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From Neighborhoods to Nations via Social

Interactions

Yannis M. Ioannides

Tufts University

http://sites.tufts.edu/yioannides/

Keynote Speech, 18th International Conference on

Macroeconomic Analysis and International Finance

University of Crete, Rethymno, Crete

May 30, 2014

Motivation Individuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth

Outline

1Motivation

2Individuals

3Firms

4Alonso-Muth-Mills

5Urban macro

6Urban emergence

7Urban macro & growth

The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth Motivation: What are social interactions all about? Moli´ereLe Bourgeois Gentilhomme1670. "Act Two. Scene IV" PHILOSOPHY MASTER: [E]verything that is not prose is verse, and everything that is not verse is prose. MONSIEUR JOURDAIN : And when one speaks, what is that then?

PM: Prose.

MJ: What! When I say, "Nicole, bring me my slippers, and give me my nightcap," that"s prose?

PM: : Yes, Sir.

MJ: By my faith! For more than forty years I have been speaking prose without knowing anything about it, and I am much obliged to you for having taught me that. Social interactions: Direct, agent-to-agent effects that are not mediated by the market. Not unlike externalities, though not aside show. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth Motivation: What are social interactions all about? Moli´ereLe Bourgeois Gentilhomme1670. "Act Two. Scene IV" PHILOSOPHY MASTER: [E]verything that is not prose is verse, and everything that is not verse is prose. MONSIEUR JOURDAIN : And when one speaks, what is that then?

PM: Prose.

MJ: What! When I say, "Nicole, bring me my slippers, and give me my nightcap," that"s prose?

PM: : Yes, Sir.

MJ: By my faith! For more than forty years I have been speaking prose without knowing anything about it, and I am much obliged to you for having taught me that. Social interactions: Direct, agent-to-agent effects that are not mediated by the market. Not unlike externalities, though not aside show. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth

Examples

Social interactions: engaging in social interactions "without knowing anything about it." •Learning new skills and influencing our choices.

•Recycling and composting because others do;

•Spending money to send our kids to schools with smart kids,or avoiding schools with too smart kids;

•Chance encounters in Silicon Valley or Austin, Texas bars leadto ideas for software innovations;

•gaining weight; attending church, synagogue or mosque;joining a gym or a country club; supporting a sports team;keeping up with college friends in person or on Facebook;enforcing, or failing to enforce, building code and zoningviolations;

The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth

Definitions

•Widely cited department chair and research productivefaculty: competing explanations.Professors with similar observable or unobservablecharacteristics have similar productivity?Correlated effect

"Follow the leader"?endogenous social effect. Chair creates environment conducive to research, by hiring the "right" people?Contextual effect

All of the above?

Important to distinguish their relative contributions. •For individuals in residential neighborhoods, schools,workplace, random encounters, serendipity •For firms: proximity to suppliers, and to competitors; mainingredient of new economic geography •For individuals: neighborhood effects, peer effects, role models The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth

Definitions

•Widely cited department chair and research productivefaculty: competing explanations.Professors with similar observable or unobservablecharacteristics have similar productivity?Correlated effect

"Follow the leader"?endogenous social effect. Chair creates environment conducive to research, by hiring the "right" people?Contextual effect

All of the above?

Important to distinguish their relative contributions. •For individuals in residential neighborhoods, schools,workplace, random encounters, serendipity •For firms: proximity to suppliers, and to competitors; mainingredient of new economic geography •For individuals: neighborhood effects, peer effects, role models The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth

Picture of the book

•Forindividualsin residential neighborhoods, schools, workplace, random encounters, serendipity: choice of neighborhood implies choice of neighborhood effects, structure of neighborhoods, cities, regions, countries. •Forfirms: location decision influenced by proximity to workers, suppliers, and competitors; main ingredient of new economic geography •Chapter 3: location decisions of individuals. Chapter 4: location decisions of firms •Chapters 3 and 4:proximitydefined asgroup membership. •Chapter 5: Economic agents operate in actual physical space, defined as distancebetween each other, to urban centers within cities. •Chapter 6: Emphasizes the role of social interactions inhuman capital spillovers; links empirical findings, from the more microeconomic treatment of the chapters 3, 4, 5, with the aggregative city-level models that follow in Chapters 7, 8 and 9. So: from basic facts about spatial patterns of wages and productivity. moving from states, regions, and counties, down to cities and their neighborhoods. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividuals Firms Alonso-Muth-Mills Urban macro Urban emergence Urban macro & growth From Neighborhoods to Nations via Social Interactions •Larger size, greater variety of intermediate goods: agglomeration of activities raises urban productivity;but, congestion is costly. •Intracity location equilibrium; intercity location equilibrium; intercity trade. Variety of city types: specialized, diversified, satellite; Geography via shipping costs •Chapters 7, 8, 9: city as unit of analysis -urban macro •Chapter 7: Different city sizes associated with different city functions •Chapter 8: takes up the single empirical fact about city sizes throughout the world that has generated interest much beyond economics: Zipf"s law! •Chapter 9: takes up economic growth in economies made up of cities: isolated vs. trading cities •Chapter 10: speculates further about social interactions as an overarching theme, linking cities, regions, nations. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth Individuals" location decisions to neighborhood formation People in choosing where to live try to do their best with their resources in the neighborhoods where they choose to locate •Neighbors remodel their house, or maintain it better than me; incentive for me to keep up; or when my friends tout a stock and also hold it: endogenoussocial effect: from deliberatedecisionsby other members of my milieu. •Individuals may value the actual characteristics of othersin their social and residential milieus:exogenous,orcontextualeffects, and are also socialeffects. People with kids like to live in neighborhoods where people have kids Proxy when you house hunt: look for pamper boxes in their trash!

Or, conduct a "Values Audit!"

•Individuals acting similarly because they have similar characteristics (or face similar institutional environments):correlatedeffects. People living near others of the same ethnic group. Neighborhoods, cities, regions: distributions of attributes/demographic characteristics:"character", outcome of individuals" decisions. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth

Basics of social interactions

•i"s actionyi= argmaxyiU(yi,E[yν(i)];·;parameters)i"s characteristics,xi; contextual effects,zν(i),i"s neighborhood (or social milieu)ν(i),expected action1 |ν(i)|? j?ν(i)E[yj] among the members ofi"s neighborhoodν(i).For quadratic U(·),endogenous social effect, conditional oni"s information, i: y i=α0+αxi+θzν(i)+β1 |ν(i)|? j?ν(i)E[yj|Ψi],+?i; (1) parametersα,θare row vectors,α0,andβare scalar, stochastic shock?ii.i.d. across observations.

Obtained from a quadratic utility function.

•Solution (Reduced form),yi(xi,xν(i),zν(i)): Ifi"s expectation = average behavior, 1 |ν(i)|? j?ν(i)E[yj|Ψi] = meanj?ν(i)[yj] The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth

Basics of social interactions: cont"d

•Reduced form:

y i=α0 •If contextual effects≡neighborhood averages of individual characteristics,zν(i)≡xν(i), y i=α0

•Estimation

•Multiplier:βα+θ1-β. •Identification problem [Blume et al. (2010)]:βα+θ1-β •Angrist (2013): "Perils" with estimations when observations for all interdependent agents used. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth

Summary of Optimum Outcomes

•Decisions determined as Nash equilibria for the neighborhood:yi= argmaxyiU(yi,E[yν(i)];·;parameters)

•Modelling/analysis: can writeIUFi(α0,α,β;xi,xν(i),zν(i);x-i,x-ν(i),z-ν(i);stats shocks),i? I.

•Interdependence can be via graph/network:

The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth A Digression on Angrist"s "Perils" in Estimating Peer

Effects

yi=α0+αxi+β1|ν(i)|? j?ν(i)E[yj|Ψi],+?i. •1 |ν(i)|? j?ν(i)E[yj|Ψi] =α1-β1|ν(i)|? j?ν(i)[xj|Ψi].• •α= 0,mean inclusive/exclusive (I>>),ˆβ= 1. •α?= 0,mean exclusive, variable group size: immune from

Angrist"s "perils."

•Best chances to avoid Angrist (2013) "Perils:" •Subjects of a peer effects investigation?= "effect-causing" peers. •OLS and 2SLS parameters,φ0,φ1,should be the same in the absence of peer effects. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth Self-selection to groups, communities, cities, regions •Unitievaluates neighborhoodνusing observablesWi,νwhich Q iand?i,ν: zero means, conditional on (are orthogonal to) regressors (xi,zn(i),Wi,ν).across the population. •ichooses "best neighborhood": Shocks no longer have zero means. Given joint distribution of (?i,?i,ν),conditional on choosingν(i) : E[?i|xi,zν(i);Ψi;i?ν(i)] [Heckman "correction"]: proportional to a y i=α0+xiα+zν(i)θ+β1 |ν(i)|? W i,-ν(i)denotes the observable attributes of all neighborhoods other than

ν(i).Immune from Angrist"s "perils."

•Main "engine" of individuals" and firms" location decisions- Ioannides (2013), Ch. 3, 4. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth

Schelling"s Models of Clustering

•Social interactions in Schelling (1978), p. 147 "self-forming neighborhood model." Individuals locate on a lattice (checkerboard) influenced preferences over skin color of neighbors. Resulting spatial equilibrium patterns in residential segregation across neighborhoods are stark. •Schelling (1978) p. 155, "bounded-neighborhood model" (neighborhood tipping model): how neighborhood composition "tips" in favor of particular groups and produces clustering of racial groups. In Schelling"s own words, "[t]hat kind of analysis exploresthe relationship between the behavior characteristics of theindividualswho comprise some social aggregate, and the characteristics of theaggregate"[ibid., p. 13]. Social outcomes, arguably unintended, magnify of individual propensities. The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth

Schelling"s neighborhood tipping model

Individualj, white, would live in a neighborhood if percentage of whites among her neighbors,o?[0,1],is at least ˜oj,o≥˜oj,where ˜ojisj"s threshold, a preference characteristic. Otherwise, individualjexits. The higher is ˜oj,the less tolerant is individualj. [Easterly (2009)] Individuals" thresholds ˜oj,distributed in the neighborhood in question according toF(˜o) : For any neighborhood with a share of white residents equal too,the percentage of white individuals who would be willing to live there are those with thresholds exceedingo. Their share is given by the value of the cumulative distribution function ato,F=F(o),whose support is [0,1].

Emergentoutcome?

•See on Figure 3.1

The University of Crete I Plenary SessionYannis M. Ioannides o x o* wF(w) MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth

Schelling"s neighborhood tipping model: Empirics

Are stable, economically and racially mixed neighborhoods feasible? Can vigilant policy tools (zoning, and mandates of mixed income housing) counter market forces driving segregation? •Card, Mas, and Rothstein (2008a; 2008b) first direct evidence in support of Schelling"s prediction that segregation is driven by preferences of white families over the (endogenous) racial and ethnic composition of neighborhoods. Neighborhood Change Database, panel of Census tracts, 1970- 2000. White population flows exhibit tipping-like behavior in most cities;

Tipping points range [5%, 20%] minority share.

US cities vary: Memphis, Birmingham: strongly held views against racial contact. San Diego, Rochester: weakly held views against racial contact. •Easterly (2009): findings not consistent with instability;agreements and disagreements with Card, Mas, and Rothstein (2008b) The University of Crete I Plenary SessionYannis M. Ioannides MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth Schelling"s neighborhood tipping model: Empirics cont"d Evidence that US "micro"-neighborhoods (5-13 households) are quite mixed, in terms of income

•Hardman and Ioannides (2004), Ioannides (2004)Joint and conditional distributions portray neighbors"characteristics conditional on the kernel"s housing tenure,race, and income. See Figure 3.2

•Wheeler and La Jeunesse (2008):Between 80 and 90 percent of income variance within USurban areas driven by within-neighborhood differences ratherthan between-neighborhood differences.Increasing numbers of foreign-born individuals increasesincome heterogeneity within but not between neighborhoods.Rising educational attainment seems to influence bothmeasures of inequality, stronger with income variation withinneighborhoods.

The University of Crete I Plenary SessionYannis M. Ioannides

Neighborhood dist85

Kernel85Stochastic Kernel

12A 10 8 6 0.6 0.8 0.4 0.2

0.00.6

0.8 0.4 0.2

0.0121086

BNeighborhood dist85

Kernel85

12 10 8 612
10 8 6 MotivationIndividualsFirms Alonso-Muth-Mills Urban macro Urban emergence Urbanmacro & growth Joint neighborhood choice and housing demand; Ioannides and Zabel (2008) Data: tract :?= 1,...,L; micro neigh :k= 1,...,K; individual :j

Preferences:

Utility

?kj= specificj,?×utility?kj×random shock utility ?kj(incomej,prices;social effectskj) Micro theory providesdiscipline: Housing demand?kj= (income j,price;ownzj,contextual effects (Zk),endog. social effectk) where endog. social effect k= mean ink?Housing demand?kj?.

Choice of neighborhood: max

?,k: Utility?kj

Ioannides and Zabel (2008): Tables 3.1, 3.2;

Associated hedonic price [Kiel and Zabel (2008)]: Table 3.3 The University of Crete I Plenary SessionYannis M. Ioannides

July, Time: :pm chapter.tex

Chapter

Table?.?.

Coefficient StandardError Coefficient StandardError

Variable

a() () () ()

Price .

Medianageofhouse .

Medianageofhouse◊incomeinstquartile .

Medianageofhouseincomeinthquartile .

.Fractionowners .

Fractionowners◊incomeinstquartile .

Fractionowners◊incomeinndandrdquartile .

Fractionowners◊incomeinthquartile .

Fractionnon-whiteintract .

Fractionnon-whiteintract◊nonwhite .

Dominantrace .

Dominantrace◊HHheadnonwhite .

FractionwithHSdegreeintract .

July, Time: :pm chapter.tex

LocationDecisionsofIndividuals

FractionwithHSdegree◊collegedegree .

Mediannumberofbedrooms .

Medianbedrooms◊HHsizeinndandrdquartiles . .

Medianbedrooms◊HHsizeinthquartile . .

Medianbedrooms◊HHheadmarried.

Medianageofresidents◊ageHHheadinthquartile .

FMLY◊ageHHheadinstquartile.

FMLY◊ageHHheadinthquartile. .

Fractionwithcommutequotesdbs_dbs46.pdfusesText_46

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