Macroeconomic Gentrification

Christophre Georges∗ Department of Economics Hamilton College

Draft: August 7, 2019

Abstract

This paper explores the macroeconomic relationship between product innovation and consumption in-equality. A rise in rents accruing to high income consumers can shift both production and R&D spendingtoward products targeted to that class of consumer. The increase in income inequality will then have amagnified impact on consumption inequality through induced changes in product quality and availability.We label this process macroeconomic gentrification. We extend the agent-based macroeconomic model inGeorges (2011, 2018) and document three channels that contribute to this process: an income channel, avariety channel, and an innovation channel. We further find that the relationship in the model betweenthe distribution of income and long run economic dynamism can be highly nonlinear.

1 Introduction

There has been substantial recent interest in thecauses and consequences of rising income inequal-ity in the U.S. and some other advanced capitalisteconomies in the past 40 years. There is also evi-dence, reviewed in the next section, that rising in-come inequality has gone hand in hand with risingconsumption inequality, not simply in consumptionlevels, but also in the composition of consumptiongoods and services.

That a variety of consumers with high incomes orrising income expectations, ranging from young pro-fessionals to the ultra rich, demand different goodsand qualities of goods from consumers with lower in-

∗Christophre Georges, Department of Economics, HamiltonCollege, Clinton, NY 13323, USA. cgeorges@hamilton.edu,315-859-4472, http://people.hamilton.edu/christophre-georges. An earlier version of this paper circulated as“Innovation and Consumption Inequality in an Agent-BasedMacroeconomic Model.” I thank Katharine Fuzesi, JohnGonzalez, Russell Glick, and Daniel Kraynack for theirexcellent research assistance. All errors are mine. JELClassification: E10, O30, E25. Keywords: Innovation,Consumption Inequality, Economic Growth, Gentrification,Agent-based Macroeconomics

comes and different class locations accords with bothacademic research and casual observation. Examplesinclude financial services, luxury apartments, fitnesstrainers, after school programs, private schools andday care, espresso drinks, high end restaurants andboutique shops, delivery services, private entertain-ment, private tours, exclusive treatment on cruisesand in entertainment venues, nannies, art galleries,and limo services.1 In gentrifying neighborhoods, theentry or quality upgrading of these goods and ser-vices displaces goods and services ranging from repairshops, to laundromats, affordable apartments, diners,basic grocery and variety stores, and so forth.2

In this paper, we propose that a similar process can

1For one example, the current and planned development inManhattan around the locations of the new Whitney Museum,High Line park, and Hudson Yards is transforming these for-merly low-rent neighborhoods. See the promotional website forthe Hudson Yards project http://livehudsonyards.com/ andthe Jeremiah’s Vanishing New York blog entry on that projecthttp://vanishingnewyork.blogspot.com/2015/02/hudson-yards-effect.html. For another example see “In an Age ofPrivilege, Not Everyone Is in the Same Boat.” New YorkTimes, April 23, 2016.

2The provision of public services, such as public transporta-tion, housing, schools, parks, and health care is a closely re-lated issue that we will not address here.

1

operate at the level of the macroeconomy. We con-sider a model in which rents accruing to high incomeworkers may induce innovation in the high incomeconsumption goods sector, to the benefit of thoseworkers, while also potentially reducing the welfare oflower income workers by reducing their income sharesand the variety and quality of goods that cater tothem. We label this process macroeconomic gentrifi-cation.

We build off of the agent based macroeconomicmodel in Georges (2011, 2018) in which both growthand business cycle dynamics are grounded in prod-uct innovation. The hedonic approach to the prod-uct space there provides a simple and flexible way tocharacterize the ongoing emergence of new and im-proved products through product innovation. Thatinnovation is driven by R&D investments that arealso a driver of aggregate demand in the model. Themodel is capable of producing complex disequilibriumdynamics due to the ongoing churning of firms andproduct shares and the tension between the near termand medium term effects of R&D investments on in-dividual firms’ profits.

In the present paper we develop an extension ofthat model in which production workers and over-head workers consume different baskets of goods. Wedocument three channels that contribute to macroe-conomic gentrification in this model: an income chan-nel, a variety channel, and an innovation channel. Anincrease in rents accruing to overhead labor shiftsrelative demand and thus production between thetwo consumer goods sectors. It also influences R&Dspending in the two sectors, yielding a period of in-creased product innovation in the one sector and in-novative stagnation in the other. We further findthat the relationship in the model between the dis-tribution of income and long run economic dynamismcan be highly nonlinear.

2 Background

There is substantial ongoing debate about the im-portance of various causes of recent increases in in-come inequality, particularly trade, technology, andmarket power. Much of the recent literature has fo-

cussed on skill biased technological change. Techno-logical advances appear to be contributing to a hol-lowing out of the middle of the distribution of la-bor income and may be progressively replacing everhigher skilled but routine types of labor (e.g., Au-tor and Acemoglu (2011), Brynjolfsson and McAfee(2014), Acemoglu and Restrepo (2018)). There is,however, also increasing interest in rent-based expla-nations (e.g., Piketty et al. (2014), Reich (2015),Furman and Orszag (2018), Keller and Olney (2018),Bota et al. (2019)). Our analysis fits into this lat-ter category. We consider changes in a variety ofincome related parameters of the model but focus onthe case of an exogenous increase in product markupswhich drives an increase in rents to salaried employ-ees in the model. While there is disagreement overthe magnitude of the increase, (Basu, 2019) a num-ber of researchers have documented a secular increasein average markups in the U.S. over the past sev-eral decades (e.g., De Loecker, Eeckhout, and Unger.2018).

There is evidence that rising income inequality inthe U.S. has gone hand in hand with rising consump-tion inequality. For example, Attanasio and Pista-ferri (2016) reviews this literature. While much ofthat literature considers inequality in levels of con-sumption spending, several recent papers also con-sider the composition of that spending. Eisenberg(2014) provides evidence that, because some up-grades in PC processors in the early 2000s replacedrather than supplemented lower performance chips,the benefits of these innovations went disproportion-ately to relatively affluent consumers. Jaravel (2019)provides evidence from scanner data that the rela-tive demand for consumer goods influences the di-rection of product innovation. He finds that innova-tions in consumer products from 2004-2013 focussedon consumer goods catering to the upper end of theincome distribution and that the skewed proliferationof product variety also led to relatively lower con-sumer price inflation for affluent consumers due to acombination of increased product variety and lowermarginal cost. Murphy (2016) provides additionalevidence that technological change has been biasednot only toward skilled labor, but also toward rela-tively high income consumption goods. He provides a

2

model in which technological change is assumed to beboth skill and sector biased, and shows that not onlyare high income workers made better off, but welfaremay fall in absolute terms for low income workers.

Our analysis complements this empirical and the-oretical literature. In our model, increases in rentsaccruing to high income workers may induce shiftsof both product quality and product variety towardthe high income consumption goods sector, to thebenefit of those workers, independently of processinnovation. Proper measurement of the increase inconsumption inequality requires adjustment for bothproduct quality and product variety.

Our approach to modeling innovation and eco-nomic growth follows in the footsteps of a rich lit-erature that documents and models product innova-tion (Chen and Chie (2005,2007,2013,2014), Valente(2012), Marengo and Valente (2010), Windrum andBirechenhall (1998), Ciarli, Lorenz, Savona, Valente(2010, 2016), Garas and Lapatinas (2017), Hidalgoet. al. (2007), Klette and Kortum (2004), Sutton(2007), Akcigit and Kerr (2018), Acemoglu, Akcigit,Bloom, and Kerr (2018)) and churning in productmarkets (Broda and Weinstein (2010), Bernard, Red-ding and Schott (2010), Hottman et al. (2016) Ar-gente, Lee and Moreira (2017)).

The current paper is also in the recent tradi-tion of agent-based macroeconomics. This litera-ture builds macroeconomic models from microfoun-dations, but in contrast to standard macroeconomicpractice, treats the underlying agents as highly het-erogeneous, boundedly rational, and adaptive, anddoes not assume a priori that markets clear. See e.g.,Dosi et al. (2005), Delli Gatti et al. (2008), Dawidet al. (2016), Hommes and Iori (2015), Russo et al.(2016), and Fagiolo and Roventini (2017), Dawid andDelli Gati (2018).

3 The MacroeconomicEnvironment

Here are the fundamental features of the model.

• There are n1 + n2 = n firms, each of whichproduces a single good. Type 1 firms produce

goods consumed by wage earning productionworkers. Type 2 firms produce goods consumedby salaried overhead workers. The total numberof firms n is fixed.

• There are m characteristics of goods that all con-sumers care about. Each good embodies distinctquantities of these characteristics at any giventime. Product innovation affects these quanti-ties.

• The probability that a firm experiences a prod-uct innovation at any time depends on its recentinvestments in R&D, which in turn is the out-come of a discrete choice rule.

• Each firm produces with both overhead and vari-able labor. It forecasts the final demand forits product by extrapolating from recent expe-rience, sets its price as a constant mark-up overmarginal cost, and plans to produce enough ofits good to meet its expected final demand giventhis price.

• There are two distinct representative consumers,one representing production workers, the otheroverhead workers. They inhabit distinct seg-ments of the product market. Each spends allof her labor income each period on consumptiongoods and searches for better combinations ofproducts to buy within her budget. The formersearches only over the n1 products (firms) thatcater to her consumer type, and the latter onlyover the n2 products that cater to her consumertype.

• If a firm becomes insolvent, it exits the marketand is replaced by a new entrant. The new en-trant selects which of the two markets to enteraccording to a discrete choice rule.

4 Consumer Preferences

The representative consumers’ preferences are de-fined on the characteristics space, and we assumethat the two types of consumers have identical utilitymappings in this space. Specifically, for either type

3

of consumer, the momentary utility from consumingthe vector z ∈ Rm of hedonic characteristics is u(z).

In addition to this utility function, each consumerhas the same home production function g(q) thatmaps bundles q ∈ Rn of products into perceived bun-dles z ∈ Rm of the characteristics.3

Here we will simplify the model in Georges (2011,2018) by removing direct product complementarities.Product complementarities are clearly important inpractice both for consumer utility and for networkeffects that may influence the macroeconomic propa-gation of microeconomic disturbances. However, wewill set these aside for the moment in order to focuson other aspects of the model.

Thus, we assume that the representative consumersimply associates with each good i a set of integercharacteristic magnitudes zi ∈ Rm per unit of thegood.4

The home production g(q) is a CES aggregatorthat, for each characteristic j, aggregates these char-acteristic magnitudes over products i:

zj =

[n∑i=1

(qi · zi,j)ρ1]1/ρ1

(1)

with ρ1 < 1. The CES form of (1) introduces sometaste for variety across products.5

The utility function u for the representative con-sumer over hedonic characteristics is also CES, so

3As noted in Georges (2011, 2018), this is essentially the ap-proach taken by Lancaster, and shares some similarities withothers such as Becker (1965) and Strotz (1957). The primarydeviation of our approach from that of Lancaster is the con-struction of our home production function g(q).

4These are the base characteristic magnitudes z-basei inGeorges(2011, 2018). Intuitively, by ignoring product com-plementarities, we are assuming that products like smartphones or spinach have characteristics (entertainment, nutri-tion, crunchiness, and so forth) that are invariant with respectto what other goods are consumed. I.e., electricity, program-ming content, salad dressing, and so forth do not indepen-dently alter the utility from consuming these goods.

5Note that if ρ1 = 1, the number of viable products in theeconomy would be strongly limited by the number of hedo-nic elements, as in Lancaster, who employs a linear activityanalysis to link goods and characteristics.

that

u =

m∑j=1

zρ2j

1/ρ2

(2)

Thus, utility takes a nested CES form. Consumersvalue variety in both hedonic characteristics and inproducts.6

5 Product Innovation

A product innovation takes the form of the creationof a new or improved product that, from the pointof view of the consumer, combines a new set of char-acteristics, or enhances an existing set of character-istics. The new product will be successful if it is per-ceived as offering utility (in combination with othergoods) at lower cost than current alternatives. Theproduct may fail due to high cost, poor search by con-sumers, or poor timing in terms of the availability ordesirability of other goods.

A product innovation is then a set of random (inte-ger) increments (positive or negative) to one or moreelements of zi. Product innovation for continuingfirms is strictly by mutation. Product innovationscan be positive or negative. I.e., firms can mistakenlymake changes to their products that consumers donot like. However, there is a floor of zero on charac-teristic values. Further, innovations operate throughpreferential attachment; for a firm that experiencesa product innovation, there is a greater likelihood ofmutation of non-zero hedonic elements.7

The probability that a firm experiences productinnovation in any given period t is increasing in itsrecent R&D activity.

6While we allow for substantial heterogeneity among prod-ucts and firms, our representation of consumers as belongingto only two types and exhibiting fixed rather than evolvingpreferences is clearly highly stylized. While, richer represen-tations of consumers and their behavior have been developedelsewhere (e.g., Windrum and Birchenhal (1998), Aversi etal.(1999), Valente (2012), Garas and Lapatinas (2017)), we canget a fair bit of mileage out of our reduced heterogeneity for-mulation while mitigating the computational bottleneck thatarises in consumer search when the numbers of firms and char-acteristics is large (see below).

7This weak form of preferential attachment supports spe-cialization in the hedonic quality space.

4

6 R&D

The R&D investment choice is binary – in a givenperiod a firm either does or does not engage in afixed amount of R&D. If a firm engages in R&D ina given period, must hire additional overhead laborr at salary rate s and so incurs additional overheadlabor costs R = r · s in that period.

In making its R&D investment decision at anytime, the firm follows a discrete choice rule. Itcompares the recent profits of two reference groups,and decides probabilistically on this basis whether toswitch its investment state. The two reference groupsare either firms with relatively high and low recentR&D investment or firms in the two markets.

For example, if the reference groups are firms withrelatively high and low recent R&D investment, thefirm observes the average recent profits πH and πL ofother firms with relatively high and low recent R&Dactivity.8 If the firm is in the lower profit group, itthen switches its R&D behavior with a probabilityrelated to the profitability differential between thetwo groups. Specifically, it switches its behavior withprobability 2Φ− 1, where

Φ =eγπ1

eγπ1 + eγπ2(3)

γ > 0 measures the intensity of choice and π1 and π2are measures of the average recent profits of the highand low profit R%D groups.9

A similar rule is followed if the firm’s current refer-ence groups are firms in market 1 and firms in mar-ket 2. Firms select between the two pairs of refer-ence groups probabilistically, and there is addition-ally some purely random variation in R&D choice.

7 Production and Employment

Each firm i produces its good with labor subject toa fixed labor productivity Ai and hires enough pro-

8Each firm’s recent profits and recent R&D activity are (re-spectively) measured as exponentially weighted moving aver-ages of its past profits and R&D activity.

9I.e., if πH > πL, then π1 = πH and π2 = πL, and firmswith relatively low recent R&D activity switch R&D on with aprobability that is greater the larger is the difference betweenπ1 and π2.

duction labor to meet its production goals period byperiod. Each firm also must employ a fixed numberh of overhead workers at salary rate s and so incursfixed overhead labor cost H = h · s in each periodas well as the additional overhead R&D labor costR = r · s in any period in which it is engaged inR&D. In this paper, our focus is on product innova-tion rather than process innovation. Consequently,we suppress process innovation and hold Ai, h, andr constant over time.

8 Consumer Search

Each of the representative consumers spends her en-tire income each period and selects the shares of herincome to spend on each good. Each period, sheexperiments with random variations on her currentset of shares. Specifically, she considers randomlyshifting consumption shares between some numberof goods in her own market (market 1 or market 2),over some number of trials, and selects among thosetrials the set of shares that yields the highest utilitywith her current income. While each consumer en-gages in limited undirected search and is not able toglobally maximize utility each period, she can alwaysstick to her current share mix, and so never selectsnew share mixes that reduce utility below the utilityafforded by the current one. I.e., the experimentationis a thought exercise not an act of physical trial anderror.

9 Entry and Exit

When a firm does not have enough working capital tofinance production, it shuts down and is replaced. Atthis time, the new firm may have the opportunity toswitch markets. In making this decision it followsa discrete choice rule based on the relative recentprofitability of firms in the two markets. The newfirm adopts (imitates) the product characteristics ofa randomly selected existing firm, and either retainsthe exiting firm’s current share of consumer demandor has some market share reallocated to it from otherfirms, depending on which market it enters. The im-

5

itated firm may or may not be in the entering firm’sown market, depending on a bias parameter, allowingsome diffusion of product innovation across marketsover time. The new firm is also seeded with startupcapital.

10 Timing

The timing of events within each period t is as follows:

• R&D: firms chose their current R&D investmentlevels (0 or 1).

• Innovation: firms experience product innovationwith probabilities related to their recent R&Dinvestments.

• Production: firms forecast sales, hire productionlabor, and produce to meet forecasted demand.

• Incomes: all firms pay wages to their productionworkers and salaries to their overhead workers.

• Consumer Search: the representative consumerssearch and update their consumption baskets.

• Sales: the consumers spend all of their labor in-comes (above) on their consumption baskets.

• Entry and Exit: firms with insufficient workingcapital are replaced. The new entrants may mi-grate across the two markets.

11 Representative AgentBenchmark

Before moving to the full heterogeneous agent model,it is worth considering a representative agent equilib-rium benchmark.

Consider the case in which all firms are identicalwith the exception of which of the two markets it in-habits (i.e., which consumer type it caters to). Thereare n1 firms catering to the consumption of produc-tion workers (market 2) and n2 firms catering to theconsumption of salaried (overhead) workers (market2). Each firm of type i starts with a 1/ni share of

the total market. Suppose further that all firms en-gage in R&D in every period and experience identicalinnovations over time.

In this case, if n1 and n2 are fixed, then the equalindividual market shares will persist, since there isno reason for consumers of either type to switch be-tween firms in its own market with identical prod-uct qualities and identical prices. Further, there is aunique equilibrium for the real production and salesof consumer goods at which demand and supply arein balance. At this equilibrium, aggregate real activ-ity depends on the markup η, the per firm overheadlabor requirements h and r, the salary rate s (foroverhead workers), the wage rate W (for productionworkers), labor productivity A (for production work-ers), and the numbers of firms n1 and n2 in the twomarkets. Specifically, at this equilibrium, total pro-duction Y = Y1+Y2 = ( 1

η−1 ) · AW ·(h+r) ·s ·(n1+n2).See below for details. Note that this total equilibriumproduction is independent of both product qualityand the relative proportion n1/n2 of the two types offirms. Further, this equilibrium is a steady state ofthe agent dynamics in the model and is locally stableunder those dynamics. If, for example, firms all startwith production less than steady state production,then since the markup η > 1, demand will be greaterthan production for each firm, and production willconverge over time to the steady state equilibrium.

We can see this as follows. Since firms within eachof the two markets are identical, they produce iden-tical quantities q1 or q2 of their goods. The totalincome (E1) spent in market 1 is the total income ofproduction workers, and in market 2 (E2) is the totalincome of overhead workers:

E1 =W

A· (n1 · q1 + n2 · q2)

E2 = (h · s+ r · s) · (n1 + n2) (4)

Each firm, regardless of market, charges an identicalprice p for its good, which is a fixed markup η onmarginal cost

p = η ·MC

MC = WA (5)

6

and so produces and sells

qi =Eini · p

(6)

units of its good.These relationships yield the following steady state

equilibrium value for (per firm) production for eachof the two firm types.

q∗1 =(n1+n2

n1) · (h+ r) · s · AW(η − 1) · η

q∗2 =(n1+n2

n2) · (h+ r) · s · AW

η(7)

Thus the total output in markets 1 and 2 in steadystate equilibrium are:

Y ∗1 =

(n1 + n2) · (h+ r) · s · AW(η − 1) · η

Y ∗2 =

(n1 + n2) · (h+ r) · s · AWη

(8)

and total steady state output is:

Y ∗ = n1 ·q∗1+n2 ·q∗2 =(n1 + n2) · (h+ r) · s · AW

(η − 1)(9)

If in this representative agent case firms followsimple one period adaptive expectations for demand,then firm level output dynamics will converge to thesteady state. For type 2 firms, demand is constant at(H +R) · (n1 + n2)/n2, so supply will converge to q∗2in a single period. For type 1 firms, output dynamicsare then given by:

q1,t =1

η· q1,t−1 +

1

η· n2n1· q∗2 (10)

Thus, given η > 1, the steady state equilibrium q∗1for type 1 firms is also asymptotically stable.

Focussing on the steady state equilibrium, we cansee that for both firm types, ∂q∗/∂(h+ r) > 0,∂q∗/∂s > 0, ∂q∗/∂A > 0, ∂q∗/∂W < 0, and∂q∗/∂η < 0. These are all demand driven.

• An increase in the salaries s or employment re-quirements h + r of overhead labor raises the

incomes of overhead workers, raising demand inmarket 2. Since more production labor is re-quired in market 2, the incomes and demands ofproduction workers for goods in market 1 alsoincreases. Thus there is a general increase in de-mand distributed across the two markets and anincrease in output and the employment of pro-duction workers in both markets.

• An increase in labor productivity A will causefirms to lower their prices, raising demand byboth types of worker and thus equilibrium out-put in both markets.

• Similarly an increase in the wage rate W of pro-duction workers or in the mark-up η will causefirms to raise their prices, lowering AD and equi-librium output.

Clearly Y ∗1 , Y ∗

2 and Y ∗ will be constant as longas there is no change in the parameters h, r, s, A,W , η and n. If we were to allow them to change,growth in the number of firms n or the productivityof production labor A would cause equilibrium to-tal production Y ∗ to grow over time, while balancedgrowth in production wages W and labor produc-tivity A would have no impact on equilibrium pro-duction. Note that, in the full heterogeneous agentmodel, while all of the parameters above are fixed, thefraction of firms adopting R&D investment, ratherthan being fixed at 1, varies endogenously, contribut-ing to the disequilibrium dynamics of the model.

Note that the steady state levels of both total andmarket level output Y ∗, Y ∗

1 , and Y ∗2 are indepen-

dent of the relative number of firms n1/n2 in the twomarkets. Thus, varying that ratio, while holding thetotal number of firms n = n1 + n2 constant, affectsthe relative sizes q∗1 and q∗2 of individual firms butleaves total production in each of the two marketsunchanged.

Further, and importantly, the relative shares of in-comes earned by each of the two classes (wage andsalary labor) and spent in the two markets in steadystate equilibrium is entirely independent of all param-eters in the model with the exception of the markup

7

η. For example the ratio Y ∗1 /Y

∗2 is:

Y ∗1

Y ∗2

=1

η − 1(11)

and both the share of real income and real consump-tion going to production workers in steady state is1/η.

The latter result is not an artifact of the perfectlyelastic supply of labor assumed here and is the casefor any elasticity of labor supply in either labor mar-ket. Equilibrium requires that the supply and de-mand for labor come into balance in this way viasome combination of output and income adjustment,given the fixed markup.10

This places a strong restriction on income inequal-ity in the model. For example, if the salary rateor employment of overhead workers increases exoge-nously (H+R increases), this will cause the economyto expand but will not lead to an increase in the in-come share of overhead labor nor the relative size orprofitability of market 2. However, an increase in themarkup η will have those effects in steady state equi-librium, while shrinking output and the employmentof production labor in both markets.

There is nothing in the analysis above that requiresprofit to be equalized across markets. Since all in-come in the model accrues to the two types of labor(production and overhead), i.e., to wages and salaries,average profit will be zero in steady state equilibrium.However, if the representative firm in one market hasgreater output q∗ than its counterpart in the othermarket, then it will spread its overhead costs overmore output and so earn positive profit, while therepresentative firm in the other market earns nega-tive profit.11

10For example, with the fixed nominal wage w assumed inthis paper, an increase in η raises prices, lowering real incomesspent in both markets. Production workers are laid off, whileoverhead workers are not (since n1+n2 is unchanged), skewingdemand toward market 2.

11On average, across the two representative firms, revenuesnet of the cost of production labor are just great enough tocover all overhead labor costs. Once we move to the hetero-geneous firm case, in the comparable steady state equilibrium,profits are distributed around zero across all firms and aroundmarket specific averages that need not be zero within eachmarket. On the one hand, firms will vary as to R&D invest-

Once, however, we add entry and exit across mar-kets, then n1/n2 can adjust to equalize profits ofthe representative firms at zero in the representativeagent steady state equilibrium. This adjustment willbe central to the main results of the paper.

Finally, but importantly, note that the representa-tive agent steady state equilibrium levels of produc-tion above are entirely independent of product qual-ity. Improvements in product quality will, however,increase consumer utility, or equivalently, the qualityadjusted value of total production at this equilibrium.Specifically, given the nested CES formulation of util-ity, if the magnitudes of all product characteristicsin a given market grow at rate g due to innovation,then the growth rate of consumer utility in that mar-ket will converge in the long run to g. If the mag-nitudes of different characteristics grow at differentrates, the growth rate of utility will converge to therate of growth of the fastest growing characteristic.All else equal, the long run growth path of utility forconsumers in that market will be lower in the lattercase than in the former case.

The CES formulation also implies that consumersin either market benefit from greater variety of prod-ucts in their market. Thus, at any moment in time,the utility of each worker type i depends on its ownincome and the past product innovation in its ownmarket, but also the number of products ni in itsown market.

ment status, with firms who are not engaging in R&D invest-ment saving overhead cost R per period. On the other hand,firms will also vary with respect to demand shares (driven byboth aggregate market shares and individual product qualitieswhich are themselves related to past investments in R&D), andfirms with relatively high demand shares are able to spreadoverhead cost over greater sales. Thus, for two firms with thesame overhead cost (i.e., the same current R&D investmentstatus), the firm with greater demand for its product will havehigher profit, while for two firms with the same product de-mand shares, the one with the lower overhead cost (lower cur-rent R&D investment) will have higher profit. Firms that facechronic losses eventually fail and are replaced.

8

12 Features of the Heteroge-neous Agent Model

As noted above, if we eliminate all heterogeneity inthe model, other than the two types of consumerand firm, there is a steady state equilibrium whichis stable under the simple dynamics described above.Assuming that all firms engage in R&D investment,steady state aggregate output is a multiple of thecombined overhead labor cost H +R and is indepen-dent of product quality. Quality adjusted output andconsumer utility, on the other hand, will grow overtime in equilibrium as product quality improves.

With firm heterogeneity, the evolution of the he-donic characteristics of firms’ products will influenceproduct market shares as well as aggregate activityand living standards. The stochastic evolution ofproduct quality with zero reflective lower bounds onindividual characteristic magnitudes will tend to gen-erate skewness in the distribution of individual prod-uct qualities and thus in the distribution of marketshares.12

Turning to aggregate activity, with heterogeneity,as discussed in Georges (2018), there is a tension be-tween the short term and medium term effects ofR&D at the firm level that feeds back with the sociallearning to allow complex dynamics to emerge in ag-gregate R&D spending. That spending in turn drivesboth aggregate output dynamics as well as innovationand thus growth in consumer utility. Any individualfirm’s R&D investment creates an aggregate demandexternality that initially impacts market 2 and then(relatively rapidly) filters throughout the economy.Any innovation that results stochastically from thatinvestment is initially purely market, and thus class,specific, though may also eventually diffuse across thetwo markets.

Here, we are particularly interested in the potentialfor the divergence of consumer utility across classes ofworker/consumers. This can be driven by changes inthe relative incomes of the two consumer classes, by

12This skewness is limited by the variety loving aspect ofconsumer preferences and the ratios m/n1 and m/n2 whichcharacterize the opportunities in each market for firms to nichein the product space.

different rates of product innovation in the two mar-kets, and by changes in relative variety across the twomarkets. Below we focus on the impact of exogenousincreases in markups and in the salary rates of over-head workers on these three drivers of consumptioninequality.

According to the analysis in section 11, an increasein the common markup will cause prices to rise andthus AD to fall in both markets. The share of incomegoing to salaried workers increases, while the sharefalls for production labor, due to the reduction inemployment of the latter. This shift in income shareswill be matched by a shift in consumption shares, butwill also have the effect of shifting profit from mar-ket 1 to market 2. This should, in turn lead to botha relative increase in R&D investment in market 2and the relocation of firms from market 1 to market2 over time. The latter will eventually wipe out thesupernormal profits in market 1 but will also perma-nently increase the relative variety of consumer goodsavailable to salaried workers. Meanwhile the periodof supernormal R&D investment should temporarilyboost product innovation that caters to the consump-tion of salaried workers.13 So in principle, we may geta triple wedge between the utility of the two classesin the form of divergent levels, variety, and qualitiesof consumption each of which persists after profitshave normalized.

As noted in section 11, we would not expect thesame to be the case for an increase in salary rates(or overhead labor requirements, or a decrease inwages), as once output and employment expand inresponse, income shares and relative market sizes willreturn to their previous equilibrium if the markup hasnot changed. We would expect this to happen on amuch faster time scale than the relocation of firmsacross markets which is required in the aftermath ofa change in the markup. However, increases in theseoverhead employment and compensation rates mayimpact innovation both directly, via increases in theoverhead costs of R&D, and indirectly via their ef-fects on turnover. Turnover rates can have positiveindirect effects on innovation if entrants imitate firms

13Note that R&D and innovation are fixed and uniformacross firms in the representative agent version of the modelin section 11, so this innovation effect is suppressed there.

9

with higher quality than firms that are exiting.

Finally, it is worth noting that in an adaptive en-vironment, profit provides incentives for individualfirms to engage in R&D and select the market to exitor enter, but these incentives can be clouded by bothnoise and general disequilibrium feedback and so maynot be entirely straightforward. Ultimately the out-comes are emergent.

13 A Computational Experi-ment

Here we conduct a computational experiment in thefull model by simultaneously increasing the markup ηand the salary rate s. Specifically, in period 2000 themarkup is increased from 2.0 to 3.0 and the salaryrate from 1.0 to 2.0. This precipitates a shift in in-come shares from production labor to overhead laborand a movement of production and production laborfrom market 1 to market 2 but is aggregate employ-ment neutral in the representative agent benchmark.

Results are illustrated first via a representativerun in Figures 1-5 below. The increase in productmarkups η lowers the real wage, while the parallelreduction in the real salary rate is more than off-set by the increase in the nominal salary rate. Wesee then demand falling immediately in market twoand rising in market one. Production and the em-ployment of production labor shifts from market oneto market two, while the utility of salaried overheadworkers rises and the utility of wage earning produc-tion workers falls.

At the same time, profits that had come into equal-ity across the two markets now increase in market2 and decrease in market 1. This has two effects.Firms that fail in market 1 begin to shift to re-enterin market 2. Similarly, R&D investment responds tothe changes in profit by tending to increase in mar-ket 2 and decrease in market 1. Thus, we have twofurther effects on relative utility – a variety effectand an innovation effect. The movement over time offirms from market 1 to market 2 offers overhead la-bor greater product variety and reduces product vari-ety for production laborers. And the temporary shift

0

2

4

6

0 1000 2000 3000 4000 5000Time

Util

ity

Utility By Consumer Type

Figure 1: Utility of production workers (U1, blue) andoverhead workers (U2, red) following a simultaneousincrease in the common markup η from 2.0 to 3.0 andthe salary rate s from 1.0 to 2.0 in time period 2000.The total number of firms in this simulation is 100.

1.0

1.5

2.0

2.5

0 1000 2000 3000 4000 5000Time

Rel

ativ

e U

tility

Ratio of Overhead to Production Employee Utility

Figure 2: Relative utility of overhead workers to pro-duction workers: U2/U1.

10

−0.8

−0.4

0.0

0.4

0 1000 2000 3000 4000 5000Time

Pro

fit

Profit By Market

Figure 3: Average profit of firms in each market.

40

50

60

0 1000 2000 3000 4000 5000Time

Num

ber

of F

irm

s

Number of Firms by Product Market

Figure 4: Number of firms in each market.

in R&D investment causes a tendency for relativelygreater product innovation in market 2 for some time.

More generally, these effects are conditioned by anumber of factors. The increase in the salary ratemakes R&D investment more expensive, and so lessprofitable in the short term. The increase in overheadcosts also drives unprofitable firms to failure morequickly, increasing turnover rates. Depending on thedegree of own market bias in imitation for reenter-ing firms, there can me more or less drift in averageproduct quality across the two markets. If reenter-ing firms can imitate firms in the other market, thendrift in product quality is limited and the high lev-

0

20

40

0 1000 2000 3000 4000 5000Time

R&

D E

xpen

ditu

re

R&D By Market

Figure 5: RandD spending in each market.

els of innovation in market 2 ultimately trickle downto product quality in market 1. In the present ex-periment, there is a high degree of own market biasand thus relatively slow diffusion of innovation acrossmarkets.

14 Monte Carlo Experiment

The qualitative results in the last section are robustover multiple simulations and simulations with largernumbers of firms. Here we replicate figures 1-5 in-creasing the number of firms to 1000 and plotting av-erage outcomes over 1000 runs with different randomseeds. We also include bands indicating one standarddeviation above and below the average for each timestep.14

To get some sense of the relative importance ofthe three factors driving consumption inequality, wecan consider a benchmark prediction from the rep-resentative agent case. With initial markup η = 2.0and n1 = n2, we would have q∗2/q

∗1 = 1.0 in steady

state. A simultaneous increase in η from 2.0 to 3.0and the salary rate s from 1.0 to 2.0, holding productquality and the number of firms in each market con-stant, would simply shift income from wage labor to

14Note that the average utility is higher in the MC experi-ment below than in the individual run illustration above dueto the larger number of firms, which yields greater variety andthus utility.

11

0.0

2.5

5.0

7.5

10.0

0 1000 2000 3000 4000 5000Time

Util

ity

Utility By Consumer Type − MC

Figure 6: Utility for production workers (U1, blue)and overhead workers (U2, red). Average across 1000runs for each time period with band indicating onestandard deviation above and below the average. Themarkup η is increased from 2.0 to 3.0 and the salaryrate s from 1.0 to 2.0 in time period 2000. The totalnumber of firms in the two markets is 1000.

1.0

1.5

2.0

2.5

0 1000 2000 3000 4000 5000Time

Rel

ativ

e U

tility

Ratio of Overhead to Production Employee Utility − MC

Figure 7: Ratio of utility of overhead workers to util-ity of production workers: U2/U1.

−0.4

0.0

0.4

0 1000 2000 3000 4000 5000Time

Pro

fit

Profit By Market − MC

Figure 8: Average profit of firms in each market.

400

500

600

0 1000 2000 3000 4000 5000Time

Num

ber

of F

irm

sNumber of Firms by Product Market − MC

Figure 9: Number of firms in each market.

100

200

300

400

500

0 1000 2000 3000 4000 5000Time

R&

D E

xpen

ditu

re

R&D By Market − MC

Figure 10: RandD spending in each market.

12

overhead labor in equilibrium, increasing Y2/Y1 andU2/U1 from 1.0 to 2.0. We see this prediction closelymatched in both the individual run in Section 13 andthe Monte Carlo experiment in Section 14, with rapidincreases in U2/U1 following the distributional shockin period 2000.

Returning to the representative agent benchmark,over time, firms would respond to the increase in rela-tive profit π2/π1 by migrating from market 1 to mar-ket 2, and n2/n1 would eventually rise from 1.0 to2.0, at which point q∗2/q

∗1 would have returned to 1.0.

Thus, the relative increase in consumption for over-head workers ultimately would express itself entirelyas an increase in product variety in the consumptionbasket of overhead workers (and a decrease in vari-ety for production workers), rather than a change inthe quantities consumed of individual goods. In thespecial case in which all firms’ product qualities arefixed and identical, in the sense that all characteristicmagnitudes zi,j are identical across both i and j, wewould see U2/U1 increase to approximately 2.38 inequilibrium, so an additional 38% increase due to theproduct variety effect. Adding in temporarily differ-ent rates of innovation would then add a third wedgebetween utility in the two markets.

We can see that in both the single run shown inFigures 1-5 and the Monte Carlo experiment shownin Figures 6-10, the utility ratios U2/U1 peak wellabove 2.38, at approximately 2.70 for the single runand 2.66 for the average of the MC runs, respectively.Following the distributional shock in period 2000,profit in market 2 rises above that in market 1 for anextended time driving both n2/n1 to increase perma-nently (and thus the variety effect) and R&D2

n2/R&D1

n1

to rise above 1 during that time (and thus an inno-vation effect). A back of the envelope measure ofthe average innovation effect in the MC experimentis the difference between 2.66 and 2.38, or an addi-tional 28% increase in U2/U1 due to the innovationeffect.

0

1

2

3

4

0 1000 2000 3000 4000 5000

Time

Util

ity

Utility By Consumer Type

Figure 11: U1 (blue) and U2 (red) in a run in which µis increased from 2.0 to 2.4 gradually during periods2000-4000. The number of firms is 100.

15 Competition, Distribution,and Economic Dynamism

Considering the markup in isolation, increasing themarkup in both markets tends to increase pricesand reduce demand, production and utility for bothclasses of workers in the short term. Innovation willcontinue over time, working against the level effectloss of utility from the loss of competition. It isstraightforward then that if the markup rises slowlyover a period of time, utility will be pulled down bythe rising markups while being pushed up by prod-uct innovation. This is illustrated in Figures 11-12,in which the common markup µ rises gradually be-tween periods 2000-4000. We see that between thenegative effect of the rising markup and the ongoingpositive effect of the background innovation, the util-ity of production workers stagnates during the transi-tional period, while impact on the growth of utility ofsalaried workers is more muted. Production workersare hit harder by the increase in markups as they facethe same price increases and thus reduced purchasingpower of a dollar of income as salaried workers, butalso face reduced employment due to the effect onaggregate demand. Figure 11 illustrates for a specificrun of the model, while Figure 12 provides MonteCarlo results.

13

0

2

4

6

0 1000 2000 3000 4000 5000

Time

Mea

n U

tility

Utility By Consumer Type

Figure 12: Same as Figure 12, but MC averages andone standard deviation band over 1000 runs with1000 firms.

Overall, the utility (U2) of salaried workers rises57.2% over the utility (U1) on average in the MCexperiment. We can again perform a back of the en-velope decomposition of this overall effect on relativeutility between the two classes into three componentswith the aid of the representative agent version of themodel. Holding both the number of products (firms)in each market and all product qualities constant, theeffect of the increase in the markup on prices wouldcause U2/U1 to increase by 40% in the model. Asfirms move from market 1 to market 2 and increasetheir R%D in that market over time in response to thetemporary increased relative profitability of market 2,the variety effect contributes an additional 12%, andthe innovation effect accounts for an additional 5%,calculated as a residual between the 57% observed inthe simulation and the 52% predicted by the model.

In the example above, the increase in the markup ismodest and impact on R&D is primarily transitional,so in the long run, there are long term level effects onthe utilities of the two classes, but long run growth iseventually reestablished. ** Further, as noted above,the diffusion of product innovation across markets ona slow time scale will cause the relative innovationeffects to eventually fade away – i.e., the boost inproduct quality in the high end market eventuallywill trickle down to the low end market – while the

income share and variety wedges will persist.More generally, the pace of long run growth in

the model is conditioned by the endogeneity of bothR&D and firm turnover. Due to the tension be-tween the short and long run effects of a firm’s R&Don it’s profit, the relationship between long run dy-namism and the distribution of income is more com-plex that that suggested by the representative agentbenchmark in Section 11. For example, increasingthe salary rate will raise the cost of R&D but alsostimulate aggregate demand. Interestingly, the im-pact on long run wellbeing can be highly nonlinear,with successive increases in salary at first stimulatingthe economy and driving up the utility of productionworkers, but after a point causing R&D spending torapidly decline, crashing the rate of innovation alongwith consumer welfare.

In Figure 13, we see the impact of the salary rateon the utility of production workers at two times inthe simulation, averaged over 100 Monte Carlo runs.

For modest increases in salary, the demand effectsoutweigh the R&D effects (on average) at both ofthese time horizons. However, as salary is increasedfurther, the number of firms engaging in R%D be-comes less stable, rising and falling in waves in indi-vidual runs, and then ultimately collapses. In Figure13, at high salary rates (over 2.5) R&D never getsa foothold, and U1 becomes stuck close to it’s lowinitial value (of approximately 0.2) in each run.

Interestingly, U1 increases monotonically in salaryin the equilibrium off the representative agent ver-sion of the model in section 11. There, all firmsare assumed to engage in R&D, and that behav-ior is sustained by a macroeconomic equilibrium inwhich firms earn zero profit. However, under thediscrete choice rules followed by firms in the hetero-geneous agent model under general disequilibrium,R&D tends to becomes increasingly fragile as thesalary rate rises to higher levels.

16 Conclusion

We extend the agent-based macroeconomic model inGeorges (2011, 2018) to investigate consumption in-equality. An increase in product markups and salary

14

0

10

20

30

40

0 1 2 3

Salary

U1

round

5000

9000

U1 vs. Salary

Figure 13: Utility of production workers (U1) in peri-ods 5000 and 9000 as the salary rate is varied acrosssimulations. Monte Carlo averages over 100 runs foreach of 30 salary values.

0.0

0.1

0.2

0.3

0.4

0 1 2 3

Salary

Ran

dD1

rate round

5000

9000

RandD1−rate vs.Salary

Figure 14: Fraction of firms in market 1 engaging inR&D in periods 5000 and 9000 in Figure 13. MonteCarlo averages over 100 runs for each of 30 salaryvalues.

rates leads to an increase in rents going to salariedoverhead labor. The increase in the share of incomegoing to this class of consumers skews production,product variety and product innovation toward goodsthat cater to that class and thus widens consump-tion inequality to a greater degree than raw incomeinequality. We illustrate how, in response to risingmarkups, this process of macroeconomic gentrifica-tion can lead to a protracted period of stagnation ofthe welfare of production workers.

Of course the process will also work in reverse. Arising share of income accruing to production laborwould shift product innovation and variety in favorof that group. Further, innovation that favors onegroup will eventually diffuse (or ‘trickle down’) to theother group. Thus, the innovation effect describedhere can be protracted, but is ultimately transitory,and can operate in different directions over the courseof history.

We also show in the model that the relationshipbetween the distribution of income and long run eco-nomic dynamism can be highly non-linear.

References

[1] Acemoglu, Daron, Ufuk Akcigit, NicholasBloom, and William R. Kerr. “Innovation, Real-location and Growth.” American Economic Re-view 108:11, November 2018. 3450–91.

[2] Acemoglu, Daron, Vasco M. Carvalho, AsumanOzdaglar, and Alireza Tahbaz-Selehi. “TheNetwork Origins of Aggregate Fluctutations.”Econometrica 80:5, 2012, 1977–1016.

[3] Acemoglu, Daron, Ufuk Akcigit, and WilliamR. Kerr. “Networks and the Macroeconomy: AnEmpirical Exploration.” NBER MacroeconomicAnnual, vol. 30, 2015.

[4] Acemoglu, Daron, Pascual Restrepo. “The Racebetween Man and Machine: Implications ofTechnology for Growth, Factor Shares, and Em-ployment.” American Economic Review 108:6,2018. 14881542.

15

[5] Aghion, Philippe, Ufuk Akcigit, AntoninBergeaud, Richard Blundell, and David He-mous. “Innovation and Top Income Inequal-ity.” Review of Economic Studies 86:1, January2019. 1–45.

[6] Akcigit, Ufuk and William R. Kerr. “GrowthThrough Heterogeneous Innovations.” Journalof Political Economy 126:4, August 2018. 1374–1443.

[7] Argente, David, Munseob Lee, and Sara Mor-eira. “Innovation and Product Reallocation inthe Great Recession.” Journal of Monetary Eco-nomics 91 2018. 1–20.

[8] Attanasio, Orazio P., and Luigi Pistaferri.“Consumption Inequality.” Journal of Eco-nomic Perspectives Volume 30, Number 2,Spring 2016, 3-28.

[9] Autor, David, and Daron Acemoglu. Skills,Tasks and Technologies: Implications for Em-ployment and Earnings. In Orley Ashenfelterand David Card, ed. Handbook of LaborEconomics vol 4b. Elsevier, 2011.

[10] Basu, Susanto. “Are Price-Cost Markups Risingin the United States? A Discussion of the Evi-dence.” Journal of Economic Perspectives 33:3.Summer 2019. 3–22.

[11] Aversi, Roberta, Giovanni Dosi, Giorgio Fagi-olo, Mara Meacci, and Claudia Olivetti. “De-mand Dynamics with Socially Evolving Prefer-ences.” Industrial and Corporate Change. 8:2.1999. 353–408.

[12] Becker, Gary S. “A Theory of the Allocationof Time.” Economic Journal, Vol. 75, No. 299,September 1965, 493-517.

[13] Bernard Andrew B., Stephen J. Redding andPeter K. Schott. “Multi-Product Firms andProduct Switching.” American Economic Re-view 100:1, 2010, 70–97.

[14] Bota, Alberto, Eugenio Caverzasi, AlbertoRusso, Mauro Gallegati, Joseph E. Stiglitz.

“Inequality and Finance in a Rent Economy.”forthcoming in Journal of Economic Behaviorand Organization, 2019.

[15] Brynjolfsson, Erik, Yu Hu and Michael D.Smith. “The Longer Tail: The ChangingShape of Amazon’s Sales Distribution Curve.”Manuscript, MIT Sloan School, 2010.

[16] Brynjolfsson, Erik, Yu Hu and DuncanSimester. “Goodbye Pareto Principle, HelloLong Tail: The Effect of Search Costs on theConcentration of Product Sales.” ManagementScience 57:8, 2011, 1373–1386.

[17] Brynjolfsson, Erik, and Andrew McAfee. TheSecond Machine Age. Norton, 2014.

[18] Broda, Christian and David E. Weinstein.“Product Creation and Destruction: Evidenceand Price Implications.” American EconomicReview 100: 3, 2010, 691–723.

[19] Carvalho, Vasco M., and Xavier Gabaix. “TheGreat Diversification and Its Undoing.” Ameri-can Economic Review 103:5, 2013, 1697–1727.

[20] Chen, Shu-Heng and Bin-Tzong Chie. “A Func-tional Modularity Approach to Agent-basedModeling of the Evolution of Technology.” inNamatame A. et al. (eds.) The Complex Net-works of Economic Interactions: Essaysin Agent-Based Economics and Econo-physics, vol. 567, Springer. Heidelberg, 2005,165–178.

[21] Chen, Shu-Heng and Bin-Tzong Chie. “Modu-larity, Product Innovation, and Consumer Sat-isfaction: An Agent-Based Approach.” IDEAL,2007.

[22] Chie, Bin-Tzong and Shu-Heng Chen. “Non-Price Competition in a Modular Economy: AnAgent-Based Computational Model.” EconomiaPolitica 30:3, December 2013. 273–300.

[23] Chie, Bin-Tzong and Shu-Heng Chen. “Compe-tition in a New Industrial Economy: Toward anAgent-Based Economic Model of Modularity.”Adm. Sci. 2014:4, 192–218.

16

[24] Chiarli, Tommaso, Andre Lorentz, MariaSavona, Marco Valente. “The Effect of Con-sumption and Production Structure on Growthand Distribution: a Micro to Macro Model.”Metroeconomica 61:1, 2010, 180–218.

[25] Chiarli, Tommaso, Andre Lorentz, MariaSavona, Marco Valente. “The Effect of Demand-Driven Structural Transformations on Growthand Technical Change.” J. Evolutionary Eco-nomics, 26:1, March 2016, 219–246.

[26] Dawid, Herbert, Simon Gemkow, Philipp Hart-ing, Sander van der Hoog, Michael Neugart.“Agent-Based Macroeconomic Modeling andPolicy Analysis: The Eurace@Unibi Model.” InChen, S.H., and M. Kaboudan (Eds.), Hand-book of Computational Economics andFinance. Oxford U. Press. 2016.

[27] Dawid, Herbert and Domenico Delli Gatti.“Agent-Based Macroeconomics.” in The Hand-book of Computational Economics, Vol 4: Het-erogeneous Agent Modeling. Cars Hommes andBlake Lebaron, eds. Elsevier, North Holland,2017. 63–156.

[28] De Loecker, Jan, Jan Eeckhout, and GabrielUnger. 2018. ?The Rise of Market Power andthe Macroeconomic Implications.? Manuscript,September, 2018.

[29] Delli Gatti, Domenico, Edoardo Gaffeo, MauroGallegati, and Gianfranco Giulioni. EmergentMacroeconomics: An Agent-Based Ap-proach to Business Fluctuations. Springer2008.

[30] Dosi, Giovanni, Giorgio Fagiolo, and AndreaRoventini. “An Evolutionary Model of En-dogenous Business Cycles.” Computational Eco-nomics 27:1, 2005, 3–34.

[31] Eizenberg, Alon. “Upstream Innovation andProduct Variety in the U.S. Home PC Market.”Review of Economic Studies 81:3, 2014. 1003-1045.

[32] Fagiolo, Giorgio, and Andrea Roventini.“Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments andChallenges Ahead.” J. Artificial Societies andSocial Simulation 20:1 January 2017.

[33] Foster, L., J.C. Haltiwanger, and C. Syver-son. “Reallocation, Firm Turnover, and Effi-ciency: Selection on Productivity or Profitabil-ity?” American Economic Review, 98(1), 2008,394–425.

[34] Furman, Jason and Peter Orszag, “A FirmLevel Perspective on the Role of Rents inthe Rise of Inequality.” in Guzman, ed. To-ward a Just Society: Joseph Stiglitzand 21st Century Economics. Columbia U.Press. 2018.

[35] Gabaix, Xavier. “The Granular Origins of Ag-gregate Fluctuations.” Econometrica 79, 2011,733–772.

[36] Garas, Antonios and Athanasios Lapatinas.“The role of consumer networks in firms? multi-characteristics competition and market shareinequality.” Structural Change and EconomicDynamics. 43. 2017. 76–86.

[37] Georges, Christophre. “A Hedonic Approach toProduct Innovation for Agent-Based Macroeco-nomic Modeling.” Manuscript. Hamilton Col-lege. 2011.

[38] Georges, Christophre. “Product Innovation andMacroeconomic Dynamics.” in S.-H. Chen etal. (eds.), Complex Systems Modeling andSimulation in Economics and Finance.Springer. 2018.

[39] Grossman, G.M., and E.Helpman. Innovationand Growth in the Global Economy. Cam-bridge, MA: The MIT Press. 1991.

[40] Hidalgo, C. A., B. Klinger, A. L.. Barabasi,and R. Hausmann. “The Product Space Con-ditions the Development of Nations.” Science317, 2007, 482–487.

17

[41] Hommes, Cars, and Giulia Iori. “Introductionto Special Issue Crises and Complexity.” Jour-nal of Economic Dynamics and Control 50,2015, 1-4.

[42] Horvath, Michael. “Sectoral Shocks and Aggre-gate Fluctuations.” J. Monetary Economics 45,2000, p. 69–106.

[43] Hottman, Colin, Stephen J. Redding, DavidE. Weinstein. “Quantifying the Source ofFirm Heterogeneity.” Quarterly Journal of Eco-nomics 131:3, 2016, 1291–1364.

[44] Jackson, Laurence Fraser. “Hierarchic Demandand the Engel Curve for Variety,.’ Review ofEconomics and Statistics 66, 1984, 8–15.

[45] Jaravel, Xavier. “The Unequal Gains fromProduct Innovations: Evidence from the US Re-tail Sector.” Quarterly Journal of Economics.2019. 718–753.

[46] Keller, Wolfgang and Will Olney. “Globaliza-tion and Executive Compensation.” NBER WP23384. June 2018.

[47] Klette, Jacob and Samuel Kortum. “Innovat-ing firms and Aggregate Innovation.” Journalof Political Economy 112:5, 2004, 986–1018.

[48] Lancaster, Kelvin J. “A New Approach to Con-sumer Theory.” Journal of Political Economy74:2, 1966a, 132–157.

[49] Lancaster, Kelvin J. “Change and Innovationin the Technology of Consumption.” AmericanEconomic Review 56:1/2, 1966b, 14–23.

[50] Lentz, R., Mortensen, D. “Optimal growththrough product innovation.” Review of Eco-nomic Dynamics 19, 2016. 4?19

[51] Marengo, Luigi and Marco Valente. “IndustrialDynamics in Complex Product Spaces: An Evo-lutionary Model.” Structural Change and Eco-nomic Dynamics, 21:1, March 2010, 5–16.

[52] Murphy, Daniel. “Welfare consequences ofasymmetric growth.” Journal of Economic Be-havior and Organization 126, 2016, 1–17.

[53] Piketty, Thomas, Emmanuel Saez, and StefanieStantcheva. “Optimal Taxation of Top LaborIncomes: A Tale of Three Elasticities.” Amer-ican Economic Journal: Economic Policy 6:1,2014, 230–271.

[54] Pintea, Mihaela, and Peter Thompson. “Tech-nological Complexity and Economic Growth.”Review of Economic Dynamics 10, 2007, 276–293.

[55] Reich, Robert B. Saving Capitalism: For theMany, Not the Few. Knopf, 2015.

[56] Russo, Alberto, Luca Riccetti, and Mauro Gal-legati. “Increasing inequality, consumer creditand financial fragility in an agent based macroe-conomic model.” J Evol Econ 26:1, 2016, 25?47.

[57] Strotz, Robert H.. “The Empirical Implicationsof a Utility Tree.” Econometrica vol. 25 no. 2,1957, pp. 269–280.

[58] Valente, Marco. “Evolutionary Demand: AModel for Boundedly Rational Consumers.” J.Evol Econ 22, 2012, 1029–1080.

[59] Windrum, Paul and Chris Birchenhall. “Isproduct life cycle theory a special case? Dom-inant designs and the emergence of marketniches through coevolutionary-learning,” Struc-tural Change and Economic Dynamics, vol.9(1), 1998, 109-134.

18