Lindahl Lecture 3: Housing, Transportation Technology and City Governments

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Lindahl Lecture 3 Housing, Transportation
Technology and City Governments

• Edward L. Glaeser
• Harvard University

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Structure of Lecture

• Transportation Technologies and Cities
• Urban Poverty
• Sprawl
• Housing Demand and Supply
• Government Policies towards Housing
• Rent Control
• Subsidized Homeownership
• General Aside on Social Capital
• Cities and Governments

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Why do the poor live in central cities?

• Poverty rate in central cities is 18 in suburbs
  it is 8
• In old metropolitan areas, poverty frises and
  then falls with distance from CBD
• In newer metro areas, poverty just declines with
  distance from CBD
• New migrants to cities are just as poor as old
  residents selection not treatment

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The AMM Model

• With two groups the willingness to pay for
  proximity per acre determines who lives closer to
  the city center
• The key willingness to pay comes is P(d) from
  P(d)adt total costs, so willingness to pay is
  for proximity is -P(d)t/a
• This means that the poor live in cities if they
  have higher commuting costs or less demand for
  land

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The Model Graphically

• House Price

Whoever has a steeper curve Lives near the center
Distance
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The Elasticity Condition

• With two groups, the question is whether
• tr / ar gttp / ap or

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Poor in Cities continued

• With income as a continuum, the key question is
  whether the income elasticity of demand for land
  is greater or less than the income elasticity of
  commuting costs.
• What is a reasonable benchmark for the income
  elasticity of commuting costs? One mode
  probably somewhat under 1.

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The Income Elasticity of Demand for Land

• If you just look at people live in single family
  detached houses .1
• This rises to .3 if you instrument with education
  for income (perm. Income)
• But apartments are the critical issue, and then
  you need to assign land to an apartment.
• Our best estimate is .3-.4.

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Why do the poor live in cities?

• The role of transport modes if the rich drive
  and the poor take transit the puzzle can be
  resolved.
• Even though the rich pay more for driving the
  marginal cost of distance is less
• Cars move (on average) at 30 mph and have a
  minute fixed cost
• Buses move at under 20 on average
• The income elasticity of auto ownership is high

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Evidence on the Role of Public Transportation

• Cross-section people who live close to public
  transportation are poorer holding distance to CBD
  fixed
• Rail in Boston, Portland, Washington
• Buses in LA
• Subways in NYC (outer buroughs)
• Panel when tracts get new access to trains, the
  poverty rate rises

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Evidence continued

• In areas where everyone drives, the rich live
  closer to the city
• The existence of subways creates a zone where the
  rich take public transport and dense cities the
  rich walk in the center
• In these cities, the relationship between income
  and distance is not monotonic
• Statistically, these subways change the urban form

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Why the 20th century transformation?

• The move to sun and sprawl both reflect the same
  phenomenon.
• Transportation costs have fallen.
• Consumer cities not producer cities.
• Car cities, not walking or PT cities.

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Reduction of the Costs of Moving Goods

• Railroads, Trucking, Highways have radically
  reduced transport costs.
• Manufacturing no longer locates near its
  suppliers/consumers.
• Manufacturing has suburbanized and left cities
  (and the US) altogether.
• Boston was typical not unique.

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Declining Transport Costs Rail
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Declining Costs More Modes
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As Transport Costs Fell Manufacturing Left Cities

• First is suburbanized
• Manufacturing firms are big users of space
• There is a strong tendency of these firms to
  locate for from the city center.
• Then it left high density counties
• And it left the U.S.
• There is no reason to think that this is
  inefficient or bad.

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Manufacturing and Density
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Manufacturing and Decline 1920-1980
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The Rise of Car Cities

• First, there was flight to the suburbs.
• Then the jobs left too Now more than 75 percent
  of Chicagos jobs are outside the classic
  downtown.
• Firms followed people (again consumer cities).
• Movement both within MSAs to edge cities and
  across MSAs to car friendly places.

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Density and Decline 1920-1980
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Car Cities in the 1990s Is there a New
Urbanism?
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Facts about Sprawl

• In most American cities, more than 80 percent of
  people live more than 3 miles from the CBD
• More than 75 percent of workers work outside that
  ring
• In cities with decentralized employment, rents
  dont rise much with distance and commute times
  dont rise

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Sprawl Cities are Car Cities

• 92 percent of trips are by car
• Even 77 percent of trips under a mile are by car
• Places with more African-Americans in the center
  have more dispersion but the differences are
  small
• Across countries, using gas prices instrumented
  for by legal origin predicts sprawl

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Is Sprawl Bad?

• Pollution potentially serious for global
  warming, but most other problems have been taken
  car of by technology
• Little land area is actually used in the U.S.
• Congestion sure, but it is not obvious that
  congestion rises with sprawl commute times
  actually fall
• Commute time by car is 23 minutes on average 47
  minutes by public transportation
• The big plus is housing size which reached over
  2000 square feet in the last few years

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Cars and Driving Times
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The Europeans and their Trains

• Fact 1 In rich European cities, people now
  drive just like in the U.S.
• Fact 2 In many cities where people rarely
  drive, commute times are very high
• Moscow 10 drive, 62 minute commute.
• Athens 36 drive, 53 minute commute.
• Paris 60 drive, 35 minute commute.
• US average is 24 minutes.

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Cars and Travel Time Internationally
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Housing Demand and Supply

• Traditionally urban literature has focused on
  housing demand using housing price hedonics to
  back out demand for place
• New literature focuses more on supply, in part
  because supply drives city growth
• In part because recent regulatory changes are
  incredibly important and underexplored

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Why Supply Matters
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And in changes
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Vacancy Rate Coefficient is .1
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Durable Housing the Basic Idea
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Durable housing is needed to explain American
Cities
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Implications of a Durable Housing Model

• Population growth rates will be skewed because
  places grow quick and decline slowly.
• There will be strong persistence of growth rates
  especially in decline
• Places with housing costing below construction
  costs will not grow
• Positive shocks increase population more than
  housing prices negative shocks decrease housing
  prices more than population
• Concave correlation between prices and growth

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Results on Durable Housing

• Highly skewed distribution of growth rates
• Coefficient of current growth on past growth is 1
  if growth was negative and .4 if growth was
  positive
• Coefficient on price growth on population growth
  is 1.8 when negative and .2 when positive
• Strong relationship between housing below
  construction cost and no population growth

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The Concave Price/Growth Relationship
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The Weather and Urban Growth

• We split the weather into positive shocks and
  negative shocks so that the same share is
  negative as had overall population declines
• The coefficient on weather and price growth is
  .006 for negative shocks and .002 for positive
• The coefficient on weather and population growth
  is .0008 for negative shocks and .068 for
  positive shocks

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A Final Implication Durable Housing and Poverty

• If cities decline by becoming less productive,
  and if productivity relates to skill level
• Then poor people will stay in declining cities
  disproportionately because they have cheap,
  durable housing
• Poor people do congregate in declining cities,
  but this disappears when you control for housing
  prices

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The Regulatory Tax

• Housing Supply Costs, in growing areas, are CSR
  where C is structural cost, S is size of
  structure and R is residual
• In most of U.S. history, the 1970, R/(CSR) is
  small less than .2 almost everywhere
• Only over the past thirty years do prices start
  to greatly exceed construction costs

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Why the gap between housing prices and
construction costs?

• Theory 1 Land is expensive
• Theory 2 Regulation prevents new construction
• RPLT where P is land costs, L is land area and
  T is regulatory tax
• We dont directly observe land costs, but we can
  estimate them hedonically
• R/Lgt10the estimate of P tax not land

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Another piece of Evidence NYC

• In New York City, apartments are always the cost
  of building up
• No matter what the fixed costs are, the marginal
  cost is technological and generally less than
  200/square foot
• Yet condo prices are now often over 600/square
  foot
• Hard to reconcile with a free market

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Other Evidence on Rising Regulation

• Declining numbers of permits
• Little correlation between prices and density
  across metro areas
• Correlation between changes in prices and changes
  in population has become negative across regions
• Places with more estimated zoning tax have
  other measures of regulation

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The Change in the Price/Quantity Relationship

• In NYC in the 50s and 60s, rising prices related
  strongly to new permits.
• In the 80s and 90s, this positive relationship
  has disappear.
• Anecdotal information strongly supports the idea
  that citizens groups can now block change,
  presumably to keep prices up.
• We dont know why this occurred.

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Supply Restrictions and Urban Dynamics

• Any restrictions on new supply will change the
  way that cities develop.
• One possible source of restricted supply is
  zoning, but limited land is certainly another.
• This will change the ways cities develop compare
  Massachusetts and Texas

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Supply and Urban Growth

• Price

MA Supply
Texas Supply
Rise in Demand
Number of Homes
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Massachusetts Population
47
Massachusetts Prices
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Texas Prices
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Texas Population
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Responses to Labor Demand Shocks
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The Costs of Rent Control

• Undersupply
• Reduced maintenance
• Social waste on rent seeking
• Misallocation (Deacon and Sonstelie, Hubert,
  Suen)
• Nat Sherman rented a six month CPW apartment for
  335/month and said the apartment happens to be
  used so little that I think the rent is fair

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Misallocation under Rent Control

• Rent

Demand
Surplus Left
Supply
DWL
Transfer
Quantity
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Misallocation under Rent Control

• Rent

abbc
a
Supply
Misallocation Loss
b
Surplus Left
DWL
Transfer
c
Demand
Quantity
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How Big is the Misallocation Loss?

• The misallocation loss is technically first order
  while the undersupply loss is second order
• Thus for sufficiently mild impositions of rent
  control the social loss is always greater from
  misallocation
• This relies on random matching better matching
  would reduce losses
• Different impacts of demand elasticity --

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Empirical Approach

• Assume that if household A consumes more of
  attribute y than household B in city 1, this will
  also be true in city 2.
• This assumes the we can rank households by
  demand.
• For any city c and subgroup i, the distribution
  of demand, f(d, x) equals f(dlc, x) for some lc.
• This assumes the all demand shifts are city
  specific

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These assumptions imply Constant Overlap

• If the share of subgroup i in the free market
  city A that rents apartments with k or fewer
  rooms is equal to the share of subgroup i in free
  market city B that rents apartments with n or
  fewer rooms, then for any other subgroup j, the
  share renting apartments with k or fewer rooms in
  city A must equal the share renting apartments
  with n or fewer rooms in city B.

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Results

• In NYC, 47 percent of high school dropouts
  consume more rooms than people with college
  degrees (31.6 percent for the U.S. as a whole)
• In NYC, 45.7 percent of people in the bottom
  third of the income distribution consume more
  rooms than people in the top third in the U.S.
  the number is 35.1 percent.

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Full Structural Estimation

• Estimate the maximum cutoff of unobserved demand
  within each demographic group associated with
  each apartment size
• Calculate the total amount of misallocation 20.9
  with correction for sampling error
• By comparison renters in Hartford (4 percent),
  Chicago, 7 percent
• Misallocation is strongest in Manhattan (26
  percent) and among long term residents

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Homeownership and Social Capital

• What is social capital?
• One view is that it is socially-relevant human
  capital that is determined by investment
• Social characteristics, including charisma,
  status and access to networks, that enable that
  person to extract private returns from
  interactions with others
• Social capital can be individual or aggregated up
  to form society-wide social capital

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Is so, then usual investment models can
understand this thing?

• Social capital should rise and fall over the
  lifecycle (it seems to)
• People in more social occupations should invest
  more (they do)
• People who are more patient or just invest more
  generally should invest more in social capital
  (they do using education)
• People who are more mobile will invest less

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Homeownership and Social Capital

• Homeowners have more expected permanence and have
  a property stake in the quality of the community
• They should invest more in local public goods, at
  least that is one of the stated reasons for
  subsidizing ownership
• But how big are these effects really?
• And are the subsidies effective?

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Are Homeowners Better Citizens?

• Using almost all measures of social capital,
  people who are homeowners are better citizens
• .25 more organization, .09 knows school head, .10
  knows US representative, .15 votes in local
  elections
• Also, .12 garmed and .1 owns a gun
• ½ of the good effects are related to permanence
• These effects however are much bigger without
  controls, because homeowners are really different
  based on observables
• The selection problem is huge

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The Endogeneity Problem

• Unsolved but two approaches first use area
  averages based on structures same basic results
• Second use GSOEP data from Germany where you have
  a panel
• Much smaller impacts in general
• With fixed effects home repair drops from .12 to
  .09
• Volunteering drops from .033 to .013 and
  poltiical participation from .04 to .008
• Effects are small but significant statistically

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But does the subsidy do anything?

• Homeownership is essentially determined by
  structure 85 percent of people in houses are
  owners, 85 percent of people in apartments are
  renters
• Incentive Problems
• Homeownership doesnt change much over time at
  all even though subsidy changes with inflation
• Across people are well, the size of the subsidy
  doesnt seem to matter

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Cities and Governments

• National governments play a huge role in shaping
  cities
• Large scale infrastructure spending
• Place based initiatives and redistribution
• Transport technologies
• Local governments are also critical
• Schools, Safety, Other Services
• Local Redistribution

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Trade and Circuses Mega-Cities

• What determines the level of primacy across
  countries?
• Krugman and Livas point to international trade
  because trade is space neutral (is it?) the
  incentive agglomerate declines
• High internal transport costs is presumably
  another reason to agglomerate

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The Political Roots of Agglomeration

• A dictators desire to invest may decline with
  distance from him
• Investment for consumption reasons
• Investment to deter unrest
• Political influence declines with political
  distance
• Physical threat declines
• Lobbying, etc., also declines
• This should be more important in unstable or
  dictatorial regimes

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Capital Cities and Transfers

• As a result, capital cities have generally
  received more benefits from government
• Sometimes these reflect dictators building
  themselves nice cities (St. Petersburg)
• Sometimes it is a response to political power of
  locals (Washington)
• Sometimes it is a response to local uprisings
  (students in Santiago)

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The Empirical Causes of Mega-Cities

• Basic specification
• Log(Primate City Population)
• aLog(Non-Urban Population)
• bLog(Urban Population)
• Country Factors
• Countries with higher levels of trade do indeed
  have smaller central cities (-.6)
• Internal investment in roads matters (but what
  about causality)

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Politics is very significant
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In regressions

• Capital City Effect .42 (only 8 non-capitals)
• Dictatorship Effect .44 (.15)
• Dictatorship Instability Interaction yields
  .7, 2.3 and -2.3 all significant
• But does primacy lead to dictatorship or
  dictatorship to primacy

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Tests for Causality

• Instrument using various political variables such
  as ethnic heterogeneity (predates the cities) and
  neighboring instability-- .5
• Between 1970 and 1986, dictatorships in 1970 had
  faster growth in the primate city
• However, there is no significant relationship
  between size of capital city and becoming a
  dictatorship

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History

• Romes growth peaked between 135 and 50 b.c.e.
  when it grew from 375k to 1,000,000.
• Strength abroad and weakness at home leades to
  redistribution to the capital
• Empire expanded in Gaul, Bithynia, Pontus,
  Cilicia and Syria
• Pompey declares all conquests are part of the
  city governemnt
• Sempronian and Clodian laws extrend the grain
  distribution to Italians in Rome
• Sulla extends citizenship to all inhabitants of
  Italy

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Other Cities

• Edo (Tokyo) expands from little to between 500k
  1 million in the 1600s
• Growth entirely related to being Shogunal capital
  for newly unified Japan
• Buenos Aires grew most between 1870 and 1914
  industry and politics (London)
• Paris and Mexico city are more overtly political

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Local Governments

• There is a strong traditional from Tiebout (and
  the Federalist papers) that suggests that many
  benefits of local governments
• Opportunities for variety
• An ability to influence outcomes through both
  voice and exit
• However, local governments are particularly bad
  at redistribution because of mobility

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Incentives and Local Governments (Public Choice,
1996)

• Inducing Local Governments to behave well
  presumably requires incentives
• Taxes can provide those incentives is governments
  want more revenue
• Property taxes have the benefit of inducing long
  time horizons for governments
• Tradeoff between income and property taxes
  involves the elasticity of demand for space
  (highly elastic income tax looks better
  inelastic housing is better)

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General Redistribution Point

• If the average tax rate (pure redistribution) is
  determined by the income level of the median
  voter t(y) and
• The income level of the median voter is
  determined by the level of redistribution y(t)
• Then the local equilibrium is determined by the
  point where y(t(x))x

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Tax Rate as a function of Income
Tax Rate for Redistribution
Income as a Function of Tax rate
Median Income in the City
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The Curley Effect

• Tiebout suggests that since localities will want
  their communities to grow, this will create good
  incentives for governments
• But what if governments dont want their cities
  to grow (as in zoning)
• Or even worse, what if they want their cities to
  lose their richest residents

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Shaping the Electorate

• James Michael Curley was the mayor of Boston on
  four separate occasions from before WWI to after
  WWII
• He was highly focused on ethnic conflict and also
  ended up in jail
• When asked in WWI, if a UK recruiter could
  recruit Bostonians of British extraction to
  fight, Curley replied Go Ahead, Take Every Damn
  One of Them

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Curleys Logic

• The rich anglo-bostonians were never going to
  vote for him
• As a result, by eliminating them he increased his
  vote share
• This requires some form of group identification
• This can also be seen in the policies of
  African-American mayors like Berry or Coleman
  Young (Detroit)