Promoting Entrepreneurship and Innovation: The Institutional Framework

Abstract

In the previous chapters, we have consistently emphasized the importance of entrepreneurship for innovation, renewal, growth, and job creation. However, these beneficial forces do not automatically reflect the individual entrepreneur’s aims. Even if factors such as social recognition and testing one’s ideas influence the desire to become an entrepreneur, the pursuit of profit plays a part that cannot be ignored. When entrepreneurs search for and attempt to create entrepreneurial rents, they are largely governed by the incentives—the reward structure—that prevail in the environment in which they pursue their entrepreneurship. These incentives are essentially determined by the institutional setup of the economic system, which is sometimes called “the rules of the game.” Good institutions or favorable rules are prerequisites for encouraging innovation and entrepreneurship and for channeling entrepreneurial effort towards socially productive venturing.

In the previous chapters, we have consistently emphasized the importance of entrepreneurship for innovation, renewal, growth, and job creation. However, these beneficial forces do not automatically reflect the individual entrepreneur’s aims. Even if factors such as social recognition and testing one’s ideas influence the desire to become an entrepreneur, the pursuit of profit plays a part that cannot be ignored. When entrepreneurs search for and attempt to create entrepreneurial rents, they are largely governed by the incentives—the reward structure—that prevail in the environment in which they pursue their entrepreneurship. These incentives are essentially determined by the institutional setup of the economic system, which is sometimes called “the rules of the game.” Good institutions or favorable rules are prerequisites for encouraging innovation and entrepreneurship and for channeling entrepreneurial effort towards socially productive venturing.

In this chapter, we will identify and analyze the most important institutions and contextual factors involved when discussing the design of policies to promote innovation and productive entrepreneurship in both new and incumbent firms.

3.1 The Rule of Law and Protection of Property Rights

A market economy, based on voluntary transactions and decentralized decision-making with the entrepreneur as its primus motor, works best in systems with well-functioning institutions in which political freedom thrives. The entrepreneur collects and exploits decentralized information; society’s institutions in the broadest sense govern accessibility and opportunities to assimilate, develop, and exploit knowledge. Studies building further on Nobel Laureate Douglass North’s research (North 1987, 1990) also find strong evidence for the notion that certain basic institutional arrangements are vital to economic growth, of which the rule of law—de facto, not only de jure—and well-defined and secure property rights seem to be the most important.¹ It is also crucial that the state treats citizens equally and impartially, and does not engage in “clientelism,” that is, favoring particular groups. The latter easily leads to corruption, which greatly distorts the driving forces of entrepreneurship (Rothstein 2011).

The lack of well-defined property rights may partly explain weak economic growth in the world’s poorest countries (de Soto 2000). When properties cannot be mortgaged because they were built without a permit or a legal title, the impact on investment is palpable. Insecure and poorly defined property rights thus undermine the use of assets in prosperity-enhancing productive activities. Kleptocracy can then become a significant element of government activities. In a nation governed by the rule of law, fewer resources are wasted on conflicts because both citizens and authorities are subject to the law, and the law in turn is rooted in public consciousness. The state is impartial, and enforcement of the law—as well as the imposition of sanctions for violations—is guaranteed by independent courts (Bingham 2011).²

Private ownership is thus central to productive entrepreneurship. By this we mean something more than simply the right to dispose of an asset and to compensation in the event of expropriation. Also crucial are the right to exploit and develop the asset, the right to the return that the asset can generate, the right to transfer all or part of these rights through sale, gift, or rent, and protection against infringements by the government or individuals.

Particularly important in a knowledge-based and innovation-driven economy are the right to ideas and a framework that enables individuals and businesses to transform ideas into new and growing firms. Examples include intellectual property law and patent law. If the protection of property rights is strong, investors can count on retaining the profits they expect from entrepreneurial activities. If legal certainty is high and the legal system is credible, it will be much safer for them to engage in long-term, often risky, projects, as the basic “rules of the game” can be expected to remain stable.

Likewise, risks are lower when entering into agreements and performing transactions with other parties. In a society governed by law with well-defined property rights, there is more room for division of labor. The opportunities are then greatly enhanced for individuals and organizations to acquire specialized skills and form combinations or trade to take advantage of the skills and capabilities of others in addition to their own. Such an environment provides favorable opportunities for entrepreneurs to exploit their ideas by gaining access to external capital and the necessary skills through contracts.

3.2 National Innovation Systems

In the debate and research on innovations and innovation-promoting institutions, the so-called national innovation systems occupy a central place. A common definition of such a system is the following (Metcalfe 1995, p. 38):

…that set of distinct institutions which jointly and individually contribute to the development and diffusion of new technologies and which provides the framework within which governments form and implement policies to influence the innovation process. As such it is a system of interconnected institutions to create, store and transfer the knowledge, skills and artefacts which define new technologies. The element of nationality follows not only from the domain of technology policy but from elements of shared language and culture which bind the system together, and from the national focus of other policies, laws and regulations which condition the innovative environment.

Similar definitions are suggested by other leading scholars in the field. For example, Patel and Pavitt (1994, p. 12) define a national innovation system as

the national institutions, their incentive structures and their competencies, that determine the rate and direction of technological learning (or the volume and composition of change generating activities) in a country.

Innovation systems that are organized appropriately have the potential to serve as powerful engines of progress. On the other hand, if poorly organized and fragmented, they may seriously inhibit the process of innovation (Freeman 1987). Metcalfe (2022) interprets innovation systems as a way of organizing labor across universities and industries while testing facilities in search of innovations. As Freeman does, he stresses the need for connectivity, but also structure, between these actors to attain the objectives of new knowledge and innovative outcomes.

Volumes of research and numerous inquiries have also been conducted exploring how such systems should be designed.³ In an overview of research on innovation systems, however, Carlsson (2007) concludes that most of this activity concerns inventions rather than innovation, and a mere two to three percent of the studies Carlsson identifies involve entrepreneurship.⁴ Policy issues are addressed in about a quarter of these works, but they are almost exclusively focused on technology policy, i.e., technological infrastructure, R&D, patents, private versus public R&D, and collaborations between public and private actors. Incentive structures, which vary between actors in an innovations system (e.g., between research institutes and in firms), are entirely overlooked and less than one percent of them concern the issue of finance.

Even more interesting is the fact that, in the extensive literature on innovation systems, the outcome of these initiatives is almost completely overlooked; less than three percent of the research into innovation systems examines success criteria (productivity, growth, innovation, patents, start-ups, etc.). From our perspective, the fact that the innovation system approach attempts to identify and analyze the mechanisms that cause existing and new knowledge to be transformed into economic growth, while it only to a minor extent includes incentive structures, is problematic in terms of its normative power. This shortcoming makes it practically impossible to analyze the incentives required for various actors and functions to work together to create the most value.

A good example of how an overly partial innovation system analysis can easily lead to erroneous conclusions is the introduction of soft loans for start-ups to stimulate innovation and entrepreneurship. Such loans are often a central component of a country’s innovation policy, even though they normally have a dubious or even counterproductive effect (Lerner 2009; Dvoulety 2017; Svensson 2017). The reason is that the underlying analysis used to justify such a measure does not take into account the specific characteristics of entrepreneurial activity (we will return to these below). Without a detail-oriented and competent provider, there are obvious risks that systems in which one main element is soft government loans will develop into a system of subsidies, where various pressure groups (regional bodies, industry representatives, interest groups) compete against each other around the pork barrel. The result is often less than optimal, despite the good intentions. We have observed this phenomenon in the Swedish innovation system. There are numerous actors (e.g., Vinnova, ALMI, Industrifonden, the AP funds, and several regional funds) at the regional and national level which all provide loans, credit guarantees and investments in sizable amounts earmarked for the promotion of new firms and innovations.⁵ Their assessed impact seems ambiguous. We need to better comprehend how these bodies interact, compete, and overlap as well as what overall impact they achieve.

It is thus unclear whether and how innovation systems, traditionally defined, lead to more innovation and higher growth. As Fig. 3.1 shows, it is also difficult to establish a simple positive macro-level relationship between aggregate innovation measures (here measured by the EU innovation index) and the economic growth rate. This observation is in line with Akcigit and Nicholas’s (2019, p. 623) observation: “Yet it is perhaps surprising how difficult it has been to establish a robust empirical link between innovation and growth. To our knowledge, no paper has actually shown that innovation is related to U.S. economic growth over the long run.”

Fig. 3.1

The EU innovation index and annual GDP growth per capita, 2014–2021, all EU member countries. Source: European Commission (2021), OECD Statistics, and Braunerhjelm (2011).

For us, this suggests that the innovation system approach does not focus sufficiently on incentives and drivers for innovation and entrepreneurship. Hence, even though the literature on national innovation systems is purportedly influenced by the Schumpeterian tradition, the entrepreneur remains largely absent therein.

3.3 Public Support for R&D

Support for firm R&D is not considered to be in breach of EU state aid rules, despite the fact that the EC Treaty prohibits general state aid for activities which, according to the Treaty, must be subject to free competition.⁶ This is justified by the fact that R&D is considered central to achieving economic growth, and that there is reason to believe that the social return on R&D investment is greater than the private return. As long as support is non-discriminatory and potentially available for all firms, it is therefore considered compatible with state aid rules.

As shown in Table 3.1, there is significant public support for R&D in wealthy countries. This support can manifest in both direct and indirect form:

• Direct support for R&D activities engages targeted firms directly. A classic example is the 1960s U.S. lunar landing project, which in its final phase employed some 400,000 people, most of them in private firms. All OECD countries today use direct support.

• Indirect support is intended to stimulate the development of knowledge in firms in general. The individual firm determines the purpose of its R&D efforts with no intervention by the state or its agencies. Indirect subsidies are designed as tax incentives, either through the granting of a deduction that exceeds the actual expenditure (Sweden had such a system in 1973–83) or as a tax rebate such as a reduced payroll tax on the salaries of R&D personnel (which Sweden has had since 2014).⁷ However, indirect support presupposes that the expenditure conforms to the R&D requirements set by the authorities, which in itself can affect its composition.

Table 3.1 Business enterprise R&D support (BERD) as a share of GDP in 27 countries 2020 (%)

An overwhelming majority of OECD countries use indirect support for R&D in the form of tax incentives.⁸ Of the 27 countries in Table 3.1, Sweden had the eighth most extensive direct subsidies as a share of GDP, but due to its low level of indirect subsidies, Sweden is well below the median for total R&D subsidies. At the same time, Sweden’s business sector R&D as a share of GDP is the fourth highest in the world after South Korea, the United States, and Japan, which means that state support constitutes a relatively small part of the business sector’s total R&D (just over five percent).

Hovdan et al. (2023) concluded in a recent survey article that both indirect and direct support to private R&D have positive input and output effects. According to the authors, the effect is somewhat larger for indirect support than for grants, which is confirmed in an econometric study for Belgium. However, they also stress that public support invariably leads to partial crowding-out as firms substitute state aid for their own R&D expenditures, thereby imposing a welfare loss on society as such. In addition, they argue that knowledge regarding how different policy instruments interact is necessary to avoid suboptimal policy designs.

In recent decades, a new kind of tax incentive has attracted increasing interest—the so-called patent boxes (alternatively called innovation boxes to underscore that these are not contingent upon granted or applied patents). This means that profits generated by patents, intellectual assets, or intellectual property rights are taxed at a considerably lower rate than corporate profits in general.

There exists no definitive evidence regarding the effect of patent boxes on innovation or economic outcomes. The effect, whether positive or negative, hinges on the design of the scheme. It was first used by some European countries as far back as the 1970s and has spread rapidly to several other countries in recent years. At present, 13 European countries, in addition to several regions, have adopted some form of patent boxes.⁹ Their popularity seems to have levelled out after the OECD linked it to the Base Erosion and Profit Shifting (BEPS) initiative and imposed several restrictions setting limits on the level of intellectual property income eligible for preferential taxation.

Interpretations of the empirical evidence of how patent boxes affect location, innovation outcomes, and R&D investments are mixed. For instance, Gaessler et al. (2021) and Miller and Pope (2015) find negative effects, while Mohnen et al. (2017) and Bradley et al. (2021) see positive effects. Obviously, one must acknowledge the vast methodological difficulties in assessing the effects of R&D support (OECD 2011).

Overall, we argue that there are several reasons why governments should be restrictive in supporting private R&D. First, there is always the risk that government support will displace private expenditure, i.e., some of the government-financed spending would have occurred in any event. Second, in addition to crowding-out effects, R&D support, in particular tax incentives, always creates distortions in how resources are allocated. These can be costly since:

• (i) We can expect firms to become increasingly skilled at defining expenditure as R&D investment eligible for subsidies, which increases costs.¹⁰

• (ii) Those industries that are R&D-intensive benefit at the expense of other industries. More specifically, many service industries will be disadvantaged, even though these industries are central to wealth and job creation.¹¹

• (iii) The tax revenue that the government foregoes through these incentives must be considered in the total assessment.¹²

The government can use additional policy measures to promote innovation, one of these is public procurement. In Sweden, this amounted to SEK 819 billion in 2020 (for the state, municipality, and county councils in total), which was more than one-sixth of GDP (Swedish Competition Authority 2020). Given its size, and the fact that public procurement can be an important part of the innovation process, there is a good reason to consider how innovation can be integrated into the procurement process and how smaller businesses can be involved.

The United States pioneered a system targeting smaller firms—Small Business Innovation Research, SBIR—in which innovation-procuring agencies were obligated to allocate a certain proportion of their funds to smaller firms. Other countries, including the Netherlands and the United Kingdom, have introduced similar systems. This model is based on a two-step procedure, where initial public financing is aimed at several potential innovators (firms), and the one that succeeds in producing the most promising prototype receives significantly more financing in the second step. In the United States, evaluation of the SBIR policy points to positive results for firm R&D, R&D collaborations, firm growth, and start-ups.¹³

More generally, public procurement to stimulate innovations is still unevenly adopted across countries and the outcomes at the country level have not yet been analyzed nor understood in depth. Uyarra et al. (2023) argue that in order to become an efficient instrument in enhancing innovation, procurement initiatives need to be integrated into a national and sectoral context and aligned with other policy initiatives.

3.4 National Systems of Entrepreneurship/Entrepreneurial Ecosystems

The essence of national systems of entrepreneurship (NSE) is defined by Acs et al. (2014, p. 479) as

the dynamic, institutionally embedded interaction between entrepreneurial attitudes, activities, and aspirations, by individuals, which drives the allocation of resources through the creation and operation of new ventures.

The insight that a more efficient allocation of resources, and the concomitant restructuring of an economy, are driven by entrepreneurs is profound and important. It clearly alludes to Schumpeter’s view on the role of entrepreneurship and creative destruction but adds rigor and stringency.

Thus, the NSE aims at integrating the country-specific context in which entrepreneurs (and potential entrepreneurs) are embedded with decisions taken by individuals identifying an entrepreneurial opportunity, where the costs and benefits of such decisions are considered (Acs et al. 2016). The NSE is clearly associated with previous research focusing on the existence of entrepreneurial opportunities (Acs et al. 2009). However, it differs from previous research by stressing reallocation and structural change at a more aggregate level. This is a definitive step forward in understanding the dynamics of innovation.

In the NSE framework, bottlenecks, or deficiencies, are identified through cross-country comparisons. Even though lessons can be learned from other countries, interdependencies and links across policy areas and other institutional characteristics tend to center on a certain country-specific context. As Acs et al. (2016) note, identifying these relationships and understanding their implications should guide the design of an NSE. Obviously, such interactions consist of a complex web, rendering it an intricate and context-dependent task to design the appropriate policy measures required to strengthen the NSE (Autio and Levie 2015).

Another related and largely overlapping concept under which one can frame the relationship between the individual opportunity and the context within which it operates concerns the entrepreneurial ecosystem approach.¹⁴ According to Stam and Van de Ven (2021), this entails a multitude of diverse and interrelated organizations and institutions that co-exist and co-evolve. The actors either compete or cooperate, depending on their specific characteristics and the circumstances under which they operate. In a well-designed ecosystem, these organizations, and the ways in which they interact, are claimed to contribute to a dynamic but complex economic environment. In such ecosystems, the different roles played by agencies and institutions are emphasized. Stam and Van de Ven also stress how endowments and access to resources are dependent on both the formal and informal institutional setup.

While these modified system versions offer valuable insights and add dynamic elements lacking in previous versions, we would argue that critically important variables that determine individual behavior, and the outcome at the aggregate level, are absent. In particular, countries are characterized by their specific formal and informal prerequisites and bottlenecks, generating direct and indirect effects, which should be analyzed within their specific context (Audretsch 2015). One of a policymaker’s most powerful instruments, taxes, will be discussed in Chap. 5 as one example. In addition, some of the institutional variables used to determine the functioning of an NSE, such as technology absorption, gender equality, R&D spending, and depth of capital markets, seem to be outcomes resulting from the structure of the NSE rather than institutional determinants (Braunerhjelm and Henrekson 2016, p. 101).

We argue that a detailed analysis of each individual economy is required to design appropriate policies. Moreover, importing “best practices” from other countries and making them work under local circumstances is a challenging task, although lessons can certainly be learned from them. This is also highlighted in the critique of national innovation systems (NIS) by NSE proponents. For instance, Autio et al. (2014) argue that the NIS literature studies entrepreneurial activities and performance based on the implicit assumption that observed differences are the outcome of institutional influences operating in the same way across countries. However, in fact, they feature a range of different formal and informal institutional settings, different cultures, norms and values, and attitudes towards entrepreneurship that affect entrepreneurial performance. Hence, even though the NSE approach provides valuable insights on how countries deviate in terms of entrepreneurial effort and orientation from a cross-country average measure taking institutions into account, an appropriate policy design requires additional disaggregation and attention to detail.

3.5 Growth at the Firm Level: The Collaborative Innovation Bloc

As we have already noted, it is not enough to create new knowledge, as much new knowledge is not in itself economically valuable. The economy therefore needs “knowledge filters” that distinguish economically relevant knowledge and convert it into economic activity (Carlsson et al. 2009). Moreover, the entrepreneurial process that causes the market order to evolve is inherently collaborative: To pursue their innovative projects, entrepreneurs engage in cooperation with a number of actors, whose complementary skills greatly increase the probability that an innovation-based venture will be successful. The actors are drawn from several skill pools, which together form what we call the collaborative innovation bloc. Thus, we recognize that entrepreneurship is crucial, but other actors are as well: early-stage financiers, key personnel, inventors, knowledgeable and demanding customers, and later-stage financiers. Successful entrepreneurship that generates rapid growth is a function of how well these actors acquire and apply their skills. The opportunities and impetus to do this are largely determined by the institutional framework, or what in everyday speech we call “the rules of the game.”

Figure 3.2 schematically captures the phases in which various actors enter the innovation and commercialization process. In the initial stage, the entrepreneur identifies potential profit opportunities; knowledgeable customers often play an important part in this process. Inventors are engaged to solve technical problems, but as the business grows, innovators and key personnel, especially experienced managers but also R&D specialists, are needed to lead more comprehensive development projects. Sometimes the process can be initiated by inventors, whose ideas are then further developed by innovators and entrepreneurs.

Fig. 3.2

The collaborative innovation bloc. Source: Elert and Henrekson (2019, 2020)

The early commercialization phase mainly involves entrepreneurs (possibly also inventors) and, to a lesser extent, other types of skilled labor. In the scale-up phase, professional managers, salespeople, and R&D specialists are activated and skilled labor is then essential. Founders, family and friends, business angels, and venture capital firms finance development in the early stages, while actors in the secondary market, later-stage financiers, enter the picture later. Figure 3.2 is obviously a simplification—for example, experienced managers and later-stage financiers can be involved much earlier, and different actors can work in parallel with each other, overlapping or lagging each other in different phases. The same person can sometimes fulfill more than one function, for example that of both an entrepreneur and a manager of the firm when it reaches a more mature stage.¹⁵

Later-stage financiers potentially fall under a number of different categories: wealthy individuals/families, closed-end investment funds, stock-market activists, institutional investors, buyout firms, stock-picking individual savers, and competitors aiming to take over the firm’s operations through a so-called trade sale. Later-stage financiers have similar skills and carry out similar functions as venture capitalists, in terms of financing and the transmission of knowledge and skills, but this selection occurs at a later stage when entrepreneurs and venture capitalists wish to exit their investments. Hence, these actors evaluate firm performance and assess whether there are potential profits in assuming control, replacing the entrepreneur and top management in the event of sustained inferior performance. An important distinction among later-stage financiers is between those who take an active part in a company in which they invest or wholly control its governance, and passive investors, such as pension funds and open-ended stock-market funds as well as physical persons who own listed shares directly.

A trade sale—selling the firm to another one, usually a firm in the same industry—is arguably the most common way of exiting. In this case, full control over the firm is handed over to the buyer, and the entrepreneur/founder leaves the business with substantial financial assets. These assets make it possible to start new firms or act as a business angel or venture capitalist. A trade sale is likely an indication that some crucial skill is lacking in the firm in its existing form, making an independent scale-up of its operations unfeasible.

Consumers are the ultimate arbiters of an innovation’s success (as such, they make the final selection), yet they seldom appear in studies of innovation. The omission is regrettable—the willingness and ability of individual consumers to dare to purchase and effectively use new products, and the openness of intermediate producers to new know-how and products, may be crucial drivers of innovation. Usually, the role of the alert, competent, and interested customer is essential to the supply of innovative products. Especially in the early stages, demanding collaborators function as particularly important sources of information regarding consumer needs and preferences, provided that they are representative of a large group of customers. Sometimes they even act as strategic partners who take an active part in the development and commercialization of products, thus having a decisive influence on the development and design of new products (Bhidé 2008).

In Fig. 3.3, we present a more detailed version of the collaborative innovation bloc. Here we observe the vital interplay between final beneficiaries and actors in the early- and later-stage markets of financing, as well as the main categories of key personnel and customers. For an innovation to have a high probability of reaching its full potential, the collaborative innovation bloc must acquire sufficient size and depth to reach critical mass, i.e., it must have sufficiently large pools of each skill from which actors can be recruited to fulfill each function in the collaborative team. A lack of requisite skills or an important actor category may significantly impede or even prevent collaborations from taking place.

Fig. 3.3

The collaborative innovation bloc—a detailed overview. Source: Elert et al. (2019)

As we have stated, part of what it means to be an entrepreneur is to be able to gather skills and productively combine them. This is where economic policy and the institutional framework underpinning the innovation bloc come into play. Whether an innovation bloc can emerge spontaneously, because of the actions of entrepreneurs and other actors, depends on conditions faced by the actors who could potentially comprise the collaborative innovation bloc. Some institutions, such as the rule of law and the protection of private property rights, may be relevant for all actors in the innovation bloc, while others are more specific, e.g., the removal of bottlenecks that hinder the emergence of a sufficient mass and variety of one or several skills in the structure.

3.6 Financing Expansion

During its life cycle, a business is dependent on different sources of finance. In Fig. 3.4, the main phases of a firm’s development are described schematically. Above all, the figure highlights how best to resolve the specific incentive problems that exist when the entrepreneur/founder lacks the means to finance the company’s development alone. Silicon Valley is the region that has come the furthest in developing contracts and formal and informal institutions to deal with these incentive problems (Ohlsson 2019). In the following, we will therefore draw some inspiration from this area.

Fig. 3.4

Central phases in the evolution of an entrepreneurial firm. Source: Henrekson and Sanandaji (2016)

For a start-up firm based on a unique and untested idea, the risks are apparent and considerable. Even in cases where the business is a success, some time usually elapses before its products reach the market, and even more to achieve a positive cash flow.

The shared risk is seldom calculable, either by the founders or by external parties, and is thus a matter of genuine uncertainty. This is a common and particularly important problem in entrepreneurial venturing. When investing in public firms, the investor often uses historical experience as a basis for an assessment of expected outcomes. When investing in a new, innovative firm, however, the investor cannot possibly predict the outcome—not even its probability distribution—before the product is fully developed and introduced in the marketplace. Before the product exists in physical form, it is impossible to predict what technical problems will arise, or whether a market even exists. The return on investment in start-ups has an exceptionally high variance, with a very high risk that the entire investment will be lost.¹⁶

The fundamental difficulty in creating the right incentives is that it is impossible to establish agreements that cover all eventualities. When the parties cannot draft contracts that are detailed enough to cover all possible outcomes, it is important that ownership and control in different situations are allocated in advance between those involved. Innovative entrepreneurship is an activity where the uncertainty is particularly great, where the value of assets is relationship-specific, and where parties with widely differing interests must cooperate. Therefore, the need is particularly great for contracts in which ownership and control are conditioned on unpredictable future outcomes.

Due to transaction costs and non-calculable risks, equity financing is often necessary. Debt financing is problematic, as firms have neither fixed assets to pledge nor a cash flow to borrow against. This means that banks have little interest in financing risky entrepreneurial firms. The problem of asymmetric information about a firm’s potential, and the risk of excessive optimism among its entrepreneurs, also make loan financing more difficult. At the same time, few founders have enough capital to finance the company themselves up to the point where the cash flow is positive or the level of uncertainty has diminished sufficiently to enable loan financing. Many new firms may therefore disappear prematurely due to a lack of capital for development and expansion. One attempt to solve this problem can be soft loans from public bodies.

However, providing access to soft financing, including public risk capital that spurs entrepreneurial success, is a difficult task and the outcome is often disappointing.¹⁷ Lerner (2020) claims that such policies may be successful if conditioned on factors such as independent governance structures, matching funds, and a careful evaluation of effects. Such elaborate policy measures are rare. One reason is that politicians may be tempted, for political reasons, to set up agencies on a regional and industry basis with the authority to issue such loans—something that entails a large number of different and unmanageable conditions, often without properly considered long-term plans.¹⁸ The most successful international experience to learn from may be that of Israel.¹⁹

Those external financiers who are usually most suitable for providing equity for the first seed phase are the so-called angel investors. These are wealthy individuals with their own experience as entrepreneurs or business leaders, and who have the time, commitment, and capital to invest in promising new business ideas. Through the angel’s own network, the company often also has access to additional capital and expertise.²⁰

In the next phase of a company’s development, there is more information about the viability of the business concept, which lowers the level of uncertainty. In this situation, the company also becomes interesting to those providing venture capital (VC). Like angel investors, VC firms are not only passive financiers, but also contribute to the development of new businesses and the commercialization of their ideas.²¹

3.6.1 Stock Options Help to Build Firms

The problem of asymmetric information is best remedied by the investor entering as a partner in the firm and thus gaining more insight into its workings. Otherwise, the investor will initially be reluctant to invest a large amount, as the information asymmetry cannot be significantly reduced until they have become a partner. Over time, however, uncertainty about the firm’s technical and commercial potential decreases, as experience is gained and more information becomes available. Agreements have therefore been developed in the market where external investors pay out financial support in several rounds (staged financing), so that there is enough, but just enough, funding available for the business to reach a certain milestone in its development. This creates many opportunities to evaluate the results at each stage—as well as to exit the investment if the firm’s performance fails to meet investor expectations.

Even if external financiers contribute a number of key competencies, the entrepreneur or founder is normally crucial to the firm’s development for several years. Still, of course, the business can arrive at a point at which it would develop better under new management, for example if the founder’s strength is in the start-up phase itself but he or she is less suited to leading a growing firm. External financiers will want a substantial ownership share in order to receive a substantial part of the value they expect to be created, but the ownership share should not be so large that the entrepreneur has insufficient incentive to contribute their unique expertise. At the same time, external investors will want an opportunity to replace the founder and/or close down the firm to minimize losses if predictions about its success are not sufficiently positive.

Normally, neither the entrepreneur nor his or her close associates have the skills or financial resources required to cope with the more capital-intensive development phase. External financiers must therefore quickly contribute a large amount of equity relative to what the founder can invest. This means the founder loses control of his or her firm, which weakens their incentives to contribute further to its development. Thus arises a dilemma.

The solution, which began to be used with great success in the United States in the 1980s, is stock options.²² External investors take control of the firm, but the founder (and other key employees) receives inexpensive stock options that guarantee they will regain substantial ownership in the future, provided that a number of stipulated “milestones” are attained. Many firms offer options with a low exercise price, similar to (free) shares. Such agreements are usually also designed with “vesting,” i.e., the purchaser may only buy shares if he or she remains in the firm and continues to contribute expertise.

The option instrument is therefore an elegant way of giving the founder and other key employees with limited or zero personal wealth a share in the future value of the firm, the creation of which presupposes their participation. A well-designed options program makes the founder/entrepreneur behave as if she herself were still the owner of the project. In practice, it is difficult to harmonize the interests of the founder and the firm by using stock options, but it is often possible to achieve a much better correspondence between the interests of the two parties than before. This solution also inhibits wage claims from those who receive the options.

3.6.2 Exit Routes

When an entrepreneurial firm’s cash flow becomes more stable and predictable, the financial risk also becomes increasingly calculable. Then it is possible to make forecasts of future growth opportunities and profitability. Once the firm is on stable ground, it is time for early-stage financiers to relinquish their ownership role. Such an exit can be made in several ways. Provided that the firm has developed satisfactorily, founders and other key employees who have received options are now able to exchange these for common stock and become major owners in the firm.

A first exit route is an initial public offering (IPO). For this to succeed, it is usually necessary, after the listing, that there be one main owner who has the motivation and ability to take responsibility and lead the firm in the medium term. An IPO can be implemented more easily if the management, normally the founder, has been granted sufficient stock options to become a major shareholder in the firm if it becomes successful. Stock options also give the founder strong incentives to remain in the firm and contribute to its development, since, in addition to securing ownership of a large part of the value created, he or she now also has the chance to become the controlling owner of a listed company.

Two other exit strategies bear mentioning. A second route is a trade sale, which means that the entrepreneur/founder leaves the business, but with financial assets that enable them to start a new business or become an angel investor and/or venture capitalist. This is often the most profitable alternative from the acquirer’s perspective, because in addition to utilizing the assets they buy, the buyer also wants to prevent competing firms from gaining access to them (Cunningham et al. 2021; Norbäck and Persson 2009). A third possibility, if the firm is performing very well, is that the founder and other senior executives buy out the VC firm in a loan-financed management buyout (MBO), possibly in collaboration with long-term private co-financiers.

In many cases, the firm will not develop in line with the business plan, which may be due to the business idea having less potential, the competition being tougher than expected, or the management—usually the founder—not performing as expected. The VC firm can then take measures such as removing the management or closing down the business to recover as much as possible of their investment. In the United States, this is achieved by the VC firm owning preferred stock or having priority loans; management instead has common stocks, or options on them, which are usually worthless if the firm is closed down (Metrick and Yasuda 2011; Bengtsson and Sensoy 2011).

The fact that the venture capitalists by and large have the power to decide whether to close down the firm or replace the management team if certain milestones are not met entails a risk that they will behave opportunistically. However, there are a number of mechanisms and forms of agreement to prevent such opportunism (i.e., alleviate the hold-up problem; Black and Gilson 1998).²³ These contracts are complex, which reflects the fact that the market is characterized by high transaction costs and great uncertainty. Such agreements stipulate the distribution of cash flow, control over board membership and voting rights, under what conditions financiers have the right to liquidate the firm and how the remaining assets and other rights are distributed in such a scenario. The outcome is conditioned on the firm’s performance, and stock options are consistently an important component of these agreements. An additional mechanism that protects the founder(s) from being held up by the investor is that the VC firm cares not only about its reputation for competence but also as a reliable business partner.²⁴

In turn, the open control protects the founder(s) from being held up by investors since outside board members are unlikely to vote to replace the founder unless performance is truly inferior (Kaplan and Strömberg 2003). A reverse hold-up problem may also arise in cases where the firm is particularly dependent on the skill of the original founder(s). To mitigate this problem, it is common for VCs to include non-compete and vesting provisions that make it more expensive for the entrepreneur to leave the firm prematurely (Kaplan and Strömberg 2003).

3.7 Human Capital and Academic Entrepreneurship

We have established that entrepreneurs are important, that information is decentralized and fragmented, that a focus on “innovation systems” is too narrow, and that financing of entrepreneurial activities entails particular complexities. We now want to broaden the perspective to the supply of entrepreneurial skills and the incentives to develop and exercise such skills productively. For individuals to acquire valuable skills, it is important to have the right incentives at all levels. We know from the United States that truly successful entrepreneurs are usually more highly educated than average, and that their success hinges on their ability to recruit competent and exceptionally motivated people to build their firms.²⁵ Potential entrepreneurs face educational and career choices several times during their lives, not least in their youth. With the wrong incentives and unclear signals, there are many instances in which an individual risks making choices that inhibit the acquisition of the knowledge that can deliver a good financial return in a successful business.

Figure 3.5 outlines the main steps in the creation of a knowledge base that can be translated into commercial activity.

Fig. 3.5

From educational choice to knowledge-based entrepreneurship. Source: Henrekson and Rosenberg (2001)

The first strategic choice the individual encounters is in high school when he or she must decide whether to start gainful employment after graduation or to advance to college. Once she has opted for college, the next decision will be whether to pursue a degree in science and technology or the humanities and social sciences. After having received a bachelor’s degree, the graduate must choose between finding employment or further studies. If the latter is chosen, the subsequent choice is between a university career or leaving academia for work outside it.

Successful scientific or technical research-based entrepreneurial activities consequently depend on academically educated and highly motivated individuals. When university professors and researchers are actively involved in spin-offs and start-ups on campus, this phenomenon is referred to as academic entrepreneurship (Shane 2009).²⁶ In addition, there are other important sources for recruitment to scientific and/or technically based entrepreneurship, such as the pool of individuals with an academic degree as well as people with an academic background who work in other firms. Finally, as pointed out by Arora et al. (2021), the diffusion of knowledge, breakthrough innovations, and general purpose technologies (GPTs) requires close interaction between academia and industry. Such interaction is also likely to spur university spin-offs. Currently, the trend seems to be in the opposite direction, characterized by an increased division of labor between university researchers (basic research) and corporate research (applied research).²⁷

Academic entrepreneurship has increased substantially over the last decades (Caputo et al. 2022). For example, 3,376 university spin-offs were documented in the United States between 1980 and 2000, another 2,885 between 2001 and 2007, and 4,539 more between 2008 and 2014. These numbers probably underestimate the true extent of academic entrepreneurship since not all instances are disclosed to universities. Moreover, faculty members may start businesses that are not based on university intellectual property rights. This development is not confined to the United States; similar patterns can be observed in other countries (Åstebro 2016). Still, the licensing of university patents to established companies strongly dominates over spin-offs as a form of technology transfer (Åstebro et al. 2019).

U.S. universities were long seen as role models for commercializing university-based inventions and for academic entrepreneurship. The reason for their superiority was typically attributed to the Bayh–Dole Act instituted in 1980. It transferred all intellectual property rights for inventions to the university, provided that it had funded the underlying research. This reform was originally claimed to have contributed to the overall surge in innovation in the United States (Merrill and Mazza 2010; Mowery and Sampat 2005). Others were more skeptical, arguing that the Bayh–Dole Act coincided with a number of other major policy changes said to be instrumental in improving commercialization of university-based research, notably tax reforms, increased federal resources to university research, and more flexible investment policies for pension funds (Kenney and Patton 2009; Lissoni et al. 2009).

Around ten years ago, and simultaneously as the effects of the Bayh–Dole Act became increasingly questioned in the United States, several countries decided to implement a similar intellectual property rights (IPR) regime (e.g., Germany, Belgium, Denmark, Japan, Norway, Finland, and China). These changes were introduced with the aim to increase technology transfer and innovations, thereby strengthening the competitiveness of firms in the respective country. However, according to the evaluations of these reforms, the results are rather bleak. In fact, relatively recent and remarkably consistent evidence from several countries (e.g., Denmark, Germany, and Norway) indicates that implementing a Bayh–Dole system appears to have been detrimental to technology commercialization, or, at best, have had no effect at all. Scholars in Europe thus seem to have applied for patents to the same extent as their U.S. colleagues and to interact as much with industry, despite the previous “Professor’s Privilege” (where IP rights remain with the inventor in academia). Czarnitzki et al. (2015) find that the volume of university citation-weighed patents decreased by 27% (19% unweighted) in Belgium. In the most extreme case of Norway, there was an approximate 50% decline in the rate of new venture creation and patenting by university-based researchers after the reform. The quality of university start-ups and patents also appears to have declined (Hvide and Jones 2018).

Granting intellectual property rights to the university may work in the United States where universities are generally independent institutions that operate in a highly competitive environment. By contrast, in most European countries, universities are part of the government sector and less exposed to competition. This makes it far less likely that universities in Europe will do their utmost to make sure that the intellectual property rights they have been granted will be exploited commercially (Henrekson and Rosenberg 2001). Here is yet another example of the difficulties encountered when adopting institutions from other countries.

Figure 3.5 shows that several links must be present and considered sufficiently attractive for the right persons to create an environment where scientific research-based entrepreneurship can flourish:

• First, there must be incentives to invest in scientific and/or technical human capital at the university, especially at the graduate level (links 1a, 1b, 1c)

• Second, strong forces are needed that drive involvement in scientific entrepreneurship, both for university employees and for non-employees with a scientific background (2a, 2b, 2c, 2d, 2e)

• Third, incentives within the university system are required to adapt student choices and course curricula to the demands of the private sector, facilitating the transition from academia to entrepreneurship and the business world. This third factor has complex repercussions throughout the decision tree in Fig. 3.5—it directly affects the universities’ propensity to be active in entrepreneurial activities (2a) but also students’ choices regarding their own development (1b, 1c, 3).

The overarching condition is that it must be profitable to acquire productive knowledge and to use it intensively. Income taxes, pay differences, and a well-functioning service sector that enables specialization are important components. Social insurance systems are also crucial—they must not weaken incentives to change jobs but rather offer an equivalent safety net under this transfer. In addition, they should not entail a high opportunity cost in the form of lost pension rights and other amenities when one switches to an employer who offers higher expected social value creation. The tax system and social insurance system interact, and their effects need to be jointly analyzed. In Chap. 4, we will explore how this interaction manifests in practice in the Swedish case.

3.8 Dynamics in Tax-Financed Welfare Services

Innovations and entrepreneurship are essential for growth and development not only in the goods-producing sector. They play at least as important a role in the service sector, in order to develop new services, streamline organizations, and reduce costs. How service production is organized—not least how competition works in these markets—is of great importance in this respect. Regulations, procurement, and monitoring affect the impetus and opportunities for entrepreneurs to attempt new ways of doing things and to experiment and innovate in the service sector.

The leading European welfare states made a strategic choice in the twentieth century: to rely primarily on tax financing and government production of healthcare, education, and social care. The public sector was then still small, the overall tax burden was low, and these services were relatively inexpensive. Welfare services were considered too important to the cohesion of society to be kept under government control and not be subject to the vagaries of market forces. Distributional aspects and positive externalities could be invoked to justify both government production and financing, especially in education and healthcare. At the time, neither the efficiency of government production, nor whether taxes were the most appropriate form of finance, were discussed.

As Table 3.2 shows, government spending as a share of GDP on education and care services is now sizable in major OECD countries. Cross-country variation around the average of 14.3% is surprisingly small, despite sometimes sizable differences within subcategories. There is also large additional private spending on several services, notably in the United States, where total spending on education and care is well above 25% of GDP.

Table 3.2 Government spending on education and care as a percentage of GDP in selected OECD countries (latest available year)

As incomes rise, the demand for services such as education, healthcare, and social care tends to rise even faster, i.e., the income elasticity of demand is larger than one. At the same time, these services are particularly labor-intensive, and in most cases, it is not possible to reduce staff without reducing quality. Machines cannot replace people in this area as they can in manufacturing. This makes it more difficult to achieve increases in productivity, although there are exceptions (mainly due to IT and development of artificial intelligence, AI). The relative cost of welfare services therefore tends to rise, especially if citizens demand that quality be raised in tandem with real incomes. Healthcare, education, and social care are therefore becoming increasingly expensive in comparison to food, travel, or mobile phones. We have already mentioned Baumol’s Cost Disease.²⁸ However, there is some potential for rationalization due to the fact that a larger proportion of total working hours can be devoted to producing the service (teaching, patient care, and the like) if activities are organized efficiently.

In a modern knowledge society, education becomes increasingly important. Both the quality of education and the time spent pursuing it need to increase. With rising incomes, the expectations of quality in child and elderly care are also heightened, at the same time as demographic development further increases the demand for the latter. Increased incomes create a rapidly increasing demand for healthcare, while technological advances make it possible to offer increasingly more treatments and interventions for conditions that were previously untreatable. New medical breakthroughs (more powerful medicines, joint replacements, keyhole surgeries, etc.) can entail lower costs for specific procedures. At the same time, however, demand rises for these services as they become available to more people—while the demand for quality rises inexorably. The overall effect is increased costs (Smith et al. 2022).²⁹

This combination of rising costs and the fact that most Western countries have reached a point at which further tax increases are not possible presents a significant challenge. There is only one solution to this dilemma: innovation. To this end, competition needs to be allowed between government and private (non-profit and for-profit) providers by establishing the so-called quasi-markets (Le Grand 2009), where consumer choice is combined with government financing. However, in order for quasi-markets to fulfill the potential of their innovative promise, the regulatory framework must be properly designed (and continually revised). Currently, the production of welfare services is surrounded by a wide range of restrictions—on funding, procurement, quality, and governance—which severely limits the framework for what can be done. Several of these restrictions need to be recast to allow for more entrepreneurship; at the same time, competence in monitoring and governing these services requires improvement (see Chap. 4).

3.9 Urbanization, Agglomeration, and Innovation

How we live, work, and travel have a crucial, and sometimes overlooked, role among the mechanisms for disseminating knowledge. We therefore wish to conclude this chapter with a section on urbanization, agglomeration, and regional labor markets—factors that are becoming increasingly important driving forces for societies that aspire to be entrepreneurial and innovative.

Since the early 1990s, urbanization has gained momentum in Sweden and other nations in the Western world. Historically, population growth has been concentrated in the larger cities, while it has declined in many medium-sized towns. In the OECD, half of the population live in cities, while roughly one-quarter live in towns and semi-dense areas and one-quarter in rural areas (OECD 2022).³⁰ The share of the population living in cities is expected to continue to increase. In 2000, 84% of the Swedish population lived in urban areas (defined as towns with at least 200 inhabitants), which had increased to 88% in 2020. Even though population growth seems to have levelled out in some cities, notably Stockholm, at the municipality level growth continues in all the larger cities in Sweden.³¹ Most forecasts indicate that this trend will continue, even though the current shift towards a larger share of employees working remotely at least part of the week may alter previous forecasts.

The main reason for urbanization is that higher population density provides not only economic benefits but also social and living amenities. Specialization and increased division of labor is one source of prosperity and higher wages, often referred to as the urban premium. Until the early 1970s, specialization and rising prosperity were primarily driven by increased standardization and mass production, aided by technology and specialized capital equipment. In step with the growth of the service sector, digitalization, and increased specialization, production of services is becoming the most important source of job creation and development, often concentrated to cities.³²

In any event, the service sector in all developed industrial countries is today significantly larger than the goods-producing sector. At the same time, the boundaries between the various sectors have become more fluid. Physical products contain components of services to an increasing extent, while many services are now produced in industrial processes. A customer who buys a truck from Scania or Volvo, or for that matter a Tesla, not only buys a vehicle but a transport service, including repairs and software that are continually updated. Traditional service firms such as banks and insurers conduct their business using powerful computers, just as in manufacturing.

3.9.1 Agglomerations and Cluster Formation

Increased specialization in the production of services does not have to mean that an individual’s tasks become simplified or more monotonous. On the contrary, the work can instead be characterized by more collaboration, continuous skill development, and high flexibility. Densely populated environments then become attractive, partly because they provide entrepreneurial opportunity due to knowledge flows and proximity to markets and customers. They also become attractive because clusters tend to emerge in dense, agglomerated areas where companies of a similar nature can collaborate, compete, and learn from each other—and benefit from the dynamic labor market for specialists typical for such clusters.³³

There is an extensive literature on existing clusters and agglomeration forces. Normally these are categorized as either Marshallian or Jacobian types of agglomerations, related to whether agglomeration effects occur within an industry (intraindustry) or are spread across industries (interindustry). The underlying mechanisms refer to the type and extent of spillovers, which are categorized as either knowledge or pecuniary spillovers. The latter refers to different types of linkages predominantly between customers and suppliers of goods or services. Some of these issues are more peripheral to our analysis, e.g., the causes and effects of agglomeration in a broader sense.³⁴

Our main focus is how clusters and agglomerations influence innovations associated with different types of microlevel interactions and spillovers. This is a dimension of clusters and agglomeration where research is relatively scarce (Combes and Gobillon 2015). There are some exceptions, such as Carlino and Kerr (2016), who also stress the importance of pursuing more research covering this perspective. Using a spatial panel data analysis, Kosfeld and Mitze (2023) find that R&D-intensive regional clusters in Germany have contributed to higher productivity within the region. They also conclude that it is not cluster diversity that matters for productivity growth but rather cluster strength. However, they did not use specific innovation data.

The improved availability of microlevel data that allows the identification of inventors, mobility, and networks has expanded the possibilities for such research. One crucial issue that deserves increased attention concerns the resilience and longevity of cores of innovation located in clusters, not least since new clusters emerge that compete with those already in existence. In the United States, innovation centers have shifted between locations such as Austin, Boston, Detroit, and Silicon Valley. In recent decades, Tel Aviv and Bangalore have also emerged as strong innovation nodes.

Sorenson (2018) addresses this issue from a different angle. He finds that entrepreneurial activity and innovation are strongly embedded in socially and spatially bounded relationships. By combining insights from sociology, economic geography, and economics, he challenges some of the established views on the microeconomic foundations of spatial formations, knowledge diffusion, and interactions between economic agents. Instead, he emphasizes how more soft ties based on family and the individual’s established networks influence location and agglomerations.³⁵

Overall, policymakers need to be better assisted by research to understand how clusters and agglomerations develop and how policy can strengthen both agglomeration and innovation (Carlino and Kerr 2016). This is a complex task, spanning several policy areas, where there are obvious knowledge gaps. However, these knowledge gaps have not stopped governments from spending billions of dollars in their attempts to promote innovative clusters.

An attempt to provide some insight into the process of emerging clusters was provided by Braunerhjelm and Feldman (2006). They analyzed whether clusters emerge by coincidence or as a result of deliberate policy efforts, and the extent to which entrepreneurs could influence policy design. They found, that, to a great extent, these can be attributed to chance and entrepreneurial initiative, but after this initial phase, policy has generally been crucial where entrepreneurs also contributed to its design. For instance, there were initially many potential “Hollywoods.” That Hollywood became Hollywood was due not only to the climate but also to the fact that policies adapted to the film industry’s needs; the government did not reject the industry as “immoral” but rather encouraged it.

Another example is Israel, which has benefited from the particular combination of a high-quality education system, the immigration of skilled engineers from Eastern Europe, and access to American venture capital. In Israel, several attempts were made to establish a high-tech sector; the big boost came only when it was realized that this required both a knowledge platform and entrepreneurial skills, which generated a supply of ideas (deal flow) that was combined with competent venture capital. The state played a crucial role in building a venture capital market, enlisting the assistance of private investors early in this process.³⁶

Furthermore, as shown by Henrekson et al. (2021) in a study of the emergence of the Silicon Valley venture capital model and the Hollywood film industry, specialized institutions that regulate these entrepreneurial ecosystems were not designed by policymakers. Instead, they emerged through actions by business entrepreneurs. Thus, Schumpeterian entrepreneurs not only created new companies; they also created new institutions as an integral part of the restructuring process. At times, efforts by identifiable entrepreneurs were crucial, while in other instances institutional change resulted from a Hayekian process of emergence fueled by the efforts of business entrepreneurs.

In general terms, the benefits of agglomeration and dense areas can be summarized as follows:

• Innovation and entrepreneurship have been shown to be concentrated activities which predominantly take place in population-dense environments (Audretsch and Feldman 1996; Moretti 2012). There is more knowledge transfer when people interact and change jobs both within and between industries. Talented entrepreneurs tend to be drawn to metropolitan environments where there are plenty of opportunities and access to specialized labor.

• The service society tends to develop in agglomerated areas. Despite globalization, an increasing share of current consumption expenditure consists of locally produced services. These can be directly related to the consumption of goods, such as retail and transport, but also specialized services per se such as restaurants, cultural experiences, specialized care, and education services. The fact that many people live in close proximity to each other within a relatively small radius is a necessary condition for many goods and services to be in supply at all.

• Agglomeration provides a market for specialized labor. Highly specialized firms demand labor with specialized knowledge and skills. For an individual employee in a large city, it is thus less risky to invest in highly specialized knowledge, as the risk of being left at the mercy of a single employer is lower than in a small town with only a few firms.

• Economies of scale result in lower costs of production. An important difference between goods and services is that the latter can usually not be stored (although there are exceptions, such as services that can be digitized). If a hair salon lacks customers, nothing is produced, even if it is staffed. The same applies to health centers and restaurants. Producing specialized services at a reasonable cost therefore requires high-capacity utilization, which as a rule presupposes a certain population density.

However, there are also forces working in the opposite direction. The advances made in digital technologies such as artificial intelligence and advanced machine learning may lead to the demise of advantages related to physical proximity. Still, there is a long way to go before agglomerated and dense areas more generally can be replaced by more remote systems. Production costs will therefore vary with population density.³⁷

There are thus strong forces that propel agglomeration and the co-location of firms/employers and individuals/employees. Every time a new job is created in the competitive sector, more jobs are created in the local service sector. The multiplier effect stems from rising income, which generates demand for salon services, restaurant visits, medical care, and so on. Successes in the competitive sector thus create more jobs in the local service sector. Based on U.S. data, Moretti (2010) finds that every new job in the competitive sector in a city generates 1.6 new jobs in the service sector in the same city. The higher the skill level, the greater the ratio, i.e., an increase in income generates correspondingly higher and more extensive knowledge spillovers. Moretti and Thulin (2013) conducted a similar study for Sweden and found the same multiplier effect here as well.³⁸

3.10 In Sum

In this chapter, we have presented some of the necessary institutions and conditions for innovative activities to take place, such as the rule of law and the security of private property rights. We have also surveyed previous research efforts to identify institutional frameworks that are intended to govern innovations, notably national innovation systems and their “progeny,” national systems of entrepreneurship and entrepreneurial ecosystem approaches. While these system approaches give valuable insights regarding the shaping and understanding of the innovation landscape, we argue that their microeconomic foundation needs to be reinforced. Finally, we addressed a number of key areas required to promote innovation and entrepreneurship, such as the financial market, access to human capital, the organization and production of welfare services, and the importance of dense and knowledge-intensive areas. One important message from this chapter is that we should not be afraid of large, expanding cities. In the next chapter, we will focus more intently on the details in designing policies required to encourage and stimulate innovative endeavors.