Innovation and Wage Inequality in Norwegian Regions: Is There a Link?

Innovation and wage inequality in Norwegian regions: Is there a link?

Gøril L. Andreassen

Thesis submitted for the degree of Master of Philosophy in Economics 30 credits

Department of Economics Faculty of Social Sciences

UNIVERSITY OF OSLO

Submitted May 2018

Innovation and wage inequality in Norwegian regions: Is there a link?

Gøril L. Andreassen c 2018 Gøril L. Andreassen

Innovation and wage inequality in Norwegian regions: Is there a link? http://www.duo.uio.no/

Printed: Reprosentralen, University of Oslo Acknowledgements

Writing a master’s thesis has been a demanding as well as a fun experience. First of all I would like to express my gratitude to my main supervisor Jørgen Modalsli and to my co-supervisor Jo Thori Lind for excellent supervision. It has been a privilege to be guided by and learn from such experienced and accomplished researchers.

I would also like to thank Statistics Norway for providing microdata on wage and on Norwegian companies, as well as oﬃce space. Writing this thesis at Statistics Norway oﬀered a great opportunity to gain insights into the work of this important institution.

Further, I would like to thank Jørgen Modalsli and Rolf Aaberge for believing in my research idea, Bernt Sverre Mehammer and Bjørn Oscar Undander for giving me a summer job where I got the idea in the ﬁrst place, Lars Løvold and Nils Hermann Ranum for granting me a two-year study leave, the Centre for the Study of Equality, Social Orga- nization and Performance (ESOP) for granting me a scholarship, and Bjarne Kvam at the Norwegian Industrial Property Oﬃce for providing data on patent applications and answering questions along the way. My thanks also go to Maria Nareklishvili, Ole Myrnes, Malin Jensen, Henrik Svensli, Susann Strømvåg and Jørgen Larsen for discussions and colloquiums during the two-year master’s program, to Susan Høivik for language editing, and to fellow students at Statistics Norway for lunches and Cake-Wednesdays.

In particular, I wish to thank the love of my life, Audun, for being who you are; and Amanda, Åse and Tord for making my life complete.

This thesis is part of the project "Urbanization: productivity, distribution, and policy," funded by the DEMOS program of the Research Council of Norway. Project leader is Jørn Rattsø at the Norwegian University of Science and Technology (NTNU), with Rolf Aaberge at Statistics Norway (SSB) and Kjell G. Salvanes at the Norwegian School of Economics (NHH) as co-project leaders.

Any inaccuracies or errors are my responsibility.

Oslo, May 2018

Gøril L. Andreassen

i Abstract

This thesis finds a significant correlation between innovation and inequality in Norwe- gian regions. High innovative activity is a useful predictor of high wage inequality, both between and within regions. The significance of the lagged values of innovative activity indicates persistence in the relationship over time.

Further, this thesis identifies six possible channels between innovation and wage inequality: research arbitrage for the inventor(s) or the innovative firm, skill-biased technical change, creation of low-wage jobs, sorting, knowledge spillover effects, and inequality leading to innovation.

Keeping constant the mean wage income level, population size, educational level, business structure and within-country location characteristics, an increase in innovative activity by one unit is associated with a 0.009 increase in the Gini coefficient. In contrast, within regions, an increase in innovative activity does not seem significantly associated with the regions becoming more unequal. Thus, the relation appears to be driven by differences between the regions.

Possible explanations of the relation between innovation and inequality in Norwegian regions may be that regions with high innovative activity adopt technology that spurs the productivity of high-skilled workers (skill-biased technical change), or that regions with high innovative activity attract high-ability workers who are more productive (sorting), or that high-skilled workers increase their productivity through knowledge spillover eﬀects in the innovative region. However, this will need further investigations.

Patents per thousand inhabitants is used as the measure of innovation. This is a measure with noise and biases because not all innovations are patented and not all patents represent innovation. However, it may be the best available.

The Gini coefficient is used as the main measure of wage inequality. In addition the Bonferroni coefficient (which is more sensitive to the lower part of the wage income distribution), and the C3 (which focus more on the upper part of the distribution) are used. The sign on the effect is the same for all three measures of inequality, but the effect of innovative activity is highest associated with the Bonferroni coefficient. This indicates

ii that innovation is associated with the lower-income group getting left behind compared to the middle and upper income groups, although the reasons for this are not clear.

iii Contents

1 Introduction 1

2 Background 4 2.1 Innovation and its role in economic growth ...... 4 2.1.1 The term innovation ...... 5 2.1.2 How to measure innovation ...... 7 2.2 Channels between innovation and inequality ...... 9 2.2.1 Innovation and the ﬁrm ...... 9 2.2.2 Innovation and the worker ...... 9 2.2.3 Possible spatial mechanisms ...... 10 2.2.4 Inequality leading to innovation ...... 11 2.2.5 Summary of possible mechanisms ...... 11 2.3 Findings on the link between innovation and wage inequality ...... 12

3 Measures, data and summary statistics 14 3.1 The patent system ...... 14 3.2 What do we measure with patents ...... 16 3.3 Data description on patent applications and patents ...... 17 3.3.1 Business sectors ...... 19 3.4 Geographical unit ...... 19 3.5 Summary statistics on patent applications and patents in Norwegian regions 21 3.6 Measurement of inequality ...... 23 3.7 Data description on wage income ...... 24 3.8 Summary statistics on wage inequality in Norwegian regions ...... 25 3.9 Control variables ...... 26

4 Empirical strategy, results and robustness 30 4.1 Empirical strategy ...... 30 4.2 Results ...... 31 4.2.1 OLS regression with control variables ...... 34 4.2.2 Panel data analysis ...... 38

iv 4.2.3 Other inequality measures ...... 38 4.2.4 Granger causality test ...... 40 4.2.5 Lagged values ...... 42 4.3 Robustness ...... 43

5 Summary of ﬁndings, discussion and concluding remarks 46 5.1 Summary of ﬁndings ...... 46 5.2 Discussion of possible channels ...... 47 5.3 Concluding remarks ...... 49

Bibliography 51

A Appendix to chapter 3 59 A.1 Tables ...... 59 A.2 Norwegian economic regions ...... 62 A.3 List of patents per thousand inhabitants per region ...... 64 A.4 List of patents per region ...... 64 A.5 List of Gini coeﬃcients per region ...... 64 A.6 Summary statistics for the control variables ...... 68

B Appendix to chapter 4 69 B.1 Dumitrescu-Hurlin test ...... 69 B.1.1 Results from the Dumitrescu-Hurlin test ...... 71 B.2 Additional regression tables ...... 72 B.2.1 Results with innovative activity as the dependent variable . . . . . 74 B.2.2 Results with mean income as the dependent variable ...... 77 B.2.3 Results without the observations in 2005 ...... 80 B.2.4 Results without the ﬁve regions with most patents ...... 83 B.2.5 Results with four time periods ...... 87 B.2.6 Additional results with NIBR regions ...... 89

v Chapter 1

Introduction

Innovation is crucial to sustained economic growth. Expanding the frontiers of what is technologically possible and adopting this technology is essentially what makes us more productive. Distribution of income varies among countries, but the empirical evidence indicates that growth is associated with income growth that involves the whole society, including the poor (Weil, 2014). Even when growth is good for most people, it may be better for one group than another, thereby leading to higher inequality. Growth in the OECD countries over the past 50 years is associated with increased pre-tax inequality (Marthinsen, 2016).

Is high inequality a problem? According to Adam Smith the answer is yes. In Wealth of Nations he wrote in 1776: "No society can surely be ﬂourishing and happy, of which the far greater part of the members are poor and miserable"(Smith, [1880], p.61). Un- equal societies score worse on basic health and welfare indicators, such as trust levels, mental health, drug abuse, teenage pregnancies and obesity (Wilkinson & Pickett, 2010); moreover, people living in areas characterized by low equality report themselves to be less happy (Glaeser, Resseger, & Tobio, 2009). Also, high inequality is associated with low economic mobility (Autor, 2014).

Furthermore, inequality can have negative impacts on growth. Lower post-tax inequality is robustly correlated with faster and more durable growth, for a given level of redistribution (Ostry, Berg, & Tsangarides, 2014). Also, low economic mobility may lead to fewer inventions and more "lost Einsteins," because children’s socioeconomic class, race, and gender are highly predictive of their propensity to become inventors (Bell, Chetty, Jaravel, Petkova, & Van Reenen, 2017).

On the other hand, there is a social value in having some inequality. A market economy needs a certain degree of inequality to create incentives to work hard and undertake risks, so wage diﬀerentials can play a productive role in a market economy (Autor, 2014), and

1 also for innovation (Acemoglu, Robinson, & Verdier, 2017; Acemoglu & Robinson, 2012).

How are the gains from innovation allocated? Are the proﬁts reaped by the majority, or by a more restricted group? Florida (2007) found that the most innovative cities in the USA were also those with the greatest inequality. Other studies have also found this relation, some between regions, others within regions (Breau, Kogler, & Bolton, 2014; Lee & Rodríguez-Pose, 2013; Lee, 2011). What, then, of Norway? Whether there is a connection between innovation and inequality in Norwegian regions is the research question of this thesis.

The effect, or the magnitude of the effect, of innovation on inequality depends partly on policy (Donegan & Lowe, 2008). Policy plays an important role in maximizing the benefits and minimizing the costs of inequality (Autor, 2014). Also the quality of institutions can matter for inequality and for long-term growth (Acemoglu, Johnson, & Robinson, 2001; Sokoloff & Engerman, 2000; Weinhold & Nair-Reichert, 2009)1. Countries differ regarding such central policy and institutional issues as welfare and tax systems; and labor markets vary when it comes to wage determination and unionization (Holden, 2016). Norway has relatively low wage disparity (Barth & Moene, 2016), with a relatively high share of the work force organized in trade unions (Holden, 2016). Is there a correlation between inequality and innovation also in Norway, with its compressed wage structure and widespread collective bargaining over wages? Since the present thesis investigates this relation in one country, the institutions and national policies are constant for all statistical units. That is an advantage for the research design, because accurately controlling for those variables can be difficult.

That this type of analysis is conducted precisely for Norway is among the contributions of this thesis to the literature. Nearly two decades earlier, Butler and Dueker (1999) conducted a study where Norway was among the countries investigated. Their research design was quite different (see section 2.3), but their finding that domestic innovation and wage inequality are significantly positively related is in line with the results in this thesis.

Further, this study oﬀers a description of innovation in Norwegian regions, with patents per thousand inhabitants as a measure. That has, to my knowledge, not been done previously: other studies have involved more elaborated regional descriptive analyses based on R&D expenditure and Statistics Norway’s innovation survey (Gunnes, Sandven, & Spilling, 2017; Gundersen, 2002).

There are many ways of approaching innovation and inequality. In this thesis, the focus is not on the drivers of innovation, but on one possible consequence of innovation: wage inequality. Neither are the consequences of regional wage inequality examined here. Patents

1In this context “institutions” mean "humanly devised constraints that structure political, economic, and social interaction" (North, 1991, p.97), understood as "the rules of the game".

2 as a measure of innovative activity is used, but without investigating the social value of the patent system or alternatives to the current system, or other possible measures of innovation such as R&D expenditure, or innovation surveys.

The thesis is organized as follows: The next chapter gives necessary background information. Chapter 3 presents measures, data description and summary statistics; chapter 4, the empirical approach, the results and robustness. Chapter 5 summarize ﬁndings, discuss about them and oﬀers some suggestions for further research. The analysis has been conducted in Stata 15.

3 Chapter 2

Background

Technological progress (...) has provided society with what economists call a “free lunch”(...).

Joel Mokyr (1990, p.3)

In this chapter the role of innovation in the economy is presented, ﬁrst at the macro level (2.1). Then the term “innovation” (2.1.1) and various approaches to measuring innovation (2.1.2) are discussed. In discussing the role of innovation in the economy at the micro level, possible channels between innovation and inequality are examined (2.2). The chapter concludes with a summary of ﬁndings on the link between innovation and inequality in other studies (2.3).

2.1 Innovation and its role in economic growth

The most decisive factor behind the world’s sustained economic growth is technological progress (see for instance Holden, 2016; Weil, 2014; Mokyr, 1990; Schumpeter, 2017). Mokyr (1990, p.4) deﬁnes technological change as outward shifts of the production possibility frontier—that is, “increases in the productive potential of the economy” or implementation of technology so that we get closer to the frontier.

Not all economic growth is dependent on technological change. Mokyr (1990) divides economic growth into four distinct processes:

1. Investment. Increases in the capital stock make available to each worker more equipment, which increases output per capita. This can be called Solowian growth.

2. Commercial expansion. Increase in the exchange of goods, services, labor, and

4 capital can increase income for all involved. These gains from trade come as a result of specialization and can be thought of as Smithian growth.

3. Scale or size eﬀects. Because of increasing returns to scale up to a point, population growth may in itself lead to per capita income growth.

4. Increases in the stock of human knowledge, which includes both technological and institutional progress. This can be referred to as Schumpeterian growth.

These processes interact. “The four forms of growth reinforce each other in many complex ways” (Mokyr, 1990, p.8). For instance, technological progress is dependent on capital, and the incentive to innovate is greater when the market is larger (Aghion & Howitt, 1998).

Not all economic growth is dependent on technological progress, but in order to sustain growth over decades, innovation is needed (Acemoglu & Robinson, 2012). In neoclassical economic models the variable for productivity or technology is A and the parameter for productivity growth or technological progress is g. This g is what aﬀects the growth rate per capita in the long run (Aghion & Howitt, 1998; Romer, 2012).

Economic growth has historically been related to increasing inequality (Weil, 2014). Ac- cording to the Kuznets hypothesis, inequality will fall when the growth reaches a certain level. Whether this actually happens, however, is much debated among researchers (Weil, 2014). In her master’s thesis Marthinsen (2016) investigated the relationship between growth and inequality in OECD countries between 1960 and 2010. She found that growth increase pre-tax inequality. Increased inequality as a result of growth can be due to skill- biased technical change (see 2.2.2), increased trade and organizational change (Marthin- sen, 2016). All three phenomena are closely associated with growth, and contribute to widening the distribution of earnings. Increased wage inequality is directly linked to the nature of the forces driving economic growth in the developed world (Marthinsen, 2016).

2.1.1 The term innovation

The terms “productivity growth” and “technological progress” are often used interchangeably in the economic literature, but they are not the same. In this section it will be explained how these terms relate to each other.

First: what is meant by “technological progress”? According to Mokyr (1990), economists normally distinguish between invention and innovation. Inventions are the outward shifts in the production frontier, whereas technological innovation refers to the adoption of the new technology. As long as an invention is not used, it provides little economic gain. But without new inventions, we cannot deploy new technology, or get economic growth

5 through technological progress. Therefore, invention and innovation are complementary, but not perfectly so. It is possible to have one without the other, but not in the long run. Technological progress lies in this combination of inventions and innovations.

Productivity in society is dependent on both the technology available and how eﬃciently the resources are used. We can divide the term “productivity” into two components (Holden, 2016; Mokyr, 1990; Weil, 2014):

• Technology, deﬁned as knowledge of how a product or machine can be produced and methods that yield higher output. This is where the production possibility frontier is located.

• Efficiency, defined as how efficiently the technology and the production factors are used in production—whether we are at the production possibility frontier or in the interior of the production function. This can depend on implementation of technology and on institutional progress.

Productivity growth is a broader term than technological progress, since it is possible to increase productivity without there being technological progress. Innovation is more than technological progress, but less than productivity growth: it includes innovation in business models and organizational innovation, but not reduced corruption.

Increases in the stock of human knowledge, or Schumpeterian growth, include technological and institutional progress. Institutional progress involves better coordination, reducing corruption, increased protection of private ownership, and innovation that is not technical. It can also pertain to better knowledge about something that does not directly increase productivity.

To sum up, all these terms (increases in the stock of human knowledge, productivity growth and innovation) include technological progress plus something else (institutional progress, eﬃciency gains, non-technical innovation). Innovation includes both technological progress and non-technological innovation. Productivity growth includes technological progress, innovation, and increase in eﬃciency that is not due to innovation—for instance, reduction in corruption. Increases in the stock of human knowledge include productivity growth and institutional progress that does not directly lead to productivity growth. See Figure 2.1, which illustrates the relations among these terms.

As noted, inventions are the outwards shifts in the production possibility frontier, whereas technological innovations concern the adoption of a technology. The total of these is technological progress. The terms “innovation” and “inventions” are often used interchangeably in the literature, for instance in Dechezleprêtre, Glachant, Haščič, Johnstone, and Ménière (2011). In this thesis, I have opted to use inventions, technological progress and innovation interchangeably, because patents are the measure of innovation used here, and both

6 Figure 2.1: The term innovation and its relation to other terms.

inventions and technological innovations (whether a product or a technological process) can be patented.

2.1.2 How to measure innovation

There are several indicators or proxies for technological progress or innovation, but it is difficult to capture everything by one measure. It is also difficult to capture just innovation and nothing else, as indicators of innovation often measure more than innovation. Thus, all measures of innovation have noise and biases. A study made by Hagedoorn and Cloodt (2003) of high-tech industries found strong statistical overlap among and between various indicators of innovative performance, which led these authors to propose that one indicator would suffice.

The measure used in this thesis is patenting. Specifically, domestic patents are used, as most patent applications are filed in the home country (Weil, 2014). Another possibility could be patents filed in the USA, as that is the largest single market (Griliches, 1998; Butler & Dueker, 1999). The number of patents can be divided by the number of inhabitants in a region, as done in this study. Other approaches are to divide the number

7 of patents by the number of ﬁrms in a region, or the size of the labor force in a region. These measures may also be combined, as with patents per R&D expenditure (Butler & Dueker, 1999).

Dechezleprêtre et al. (2011), Griliches (1998) and OECD (2009) argue that patents are a good indicator of innovative activity. “Patents have a close (if imperfect) link to invention. Most signiﬁcant inventions from businesses are patented, whether based on R&D or not” (OECD, 2009). Patents represent innovation on the supply side in the market of inventions. However, some of the patented inventions can also be used by those that invented them, therefore some adoption of technology may be picked up by patents as a measure of innovation. Patents as a measure of innovation is discussed in greater detail in section 3.2.

Maybe a better measure of the adoption of technology is Total Factor Productivity (TFP), which measures the growth in output that is not due to increases in input of labor or capital. However, there are several challenges related to measuring Total Factor Productivity (NOU 2013: 13 (The Holden III-Commission), 2013; Nagaoka, Motohashi, & Goto, 2010). As the use of capital as such can be difficult to measure correctly, it is usual to use labor productivity: that is gross output (at constant prices) divided by the number of hours worked (NOU 2013: 13 (The Holden III-Commission), 2013). But that is challenging too, because what is the price increase due to inflation and what is due to quality improve- ments (NOU 2013: 13 (The Holden III-Commission), 2013)? Also, both Total Factor Productivity and labor productivity may be affected by factors other than technological adoption, such as improved organizational efficiency. Further, information on gross output is not available at a lower regional level than the counties, at least is that the case for Norway.

Innovation surveys can provide insights into the innovation intensity of an economy, and can measure both supply and demand regarding inventions and innovation. Statistics Nor- way’s innovation survey among Norwegian companies asks whether the firms have made any innovations and have adopted new technology recently (SSB, 2017b). However, this measure may be subjective; further, it includes only firms with more than five employees. Innovations made by private persons or small companies, for instance start-ups, are not covered. Also, the survey is based on a representative sample, not the whole population, so that analysis of subsamples such as different regions within a national survey must be based on estimates (Gundersen, 2002).

A company’s turnover rate can be used to measure innovation, since innovation involves creative destruction (Aghion & Howitt, 1998). However, it is possible to have innovation without creative destruction (Aghion & Howitt, 1998), and this measure does not capture the innovations made by large, stable companies. The turnover rate may be relevant

8 for measuring whether an economy is restructuring—for instance, whether the Norwegian economy is shifting from a resource economy to a knowledge economy (NOU 2016: 3 (The Productivity Commission), 2016).

Other possible measurements of innovation are:

• R&D expenditure is often used as a proxy for innovation and technological progress. It measures the resources allocated to research by the ﬁrm or the nation. However, it captures only the input (Nagaoka et al., 2010). A company may spend millions on R&D without ﬁnding a solution to the problem it wanted to solve.

• Share of the labor force working in knowledge-intensive industries: a broad measure which captures far more than just innovative activity.

2.2 Channels between innovation and inequality

There may be several possible channels where innovation and inequality are connected. At the micro-level the mechanisms can work between and within ﬁrms, they can inﬂuence the worker, or work at the geographical level. Here six possible mechanisms are presented.

2.2.1 Innovation and the ﬁrm

Firms invest in research. If this results in a new invention, the firm may be granted monopoly rights (patents). The firm with the most recent invention gets research arbitrage until someone else invents an improved replacement, and the firm producing the second- best product becomes obsolete (Aghion & Howitt, 1998).

The research arbitrage resulting from the invention is shared between the company’s workers and owners. How that gain is shared will depend on the workers’ negotiation power (Barth & Moene, 2016). The research arbitrage from innovation may lead to regional wage inequality between those in the ﬁrm who get the gains from innovation and those in the region who do not. In the market of inventions, this channel is a supply-side channel.

2.2.2 Innovation and the worker

Technological change is central in explaining the growing wage inequality since the 1980s (Aghion & Howitt, 1998). Wages are an increasing function of the workers’ productivity, but are also determined by the workers’ bargaining power (Barth & Moene, 2016). Tech- nology can complement the workforce, make it more productive and therefore increase

9 their wage—or it can substitute for workers, making them unemployed (Acemoglu & Au- tor, 2011). This complementarity or substitution eﬀect can happen to highly skilled or low-skilled workers, generating skill-biased demand shifts (Acemoglu & Autor, 2011). A central theory, called skill-biased technical change, holds that the technological develop- ments of recent decades have been directed at highly skilled workers: demand for such workers rises, increasing their wage premium compared to unskilled workers (Aghion & Howitt, 1998; Autor, 2014). Dispersion of earnings is the result.

Faggio, Salvanes, and Van Reenen (2010) found that most of the increase in wage inequality over the past three decades can be explained by differences in labor productivity between firms within a given industry (and not within individual firms). This increased dispersion of productivity between firms appears to be due to adoption of new technologies.

Whether we can measure if skill-biased technical change is one of the channels between innovation and inequality with patents as the measure of innovation depends on who deploys the technology that is patented. If the adopter of the inventions is the ﬁrm that has made it, the eﬀect can be picked up. But if the invention is sold to another part of the country or abroad, this skill-biased technical change cannot be measured, since it focuses on the deployment of innovation. In the market of inventions, skill-biased technical change is a demand-side channel.

2.2.3 Possible spatial mechanisms

Having presented some possible channels for the ﬁrm and for the worker, three possible mechanisms that are related to the geographical or spatial context are now discussed.

The first possible geographical mechanism is the creation of low-wage jobs in an area where affluent groups are demanding more personal services (Donegan & Lowe, 2008; Lee, 2011; Florida, 2007). Such creation of low-wage jobs assumes a flexible wage system, which is more the case for the USA than for Europe and Norway (Holden, 2016). The wage system is more compressed in Norway and those on the lower rungs of the wage scale have higher bargaining power (Barth & Moene, 2016), making it harder for the affluent to employ people for low wages. The shifts in the labor market is expected to be lower in Norway than in the USA, although this might be seen in Norway as well, for instance in food and cleaning services.

Another possible spatial mechanism is sorting. Innovative regions may attract highly skilled workers (Lee, 2011). "The sorting eﬀect will skew the distribution of skill levels in a population, and so create local inequality" (Lee, 2011). This may lead to cumulative processes, as "innovative cities attract the highly skilled who then can increase levels of

10 innovative activity"(Lee, 2011).

A third possible geographical mechanism is that knowledge spillover effects influence inequality. Knowledge spillover across agents and firms can lead to innovation (Glaeser, 1999; Audretsch & Feldman, 1996). This explanation of innovation takes as its starting point that cities are the most innovative areas and that agglomeration effects through external spillover effects play an important role2. As determinants of innovative activity are not in focus in this thesis, that issue will not be entered further into. However, as- suming that external knowledge spillover can explain some of the innovative activity in Norwegian regions, this channel may both increase and reduce inequality, depending on who benefits from the knowledge spillover.

Highly skilled workers have better capacity to increase productivity and human capital as a result of knowledge spillover than do low-skilled workers. This can result in innovative or other productive activity that increases these worker’s wages, thereby increasing inequality in innovative regions. On the other hand, knowledge spillovers may enable those with fewer skills to learn from the highly skilled, increasing their productivity as they have a greater range of potential learning partners (Glaeser, 1999). Knowledge spillover eﬀects can thus act both to increase and to reduce inequality.

2.2.4 Inequality leading to innovation

Another possible channel is that inequality leads to innovation. For example, the local market of highly aﬄuent people demands new or niche products. Or it could be that greater dispersion of income leads to a more diverse range of preferences, and that this in turn leads to innovation (Lee, 2011). The sectors active in patenting in Norway are mostly business-to-business, such as industry, oil and gas and consultancy. However, there are exceptions. Producers of furniture and sports equipment are businesses that patent, and their innovations may be driven by aﬄuent potential customers who demand such products. However, the market would be at least the whole of Norway, not only the regional consumer market—so regional inequality is less likely to drive the innovation.

2.2.5 Summary of possible mechanisms

There are several possible channels where innovation and inequality can be linked. Most of them lead to a positive correlation between innovation and inequality. The possible mechanisms and whether each mechanism leads to innovation being positively or negatively correlated with inequality are summarized in Table 2.1.

2Agglomeration eﬀects mean that productivity rises with density (Glaeser & Gottlieb, 2009).

11 Table 2.1: Possible channels and their eﬀect on inequality.

No. Mechanism Eﬀect Section

1 Research arbitrage + 2.2.1 for the inventor(s)/innovative ﬁrm (in the market of inventions: supply-side channel)

2 Skill-biased technical change + 2.2.2 (in the market of inventions: demand-side channel)

3 Creation of low-wage jobs + 2.2.3

4 Sorting + 2.2.3

5 Knowledge spillover eﬀects ± 2.2.3

6 Inequality leading to innovation + (reversed causality) 2.2.4

2.3 Findings on the link between innovation and wage inequality

When asked to mention an innovative region, people tend to name California’s Silicon Valley—which is also an area with large inequalities (Florida, 2007). Florida (2007) found that innovation and inequality are positively linked, not just in Silicon Valley, but in a cross-section of US metropolitan areas.

Breau et al. (2014) have investigated the relationship between innovation and wage inequality in Canadian cities, 1996–2006. The study found a significant relationship between innovation and wage inequality, both within and between cities. They controlled for other variables, such as city size, share of visible minority, education, year dummies for the be- ginning and the end of the period, and region fixed effects. The result held true for different measures of inequality. The authors did not control for mean income, but argue that city size is a proxy for general forces of agglomeration.

Lee (2011) investigated this relationship in Europe in a panel data analysis. His results indicated that innovation and inequality within regions are positively linked when using patent applications as the measure of innovation. However, no significant results emerged when innovation was measured by the proportion of employment in knowledge-intensive industries. The author uses region fixed effects, but no time fixed effects.

12 Lee and Rodríguez-Pose (2013) have compared the link between innovation and wage inequality in Europe and the USA through a panel data analysis. They use household surveys in the USA and 13 European countries3. They found a relationship between innovation and inequality within European regions, but in the USA they found only limited evidence of such a link. The authors used diﬀerent measures when controlling for mean income for Europe and for the USA: GDP per capita for Europe, but the median wage for the USA. That may have inﬂuenced their results when comparing Europe and the USA.

Butler and Dueker (1999) used a somewhat different set-up and research question. They investigated whether domestic and foreign innovation affected domestic wage inequality in 11 developed countries, including Norway. Their measure of innovation was patents granted in the USA per R&D dollar. Using this homogeneous unit for measuring innovations across countries made it easier to compare innovative activity in different countries. Butler and Dueker (1999) found that domestic innovation was associated with greater wage inequality between high- and low-tech workers; further, that foreign innovation was associated with lower wage inequality between high- and low-tech workers, and that foreign and domestic innovation impacted domestic wage inequality to roughly equal and opposing degrees.

This chapter has focused on the crucial role played by innovation in relation to sustained economic growth. Noting how the often interchangeably used terms “productivity growth,” “technological progress,” and “innovation” relate to each other, various approaches for measuring innovation are discussed. Then possible channels between innovation and inequality are examined. Findings from other countries have indicated that innovation and inequality are positively linked. Chapter 4 examines whether this is the case also for Norway, but ﬁrst important measures, data and summary statistics will be presented in chapter 3.

3The countries were Austria, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Portugal, Spain, Sweden and the United Kingdom. They divided the European countries into 93 regions, with the whole of Denmark as a single region and the same for Finland. For the USA, they examined 70 metropolitan areas.

13 Chapter 3

Measures, data and summary statistics

Number of patents granted per thousand inhabitants is the measure of innovation used in this thesis, and the Gini coeﬃcient is the main measure of wage inequality. In this chapter these measures are presented, as well as descriptions of the data, the geographical unit employed in this study —the economic regions, and the control variables. In addition summary statistics are presented.

3.1 The patent system

The main diﬀerence between Schumpeterian growth and growth from greater access to production factors is that Schumpeterian growth is based on knowledge and ideas (see section 2.1). An important feature of knowledge and ideas is that they are non-rival: one person’s use of an idea does not stop others from using it (Holden, 2016). A further important feature is that knowledge and ideas build on each other—often referred to as the “standing on shoulders”-eﬀect (Golombek, Greaker, & Kverndokk, 2015).

Expanding the knowledge base of the world is both important and useful—but, in a market economy the economic incentives for investing in the development of new products may be too weak. If a company develops a new product and another company can copy it and sell it too, then the company whose innovations led to the new product will have incurred costs, whereas the gains will have to be shared with all those who sell the resultant product. If, however, the company can acquire property rights to its technical solution, this will create an incentive to invest in research. That is the rationale for patent systems (Holden, 2016).

Against the patent system, some argue that it hinders innovation rather than promoting it (Holden, 2017). However, using patents as a proxy for innovative activity does not

14 mean ignoring the problems entailed in the patent system. Whether patents provide the right incentive for innovation is simply not the subject of this thesis.

Patents ensure property rights and exclusive rights to inventions. “[P]atent protection means that the invention cannot be commercially made, used, distributed, imported or sold by others without the patent owner’s consent” (World Intellectual Property Organi- zation, 2018a).

A patent is granted to something that represents a novel technical solution: a concrete solution to a technical problem (Norwegian Industrial Property Oﬃce, 2018c). Patents may be granted to both products and technological processes. A patent will not be granted if the application does not contain detailed information about how the technical solution is to be implemented in practice. It is not possible to patent a great idea without information on how to apply it. The invention described in the patent application must be replicable (World Intellectual Property Organization, 2018a).

“Patents are territorial rights. In general, the exclusive rights are only applicable in the country or region in which a patent has been filed and granted, in accordance with the law of that country or region” (World Intellectual Property Organization, 2018a). The inventor will often apply for a patent in her home country first (Weil, 2014). If protection is wanted in other markets as well, she can either file for a patent also in that country, or file for an international patent application under the Patent Cooperation Treaty (PCT). Through the PCT applicants can simultaneously seek protection for an invention in a very large number of countries (World Intellectual Property Organization, 2018b).

The patent application is to be kept secret for the first 18 months from the date of filing. If the application is withdrawn within the first 18 months, the application will never be made public (Norwegian Industrial Property Office, 2018a). The Norwegian Industrial Property Office must give an initial assessment of the application within the first seven months. Often this assessment holds that the quality is too low (email from Bjarne Kvam, Norwegian Industrial Property Office, March 9, 2018). Such applications are therefore shelved or withdrawn, and never become public.

After 18 months, the application becomes publicly available. This means that the inventor has property rights to the invention as such, but the invention is known to the public. Others can therefore use the ideas from the invention and build on them. This arrangement is a compromise between creating incentives for innovation and building a common knowledge base.

Average processing time for national patents applications in Norway is 4.1 years (Norwe- gian Industrial Property Oﬃce, 2018b). The invention is protected from the time of the ﬁling date and for 20 years, if the inventor pays the annual fee for retaining the patent.

15 Norway has lower patenting activity than other North European countries. In 2016, Swe- den, Finland and Germany had approximately twice as many patents per capita, and Denmark about 30 percent more patents per capita than Norway (World Intellectual Property Organization, 2018c). According to NOU 2016: 3 (The Productivity Commis- sion) this can be because the Norwegian economy is a resource economy and has not yet made the transition to a knowledge economy. Norwegian ﬁrms that apply for patents are generally either very large (over 500 employees) or very small (fewer than 5 employees) (SSB, 2017a).

3.2 What do we measure with patents

Patent applications indicate that a company or an individual has found something that is seen as new and worth spending time and money on—first, in developing it, then in applying for a patent. If the patent office decides to grant a patent, that means that it agrees that this is a novel incremental improvement: that it "does represent a minimal quantum of invention that has passed both the scrutiny of the patent office as to its novelty and the test of the investment of effort and resources by the inventor and his organization into the development of this product or idea, indicating thereby the presence of a non-negligible expectation as to its ultimate utility and marketability"(Griliches, 1998, p.296).

Not all patents represent innovation (Nagaoka et al., 2010). Firms may use patents strategically to block other companies (OECD, 2009). The patent system may thus increase the propensity to patent, not the propensity to innovate (Weinhold & Nair- Reichert, 2009).

Not all innovations are patented (Nagaoka et al., 2010). Firms may use other strategies to protect their inventions, for instance secrecy and lead-times (Nagaoka et al., 2010). Product innovation is patented more often than process innovation (Nagaoka et al., 2010). Industries vary in their propensity to patent (OECD, 2009). As not all innovations are technical, and patents are related to technical solutions, not all kinds of innovations are counted by patents. As noted in section 2.1.1 , “innovation” covers more than technical solutions—for instance, innovation in business models.

Patent statistics do not indicate whether a given invention is commercially successful. Several studies have shown that the value distribution of patents is highly skewed (OECD, 2009). In counting patents granted, we do not distinguish between inventions of high quality and innovations that are less important.

Patents are generally related to the supply of inventions. A patent grant does not indicate

16 who adopts the invention or whether that takes place in the same region as where the invention was made. If the invention is made by and for a given ﬁrm, supply and demand of the invention are probably located in the same region, but technology adoption may spread over more than one region if the ﬁrm has production sites several places in Norway and/or abroad.

What, then, do patents indicate? Patents are often referred to as a measure of output of R&D activity, whereas R&D expenditure is a measure of input. However, patents mean more than an increase in the knowledge base: they represent an increase in the knowledge base plus biases and “noise”. Also, it is not only a question of research that leads to an increased knowledge base: it is research plus an error term. Figure 3.1 presents a path analysis diagram of the knowledge function, made by Griliches (1998). As seen in the Figure, there are unobserved inﬂuences on patenting (P), additions to economically valuable knowledge (K˙ ) and indicators of expected or realized beneﬁts from inventions (Z). Because of this noise and the biases entailed in patents and other measures, we cannot conclude that patents are a measure of output. Rather, they are an indicator of innovative activity (Griliches, 1998).

3.3 Data description on patent applications and patents

The patent data employed in this thesis are supplied by the Norwegian Industrial Property Office and cover only Norwegian patent applicants: thus, in this study only patents filed in Norway by Norwegian applicants are counted. Inventors are most likely to apply for patents in their own country first (Weil, 2014); some inventors might not do that, but they are not counted in this thesis. Further, key defense-related inventions are kept secret, and are therefore not included in the figures received from the Norwegian Industrial Property Office.

The Norwegian Industrial Property Office has provided two datasets on publicly available patent applications. One dataset is from 1990–2006, the other from 2007–2015. The datasets contain information on the status of the applications as of September 20, 2017, the postal code of the applicants and the firm identifier (organization number). As the datasets did not have information about the postal code of the inventors, the Norwegian Industrial Property Office provided two additional datasets that were merged with the original datasets4.

The two patent datasets were linked with the business register of Statistics Norway that

4Some of the applications in the two datasets in the same time period did not match. The reason could be that the nationality of the main applicant was changed because of acquisitions, mergers, etc. Only those that were part of the original dataset were retained.

17 covers all Norwegian enterprises. Through that information about the NACE code and the organizational form was made available5. As the business register started in 1995, the period investigated starts in 1995 as well.

Patents is allocated to regions on the basis of the postal code of the inventor(s), because if the main office of a company applies for a patent where the inventive activity took place at a plant in another region, the allocation of patents could be assigned to a region where the actual inventive activity did not happen. Many patent applicants have affiliations in other regions than the main office. On the other hand, the inventors might be long-distance commuters who live in another region than where they work. One way of reducing this problem is to use larger regions than municipalities (OECD, 2009). But still, someone may be living in, say, the western part of Norway and be working in Oslo. However, I hold that allocation is more correct this way than if it were skewed in the direction of the main offices. The allocation method is in line with the method used by the EU’s statistical bureau Eurostat (2016) and the same as that used by Breau et al. (2014). An additional advantage of this method is that official income data are also based on the individual’s municipality of residence.

However, there is also a disadvantage: postal codes for inventors are missing for several observations the ﬁrst four years under study6. Therefore results for the period 1995–1998 must be interpreted with caution. For the whole period, information is missing as to regions for 10 percent of the patents; see Table A.1.

Behind some applications, there are teams of inventors. Based on Breau et al. (2014) and Eurostat (2016), a proportional fraction of the patent is assigned to each inventor’s region. This means that if one inventor is from Oslo, one from Rjukan and one from Drammen, each of these regions gets a value of one third. Even if the applier is Norwegian, some of the inventors can be foreign. On average 5 percent of patents come from foreign inventors, see Table A.1.

The number of patents actually granted is used as the main measure of innovation, not the number of patent applications. Such a patent will have passed the scrutiny of the patent oﬃce in addition to the investment of time and resources by the inventor/ﬁrm (Griliches, 1998). To the category patents granted both patents granted and patents ceased are assigned7. Patent counts are standardized by the numbers of inhabitants in the region divided by 1000, which yields patents per thousand inhabitants.

The number of publicly available patent applications in a region is used as a test of

5NACE codes are EU’s statistical classiﬁcation of economic activities. 6For 1995–1998, on average half of these postal codes are missing, as against an average of 3 percent for the rest of the period (1999–2015). 7The other categories are “Refused”, “Permanently shelved”, “Shelved (may be resumed)”, “Ceased/canceled”, “Withdrawn” and “Pending”.

18 robustness. The total number of patent applications is not used, only the total number of publicly available patent applications, see section 3.1. Thus, in referring to patent applications in this thesis, publicly available patent applications is what is meant.

3.3.1 Business sectors

The patents is grouped by discretion, based on the NACE code, with five business sectors (see Table A.2). If the business is a large one, the NACE codes is grouped by the type of business assumed to use the patented invention. For instance, both “building oil platforms and modules” and “services related to extraction of oil and gas” are categorized together in the group “oil and gas”. However, some firms may have operations related to oil and gas, but be classified under a more general NACE code, for instance “other professional, scientific and technical activities”.

As some NACE codes do not give indications of the kind of industry that uses the invention, such as the “other professional, scientific and technical activities” code, those NACE codes are grouped together on the basis of their activity and this group is called “scientific and technical research, development, consultancy and other activities” (short: “scientific activity”). Patent-owning firms within this sector include businesses that deal with either only oil and gas, or a combination of maritime business, for instance both seismic activity and fishery, but also firms that work with digitalization and firms with a broad range of applications of their technology not limited to one sector. Having grouped patents into these two categories, the rest is divided into “other industry”, “other services” and “primary industry and mining”8.

3.4 Geographical unit

The economic regions as developed by Statistics Norway at the level between counties and municipalities is used as the geographical unit. This division is based on criteria such as labor market and trade (Hustoft, Hartvedt, Nymoen, Stålnacke, & Utne, 1999)9. In Norway there are 89 economic regions in total. Maps of the regions can be found in the Appendix in Figure A.1 and Figure A.2.

8Note that production of forestry and agricultural machinery is grouped into “other industry”, not “primary industry,” even if the forestry and agriculture sectors use the machinery. This is because a distinction between industry and agriculture is interesting, but it could be argued that this should have been part of the primary sector. In any case, there are not many patents within this NACE code. 9When it was established it was part of the oﬃcial EU classiﬁcation known as NUTS4 (Nomenclature des Unités Territoriales Statistiques) (Hustoft et al., 1999), but today the EU no longer operates with an entity between counties and municipalities. NUTS3 is equivalent to county level; the next level is LAU (Local Administrative Unit), the municipalities (Eurostat, 2018).

19 Municipalities are the smallest geographical unit in the economic region, and it is not possible to cross county borders. A center is defined for each region. The classification is mainly based on commuting data, and municipalities that are merged have at least 10 percent mutual commuting. But when that criterion is not sufficient for determining where a municipality belongs, the next criteria are retail sales per inhabitant, number of inhabitants in the largest settlement in the municipality, information about moving patterns, newspaper region—and discretion. The main problem with these regions is that they do not cross county borders and are therefore a hybrid between administrative region and functional region. Moreover, this classification is quite old and is thus not based on the latest commuting data10.

There are other alternatives than Statistics Norway’s economic regions. First, the Nor- wegian Institute for Urban and Regional Research (NIBR) developed a division into residential and labor market regions (Gundersen & Juvkam, 2013), in a report commissioned by the Ministry of Local and Regional Government11. In total there are 160 residential and labor market regions, called the “NIBR regions”. Also here municipalities are the smallest geographical unit. But unlike the economic regions, if a municipality does not have enough commuting in or out, the region is identical with the municipality. Further, the regions can cross the county borders. This means that the regions are both larger and smaller than the economic regions. One attractive feature here is that the regions are actually functional entities, based on urban settlements, commuting data and maximum traveling time (Gundersen & Juvkam, 2013).

The 46 “Bhuller regions” (Bhuller, 2009) represent a second alternative. They were developed because the economic regions of Statistics Norway cannot cross county borders. The problem with this classiﬁcation is that it has a lower limit to how small a “region” can be (17,500 inhabitants). In practice, then, in rural parts of Norway this division of regions is not based on functionality at all. In addition, the categorization has a sequential approach, whereby a municipality connected to a small region can become part of a larger region, even if there is little commuting to the larger region, whether actual commuting or potential commuting based on traveling time. This in turn means that regions also in the urban areas can become very large, without being based on functionality. Because of these limitations the Bhuller regions will not be used.

In this study the economic regions of Statistics Norway is used, an oﬃcial division that has been broadly consulted and employed. Many scholars use this geographical unit (see for instance Rattsø and Stokke (2014)); the regions are of a suitable size and are based primarily on commuting data, so that they almost represent labor markets. Thus, among

10However, in the USA, studies using commuting zones (CZ) often use commuting data from the 1990 census (see for instance Chetty and Hendren (2016)) and not more recent data. 11now called the Ministry of Local Government and Modernization.

20 the options, Statistics Norway’s economic regions seems to be the best option. The NIBR regions are used to do robustness tests.

During the period under study (1995—2015) there have been seven mergers of municipalities, as well as one case of a municipality changing county (SSB, 2017). The municipality structure of 2015 is used and the municipalities that merged in the course of the period is treated as having been merged throughout the whole period.

3.5 Summary statistics on patent applications and patents in Norwegian regions

As shown in Figure 3.2, in Norway there are between roughly 1000 and 1200 patent applications per year12. Substantial numbers of applications are withdrawn, reducing the total to some 500 to 750 publicly available patent applications from Norwegian applicants each year. Some 200 to 500 of those are granted patents. The number of patents granted may seem to be decreasing in 2015, but that is probably because 46 percent of all applications in 2015 were still pending.

Patents are assigned to the year they are filed, not the year the patent was granted. Protection of the invention in question is effective from the day the patent application was filed.

On average for the whole period, 66 percent of the publicly available patent applications were granted. For patents applied for by private individuals the share is slightly lower, 62 percent, whereas companies have a 68 percent success rate. Companies hold 78 percent of the patents, see Table 3.1.

Table 3.1: Share of patents and patents applications between companies and private individuals.

Total number Companies Private individuals in the period 1995-2015 Publicly available applications 12 716 9 586 (75 %) 3 130 (25 %) Patents granted 8 403 6 556 (78 %) 1 847 (22 %) Share granted 66 %

Table 3.2 shows that the distribution of patents in the regions is heavily skewed to the left with a long, small tail to the right. The mean number of patents per thousand inhabitants

12The number of applications in total is the ﬁgure that can be found at ssb.no. This include only national applications with Norwegian applicants, whereas the other ﬁgures include national applications and PCT applications, both with Norwegian applicants (see section 3.1).

21 Table 3.2: Summary statistics on patents and patents applications per year per region.

mean p25 p50 p75 p99 max min Patents/thousand inhabitant 0.05 0 0.03 0.08 0.31 0.61 0 Patents 3.81 0 1 3 54.25 78.73 0 Patent applications/thousand inhabitant 0.07 0 0.05 0.11 0.38 0.76 0 Patent applications 5.52 0 1 4 73 111.00 0 Observations 1869 per year is 0.05 and the median is 0.03. The top percentile is 0.31, and the maximum number of patents per thousand inhabitants in a Norwegian region in a year is 0.61. The top ﬁve regions with most patents per thousand inhabitants in total in the period is Flekkefjord, Stavanger/Sandnes, Bærum/Asker, Ulsteinvik and Kongsberg. The whole list of the ranking of the regions based on patents per thousand inhabitants can be found in Table A.6.

The mean number of patents per region per year is 3.81, the median is 1 and the highest percentile is 54.25. The top ﬁve regions with most patents in total for the whole period are Stavanger/Sandnes, Oslo, Trondheim, Bærum/Asker and Bergen. The whole list of the ranking of the regions based on patents can be found in Table A.7.

Table 3.3: Total patents granted broken down to business sectors in the period 1995–2015. (Private individuals granted patents are not included since NACE codes are related to companies.)

Rank Category Number of patents Share 1 Other industry 2176 33 % 2 Scientiﬁc and technical research, development, consultancy and other activity (Short: Scientiﬁc activity) 1858 28 % 3 Oil and gas 998 15 % 4 Other service 858 13 % 5 Unknown 617 9 % 6 Primary industry and mining 49 1 % Total 6556

Table 3.3 shows the share of patents per business sector (see section 3.3.1 and Table A.2 for details on the categorization). 48 percent were granted to industry and 42 percent to the service sector, with the largest share going to scientiﬁc and technical research, development, consultancy and other activity. There has been an increase in patents related to oil and gas in the period. Further, 60 percent of the patents went to limited liability stockholder companies (AS) and 10 per cent to public/state companies (ASA), see Table A.3. Only 1 percent of the patents went to companies with sole proprietorship, but some could be classiﬁed in the “private individuals” category (see Table A.3).

22 3.6 Measurement of inequality

The Gini coeﬃcient is the most commonly used measure of inequality in income distributions. It satisﬁes the four criteria for measures of inequality (Ray, 1998):

1. The anonymity principle. Who earns the income does not matter.

2. The population principle. What matters are the proportions of the population that earn diﬀerent levels of income, not the size of the population.

3. Relative income principle/scale invariance. It is the relative income share that matters, not absolute income. The reason for this relative inequality measure is that we want to distinguish between inequality that rises merely because mean income is increasing, and inequality that rises because of other forces in the economy (Mi- lanovic, 2016). This principle also means that it does not matter what currency the income shares are measured in, as long as this is the same for the same population.

4. The Pigou-Dalton principle of transfers. When income is transferred from the relatively rich to the relatively poor (a progressive transfer) without the poor becoming richer than the rich, then inequality is reduced.

The Lorenz curve graphically represent income distributions. The share of the population ordered by their income is on the x-axis, and on the y-axis is the share of population that is part of each income class (Ray, 1998). The Gini coefficient is calculated by taking the average of all pairwise income differences and dividing it by two times the mean income. The Gini coefficient summarizes the information content of the Lorenz curve and ranks intersecting Lorenz curves (Aaberge, 2007).

A problem with the Gini measure is that it has little sensitivity to what happens in the lower and upper parts of the income distribution. Further, the measure does not identify where in the income distribution the change in income inequality may have oc- curred. When some researchers want to complement Gini with other inequality measures they often use Theil’s or Atkinson’s measures (see for instance Breau et al. (2014), Lee (2011)). Aaberge (2007, p.305) argue that, as these have other theoretical foundations than the Gini, it is "diﬃcult to evaluate their capacity as complementary measures of inequality". Therefore the two other measures (C1 and C3) use a transformation of the Lorenz curve (the scaled conditional mean curve) in order to "supplement each other with regard to sensitivity to transfers at the lower, the central and the upper part of the income distribution" (Aaberge, 2007, p.306).

These two other measures of inequality that are closely related to the Gini coeﬃcient is also used (Aaberge, 2007; Kvile, 2017). Thus:

• the Bonferroni coeﬃcient, which focuses more on the lower part of the distribution

23 (C1).

• the Gini coeﬃcient, which focuses on the middle of the distribution (C2).

• the C3, which focuses more on the upper part of the distribution.

3.7 Data description on wage income

Statistics Norway has provided a dataset on wage income for the entire population in all municipalities in Norway between 1995 and 2015, based on tax data and other register data. Incomes are on the individual level, not the household level. Wage income includes both labor income and net business income, before tax. The wage income for the population aged 25–62 years is used, categorized by municipality of residence, not the municipality where they work.

Labor income includes the pay individuals get from their employers, as well as taxable benefits in kind (SSB, 2006a). Net business income includes income from agriculture and forestry, fishery, and from other business activity. Any business deficits are deducted (SSB, 2006b).

Parental benefits and sickness benefits are included in wage income until end 2005. For a limited period, rehabilitation benefits (2002–2010) and temporary disability benefits (2004–2010) are included in wage income (SSB, 2006a). However, pensions and unemployment benefits are not included in the wage income.

The aim was to measure inequality among wage-earners in a region. Therefore, in order to include only those who are full-time workers, or at least in nearly full-time work, only persons who earned more than twice the basic unit of income (G) used by the Norwegian Labour and Welfare Administration (NAV) are included (NAV, 2017).

During the period under study (1995–2015), there was one tax reform, with changes implemented from 2006. During the years prior to this, large dividends were paid. As regards wage income, the major change was that maximal marginal tax was reduced to the same level as maximal marginal tax for capital income (48.16 percent) (The Research Council of Norway, 2016). Inequality levels in several rural regions increased drastically in 2005, then returned to earlier levels13. The mean for the whole country was higher in 2005 than in 2015, which indicates that incomes in 2005 were somehow inﬂuenced by the tax reform. See Table A.4 for details about the distribution of inequality levels in the regions in 2005 compared to the year prior and the year after. The reasons for the high inequality in 2005 is unknown.

13For instance Hallingdal, Rjukan, Setesdal, Valdres, and Oppdal.

24 This thesis examine wage income, not total income including capital income. The reason is that capital is not necessarily linked to the geographical location where it is earned; it is more probable that the worker lives close to the company than the owner of the company. When not including capital income in the analysis capital income from companies with one sole proprietor (enkeltpersonforetak) is excluded, which ideally should have been part of the analysis. However, such companies hold less than 1 percent of the patents granted (see Table A.3).

3.8 Summary statistics on wage inequality in Norwe- gian regions

The Gini coeﬃcients of the regions are skewed to the left, see Table 3.4. Most regions have relatively low wage inequality, although a few exhibit high wage inequality. Wage inequality in the regions has increased among the upper part of the distribution, as shown in Table 3.4. There has been little change in the mean and the lower percentiles between 1995 and 2015, but the highest percentile has increased from 0.297 in 1995 to 0.332 in 2015. In other words, most—but not all—regions have not experienced much increase in wage inequality over these two decades.

The regions with highest wage inequality on average in the period are Bærum/Asker, Stavanger/Sandnes, Oslo, Ulsteinvik and Follo. Those with lowest wage inequality on average in the period are Mo i Rana, Grong, Mosjøen, Tynset and Røros. A list of the average of the Gini for the whole period for each region can be found in Table A.8.

The Gini coefficient for the whole country is also calculated. The mean for the whole period is 0.256, which is 0.026 higher than the mean for the regions for the whole period. That regional inequality on average is lower than the national inequality, means that people are stratified across space so that people in similar income groups gather in the same regions (Glaeser et al., 2009). The five regions with highest inequality on average during the period are the only regions that have higher Gini than the national Gini, see Table A.8.

Table 3.4: Summary statistics for Gini in the regions.

mean p25 p50 p75 p99 max min Gini in 1995 (N=89) 0.228 0.218 0.226 0.237 0.297 0.297 0.197 Gini in 2015 (N=89) 0.232 0.218 0.227 0.242 0.332 0.332 0.194 Gini for the whole period (N=1869) 0.230 0.217 0.224 0.241 0.309 0.346 0.190 Gini in Norway (N=21) 0.256 for the whole period

25 The results from calculation of Gini’s nuclear family are shown in Table 3.5. There has been a greater increase in inequality in the higher end of the distribution than in the lower and middle parts; further, the increase in inequality has been higher for the country as a whole than in the regions on average14.

Table 3.5: Nuclear Gini. (Without the 2005 observations.)

C1 C2 = Gini C3 Average 1995-2004 in the regions 0.318 0.226 0.182 Average 2006-2015 in the regions 0.323 0.232 0.188 Percentage change 1.3 % 2.4 % 3.3 % Average 1995-2004 in Norway 0.342 0.250 0.205 Average 2006-2015 in Norway 0.352 0.260 0.216 Percentage change 2.7 % 4.3 % 5.5 %

3.9 Control variables

In addition to the variables inequality and innovative activity in the regression, other variables that are assumed to inﬂuence both innovative activity and inequality, are used as control variables. Here they are presented brieﬂy. See Table A.9 for summary statistics on the control variables.

Mean wage income. The mean wage income in the regions are calculated based on the income data described in section 3.7. In the regression, the logarithm of the mean wage income is used because then the regression coeﬃcient can be interpreted as relative change, not absolute changes15.

The mean wages have not been adjusted for inflation because the inflation is picked up by the year fixed effects when the logarithm of the wage is taken16.

Because of lack of other measures of regional growth, the log mean income is used as a proxy for how prosperous the region is, and changes in the log mean income as a proxy for regional growth. This is line with the method of Rattsø and Stokke (2014) and Modalsli (2014).

Population size. Statistics Norway’s oﬃcial data on populations in the municipalities is used. As there is considerable overlap between populous regions and urban regions,

14The calculations are done without the 2005 observations, as that year has a different income distribution than other years, see section 3.7 and Table A.4. 15 ∆X The regression coefficient can be interpreted as percentage change because X ≈ ln(X +∆X)−ln(X) for small values of ∆X. 16 With Y as income and π as inflation, it can be written like this: log((πY )it)=log(πt) + log(Yit). The log(πt) is the same for all regions and are picked up by the year fixed effects.

26 population size is used as a proxy for the urbanity of a region. In the regression, the logarithm of population size is taken, to be able to interpret the regression coeﬃcient as percentage change.

Education. Statistics Norway’s oﬃcial data on the educational level of residents above 16 years in each municipality for each year 1995–2015 is used. The population is divided into shares by highest educational achievement: primary school, high school and higher education. Higher education is further divided into maximum 4 years (short higher education) and more than four years (long higher education). There is also a group for which no information is available as to education. Primary school is omitted in order to avoid perfect multicollinearity.

Business structure. Statistics Norway’s oﬃcial data on employment in diﬀerent sectors is used, based on the two-digit NACE codes. The NACE code system was changed twice during the period 1995–2015. The shares in each business sector in each economic region is calculated based on the employee’s residence municipality, as the patent data are based on the inventor’s postal code and wage data are based on the municipality of residence. However, for the period 1995–1999 only data on the municipality of the employee’s work place are available.

In section 3.3.1 it is explained how the categorization of the business sectors based on the NACE code is done. Since only two-digit NACE codes are available for the municipalities, while the patents are categorized by the ﬁve-digit NACE codes, the categorization of the patents and the work force is not identical. The diﬀerences can be seen in Table A.2. This might be a problem for the analysis, but that was the data that were available.

The category other service is omitted in order to avoid perfect multicollinearity.

Regional dummies. Dummy variables for each part of the country is constructed in order to control for regional culture and other specific characteristics. See Table A.5, showing which counties belong to which part of the country (here there are no surprises for those familiar with Norwegian geography). Trøndelag (in the middle of Norway) has been omitted to avoid the problem of perfect multicollinearity. Regional characteristics on a more detailed level is controlled for with the region fixed effects.

Now that measures, data and summary statistics are presented, the link between innovation and wage inequality in Norwegian regions will be examined in a regression model.

27 Figure 3.1: Knowledge production function. Figure from Griliches (1998).

28 1200 1000 800 600 400 200 0 1995 2000 2005 2010 2015 year

Publicly available applications Patents granted Patents + pending applications Applications in total

Figure 3.2: Number of publicly available applications per year and number of granted patents per year. Note: See footnote 12.

29 Chapter 4

Empirical strategy, results and robustness

Previous chapters have discussed the role of innovation in the economy, possible channels between innovation and wage inequality, and presented measures, data and summary statistics. This chapter uses a regression model to investigate whether there is a link between innovation and wage inequality in Norwegian regions.

4.1 Empirical strategy

Is high innovative activity associated with high inequality? The control variables used here include log mean wage income, population size, share of education levels, the business structure, time ﬁxed eﬀects and a dummy variable for the part of the country in which the region is located or a dummy variable for each of the 89 regions.

Region fixed effects and dummy variables for location within Norway control for unobserved regional heterogeneity that is constant over time—perhaps special characteristics of that region, like culture or geography. Dummy variables at the specific regional level are naturally more fine-grained than dummy variables on the level of the general part of the country. With the region fixed effects it can be investigated whether correlations between inequality and innovative activity within the regions are significant.

Year fixed effects vary each year, but remain the same for all regions. This captures time- trends that apply to all the regions—for instance a downturn or upturn in the Norwegian economy. Controlling for time fixed effects means eliminating the confounding factor of the common time trend.

30 The model looks like this:

0 Iit = β1Pit + γ Xit + αi + δt + εit (4.1) where I is inequality, P is innovative activity, X is the vector with control variables, α represents the dummy variables for the different parts of the country or the regions, δ is the time fixed effects and ε is the error term.

In order to correct for correlated error terms, standard errors are clustered by regions.

4.2 Results

Cross-section OLS regression yields a significant positive relationship between the Gini coefficient and innovative activity (see Table 4.1). That means that regions with relatively high innovative activity are also more unequal. The positive correlation between innovative activity and inequality is robust to all four specifications of the innovation measure, and with or without time fixed effects. The size of the effect is smaller for patent applications than for patents, as expected. Time fixed effects are used on all the following regressions, with patents per thousand inhabitants as the main measure of innovative activity. One more patent per thousand inhabitants is associated with a 0.14 increase in the Gini coefficient—a considerable increase in inequality. However, this is without control variables and is therefore probably not the actual effect.

To identify which control variables to include in the regression analysis, what variables that are assumed to influence inequality and also have explanatory power over innovative activity are explored. Many of the confounding factors seem to be taken up in the mean wage income. (For detailed results, see Table B.3 in the Appendix.) When innovative activity is regressed on log mean income, at the same time as population size, education, business structure and regional characteristics are controlled for, two variables—education and population size—are no longer significant (Table B.3, column (6))17. However, the share of labor force in the region working in the oil and gas sector and in the other industry sectors remains significant. Also, Northern Norway and Eastern Norway are significantly negatively correlated with innovative activity, unlike other parts of Norway18. It is not possible to determine whether some of the significance is due to correlation between the control variables19. Summing up: for the variables included here, mean wage income

17The omitted variable Primary school is also not significant (checked with the Stata command lincom). 18The omitted variable Trøndelag is not significant (checked with the Stata command lincom). 19When regressing only the dummy variables for part of the country on innovative activity, Northern, Western and Southern Norway emerge as significant (table B.3, column (5)). However, when all control variables are regressed simultaneously, other parts of the country become significant, and those that were

31 appears to be the most important control variable, but other variables seems to have explanatory power as well.

signiﬁcant are no longer so, except Northern Norway (table B.3, column (6)).

32 Table 4.1: OLS regression with Gini as the dependent variable and different specifications of the explanatory variable. First alone, then with year fixed effects. (1) (2) (3) (4) (5) (6) (7) (8) Patents/thousand inhabitants 0.137∗∗∗ 0.140∗∗∗ (0.0303) (0.0320)

Patent applications/thousand inhabitants 0.122∗∗∗ 0.124∗∗∗ (0.0259) (0.0268)

Patents (divided by 1000) 1.471∗∗∗ 1.476∗∗∗ (0.228) (0.233)

Patent applications (divided by 1000) 1.061∗∗∗ 1.060∗∗∗ (0.176) (0.178) Year Fixed Eﬀect No Yes No Yes No Yes No Yes 33 Observations 1869 1869 1869 1869 1869 1869 1869 1869 Adjusted R2 0.196 0.221 0.244 0.270 0.400 0.422 0.420 0.442 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 4.2.1 OLS regression with control variables

High income levels are associated with high innovative activity (see Table B.4 and Table B.5). One more patent per thousand inhabitants is associated with a 47 percent increase in mean wage income. More prosperous regions are likely to have a higher share of highly skilled workers and more economic activity, and therefore also more innovative activity. As more prosperous areas are also more unequal, controlling for the log mean income reduces the coeﬃcient on innovative activity considerably (column (2) in Table 4.2). Furthermore, the adjusted R2 increases from 0.2 to 0.8 when mean wage income is included as a control variable. This indicates that mean income is a confounding factor in the regression analysis, and the regression in column (2) in Table 4.2 where mean wage income is controlled for appears more credible than column (1) in Table 4.2. For a given mean wage level, one more patent per thousand inhabitants is associated with a 0.026 increase in the Gini coeﬃcient.

Controlling for population size in addition to log mean income does not change the coefficient greatly (column (3) in Table 4.2). This indicates that the effect of population size is picked up by the mean income, probably because population size is an indication of how urban the region is, and urban regions are generally more prosperous than rural areas (Rattsø & Stokke, 2014). Only controlling for population size and not log mean income reduces the coefficient on innovative activity (see B.2, column (1)), but not as much as when controlling for the log mean income.

Further, areas where a large share of the population has higher education tend to have more innovative activity. When education is controlled for (Table 4.2, column (4)), the coefficient does not change much. That indicates that most of the effect of education on innovative activity is already captured by the mean income variable, as expected based on the results in Table B.3. Keeping the mean wage income level and the education level (and the population size) constant, each one-unit increase in innovative activity is associated with a 0.021 increase in the Gini coefficient.

As businesses differ in their propensity to innovate and to patent and business structure is expected to influence inequality levels in the regions, business structure could be expected to change the coefficient on innovative activity. Adding control variables for the business structure reduces the coefficient somewhat. For a given level of mean wage income, population size, education level and business structure, one more patent per thousand inhabitants is associated with a 0.015 increase in inequality (Table 4.2, column (5)). Controlling only for the log mean income and business structure, instead of adding it to the other control variables, yields no change in the coefficient on innovative activity (see Table B.2, column (2)).

34 Region fixed effects are controlled for in another Table (Table 4.3), but it seems reasonable to assume that there might be special characteristics that affect both innovative activity and inequality at a less fine-grained level than that of the regions. Therefore what part of the country the region is, is controlled for. When the dummy variables for the part of the country where the regions are located is added, the coefficient is reduced again, remaining significant at the 10 percent level (column (6) in Table 4.2)20.

Keeping constant the mean wage income level, the population size, the education level, the business structure and within-country location characteristics, an increase in innovative activity by one unit is associated with a 0.009 increase in the Gini coeﬃcient21. Thus, if the Gini is 0.221, one more patent per thousand inhabitants is associated with an increase in the Gini to 0.23022.

It might be that some variables that influence both innovative activity and inequality have been omitted here. That would make the coefficient on innovative activity both biased and inconsistent. However, if we assume that the model includes most important variables that determine both inequality and innovative activity, then one more patent per thousand inhabitants will be associated with a 0.009 increase in the Gini coefficient.

20If only controlling for log mean income and what part of the country the region is, the coefficient is reduced compared to only controlling for log mean income, see Table B.2, column (3). 21Without the observations from 2005 this coefficient is 0.012 and significant at the 5 percent level (see Table B.7, column (6)). 22As innovative activity is expected to be followed by economic growth, also the relation between mean income and innovative activity is investigated. For the mean income, keeping all control variables constant, a one-unit increase in innovative activity is associated with an 11 percent increase (see Table B.5, column (5)).

35 Table 4.2: OLS regression with Gini as the dependent variable, year ﬁxed eﬀects and control variables.

(1) (2) (3) (4) (5) (6) Gini Gini Gini Gini Gini Gini Patents/thousand inhab. 0.140∗∗∗ 0.0259∗∗∗ 0.0252∗∗∗ 0.0214∗∗∗ 0.0145∗∗∗ 0.00922∗ (0.0320) (0.00735) (0.00728) (0.00671) (0.00500) (0.00506)

Log mean income 0.241∗∗∗ 0.250∗∗∗ 0.241∗∗∗ 0.230∗∗∗ 0.235∗∗∗ (0.0118) (0.0142) (0.0173) (0.0203) (0.0222)

Log inhabitants -0.000963 0.0000247 0.000548 -0.000169 (0.00109) (0.00114) (0.000903) (0.000722)

High school 0.0824∗∗∗ 0.0542∗∗ -0.0372∗ (0.0271) (0.0272) (0.0223)

Short higher education -0.122∗∗ -0.0842∗∗ -0.0342 (0.0466) (0.0409) (0.0376)

Long higher education 0.164∗ 0.182∗∗ 0.0811 (0.0864) (0.0746) (0.0658)

Education unknown 0.307∗∗∗ 0.163 0.0920 (0.107) (0.101) (0.0797)

Oil and gas 0.00186∗∗∗ 0.00230∗∗∗ (0.000472) (0.000590)

Other industry 0.0000459 -0.000122 (0.000168) (0.000146)

Scientiﬁc activity -0.000580 -0.000918∗∗∗ (0.000358) (0.000283)

Primary industry 0.000101 0.000469∗∗ (0.000191) (0.000194)

Business unknown 0.0263∗∗∗ 0.0163∗∗∗ (0.00328) (0.00231)

Northern Norway -0.0107∗∗∗

36 (0.00204)

Western Norway -0.00219 (0.00176)

Southern Norway 0.0114∗∗∗ (0.00194)

Eastern Norway 0.00215 (0.00169) Year Fixed Eﬀects Yes Yes Yes Yes Yes Yes Observations 1869 1869 1869 1869 1869 1869 Adjusted R2 0.221 0.798 0.799 0.818 0.853 0.896

Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

37 4.2.2 Panel data analysis

Is an increase in innovative activity associated with increased wage inequality within the regions? Panel data analysis indicates that this is not the case, see Table 4.3, column (1)23. This indicates that diﬀerences between the regions are what drive the correlation between innovative activity and inequality.

When patents are not divided by the number of inhabitants in the region, but the number of patents per region is used instead, the relation between innovative activity and inequality is significant at the 1 percent level with both region fixed effects and year fixed effects, see Table 4.3, column (3). This means that an increase in number of patents granted is associated with higher inequality within the regions. However, these results are entirely driven by the five regions that have most patents in the period24. When these are omitted, the coefficient on innovative activity is no longer significant. The specific results without the five regions can be found in Table B.10. To check whether these five regions drive all the results, further investigations were conducted in the robustness-section (section 4.3). The robustness check shows that the five regions alone do not drive the main results.

Table 4.3: Panel data with Gini or log mean income as the dependent variable, patents/thousand inhabitant and patents as the independent variable, year fixed effects and region fixed effects. (1) (2) (3) (4) Gini Log mean income Gini Log mean income Patents/thousand inhabitants 0.00137 0.0129 (0.00297) (0.00894)

Patents (divided by 1000) 0.175∗∗∗ 0.448∗∗∗ (0.0509) (0.131) Region Fixed Eﬀects Yes Yes Yes Yes Year Fixed Eﬀects Yes Yes Yes Yes Observations 1869 1869 1869 1869 Adjusted R2 0.927 0.998 0.928 0.998 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

4.2.3 Other inequality measures

Do changes in the wage income distribution associated with changes in innovative activity apply mainly to the lower income group, the middle income group, or the upper income

23Neither is an increase in innovative activity associated with income growth within the regions (see Table 4.3, column (2)). 24These regions are Stavanger/Sandnes, Oslo, Trondheim, Bærum/Asker, and Bergen. See Appendix, Table A.7 for a list of the ﬁgures on patents by regions.

38 group? The two other measures of inequality are regressed on innovative activity (see Table 4.4). These two measures are the Bonferroni coeﬃcient, which adds more weight to the lower part of the distribution (C1) and C3, which adds more weight to the upper part of the distribution. C2 is the Gini coeﬃcient (which adds most weight to the middle of the distribution).

The signs on the coeﬃcients are all the same and the magnitudes are in the same range.

The coefficient is higher on C1 than on the Gini, whereas the coefficient on C3 is lower than on the other two. When log mean income is controlled for, the effect is still strongest on C1 and weakest on C3. The difference between the coefficients on C1 and C3 is roughly 0.01. That means that, for a given mean income level, the effect of a one-unit increase in innovative activity is associated with a 0.030 increase in C1, whereas on C3 the increase is associated with a 0.021 increase. The Gini coefficient (C2) is in-between the two.

Table 4.4: Gini’s nuclear familiy. (1) (2) (3) (4) (5) (6) C1 C1 C2 C2 C3 C3 Patents/thousand inhabitants 0.154∗∗∗ 0.0299∗∗∗ 0.140∗∗∗ 0.0259∗∗∗ 0.128∗∗∗ 0.0209∗∗∗ (0.0337) (0.00788) (0.0321) (0.00736) (0.0302) (0.00665)

Log mean income 0.263∗∗∗ 0.241∗∗∗ 0.226∗∗∗ (0.0115) (0.0118) (0.0114) Year Fixed Eﬀects Yes Yes Yes Yes Yes Yes Observations 1869 1869 1869 1869 1869 1869 Adjusted R2 0.220 0.816 0.221 0.798 0.221 0.799 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

39 4.2.4 Granger causality test

A Granger causality statistic tests whether lagged values of X can predict the current value of Y . The idea is to investigate whether something that allegedly causes something in fact occurs before the consequence (Angrist & Pischke, 2008). This is in fact not a test of causality, but of whether innovative activity is a useful predictor of inequality (Stock & Watson, 2015). A Granger causality test is performed, to test whether past values of innovative activity help predict current values of inequality, with lagged values of inequality as control variables25. The test is also conducted the other way around, to see whether past values of inequality can predict current values of innovative activity (with lagged values of innovative activity as control variables).

The results of the cross-sectional Granger causality test, presented in Table 4.5, show that, for given levels of mean income, innovative activity is a useful predictor of inequality on the cross-sectional level. Regions with high innovative activity are predicted to also have high wage inequality, but the coefficient is only 0.005 (Table 4.5, column (1)). That means that innovative activity as such is not a very strong predictor of inequality, probably because inequality is determined by many other factors as well. Further, we can predict that there will be a 75 percent persistence between the regions’ inequality level from one year to another, keeping the mean wage level constant (but inflation is taken out in the year fixed effects, see footnote 17).

Regions with high innovative activity in one year are predicted to experience that the next year too, but only with a 22 percent persistence (Table 4.5, column (2)). Inequality can be rejected as a predictor of innovative activity (Table 4.5, column (2)), which indicates that reversed causality is not an issue here.

However, this only tests the predictive content in innovative activity on inequality at the cross-sectional level. A Granger causality test within each region will show whether past values of innovative activity within the regions are a useful predictor of current inequality in the same region, with lagged values of inequality as controls. Here a Dumitrescu-Hurlin test is conducted (Dumitrescu & Hurlin, 2012), which extends the Granger causality test and is designed to detect Granger-causality in panel data (Lopez & Weber, 2017). The test takes into account an important issue: the possibility of heterogeneous eﬀects (Dumitrescu & Hurlin, 2012). The regression model looks like this:

K K X X Ii,t = αi + βikIi,t−k + γikPi,t−k + εi,t (4.2) k=1 k=1 where Ii,t and Pi,t are the observations of inequality and innovative activity in region i

25Along the lines of Lind, Moene, and Willumsen (2014).

40 Table 4.5: Granger causality test with log mean income as control. (1) (2) Gini Patents/thousand inhabitants L.Gini 0.752∗∗∗ -0.247 (0.0627) (0.186)

L.Patents/thousand inhabitants 0.00535∗∗ 0.220∗∗∗ (0.00215) (0.0568)

Log mean income 0.0666∗∗∗ 0.134 (0.0166) (0.114) Year Fixed Effects Yes Yes Observations 1780 1780 Adjusted R2 0.930 0.423 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 in period t. Coefficients can differ across regions, but are assumed to be time-invariant (Dumitrescu & Hurlin, 2012). K is the lag order and is the same for all regions. The panel must be balanced (as is this one) and stationary. Tests for stationarity have been conducted with different unit root tests in Stata (Breitung, 2001; Levin, Lin, & Chu, 2002). They both reject the 0-hypothesis that the panel contains unit root, which strengthen the hypothesis that the panel is stationary for both variables.

The Dumitrescu-Hurlin test determines the existence of Granger causality by testing for signiﬁcant eﬀects of past values of innovative activity on the present value of inequality, and vice versa. The test assumes that there may be Granger causality for a sub-group of the regions (Dumitrescu & Hurlin, 2012). The Dumitrescu-Hurlin test performs regression for each individual region i based on equation 4.2 and performs an F-test. See the Appendix (section B.1) for a formal presentation of the hypothesis.

The results from the Dumitrescu-Hurlin test with 1-5 lags do not reject the hypothesis that innovative activity does Granger-cause the inequality for at least one region. The speciﬁc results are found in Table B.1, column (1) in the Appendix. Rejecting the 0-hypothesis does not exclude that there is no Granger-causality for some regions.

As it is not possible to use other control variables than the lagged values in the test, we cannot know whether the test is signiﬁcant when controlling for log mean income, which is assumed to be an important confounding factor in the relation between innovative activity and inequality.

The test rejects the hypothesis that inequality Granger-causes innovative activity (see Table B.1, column (5)).

41 4.2.5 Lagged values

Further, Gini is regressed on lagged values of innovative activity. All ﬁve lagged values of innovative activity are signiﬁcantly correlated with inequality, also when mean income is controlled for, see Table 4.6. This indicates persistence of the relation between innovative activity and inequality.

Table 4.6: OLS with Gini as the dependent variable, time ﬁxed eﬀects and 1-5 lagged values of innovative activity as the independent variables. (1) (2) (3) (4) (5) (6) Patents/thousand inhab. 0.0259∗∗∗ 0.0168∗∗∗ 0.0131∗∗∗ 0.0106∗∗ 0.0105∗∗ 0.00916∗∗ (0.00735) (0.00514) (0.00464) (0.00426) (0.00410) (0.00391)

L.Patents/thousand inhab. 0.0192∗∗∗ 0.0114∗∗∗ 0.00941∗∗∗ 0.00690∗∗ 0.00658∗∗ (0.00529) (0.00394) (0.00345) (0.00316) (0.00301)

L2.Patents/thousand inhab. 0.0204∗∗∗ 0.0161∗∗∗ 0.0148∗∗∗ 0.0129∗∗∗ (0.00516) (0.00429) (0.00393) (0.00386)

L3.Patents/thousand inhab. 0.0130∗∗∗ 0.00866∗∗ 0.00672∗∗ (0.00429) (0.00331) (0.00301)

L4.Patents/thousand inhab. 0.0121∗∗∗ 0.00779∗∗ (0.00446) (0.00332)

L5.Patents/thousand inhab. 0.0128∗∗∗ (0.00423)

Log mean income 0.241∗∗∗ 0.239∗∗∗ 0.237∗∗∗ 0.237∗∗∗ 0.237∗∗∗ 0.238∗∗∗ (0.0118) (0.0119) (0.0122) (0.0124) (0.0127) (0.0130) Year Fixed Eﬀects Yes Yes Yes Yes Yes Yes Observations 1869 1780 1691 1602 1513 1424 Adjusted R2 0.798 0.804 0.808 0.810 0.811 0.811 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

42 4.3 Robustness

To examine the robustness of the result, four different specifications are tested. The first is to omit the observations for 2005 because wage income distribution are different in that year; the second involves omitting the observations of the five regions with most patents in total; the third is to test whether the main result is robust to shorter time-periods; and the last is to test whether the main result holds also for other geographical units. As will be shown, the main result holds for all these tests of robustness.

Since wage inequality in 2005 has higher right tails than in other years (see section 3.7 and Table A.4), it has been investigated whether the results in Table 4.1 and Table 4.2 are signiﬁcant without the year 2005: indeed, they are signiﬁcant and not much changed. See Table B.6 and Table B.7 in the appendix.

As to the second test: in section 4.2.2 it was noted that the finding that patents—not patents per thousand inhabitants—were significantly correlated with inequality within the regions was driven by the five regions with most patents in total (not patents per thousand inhabitant) (Table 4.3, column (3) and (4)). The results of Table 4.3 without the five regions can be found in Table B.10. Therefore, it is investigated whether the other results were also driven by the five regions: and they were not. The main results in Table 4.2 are the same. Table 4.2 without the five regions can be found in Table B.9. The coefficient on regression number (1) becomes smaller (0.915) without the five urban regions, while when controlling for log mean income the difference between with or without the five regions becomes negligible. Whether the regression results in Table 4.1 and Table 4.5 are driven by the five regions that have the highest number of patents are also checked: and the main results stay the same. For details, see the appendix, Table B.8 and Table B.11, respectively.

When the Dumitrescu-Hurlin test is performed and the five regions with most patents omitted, the test is no longer significant for the first lag, but the results for all other lags are significant (see table B.1, column (2)). When the test is performed without the five regions with most innovative activity per thousand inhabitants in total, but with the five regions with most patents in total included, it is still significant (Table (B.1, column (3))26. When the five regions with most patents and the five regions with most patents per thousand inhabitants are omitted, the result is no longer significant for the first lag, but remains significant for all the other lags. This indicates that the results of the Dumitrescu-Hurlin test is not driven by a small group of regions, at least when testing for more than 1 lag.

26The ﬁve regions with most innovative activity per thousand inhabitants are Flekkefjord, Sta- vanger/Sandnes, Bærum/Asker, Ulsteinvik and Kongsberg, see Table A.6.

43 The third robustness test, whether the main result is robust to shorter time-periods, shows that this is so. For the period 1995–1998, half of the patents are not allocated to a region (see section 3.3), and the result is not significant. For the three periods from 1999 and onwards, the coefficient is significant, see Table B.12. If 2005 is excluded, the coefficient changes and also becomes significant at the 1 percent level instead of the 10 percent level. Also if the five regions with the highest number of total patents are omitted, the result is significant for shorter time-periods (Table B.13).

The final robustness test is whether the main result remains unchanged with a different geographical unit. The regression analysis with NIBR’s residential and labor market regions can be found in Table 4.7. NIBR regions are described in section 3.4. The positive correlation between inequality and innovative activity remains, with the NIBR regions (column (1)) and also when log mean income is controlled for (column (2)). This indicates that the relation between innovative activity and inequality is robust to different geographical units. However, the magnitude of the effect is different when using NIBR regions than when using Statistics Norway’s economic regions. Further, when region fixed effects are controlled for, the result remains significant (column (3))—unlike with the economic regions. However, for constant levels of mean wage income, increasing innovative activity is not associated with increased inequality within NIBR regions (see Table 4.7, column (4)).

The results for the NIBR regions are more sensitive to removal of the regions with most patents. When they are omitted, the signiﬁcance of the results in column (2), (3) and (4) disappears (see table B.14). Thus, unlike with the economic regions, without the regions that have most patents, for a given level of mean wage income, there is no signiﬁcant relationship between innovative activity and inequality in NIBR regions.

Table 4.7: Regression with the NIBR regions. (1) (2) (3) (4) Patents per inhabitant 2.891∗∗∗ 0.466∗∗ 0.0822∗ 0.0370 (0.635) (0.192) (0.0457) (0.0328)

Log mean income 0.222∗∗∗ 0.235∗∗∗ (0.0158) (0.0340) Year Fixed Eﬀect Yes Yes Yes Yes

Region Fixed Eﬀect No No Yes Yes Observations 3289 3289 3289 3289 Adjusted R2 0.099 0.574 0.799 0.857 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

The regression analysis shows that there is a link between innovation and wage inequality

44 in Norwegian regions, and that the correlation appears to be driven by diﬀerences between the regions. In the next chapter these results are discussed in further detail.

45 Chapter 5

Summary of ﬁndings, discussion and concluding remarks

In chapter 4 the link between innovation and wage inequality was examined through a regression model. This chapter summarizes the ﬁndings, and discusses whether it is possible to trace what channels might be involved. The chapter ends with concluding remarks and suggestions for further research.

5.1 Summary of ﬁndings

There is a significant correlation between innovative activity and inequality in Norwe- gian regions. Keeping constant the mean wage income level, population size, education level, business structure and within-country location characteristics, a one-unit increase in innovative activity has been shown to be associated with a 0.009 increase in the Gini coefficient. In contrast, within the regions studied, higher innovative activity is not associated with an increase in inequality, so the relation seems to be driven by differences between the regions.

High innovative activity is a useful predictor of high wage inequality, both between and within regions. The Dumitrescu-Hurlin test showed that, at least within some regions, the hypothesis that innovative activity Granger-causes inequality is not rejected. The results are significant for 1 to 5 lags. In addition, the significance of all the five lagged values of innovative activity indicates persistence in the relationship over time.

The results for Gini’s nuclear family are with the same sign and in the same range of the magnitude for all three measures. The coeﬃcient on C1, which places most weight on the lower part of the income distribution, is somewhat higher than on C3, which focus more on the upper part. For a given mean income level, one unit increase in innovative activity

46 is associated with a 0.030 increase in C1, whereas on C3 the increase is associated with a 0.021 increase in inequality.

The main results are robust to diﬀerent geographical units and other tests of robustness.

5.2 Discussion of possible channels

As we saw in section 2.2 (summed up in Table 2.1) there are several possible channels between innovation and inequality. Can these be traced in the ﬁndings?

If there is research arbitrage for the innovative firm or the inventor(s) that increase wages for some (channel 1), one possible way of detecting it may be a significant correlation between inequality and innovation within the regions. However, that is not the case. This might be because the research arbitrage is allocated at different times for different patents: some when the patent application is filed, others when the product has commercial success. That would make it difficult to identify the research arbitrage statistically. Or, perhaps some firms that invent in Norway do not amass large research arbitrage that can be traced back to wages because it is more a matter of survival: if they fail to invent, they will have to shut down, losing out to competitors in other countries with lower wages. In that case, the research arbitrage from inventing concerns not higher wages, but the very survival of jobs with Norwegian wage levels. But this is speculation. Further investigations on the company level are needed to clarify the relation between innovative activity, research arbitrage and wages.

A third possible explanation of the absence of a significant correlation between innovation and inequality within regions may be that most patents are not particularly valuable or innovative. In that case, there is simply no effect to capture, so there will generally be no research arbitrage when the company files patent applications.

A fourth possible explanation is that the measure of innovative activity could involve measurement error, as noted in section 3.2. Measurement error in the regressor will bias the coeﬃcient towards zero (Angrist & Pischke, 2008).

A fifth explanation could be that the capital owners, not the workers, get all the research arbitrage from the innovation, so the gain from innovation is not captured in terms of wages. However, that seems unlikely since the inventor(s) will probably have at least some bargaining power. For inventors with companies with sole proprietorship that take out the research arbitrage in capital income, however, the effect is not captured in this analysis. This might partially explain why we find no significance between innovation and inequality within a given region. However, companies with sole proprietorship own only 1 percent of the patents, so that cannot be the whole explanation.

47 To sum up regarding channel 1: We ﬁnd no traces of research arbitrage causing increased wages and increased inequality in innovative regions, but the reason is highly uncertain. Whether it is because of measurement error or research arbitrages not being present, for various reasons, requires further investigation.

The positive correlation between innovation and inequality seems driven mainly by differences between the regions studied. It could be that part of the explanation of the inter-regional differences is skill-biased technical change, where adoption of technology makes the high-skilled workers more productive (channel 2). Patents are not a precise measure for the adoption of technology, but may capture some technology adoption because some firms will adopt the technology they have developed themselves. Therefore, it may be that patents sometimes capture the effect of technology making high-skilled workers more productive, and that those workers earn more than others in the region.

The results for Gini’s nuclear family indicate that innovative activity is associated with the lower-income group getting somewhat left behind compared to the middle- and upper- income group. However, we cannot say whether this is due to the lower-income group entering the labor market from unemployment, or to the lower-income group entering into service jobs for the inventive group (channel 3, which in chapter 2 was found not very likely in Norway, see section 2.2.3), migration, or to the middle and upper income group simply leaving the lower-income group behind.

An alternative explanation is that, if patents are not such a good measure of innovative activity, but rather a measure of the propensity to patent, it could be that the business sectors that patent the most per inhabitant also are the ones that drive inequality up. What we see would then be a correlation not between innovation and inequality, but between high propensity to patent and high inequality.

Another possible explanation for the positive correlation between innovation and inequality may be sorting, with many high-skilled workers gathered in certain regions characterized by high innovative activity (channel 4). When higher education is controlled for, the correlation between innovative activity and inequality remains signiﬁcant, but perhaps the variable “higher education” does not capture certain speciﬁc abilities of those workers who are attracted to the region. Such high-ability workers may be more productive precisely because of their high ability; if they can command higher wages, that in turn will increase inequality in that area, compared to workers without similar abilities. Perhaps high innovative activity is an indicator of high-ability workers being present in the region.

Expanding on the explanation about high-ability workers, the results could indicate not just sorting, but knowledge spillover effects among high-ability workers (channel 5). These effects are assumed to be linked to cities and agglomeration effects (see section 2.2.3)27.

27The population size of the regions do have a signiﬁcant eﬀect on innovative activity, if mean income

48 Perhaps innovative activity makes those with high abilities increase their productivity and therefore their wages, but not in ways directly linked to inventing and patenting. However, it could be that knowledge spillover effects are also present among those with fewer skills as well, so that without these effects the inequality would have been higher, not lower, in innovative regions. The results from the Gini’s nuclear family indicate that those in the lower-income groups are somehow left behind. Perhaps that is because the wages of workers in the middle- and upper-income groups are increasing more than for the lower-income group, partly because of knowledge spillover effects for both the higher- and middle-income groups —but this is as yet only speculation.

For the last channel (channel 6)—of inequality causing innovation—the results from both Granger causality tests strongly indicate the converse: that inequality does not cause innovative activity.

To sum up, a possible explanation of the positive correlation between innovative activity and wage inequality is that some workers in the regions with most innovative activity have higher wages because they are more productive. Such increased productivity may come about because the regions with high innovative activity adopt technology that increases the productivity of high-skilled workers (skill-biased technical change); or secondly, because high-ability workers gather in innovative regions; or thirdly, that high-skilled workers increase their productivity through knowledge spillover eﬀects within the innovative region. However, this warrant further investigations.

5.3 Concluding remarks

This thesis finds a significant correlation between innovative activity and inequality in Norwegian regions. The relation seems to be driven by differences between the regions.

Several issues touched on in this thesis require further study. First, it would be of interest to compare the outcome of Statistics Norway’s innovations surveys to patents, to see whether there are large statistical overlaps between patents as a measure of innovative activity and the innovation survey. This could offer further insights into both measures of innovation. Second, it would be relevant to scrutinize how wage distribution changes in innovative regions, and whether the higher inequality, especially for the lower part of the income distribution, is due to the poorer group entering the labor market from previous unemployment, or to migration, or to the middle and upper groups simply leaving the lower group behind. Third, it would be of interest to find why the wage income distribution is not controlled for (see Table B.3, column(7)). If mean income is controlled for (see Table B.3, column (6)), the population size is no longer significant. However, it may be argued that to control for the mean income is to actually control for the agglomeration effects, and that therefore, in order to actually “catch” the agglomeration effects it is needed to leave mean income out of the regression.

49 in 2005 resulted in inequality levels higher than any other years. Fourth, company-level study of whether being granted patents is followed by wage growth could indicate the presence of research arbitrage. For ﬁrms where the inventor is also the owner, it would be necessary to examine capital income in order to trace possible research arbitrage.

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55 List of Figures

2.1 The term innovation and its relation to other terms...... 7

3.1 Knowledge production function. Figure from Griliches (1998)...... 28 3.2 Number of publicly available applications per year and number of granted patents per year. Note: See footnote 12...... 29

A.1 Map of the regions in Northern Norway ...... 62 A.2 Map of the regions in Southern Norway ...... 63

56 List of Tables

2.1 Possible channels and their eﬀect on inequality...... 12

3.1 Share of patents and patents applications between companies and private individuals...... 21 3.2 Summary statistics on patents and patents applications per year per region. 22 3.3 Total patents granted broken down to business sectors in the period 1995–2015. (Private individuals granted patents are not included since NACE codes are related to companies.) ...... 22 3.4 Summary statistics for Gini in the regions...... 25 3.5 Nuclear Gini. (Without the 2005 observations.) ...... 26

4.1 OLS regression with Gini as the dependent variable and different specifications of the explanatory variable. First alone, then with year fixed effects. 33 4.2 OLS regression with Gini as the dependent variable, year fixed effects and control variables...... 36 4.3 Panel data with Gini or log mean income as the dependent variable, patents/thousand inhabitant and patents as the independent variable, year fixed effects and region fixed effects...... 38 4.4 Gini’s nuclear familiy...... 39 4.5 Granger causality test with log mean income as control...... 41 4.6 OLS with Gini as the dependent variable, time fixed effects and 1-5 lagged values of innovative activity as the independent variables...... 42 4.7 Regression with the NIBR regions...... 44

A.1 Share of unknown regions and foreign inventors...... 59 A.2 Five categories for diﬀerent types of businesses...... 60 A.3 Organizational form of the entity that was granted patent...... 61 A.4 Wage inequality in 2005 compared to 2004 and 2006, indicating a diﬀerent income distribution this year than other years...... 61 A.5 What parts of the country the counties belong. Which regions belong to which county can be found in Figure A.2 and Figure A.1...... 61

57 A.6 Patents per thousand inhabitants in total between 1995-2015. (Inhabitants in 2015 Figures.) ...... 65 A.7 Patents in total between 1995-2015. (The ﬁgures are rounded up to the closest integer.) ...... 66 A.8 The regions listed from highest wage inequality on average between 1995- 2015 to lowest wage inequality...... 67 A.9 Summary statistics for the control variables...... 68

B.1 Results from the Dumitrescu-Hurlin test: Z˜-values. The column title refer to the dependent variable...... 71 B.2 Additional OLS regression with Gini as the dependent variable, year fixed effects and controls ...... 72 B.3 OLS with patents/thousand inhabitants as the dependent variable. . . . . 75 B.4 OLS regression with log mean income as the dependent variable, year fixed effects and different specifications of the independent variable...... 77 B.5 OLS regression with log mean wage income as the dependent variable, year fixed effects and control variables...... 78 B.6 OLS regression with Gini as the dependent variable and different specifications of the explanatory variable. First alone, then with year fixed effects. Without the 2005 observations...... 80 B.7 OLS regression with Gini as the dependent variable, year fixed effects and controls. Without the 2005 observations...... 81 B.8 OLS regression with Gini as the dependent variable and different specifications of the explanatory variable. First alone, then with year fixed effects. Without the 5 regions with most patents...... 83 B.9 OLS regression with Gini as the dependent variable, year fixed effects and controls. Without the 5 regions with most patents...... 84 B.10 Panel data with Gini or log mean income as the dependent variable, patents/thousand inhabitant and patents as explanatory variable, year fixed effects and region fixed effects. Without the 5 regions with most patents...... 85 B.11 Granger causality test without the 5 regions with most patents, with log mean income as control variable ...... 86 B.12 OLS with Gini as the dependent variable and year fixed effects for 4 time periods...... 87 B.13 OLS with Gini as the dependent variable and year fixed effects for 4 time periods. Without the 5 regions with most patents...... 88 B.14 Regression with the NIBR regions without Oslo, Stavanger/Sandnes, Trond- heim and Bergen (Bærum/Asker is included in Oslo)...... 89

58 Appendix A

Appendix to chapter 3

A.1 Tables

Table A.1: Share of unknown regions and foreign inventors.

Total number in the period 1995-2015 Share Patents granted 8403.0 Patents where region is unknown 871.3 10% Patents with foreign inventor 414.8 5% Patents granted with domestic inventor where region is known 7116.9 85% Note: Behind some applications, there are teams of inventors. A proportional fraction of the patent is assigned to each inventor’s region. This means that if one inventor is from Oslo, one from Rjukan and one from Drammen, each of these regions gets a value of one third. Therefore the patents have decimals.

59 Table A.2: Five categories for diﬀerent types of businesses.

Category Type of business NACE code 2-digit NACE code (2007-standard) (2007-standard) Oil and gas Building oil platforms and modules and services related to oil and gas. 6, 9, 30.113, 46.630 6, 9 Scientiﬁc and technical research, Firms that work with a broad range of technologies 58.290, 62.010, 62.020, 58, 62, 71, 72,(73), 74, 85 development, consultancy and within digitalization, medicine, oil and gas, 71.121, 71.122, 71.129, other activities defense, maritime technology and renewable energy, 72, 73, 74, 85.421, (Short: scientiﬁc activity) computer programming, consultancy and related activities. 85.422, 85.423 The patenting activity at the universities and colleges. Some of the companies in this category are also related to oil and gas, but it is not possible to sort out how many. Other industries Production of aluminum, fertilizer, plastic, From 10-43, From 10-43,

60 machinery, cranes, valves, lifting and handling equipment, except those in oil and gas except those in oil and gas agriculture and forestry machinery, vehicle equipment, furniture and sports equipment, building of ships and firms that work with production of hydro power. Other services The rest of the service sector. From 45-99, except From 45-99, except those in the scientific those in the scientific activity-category activity-category Primary industry and mining 1, 2, 3, 5, 7, 8 1, 2, 3, 5, 7, 8 Table A.3: Organizational form of the entity that was granted patent.

Organizational form Percent Limited liability stockholder companies (AS) 60 % Private individuals 22 % Public/state companies (ASA) 10 % Unknown 5 % Foundation 1 % Companies with sole proprietorship 1 % The rest 1 % Note: Those that are classiﬁed as private individuals are those that were not registered with a business identiﬁer (organizational number) in the patent dataset.

Table A.4: Wage inequality in 2005 compared to 2004 and 2006, indicating a diﬀerent income distribution this year than other years.

mean p90 p95 p99 Gini 2004 0.228 0.248 0.259 0.313 Gini 2005 0.241 0.281 0.296 0.346 Gini 2006 0.232 0.257 0.273 0.331 Observations 89

Table A.5: What parts of the country the counties belong. Which regions belong to which county can be found in Figure A.2 and Figure A.1.

Part of the country Counties Northern Norway Finnmark, Troms, Nordland Trøndelag (omitted) Nord-Trøndelag, Sør-Trøndelag Western Norway Møre og Romsdal, Sogn og Fjordane, Hordaland, Rogaland Southern Norway Vest-Agder, Aust-Agder Eastern Norway Østfold, Akershus, Oslo, Hedmark, Oppland, Buskerud, Vestfold, Telemark

61 A.2 Norwegian economic regions

Figure A.1: Map of the regions in Northern Norway

62 Figure A.2: Map of the regions in Southern Norway

63 A.3 List of patents per thousand inhabitants per region

See Table A.6.

A.4 List of patents per region

See Table A.7.

A.5 List of Gini coeﬃcients per region

See Table A.8.

64 Table A.6: Patents per thousand inhabitants in total between 1995-2015. (Inhabitants in 2015 Figures.)

Rank Region Patents per 1000 Rank Region Patents per 1000 inhabitants inhabitants 1 Flekkefjord 3.74 46 Gjøvik 0.79 2 Stavanger/Sandnes 3.54 47 Lyngdal/Farsund 0.78 3 Bærum/Asker 3.10 48 Askim/Mysen 0.75 4 Ulsteinvik 3.07 49 Vesterålen 0.72 5 Kongsberg 2.86 50 Egersund 0.68 6 Ålesund 2.42 51 Røros 0.66 7 Trondheim 2.38 52 Odda 0.60 8 Jæren 2.33 53 Steinkjer 0.58 9 Risør 2.17 54 Rørvik 0.55 10 Sunndalsøra 2.01 55 Hønefoss 0.53 11 Skien/Porsgrunn 1.97 56 Tromsø 0.53 12 Kristiansand 1.67 57 Oppdal 0.50 13 Holmestrand 1.65 58 Hamar 0.45 14 Florø 1.62 59 Orkanger 0.44 15 Ørsta/Volda 1.57 60 Alta 0.43 16 Tønsberg/Horten 1.55 61 Halden 0.43 17 Oslo 1.52 62 Vest-Telemark 0.42 18 Haugesund 1.47 63 Levanger/Verdalsøra 0.42 19 Follo 1.42 64 Kongsvinger 0.40 20 Nordfjord 1.42 65 Førde 0.40 21 Arendal 1.36 66 Sandnessjøen 0.40 22 Lillesand 1.36 67 Vadsø 0.39 23 Mandal 1.34 68 Lofoten 0.37 24 Drammen 1.33 69 Bodø 0.35 25 Bergen 1.31 70 Setesdal 0.34 26 Sunnhordland 1.25 71 Hammerfest 0.34 27 Brønnøysund 1.21 72 Mo i Rana 0.34 28 Frøya/Hitra 1.14 73 Ullensaker/Eidsvoll 0.33 29 Lillehammer 1.06 74 Elverum 0.30 30 Moss 1.05 75 Midt-Gulbrandsdalen 0.30 31 Hallingdal 1.05 76 Tynset 0.30 32 Sande/Svelvik 1.03 77 Nord-Gulbrandsdalen 0.28 33 Voss 1.01 78 Hadeland 0.26 34 Narvik 1.00 79 Finnsnes 0.26 35 Notodden/Bø 0.98 80 Høyanger 0.23 36 Stjørdalshalsen 0.92 81 Namsos 0.20 37 Molde 0.90 82 Grong 0.19 38 Sogndal/Årdal 0.88 83 Harstad 0.19 39 Surnadal 0.86 84 Mosjøen 0.18 40 Kristiansund 0.85 85 Kirkenes 0.15 41 Lillestrøm 0.84 86 Andselv 0.13 42 Brekstad 0.84 87 Valdres 0.11 43 Kragerø 0.83 88 Nord-Troms 0.09 44 Fredrikstad/Sarpsborg 0.81 89 Rjukan 0.08 45 Sandefjord/Larvik 0.81

65 Table A.7: Patents in total between 1995-2015. (The ﬁgures are rounded up to the closest integer.)

Rank Region Patents Rank Region Patents 1 Stavanger/Sandnes 987 46 Sunndalsøra 21 2 Oslo 985 47 Risør 20 3 Trondheim 585 48 Kongsvinger 20 4 Bærum/Asker 558 49 Egersund 17 5 Bergen 554 50 Voss 17 6 Drammen 242 51 Sande/Svelvik 16 7 Ålesund 230 52 Brønnøysund 16 8 Skien/Porsgrunn 222 53 Levanger/Verdalsøra 15 9 Kristiansand 199 54 Lyngdal/Farsund 15 10 Follo 178 55 Halden 14 11 Lillestrøm 176 56 Brekstad 13 12 Tønsberg/Horten 163 57 Kragerø 12 13 Haugesund 159 58 Elverum 12 14 Jæren 130 59 Førde 12 15 Fredrikstad/Sarpsborg 117 60 Mo i Rana 11 16 Arendal 111 61 Alta 11 17 Kongsberg 95 62 Frøya/Hitra 11 18 Ulsteinvik 87 63 Orkanger 10 19 Sunnhordland 74 64 Lofoten 9 20 Flekkefjord 63 65 Hammerfest 9 21 Moss 62 66 Surnadal 8 22 Molde 58 67 Hadeland 8 23 Gjøvik 56 68 Odda 8 24 Tromsø 45 69 Harstad 6 25 Nordfjord 41 70 Sandnessjøen 6 26 Lillehammer 41 71 Vest-Telemark 6 27 Hamar 41 72 Vadsø 6 28 Askim/Mysen 38 73 Rørvik 6 29 Sandefjord/Larvik 37 74 Nord-Gudbrandsdalen 5 30 Mandal 34 75 Røros 5 31 Ørsta/Volda 31 76 Finnsnes 5 32 Kristiansund 31 77 Oppdal 5 33 Bodø 28 78 Tynset 5 34 Narvik 28 79 Namsos 4 35 Florø 26 80 Midt-Gudbrandsdalen 4 36 Notodden/Bø 24 81 Mosjøen 3 37 Sogndal/Årdal 24 82 Setesdal 3 38 Ullensaker/Eidsvoll 24 83 Høyanger 2 39 Stjørdalshalsen 23 84 Andselv 2 40 Holmestrand 23 85 Valdres 2 41 Steinkjer 22 86 Kirkenes 2 42 Vesterålen 22 87 Nord-Troms 1 43 Hallingdal 22 88 Grong 1 44 Lillesand 21 89 Rjukan 1 45 Hønefoss 21

66 Table A.8: The regions listed from highest wage inequality on average between 1995-2015 to lowest wage inequality.

Rank Region Average Gini Rank Region Average Gini 1 Bærum/Asker 0.319 46 Hamar 0.224 2 Stavanger/Sandnes 0.298 47 Hadeland 0.223 3 Oslo 0.285 48 Nordfjord 0.223 4 Ulsteinvik 0.267 49 Lofoten 0.223 5 Follo 0.261 50 Høyanger 0.223 6 Bergen 0.255 51 Førde 0.223 7 Kongsberg 0.255 52 Tromsø 0.222 8 Arendal 0.254 53 Levanger/Verdalsøra 0.222 9 Haugesund 0.254 54 Voss 0.222 10 Tønsberg/Horten 0.253 55 Bodø 0.222 11 Kristiansand 0.248 56 Midt-Gudbrandsdalen 0.221 12 Jæren 0.248 57 Halden 0.221 13 Lyngdal/Farsund 0.248 58 Elverum 0.221 14 Lillesand 0.248 59 Ørsta/Volda 0.220 15 Drammen 0.247 60 Sunndalsøra 0.220 16 Sandefjord/Larvik 0.246 61 Oppdal 0.220 17 Flekkefjord 0.245 62 Harstad 0.219 18 Kristiansund 0.245 63 Valdres 0.219 19 Skien/Porsgrunn 0.244 64 Hammerfest 0.218 20 Egersund 0.244 65 Vesterålen 0.218 21 Mandal 0.244 66 Rørvik 0.217 22 Sunnhordland 0.242 67 Gjøvik 0.217 23 Moss 0.241 68 Namsos 0.217 24 Ålesund 0.239 69 Orkanger 0.217 25 Lillestrøm 0.238 70 Sandnessjøen 0.217 26 Trondheim 0.238 71 Odda 0.217 27 Hønefoss 0.237 72 Kongsvinger 0.217 28 Holmestrand 0.237 73 Alta 0.216 29 Setesdal 0.236 74 Finnsnes 0.216 30 Kragerø 0.235 75 Brønnøysund 0.214 31 Sande/Svelvik 0.235 76 Nord-Troms 0.213 32 Florø 0.234 77 Sogndal/Årdal 0.212 33 Hallingdal 0.233 78 Narvik 0.212 34 Stjørdalshalsen 0.231 79 Andselv 0.211 35 Risør 0.229 80 Steinkjer 0.211 36 Ullensaker/Eidsvoll 0.229 81 Nord-Gudbrandsdalen 0.210 37 Rjukan 0.228 82 Kirkenes 0.209 38 Fredrikstad/Sarpsborg 0.228 83 Surnadal 0.205 39 Askim/Mysen 0.228 84 Vadsø 0.204 40 Vest-Telemark 0.228 85 Røros 0.201 41 Brekstad 0.226 86 Tynset 0.200 42 Molde 0.226 87 Mosjøen 0.198 43 Lillehammer 0.226 88 Grong 0.197 44 Notodden/Bø 0.225 89 Mo i Rana 0.195 45 Frøya/Hitra 0.224

67 A.6 Summary statistics for the control variables

Table A.9: Summary statistics for the control variables.

Mean Median Standard Max Min deviation Mean income 1995 215 037 211 949 16 659 288 649 189 949

Mean income 2015 522 458 514 165 38 531 740 678 466 358

Population size 51 353 24 280 78 429 647 676 5 190

Education Share primary school 34% 34% 0% 54% 16% Share high school 45% 45% 0% 53% 29% Share short higher education 16% 16% 0% 31% 8% Share long higher education 4% 3% 0% 19% 1%

Business structure Share oil and gas 1% 0% 1% 14% 0% Share other industry 23% 22% 6% 46% 8% Share scientiﬁc activity 8% 7% 14% 4% 1% Share primary industry and mining 5% 4% 4% 18% 0% Share other service 63% 63% 7% 84% 46% Observations 1869 Note: Mean income is not adjusted for inﬂation.

68 Appendix B

Appendix to chapter 4

B.1 Dumitrescu-Hurlin test

This section is based on Dumitrescu and Hurlin (2012) and Lopez and Weber (2017). The regression model is given by this equation (the same as equation 4.1):

K K X X Ii,t = αi + βikIi,t−k + γikPi,t−k + εi,t (B.1) k=1 k=1

The procedure to determine the existence of Granger-causality is to test whether past values of P is signiﬁcant for present values of I.

The null hypothesis is deﬁned as:

H0 : γi1 = ... = γiK = 0 ∀i = 1, ..., N

The alternative hypothesis is deﬁned as:

H1 : γi1 = ... = γiK = 0 ∀i = 1, ..., N1

γi1 6= 0 or... orγiK 6= 0 ∀i = N1 + 1, ..., N

where N1 ∈ [0,N − 1] is unknown. N1 < N

28 The test perform F-tests to test the H0-hypothesis and retrieve Wi . Wi is the standard adjusted Wald statistic for region i observed during T periods. The test compute the

28 See Dumitrescu and Hurlin (2012) p.1453 for the mathematical deﬁnition of Wi.

69 average of the N individual Wald statistics:

N 1 X W = W (B.2) N i i=1

Rejecting H0 does not exclude that there is no signiﬁcance for some regions.

Under the assumption that Wald statistics Wi are independently and identically distributed across regions, it can be shown that the Z˜ follows a standard normal distribution (Dumitrescu & Hurlin, 2012; Lopez & Weber, 2017):

r N T − 3K − 5T − 3K − 3 Z˜ = W − K −−−→d N(0, 1) (B.3) 2K T − 2K − 3 T − 3K − 1 N→∞

For large N and small T datasets (like the one in this thesis), Z˜ is the favored statistics.

70 B.1.1 Results from the Dumitrescu-Hurlin test

Table B.1: Results from the Dumitrescu-Hurlin test: Z˜-values. The column title refer to the dependent variable.

Inequality (A) Inequality (B) Inequality (C) Inequality (D) Innovative activity (A) (1) (2) (3) (4) (5) 1 lag 2.4789 1.6275 2.1915 1.6284 -1.4164 2 lags 3.1135 2.9776 3.3219 3.0991 - 3 lags 2.3996 2.2103 2.5823 2.3233 - 4 lags 2.1878 2.1204 2.3660 2.1975 - 5 lags 8.9724 9.1049 9.0169 9.0089 - A: All observations included (N=89). B: The five regions with most patents in total excluded (N=84). C: The five regions with most patents per inhabitant excluded (N=84). 71 D: The five regions with most patents in total and the five regions with most patents per inhabitant excluded (N=79). B.2 Additional regression tables

Table B.2: Additional OLS regression with Gini as the dependent variable, year ﬁxed eﬀects and controls

(1) (2) (3) (4) Patents/thousand inhabitant 0.103∗∗∗ 0.0146∗∗∗ 0.0127∗∗ 0.0344∗∗∗ (0.0199) (0.00475) (0.00610) (0.0114)

Log inhabitants 0.0107∗∗∗ 0.00186 (0.00184) (0.00154)

Log mean income 0.242∗∗∗ 0.235∗∗∗ (0.0171) (0.0134)

High school -0.0546 (0.0427)

Short higher education 0.0275 (0.0650)

Long higher education 0.294∗ (0.156)

Education unknown 0.393∗∗∗ (0.141)

Oil and gas 0.00201∗∗∗ 0.00541∗∗∗ (0.000400) (0.000824)

Other industry 0.000136 0.000360∗ (0.000133) (0.000192)

Scientiﬁc activity -0.000285 0.000838 (0.000396) (0.000559)

Primary industry and mining 0.000118 0.000153 (0.000182) (0.000386)

Business unknown 0.0282∗∗∗ 0.0123∗∗∗ (0.00401) (0.00429)

Northern Norway -0.00967∗∗∗ -0.00660∗

72 (0.00251) (0.00393)

Western Norway -0.00148 0.00428 (0.00218) (0.00375)

Southern Norway 0.0105∗∗∗ 0.0159∗∗∗ (0.00205) (0.00361)

Eastern Norway 0.000524 0.00847∗∗ (0.00208) (0.00379) Year Fixed Eﬀects Yes Yes Yes Yes Observations 1869 1869 1869 1869 Adjusted R2 0.439 0.844 0.855 0.746

Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

73 B.2.1 Results with innovative activity as the dependent variable

In order to understand better what happens with the control variables and their relation to innovative activity, an equation with innovative activity as the dependent variable is estimated. X is a vector of the control variables.

0 Pit = γ Xit + αi + δt + εit (B.4)

74 Table B.3: OLS with patents/thousand inhabitants as the dependent variable.

(1) (2) (3) (4) (5) (6) (7) Log mean income 0.395∗∗∗ 0.302∗∗∗ (0.0461) (0.0745)

Log inhabitants 0.0173∗∗∗ -0.00260 0.00889∗ (0.00478) (0.00493) (0.00515)

High school 0.352∗∗∗ -0.163 0.380∗∗∗ (0.106) (0.113) (0.103)

75 Short higher education 0.156 -0.00354 0.0670 (0.213) (0.214) (0.210)

Long higher education 0.826∗∗ 0.138 0.711∗ (0.362) (0.299) (0.378)

Education unknown 1.155∗ -0.538 1.005 (0.665) (0.346) (0.622)

Oil and gas 0.0127∗∗∗ 0.00538∗∗ (0.00198) (0.00270)

Other industry 0.00250∗∗∗ 0.00155∗∗ (0.000623) (0.000709) Scientiﬁc activity 0.00809∗∗∗ 0.00151 (0.00155) (0.00214)

Primary industry -0.00128 -0.000541 (0.000825) (0.000888)

Business unknown 0.0228 0.0243∗ (0.0171) (0.0132)

Northern Norway -0.0170∗ -0.0223∗∗ (0.00973) (0.0102)

Western Norway 0.0390∗∗∗ 0.00340 76 (0.0141) (0.0115)

Southern Norway 0.0426∗∗ 0.0114 (0.0190) (0.0174)

Eastern Norway 0.0104 -0.0160∗ (0.0114) (0.00924) [1em] Year Fixed Eﬀects Yes Yes Yes Yes Yes Yes Yes Observations 1869 1869 1869 1869 1869 1869 1869 Adjusted R2 0.215 0.093 0.136 0.223 0.123 0.272 0.143

Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 B.2.2 Results with mean income as the dependent variable

Since prosperous regions are expected to have higher inequality and also have more innovative activity, an equation with mean income (Y) as the dependent variable is estimated:

0 Ln(Yit) = β2Pit + γ Xit + αi + δt + εit (B.5)

Table B.4: OLS regression with log mean income as the dependent variable, year fixed effects and different specifications of the independent variable. (1) (2) (3) (4) Patents/thousand inhabitants 0.473∗∗∗ (0.117)

Patent applications/thousand inhabitants 0.422∗∗∗ (0.0975)

Patents (divided by 1000) 5.378∗∗∗ (0.881)

Patent applications (divided by 1000) 3.875∗∗∗ (0.661) Year Fixed Eﬀects Yes Yes Yes Yes Observations 1869 1869 1869 1869 Adjusted R2 0.945 0.948 0.962 0.963 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

77 Table B.5: OLS regression with log mean wage income as the dependent variable, year ﬁxed eﬀects and control variables.

(1) (2) (3) (4) (5) Patents/thousand inhabitants 0.473∗∗∗ 0.311∗∗∗ 0.221∗∗∗ 0.105∗∗∗ 0.107∗∗∗ (0.117) (0.0662) (0.0413) (0.0318) (0.0290)

Log inhabitants 0.0465∗∗∗ 0.0221∗∗∗ 0.0110∗ 0.00863 (0.00612) (0.00606) (0.00591) (0.00597)

High school 0.268∗∗ -0.0552 -0.0740 (0.114) (0.120) (0.161)

Short higher education 0.354 0.295 0.263 (0.262) (0.249) (0.261)

Long higher education 1.133∗ 0.831 0.905 (0.618) (0.550) (0.548)

Education unknown 2.342∗∗∗ 1.390∗∗∗ 1.283∗∗∗ (0.756) (0.511) (0.472)

Oil and gas 0.0136∗∗∗ 0.0132∗∗∗ (0.00227) (0.00254)

Other industry 0.00229∗∗∗ 0.00205∗∗ (0.000790) (0.000813)

Scientiﬁc activity 0.00730∗∗∗ 0.00748∗∗∗ (0.00212) (0.00202)

Primary industry and mining -0.00212∗ -0.00134 (0.00111) (0.00126)

Business unknown -0.00411 -0.0169 (0.0159) (0.0134)

Northern Norway 0.0174 (0.0129)

Western Norway 0.0275∗∗

78 (0.0123)

Southern Norway 0.0191 (0.0120)

Eastern Norway 0.0269∗∗ (0.0135) Year Fixed Eﬀects Yes Yes Yes Yes Yes Observations 1869 1869 1869 1869 1869 Adjusted R2 0.945 0.968 0.978 0.984 0.985

Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

79 B.2.3 Results without the observations in 2005

Table B.6: OLS regression with Gini as the dependent variable and different specifications of the explanatory variable. First alone, then with year fixed effects. Without the 2005 observations. (1) (2) (3) (4) (5) (6) (7) (8) Patents/thousand inhabitants 0.138∗∗∗ 0.143∗∗∗ (0.0307) (0.0326)

Patent applications/thousand inhabitants 0.121∗∗∗ 0.125∗∗∗ (0.0261) (0.0271)

Patents (divided by 1000) 1.501∗∗∗ 1.515∗∗∗ (0.230) (0.234)

∗∗∗ ∗∗∗

80 Patent applications (divided by 1000) 1.073 1.075 (0.177) (0.179) Year Fixed Effects No Yes No Yes No Yes No Yes Observations 1780 1780 1780 1780 1780 1780 1780 1780 Adjusted R2 0.209 0.225 0.254 0.271 0.424 0.437 0.444 0.456 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 Table B.7: OLS regression with Gini as the dependent variable, year fixed effects and controls. Without the 2005 observations.

(1) (2) (3) (4) (5) (6) Patents/thousand inhab. 0.143∗∗∗ 0.0288∗∗∗ 0.0283∗∗∗ 0.0249∗∗∗ 0.0178∗∗∗ 0.0120∗∗ (0.0326) (0.00713) (0.00709) (0.00661) (0.00497) (0.00503)

Log mean income 0.237∗∗∗ 0.242∗∗∗ 0.231∗∗∗ 0.217∗∗∗ 0.222∗∗∗ (0.0122) (0.0146) (0.0177) (0.0203) (0.0220)

Log inhabitants -0.000502 0.000362 0.000823 0.0000494 (0.00106) (0.00113) (0.000893) (0.000722)

High school 0.0796∗∗∗ 0.0479∗ -0.0442∗∗ (0.0264) (0.0264) (0.0214)

Short higher education -0.118∗∗ -0.0784∗ -0.0343 (0.0456) (0.0401) (0.0374)

Long higher education 0.170∗ 0.189∗∗ 0.0895 (0.0863) (0.0757) (0.0669)

Education unknown 0.301∗∗∗ 0.157 0.0851 (0.105) (0.101) (0.0815)

Oil and gas 0.00204∗∗∗ 0.00237∗∗∗ (0.000473) (0.000593)

Other industry 0.0000737 -0.000107 (0.000160) (0.000139)

Scientiﬁc activity -0.000541 -0.000817∗∗∗ (0.000341) (0.000262)

Primary industry 0.0000606 0.000409∗∗ (0.000193) (0.000191)

Business unknown 0.0254∗∗∗ 0.0157∗∗∗ (0.00334) (0.00237)

Northern Norway -0.0107∗∗∗

81 (0.00205)

Western Norway -0.00152 (0.00176)

Southern Norway 0.0115∗∗∗ (0.00195)

Eastern Norway 0.00197 (0.00171) Year Fixed Eﬀects Yes Yes Yes Yes Yes Yes Observations 1780 1780 1780 1780 1780 1780 Adjusted R2 0.225 0.806 0.806 0.826 0.862 0.906

Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

82 B.2.4 Results without the ﬁve regions with most patents

Table B.8: OLS regression with Gini as the dependent variable and different specifications of the explanatory variable. First alone, then with year fixed effects. Without the 5 regions with most patents. (1) (2) (3) (4) (5) (6) (7) (8) Patents/thousand inhabitants 0.0904∗∗∗ 0.0915∗∗∗ (0.0144) (0.0149)

Patent applications/thousand inhabitants 0.0798∗∗∗ 0.0808∗∗∗ (0.0124) (0.0126)

Patents (divided by 1000) 2.490∗∗∗ 2.512∗∗∗ (0.322) (0.330)

∗∗∗ ∗∗∗ 83 Patent applications (divided by 1000) 1.903 1.909 (0.234) (0.238) Year Fixed Effects No Yes No Yes No Yes No Yes Observations 1764 1764 1764 1764 1764 1764 1764 1764 Adjusted R2 0.130 0.165 0.153 0.191 0.242 0.278 0.259 0.296 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 Table B.9: OLS regression with Gini as the dependent variable, year fixed effects and controls. Without the 5 regions with most patents.

(1) (2) (3) (4) (5) (6) Patents/thousand inhab. 0.0915∗∗∗ 0.0266∗∗∗ 0.0255∗∗∗ 0.0209∗∗∗ 0.0153∗∗∗ 0.00963∗ (0.0149) (0.00712) (0.00704) (0.00606) (0.00503) (0.00518)

Log mean income 0.220∗∗∗ 0.230∗∗∗ 0.225∗∗∗ 0.220∗∗∗ 0.228∗∗∗ (0.0157) (0.0180) (0.0177) (0.0226) (0.0243)

Log inhabitants -0.00116 0.000778 0.00120 0.000294 (0.00126) (0.00123) (0.000975) (0.000749)

High school 0.0890∗∗∗ 0.0576∗∗ -0.0327 (0.0259) (0.0272) (0.0226)

Short higher education -0.0641 -0.0576 0.00538 (0.0490) (0.0446) (0.0406)

Long higher education -0.00954 0.0738 -0.0469 (0.101) (0.102) (0.0801)

Education unknown 0.295∗∗ 0.168 0.0862 (0.144) (0.143) (0.112)

Oil and gas 0.00217∗∗∗ 0.00264∗∗∗ (0.000740) (0.000807)

Other industry 0.0000492 -0.000105 (0.000168) (0.000148)

Scientiﬁc activity -0.000458 -0.000918∗∗∗ (0.000421) (0.000328)

Primary industry 0.0000312 0.000427∗ (0.000210) (0.000216)

Business unknown 0.0258∗∗∗ 0.0150∗∗∗ (0.00362) (0.00246)

Northern Norway -0.0104∗∗∗

84 (0.00210)

Western Norway -0.00271 (0.00183)

Southern Norway 0.0112∗∗∗ (0.00197)

Eastern Norway 0.00254 (0.00183) Year Fixed Eﬀects Yes Yes Yes Yes Yes Yes Observations 1764 1764 1764 1764 1764 1764 Adjusted R2 0.165 0.668 0.670 0.705 0.753 0.828

Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

Table B.10: Panel data with Gini or log mean income as the dependent variable, patents/thousand inhabitant and patents as explanatory variable, year fixed effects and region fixed effects. Without the 5 regions with most patents. (1) (2) (3) (4) Gini Log mean income Gini Log mean income Patents/thousand inhabitants -0.000369 0.00843 (0.00275) (0.00871)

Patents (divided by 1000) 0.114 0.350 (0.0727) (0.233) Region Fixed Eﬀects Yes Yes Yes Yes

Year Fixed Eﬀects Yes Yes Yes Yes Observations 1764 1764 1764 1764 Adjusted R2 0.886 0.998 0.887 0.998 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

85 Table B.11: Granger causality test without the 5 regions with most patents, with log mean income as control variable (1) (2) Gini Patents/thousand inhabitant L.Gini 0.746∗∗∗ -0.256 (0.0651) (0.206)

L.Patents/thousand inhabitant 0.00551∗∗ 0.205∗∗∗ (0.00220) (0.0614)

Log mean income 0.0651∗∗∗ 0.108 (0.0170) (0.122) Year Fixed Eﬀects Yes Yes Observations 1680 1680 Adjusted R2 0.880 0.347 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

86 B.2.5 Results with four time periods

Table B.12: OLS with Gini as the dependent variable and year ﬁxed eﬀects for 4 time periods. (1) (2) (3) (4) (5) 1995-1998 1999-2003 2004-2009 2004-2009, without 2005 2010-2015 Patents/thousand inhabitants 0.0254 0.0215∗∗ 0.0168∗ 0.0244∗∗∗ 0.0383∗∗∗ (0.0184) (0.00884) (0.00953) (0.00735) (0.0100)

Log mean income 0.208∗∗∗ 0.216∗∗∗ 0.263∗∗∗ 0.252∗∗∗ 0.265∗∗∗ (0.0139) (0.0127) (0.0121) (0.0118) (0.0124) Year Fixed Effects Yes Yes Yes Yes Yes Observations 356 445 534 445 534 Adjusted R2 0.770 0.808 0.805 0.835 0.814 Clustered standard errors in parentheses 87 ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 Table B.13: OLS with Gini as the dependent variable and year fixed effects for 4 time periods. Without the 5 regions with most patents. (1) (2) (3) (4) (5) 1995-1998 1999-2003 2004-2009, with 2005 2004-2009, without 2005 2010-2015 Patents/thousand inhabitants 0.0256 0.0204∗∗ 0.0177∗ 0.0260∗∗∗ 0.0412∗∗∗ (0.0189) (0.00810) (0.00973) (0.00692) (0.0101) 88 Log mean income 0.187∗∗∗ 0.195∗∗∗ 0.250∗∗∗ 0.230∗∗∗ 0.243∗∗∗ (0.0169) (0.0157) (0.0194) (0.0165) (0.0177) Year Fixed Effects Yes Yes Yes Yes Yes Observations 336 420 504 420 504 Adjusted R2 0.648 0.682 0.678 0.707 0.664 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01 B.2.6 Additional results with NIBR regions

Table B.14: Regression with the NIBR regions without Oslo, Stavanger/Sandnes, Trond- heim and Bergen (Bærum/Asker is included in Oslo). (1) (2) (3) (4) Patents per inhabitant 5.648∗∗∗ 0.131 0.794 0.0188 (1.373) (0.327) (0.536) (0.230)

Log mean income 0.217∗∗∗ 0.236∗∗∗ (0.0182) (0.0342) Year Fixed Eﬀects Yes Yes Yes Yes

Region Fixed Eﬀects No Yes No Yes Observations 3205 3205 3205 3205 Adjusted R2 0.069 0.773 0.519 0.839 Clustered standard errors in parentheses ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01