Microsoft Word - regulate33.docMatthew E. Kahn1
UCLA and NBER
April 2008 Preliminary and Incomplete mkahn@ioe.ucla.edu
1 I thank seminar participants at USC, Research Triangle, and the summer 2007 UBC Real Estate Conference. I thank Jeff Zabel for kindly sharing data with me. Ed Glaeser provided useful suggestions. This research was supported by the Richard S. Ziman Center for Real Estate at UCLA.
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Introduction
Housing supply regulation raises the cost of building new housing. The
consequences of housing supply regulation have been documented in a number of recent
studies including Fischel 2000, Mayer and Somerville 2000, Quigley and Raphael 2004,
Glaeser, Gyourko and Saks 2005, Schill 2005). By limiting supply in some of the most
desirable cities in the United States, such regulation may contribute to the extraordinary
price appreciation that has been observed in major coastal cities such as San Francisco,
Los Angeles, Boston and New York City (Gyourko, Mayer and Sinai 2006).
Why do housing supply regulations differ across space? One explanation focuses
on the median voter’s narrow self interest. Home owners have a financial incentive to
discourage new construction because it reduces the scarcity value of their asset (Fischel
1999). Richer communities may engage in fiscal zoning to keep the poor out. Minimum
lot zoning reduces the likelihood that new entrants will be much poorer than incumbents.
Communities may also enact housing supply regulation to preserve and enhance
local quality of life. Environmentalist communities are especially likely to pursue such
goals. Environmentalists may seek to block local growth to preserve local public goods
such as open space, bike paths and clean air and to preserve the character and culture of
their community.
Academics have also posited that communities may justify anti-growth sentiment
using environmental concerns as a politically correct justification for less noble reasons.
“The actual issues that lead people to oppose homebuilding are hard to discover. By far the most frequent objections that growth opponents raise have to do with environmental impacts. These range from harm to wildlife to destruction of natural resources to increases in air pollution. Yet to label all protest as environmentalism would be a mistake. Many growth opponents use environmental arguments to mask other motives such as fears of property tax increases or anxieties about keeping their community
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exclusive. Environmental rhetoric has become a valued currency for public debate with much greater voter appeal than arguments that appear more narrowly self interested. As a result people who are not environmentalists in any sense borrow it for their own purposes.” (Frieden 1979 page 8)
While the environmental “activism” hypothesis is intriguing, it is challenging to
test because of the difficulty of identifying credible measures of community
environmentalism. Put simply, what evidence can be collected to document that
Berkeley, California’s population is a “greener” community than a Republican
community in Orange County California?
This paper uses California and national level data to test community
environmentalism’s role in determining new housing supply. California provides an
ideal setting for studying this issue because of its clear spatial variation in where
environmentalists do (i.e Berkeley) and do not live. It is well known to be an innovative,
regulatory leader as demonstrated by Gov. Arnold Schwarzenegger recently signing the
first cap on greenhouse gas emissions (AB32).
Detailed political registration and voting data on binding California propositions
provide a rich data set for identifying environmentalist communities. California is the
nation’s leading state in voting on direct environmental initiatives (Kahn and Matsusaka
1997, Matsusaka 2005). In recent research, I have documented that data from political
markets such as a community’s share of Green Party members is useful for proxying for
community environmentalism (Kahn 2007, Kahn and Morris 2007). In these papers, I
have documented that in communities whose population votes and registers as “greens”
that such communities are more likely to purchase Toyota Prius vehicles, have
environmentalist stores locate within their borders and feature a workforce that
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disproportionately commutes using public transit, walking and biking. The
environmentalism indicators I introduce below are notable because they are based on
actual choices of community members rather than stated attitudes in surveys. I also
examine a second national level data set. While the national data offers a larger sample,
my community environmentalism measures are not as high quality as my California
measures.
This paper proceeds in four steps. First, I sketch a dynamic process through
which environmentalist communities form in some areas but not in others. Second, I
present my estimation strategy for testing for environmentalism’s role in determining
new housing supply. Section Three discusses the data sources. The results are
presented in Section Four and Section Five concludes. The paper’s major findings are
Where do Environmentalist Communities Form?
This paper’s core goal is to test whether there is less new housing construction in
environmentalist communities. To begin to study this issue requires an understanding of
where such communities form within urban areas. Within metropolitan areas, greens are
not “randomly assigned” across communities.
People differ with respect to their support for environmental causes. For reasons
that lie outside this paper’s focus, some people are environmentalists and others are not.
The heterogeneous population Tiebout sorts into sub-communities. This sorting provides
a source of variation to test this paper’s main hypothesis.
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Within a metropolitan area, environmentalists are likely to cluster in high density
areas, featuring opportunity to walk, located near public transit stations and in high
environmental amenity areas such as Berkeley, Santa Monica and Santa Cruz. Small
initial differences in exogenous spatial attributes such as proximity to the ocean can have
a social multiplier effect. As environmentalists move to a nice community, green
businesses such as organic restaurants would be more likely to locate near this
community. Such “endogenous” green amenities would only further encourage
environmentalists to move to this community. Such a green community would act as a
club financing “green” infrastructure such as bike lines. A social multiplier effect takes
place such that a community that develops a “green” reputation attracts stores that cater
to this group and this attracts more “like-minded” people (Waldfogel 2007). Community
social interactions would only reinforce this dynamic as a type of social multiplier effect
feeds on itself.
Why would an environmentalist community engage in “extra” zoning? The
median voter may believe that growth threatens the area’s natural capital and overall
quality of life. The residents of the area may believe that due to their environmentalism
they have been able to overcome tragedy of the commons problems such as litter that
degrade quality of life in other communities. They may fear that this social ethic could
be “diluted” if growth takes place. In this sense, environmentalist areas may be like
religious communities and as such are club goods. Iannaccone (1992) and Berman
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(2000) have argued that club member utility is higher if the size of the group is larger and
if the average devotion to the cause is higher. Housing supply limitation measures may
help to self-select only those environmentalists who are committed to the cause and this
raises the average devotion to the group.
To test for environmentalism’s role in determining new housing production across
different communities, I will use data from California and the nation as a whole. In one
set of results I will present below, the dependent variable will be the count of annual new
single detached housing permits in a place in a given calendar year.
New Housingjlt = αl + γ Greenjlt-n + φ Xjlt + U jlt (1)
Controlling for other baseline community characteristics, (X), and geographical
fixed effects (such as county or metropolitan area fixed effects), I test whether γ is less
than zero. This would indicate that there is less new housing production in
environmentalist communities. This regression approach is an indirect way for
documenting environmentalism’s consequences for housing supply limitations. Below, I
will present some direct evidence where the dependent variable will be a measure of the
housing supply regulations that a place has enacted at a point in time (based on the
Wharton Survey).
In interpreting estimates of equation (1), there are three distinct reasons why I
may find that γ is negative. The first explanation is the “treatment effects” hypothesis.
The presence of environmentalists creates a voting coalition that seeks policies to limit
growth. The second explanation emphasizes selection effects. Greens locate in
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communities that for exogenous reasons are difficult to build new housing. A third
explanation is that greens locate in “hippie communities” that block growth for reasons
independent of environmental concerns.
There are plausible reasons to question the exogeneity of “Green” in equation (1).
The unit of analysis in the California regressions will be a place/year such as Santa
Monica in 1996. The error term in equation (1) will reflect unobserved demand factors.
Greens could be agglomerating in the most desirable areas of a metropolitan area or their
costly actions may make an area a more attractive place to live. In both the “selection”
and “treatment” cases, developers will seek to build in such places. In this case, the
presence of Greens signals that a place has high quality of life for exogenous reasons. If
greens cluster in the best parts of a metropolitan area, then E( U jt-n | Green) will be
greater than zero. Environmentalists may cluster in places with high unobserved (to the
econometrician) quality of life and their actions may improve an area’s quality of life
increasing the demand to live there. The net effect of either the selection or the
treatment mechanism would be that E( U jt | Green) would be positive and OLS estimates
of equation (1) would be biased against my core hypothesis that γ is less than zero.
Conversely, it is possible that E(U|green)<0. Environmentalists could cluster in
“hippie” places where anti-business sentiment creates red-tape that hinders development.2
In this case, the environmentalists may not be causing the hurdles to development. They
2 “First, just navigating the process adds explicit financial and time costs. The effects of those direct costs are similar to those of fees. Second, both the outcome and the length of the regulatory process are uncertain. Developers do not know the extent to which local authorities will demand costly changes in project density, design, or type before granting a final approval. For risk adverse developers, this uncertainty will reduce the level of new construction above the direct effect of higher mean construction costs (Mayer and Somerville 2000).”
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may merely settle in such areas. In this case, my estimates of equation (1) will over-
estimate the effect of “environmentalism” because the environmentalism measures I
present below may in part reflect the omitted variable of “anti-business” ideology. I will
address this below by creating some measures of community “liberalism” and testing
whether controlling for such community ideology measures reduces the effect of my
environmentalism measures. Clearly, the validity of this test hinges on the absence of
collinearity. I need there to be environmentalist communities that are not liberal and
vice-versa.
instrumental variables strategy for reducing the correlation between the error term in
equation (1) and the environmentalism indicators. Below, I will contrast OLS and IV
estimates of this equation. The instrument set will be lagged political party registration
variables from the year 1972 and measures of the city’s urban in 1970. I will discuss the
validity of such variables as instruments below. In equation (2), I present the “first
stage” of TSLS.
Greenjt = α + γ Politicsjt-n + φ historyjt-n + ψ jt (2)
In equation (2), a California city j’s time t environmentalism is modeled as a
function of the community’s past political party of registration shares “Politics” and of its
past attributes such as its lagged population density. The lag structure in equation (2) will
be 18 years when the variable I instrument for is 1990 environmentalism and it will be 8
years when I instrument for year 2000 environmentalism in estimating equation (1).
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If the error term in equation (1) has a permanent component, then my political
instruments are unlikely to resolve the environmentalism endogeneity issue. For
example, if greens always cluster in the most beautiful part of a metropolitan area and if
this beauty is a fixed effect, then my instrument set (the lagged political variables) will
also be correlated with the error term in equation (1).
Data Sources
Identifying California Greens
The percentage of each census tract’s voters who are registered with the Green
Party.3 The Green Party is well known for its environmentalism activism, and California
is the state with the highest count of Green Party registered voters both in absolute terms
and as share of all registered voters.4 But even in California, the Green Party’s
membership is small. Across 7002 California census tracts in the year 2000, the average
tract’s Green Party share is 0.009 and the median is 0.005. This makes Green Party
membership a costly political choice, because in California, members of this party lose
the right to vote in another party’s primary election.
In California, voters have the opportunity to participate in lawmaking through
ballot initiatives (Matsusaka 2005). Many of these initiatives are related to
3 The Berkeley IGS (see http://swdb.berkeley.edu/) provides data for each California census tract's count of registered Green Party Voters in the year 2000. It is important to note that voting precincts and census tracts spatially overlap but they do not coincide. To translate the voting precinct data into census tract data, The Berkeley IGS takes the precinct data (there are over 1700 Precincts in Los Angeles County alone) and uses a statistical procedure based on ecological inference to create the census tract data. 4 See http://cagreens.org/platform/ecology.htm
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environmental issues. Voting patterns based on these binding votes is informative about
a community’s environmentalism.
Here is a brief summary of three propositions I study.
Proposition 185 in 1994: This measure imposes a 4 percent sales tax on gasoline not diesel fuel beginning January 1, 1995. This new sales tax is in addition to the existing $.18 per gallon state tax on gasoline and diesel fuel and the average sales tax of approximately 8 percent imposed by the state and local governments on all goods, including gasoline. Revenues generated by the increased tax will be used to improve and operate passenger rail and mass transit bus services, and to make specific improvements to streets and highways. The measure also contains various provisions that generally place restrictions on the use of certain state and local revenues for transportation purposes. (www.calvoter.org/archive/94general/props/185.html)
In the results I report below I will use factor analysis to extract an environmentalism
factor based on a place’s share of registered Green Party voters in 1992 and its share of
voters who voted in favor of Proposition 185 in 1994. This factor will represent the early
1990s environmentalism indicator for a place.
The year 2000 place environmentalism index is based on factor analysis of four
variables. The place’s Green Party registered voter share is the first indicator. The next
two are the share of the place that voted in favor of the following propositions.
Proposition 12 in March 7, 2000, The 2.1 billion dollar "Safe Neighborhood Parks, Clean Water, Clean Air and Coastal Protection Bond Act of 2000" (2000 Bond Act). This proposition authorizes the state to sell $2.1 billion of general obligation bonds to fund many designated programs. About $940 million of the bond money would be granted to local agencies for local recreational, cultural, and natural areas. The remaining $1.16 billion would be used by the state for recreational, cultural, and natural areas of statewide significance. Proposition 12 will help make our parks safer, keep our water free of pollution, improve air quality, and preserve our natural resources.http://ca.lwv.org/lwvc.files/mar00/pc/prop12.html Proposition 13 in March 2000: This proposition authorizes the state to sell $1.97 billion of general obligation bonds to spend on programs designated to provide: Safe Drinking Water ($70 million) Flood Protection ($292 million) Watershed Protection
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($468 million) Clean Water and Water Recycling (355 million) Water Conservation ($155 million) Water Supply, Reliability and Infrastructure ($630 million) http://ca.lwv.org/lwvc.files/mar00/pc/prop13.html.
My final measure of environmentalism is the count of hybrids registered in each
California zip code divided by the zip code’s count of households. Given the reputation
of these cars as “green vehicles,” and the fact that hybrid owners' savings in fuel costs are
far smaller than price differential between hybrids and comparable conventional models,
hybrid ownership represents a costly "green" choice and is a strong indicator of
environmentalist beliefs. Using factor analysis on these four measures of place
environmentalism yields an intuitive index. Based on 349 places in California, the top
ten “greenest” places are: Albany, Berkeley, Fairfax, Belvedere, Piedmont, Mill Valley,
Larkspur, Portola Valley, Sausalito and Palo Alto. Other notable places include; Santa
Monica (ranked #12), Malibu ranked #17 and Santa Cruz ranked #23. The “brownest”
five are; Woodlake, Montclair, Ripon, Gonzales, and Escalon.
The instrument set will include a city’s political party registrations such as the
1972 share of the city’s registered voters who were registered Democrats. In estimating
equation (2), I expect that a city’s “liberalness” persists over time such that cities with
larger share of liberal voters (i.e registered voters from the Democratic Party and the
Peace and Freedom Party) in 1972 have larger environmentalism scores in the 1990s.
In a series of case studies about California, Frieden (1979) argues that
some home owner communities voice environmentalist views as a political correct means
of achieving their NIMBY goals. My measures of environmentalism are based on
political choices not on stated rhetoric. It is possible that a community of “fake greens”
will voice words justifying their anti-growth stance based on environmental arguments
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but based on political data not reveal any “true” environmental leanings. My
environmentalism measures are unlikely to suffer from this problem.
Identifying Greens: National Measure #2
In previous work based on California data, I have documented that
Representatives whose constituents are environmentalists (based on their initiative voting
and political affiliations such as being members of the Green Party) are more likely to
have sharply pro-environment voting records (Kahn 2007). To measure a
Representatives’ “pro-environment” voting record, I use data from the League of
Conservation Voters.
The annual League of Conservation Voters’ (LCV) “Scorecard” determines which
roll call votes are important pieces of environmental legislation and identifies what is the
“pro-environment” vote on each specific issue (see www.lcv.org).
“This Scorecard represents the consensus of experts from 19 respected environmental and conservation organizations who selected the key votes on which Members of Congress should be graded. LCV scores votes on the most important issues of the year, including environmental health and safety protections, resource conservation, and spending for environmental programs. … Dedicated environmentalists and national leaders volunteered their time to identify and research crucial votes.”
A Representative’s voting record is likely to be positively correlated with
unobserved constituent ideology if Representatives vote partially based on their
constituent’s preferences. If Representatives voted their own ideology on environmental
bills, then their voting records would not be informative about the preferences of the
median voter in their district.
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It is important to note that my measures of environmentalism are based on costly
“revealed preference”. This matters because researchers such as Friedan (1979) have
argued that self interested home owners embrace environmentalism, not out of “true”
ideology but instead to cloak themselves in a politically correct coating of
environmentalism that allows them to achieve their goals of banning new construction
without explicitly stating their “true” motivations.
Measuring New Housing Supply
I focus on the count of new construction being permitted and observed in different
physical jurisdictions at different dates. The first data set I examine is annual building
permits for each Californian city are based on Zabel and Peterson (2006) data obtained
from the California Industry Research Board (CIRB) http://www.cirbdata.com/. The
dataset includes the total number of permits granted each year for different types of
housing structures for more than 400 FIPS places over the period 1990-2004, as recorded
by the CIRB. This represents the incorporated subset (with minor exceptions) of all
California FIPS places and encompasses the majority of all land within FIPS boundaries.
Within the sample, places that featured a large share of new development in 1990 also
had high levels of new development in 2004. The correlation between the 1990 and 2004
new development share (defined as place j’s count of new permits/total sample new
permits) is .818. For the nation as a whole, I use data from the 5% IPUMS Census of
Population and Housing micro data. The micro data reveals whether a specific household
lives in a single detached home and the age of the housing unit.
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Evidence from California
In Table One, I report California housing permit regressions using place level data
from 1990 to 2004. The unit of analysis is a place/year. The Table reports four OLS
regressions of equation (1). Standard errors are clustered by place. In each of these
regressions, I control for the place’s share of housing units that are owner occupied, and
the place’s population size, average income and population density. County fixed
effects and year fixed effects are included in each regression. The environmentalism
indicator is based on a place’s environmental factor constructed using information on its
Green Party registration share in 1992 and the share of votes in favor of Proposition 185
in 1994.
Table One reports four OLS estimates of equation (1). In columns (1) and (2) the
dependent variable is the log(1+total single family home permits). The results in these
columns differ because I weight the regressions by a place’s 1990 population in column
(1) while in column (2) the results are unweighted. In all of the California regressions I
report, the standard errors are clustered by place. All else equal, a standard deviation
increase in a place’s environmentalism reduces its single family permits by 34% in the
population weighted regression and by 24% in the unweighted regression (see column
(1)). In columns (3) and (4), I report results where the dependent variable equals a
place’s total log(1+total housing units). Environmentalist places are issuing a larger
share of their new permits as multi-family dwellings. The results in column (3) indicates
that a standard deviation increase in community environmentalism reduces total housing
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permits by 20%. This estimate is smaller than the estimate of 34% reported in column
(1) for single detached housing units. These findings are robust to controlling for other
community political ideology measures. I continue to explore the robustness of these
results to including more place level controls.
In future work, I will incorporate place specific measures of available infill
development land. This data has been created in a project led by John Landis (see
http://infill.gisc.berkeley.edu/index.html). In future work, I will create measures of a
California place’s “liberalism” based on its voting on propositions that have no obvious
environmental component. I will study how the environmentalism coefficient is affected
by controlling for this measure of place liberalism.5
To provide some evidence on the causal link between place environmentalism and
regulatory intensity, I use data from the Wharton Regulation Index (see
http://real.wharton.upenn.edu/~gyourko/Wharton_residential_land_use_reg.htm).
Gyourko, Saiz and Summers (2008) have created a measure of regulatory severity. In a
place level regression of their Wharton Residential Land Use Regulation Index on my
year 2000 environmentalism indicator (weighted by place population) yields:
reg WRLURI p2 [w=Pop]
5 http://en.wikipedia.org/wiki/California_Proposition_22_(2000)
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Source | SS df MS Number of obs = 179 -------------+------------------------------ F( 1, 177) = 54.15 Model | 32.8429086 1 32.8429086 Prob > F = 0.0000 Residual | 107.349181 177 .606492546 R-squared = 0.2343 -------------+------------------------------ Adj R-squared = 0.2299 Total | 140.192089 178 .787596007 Root MSE = .77878 ------------------------------------------------------------------------------ WRLURI | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- p2 | .6183516 .0840287 7.36 0.000 .4525247 .7841786 _cons | .8955058 .0629615 14.22 0.000 .7712539 1.019758 ------------------------------------------------------------------------------
And, including county fixed effects yields:
Linear regression, absorbing indicators Number of obs = 179 F( 1, 135) = 38.66 Prob > F = 0.0000 R-squared = 0.5524 Adj R-squared = 0.4098 Root MSE = .6818 ------------------------------------------------------------------------------ WRLURI | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- p2 | .8852921 .1423755 6.22 0.000 .6037171 1.166867 _cons | .8192679 .0651948 12.57 0.000 .6903327 .948203 -------------+---------------------------------------------------------------- fips | F(42, 135) = 2.284 0.000 (43 categories)
Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- WRLURI | 179 .5866114 .7194855 -1.268611 3.592063 p2 | 179 .0801588 .8992566 -1.640422 3.164381
(april15b.do)
In addition to using place/year level data, I also use household level Census data
from the 2000 IPUMS data 5% sample. This allows an examination of whether
observationally identical households live in different housing types depending on their
community’s degree of environmentalism.
In Table Two, I report three linear probability models using the year 2000 Census
Micro data. The sample includes all households who live in a PUMA (the geographical
unit) whose centroid is within 30 miles of the closest central business district (CBD).
The regression equation is reported below.
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Housing Consumption = MSA + demographics + B*Distance to CBD + δ*Green + U
In column (1), the dependent variable is a dummy that equals one if the household
lives in a house that was built between 1990 and 2000. If environmentalist communities
are slowing growth, then all else equal, in these communities people should be less likely
to live in new single detached housing. In Table Two, I control for the household’s
income and socio economic status and the community (the PUMA) physical distance
from the CBD and metro area fixed effects. Holding all of these factors constant, I focus
on the estimates of the year 2000 environmental factor. As shown in Table Two, a
standard deviation increase in a PUMA’s environmentalism factor is associated with a
1.3 percentage point reduction in the probability that the household lives in new housing
a 6.3 percentage point reduction in the probability that the household lives in a single
detached home. In the third column of Table Two, I present some evidence that my
measure of community environmentalism correlates with behavior that one would expect
that environmentalists would engage in. Controlling for a PUMA’s distance to the CBD,
an extra standard deviation of community environmentalism increase the probability that
a household head commutes using public transit, bicycle or walking by 1.6 percentage
points. This is a large effect given that the sample average for workers commuting by
these modes is 4.5%.
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In this section, I contrast OLS and IV results using my California sample from
1990 to 2004 and from 2000 to 2004. Table Three reports two estimates of my first stage
regression. In column (1), the dependent variable is a place’s environmental factor based
on the 1990s data. The explanatory variables include metro area fixed effects and data
from the 1970 Census and the 1972 Party Registration data. As shown in column (1),
richer places, and places with a larger share of Democrat registered voters in 1972 and
places with more Peace and Freedom voters and fewer American Independent voters are
statistically significant positive correlates in determining a community’s
environmentalism level based on the 1990s indicator. Reading the platform for these last
two parties indicates that these results are intuitive. The Peace and Freedom party is a
socialist party while the American Independent party opposes immigration, abortion and
would end the federal income tax.6 Column (1) does feature some surprises. Neither
distance to the CBD nor community density is statistically significant.
In column (2) of Table Three, I report a similar regression but this time using the
data on the community’s year 2000 environmentalism factor as the dependent variable.
Controlling for a place’s log of average household income and its population density and
distance to the CBD, communities with more Democrats, Green Party members and
Peace and Freedom registered voters (based on 1992 registration data) are more likely to
be environmentalists. The results reported in Table Three should boost one’s confidence
that lagged political registration variables do predict community environmentalism.
6 In the instrumental variables results I report below, I do not use lagged place per-capita income as an instrument. It is included in Table Three to provide some descriptive evidence concerning which places are “green”.
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The instrument set can be thought of as identifying which places are historically
liberal communities. Given that liberal communities are often environmentalist
communities, this logic satisfies the first condition for being a valid instrument set.
If the error term in equation (1) does not contain a persistent fixed effect, then transitory
demand shocks embedded in this error term will be uncorrelated with this instrument.
Tables Four and Five present a set of OLS and IV results for comparable samples.
Table Four focuses on data from 1990 to 2004 and instruments for place
environmentalism in 1990. Table Five presents OLS and IV estimates of equation (1)
using data from 2000 to 2004 and instruments for environmentalism in the year 2000.
Table Four’s results are based on 279 urban places in California for which I have data on
their permits over the years 1990 to 2004, the place’s census demographics in 1990, its
environmentalism index based on the 1990s data and I have data on its 1972 political
party registration data.7 In Table Four, I include metropolitan area fixed effects.
Controlling for 1990 base year attributes, both the OLS results (see column 1) and the IV
results (see column 2) indicate that environmentalist communities issue fewer new single
family housing permits. The results indicate large effects due to environmentalism. A
standard deviation increase in a place’s 1990s environmentalism reduces new single
detached permits by roughly 40%. The right columns of Table Four show that for the
subset of California places for which I have 1972 political voting registration data for, a
community’s environmentalism does not have a statistically significant effect on the total
count of permits the place issues. I conclude that environmentalist communities seek to
7 The place’s population density in 1970 is also used as an instrument. Recall that in estimating equation (1), I control for population density in the base year which is 1990 in Table Three and 2000 in Table Four.
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limit single family homes relative to multifamily homes. It is important to note that my
regressions do include baseline controls for the place’s distance to the CBD and its
population density.
Table Five presents the results based on the California places over the years 2000
to 2004. In these regressions, the base year demographics are from the year 2000 and I
use the year 2000 environmentalism indicator. In the instrumental variables regressions
reported in columns (2) and (4), I instrument for the year 2000 environmentalism
indicator using the 1992 political party registration shares listed in Table Two’s column
(2). The results for community environmentalism are similar to those reported for the
1990 to 2004 sample in Table Four. Environmentalist communities are issuing fewer
permits for new single detached homes but the magnitude of this effect is smaller for total
permits issued. It is interesting to note that unlike in other specifications, the place’s
share of the housing stock that is owner occupied has a negative effect on issuing new
permits.
Equation (1) identifies the effect of environmentalism using cross place variation
in environmentalism in the base year (either 1990 or 2000). An alternative strategy
would be to build a panel data set and try to exploit within place changes in
environmentalism. To estimate a first difference regression version of equation (1)
requires an environmentalism measure that can be compared at two points in time.
Intuitively, I need an observable measure of Berkeley’s environmentalism in 1990 and
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2000. To proxy for community environmentalism using a comparable variable I use each
place’s Green Party voter registration share. In 1990, I merge on the 1992 voter
registration share and in 2004, I merge on the year 2000 voter registration share.
I estimate a panel regression of equation (1) with place fixed effects using two
observations for each place. In these regressions, I use the 1990 and 2004 data on
housing permits. Including place fixed effects, I estimate two regressions. Below, I
report standard errors in parentheses. There are 865 observations in each regression and
the unit of analysis is a place/year.
Log(1+total single family) = place + Year 2000 -3.54*Green + 1.81*log(pop) (6.32) (.42)
Log(1+total units) = place + Year 2000 -14.30*Green + 1.83*log(pop) (5.92) (.39)
These fixed effects results support the claim that there is less new housing unit
growth in communities experiencing a growth in their share of environmentalists. A one
percentage point increase in Greens is associated with a 14% reducing in new housing
permits. Surprisingly, the single family homes regression yields an insignificant
coefficient estimate.
Evidence from the Entire United States
I now turn to using national data from the year 2000 5% IPUMS sample of the
Census of Population and Housing. For the nation as a whole, the empirical challenge is
that there are no national level environmental referenda where communities reveal their
“environmentalism” through their binding voting behavior. To get around this problem,
I use data on each Congressional Representative’s year 2000 League of Conservation
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Voters (LCV) score. Geocorr is used to match each Census PUMA identifier to a
Congressional Representative. If a Representative’s voting patterns are positively
correlated with the district’s median voter’s preferences, then the LCV score does
embody useful information about a community’s environmentalism (Peltzman 1984).
By merging micro data with the voting record of one’s Representative, I can
provide more tests of the hypothesis that “green” communities slow new development.
In Table Six, I report four linear probability models using the Census micro data. These
regression models are identical to the California models reported in Table Two but in this
case I use the LCV score (based on the 106th Congressional votes on key environmental
legislation) as my measure of community environmentalism. In columns (1) and (2), the
dependent variable is a dummy variable that equals one if the household lives in a new
house (age less than or equal to ten). Controlling for the household head’s age,
household income, the head’s industry index, and the household’s residential area’s log
population density and distance to the closest Central Business District, I test for the role
of environmentalism in determining attributes of the types of homes that people live in.
As shown in columns (1) and (2), when I include either state fixed effects or metro area
fixed effects, I find a negative and statistically significant effect of Representative
environmentalism on the probability that a constituent household is living in new
housing. Based on the regression results reported in column (1), a 25 point increase in a
Representative’s LCV score reduces the likelihood that a household is living in new
housing by 1.5 percentage points. The results in column (3) switch the dependent
variable to a dummy that equals one if the household lives in a single detached home.
Again, all else equal, I find that green constituents are less likely to live in a single
23
detached home. Note that this regression controls for both the PUMA’s population
density and its distance to the nearest CBD. Finally, in column (4) I confirm again that
environmentalist constituents are more likely to not commute by private automobile.
In results available on request, I have used the national sample to test for the
relative importance of community environmentalism versus community liberalism in
determining housing supply conditions. To proxy for community liberalism, I include
include the Poole-Rosenthal (1997) measures of the Representative’s political ideology.8
I find that my environmentalism estimates are robust to including this liberalism measure
in regressions based on equation (1) using the household micro data. The “liberalism
measure” itself is statistically insignificant.
The Incidence of Housing Supply Regulation in Environmentalist Areas
Environmentalists settle in high amenity areas in California such as Santa Cruz,
Malibu and Berkeley. In addition to self selecting into such communities, an intended
consequence of local “good citizenship” and activist political participation is to make the
community a more desirable place to live. This paper has documented that all else equal
that there is less new construction in environmentalist communities. This effect is
8 My measure of Representative ideology is the Poole and Rosenthal two factors (see http://voteview.com/dwnomin.htm). They conduct a principal-components factor analysis of Congressional voting patterns to assign each representative in each Congress a point in a two dimensional ideology space. In the political science literature, this is the most commonly used measure of legislator preference. It is important to note that Poole and Rosenthal (1997) use all Congressional Roll Call votes, not simply the environmental votes, to create their indices. They interpret the first dimension as measuring whether a legislator is a liberal or a conservative.
24
especially large for new single family homes. If such communities feature high demand
and inelastic supply, then home prices will be quite high in such communities. For
incumbent home owners, the economic returns to introducing housing supply regulations
are highest in locations where demand is inelastic. Perhaps it is no accident that cities
with the highest regulatory taxes (based on the results reported in Glaeser, Gyourko and
Saks (2005)) have the highest quality of life (based on the results reported in Gyourko
and Tracy 2001). Forward looking home owners may recognize that the economic
returns from blocking new development are highest in such desirable areas.
Do renters in high quality of life areas benefit from activist environmental policy
that blocks new construction? A congestion theory of local externalities would argue yes
while other urban experts are less sure.
“A closer look at how the growth control and environmental coalition operates in local controversies shows that its effects are far less benign. It has made a clear and substantial contribution the escalation of new home prices; yet its success in discouraging homebuilding has failed to produce important environmental benefits for the public at large. Instead, it has protected the environmental, social and economic advantages of established suburban residents who live near land that could be used for new housing.” (Frieden 1979 page 4)
This paper has focused on estimating partial equilibrium models and has not
investigated where growth is deflected to as green communities limit such growth.
The general equilibrium effects induced by local regulatory efforts merits future research.
It is possible that an unintended consequence of environmentalist communities engaging
in open space preservation is a “browning” of the metropolitan area as growth is
deflected to more outlying areas. Consider the example of Marin County north of the
Golden Gate Bridge in San Francisco. As William Fischel observes,
25
“It has large amounts of open space on which development could easily occur but does not. Tens of thousands of commuters from far away suburbs and exurbs pass through the Marin County corridor on U.S. Route 101 on their way to work in San Francisco. Marin’s open space is an asset for those who live near it and it probably provides some pleasures for those who drive through it daily. But it also represents an enormous waste in the form of excessive commuting and displacement of economic activities to less productive areas (Fischel 1999 page 162).”
To answer how local growth regulation affects a metro area’s greenness requires
estimates of where “deflected” residents (those who would have moved to Marin) do
move to.
Conclusion
A growing consensus has emerged that housing supply and land use regulations
have contributed to raising home prices in certain desirable cities and communities. This
raises the question of why certain jurisdictions introduce such regulations while other
communities do not. The home owner hypothesis makes an intuitive claim that self
interest encourages the median voter/home owner to support policies that limit new
construction.
A second possible explanation for why some communities limit growth is the rise
of environmentalism. This paper has tested the claim that environmentalist communities
are more likely to engage in slow growth. This paper’s empirical contribution has been
to develop credible indicators of community environmentalism based on costly revealed
preference indicators. I showed how political outcome measures (voting on initiatives
and Congressional voting, rather than respondent surveys) can be used to study the role
of this ideology as a determinant of community land use patterns.
26
Using data from both California and national data, I documented that new housing
is less likely to be constructed in environmentalist communities. This paper’s “outcome”
research has focused on the total effect of community environmentalism. I have not
attempted to pinpoint any specific regulation’s impact. Several recent studies have
sought to estimate how specific regulations affect the production of new housing. Some
attempts to look at specific environmental regulations and how they affect housing
supply. Some of these housing supply regulations are directly tied to environmental
issues (Zabel and Peterson 2006, Hanek and Chen 2007, Quigley and Swoboda 2006).
The challenge in evaluating any one regulation’s impact is that there is likely to be a
positive correlation between the adoption of various regulations. This paper has
implicitly assumed that communities enact a bundle of regulations and this hampers
efforts to disentangle the effects of any one specific regulation in limiting growth.
27
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Hanak, Ellen and Margaret K. Browne, 2006. “Linking Housing Growth to Water Supply: New Planning Frontiers in the American West,” Journal of the American Planning Association, Vol. 72(2). Helsley, Robert W. and William C. Strange, 1995. “Strategic Growth Controls,” Regional Science and Urban Economics, Vol. 25: 435-460. Iannaccone, Laurence R. 1992. Sacrifice and Stigma: Reducing Free-Riding in Cults, Communes, and Other Collectives Journal of Political Economy, vol. 100, no. 2, 271- 291 Ihlanfeldt, Keith R. and Timothy M. Shaughnessy, 2004. “An Empirical Investigation of the Effects of Impact Fees on Housing and Land Markets,” Regional Science and Urban Economics, Vol. 34(6): 639-61 Kahn, Matthew. 2007. Do Greens Drive Hummers or Hybrids? Environmental Ideology as a Determinant of Consumer Choice, Journal of Environmental Economics and Management. September. Kahn, Matthew, and John Matsusaka. 1997. “Demand for Environmental Goods: Evidence from Voting Patterns on California Initiatives.” Journal of Law and Economics 40, 1: 137–73. Kahn, Matthew and Eric Morris. 2008. Walking the Walk: Do Green Beliefs Translate Into Green Travel Behavior? UCLA Working Paper. Landis, John D. 1992. “Do Growth Controls Work? A New Assessment,” Journal of the American Planning Association 58 (4): 489–508. Landis, John D. 2006. Growth Management Revisited. Journal of the American Planning Association. 72(4) 411-430. Levine, Ned, 1999. “The Effects of Local Growth Controls on Regional Housing Production and Population Redistribution in California,” Urban Studies, Vol 36(12): 2047-2068. Mayer, Chris. and Tsur. Somerville (2000) “Land Use Regulations and New Construction,” Regional Science and Urban Economics, 30, 639-662. Peltzman, Sam. Constituent interest and congressional voting, Journ. Law and Econ. 27(1984) 181-210.
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Pollakowski, Henry O., and Susan M. Wachter. 1990. “The Effects of Land-Use Constraints on Housing Prices,” Land Economics 66 (3): 315–324. Poole, Keith, and Howard Rosenthal. (1997). Congress: A Political-Economic History of Roll Call Voting. Oxford University Press. Quigley, John M., and Stephen Raphael, 2004. “Regulation and the High Cost of Housing in California,” American Economic Review. Quigley, John, Steven Raphael and Larry A. Rosenthal Local Land-use Controls and Demographic Outcomes in a Booming Economy Urban Studies, Vol. 41, No. 2, 000–000, February 2004 Quigley, John M., and Larry A. Rosenthal. 2005. “The Effects of Land Use Regulation on the Price of Housing: What Do We Know? What Can We Learn?” Cityscape 8 (1): 69–138. Quigley, J.M. and A.M. Swoboda, “The Urban Impacts of the Endangered Species Act: A General Equilibrium Analysis,” Journal of Urban Economics (2006). Schill, Michael. 2005. “Regulations and Housing Development: What We Know” Cityscape 8 (1): 5-20. Zabel, Jeffrey. and R. Paterson (2006a) “The Effects of Critical Habitat Designation on Housing Supply: An Analysis of California Housing Construction Activity,” Journal of Regional Science, 46 (2006): 67-95. Zabel, Jeffrey. and R. Paterson (2006b) “The Impact of Critical Habitat Designation on Housing Markets in California.
Table One: The Determinants of New Housing Permits Issues by California Cities Between 1990 to 2004
Column (1) (2) (3) (4)
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Environmentalism Indicator -0.3445 0.0612 -0.2803 0.0659 -0.2052 0.0476 -0.1727 0.0710 % Owner Occupied -0.4742 0.5836 -0.3429 0.5248 -1.3662 0.5750 -1.0223 0.5286 log(Place Population) 1.0877 0.0267 1.0622 0.0464 1.2322 0.0258 1.1321 0.0483 log(Distance to CBD) 0.1640 0.0985 0.2341 0.1064 0.1730 0.0973 0.1982 0.1026 log(population density) -0.3904 0.1059 -0.2581 0.0743 -0.4436 0.1086 -0.2713 0.0733 Log(average household income) 1.2825 0.2520 0.9435 0.1838 1.3922 0.2713 0.8509 0.1830 Constant -19.8717 3.0301 -17.4466 2.4939 -20.9316 3.0468 -15.7076 2.5014
Observations 5113 5113 5113 5113 R2 0.7420 0.5530 0.7830 0.5490 count of places 348 348 348 348 Fixed Effects county county county county Unit of analysis Place/year Place/year Place/year Place/year weighted by place population Yes No Yes No Year Fixed Effects Yes Yes Yes Yes
This table reports estimates of equation (1) in the text. Place level attributes are based on 1990 values. The sample includes California places that are located within 30 miles of a Metropolitan area's Central Business District. In columns (1) and (3), the dependent variable is the log(1+Permit Count). "Share of Single Family" is defined as total annual single family housing permits divided by total housing permits. Environmentalism indicator is based on the 1990s data namely the place's share of Green Party voters in 1992 and the share of the place that voted in favor of Proposition 185 in 1994. Standard errors are clustered by place. The Environmental indicator has a mean of zero and a standard deviation equal to one. april15_08.do
Single Family Permits Total Family Permits
Table Two: California Level Findings Based on Year 2000 Household Level Data
Column (1) (2) (3) Dependent Variable Green Commuter
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Environmentalism Indicator -0.0130 0.0071 -0.0627 0.0167 0.0155 0.0052 log(Household Income) 0.0063 0.0009 0.0560 0.0017 -0.0014 0.0003 Age -0.0014 0.0001 0.0053 0.0002 -0.0010 0.0001 Duncan Socioeconomic Index 0.0007 0.0001 0.0013 0.0001 0.0002 0.0000 log(distance to closest Metro Area CBD) 0.0433 0.0106 0.0491 0.0196 -0.0266 0.0065 Constant -0.3193 0.1038 -0.8240 0.1866 0.3601 0.0643 Mean of Dependent Variable 0.1200 0.5560 0.0450
Unit of Analysis Household Household Household Fixed Effect MSA MSA MSA Observations 539563 539563 539563 R2 0.047 0.123 0.046
The sample includes all California households living in a PUMA whose centroid is within thirty miles of a CBD. This table reports household level linear probability models. "New Housing" is a dummy variable that equals one if the household lives in a dwelling that was built between the years 1990 and 2000. "Single Detached Home" is a dummy that equals one if the person lives in a single detached home. "Green Commuter" is a dummy variable that equals one if the household head commutes to work using public transit, a bicycle, or walks. equals one if the person lives in a single detached home. The environmentalism indicator is based on the year 2000 data that includes the PUMA's share of Green Party voters, the share of votes in favor of Proposition 12 and 13 in the year 2000 and the PUMA's share of hybrid vehicles. Standard errors are clustered by PUMA.
oct31.do
Table Three: Political and Demographic Correlates of Community Environmentalism
Dependent Variable = Green Factor for California Places
1990 2000 (1) (2)
Coef. Std. Err. Coef. Std. Err.
log(distance to CBD) -0.0836 0.0632 log(Population Density in 1970) 0.0421 0.0339 log(average household income in 1970) -0.1187 0.1707 % Democrat in 1972 -0.3774 0.3842 % American Independent in 1972 -21.4846 7.7437 % Peace and Freedom in 1972 8.4872 3.7357 % Miscellaneous in 1972 -0.8335 3.0554 % Declined in 1972 11.6264 2.3129 Constant 1.4666 2.3187
log(distance to CBD) 0.0793 0.0508 log(Population Density in 1990) 0.0808 0.0212 log(average household income in 1990) 0.6159 0.1256 % Democrat in 1992 3.7201 0.3722 % American Independent in 1992 -21.0130 7.2568 % Peace and Freedom in 1992 36.3030 12.7599 % Libertarian in 1992 12.4752 10.4796 % Green Party in 1992 28.6558 3.9135 % Declined in 1992 2.1890 1.1686 Constant -10.1386 1.7588 Metropolitan Area Fixed Effects Yes Yes
Observations 283 568 R2 0.32 0.5030
The omitted category is a place's share of Republican Party registered voters.
Table Four: The Determinants of New Housing Permits Issues by California Cities Between 1990 to 2004
Column (1) (2) (3) (4) Estimation Method OLS IV OLS IV
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Environmentalism Indicator -0.3754 0.0859 -0.5645 0.1782 -0.0864 0.0842 -0.0123 0.1748 % Owner Occupied 0.0253 0.6412 -0.4254 0.7238 -0.8206 0.6080 -0.6441 0.6820 log(Place Population) 1.1055 0.0296 1.1011 0.0298 1.2383 0.0273 1.2401 0.0270 log(Distance to CBD) 0.2433 0.1035 0.2291 0.1021 0.1320 0.1037 0.1376 0.1031 log(population density) -0.4806 0.1253 -0.4552 0.1214 -0.4164 0.1231 -0.4263 0.1191 Log(average household income) 1.0024 0.2967 1.1724 0.3264 1.2594 0.3075 1.1928 0.3454 Constant -14.9750 3.4855 -16.6460 3.6735 -17.7431 3.3295 -17.0885 3.6257
Observations 4169 4169 4169 4169 R2 0.7530 0.7510 0.7940 0.7940 count of places 279 279 279 279 Fixed Effects year year year year Fixed Effects MSA MSA MSA MSA Unit of analysis Place/year Place/year Place/year Place/year This table reports estimates of equation (1) in the text. Place level attributes are based on 1990 values. The sample includes California places that are located within 30 miles of a Metropolitan area's Central Business District. In columns (1) and (3), the dependent variable is the log(1+Permit Count). "Share of Single Family" is defined as total annual single family housing permits divided by total housing permits. Environmentalism indicator is based on the 1990s data namely the place's share of Green Party voters in 1992 and the share of the place that voted in favor of Proposition 185 in 1994. Standard errors are clustered by place. The omitted category is a place not in Los Angeles, San Francisco or San Diego. The instruments include a place's density in 1970 and its political party registration shares in 1972. weighted april13_08.do
Single Family Permits Total Permits
Table Five: The Determinants of New Housing Permits Issues by California Cities Between 2000 to 2004 Column (1) (2) (3) (4) Estimation Method OLS IV OLS IV
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Environmentalism Indicator -0.1905 0.1050 -0.4124 0.1278 -0.1182 0.1015 -0.2080 0.1226 % Owner Occupied -0.5847 0.6664 -1.0572 0.7229 -1.3331 0.6593 -1.5243 0.6912 log(Place Population) 1.0505 0.0558 1.0237 0.0566 1.1662 0.0563 1.1554 0.0568 log(Distance to CBD) 0.0764 0.1197 0.0920 0.1215 0.0735 0.1100 0.0798 0.1110 log(population density) -0.3364 0.0872 -0.3151 0.0894 -0.3296 0.0849 -0.3210 0.0857 Log(average household income) 0.9725 0.2556 1.2302 0.2876 1.0256 0.2411 1.1299 0.2632 Constant -15.4718 3.1942 -18.2436 3.4964 -16.5221 3.0071 -17.6397 3.1979
Observations 1734 1734 1734 1734 R2 0.5680 0.5640 0.5810 0.5800 count of places 348 348 348 348 Fixed Effects county county county county Fixed Effects Year Year Year Year Unit of analysis Place/year Place/year Place/year Place/year Weighted by Population No No No No
This table reports estimates of equation (1) in the text. Place level attributes are based on 1990 values. The sample includes California places that are located within 30 miles of a Metropolitan area's Central Business District. The dependent variable is the log(1+Permit Count). Environmentalism indicator is based on the 2000s data namely the place's share of Green Party voters in 2000 and the share of the place that voted in favor of Proposition 12 and 13 in 2000 and the share of hybrid vehicles in the place. The environmentalism indicator from the 2000s is instrumented for using the place's 1992 political party registration shares and the share of the place's voters who voted in favor Proposition 185 in 1994.
april9_08.do
Single Family Permits Total Permits
Table Six: National Level Findings Based on Year 2000 Household Level Data
Column (1) (2) (3) (4)
Dependent Variable
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Representative League of Conservation Voters Score in 106th -0.0006 0.0001 -0.0003 0.0001 -0.0008 0.0001 0.0004 0.0000 log(Household Income) 0.0136 0.0005 0.0113 0.0006 0.0554 0.0009 -0.0012 0.0003 Age -0.0021 0.0001 -0.0019 0.0001 0.0053 0.0001 -0.0010 0.0000 Duncan Socioeconomic Index 0.0008 0.0000 0.0008 0.0000 0.0016 0.0000 0.0004 0.0000 log(PUMA Population Density) -0.0079 0.0025 -0.0472 0.0050 -0.0459 0.0041 0.0100 0.0016 log(distance to closest Metro Area CBD) 0.0405 0.0047 0.0572 0.0067 0.0302 0.0061 -0.0140 0.0026 Constant -0.2030 0.0612 -0.0359 0.0990 -0.1939 0.0815 0.1305 0.0326 Mean of Dependent Variable 0.1680 0.1630 0.5910 0.0460
Unit of Analysis Household Household Household Household fixed effect state metro area state state Data Sample 2000 2000 2000 2000 Observations 509457 362597 509457 509457 R2 0.062 0.089 0.118 0.063
This table reports household level linear probability models. "New Housing" is a dummy variable that equals one if the household lives in a dwelling that was built between the years 1990 and 2000. "Single Detached Home" is a dummy that equals one if the person lives in a single detached home. "Green Commuter" is a dummy variable that equals one if the household head commutes to work using public transit, a bicycle, or walks.
New Housing New Housing Single Detached Home Green Commuter