Lindahl Lecture 1: The Economics of Cities

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Lindahl Lecture 1 The Economics of Cities

• Edward L. Glaeser
• Harvard University

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Cities Result from Three Forces

• Agglomeration Economies and Social Interactions
• These are the magic of urban areas
• Urban Technologies Bricks and Mortar, Trains and
  Cars
• Government Policies
• Both local and national

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The Plan of these Lectures

• In lecture 1, I will focus on agglomeration
  economies what makes cities productive and
  attractive.
• In lecture 2, I will focus on social interactions
  and other effects of proximity
• In lecture 3, I will address both the urban
  technologies and the role of government

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Plan of this Lecture

• Overview of Urban Economics
• Measuring Agglomeration based on location
  patterns
• Lessons from Urban Growth
• Urban Labor Markets
• Learning, Information and Cities some theory

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The Heart of Urban Economics Spatial Equilibrium

• Workers must be indifferent across space
• U(Wages, Amenities, Prices)U
• Higher wages must be offset be either lower
  amenities or higher prices.
• Firms must be as well Profits(Wages, Prices,
  Productivity)0
• Higher wages must be offset by either higher
  productivity or higher prices.
• There is also a housing supply equilibrium that
  will be addressed in lecture 3.

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An Easy Example

• Assume wages are fixed at w and that commuting
  costs equal tdistance from the city then
  spatial equilibrium implies that rents must
  decline by tdistance from city.
• Rents or housing values will be higher in areas
  with higher amenities or better schools.

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Housing Prices and Temperature 1990
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Why locate together?

• Cities can come in principle for two reasons
• First, a desire to be next to some exogenous
  attribute, like a mine or a port
• Second, a desire to be next to the other
  inhabitants of the city

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Why Cities?

• As such, cities are defined as the absence of
  physical space between people and firms
• They always occur in an attempt to eliminate
  transportation costs for goods, people and ideas
• The empirical questions revolve around which are
  these are more important

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Moving Goods, People and Ideas

• Cities are originally about moving goods
• Every large city in the U.S. before 1880 is on a
  river and most are where the river meets the sea.
• Local Feedback where producers move to be close
  to consumers (Krugman).

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Moving People

• Modern big cities specailize in business
  services these require fact to face contact.
• Cities allow works to switch employers and
  industires, which provides insurance and better
  search.
• Proximity to other people isnt just productive,
  its also fun (city as marriage market).

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Moving Ideas

• Ideas, like everything else, move better over
  short distances (face-to-face)
• Jaffe, Trajtenberg and Henderson show that patent
  citations are geographically localized.
• Idea-intensive industries (finance, the arts)
  remain core parts of urban growth.
• Urban edge in idea production makes cities
  important (Athens, Florence).

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The Impact of Proximity

• While city location is a choice, it is also
  interesting because it shapes outcomes
• Firms may be more productive in dense areas
  (Ciccone and Hall, 1996)
• Workers may learn more quickly in dense areas
• It may be easier to steal in dense areas
• Our beliefs are formed by our neighbors

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Measuring Agglomeration (Ellison, Glaeser, JPE,
1997)

• How should we measure the amount of agglomeration
  or people or industry?
• I think measures should generally be
  model-driven, i.e. reflect a parameter in some
  sort of a model.
• Assume profits have the following form

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Where we assume

• Individual shocks follow a weibull distribution
• The spillover effect takes on a value of 1 with
  probability gs
• The mean and variance of profits are

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Together these assumptions give us that
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Properties of the Index

• Easy to compute with available data
• Easy benchmark with no spillover/natural
  advantage version
• Comparable across industries with different sizes
  of firms
• Comparable across different levels of aggregation
• Not good at dealing with issues of actual location

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Facts on agglomeration

• Median estimate of gamma is .026 mean is .051.
• A few industries are extremely concentrated fur
  goods (.6), costume jewelry (.3)
• Many are not cane sugar refining,
• A few really change when we correct for plant
  size (vaccuum cleaners)

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Does Natural Advantage Explain Agglomeration
(AER, 1999)

• To extend the JPE paper, we try to control for
  local characteristics

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What do the variables mean?

• The delta is industry specific and allows
  different industries to respond differently to
  costs
• The beta is the coefficient to estimate that is
  cost specific (i.e. electricity, labor, etc.)
• The y variables are state cost specific (i.e.
  price of electricity in kansas)
• The z variables are industry cost specific (i.e.
  how much does that industry use that input)

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The Empirical Strategy

• Regress state/industry shares on characteristics
  and then ask how much is explained
• Characteristics include energy prices, labor
  costs, proximity to the coast, proximity to
  consumers, etc.
• Some are quantities some are prices.

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Overall Results

• Controlling for all of these variables reduces
  the mean gamma across industries from .051 to
  .048.
• When we allow 2 and 3 digit industry dummies (we
  are using 4 digit industries) concentration falls
  to .045 and .041
• Since many industries arent very concentrated,
  this explains some portion of those industries,
  but little of the highly concentrated industries.
  

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The Dynamics of Industrial Concentration (REStat,
2002)

• How permanent are concentrations of industries
  Krugman (1991), e.g.

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Empirical Results

• Use the Census Longitudinal Research Database
  with plant level data for all manufacturing
• Estimates of beta are around -.06 for 5 year
  patterns
• Mean reversion would cause concentration to
  decline by 12 percent every 5 years,
• But this is made up for by the concentration of
  new firms

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An Extension New Births, Closures, Etc.

• We can extend the methodology to look at what
  sort of changes create mean reversion
• Closures are more likely in places with initial
  concentration
• New Openings are more likely in places with less
  initial concentration (equally of affiliated and
  unaffiliated plants)

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Co-Agglomeration (NBER Working Paper, 1997,
Dumais E/G)

• To add to our knowledge of the sources of
  agglomeration, we look at which industries
  colocate near one another.
• Changes specification regress growth in
  employment on presence of other industries in
  initial period.
• Levels specification regress employment on
  presence of other industries (BUT THERE IS A
  REFLECTION PROBLEM)
• All measures of colocation are normalized to have
  standard deviation of 1

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Suppliers and Consumers

• Use Input-Output matrices to calculate the extent
  that an industry buys to or sells from other
  industries.
• Use that matrix to calculate the extent that a
  state or MSA is supplier or customer
• In levels, .06 for customers, .01 for suppliers.
• In State changes, .04 and .03
• In MSA changes, .02 and 0

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Labor Supply

• Use occupation data to figure out who uses the
  same type of workers
• Calculate a similarity index across and ask which
  places have industries that use similar workers
• In state levels, the coefficient is .41
• In MSA changes, the coefficient is.43
• In State Changes, the coefficient is .18

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Idea Flows

• Option 1 Use the Scherer input output matrix for
  patent flows
• Option 2 Use patents of co-ownership, excluding
  those firms with supply/demand relationship
• In levels, the coefficients are .04 and .03
• State change coefficients are -.01 and .06
• MSA changes coefficients are 0 and .08

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Urban Growth Underpinnings
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• Worker utility equals CN-mw U, where C is a
  consumption amenity index.
• There is a fixed supply of Z in the city denoted
  Z.
• Using the first order condition for firms, and
  these two conditions then gives us, using the
  notation that lnxx

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This implies that

• City size will be a function of consumer
  amenities, fixed factors of production,
  reservation utility levels and so forth.
• Wages will also rise with productivity this is
  being offset by lower amenity levels.
• These equations are then first differenced to
  provide estimating equations

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To close the model

• Assume changes in A and changes in C are
  functions of initial characteristics and then
  regress changes in population (or employment) and
  income on initial conditions.
• Higher employment means either more productivity
  or better amenities
• Higher wages means either more productivity or
  worse amenities.

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Testing the New Growth Theory (GKSS, JPE, 1992)

• Under what conditions are new ideas created?
• Marshall/Arrow/Romer high concentration, big
  firms
• Jacobs diversity, lots of little firms
• Michael Porter high concentration little firms

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The Empirical Test

• Using city-industry (i.e. steel in Pittsburgh)
  growth between 1956 and 1987, GKSS look at what
  predicts growth
• Concentration of the industry (share in city
  relative to share in U.S.)
• Initial Employment in the City-Industry
• Competition (or firm size relative to national
  average)
• Diversity in other industries

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Results

• Average firm size (competition) always predicts
  more growth what does it mean?
• There is substantial mean reversion
• Relative size is sometimes good/sometimes bad (no
  clear pattern)
• Diversity is good in our paper (not obvious how
  robust)

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Subsequent Work Climate and the Consumer City

• In 1900, cities had to locate in places where
  firms had a productive advantage.
• In 2000, cities increasingly locate in places
  with attractive amenities.
• The move to warm, dry places.
• The continued resilience of a few big consumer
  cities (NYC, Chicago, Boston, San Francisco).

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Climate is the most reliable predictor of city
growth
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Best thought of as a regional effect
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Other correlations between pop. growth and
consumer amenities

• 35 percent correlation with temperature and 12
  percent with dryness
• 24 percent correlation with proximity to ocean
  (Rappaport Sachs)
• 14 percent correlation with theaters
• In France, 45 percent correlation with
  restaurants and 33 percent with hotel rooms
• In UK, 31 percent correlation with tourist nights

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Other facts

• Real wages used to decline with city size, now
  they rise (to be discussed later)
• Amenities (high housing prices relative to wages)
  strongly predict later population growth
• Housing price growth in central cities has boomed
• Reverse Commuting has increased

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Urban Growth is Very Persistent
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The Rise of the Skilled City (JME, 1995, BWPUA,
2004)

• One fact that is regularly observed is the more
  skilled cities grow more quickly (Cityscape,
  1994)
• Simon and Nardinelli show this going back to
  1880.
• Are skilled cities more innovative?
• Is the productive value of being around skilled
  workers rising?

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What does the rise of the skilled city mean?

• Or, perhaps are skilled cities become more
  attractive places to live?
• Test using wage changes, housing price changes
  and income changes
• The skill premium (i.e. the extra wages
  associated with being around skilled people) are
  rising quickly
• Housing prices rise almost enough to keep real
  wages constant

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Skills and City Growth
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Also predicts growing income
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Interpretation

• The natural interpretation of this is that skills
  are working through labor demand, not labor
  supply.
• But it is true that the skills effect still works
  within metro areas (which are common labor
  markets)
• One startling fact is that skills matter for
  older, colder places, not newer warmer cites the
  reinvention hypothesis

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Declining Regions
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Growing Region (the West)
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The Reinvention Hypothesis

• An alternative interpretation skills matter in
  times of shock (Schultz, Welch).
• Skilled cities excel because they permit
  innovation.
• As such, the key to reinvention is to keep
  skilled people from leaving.

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Bostons Growth is one of Reinvention

• In 1630, Winthrop comes to Boston for
  consumption, not production reasons.
• City on the Hill-- a religious community.
• All other colonies are about production.
• Original export industry is some fishing and
  selling goods to new immigrants.

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Americans first city

• Boston is founded in 1630 with 150 settlers.
• Location is determined by the Charles river and
  clean water.
• Population rises to 7,000 in 1690.
• Population is 17,000 is 1740 when the city is
  overtaken by Philadelphia.

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The 1640 Crisis and Its Resolution

• In the early 1640s, the flow of immigrants
  subsides.
• English revolution
• Bostonians respond by reinvention, not exit.
• Respond by selling basic foodstuffs and wood, but
  now to other colonies.

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The Colonial Model for Boston

• New England exported to other colonies
• 73 percent to the Southern Colonies and Caribbean
  (1770)
• 13 percent to England
• Goods were basic commodities
• 35 percent is fish (to West Indies 1770)
• 32 percent livestock
• 21 percent woodstock

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Basic Model

• Land in Virginia and Haiti is worth more growing
  tobacco and sugar
• The North has little it can export to Europe, so
  its land is worth less and it grows commodies.
• North is poorer than South in the 1700s.

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The 19th Century Reinvention

• But after 1790, Boston begins to grow again.
• Growth from 18,000 in 1790 to 90,000 in 1840
• Kept pace with national population growth.
• It is maritime, not manufacturing.
• 10,000 in maritime trades
• 5,000 in manufacturing (less than Lowell)

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Boston as a Share of the U.S.
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What Happened?

• Bostons port is still inferior to NYC.
• Between 1821 and 1841, Bostons share of trade
  drops from 21 percent to 10 percent.
• But Bostonians increasingly own and man the
  ships.
• Bostons share of registered tonnage rises from
  45 to 58 percent between 1811 and 1851.
• Yankees captures New York Port around 1820 and
  dominated its activity until the Civil War
  (Albion, 1931).

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What Happened, Continued

• Bostons comparative advantage was in human
  capital both at the high end (merchants) and in
  sailers.
• Over the 1790-1840 period, technology and
  politics increased globalization of trade.
• China trade and South Africa
• Whaling far from New England
• Clipper Ships
• The human capital became more important than the
  port location.

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Live by the Clipper Ship, Die By

• In the 1840s, steam ships start becoming more
  important than sail.
• Bostons human capital becomes far less valuable.
• Bostons loses it maritime dominance, never to
  regain it.

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But Reinvention Once Again

• Over the 1840-1920 period, Boston would continue
  to boom.
• Manufacturing replaced maritime.
• Improvements in engine technology helped the city
  in two ways
• Freed Manufacturing form river power
• Created Rail Networks

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And then theres the Irish

• Boston starts becoming Irish in the 1840s.
• The Potato famine coincides with last era of
  Boston maritime dominance.
• As a result, its cheaper for the Irish to go from
  Liverpool to Boston than to NYC this will not be
  true for later migrants.

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The Twentieth Century

• Manufacturing left cities
• Car cities replaced higher density areas
• People fled cold places
• The rich fled redistributive cities.

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In the 1970s, Boston was in bad shape

• Population had been declining for decades
• The economy was in shambles
• Housing cost less than new construction in most
  of the area.

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But since 1980, the city has surged

• Population has grown modestly
• The economy has grown robustly
• Housing prices have soared.

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Economic Growth since 1980

• In 1980, per capita income is the Boston Metro
  Area was 7547 which meant it ranked 61st in the
  nation.
• In 1994, personal income was 26,093 tenth in the
  nation.
• In 1996, average annual pay was 34,383, sixth in
  the nation.

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Middlesex County Employment

• Professional, Scientific and Technical Services
  110,000 jobs or 13 percent
• Educational Services 64,000 jobs or 7 percent
• Administrative and Support Services 64,00 jobs
  or 7 percent
• Computer and Electronic Manufacturing 58,000
  jobs or 7 percent/

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Urban Wages (JOLE, 2001)

• Wages are higher in big cities than in small
  towns
• This is a nominal wage difference, not a real
  wage difference
• There is no labor supply puzzle, but there is a
  labor demand puzzle. b

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Nominal Wages and City Size(Slope.073,
R-Squared.3)
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Real Wages and City Size 1970
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Real Wages and City Size Today
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Is it Selection?

• Wage Premium for metropolitan area residence
  .2-.35 depending on source
• What about the real wage facts today
• Controlling for standard omitted factors
  (education, industry, occupation) makes little
  difference
• Controlling for AFQT in the NLSY makes no
  difference

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More on selection

• Parents location when used as an instrument
  predicts higher wages today.
• But individual fixed effects regressions do
  generally eliminate much of the city effect
• .28 to .05 in PSID
• .24 to .1 in NLSY
• Whats going on here?

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The Learning Hypothesis

• If cities increase human capital only slowly,
  then this can explain the individual fixed effect
  results without selection
• Urban dummy is small for young workers (under 10
  percent)
• But rises more than 15 percent over time
• Also true in fixed effect regressions a 15
  percent increase over time

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Analysis of Movers

• Ashenfelter dip before leaving or moving to
  cities
• 7 percent gain or so within a few years
• Increasing wage gains over time
• The NLSY results show somewhat quicker wage
  growth
• People who leave cities dont face wage losses.

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Learning in Cities (Journal of Urban Economics,
1999)

• To understand the previous section, a brief model
  with two skill levels (the paper does a more
  general distribution).
• Your probability of becoming skilled involves (1)
  meeting a skilled person in your industry and (2)
  imitating that person (with prob. C)
• If the share of skilled people in an area is s,
  then the probability of becoming skilled from
  each interaction is cs/I.

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More on learning

• The key assumption is that the number of meetings
  is a function of city size or density, or D(N)
  where N is population.
• The probability of becoming skilled in a period
  equals 1-(1-cs/I)D(N)
• If city renttransportsaN/2, and unskilled
  wagesw and the gain from being skilled is V then

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Closing the learning model

• Spatial equilibrium requires
• (1-(1-cs/I)D(N) )V-aN/2(1-cs/I)D(N) V-aN/2
• The gains from extra learning are offset by
  higher rents.
• If there are just two locations one with no
  learning and the other, a city then

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Comparative Statics

• City size rises with returns to learning,
  discount factor and falls with A.
• The skill level of the city will itself also be a
  function of the learning parameters.
• With multiple skill levels, the skill
  distribution is uniform.

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Information Technology and the Future of Cities
(Journal of Urban Economics, 1998)

• So cities exist in part to speed information
  flows
• Doesnt that mean that information technology
  will kill cities?
• Not so fast the key question is whether
  face-to-face interactions and electronic
  interactions are complements or substitutes

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A Simple Model

• Step 1 learn reservation value (denoted j with
  cumulative distribution R(j) and choose whether
  or not to collaborate
• Step 2 learn match quality a, which means match
  returns are af(i) where i is intensity
• Step 3 produce intensity using elecronic media
  (phones) or face-to-face

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Phones vs. Face-to-Face

• Two technologies differ in their fixed costs and
  in their power
• iPBPT and ifBf(T-T), where BfgtBp
• Phones dont have fixed costs, but they are worse
  at creating intimacy.
• Use phones whenever desired i is low.

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Solving the model

• There are two cutoff values for a
• The lower values determines a level of a at which
  is makes sense to end the relationship
• A higher values above which people use face to
  face interactions
• Better electronic technologies are increases in
  BP, which impacts several margins

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Improvements in Technology

• First it decreases the cutoff of a at which you
  interact at all.
• Second, it increases the cutoff at which you
  switch from phones to face-to-face.
• Third, it lowers the cutoff for the initial
  participation decision.

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What does this mean?

• First, improvements in technology may actually
  increase the amount of face-to-face contact, by
  increasing the number of people who work
  together.
• Second, if cities are a technology for lowering
  the fixed costs of face-to-face, then demand for
  cities will rise if improvements in technology
  raise face-to-face contact.
• Third, the key condition for this to hold is that
  people in cities use phone technology more.

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What does the data say?

• Fact 1 Phones and cities go together across
  countries, and over time.
• Fact 2 Business travel has risen over the past
  20 years (face to face)
• Fact 3 Co-authorship and other forms of
  interaction are rising steadily.
• Fact 4 High tech industries are particularly
  likely to urbanized

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More on the data

• Fact 5 Silicon Valley is clustered
• Fact 6 People in cities often use electronic
  forms of interaction more, not less.
• Overall there is no compelling case that cities
  and technology are complements, but none that
  they are substitutes either.