories of economic growth focus on endogenous technological change to explain growth. These endogenous growth models were initiated by Romer (1986). They stipulate that technological innovation is created in the research and development (R&D) sectors using human capital and the existing knowledge stock. Consequently, innovation enables sustainable economic growth, given that there are constant returns to innovation in terms of human capital employed in R&D sectors. This paper will build upon the aforementioned findings. It uses country panel data to test the validity of R&D based endogenous growth models using the most updated data. It takes R&D expenses as percentage of GDP and trademarks as some of the proxies of innovation and measures in what ways these indicators could make an impact on GDP growth. For the R&D expenses as percentage of GDP indicator, it leverages on studies by Leonard (1979) in which the lagging effects for these expenses were identified in a range of three to nine years. His findings are helpful in assessing the rate of return of R&D expenses. This means a period of three 9

14 to nine years of incubation is expected for these investments, prior to generating impact on economic growth. 10

15 3. Conceptual Framework and Methodology This study seeks to determine whether better performing results in country-level science and technology indicators positively correlate with better results in national economic growth. It also seeks to identify specifically which of these indicators, such as R&D expenses and trademarks, have a stronger relationship with economic growth. The unit of analysis is country-year. The dependent variable (Y) is composed of: economic data, such as GDP growth. On the other hand, the independent variable (X) is based on innovation proxies such as R&D expenditures as percentage of GDP, patents and trademarks, among others. This paper uses panel data techniques on 74 countries for, on average, a 13-year period, being 2014 the latest available year. The study reviews topic of innovation from a general perspective, understanding in which ways can innovation be measured and externalized and then specifically what degree of impact it has on economic growth for developed economies. To have a general data base to assess impact at the independent variable level, I used the Conference Board Total Economy Database. For the innovation-related indicators and its control variables, I used the World Bank Development Indicators database. 11

16 My research examines the effect of innovation on GDP, using proxies for innovation such as R&D expenses as percentage of GDP and trademarks. In addition, it uses econometric techniques and controls for innovation and production determinants, such as gross enrollment ratio in secondary education, foreign direct investment, and labor force stock. Specifically, the study employs a time-invariant fixedeffects model to overcome possible omitted variable bias pertinent to unobservable country-specific characteristics. It includes R&D expenditures as percentage of GDP and trademark applications. But also, it controls for other variables such as gross enrollment ratio in secondary education, foreign direct investment, and labor force stock. To capture these elements, I use a regression model written as follows: = The dependent variable ( ) represents GDP growth. My key independent variable is R&D expenditures as percentage of GDP in a country i at a time t. Additionally, the regression includes control variables, such as gross enrollment ratio in secondary education, foreign direct investment, and labor force stock. Country specifics characteristics are captured by ( ). Time-fixed 12

17 effects are captured by ( ). Finally, ( 0 ) represents the constant term and ( ) is the error term. 13

18 4. Data Description This study retrieved data from two publicly available data banks: the World Bank s Development Indicators (WBDI) and the Conference Board s Total Economy Database. The key independent variable is R&D expenses as percentage of GDP, obtained from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics. Expenditures for research and development are current and capital expenditures (both public and private) on creative work undertaken systematically to increase knowledge, including knowledge of humanity, culture, and society, and the use of knowledge for new applications. R&D covers basic research, applied research, and experimental development. 1 This indicator is being used to proxy for knowledge and innovation. Also, for this variable it is critical to consider its lagging effects. The rates of return to R&D are difficult to estimate. Leonard (1971) reports that the effect of R&D upon growth on the average begins in the third year after the R&D investment and continues with steadily rising influence for at least nine years after the initial input year. This study will then assume 6-year average lag period. The data is sourced from WBDI 2. 1 World Bank, Research and development expenditure (% of GDP) (accessed 10/15/2016) 2 Ibid 14

19 Another independent variable takes from the science and technology indicators is trademarks, obtained from the World Intellectual Property Organization (WIPO), WIPO Patent Report: Statistics on Worldwide Patent Activity. Trademark applications filed are applications to register a trademark with a national or regional Intellectual Property (IP) office. A trademark is a distinctive sign which identifies certain goods or services as those produced or provided by a specific person or enterprise. A trademark provides protection to the owner of the mark by ensuring the exclusive right to use it to identify goods or services, or to authorize another to use it in return for payment. The period of protection varies, but a trademark can be renewed indefinitely beyond the time limit on payment of additional fees. Direct resident trademark applications are those filed by domestic applicants directly at a given national IP office. 3 This variable is included given that trademarks can be an output of innovation in a given country. The data is centralized by the World Bank Development Indicators but sourced from the aforementioned WIPO Patent Report 4. One control variable is the gross enrollment ratio in secondary education, which is obtained from the UNESCO Institute for Statistics. Total enrollment in secondary education, 3 World Bank, Trademark applications, direct resident (accessed 10/15/2016) 4 Ibid 15

20 regardless of age, are expressed as a percentage of the population of official secondary education age. 5 This indicator is used as a control variable for the knowledge stock available in the country. The data is centralized by the World Bank Development Indicators but sourced from the UNESCO Institute for Statistics 6. Another control variable is foreign direct investment. Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars. 7 This variable is used as a control variable for the country s overall investments. The data is centralized by the World Bank Development Indicators but sourced from the International Monetary Fund, Balance of Payments database, supplemented by data from the 5 World Bank, Gross enrolment ratio, secondary, both sexes 10/15/2016) 6 Ibid 7 World Bank, Foreign direct investment, net inflows (current US$) (accessed 10/15/2016) 16

21 United Nations Conference on Trade and Development and official national sources 8. The last control variable is labor. Total labor force comprises people ages 15 and older who meet the International Labour Organization definition of the economically active population: all people who supply labor for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or parttime workers, in general the labor force includes the armed forces, the unemployed, and first-time job-seekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector. 9 This indicator is used as a control variable for the labor stock. The data is centralized by the World Bank Development Indicators but sourced from the International Labour Organization, using World Bank population estimates 10. Data on GDP growth is taken from the Total Economy Database (TED) of the Conference Board. This key variable represents the annual growth of GDP in percent change. 8 Ibid 9 World Bank, Labor force, total (accessed 10/15/2016) 10 Ibid 17

22 Table 1 lists the countries used in my analysis. It covers 74 countries from different regions. Total years per country range from six to nineteen years, with an average of thirteen years per country. Table 1: Countries Used in the Analysis. Countries Argentina Croatia India Mexico Slovenia Armenia Cyprus Indonesia Moldova South Africa Australia Czech Ireland Morocco Spain Republic Austria Denmark Israel Mozambique Sudan Azerbaijan Ecuador Italy New Zealand Sweden Belarus Egypt Japan Norway Switzerland Belgium Estonia Kazakhstan Pakistan Tajikistan Bolivia Finland Korea Peru Thailand Bosnia & Herzegovina France Kyrgyz Rep. Philippines Tunisia Brazil Georgia Latvia Poland Turkey Bulgaria Germany Lithuania Portugal Ukraine Canada Greece Macedonia Romania United Kingdom Chile Guatemala Madagascar Russian Federation United States China Hungary Malaysia Singapore Uruguay Colombia Iceland Malta Slovak Republic Costa Rica In addition, Table 2 summarizes the definition of variables in the model used. The table also includes the Stata code for each of the variables considered. 18

23 Table 2: Definition of Variables. Variable Name Variable Definition GDPGrowth GDP Growth gb_xpd_rsdv_gd_zs R&D expenditure as % of GDP NEWip_tmk_totl Trademark applications, total se_sec_enrr Gross enrollment ratio secondary, both sexes NEWbn_klt_dinv_cd Foreign Direct Investment, net (BoP current US$) NEWsl_tlf_totl_in Labor force, total 19

24 5. Regression Results Table 3 presents the regression results from the Stata output. Table 3: Regression Results. Fixed-effects (within) regression Number of obs = 678 Group variable: countryid Number of groups = 76 R-sq: Obs per group: within = min = 1 between = avg = 8.9 overall = max = 13 F(17,75) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 76 clusters in countryid) Robust GDPGrowth Coef. Std. Err. t P> t [95% Conf. Interval] gb_xpd_rsdv_g~s L NEWip_tmk_totl se_sec_enrr NEWbn_klt_din~d NEWsl_tlf_tot~n Year _cons sigma_u sigma_e rho (fraction of variance due to u_i) 20

25 To estimate the effect of innovation on economic growth in my sample of countries, I used fixed-effects and lagged R&D expenses as percentage of GDP have been for six years. Table 3 indicates that the coefficient of R&D expenses as percentage of GDP is positive and statistically significant. According to these results a one percentage point increase in R&D expenditures as percentage of GDP increases GDP growth by 2.28 percentage points, lagged to six years and controlling for gross enrollment ratio in secondary, foreign direct investment, and labor force. This might suggest that successful investments in R&D take 6 years to provide returns in the form of GDP growth, controlling for the aforementioned variables. Trademarks applications filed have a negative and statistically significant effect. A one percent increase in trademarks applications decrease GDP growth by 5/1,000,000 percentage points, controlling for gross enrollment ratio in secondary, foreign direct investment, and labor force. Even though this inverse relationship is unexpected, it can be stated that its impact is relatively small. Conversely, as expected, labor force has a positive and statistically significant effect on GDP growth. A one percent increase in labor force increases GDP growth by 0.12/1,000,000 percentage points. Similar to the previous case, it should also be stressed that the impact measured is relatively small. In addition, 21

26 control variables for secondary school enrollment and foreign direct investment are not statistically significant for this sample. 22

27 6. Policy Analysis Based on these results and from a policy analysis perspective, governments should give importance to R&D efforts in the pursue of GDP growth. However, it should be approached as a long-term strategy, given the years required between the time of the investments and the appearance of its results as GDP growth. Thus, national consensus between different parties and social actors should be built around these innovation-related efforts carried by the government, in order to ensure consistency and sustainability throughout time. Otherwise, efforts might be interrupted as a consequence of political changes and thus the expected returns of these expenses would never come to fruition. Another important aspect that government should considered is to develop a comprehensive tool for the different potential externalities caused by R&D expenses. For example, performance measurement of innovation-related investments could include more direct metrics like time reduction on a given process or even job creation produced from these initiatives. This would enhance the ability to further track the impact of R&D-related efforts from a regional, industrial and societal perspective. In other words, multiple iterations in this field, would allow a government to determine which type of innovations (i.e.: new products, new processes, among others) provide higher growth returns. The deep 23

28 dive in the analysis can also be made by comparing different regions, industries and demographics on a given country as recipients of these efforts in order to be determine the most efficient paths and maximize the potential return of these investments. 24

29 7. Conclusion The goal of this research was to determine whether there is a significant relationship between countries R&D efforts in research and innovation and their GDP growth. The results show that there is a strong positive relationship between innovation (R&D expenditure as percentage of GDP) and GDP growth. However, it is worth noticing that these results are evident six years after the occurrence of the investments, consistent with literature. In addition, the model shows a negative relationship between trademarks and the dependent variable (GDP growth). Even though the size of this impact is relatively small, this might shed light on the relevance of trademarks for GDP growth. Thus, other variables such as a specific type of patents should be studied to assess if they have a more direct relationship with GDP growth. However, given that trademarks are not a complete measure of innovation and research activities, the result should not be interpreted as a rejection of innovation s impact on GDP growth. 25

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