Grease or sand – the effect of corruption on innovation in
developing countries
Master Thesis in MSc Business Administration - Track of International Management
Luisa Burkard (UvA ID: 11815205)
22nd June 2018 Final version
Supervisor: Dr. Mashiho Mihalache Second reader:Dr. Niccolò Pisani
Statement of originality
This document is written by Student Luisa Burkard who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents Abstract ... 4 1. Introduction ... 5 2. Literature review ... 9 2.1. Innovation ... 10 2.2. Corruption ... 12
2.3. Grease and sand – the effects of corruption ... 14
2.4. Economic freedom ... 17
2.5. Research gap and question ... 20
3. Theoretical framework ... 21
3.1. Corruption and innovation ... 21
3.2. Economic freedom as moderator ... 24
3.2.1. Legal system and security of property rights... 25
3.2.2. Freedom to trade internationally ... 27
3.3. The conceptual model ... 29
4. Data and methodology ... 29
4.1. Sample and data collection ... 30
4.2. Variables and measures ... 31
4.2.1. Independent variable ... 31 4.2.2. Dependent variable ... 32 4.2.3. Moderator variables ... 33 4.2.4. Control variables ... 34 4.3. Statistical methodology ... 35 5. Results ... 37 6. Discussion ... 42 6.1. Findings ... 42
6.2. Academic contributions and managerial implications ... 45
6.3. Limitations and future research... 47
7. Conclusion ... 50
References ... 54
Appendix ... 65
Table of figures Figure 1: Conceptual model ... 29
Figure 2: Correlation of corruption and innovation performance for 2015 ... 38
Table of tables Table 1: Variables ... 31
Table 2: Mean, standard deviation and correlation...39
Abstract
This study examines the relationship between corruption and innovation in developing countries. It is tested how the two moderators, development of the legal system and protection of property rights as well as the freedom to trade internationally, both taken from the concept of economic freedom, influence the generally proposed negative relation of corruption and innovation. The purpose of this study is to gain insights into the environments in which corruption can “grease the wheel”. A four year panel data study is conducted on the basis of data from 87 developing countries. The results indicate that corruption is deterring to innovation and that the development of institutions such as the legal system and property rights as well as freedom to trade is crucial for its diminution. However, no evidence is found supporting the hypothesis that corruption can be beneficial under certain conditions or act as a facilitator. Academic and practical contributions are presented by adding to the debate of “grease the wheel” versus “sand in the wheel” and by suggesting fitting anti-corruption policies for developing countries.
Key words
1. Introduction
The amount of bribes paid by businesses and individuals worldwide is estimated to be $1.5 trillion each year, which translates to approximately 2% of the global gross domestic product (GDP) (World Bank 2017). Consequently, it is no surprise that during the last decades, many anti-corruption conventions and policies (United Nations Convention against Corruption, Anti-Corruption Summit London 2016, Organisation for Economic Co-operation and Development (OECD) Anti-Bribery Convention) have been put in place and that the reduction of corruption is one of the main concerns of the United Nation’s Sustainable Development Goals (United Nations 2017).
However, corruption, especially in developing countries, is still considered to be an acute issue for society and business alike (Olken & Pande 2012). Despite the constant fight against corruption, two thirds of all countries worldwide have a serious problem regarding bribery and graft, whilst most countries make little to no progress in improving their situation (Transparency International 2018). It seems that anti-corruption policies struggle to find the right approach, which is indeed difficult as corruption itself and the environments where it is present are very heterogeneous.
A new direction of corruption research was introduced in the late 1990s with the creation of the Corruption Perceptions Index (CPI) by Transparency International. It established a foundation for empirical academic research by collecting worldwide country-level data, offering an appropriate tool to measure corruption. This has given buoyancy to numerous studies concerning this new stream of research (Olken & Pande 2012; Robertson & Watson 2004).
Corruption can be defined as the abuse of public power for private gain (Cuervo-Cazurra 2006; Rodriguez, Uhlenbruck & Eden 2005) and is generally seen as an ethically incorrect phenomenon
yielding negative outcomes (Warren 2004). Bribes paid in a corrupt environment are additional costs in doing business, the money paid as bribes is inefficiently allocated, and corruption erases trust, which then increases risk and uncertainty for potential investors (Goudie & Stasavage 1997; Robertson & Watson 2004). Taking these arguments into account, it seems clear that corruption is detrimental to economic activity. Many researches have been published, arguing that corruption has a negative impact on growth (Mauro 1995; Méon & Sekkat 2005), foreign direct investments (FDIs) (Cuervo-Cuzzara 2006; Habib & Zurawicki 2002; Robertson & Watson 2004; Shleifer & Vishny 1993; Wei 2000) and entrepreneurship (DiRienzo & Das 2015; Dutta & Sobel 2016; Mahagaonkar 2008).
Nevertheless, despite the aforementioned findings, the consequences of corruption are still controversially discussed (Dutta & Sobel 2016; Méon & Sekkat 2005). An opposing stream of research investigates whether corruption may in fact have a positive effect on the economy, how it can be beneficial for development or under which circumstances corruption plays the role of a facilitator. The so-called “greasing the wheel” hypothesis finds support in studies which show that corruption has a positive impact on economic growth and development (Leff 1964; Nye 1967) and that routine corruption can be efficiency-enhancing (Dreher & Gassebner 2013). Egger and Winner (2005) find that corruption can even be an incentive for firms to realize FDIs in certain countries. Méon and Weill (2010) examine the effect of corruption on efficiency and observe that corruption is less detrimental to efficiency in countries where institutions are less effective.
Thus, it can be concluded that no clear consensus has been reached regarding this controversial body of studies and that the existing and ongoing investigation on corruption in developing
countries suggests a large and promising research agenda (Olken & Pande 2012). An answer to the question, if corruption is “sand in the wheel” or “greases the wheel”, has not yet been found.
It is widely accepted that innovation is one of the main drivers of national economic growth and has become increasingly important in the global, knowledge-based economy (DiRienzo & Das 2015), which makes it crucial to create an environment that fosters innovation and offers incentives for entrepreneurs. Thus, the urgency becomes apparent to research how innovation is influenced by corruption, which environments are most beneficial for innovation despite the existence of corruption and what the findings recommend to managers and policy makers for the fight against it.
Therefore, addressing the aforementioned gap, this study focuses on the effect of corruption on innovation. The empirical connection between corruption and innovation has not been extensively explored (Anokhin & Schulze 2009; Mahagaonkar 2008) and the above-mentioned observations suggest that there is more to the relationship between corruption and development than typically assumed (Blackburn & Forgues-Puccio 2009). The researchers that recently turned to innovation find mixed results concerning corruption and entrepreneurship. Negative impacts of corruption are found (Anokhin & Schulze 2009; DiRienzo & Das 2015; Dutta & Sobel 2016) while Aidis, Estrin and Mickiewicz (2012) suggest no direct country-level effect of corruption hindering entrepreneurial activity. The view that corruption can act as a facilitator under certain conditions is for instance supported by Dreher and Gassebner (2013) and Belitski, Chowdhury and Desai (2016).
Consequently, this study aims to find an answer to the research question how corruption affects innovation in developing countries and how the two concepts of economic freedom, the development of the legal system and security of property rights as well as the freedom to trade
internationally, moderate this relationship. The underlying factors of economic freedom are analyzed in more detail to gain insights about the environments in which corruption can “grease the wheel” on country-level. It is proposed that the negative effect of corruption on innovation in developing countries can be reduced and even turned into a positive effect when the environments of low development of the legal structure and little protection of property rights as well as low freedom to trade internationally are taken into account. It is argued that corruption in this contextual environment is expected to be a facilitator to by-pass over-regulation or to substitute for missing laws and thus, leading to increased innovation. This is tested by a linear mixed effects model using panel data of a time period of four consecutive years from 2012 to 2015 obtained from 87 developing countries.
The results indicate that the influence of corruption on innovation performance in developing countries is indeed negative and does not possess beneficial qualities. No significance is found for the moderating effects of the factors legal system and property rights and freedom to trade internationally.
However, numerous contributions to existing research are offered through this study. First, light is shed onto the effect of corruption on innovation which has not yet been sufficiently researched (Blackburn & Forgues-Puccio 2009; Mahagaonkar 2008; Méndez & Sepúlveda 2006; Mohamadi, Peltonen & Wincent 2017; Svensson 2005). Evidence is added that corruption in fact is “sand in the wheel” for the expansion of innovation in developing countries. This contradicts with the view that especially countries with ill-developed institutions can benefit from corruption (Leff 1964; Méon & Weill 2010). Secondly, although the two factors of economic freedom do not show significance in the conducted analysis, it is crucial to take the institutional environment of the developing countries, where corruption and innovation are measured, into account. Only thus,
the particular conditions for each country can be accounted for, which can be extremely diverse in the context of developing countries (Aubert 2010), and significant and meaningful results can be obtained. Thirdly, in line with the second contribution, it is argued that not only economic freedom or other measurements of institutional development should be incorporated into future researches, but regarding economic freedom, the differentiated effects of the single factors of economic freedom should be used and not the index as a whole, which can lead to false assumptions and conclusions regarding the influence of corruption on innovation performance in a country (Dempster & Isaacs 2017; Goel & Nelson 2005; Heckelman & Stroup 2005). Certain implications regarding anti-corruption policies and managerial suggestions are given on the foundation of the findings.
The reminder of this paper is structured as follows: First, this study reviews the academic debate of “sand in the wheel” versus “grease the wheel” and provides an overview of the existing research. The main concepts of innovation, corruption, and economic freedom are reviewed to provide a theoretical base and identify the research gap. In the next chapter, the conceptual model is explained, and the hypotheses are deducted from theory. Chapter 4 describes the methodological basis of the analysis. Subsequently, the results are listed and discussed in regard to findings, limitations and further research agenda. Finally, a conclusion is provided and the main contributions are highlighted.
2. Literature review
In this chapter, the underlying concepts of innovation, corruption and economic freedom in developing countries are defined and examined in greater detail. The potential positive and negative effects of corruption on an economy and accompanying literature are reviewed and
insights into the scholarly debate of the “grease” or “sand” effect of corruption are given. Finally, the research gap and research question are clarified.
2.1. Innovation
Many definitions of innovation have been proposed. As one of the first, Schumpeter (1934) identifies innovation as critical for economic change. It can appear as radical, disruptive, or incremental innovation. He also describes different types of innovation, namely product, process, marketing, and organizational innovation. Aubert (2005) supports this in pointing out that innovation does not necessarily imply technological novelty but can also describe new organizational developments. Apart from that, innovation does not have to be new to the market or to the world, but can simply be unknown to a firm or a region and thus, have an impact on productivity and employment (Zanello et al. 2016). The OECD Oslo Manual (2005, p. 46) states that “an innovation is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organisational method in business practices, workplace organisation or external relations” and additionally, in line with Schumpeter, defines the aforementioned four types of innovation.
Innovation is strongly influenced by factors of the environment where it takes place and is embedded in a broader innovation system (Aubert 2010; Srholec 2011). The national economy as well as the technological and institutional framework play a major role in providing the foundation. They include institutions, human capital and research, infrastructure, market sophistication, and business sophistication as proposed by the Global Innovation Index (GII).
Compared to developed countries, innovation in developing countries happens quite differently. As innovations spread, they oftentimes enter developing countries after their introduction to developed ones. This so-called catching-up makes it possible for developing countries to tap into
existing knowledge and know-how from foreign countries (Zanello et al. 2016). However, it cannot be assumed that all developing countries are at the same stage of development. Developing countries range from rising, growing power states to fragile countries, lacking basic development structures (Aubert 2010). For catching up, an accelerated way of innovation, to happen some conditions have to be met. Human and financial resources have to be available as well as the capability of firms and industries to absorb this knowledge. Furthermore appropriate institutions and policies are required to guide incentives and facilitate the process (Anokhin & Schulze 2009; Aubert 2010). Additionally, the diffusion of innovation in developing countries also depends on the nature of the innovation. An innovation with little technological advancement is more probable to spread easily as the channels of transmission and the needed skills and resources are less demanding. Infrastructure is also a factor which determines the successful diffusion of an innovation, as information and communication technology (ICT) and infrastructure development is needed to electronically and physically spread innovation (Zanello et al. 2016).
These conditions however remain partly unmet in some developing countries. It can be observed that the overall environment in which innovation has to grow is more challenging for developing countries as their innovation systems are fragmented and not well developed (Aubert 2005). Consequently, firms and entrepreneurs have obstacles to overcome. Those impediments which have to be faced in developing countries arise from inappropriate business and governance climates and insufficient education (Aubert 2005; Bradley et al. 2012). Political instability and weak law enforcement discourage foreign investments and the opportunity of diffusion of innovation from foreign countries. The national innovation systems are outdated or resource-constrained and thus, lack the ability to meet the original objectives for which they were designed
These barriers to the creation and diffusion of innovation can be separated into external factors such as political, economic and institutional constraints and internal factors, such as capabilities and resource constraints (Zanello et al. 2016). One of the external barriers, namely corruption, is examined in greater detail in the following chapter as it appears as highly influential regarding a country’s ability to innovate.
2.2. Corruption
The level and patterns of corruption vary strongly across countries, institutions and historical periods (Shen & Williamson 2005), which might provide an explanation to the fact that no precise definition of corruption exists. However, this study refers to corruption as the abuse of public power for private gain as used by Cuervo-Cazurra (2006) and Rodriguez, Uhlenbruck and Eden (2005). This definition emphasizes public corruption, although corruption also describes collusion between firms or the misuse of corporate assets that translate to increased costs for consumers and investors (Svensson 2005).
Corruption is a reaction to state-imposed rules. These rules can have a beneficial or harmful character. Paying a bribe to a government official to prevent getting a fine for running a red light is an example of a benevolent rule which exists to make traffic safer. Contrarily, corruption can be a reaction to over-regulation or inefficient institutions and people pay bribes to by-pass these rules put in place (Djankov et al. 2003).
To answer where corruption originates from and why it is especially present and problematic in developing countries (Shen & Williamson 2005), a look has to be taken at its determinants. There are different theories that offer explanations. The institutional theories argue that the quality of institutions - and through these institutions the prevalence of corruption as it is determined by them - is shaped by economic factors present in the country (de Vaal & Ebben 2011). The human
capital view suggests that growth in human capital and income influences the development of institutions. Another theory assumes that institutions are inherited and explain different effects of former colonies on institutional development. The influence of religion on institutions is also used as explanation for different levels of corruption (Svensson 2005). Shen and Williamson (2005) group these determinants into five groups, namely political factors, economic factors, state strength, juridical factors and cultural and social structural factors.
So far, corruption has mainly been studied by two different streams of research. One of them focuses on the economic and the other on the cultural effects. A third stream connecting both directions has evolved only recently (Robertson & Watson 2004). In line with the former, Shleifer and Vishny (1993) give an introduction to the topic of corruption and on the one hand, examine the economical outcome and on the other hand, show how corruption differs regarding contrasting cultural environments. They find that weak governments not controlling their agencies experience high levels of corruption. As corruption is illegal and happens in secrecy, it is distortionary and costly, which in turn leads to less growth and hinders development especially in developing countries. The argumentation why corruption is costly follows this line of reasoning: If the central government is weak, other governmental agencies and bureaucracies can step in and ask for bribes themselves in return for granting a permit for instance. The second reason is that secrecy, which is needed for corruption, shifts investments to projects that are more difficult to oversee, so that corruption will not be discovered. These projects can be less efficient or profitable than what would have been the first choice without the distortion due to corruption (Shleifer & Vishny 1993). The first cross-country study of corruption and growth by Mauro (1995) finds that corruption lowers investment and as a consequence economic growth. Following this, a great body of academic research has focused on corruption and its effects. This
2.3. Grease and sand – the effects of corruption
There are two general effects of corruption that have been found and are used as main arguments against it. First, corruption is costly as bribes add to the costs of doing business and a corrupt environment increases risk and uncertainty, thus deterring investments (Goudie & Stasavage 1997; Robertson & Watson 2004). The second effect is distortion: For government officials to secretly extract rent, they have to go through a variety of cover-up processes (Shleifer & Vishny 1993).
Olken and Pande (2012) describe what these two effects mean for firms, the government and correcting externalities in greater detail. Firms bribe tax officials to avoid paying taxes. It then depends on the level of taxation and in which other cases the firm has to pay bribes in order to evaluate if this is cheaper for the firm or not. In addition, firms might change their production choices to avoid corruption, if this is especially present in a certain area, resulting in less profitability. The government might not realize the most efficient project because with the added costs of bribing, it is not cost-efficient anymore. Regarding distortion, government officials who want to secretly extract rent have to go through a variety of processes as cover up, which makes it more complex, expensive and projects are chosen not because they are the most efficient but because there it is easier to extract rents. The impact on correcting externalities becomes apparent in regards to laws that are undermined when, for instance, the amount of paid bribes to government officials for dropping charges or fines is lower than the official fine that had to be paid otherwise.
All these negative effects lead to the hypothesis that corruption is “sand in the wheel” regarding investments, growth and development of a country. Corruption is not helpful and has no positive
outcomes but hinders development. The money paid for bribes are inefficiently allocated resources (Robertson & Watson 2004).
Mauro (1995), one of the supporters of the “sand in the wheel” hypothesis, identifies the channels through which corruption and other institutional factors affect economic growth and tries to quantify the magnitude of these effects. He concludes that corruption decreases investments and thus, economic growth. Furthermore, Méon and Sekkat (2005) find a negative effect of corruption on growth and show that even in difficult environments, where corruption is expected to substitute for lacking or ill-developed institutions, corruption does not compensate for bad governance. Regarding the effect of corruption on FDI, it is argued that countries with a high level of corruption do not experience high levels of trust from investors, as corruption leads to risk and uncertainty. In addition, corruption acts like an irregular tax on business operations in the country in question and thus makes the investments there costlier (Cuervo-Cazurra 2006; Habib & Zurawicki 2002; Mauro 1995; Shleifer & Vishny 1993; Wei 2000). Cuervo-Cazurra (2006) nevertheless finds differences concerning the countries where the FDIs come from. The country of origin of the FDIs modifies the relationship of corruption and FDIs in the way that countries who have signed anti-bribery laws invest less in corrupt host countries than countries that have not signed these laws and have themselves a higher level of corruption. This means that corruption does not affect investors equally. Another study turns this relationship around and investigates the effects of FDIs on corruption and finds that the more rapid the rates of change in FDIs are, the higher the level of corruption in a country (Robertson & Watson 2004).
The contradicting hypothesis states that corruption can “grease the wheel”, which suggests that corruption helps to compensate for bad governance and enhance efficiency if it is a routine kind of corruption (Blackburn & Forgues-Puccio 2009; Dreher & Gassebner 2013). It is not stated that
corruption is beneficial in general, but under certain conditions, for instance when over-regulation or other aspects of defective governance exist (Leff 1964; Méon & Weill 2010). The explanation can be given the following way: Corruption and especially bribes can in some cases facilitate the issuing of permits, speed up processes and help to circumvent inefficient regulations (Méon & Weill 2010). Leff (1964), one of the first to postulate a positive effect of corruption, argues that bribes work like a piece rate for government officials, motivating them to speed up their work and that entrepreneurs who bribe can overcome extensive regulations and thus, increase innovation. This is supported by Nye (1979) who finds that corruption has a positive impact on economic growth and development, but only in less developed countries. This may indicate that the contextual environment of the respective country plays a major role with respect to the impact of corrupt behavior. Corruption is also less harmful in countries where an organized corruption network is in place. These countries show lower levels of bribes, higher research activity and more growth than countries with unorganized corruption networks (Blackburn & Forgues-Puccio 2009; de Vaal & Ebben 2011). The underlying explanation is that in an unorganized network, all government officials act as independent monopolists who try to maximize their profits. However, when they are organized and act as a joint monopoly to maximize their joint bribe income, externalities are internalized, which then reduces the overall bribe level and increases efficiency. Studies regarding the relationship between corruption and FDI also show support for the “grease the wheel” hypothesis. Corruption in the host country can be an incentive for investors to realize FDIs (Egger & Winner 2005). The study takes a closer look at the short-term and long-term impacts and finds positive effects of corruption on FDIs in both cases. The authors argue that the level of perceived corruption in a country is correlated with the determinants of the institutional environment. Those are difficult to measure and oftentimes not controlled for in earlier studies, thus explaining the negative effect of corruption on FDIs
found before. With their approach, they find that the positive effect of the short run is smaller than of the long run effect, indicating that in the long run the negative effects of corruption on FDIs are less severe.
Much attention has recently been devoted to the relationship between economic freedom and corruption, but no theory is yet proposed that explains the coherence sufficiently (Chafuen & Guzmán 2000; Pieroni & d’Agostino 2013). Thus, the next chapter gives insights into the concept of economic freedom and the accompanying academic literature.
2.4. Economic freedom
The concept of economic freedom can be taken back to Adam Smith (1799) who introduces the modern political economy and with it, the logical individualism. This is one of the building blocks of economic freedom, as it is compounded out of personal choice, voluntary exchange, freedom to compete, and protection of persons and property (Graeff & Mehlkop 2003; Sturm & de Haan 2001). These factors ensure that individuals can decide how to spend their time, which products and goods they want to produce, and who to trade with. Thus, it entails the freedom of choice in conducting business (Goel & Nelson 2005) as well as the security of property. An individual is free to choose for itself, but the resources, time and decisions of others are to themselves and no demand can be made towards them (Gwartney & Lawson 2003). Regarding institutions and policies, economic freedom implies that property rights are protected by law, infrastructure for voluntary exchange is provided, contracts are enforced and the stability of money is granted. Institutions and policies play the role of a grantor but in some cases have to retract. Thus, the role of the state is twofold; it makes sure that economic freedom is both protected and unconstrained (Beach & Miles 2006; Yamarik & Redmon 2017). Economic freedom entails that individuals are free to choose and change employment and which business
activities to engage in and that a self-regulating market exists. Taxes, government spending and regulations reduce economic freedom, as this lessens personal choice and market coordination (Gwartney & Lawson 2003).
One of the first to develop a systematic measurement of economic freedom, then called economic liberty, are Scully and Slottje (1991). Since then, more measurements have been introduced. One of the most widely used is the Economic Freedom of the World (EFW) index by Gwartney, Lawson and Block from the Fraser Institute, introduced in 1996. Since then, it measures the “consistency of a nation’s policies and institutions with economic freedom” (Gwartney & Lawson 2003, p. 405). Due to its availability for a large number of countries and over a long period of time, the EFW index has been used in an array of empirical work (Graeff & Mehlkop 2003; Sturm & de Haan 2001). The index consists of five main areas, which are size of government, legal system and security of property rights, sound money, freedom to trade internationally and regulation. These areas possess subcategories, which again are divided, so the index in total integrates 42 distinct components, which are elucidated in the next paragraphs. The EFW index rates them from least free (with a very dominate state) to most free (with a minimal state), however the purpose of the index is not to rank countries regarding their superiority over others. Depending on the country, in some cases more regulation might enhance growth, while in other cases less regulation and more freedom could promote innovation (Gwartney & Lawson 2003).
The role of economic freedom has been diversely examined, often with a focus on entrepreneurship. Dempster and Isaacs (2017) investigate the link between productive and unproductive entrepreneurial activities as moderated by economic freedom and find that economic freedom is a determinant of the level of entrepreneurial activity across countries and
that it also moderates the relationship between human capital, corruption, and productive entrepreneurship. They include the EFW index as a control variable and their results suggest that in environments with very low levels of economic freedom, corruption is necessary for entrepreneurship. However, as aforementioned, they focus on two different kinds of entrepreneurial activity in a country and it has to be tested how generalizable their findings are and if they are applicable to innovation.
Other studies examine the relationship of economic freedom and corruption directly and discover that economic freedom decreases corruption, if treated as exogenous (Apergis, Dincer & Payne 2012; Shen & Williamson 2005). Brunetti, Kisunko and Weder (1997) and Goel and Nelson (2005) find that the monetary component of economic freedom helps the most in battling corruption. Graeff and Mehlkop (2003) propose that the relationship between economic freedom and corruption differs from country to country, depending on its wealth.
Many researchers highlight that economic freedom matters and affects both, innovation and corruption, but it is difficult to say in which way exactly. One of the reasons is that the individual factors of the EFW index affect innovation and corruption differently (Dempster & Isaacs 2017; Goel & Nelson 2005; Graeff & Mehlkop 2003; McMullen, Bagby & Palich 2008; Pieroni & d’Agostino 2013). Thus, it is problematic to say that all EFW components are deterring to corruption and to state that economic freedom reduces corruption. The complexity arise from the following: On the one hand, an economically unfree country with numerous rules, restrictions and taxes on free trade offers many opportunities to bribe, especially to avoid these excessive rules, and thus corruption can reach high levels as a result. On the other hand however, corruption is an existing phenomenon in an economically free country as well. There, corruption and bribes are used to stay ahead of the competition (Graeff & Mehlkop 2003).
In line with this, Goel and Nelson (2005), when examining deterrents of corruption, find that political freedom does not show a significant effect, while economic freedom does, but not all components are equally effective. Monetary policy has a strong negative influence on corrupt activity and countries with less regulation regarding financial sectors experience lower levels of corruption. Dempster and Isaacs (2017) identify that a smaller government and low trade restrictions show positive impacts on entrepreneurial activity, but well-developed legal systems and efficient protection of property rights can impact it negatively.
Thus it becomes apparent that the varying effect of economic freedom has to be further examined. What do these observations mean for innovation? To be able to provide answers, this study does not use the whole EFW index as moderator, but the index is divided back into the five single components and it is focused on two of them: legal system and security of property rights as well as the freedom to trade internationally. Both are elucidate in more detail in chapter 3.2.
2.5. Research gap and question
As aforementioned, depending on context, corruption can have extremely varying effects on innovation and has not yet been researched sufficiently on country-level (Blackburn & Forgues-Puccio 2009; Mahagaonkar 2008; Méndez & Sepúlveda 2006; Mohamadi, Peltonen & Wincent 2017; Svensson 2005). Méon and Sekkat (2005) summarize the ongoing debate of “greasing the wheel” versus “sand in the wheel” with the statement that not enough evidence exists to rigorously reject the “grease the wheel” hypothesis as no consensus has been reached. This is why this study tests the “greasing the wheel” hypothesis concerning the country-level effects of corruption on innovation in developing countries. As moderators, two factors of economic freedom, legal system and security of property rights and freedom to trade internationally, are used to test if the proposed negative effect of corruption on innovation can be positively
moderated by economic freedom. The enquiry focuses particularly on developing countries as they are prone to experience higher levels of corruption and the institutional and governance-related environments are challenging (Olken & Pande 2012; Svensson 2005).
3. Theoretical framework
In this chapter the theoretical framework is developed which is later on used to answer the research question. First, the link between corruption and innovation is examined in greater detail and the debate of “grease the wheel” versus “sand in the wheel” is focused onto the effect of corruption on innovation. From this theoretical foundation, the first hypothesis is deducted. The second part addresses the link between economic freedom and the proposed negative main relationship of corruption and innovation and examines the moderating character of the two factors taken from the concept of economic freedom, namely legal system and property rights and freedom to trade internationally. Thirdly, the conceptual model is presented and an overview of the proposed relationships and hypotheses is given.
3.1. Corruption and innovation
Now that the underlying conflict of “sand in the wheel” versus “grease the wheel” has been introduced, it is important to explain how far research is advanced regarding the effect of corruption on innovation. The observed lack of consensus concerning the effects of corruption on entrepreneurship, growth and FDIs is also true for innovation, although less research has been conducted in this field so far (Mahagaonkar 2008; Méndez & Sepúlveda 2006; Mohamadi, Peltonen & Wincent 2017; Svensson 2005).
For once, supporting the “grease the wheel” hypothesis, corruption can make it easier to start a business and obtain licenses, patents and permissions in countries with a heavy bureaucratized structure. This translates to a time advantage for the ones engaging in corruption (Dutta & Sobel 2016; Mahagaonkar 2008). A relationship based on corruption between firms and government officials can reduce uncertainty and risk and thus, facilitate long-term planning, in countries with little developed administration and low monitoring levels (Mahagaonkar 2008). Corruption, as aforementioned, can act as incentive for investments, which are needed for innovation (Egger & Winner 2005). As many developing countries experience high regulative governance and an environment of political risk, corruption can facilitate the process of investment, reduce risk and help to jump policy hurdles, thus leading to increased innovation performance (Mahagaonkar 2008). Dreher and Gassebner (2013) find that corruption can positively moderate the effect of regulation on entrepreneurship and Paunov (2016) argues that the effect of corruption is asymmetrical on firm-level, showing that corruption does not reduce overall patenting. Regarding African developing countries, Mahagaonkar (2008) investigates corruption on firm-level and finds that product and organizational innovation are negatively affected by corruption while process innovation shows no effect and marketing innovation is, in fact, facilitated by corruption.
However, this contradicts with the view that corruption acts as “sand in the wheel”: As the aforementioned reasons are mostly direct effects of corruption on innovation, there are direct and indirect effects which need to be considered regarding the negative impacts of corruption on innovation performance. First, corruption reduces the effectiveness of the government itself, leads to lower income of potential consumers and creates a climate where trust in institutions is lacking, consequently creating a non-favorable environment for innovation (DiRienzo & Das 2015; Dutta & Sobel 2016; Graeff & Mehlkop 2003; Sturm & de Haan 2001). Secondly, due to corruption, short-term, flexible investments are preferred to long-term investments, as corruption
threatens the incentives to engage in long lasting innovative activity and investments are channeled away from productive projects. This, as a result, decreases innovation performance of a country (Goedhuys at al. 2016; Hall & Levendis 2017). Thirdly, once corruption networks are established in developing countries, new firms which may introduce innovations, are excluded due to the secretively of these corruption cartels (Alam 1990; Goudie & Stasavage 1997). Fourthly, innovative firms increasingly suffer from corruption, as they are more frequently in need of obtaining licenses and permits from the government than established firms (Goudie & Stasavage 1997). Fifthly, corruption increases transaction costs to conduct business (DiRienzo & Das 2015) and finally, corruption hampers innovation performance of a country because government officials delay granting patents or permits to reach even higher bribes (Mahagaonkar 2008). DiRienzo and Das (2015) who study the effect of corruption on innovation on country-level suggest that corruption negatively affects innovation and that this relationship depends on the economic development of the country in question. They find that a decrease in corruption has a greater positive impact on innovation in less developed countries. Dutta and Sobel (2016) agree that corruption hurts innovation, but go even further and postulate that despite bad business climates, the impact of corruption on innovation performance is still negative. It is argued that even if a positive direct effect of corruption on innovation exists, the overall negative effect of corruption, consisting of the direct and indirect effects, hampers innovation performance (DiRienzo & Das 2015).
Due to the aforementioned arguments, the main relationship of corruption and innovation is hypothesized to be negative:
H1: The higher the level of corruption, the lower will be the level of innovation performance in a certain country.
3.2. Economic freedom as moderator
In this chapter the moderators of the relationship between corruption and innovation are introduced, which are taken from the concept of economic freedom. As mentioned before, the EFW index consists of the following factors: size of government, legal system and security of property rights, sound money, freedom to trade internationally and regulation (Fraser Institute 2018). This study focuses on two of these components, legal system and security of property rights as well as freedom to trade internationally. Theoretical foundations are given to explain how these moderators are expected to affect the hypothesized negative relationship of corruption and innovation and thereby show that corruption can, under certain circumstance, act as “grease” and thus result in a higher level of innovation.
The general idea behind the hypotheses that an environment of low economic freedom regarding certain components leads to increased innovation on country-level is backed up by Hall and Levendis (2017), who argue that corruption may be beneficial, when many regulations are in place and little economic freedom is present in a country. Dempster and Isaacs (2017), who study the impact of corruption on productive and unproductive entrepreneurial activity with the EFW as a control variable, find that in countries with very low economic freedom, corruption seems to be necessary to engage in productive entrepreneurial activity. Houston (2007) explains this with the expansionary and restrictive economic effects that corruption has, as talked about before, the sand and the grease. It depends on the magnitude of the two effects to conclude if corruption in the end has a negative or positive outcome on country-level and this is crucially dependent on the degree to which law protection is enforced in a country. When a country experiences low levels of economic freedom, it shows larger positive effects from corruption, as corrupt activities substitute for missing or misguided law. Corruption is used as a facilitator and creates value,
when it enables trades that otherwise would not have been conducted (de Jong & Bogmans 2011).
The available data for 87 developing countries provides a sufficient foundation for the analysis to examine part of the institutional environment which can be found in each country. As they differ tremendously and build varying contexts, the impacts of the two concepts legal system and security of property rights as well as freedom to trade internationally have to be analyzed in more detail regarding their direction of impact.
3.2.1. Legal system and security of property rights
This factor consists of judicial independence, impartial courts, protection of property rights, military interference in rule of law and politics, integrity of the legal system, legal enforcement of contracts, regulatory costs of the sale of real property, reliability of police and business costs of crime (Gwartney & Lawson 2003). This area secures the ownership rights and efficient allocation of resources. For countries that show deficits in this area, it is increasingly difficult to succeed in the other four areas of the EFW index (Fraser Institute 2018).
It should be of uttermost concern for countries to have a well-developed legal system and to secure property rights. The protection of property rights is viewed as one of the most important roles of the state by political philosophers as diverse as David Hume and Karl Marx and seen as an integer part for a country’s development by North (Acemoglu & Verdier 1998). This importance becomes evident, as the protection of property rights motivates innovation by creating an environment favorable for the accumulation of human knowledge. When these property rights are protected, the government ensures that inventors can profit from inventing (Gould & Gruben 1996). This reasoning is confirmed by a number of studies which show that the higher developed and better protected the intellectual property laws in a country are, the greater the positive impact
is that can be found in regards to a country’s level of innovation performance (Chen & Puttitanun 2005; Sweet & Maggio 2015). It is theoretically described and empirically measured that strongly protected intellectual property rights stimulate technological innovation by incentivizing the inventors and thus are significant determinant of economic growth (Woo, Jang & Kim 2015).
However, in developing countries, the legal system is oftentimes lacking matureness and property rights are weak and not protected extensively. This has far-reaching consequences: Court is time and cost consuming and prevalently inefficient in implementing laws which relate to property, bankruptcy, contracts, commercial activities and taxes. Where the legal system fails to enforce contracts, the free market is undermined and incentives for agents to participate in productive activities are lacking (Acemoglu & Verdier 1998). Similarly, firms and business owners cannot rely on courts to ensure patents or property rights by law. Consequently, it is increasingly risky to pursue innovation as investments are uncertain and the “find and keep” component related to innovation and entrepreneurship is not granted (DeSoto 2000; Harper 2003). Inventors have to be granted patents and monopoly rights for their innovations for a certain period of time, because otherwise engaging in innovation is not profitable (DiRienzo & Das 2015). With little incentives given innovation activity decreases (Gould & Gruben 1996).
As reaction to this environment, individuals try to settle disputes extrajudically, bribe to acquire missing permits or engage in other ways of corrupt behavior (Tonoyan et al. 2010). Houston (2007) finds that in an environment as described, corrupt activities substitute for missing or misguided law. Accordingly, corruption can potentially lead to more innovation due to bribes which can be used to speed up processes and secure jurisdiction in favor of one’s goal. Productive investments and trades that otherwise would not occur, are made possible through corruption, as it lubricates the flow of commerce, when the given legal structure does not support
the innovators. He finds, by studying the impact of corruption on economic growth, moderated by certain factors of economic freedom, the gains from corruption to be greater than the money spent on bribing. Consequently, in environments with low development of the legal structure and protection of property rights, innovation performance can increase through the use of corruption.
Thus it is hypothesized:
H2: The negative relationship between corruption and innovation performance is positively moderated by the development of the legal system and security of property rights.
3.2.2. Freedom to trade internationally
This factor of economic freedom describes the international exchange in a country. It consists of tariffs, regulatory trade barriers, black-market exchange rates and controls of the movement of capital and people. Freedom to trade internationally is rated highly, if tariffs and quotas are low, clearance is an uncomplicated process and currency can be easily converted. Furthermore, smooth movement of physical and human capital has to be assured (Fraser Institute 2018).
There are numerous reasons why international trade is crucial for a country’s development. Firstly, freedom to trade internationally increases investments to the country in question. With these investments, economical as well as development in general is introduced (Edwards 1992). Secondly, trading internationally offers improved resource allocation - a greater choice of goods for less money - as it allows the lowest-priced goods to enter the market. Thus, purchasing power increases and thereby the livelihood in the country. This is beneficial to national economic growth (Dornbusch 1992). Thirdly, increased competition is another factor that arises due to international trade: With increased international trade, domestic firms face a more competitive environment. Therefore, incentives to cut costs and to increase efficiency are given. Additionally,
know-how is granted, which in turn leads to catch-up and exponential growth (Ben-David & Loewy 1998; Dornbusch 1992).
On the contrary, a country that scores low in this area experiences restrictions of trade and movement of capital which can oftentimes be observed in developing countries. These protectionist restrictions negatively affect innovation as, for instance, FDIs have a positive effect on innovation on country-level as knowledge is transferred and foreign capital is needed to trigger innovation (McMullen, Bagby & Palich 2008; World Bank 2005). With restrictions for international trade, this is prevented. Governments that impose restrictions or tariffs on goods and services also increase costs of doing business in the market. Additionally, protectionist limitations on trade freedom favor known products over innovative new products. This constrains the opportunities that individuals consider when choosing whether to engage in entrepreneurial activity or not. New profit possibilities are not being chosen and thus, the level of entrepreneurial activity decreases (McMullen, Bagby & Palich 2008).
These drawbacks can be avoided by employing a certain degree of corruption: Bribe paying can act as “grease” in this case and improve the situation. Corruption makes trades possible that would not have been executed otherwise (Houston 2007). Cross-border transactions are facilitated and frequent bribe paying can reduce long waiting times or low quality of customs (de Jong & Bogmans 2011). If the gained time advantage can be translated into monetary advantage, which is greater than the bribe paid, then bribery compensates for the bad quality of customs. Bribery and corruption thus sustain such businesses operating in the informal economy and act as substitutes for policies regarding international trade (Houston 2007). As a result of increased international trading due to corruption, a higher level of innovation performance in a country can be achieved.
Thus it is hypothesized:
H3: The negative relationship between corruption and innovation performance is positively moderated by the freedom to trade internationally.
3.3. The conceptual model
The conceptual model summarizes the theoretical framework of this study with all previously derived hypotheses.
Figure 1: Conceptual model
As illustrated by figure 1, the main relationship of the model is the effect of corruption on innovation performance in developing countries, which is hypothesized to be negative. The moderators are factors of the concept of economic freedom, namely legal system and security of property rights and freedom to trade internationally. It is tested how these moderators influence the effect of corruption on innovation and thus, it is answered if corruption can in certain environments “grease the wheel”.
4. Data and methodology
This research builds on a quantitative analysis of secondary data and follows an explanatory, deductional approach as it is trying to establish causal relationships between different variables.
Legal system and security of property rights Freedom to trade internationally
Innovation performance H1 (-) H2 (+) H3 (+) Level of corruption
The data gathered is partly raw and partly compiled. It will be used for a macroeconomic, panel study, regarding a consecutive four year period for the years 2012 to 2015. With this approach, change during the years can be analyzed (Baltagi 2008).
In the following chapters, the empirical setting, the used variables as well as their measurement and the further used statistical methods are explained.
4.1. Sample and data collection
As this study is concerned with innovation in developing countries, the unit of analysis and the underlying population of interest are developing countries and all variables used are measured on country-level.
The term developing countries is highly controversial. The ongoing discussion about the definition has been moving away from the term developing countries to terms which emphasize the fact that the country in question is not a developed one. The World Bank, for instance, used the terms of developed and developing countries as distinction till 2016, but now classifies countries by four income-group levels, using the gross national income (GNI) per capita. The United Nations (UN) uses the differentiation between developed and developing countries, however not to judge the process and degree of development of a country, but to divide countries in these two categories for statistical reasons (World Economic Situation Prospects 2017).
Likewise, a defined body of countries is needed for the analysis that this study conducts. The Human Development Report (HDR), released by the United Nations Development Programme (2016), includes a list indicating 149 countries and territories as developing regions. This list includes territories where the legal status in unclear. The population of interest is defined by this list (see appendix 1).
The final sample, so the countries used for this study, is drawn on a convenience sample as they are selected due to availability of data. It is made sure that they represent the population accordingly to its diversity (higher and lower levels of corruption, income and innovation, different forms of government as well as varying levels of economic freedom). Since used data is collected from developing countries, it occurs that variables are missing for certain years and countries. Due to this, 62 countries were not suitable for the analysis and thus, the final sample includes 87 countries (see appendix 2). As a four year period is analyzed, 348 data points are available which represent a sufficient sample size. With the amount of 87 countries a wide distribution of corruption and innovation levels exists, which limits the potential sample selectivity bias and increases the power of the results (Yamarik & Redmon 2017).
4.2. Variables and measures
In this chapter, the used variables and their data collection processes are described.
Name of the variables Acronym Operationalization
Corruption IV_CPI_inversedit Perceived corruption in country i in year t
Innovation performance DV_GIIit Innovation performance in country i in year t
Legal system and security of property rights
MV1_legal_systemit Development of the legal system and security of
property rights in country i in year t Freedom to trade
internationally
MV2_international_tradeit Degree of freedom to trade internationally in
country i in year t
GDP log_CV1_GDPit GDP per capita in PPP in country i in year t
Population size log_CV2_populationit Population size in country i in year t
Table 1: Variables
4.2.1. Independent variable
The level of corruption is measured by the Corruption Perceptions Index (CPI) introduced by Transparency International. Data of this index is obtained through interviews with business
data, no certain proof of validity exists. However, the CPI has been proposed as one of the most adequate measures of corruption with strong validity (DiRienzo & Das 2015; Dreher & Gassebner 2013; Houston 2007; Robertson & Watson 2004) and is used by a wide array of studies (e.g. Blackburn & Forgues-Puccio 2009; Habib & Zurawicki 2002; Paunov 2016). Furthermore, Wilhelm (2002) compares the CPI with two other measures of perceived corruption and finds high validity. Thus, the CPI is used as a measure for perceived corruption in this study.
The CPI is a comprehensive data source and its availability regarding the period of time and number of countries makes it valuable. In 2015, data is available for 168 countries and territories (CPI 2015). The data for the variable are collected by downloading the indices of the CPI from the Transparency International website for the years of 2012 till 2015 for the developing countries in question.
The CPI measures corruption on a scale from 0-100, where 0 is the most and 100 the least corrupt. To use it as an indicator for corruption in this study, all scores are subtracted from 100, to inverse the score, so that 0 stands for no corruption and 100 for the maximum of corruption (as seen in Méon & Sekkat 2005; Robertson & Watson 2004). This variable then has a ratio level of measurement.
4.2.2. Dependent variable
Following earlier studies (e.g. DiRienzo & Das 2015; Esteves & Feldmann 2016; Mercan & Goktas 2011), innovation performance is measured by the Global Innovation Index (GII). This index takes input as well as output factors into consideration and calculates an overall index for the level of innovation performance in a country.
The GII is one of the most commonly used measures of innovation on country-level and offers comprehensibility regarding measurement methods (DiRienzo & Das 2015). In 2015, data is
available for 141 economies (GII 2015). The data for the variable are collected by downloading the indices of the GII from the past reports for the years of 2012 till 2015 for the developing countries in question.
The GII measures innovation performance on a scale from 0-100, with countries closer to 0 possessing the least and countries closer to 100 possessing the greatest ability to innovate. This variable has a ratio level of measurement.
4.2.3. Moderator variables
The moderators, namely legal system and property rights and freedom to trade internationally, are taken from the EFW index which is extensively used in empirical research because of its availability regarding time period and number of countries (Sturm & de Haan 2001). The index is composed of five main areas as aforementioned and consists of a total of 42 distinct variables for the obtained data in 2015. This study does not use the EFW index itself, but two of its five main area indicators, because they ensure a more detailed analysis than the weighted summary. The EFW has been criticized for its weighting issue of the subcomponents as well as for the ignorance of accounting for the differing effects each subcomponent can have on a dependent variable (Heckelman & Stroup 2005). Thus, this study does not use the EFW as a whole, but two of the subcomponents separately to test their individual influences on the main relationship.
The data for the two moderators are collected by downloading the indices of the EFW from the past reports from the Fraser Institute website and its areas for the years of 2012 till 2015 for the developing countries in question. As noted by the Fraser Institute (2018), researchers who use the data for a longitudinal study are encouraged to use the chain-linked index offered as it is the most consistent through time. Due to changes in numbers and composition of the components, it is
difficult to directly compare numbers from year to year. Missing numbers for certain countries have been added through “backcasting”.
The five main areas are measured on a scale from 0 to 10, with countries close to 0 being the least and countries close to 10 the most economically free. In regards to the first moderator, legal system and property rights, this shows that a country with a high score has a well-developed legal system and property rights are secured while for the second moderator a high scores indicates that the freedom to trade internationally is granted and customs are efficient. These variables have a ratio level of measurement.
4.2.4. Control variables
Two control variables are added to check for sample bias and alternative effects other that the ones proposed (H1, H2 and H3).
Prior research (Dutta & Sobel 2016; Furman, Porter & Stern 2002; Treisman 2000) shows that a country’s GDP can have varying effects on innovation. It is important to control for economic development as it can lead to differences in levels of technology, finance and institutions and thus, shape the environment where innovations take place (Treisman 2000). This is measured by the GDP per capita and data is obtained from the World Bank.
Population size is added as second control variable. Following prior studies (e.g. Anokhin & Schulze 2009; Dutta & Sobel 2016; Swaleheen & Stansel 2007), population size of a country is also controlled for as a nation’s size can increase innovation performance. Data is obtained from the World Bank.
4.3. Statistical methodology
The developing countries for this study were chosen due to data availability, thus no missing data has to be recorded and no missing value has to be deleted or replaced. This makes the dataset a strongly balanced dataset, as only cases, where no missing data for any variable was recorded, were analyzed (Torres-Reyna 2007; Yaffee 2003). One modification is performed on the variable for corruption (IV_CPI_inversed), which is inversed because its scale was counterintuitive (following Méon & Sekkat 2005 and Robertson & Watson 2004).
As the used data is panel data, it is checked and assured that the used dataset is actually longitudinal. For this, entities – in this case developing countries - have to be fixed, the length of the periods has to be consistent (twelve months in this study) and only one value for each entity and period of time can exist (Park 2011). As the same entities are observed for each time period, this data is called a fixed panel. Additionally, it can be called a short panel because it consists of many countries (large n), but few years (small T), in contrast to a long panel where this is reversed (Colin & Trivedi 2009). The analysis is conducted through the software Stata 15.1, which offers the most options and convenience for panel data analysis.
Certain assumptions have to be checked in order to commence this analysis. The first step of the analysis is to explore the data descriptively. Mean and standard deviation of all variables are reported and checked (see table 2, chapter 5). Secondly, to investigate if any variables are skewed, histograms are plotted. Here it becomes apparent that both control variables, GDP and population, show positive skewness and have an asymmetrical distribution. Due to this skewness, they are both logarithmized and new variables are created, called log_CV1_GDP and log_CV2_population. Third, the variables are checked for multicollinearity, as it is oftentimes present in a panel dataset and Field (2009) strongly suggests checking correlations between
variables before a regression analysis is conducted. To do so, the correlations of the variables are examined (see table 2, chapter 5). As no values show correlation of less than -0,8 or greater than 0,8, multicollinearity does not appear as a serious concern. Fourth, the assumption of normality is tested. The distribution of the residuals is checked to make sure that they are normally distributed. Finally, it is tested for homoscedasticity. The residuals of the independent variables are plotted against the fitted values. It shows that the size of the residuals is similar across values, thus no heteroscedasticity is present.
Following the strategy suggested by Verbeke and Lesaffre (1996), a linear mixed-effects model is used to test the hypotheses H1, H2 and H3. A mixed effects model, also known as multilevel or hierarchical model, contains both fixed and random effects (Hsiao 2007). The fixed effects are the same as standard regression coefficients, but the random effects are not directly estimated. Instead, they are summarized in regards to their estimated variances and covariances. Using this method, individual change and systematic differences over time can be uncovered and analyzed (Shek & Ma 2011). A mixed effects model is used because a reduced number of model parameters is needed and thus, the number of degrees of freedom remains high. The modeling of this is simpler. On the contrary, a fixed effects model reduces the number of degrees of freedom as it has to model a regression coefficient for each of the 87 countries. It is oftentimes assumed that fixed or random effects models are the only choices for panel data, but this is a frequently encountered misjudgment (Park 2011). A generalized linear model (GLM) is not used as it violates the assumption of independence of the single observations (Shek & Ma 2011). This mixed effects model is appropriate to use for data such as panel data, when data is gathered over time on the same entities (Fox 2002).
The model of this multilevel mixed-effects linear regression has the following form:
Yij = βoj + β1jXij + eij with
Yij: score on the dependent variable for an individual observation at level 1 (i referring to individual case, j referring
to group)
Xij: Level 1 predictor
βoj: intercept of the dependent variable in group j (level 2)
β1j: slope for the relationship in group j (level 2) between level 1 predictor and the dependent variable
eij: random errors of prediction for level 1 equation
On level 1, the fixed effects are the effects of the years, while the random effects are specified at level 2, identified by the group variable, which are the 87 developing countries in this case.
5. Results
The two most corrupt countries, out of the used sample of developing countries and throughout the four year period that is being analyzed, are Angola and Yemen. They both have an average score of 80,5 over the observed four years. Regarding innovation performance, Malaysia has the highest score over the four year period with 46,65.
Looking at table 2, the mean, standard deviation and correlations of the variables can be observed. In the case of the dependent variable, innovation performance, the mean of all 87 countries is 62,696 and the standard deviation is 11,448. There are certain findings to note regarding the correlation matrix. As mentioned before, the correlations between the independent variables are unproblematic, as no value is lower than –0,8 or higher than 0,8, indicating that multicollinearity is not a serious concern in this estimation. There are, however, relatively high correlations to be observed, but these figures are well expected. The high negative correlation between innovation performance and corruption of -0,579 shows support for H1 (see figure 2 for data of 2015).
