Tight and Loose Coupling in Organizations

2.1 Consumer

The taste of an infinitely-lived consumer evolves according to a stochastic process ωtt≥1, where time-ttasteωt has an outcome in N.

Each period, the consumer buys an item, whose value to him is determined by the expertise of the experts who produce it. He values the item at πn−m if it has been produced by n∈0,1,2 experts in style ωt and m∈0,1,2 experts in any other styles. These values satisfy

Condition 1. π2>π1>π0=π−1=π−2=0.

Condition 1 implies that, if the consumer’s taste changes from ωt to ωt+1≠ωt, then the very item that he valued at π2>0 at t he values at π−2=0 at t+1. ^[3] Two experts in style ωt produce a more valuable item than a single expert in style ωt does (i. e., π2>π1). Experts in different styles produce a worthless item (i. e., π0=0).

The taste process ωt is a Markov chain depicted in Figure 2. The chain satisfies, for ω0=1 and for all t≥1, Prωt=ωt−1+1=δ and Prωt=ωt−1=1−δ, for some volatility parameter δ∈0,1, which captures the consumer’s desire for novelty. Parameter δ is determined by an increasing and differentiable demand for novelty function δ˜:0,1→0,1, which associates with the firm’s innovation rate θI (described shortly) a unique volatility δ=δ˜θI.

Figure 2:

The evolution of the consumer’s taste.

In words, the consumer enjoys promiscuity in his consumption of styles. Once ripe for change, he stops enjoying the current style and regains enjoyment only if he consumes the successor style. These novelty-seeking preferences resemble, but are not equivalent to, habit-formation preferences (see, e. g., Abel 1990). The distinguishing feature of novelty-seeking preferences is that the consumer who dislikes novelty (i. e., δ is low) will be hurt by being forced to consume new styles in rapid concession, whereas the consumer who forms habits is always better off from consuming increasing quantities or qualities.

2.2 Firm

An infinitely-lived firm’s time-taction

is in the set A≡({0}∪ℕ)×({0}∪ℕ). The action’s interpretation is that, in any period t, at no cost, the firm can employ at most two experts, indexed by j∈1,2. The expertise of expert j is denoted by ajt and either indexes a style in IN or has ajt=0, meaning that expert j’s position is left vacant. Expert j is a match (for the consumer’s taste ωt) if ajt=ωt, is a mismatch if ajt≠ωt, and is absent if ajt=0.

The firm is assumed to price discriminate perfectly, and so its profit from selling the item produced by n matching and m mismatching experts is πn−m, which is also the consumer’s valuation. Using the equivalent notation πn−m≡πn−m (for type-setting convenience), the firm’s period-t profit can be written as

where the argument of π is the difference between the numbers of matches and mismatches.

The firm’s behavior is influenced by its organizational design, chosen at time zero by the firm’s founder. An organizational design is an element θL,θI in the set 0,1×0,1, where

1. the coupling typeθL∈0,1 is the probability (restricted to 0 or 1 without loss of generality) with which, in any period t, the firm’s coupling is loose; with probability 1−θL, the firm’s coupling is tight;

2. the innovation rateθI∈0,1 is the probability with which, in any period t, the firm innovates.

Figure 3:

Time-t determinants of the firm’s behavior.

The firm’s period-t employment strategy is a given stochastic function α:IN2×0,1→A that maps period-t−1 and period-t target styles θSt−1,θSt and the coupling type θL into the types of experts employed at time t:

The interpretation of the specified strategy is summarized in Table 1. If coupling is loose (which occurs with probability θL), the firm employs a single expert, who matches θSt. If coupling is tight (which occurs with probability 1−θL), the firm retains one expert, of type θSt−1, from the last period and hires another expert to match the current target style, θSt.

The employment strategy α can be motivated by a search friction in the labor market for experts. This friction precludes the firm from identifying and hiring more than one expert in the course of a single period. The firm is thus left with two options: retain one previous-period expert and replace the other (this is the case of tight coupling) or fire all last-period experts and hire a new one (this is the case of loose coupling). The two cases deliver the firm sizes of two experts and one expert, respectively. ^[4]

In the light of the employment strategy that it induces, the coupling type θL can be interpreted to parametrize the firm’s degree of self-disruption (the term coined by Christensen 1997). The firm with θL=1, periodically fires its experts and starts all over again. If this firm’s target style remains unchanged, the firm hires simply to rebuild what it destroyed the previous period. If the firm’s target style changes frequently, however, self-disruption enables the firm to turn over its expert force quickly, which is especially valuable when the economic environment is volatile (i. e., the consumer’s taste is fickle), as will be shown.

The interpretation of the innovation rate θI stems from the way it affects the target style, which follows the stochastic process θSt. This process is an outcome of a reinforcement-learning dynamics (Sutton and Barto 1998). Technically, θI controls the probability of the transition towards the higher target style in the process θSt. To describe the stochastic process θSt, define an auxiliary, best-practice, process qtt≥0, where qt registers the style that was targeted in the past and targeting which in the current period would have generated a superior profit. Formally, for some q0∈IN and for t≥1,

The initial condition q0 in eq. [2] determines whether the firm begins by knowing the consumer’s taste (q0=ω0) or not knowing it (q0≠ω0). ^[5] Condition θSt=ωt in eq. [2] is equivalent to saying that the firm’s period-t profit is weakly higher if all experts match θSt than if all experts match θSt−1. In this sense, the firm registers the best practice among past practices, perhaps, by imitating (unmodelled) similar firms that have experienced the highest profit. ^[6]

In any period t, with probability θI, the firm innovates on the best practice qt, whereas with probability 1−θI, the firm repeats the best practice:

According to eq. [3], innovation leads to catching up with the consumer’s taste (i. e., θSt+1=ωt+1) if the best-practice is lagging behind (i. e., if qt−1<ωt), and leads to overshooting the consumer’s taste otherwise (i. e., if qt−1=ωt). ^[7]

2.3 The Founder’s Problem

The founder’s payoff as a function of the organizational design θˉ≡θL,θI is denoted by Fθˉ and is defined to be the limit of the expected present discounted value of the firm’s profits, denoted by Vθˉ;β, as the discount factor β∈0,1 converges to 1:

where

The right-hand side of eq. [5] depends on θˉ directly and also indirectly, through the target-style process θSt, which it induces. The initial condition q0=ω0 in eq. [5] means that, in period 1, unless it innovates, the firm matches the consumer’s taste. By inspection of eq. [5] when β→1, the founder’s payoff is the long-run average profit, or equivalently but informally, the expected profit in a “randomly” chosen period “far enough” in the future.

An optimal organizational design solves the founder’s problem

2.3 Equilibrium

1. δˆ is induced by the demand for novelty: δˆ=δ˜θˆI.

2. θˆL,θˆI solves the founder’s problem in eq. [6].

Part 1 of Definition 1 requires that the volatility that the founder takes as given agree with the volatility (novelty seeking) that the firm’s innovation rate provokes in the consumer. That the founder takes the volatility as given is implicit in part 2 of the definition.

3.1 The Founder’s Value Function

The founder’s problem in eq. [6] is solved in two steps. First, maximize a loosely coupled and a tightly coupled firms’ payoffs separately by selecting an optimal innovation rate θI for each of them. Second, set θL=1 if the loosely coupled firm’s maximized payoff is the weakly greater of the two, and set θL=0 otherwise. This subsection fixes an organizational design θL,θI and derives the expression for the founder’s payoff, which is then subjected to the described two-step procedure.

It is convenient to recursively rewrite the definition of the expected present discounted profit defined in eq. [5]. For brevity, denote this value by V+. An auxiliary expected present discounted profit is denoted by V− and is defined to differ from V+ only in that the expectation in eq. [5] is conditional on q0≠ω0, instead of q0=ω0. The implicit equation for V+ is

where Condition 1 has been used to substitute π0=π−1=π−2=0. The first line in eq. [7] captures the case in which the firm matches the consumer’s taste if neither the consumer’s taste nor the target style has changed (i. e., ωt=ωt−1 and θSt=θSt−1), which occurs with probability 1−δ1−θI. In this case, a loosely coupled firm (whose probability is θL) employs one matching expert, whereas a tightly coupled firm (whose probability is 1−θL) employs two matching experts. At the end of period t, the firm registers the style that matches the taste (i. e., qt=ωt), and hence the continuation value is V+. The only case in eq. [7] in which the continuation value switches to V− occurs when the taste changes and the firm fails to innovate (i. e., ωt≠ωt−1 and θSt=θSt−1), which has probability δ1−θI. The case in which the taste does not change but the firm innovates leads to the continuation value V+ because the firm registers the previous period’s target style as the best practice (i. e., qt=qt−1).

The implicit equation for V− is constructed analogously

In eq. [8], the first term captures the case in which the firm does not innovate and hence, having started out mismatching the consumer’s taste continues mismatching it and expects the continuation value V−. The second term captures the case in which the firm innovates and catches up with the consumer, irrespective of whether the consumer’s taste changes in that period.

The system of linear eqs [7] and [8] admits a unique solution, whose component V+ is of primary interest. From eq. [4], the founder’s payoff is

where the argument of F has been suppressed. Explicitly computing and rearranging the expression in the above display can be shown to yield

where FL is defined to be the founder’s expected payoff conditional on θL=1, and FT is defined to be the founder’s expected payoff conditional on θL=0. ^[8] These two conditional payoffs are

In words, a loosely coupled firm’s payoff, FL in eq. [10], equals the payoff from employing an expert in the consumer’s preferred style multiplied by the frequency with which such employment occurs. ^[9] A tightly coupled firm’s payoff, FT in eq. [11], equals the payoff from employing two experts in the consumer’s preferred style multiplied by the frequency with which such employment occurs.

The following two subsections analyze separately the payoff-maximizing innovation rates for the loosely coupled and tightly coupled firms. This analysis, which is also of independent economic interest, will inform the founder’s choice of coupling.

3.2 In a Loosely Coupled Firm, Optimal Innovation is Increasing in Volatility

Define the threshold

Lemma 1 describes how a loosely coupled firm’s (LCF’s) payoff-maximizing innovation rate depends on volatility. Figure 4 illustrates this lemma.

Figure 4:

Loosely coupled firm.

Differentiating θI,L, one can verify that θI,L is weakly increasing in δ and strictly so when δ<δL∗.

Substituting θI,L into FL gives the payoff

By differentiating FL in the above display, one can verify that FL is weakly convex and is uniquely minimized at δ=1/3. By convexity, FL is maximized at a boundary point; indeed, by substitution, FL can be verified to be maximized at both boundary points, δ=0 and δ=1. ☐

In Lemma 1, LCF’s payoff is maximal either when there is no volatility, and so the firm always matches the taste by never innovating, or when the volatility is maximal, and so the firm always matches the taste by innovating in every period. Both scenarios yield the same payoff because, in either scenario, the firm starts afresh each period, hiring experts one by one, to match the state.

3.3 In a Tightly Coupled Firm, Optimal Innovation is Increasing in Volatility

Lemma 2 describes how a tightly coupled firm’s (TCF’s) payoff-maximizing innovation rate depends on volatility. Figure 5 illustrates this lemma.

Figure 5:

Tightly coupled firm.

By contrast to LCF, TCF does not enjoy the same payoff when δ=0 and when δ=1. Its payoff is higher when δ=0. When δ=0, TCF does not innovate, and its experts match the consumer’s taste. When δ=1, whatever TCF does, it cannot avoid employing at least one mismatching expert, and so its payoff is zero.

3.4 The Firm is Loosely Coupled whenever Volatility is High

When δ=0, Lemmas 1 and 2, and Condition 1 imply that LCF’s payoff is higher than TCF’s: π2>π1. When δ=1, the same lemmas and the same condition imply that LCT’s payoff is higher than TCF’s: π1>0. Hence, by continuity, θL=0 if δ is near 0, and θL=1 if δ is near 1. Theorem 1 interpolates: the optimal θL is weakly increasing in δ. In addition, Theorem 1 establishes that the loose coupling and innovation are complements in the organizational design, meaning that, for any δ, the payoff-maximizing value of θI is weakly higher for LCF than for TCF.

Normalize π2=1, so that π1∈0,1. Consider two cases: δ<δL∗ and δ≥δL∗.

if δ<δL∗, then

implying

where the equality is by differentiation, the first inequality is by π1∈0,1, and the last inequality can be verified directly.

If δ≥δL∗, then

implying

The above cases imply that FL intersects FT at most once and from below. That FL intersects FT is established by the continuity of both functions and by observing that FL0=π1<1=FT0 and FL1=π1>0=FT1. Hence, there exists a δ∗∈0,1 such that the founder sets θL=1δ≥δ∗.

To establish the complementarity of θL and θI, by Lemmas 1 and 2, it suffices to establish that θI,Lnpδ≥θI,Tδ. Indeed, combiniFTng eqs [13] and [14] gives

where the inequality is by inspection. ☐

Figure 6(a) combines Figures 4(b) and 5(b) to illustrate the optimality of loose coupling when volatility is high. Figure 6(b) combines Figures 4(a) and 5(a) to illustrate that the optimal innovation rate rises in volatility – discontinuously so at, when the coupling type optimally switches from tight to loose.

Figure 6:

The founder-optimal choices of the coupling type and the innovation rate as volatility varies.

4.1 Existence, Multiplicity, and Stability

4.1.3 Stability

All equilibria in which the inverse demand function intersects the supply function from below are Lyapunov stable. In Figure 7(a), equilibria A and B are stable. In general, the minimal-innovation and the maximal-innovation equilibria are stable. Stable equilibria obey the “intuitive” comparative statics. That is, when the demand for novelty shifts so that, at any given level of innovation, the consumer demands marginally more novelty, the stable-equilibrium innovation rate marginally increases. The intuitive comparative statics are highlighted in the following observation.

Figure 7:

Equilibrium A has more innovation and higher welfare than equilibrium B Equilibrium C has least innovation and the greatest welfare among all equilibria.

Observation 1:. If the consumer demands more novelty for every innovation rate, then the innovation rate in the smallest and in the largest equilibria weakly increases.

4.2 Welfare Comparisons

Welfare is defined as the expected present discounted sum of the consumer’s and firm’s payoffs as the discount factor β∈0,1 converges to one. This measure coincides with the founder’s payoff because, by assumption, the firm price-discriminates perfectly, and so the consumer’s payoff is zero. This measure has an alternative (informal) interpretation as the expected sum of the consumer’s and firm’s payoffs in any period that is chosen “uniformly at random.”

5.1 Organizational Theory: Tight and Loose Coupling

The paper operationalizes the concepts of loose and tight coupling, discussed by Roberts (2004, Chapter 2). Various aspects of organizational design can be tightly or loosely coupled: information technology (e. g., standardized IT platforms vs. individually chosen platforms), production processes (e. g., just-in-time vs. inventory-dependent), and human-resource policies (e. g., permanent employment or low turnover vs. high turnover). The present paper focuses on the human-resource policies of low turnover (tight coupling) and high turnover (loose coupling).

Roberts (2004) emphasizes that the individual features of organizational design are often complements and ought not to be optimized independently of each other. In the present model, this complementarity emerges between coupling (loose or tight) and the innovation rate. As the consumer’s novelty seeking varies, the coupling type and the innovation rate covary.

The intellectual roots of the concept of loose coupling go back to the bounded rationality paradigm, which emphasizes the constrained optimality of simple behavioral principles. In the context of bounded rationality, Simon (1969) anticipates loose coupling when he describes the merits of modularity in organizations. The terms “loose coupling” and “tight coupling” have first been used by Glassman (1973) to describe an evolved characteristic, the degree of interdependence of the components of living organisms and of societies. These concepts have been developed further and popularized in organizational economics by Weick (1976).

The lack of a tight definition of loose coupling and the traditionally informal nature of the discourse in organizational theory have freed the researchers to entertain rich interpretations but have hindered the synthesis of formal models of coupling. Informally, Cameron (1986) acknowledges that organizations face trade-offs between loose and tight coupling and may incorporate elements of both (which proves to be suboptimal in the model; see Footnote 8). Orton andWeick (1990) and Sanchez and Mahoney (1996) emphasize the role of loose coupling in organizations’ adaptability to change (consistent with the conclusion of Theorem 1). Weick and Quinn (1999) link tight coupling to “a preoccupation with short-run adaptation rather than long-run adaptability,” just as the present paper does by assuming that switching the types of all experts is harder for a tightly coupled firm than it is for a loosely coupled one.

In the organizational economics literature, coupling is usually studied in the context of several subunits within an organization. ^[12] By contrast, my model can be interpreted to have a single unit. This assumption does not render the concept of coupling vacuous, because of the model’s dynamic features. The firm’s single unit today can be tightly or loosely coupled with the corresponding unit tomorrow. Indeed, dynamics is the only reason why the trade-off between tight and loose coupling emerges in any discussion of coupling in organizational economics.

The analyzed model does not purport to capture the broad usage of loose coupling encountered in the literature. Reading Orton and Weick (1990) suggests the following (still partial) conceptualization of this broad usage: Organizational units are loosely coupled if they are interdependent but to a lesser degree than is first-best optimal, where the first-best optimal maximizes the organization’s objective function (e. g., profit) while, crucially, assuming unbounded cognitive ability of both the designer and the organization’s members. Thus, loose coupling is observed when tight coupling – first-best optimal by definition – is impossible to discover or implement. This paper’s model operationalizes the described broad concept by building on its critical element: bounded rationality. One can conceive of different kinds of bounded rationality, in a variety of contexts; the proposed operationalization selects but one, and in that, it is limiting.

Furthermore, no model can do complete justice to loose coupling, even in principle. According to Orton1990, loose coupling is a dialectical concept. A dialectical concept is defined to have multiple dimensions, which can be neither fully specified nor even enumerated. ^[13] As a result, a dialectical concept cannot be modelled formally. Instead, intentionally open-ended, it is intended to stimulate a continual conversation. ^[14]

The present paper contributes to such a conversation by focusing on but an aspect of loose coupling. Inevitably, omitted are such diverse phenomena as employees’ sense of self-determination, reduced conflict, psychological safety, and job satisfaction and loneliness – all associated in the literature with loose coupling. Omitted are also alternative modes of coupling: between an organization and its customers, between finding solutions to applied problems and actually acting on these solutions, between various goals and missions of an organization, between intentions and actions, and along the vertical dimension in a hierarchical organization. Omitted is also the interpretation of loose coupling as a measurement error due to the researcher’s inability to see the fine strings tightly tying together the organizational units. Instead, emphasized are bounded rationality and dynamics, whose centrality to loose coupling has been acknowledged by Orton and Weick (1990).

5.2 Organizational Theory: Corporate Culture

One can interpret the firm’s organizational design as a component of corporate culture, consistent with the vision of corporate culture described by Kreps (1990). Because some contingencies are hard to foresee (or contract upon), corporate culture evolves as a collection of simple principles, which will likely not lead to first-best outcomes. In the present model, the coupling type and innovation rate are such principles; they do not lead to first-best outcomes, if only because the firm’s innovation rate is independent of whether the firm’s experts matched the consumer’s taste in the previous period. Kreps (1990) also posits that corporate culture will be roughly aligned with the contingencies that are likely to arise, as it is in the present model.

5.3 Strategy: Creative Disruption

The model’s LCF, which periodically fires its experts and starts all over again, resembles the self-disrupting innovator of Christensen (1997). Indeed, suppose we observe a long history during which the consumer’s taste is unchanged, but this history is unrepresentative because the underlying demand for novelty is high. Initially, this history delivers a lower profit to LCF than it would have to TCF, which would not have periodically fire its experts. So in a sense, LCF “self-disrupts,” but it does so only to rebuild what it has disrupted in the following period. Because the history of the unchanging taste is unrepresentative, however, LCF is bound to eventually outperform TCF. Thus, LCF may look unprofitable at first but is more profitable than TCF on average, in the long run – which is a defining feature of a successful self-disruptor, according to Christensen (1997). ^[15]

5.4 Preference Theory: Novelty

In economics, the idea that individuals like novelty for novelty’s sake goes back at least to Scitovsky (1977). Inspired by research in psychology, ^[16] he argues that if individuals’ inherent desire for novelty is not satisfied by challenging work, latest gadgets, and fashion, this desire will find its outlet in violence. A constant stream of novelty is necessary to keep individuals content with peaceful co-existence. Hence, for Scitovsky, as in the present paper’s model, economic change need not lead to economic growth.

A branch of marketing literature studies the determinants of consumer innovativeness, of which novelty seeking is a prominent component (Hirschman 1980; Tellis, Yin, and Bell 2009). Steenkamp, ter Hofstede, and Wedel (1999) survey consumer innovativeness in Europe and conclude that it varies with country characteristics, such as individualism. A way to operationalize the concept of novelty-seeking is to identify it with the willingness to adopt new goods as long as this willingness cannot be attributed to economic factors (e. g., income). The work of Erumban and de Jong (2006) is suggestive; they report that information-technology adoption within a country is positively correlated with that country’s measure of individualism.

Habituation to novelty is a robust scientific fact (Cerbone and Sadile 1994). The model’s assumption that the consumer’s demand for novelty is increasing in the firm’s innovation rate is consistent with this fact.

5.5 Evolutionary Biology: The Disposable Body Hypothesis

The founder’s choice of whether to promote loose coupling instead of tight coupling resembles Nature’s (i. e., Evolution’s) choice of whether to equip an organism with a disposable body, instead of a perdurable one. Having considered the trade-off, Dawkins (1982, Chapter 14) concludes that the evolution of complex structures is more effectively accomplished by periodically rebuilding an organism, instead of tinkering incrementally with a growing or grown organism. The present paper makes an analogous argument for firms.

TCF resembles a perdurable-body organism. To adapt to the consumer’s ever-changing taste for styles, TCF must endure the costly episodes of employing experts with conflicting expertise. By contrast, LCF resembles a disposable-body organism. LCF avoids the costly episodes of adaptation by undergoing periodic regeneration, which is wasteful when the consumer’s taste does not change. This regeneration is worth the waste, however, when the consumer taste is fickle.

The exploration of the evolutionary hypothesis is identified by Weick (1976) as one of the seven priorities in the study of loose coupling: “If one adopts an evolutionary epistemology, then over time one expects that entities develop a more exquisite fit with their ecological niches. Given that assumption, one then argues that if loosely coupled systems exist and if they have existed for sometime, then they bestow some net advantage to their inhabitants and/or their constituencies. It is not obvious, however, what these advantages are.” In the model, these advantages are identified with increased adaptability to the volatile environment.

5.6 Evolutionary Theory: The Selfish Meme

In the model, the elements of organizational design (the innovation rate and the coupling type) and the target style can be interpreted as memes. Dawkins (1976, Chapter 11) introduces meme, an idea whose content contributes to its likelihood of being replicated. In the model, meme selection can be interpreted to occur at two frequencies. At the high frequency, at the end of every period, the firm designates for survival that target style which would have delivered the highest profit given the consumer’s current taste. At the low frequency, the firm designates for survival that organizational design which leads to the highest expected profit in the long run given the volatility of the environment. In both cases, meme replication favors the more profitable memes.

The multiple-frequency approach to meme evolution has been espoused by Deutsch (2011, Chapter 15) to speculate why some societies progress and others stagnate. He surmises that individuals have a natural proclivity to innovate (i. e., to mutate high-frequency memes), which stagnant societies suppress with a (low-frequency) thou-shalt-not-innovate meme. This meme survives in a stagnant society better than it would have in a progressive one because of another (low-frequency), thou-shalt-not-reason-critically, meme, which ensures that innovative fallacies are not discarded in favor of innovative truths.

The model’s direct counterpart for the (inverse of) thou-shalt-not-innovate meme is the innovation rate. The though-shalt-not-reason-critically meme has no direct counterpart in the model, but the coupling type plays a similar role by making the innovation meme more or less profitable. In particular, as the consumer’s demand for novelty varies, loose coupling and high innovation rate covary, just as critical reasoning and innovation do in Deutsch’s narrative.

6.1 Hypothesis

Observation 1 predicts that if demands for novelty can be ordered across economies, then the highest and the lower equilibria across these economies obey the same order. Figure 7(a) suggests that also multiple equilibria in a single economy can be ordered, with higher innovation equilibria being associated with higher novelty seeking. This positive association prevails simply because the demand for novelty slopes upwards, and all equilibria lie on this curve. These observations inspire the following hypothesis.

Hypothesis 1. Industries or countries that innovate more have consumers who seek novelty more.

Of course, multiple theories may lead to Hypothesis 1. The empirical analysis that follows is suggestive, not dispositive. Its goal is to see whether evidence corroborating the hypothesis can be amassed. The analysis raises more questions about the measurement of innovation and novelty seeking than it answers.

6.2 Data

The unit of observation is a country in 2011. The drug prevalence, taken from the World Drug Report 2012, refers to the ratio of the number of afflicted individuals of ages 15 to 64 to the total population. The patent data are the 2011 entry from the OECD database.

6.3 Evidence

Bardo, Donohew, and Harrington (1996) observe that drug addictions and novelty seeking share a common neurobiological cause and review behavioral evidence for their correlation. Linden (2011) articulates the mechanism. ^[17] To reach the same amount of pleasure, individuals with genetically suppressed dopamine signaling need greater stimulation than the individuals whose dopamine signaling is not suppressed. Cocaine and Ecstasy (a.k.a. MDMA) are stimulants that compensate for the suppressed dopamine signaling by blocking dopamine reuptake, thereby activating dopamine receptors more effectively. Novelty activates the same pleasure circuitry in the brain as stimulant drugs do, and more so in the individuals susceptible to addictions. ^[18]

Inspired by physiological and behavioral links between addictions to stimulants and novelty seeking, one might regard cocaine prevalence and Ecstasy prevalence as proxies for novelty seeking. Per-capita patents are taken to be a proxy for innovation rate.

To assess Hypothesis 1, Figure 8 reports partial regression results: the residuals from regressing log per-capita patents on log-per-capita GDP plotted against the residuals from regressing log per-capita cocaine and ecstasy prevalence on log-per-capita GDP. Controlling for the GDP ensures that the positive relationship (if any) between patents and drug prevalence is not driven by income. Figure 8 documents a statistically significant positive relationship for Ecstasy, consistent with Hypothesis 1. For cocaine, the relationship is positive, but not statistically significant.

Figure 8:

Stimulants. Partial regression plots of log per-capita patents against log drug prevalence.

Figure 9 plots the relationship between patents and amphetamines, cannabis, and opiates. Opiates are depressants; cannabis are a bit of everything (stimulant, depressant, tranquilizer, and hallucinogen), but not much of a stimulant; “trip” reports in online forums indicate that amphetamine (when not referring to Ecstasy), a stimulant, causes a surge in energy but not the bliss (dopamine rush) reported by Ecstasy users. No statistically significant relationship is observed, consistent with the interpretation that amphetamines (except Ecstasy), cannabis, and opiates are poor proxies for novelty seeking.

Figure 9:

Other drugs. Partial regression plots of log per-capita patents against log drug prevalence. None of the fitted linear relationships is statistically significant.