The effects of inequality on growth through research and development

Contents

List of Abbreviations

1 Introduction

2 Related literature
2.1 Endogenous growth
2.2 Inequality, demand and investment incentives

3 The effects of inequality on growth
3.1 Demand induced innovations
3.2 The sources of technological progress
3.3 Competition and IPR policy

4 Conclusion 14 References

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

1 Introduction

The last four decades have seen a clear trend of rising inequality across countries OECD (2015). This observation has initiated an intense debate among researches, policy makers and civil society about the consequences of whether or not to counter­act this trend. Discussions are mostly framed within the conception of an equity ver­sus efficiency trade-off based on the premise that, even if equity might be desirable, some level of inequality is necessary to maintain economic performance incentives. However, recent theoretical and empirical literature has pointed out that inequality might also have negative economic consequences such as lower economic growth. For instance, Neves, Afonso, and Silva (2016) find in a meta-study that 36 out of 41 analyzed estimations suggest a negative effect of inequality on growth, though the average effect of the Gini-coefncient on growth rates seems to be relatively small.

Theorists have argued about different channels through which inequality might af­fect growth, such as political instability (Alberto Alesina and Perotti 1996; Keefer and Knack 2002), redistribution policies (A. Alesina and Rodrik 1994; Persson and Tabellini 1994) or inefficient investments in human and physical capital through mar­ket imperfections (Aghion, Caroli, and Garcia-Pefialosa 1999; Galor and Zeira 1993, respectively). However, Zweimuller (2000a) points out that the role of demand on in­centives to realize productive investments has been so far neglected by the literature. As an answer to this gap, a framework that allows to examine this channel has been elaborated through the works of Zweimuller (2000b), Matsuyama (2002), Foellmi and Zweimuller (2004, 2006, 2017), Foellmi, Wuergler, and Zweimuller (2014), Hati-poglu (2012), among others. The common question that guides this research body is, how does inequality affect economic growth through demand induced innovations?

Three elements are commonly present in the approach. The first one is the as­sumption of non-homothetic preferences, meaning that individuals satisfy their basic needs before demanding less necessary "luxurious" goods. Second is the argument that innovation made through investments in research and development increases technological progress, fostering long-term growth. The third one describes how in­come (and wealth) distribution determines profits by shaping the size of the markets and the height of prices for goods produced by innovators.

Foellmi and Zweimuller (2006) combine these elements in a theoretical moden and identify two effects of higher inequality on innovators' profits and therefore on the incentives to undertake growth enhancing investments. First, a negative market size effect is due to reduced demand from lower-income households. Second, a positive price effect is caused by higher willingness to pay of the rich for newly invented goods. They show that, overall, positive price effects dominate, and higher inequality increases growth.

Nonetheless, diverging results can be found in models where different assumptions are made about innovations and the role of competition. For instance, Foellmi, Wuergler, and Zweimiiller (2014) show that including the possibility of cost-reducing process innovation can change the effects of inequality on growth, depending on which type of innovation is more important for total factor productivity. Foellmi and Zweimiiller (2017) argue that the presence of competition can allow market-size effects to dominate and Hatipoglu (2012) finds that finite patent length potentially changes the direction of the effect of higher inequality on growth.

In this seminar paper, I review some of the approaches to the theory that links inequality to growth through the channel of innovations, focusing on the model de­veloped in Foellmi and Zweimiiller (2006). In the next section, I connect the latter paper to works from the literature of endogenous growth and demand induced inno­vations. In section 3, I summarize the model by Foellmi and Zweimiiller (2006) and outline the results of models with differing assumptions. A conclusion is provided in section 4.

2 Related literature

2.1 Endogenous growth

The channel described by Foellmi and Zweimiiller (2006) combines two main ar­guments. The first one is that innovation affects growth rates by enhancing pro­ductivity. The idea that increasing total factor productivity (TFP) is the cause of long-term growth was initially modelled by Solow (1956) and Swan (1956), where a constant rate of technological progress causes TFP to increase, maintaining positive returns to investment and allowing the economy to constantly increase its output level. The rate of technological progress, however, is assumed to be the outcome of exogenous scientific advancements, among other factors external to the economic process.

One approach to explain increases in TFP within the model was put forward by Romer (1986), who, based on the work by Arrow (1962), assumed that firms become more productive through production experience because they learn to use input factors more efficiently and thereby improve their production process. This process of learning-by-doing (lbd) by individual firms spills over to the entire industry. The assumption captures the premise that knowledge, e.g. production experience, is a non-rival and non-excludable good. Because of this, aggregate production exhibits increasing returns to factor accumulation, causing long-run economic growth.

Deviating from the assumption that knowledge is only a by-product of production experience, Romer (1990) proposes a model where increasing productivity is the cause of an expanding variety of capital types that can be used in production.¹ In this model, the blueprints for new types of capital goods are designed by a research and development sector and the knowledge contained in those blueprints constitutes a form of non-rival but excludable goods if the use of the blueprints for production is protected by intellectual property rights (IPR). During the process of research, however, knowledge and ideas are generated that are non-excludable, e.g. because researchers can use the knowledge used for one design in their future work. The amount of resources destinated to R&D depends on the price payed for the blueprints by producers of capital goods, thus, profits from R&D investments determine the rate of technological progress. Therefore, Romer (1990) initiated a type of models that endogenously explains long-run growth based on innovation activities oriented by profit incentives.

Grossman and Helpman (1997, p.43 ff) apply the framework of the Romer (1990) model, but use it to model the creation of new types of differentiated consumer goods instead of new capital types. They assume that producers of differentiated products have a monopoly position in the supply of their goods, either due to patent protection or because of a first-mover advantage that prevents entrants from engaging in price competition. Grossman and Helpman argue that the amount of investment on R&D, and hence the rate of technological progress, depends on the expected profits from selling a new product to consumers. Moreover, they point out to the resource constraint the economy faces because both, industry and R&D, demand labor for their productive activities and, therefore, any change that relaxes this constraint (such as an exogenous increase in the economy's labor endowment or higher labor productivity) allows both sectors to achieve higher output levels.

Departing from the way to model endogenous growth in Grossman and Helpman (1993), Foellmi and Zweimuller (2006) extend the model by assuming hierarchic preferences and heterogenous income levels. This variation changes the symmet­ric demand structure and allows them to model the link between inequality and innovation incentives.

2.2 Inequality, demand and investment incentives

The second argument puts forward a causal relationship between inequality and firm's incentives to realize investments. In general terms, it states that income distribution affects profit expectations by shaping the size of markets and the height of prices.

On the one hand, market-size effects are characterized by Zweimuller (2000b), who develops a model based on hierarchic preferences and innovation-driven endogenous growth. Since lower-income households cannot afford the high prices of new goods, only the rich consume new goods and expansion to mass production is delayed by the time it takes for incomes of the poor to grow and prices to decrease. His analysis leads to the conclusion that a redistribution from rich to poor increases the size of markets by reducing the time it takes for the poor to afford new goods, thereby increasing innovation incentives and growth.

On the other hand, price effects of inequality on demand are modelled by Foellmi and Zweimuller (2004), who look at the case of imperfect competition when monopolists produce differentiated goods. Assuming non-homothetic preferences and an unequal distribution of incomes, they examine how an increase in inequality affects product variety and monopolist's mark-ups. They conclude that, when poorer households are more price-sensitive than richer households because of hierarchic preferences, an increase in inequality reduces aggregate price sensitivity, allowing monopolists to charge higher mark-ups and more firms are attracted to invest in the production of differentiated products.

The effect of inequality on innovation through market demand is examined by Mat-suyama (2002), who develops a theoretical model to explain the sequence in which different production sectors evolve and lead to the development of mass consumption markets. In a context where the productivity of firms increases through learning-by-doing, high prices and demand of the rich allow innovative industries to take off and increase productivity, which in turn reduces prices and makes new goods affordable for the poor. The poor's demand then increases productivity and pulls prices further down, giving chance for the rich to afford new goods and starting the cycle for the next industry. Matsuyama argues that some level of inequality is necessary for the process to begin, though extreme levels of inequality would break the process at an early stage if the poor cannot afford new goods even after initial price reduction.

Matsuyama (2002) identifies a channel that links inequality to productivity and in­novation, underlining that the extent to which learning by doing is possible depends on the size of the markets and the rich consumer's capacity to pay high prices for lux­ury goods. However, it leaves the question open about how innovation is affected by inequality when the former is the consequence of deliberate (investment) decisions of individuals and not only a by-product of production. Foellmi and Zweimiiller (2006) set their analysis at this point by modeling innovation as an activity fostered by private incentives.

3 The effects of inequality on growth

3.1 Demand induced innovations

I start this section presenting the model developed by Foellmi and Zweimiiller (2006) (from now on also referred to as FZ model or FZ framework). This model describes an industry sector in which differentiated goods are produced by monopolists who have acquired life-long patents for the production-rights of a particular good. The blue prints to produce new types of goods are created by R&D firms, so one possible interpretation of this is that industry firms get access to the production-rights by buying the patents from the R&D sector.

Technological progress is modeled as a consequence of spillovers from research activ­ities. This assumption can be understood by the non-rivalry and non-excludability of knowledge: because of this, the ideas generated in the process of creating blue prints for new types of goods can be implemented by producers of goods and re­searchers in their work, thereby increasing productivity of labor in the industry and the R&D sector.

The FZ model assumes the labor market to be perfectly competitive, which means that wages are determined by the level of labor productivity.² As new goods are invented and productivity grows, wages increase in the labor market, making house­holds richer and increasing their willingness to pay for consumption goods.

F(t) = F/N{t) and b(t) = b/N(t) denote the amount of labor needed for production of one blue print and one unit of consumption goods, respectively. F and b are exogenous technology parameters and N(t) indicates the amount of differentiated goods. Setting manufacturing labor costs as the numeraire, w(t) = N(t)/b describes the market clearing wage. With a higher amount of innovations N(t), thus, labor requirements decrease and wages increase.

Preferences are assumed to be non-homothetic, meaning that individuals derive utility from the consumption of differentiated products, although they regard some goods as more important or more necessary than others, thereby ordering goods in a hierarchy. This element allows the model in Foellmi and Zweimiiller (2006) to describe endogenously the life-cycle of products as well as the timing of innovation and market expansion.

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The period-utility function of the FZ model is presented in equation (1) with c(j) describing the consumption of good j, j £ [0, oo) identifying the differentiated good and denoting its rank within the hierarchy, such that a higher-ji good has lower priority because it yields lower utility. Since individuals demand the goods that give them the highest satisfaction, their demand will follow the hierarchy, meaning that before consuming a good with a lower position in the hierarchy, they will first consume all the goods with higher priority. Parameter 7 describes the distance in priority between one good and the next in the ladder of priorities, which Foellmi and Zweimtiller call the "steepness" of the hierarchy. For example, with low values of 7, basic needs are only slightly more important than luxury goods, with high values of 7 basic needs are much more important.

In this framework, individuals choose the type and amount of differentiated goods that maximize their utility given their budget, composed of labor incomes and initial wealth. Foellmi and Zweimiiller show that individual z's willingness to pay z(i) for a particular good can be described as a function of its income and of the position of that good in the hierarchy. Hence, the wealthier an individual and the higher the priority of a particular good, the higher the individual's willingness to pay for that good. The population is divided into rich (R) and poor (P) individuals, where the differences between the two groups are the levels of income and wealth, such that z(P) < z(R).

The FZ framework highlights the decisions taken by sellers of differentiated goods. Because of their monopoly position, producers have price-setting power and will choose the price that maximizes revenues for a given market demand. Here, income distribution determines the markups monopolists can achieve. Since rich and poor people have different willingness to pay and therefore different demand schedules, there is a level of prices at which poor individual's demand will be zero, while the rich will keep demanding positive amounts. Therefore, every monopolist faces a trade-off between selling at high prices in relatively small quantities to the rich or selling larger quantities to the masses at lower prices that the poor can afford to pay.

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¹ More capital types means that the production of capital goods becomes more specialized. This form of technological progress formalizes the argument of classical economists that specialization fosters learning-by-doing and economies of scale (Romer 1987; Silvestre 2018)

² The model assumes that households supply labor inelastically.