6    Cognitive capitalism, an empirical and theoretical analysis

Andrea Fumagalli and Stefano Lucarelli

Introduction

The purpose of this chapter is to provide a theoretical framework of cognitive capitalism and to discuss the conditions of stability and instability of the model. In the first part of the chapter we present a panel data analysis to support the hypothesis that welfare-state systems, by increasing knowledge, is positively correlated with the increasing value of immaterial capital. In other words, the generation of knowledge and its spatial diffusion are the basic features of cognitive capitalism. However, the capitalist system is inherently an unstable system, subject to indecision and crises, in which transformations along time imply to renew its theoretical analysis. Cognitive capitalism defines a form of accumulation without a viable mode of regulation among social classes. Particularly knowledge exploitation and capital-gains allocation are deregulated. As we argue in the second part of the chapter, on the demand side the increasing polarization of income distribution penalize effective demand not only by reducing the level of consumption but also by negatively affecting the investment. Indeed, knowledge-learning process and network economies have to be supported. A too-high rate of precariousness can negatively affect productivity, with the risk to worsen financial gains, notwithstanding a pragmatic monetary policy.

The role of knowledge in the transition from Fordism to cognitive capitalism: some empirical evidence

The starting point for the formation of cognitive capitalism is the process of diffusion of knowledge generated by the development of mass schooling and the rise of the average level of education. As shown in the previous chapters, the scholars that proposed the cognitive capitalism thesis (first of all Carlo Vercellone [2015]) affirm that, starting from the 1970s, the increase of immaterial capital, already described by Kendrick (1976, 1994) for the United States, does not mainly depend on R&D investments but on the positive effects of the social policies promoted by welfare-state systems.¹

An empirical confirm of this thought may be presented by considering the following European countries for the period 1995–2014: Finland, France, Germany, Greece, Italy, Spain and the UK. We present a panel data to estimate the correlation between the value of immaterial capital and the following dependent variables:

• Public expenditures in the healthcare system (Health), from OECD health statistics;
• Public expenditures in education (EDU), from the World Bank education statistics;
• General expenditure in R&D (GERD), from Eurostat;
• GDP, from Eurostat.

In order to describe the long-term effects of the welfare state’s social policies, we built other lagged variables for Health and EDU:

• Health(−5): that describes the five years lagged public expenditures in the healthcare system;
• EDU(−5): that describes the five years lagged public expenditures in education;
• EDU(−17): that describes the seventeen years lagged public expenditures in the healthcare system.

We also consider various time dummies and especially a dummy that measures the presence of the European crisis since 2008 to 2014 (named simply “Crisis”). To measure the dependent variable (immaterial capital), we use the data provided by Corrado et al. (2012). These authors identified all the relevant intangible asset types, that are:

• computerised information: software and databases;
• innovative property: R&D, design, product development in financial services, mineral exploration and spending on the production of artistic originals;
• economic competencies: market research, advertising, training, organisational capital (own account and purchased).

The data elaborated by Corrado et al. (2012) cover the period 1995–2010. We use a simple statistical forecasting model based on the moving average method to complete the historical series until the 2014 (see Table 6.1).

Table  6.1  Intangible assets (in millions of national currency)

Source: Our computations on dataset www.INTAN-Invest.net. Accessed September 2017.

Notes: New intangibles and national account intangibles; gross fixed capital formation, current prices, millions of national currency.

The data show a notorious dichotomy that characterises European countries: the core European countries presents the higher values. Our results are presented in Table 6.2 where we compare six different models: the first two by using the Pooled OLS empirical methodology, the second four by using the Fixed Effects methodology. As known, Fixed Effects is a feasible generalised least squares technique which is asymptotically more efficient than Pooled OLS when time constant attributes are present.

Table  6.2  Panel estimations: intangible assets determinants in Europe

Notes: 140 observations: 7 cross-sectional units; time-series length = 20p-value <0.01***, p-value<0.05**, p-value<0.1*

All the models we tested clearly show that the magnitude of the single coefficients related to healthcare and education public expenditures are higher, in absolute value, than the magnitude of the coefficient related to R&D (GERD).

The most relevant variable seems EDU(−5), that is statistically significant with a positive coefficient, in model 1, 2, 3, 5 and 6. Model 5 and 6 show that time dummies are meaningful and positively correlated with “Intangibles.” Their presence does not affect the relevance of EDU(−5), which remains positively correlated with the dependent variable. It also contributes to show the importance of Health and Health(−5), which have a high positive coefficient.

Models 2 and 6 show a negative correlation between the dependent variable and the dummy variable that describes the recent European economic crisis.

The empirical analysis is coherent with the idea that past generation of knowledge promoted by a society based on public education and healthcare represents the engine for the actual value if the immaterial capital (measured by today’s intangible assets). Differently from the Fordist phase, the present diffusion of knowledge no longer depends upon technological transfers of machineries alone, but rather upon relational flows generated by immaterial process.

Knowledge-learning process and network economies

If knowledge is the basis of accumulation, it becomes unavoidable to analyze how its exchange and diffusion affect the dynamics of productivity. The peculiarities of cognitive capitalism are its ability to enlarge both knowledge-learning process (λ) and network economies (k). The variable λ depends on the degree of cumulativeness, opportunity and appropriability (Nelson and Winter 1982). Here, opportunity is defined as the expected rate of profit (P^e) and, therefore, the higher the expected profit in adopting a new technology, the higher is the speed of its diffusion. Cumulativeness and appropriability represent the capacity of new knowledge to generate further innovation while avoiding the possibility of its imitation, thanks to the existence of intellectual property rights (IPR). The variable k depends on the level of income (Y) and positive externalities (E). When λ is constrained by IPR, we shall see that the consequence is that the greater is the degree of appropriability of knowledge, the smaller becomes its capacity of diffusion—affecting, de facto, its ability to positively influencing the associated productivity.² While it is during the learning process that the generation of knowledge occurs, network economies define the way in which the produced knowledge is diffuse. To a higher level of knowledge corresponds, in terms of its generation (λ) and diffusion (k), more innovative technologies. From a systemic perspective, an innovation is a change in the economic process occurred as a result of the investment activity. Whether the investment is devoted to the already existing technology or to new technologies will establish the amount of innovation. The crisis of Fordism led to a new investment activity based on new sources of growth (electronic marketing, informational goods, encoding software, control over the quality of information, branding, control over the lifestyles, etc.). In a social system geared around innovation and production, investment policies depend upon R&D and “learning by doing” strategies and process. In cognitive capitalism, the impact of new ICT based on computer science, micro-electronics and the new organizational productive changes (just-in-time, zero stock) have speed up the “learning by doing” processes, spreading them well beyond the firm (Venturini 2006). At the same time, part of the R&D process unfolds itself within territories each having one or more specific competencies. Where to locate economic activities is mainly determined by the search on the part of the firm of advantages in the development of its competencies (Mouhoud 2006: 300). Consequently, the productivity entailed by the exchange of knowledge cannot be assimilated to material productivity.

Some notes about financial markets and production

The realization of production is compensated by financial markets acting as a multiplier of aggregate demand and by the processes of globalization (delocalization, outsourcing, lower labour costs). The efficiency of the system is assured by both the growth of financial markets—primary source of surplus distribution—and by massive processes of outsourcing and delocalization characterizing advanced countries (which are by definition the places where the accumulation of knowledge occurs more intensely). In this context, the capital–labour compromise, based on the connection between productivity gains and real wage dynamics, is declining, with subsequent effects on polarization of income distribution.

Second, an income distribution that penalizes workers, negatively affects learning and network economies, because these last ones require higher remunerations in order to be better exploited. Consequently, the loss of productivity gains reduces the efficiency of the system. The high degree of precariousness on the one hand represents the necessary precondition for perpetrating a situation of exploitation and command within the relationship between capital and labour; and, on the other, it represents an obstacle to the development of knowledge. In such a context, a new form of the capitalistic exploitation is the production of political lines in order to improve the financialization of social production. In this respect, exploitation in cognitive capitalism has been defined as “the seizure, the centralization, and the expropriation of the form and the product of social co-operation,” “the political sign of domination above and against the human valorisation of the historical/natural world,” the “command above and against productive social cooperation” (Negri 1997).

In the above framework, aggregate demand is influenced both by the dynamic of the financial markets and by the capital gains deriving from the internationalization of production. With the weakening of the wage–productivity nexus, these dynamics had a greater impact on consumption and the investment activity. In a finance-led economy in order to avoid a demand crisis, the wage regulation ought to be based upon the distribution of capital gains. However, the shortcomings intrinsic to this approach are, first, that given the widespread uncertainty generated by working precariousness, knowledge loses its generative capacity, and, second, as there is no guarantee that the overall produced wealth will be re-invested into the financial market or elsewhere, a finance-led growth is always at risk of instability.

As far as the supply side is concerned, changes in the ability to generate new knowledge, as a basic condition for the spread of new technologies, depend on the characteristics of the environment in which R&D activities are organized. This environment is positively affected by the income level and by a set of variables, such as education, an overall macroeconomic and political stability, a fair wealth redistribution, a balance between material and immaterial activities, and the existence of a good system of infrastructures, which we define as positive externalities.

The power of finance capital resides in its ability to impose the criteria of financial returns. Companies, in order to obtain liquidity for mergers and acquisitions (M&A) run into debts. Through the M&A strategy the company control technologies, skills, and know-how of other potential competitors. Thus, business expectations should increase, managers should sustain the positive dynamics of shareholder values on one hand, and pay the debts to the banks, on the other hand. More importantly, indebtedness is not directed to capital expenditures, but it is a powerful means of satisfying the financial criteria of shareholder value. Such a process requires specific monetary policies by a massive injection of liquidity and lowering of interest rates to prevent the emergence of financial bubbles.³

Monetary policy may sustain the financial boom of a knowledge-based economy. But each financial boom has a double result: from one side, the positive dynamics of shareholder values favours the increase in aggregate consumption, from the other, because of its unequal allocation, leads to a distorted income distribution.

The model

Building upon the French regulation theory (see Boyer 2004a, 2004b), our formalization⁴ will highlight first the dynamic function of productivity as key variable of the supply side and, second the dynamic function of aggregate demand, composed of private consumption, increase in investment, and public expenditure as autonomous variable. Although the generation of knowledge, its spatial diffusion, and financialization affect open economies, including third-world economies, we have chosen to deal only with the pure case of the closed economy in which knowledge-learning process, network economies and financial dynamics develop entirely in the domestic arena. In such a context, we will clarify under which conditions productivity and aggregate demand dynamics can provide a stable rate of growth. The model is described by a linear differential equations system in Figure 6.1.

Figure  6.1  The equation system

Equations from (1) to (4) describe supply-side dynamics, based on productivity. It is supposed, as already showed, that this latter mainly depends on dynamic scale economies (Equation 1):

• network economies (k˙ ) are positively correlated to the level of production as proxy of the value of the spatial diffusion of economic activities, and to externalities E, supposed to be exogenous (Equation 2);
• learning economies (λ. ) are a positive function of investment activity and a negative function of exogenous IPR (that may also comprehend the innovative tools used in contemporary digital platforms, like Facebook, to accumulate users’ private information) (Equation 3).

Productivity changes are also related to changes in volume of output (Ẏ): as the so-called Verdoorn Law affirms, in the short run an increase in output can determine a more efficient use of labour, realizing static scale economies. Our productivity equation is similar to the Sylos Labini’s one π˙=+bY˙+cw˙Pma(t−n)+dIt−n−eI , where the w/P_ma ratio represents the incentive to save labour either absolutely or by introducing labour-saving machines (Ricardo’s effect), I_t-n is the long-run effect and I the short-run effect of investment.

The most relevant difference is that in order to describe productivity in cognitive capitalism, we divide the Smith’s effect (bẎ in Sylos’ equation) separating static economies (dẎ) from dynamic economies (ak˙+bλ˙).⁵

Investment is composed both by routine investment (I_k) and investment in innovation and knowledge (learning and human capital) (I_λ) [4]. Routine investment. traditionally depends on demand expectations and on realized production level in the previous period (σẎ) (Equation 4a). Investment in innovation and knowledge is characterized by very high potential returns and, at the same time, by possible catastrophic losses, since we suppose that this type of investment is strictly correlated to capital-gains dynamics (ĊG), through the parameter γ (Equation 4b). Capital gains are supposed obtained by the dynamics of systemic productivity gains (π˙ ),⁶ through the parameter β; given the numerous sources of uncertainty intervening in financial markets, capital-gains dynamics need to be sustained by monetary policy (μ) (Equation 4c). Indeed, especially in the past two decades, the most relevant macro-economic changes depend on monetary policy: particularly the USA Fed has played a decisive role, averting deflation by bringing the interest-rate curve to very low levels over the 2001–2008 period. The result has been a massive transfer of corporate risk onto households, which thus saved company profitability (Aglietta and Rebérioux 2005: 4). It is worth noticing that σ, γ, and β are strategic parameters that denote firms’ behaviour in terms of investments and allocation of capital gains. On the contrary, all the other parameters, a, b, c, d, e, and h, represent the characteristics of existing technology.

The second part of the model—from (5) to (8)—describes the demand side. In a very traditional Keynesian way, the aggregate demand is composed by consumption (C_n), investment (I) and exogenous public expenditures (G). Consumption (C_n) is supposed to be dependent on the total labour income. Total labour income is not only intended as the overall amount of wage but even as the earnings from financial activities. In cognitive capitalism a share of capital gains is, in fact, distributed to some categories of workers (especially high-skilled).⁷ The effect is to induce a sort of “financial income multiplier.” It operates through the expected capital gains by sustaining effective demand.

Equilibrium is defined by the equality between the rate of growth of output and the rate of growth of demand (9). By simplifying and substituting where necessary, the system can be reduced to two linear differential equation models, (10) and (11) (see Figure 6.2).

Figure  6.2  The equilibrium condition

Productivity dynamics (10) is positively correlated to network and learning economies; moreover, the impact on productivity depends upon the 1/β*, according to the level of the propensity to invest based on financial capital gains (β) and to the effects of the learning economies on productivity itself (bh).

Financial markets fix the norm of profitability. Positive expectations on financial activities partially depend on the efficiency of knowledge generation and diffusion (tacit and codified knowledge), according to the exploitation of learning and network economies (exploited codified knowledge). Therefore, the impact of “financial multiplier” (1/β*) on productivity is as much stronger as greater are the impact of investment on learning economies (h) and the impact of the learning economies on productivity (b).

1/β* can be indirectly influenced by monetary policy through the parameter μ. Nevertheless, it should be considered that the impact of monetary policy on capital gains is not able alone to strictly determine its dynamics, since it is intermediated by the parameter γ.

If we assume that β*>0, then the angular coefficient (B) of productivity line (10) is always positive.

If we assume μ = 0, then the intercept of productivity line (A) is positive only if aE > bIPR.

Result 1a

The higher the negative impact of IPR on knowledge diffusion, the lower the positive effect of network economies on productivity. As a result, the generation of knowledge and its spatial diffusion through the learning process are the basic features of cognitive accumulation.

If we consider the role of expansionary monetary policy, μ > 0, then the intercept of productivity line (A) is positive only if aE + γμ(bh+c) > bIPR.

Result 1b

The lower the positive effect of network economies on productivity, the more incisive should be the monetary policy to sustain the impact of investment on productivity. As a result, the monetary policy pragmatism may preserve the generation of knowledge and its spatial diffusion only if the monetary push is used to sustain the investment in innovations and knowledge. From 2000 until 2010, money supply has been targeted to sustain more financial liquidity than the traditional credit system, in order to provide capital-gains stability.

Considering equation (11), there is a positive correlation between demand and productivity if and only if γβ+α(1 − γ) > αw. In order to discuss this condition, consider that:

• γ defines the propensity to invest based on financial capital gains;
• α(1 − γ) is the consumption level only based on share of capital gains which is distributed to shareholders and to high-skilled workers, according to individual bargain;
• αw is the traditional Keynesian demand of consumption goods based on wage rate.

Result 2

Output growth increases if the sum of investment and consumption deriving from capital-gains allocation is greater than consumption deriving from wage bargaining. We should emphasize that wage rate becomes the variable of adjustment to preserve the wealth effect by finance-led growth regime.

At last, by analysing the intercept of output line (C), it is easy to note that it is always positive and increasing according to the level of public expenditure (G) and the income multiplier (1/α*).

A discussion of the model’s solutions

The equilibrium level of output and employment are calculated in the following equations:

Y˙*=C+DA1−DB

[12]

N˙*=C(1−B)+A(D−1)1−DB

[13]

According to Boyer (2004a: 81), the condition of stable equilibrium for the economic system is first of all defined by a smooth increase in employment: N* > 0. (see Fig. 6.3).

By means of easy algebra, it is possible to verify that condition (14) can be reworded as follows:

αw+α*<γβ+α(1−γ)<αw+α*β*(bh+c)σ+ae+d

[14^*]

Result 3

The stability condition of the economic system depends on the propensity to invest and the wealth effect both produced by capital-gains allocation. It follows that the allocation of capital gains (subtracting wage rate) should be regulated:

1. It should be higher than the inverse of income multiplier (α*).
2. It should be lower than the positive combination of α*β*, weighted by the sum of the impacts of investment ([bh+c]σ), externalities (ae), and static scale economies (d) on productivity.

Figure  6.3  The stability condition

Consequently, consumption and the demand regime are directly affected by financialization.

In order to avoid a demand crisis, the wage de-regulation ought to be compensated upon the wealth effect stimulating by capital gains. On the other hand, knowledge effects on productivity must be preserved, and financial norms should not have negative impact on financial productivity multiplier. When financial gains misrepresent the real effects of investment, dynamic scale economies and static scale economies on productivity, then financial bubbles emerge.

Without a mode of regulation that guarantees that the overall produced wealth will be re-invested into the dynamic learning and network economies and without a policy that controls financial bubbles, a finance-led growth is always at risk of instability.

Concluding remarks

In cognitive capitalism, capital becomes productive of value by the private appropriation of the “commons,” like tacit and codified knowledge. Capital is valorised by controlling the life-cycle of knowledge. In the long run, the exploitation of learning economies and network economies, and the central role of precariousness and subalternity, which prevents a new form of wages regulation, push the system into a zone of structural instability. As particularly shown in the recent debate about social platforms, exploitation is therefore realised by an armoury of instruments aimed at controlling the time of social cooperation.

Social productivity depends upon two factors which are inversely correlated:

1. On the one hand, the diffusion of different innovative tools (i.e. social platforms’ ads) and norms (i.e. various forms of IPR) guarantee the appropriation of knowledge. The effect of this is that of limiting the diffusion of knowledge, making it artificially scarce resource.
2. On the other hand, there is the need of capital to create a “virtuous circle” so as to create the optimal condition for the circulation of knowledge and information and to increase their diffusion in order to accelerate the generation of new innovations and the codification of tacit knowledge.

The trade-off is currently unsolvable at the level of simple market exchange. A high degree of IPR leads to a deterioration of network economies and learning processes. Consequently the rate of growth of productivity will decrease.

On the demand side, a relevant role is played by the allocation procedure of capital gains generated in the financial markets. As is shown in the model above, the role of financial markets should be regulated. Specifically, the regulation has to consider the dynamics of capital gains that should be confined in a specific path: it must be higher than a first limit by allowing a positive effect on aggregate demand but lower than the general impact on productivity generated by investment activity propensity.

A distribution of the productivity gains that penalizes workers negatively affects learning and network economies. The absence of a fair social compromise determines also the instability of the finance-driven growth, even if monetary policy sustains the financial boom.

We may conclude that the unsolved political problems in cognitive capitalism resides in the fact that the unfair income distribution undermines the ability to generate knowledge and the excessive appropriability of technologies leads to a lower diffusion of knowledge and learning processes. In the long run, the absence of a viable social compromise based upon a fair distribution of productivity gains and the prevalence of individual bargain do not allow a valorisation of learning and network economies.

Notes

1    Following Kendrick, increases in non-tangible capital (mainly education, training, health, and R&D) largely explain the growth in total tangible factor (capital) productivity in the whole economy.

2    This argument can be presented in terms of tacit and codified knowledge, see Malerba and Orsenigo (2000).

3    The term “bubble” describes a situation on which current stock market prices are no longer justified by future dividends.

4    See Fumagalli and Lucarelli (2007).

5    Sylos Labini introduced his productivity equation for the first time in Sylos Labini (1983: 174).

6    In such a contest, social productivity represents the stock of cognitive resources activated by the cooperation, which are spread along the territory in a row. Changes in financing of investment activity—for instance the venture-capital boom—can be explained by the increasing relevance of social productivity (Marazzi 2005).

7    We could also consider the segmentation of labour market between stable and precarious work activity by breaking down into two parts the equation (8).

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