Asset Prices and Armageddon: Do Evangelicals’
‘End Times’Beliefs A¤ect U.S. House Prices?
Christopher Crowe
Research Department, International Monetary Fund
700 19th Street NW, Washington, DC 20431.
ccrowe@imf.org.
March 31, 2008
Abstract
According to surveys, around a quarter of Americans expect the world
to end as prophesied by the Bible during their own lifetime. This paper undertakes the …rst test of whether these ‘end times’ beliefs a¤ect
economic behavior, using a 10-year panel of house price data across 363
Metropolitan Statistical Areas (MSAs). It identi…es a causal e¤ect by
interacting a time-varying proxy for the perceived probability of the ‘end
times’occurring soon with a geographically-varying proxy for the proportion of believers in Biblical prophecy, both of which are exogenous with
respect to changes in house prices, controlling for time and area …xed effects. The paper uncovers a signi…cant positive e¤ect that is robust across
samples, speci…cations, and alternative data sources. One explanation for
this positive e¤ect is that believers in Biblical prophecy face a tension –
between their belief that the end of the world is imminent and Biblical
injunctions to behave responsibly in the meantime – that could be reduced by holding illiquid or ‘commitment’ assets to lock in responsible
behavior, generating a premium on such assets. Data on mortgage applications support this interpretation. The paper therefore supports models
–such as Laibson’s (1997) ‘golden eggs’model of hyperbolic discounting –
that incorporate time inconsistent preferences and predict a commitment
premium.
JEL Classi…cations: E21, R21, Z12.
This work re‡ects the views of the author alone and does not re‡ect the views of the IMF,
its Executive Board or Management. The author would like to thank Marcos Chamon, Julian
Di Giovanni, Andre Faria, Rodney Ramcharan and Romain Ranciere for helpful discussions;
Giovanni Dell’Ariccia, Deniz Igan and Luc Laeven for making their mortgage application data
available; and Paul S. Boyer and participants at the IMF Research Department’s Brown Bag
seminar for comments on an earlier draft of the paper. All remaining errors are my own.
1
1
Introduction
According to opinion surveys, half of Americans believe that the world will
end as prophesied in the Bible. Around a quarter expect the end to come in
their own lifetime. Dispensational Premillenialism, a once-obscure nineteenth
century interpretation of Biblical prophecy centered on the ‘rapture’or accession
of true believers to heaven, is now the dominant eschatological (end times)
framework amongst evangelical Protestants, who make up around one-quarter
of the U.S. population.1 These beliefs appear to be becoming more widespread,
promulgated by both traditional and new media, and entering both the political
realm and mainstream secular culture.2
End times beliefs might be expected to have a profound impact on economic
decision-making, particularly with respect to intertemporal choices. Perhaps of
greater interest to economists, tracing their impact could also provide insights
into how discounting behavior incorporates beliefs about the future and shapes
economic outcomes more generally. However, up to now there has been no study
of the economic impact of these beliefs.
This paper has two goals. First, it attempts to test whether ‘end times’beliefs have any measurable impact on households’intertemporal choices. Second,
it argues that any e¤ect could shed light on theories of portfolio choice, and
in particular the demand for assets with di¤erent liquidity characteristics. This
study faces a number of empirical and conceptual hurdles. The …rst is to identify
a suitable dependent variable. Asset prices seem the obvious candidate, since
they are inherently forward-looking and should therefore respond to changes in
beliefs about the future. Housing assets are the most signi…cant and widely-held
1 Dispensational premillenialism has deep roots among American conservative Protestants,
but its interpretation of the Bible’s account of the ‘end times’was not always in the ascendancy
(Boyer, 1992; Grenz, 2004; Kilde, 2004). However, global events since the second world
war, including in particular the dropping of the atomic bombs on Japan, the creation of the
state of Israel, and the Cold War, lent credence to this belief system. Premillenialism (or
dispensationalism) is now the dominant eschatological viewpoint for conservative evangelical
Christians (Grenz, 2004). Central to the dispensationalist philosophy is the belief, based on
a literal reading of the Bible’s book of Revelation, that a period of ‘tribulation’ – a time of
extreme and terrible events during which the Antichrist will rule on earth – will commence
with the ‘rapture’ or physical ascension of true believers to heaven. The tribulation will end
after seven years with the ‘Battle of Armageddon’in which Jesus Christ will return to earth,
vanquish the forces of evil in concert with Israel (whose Jewish population will convert to
Christianity), and establish a 1,000-year earthly kingdom. A …nal battle between good and
evil and judgement day will follow (Grenz, 2004).
2 A 1999 poll for Newsweek found that 40 percent of Americans believed that the world
would end in the Battle of Armageddon (Newsweek, October 24, 1999). Of these, 47 percent
expected it to occur in their lifetime, while 15 percent expected it to occur within the next
year. A 2004 poll for Newsweek found an apparent uptick in these beliefs, with 55 percent of
the population reportedly believing that the faithful would be taken to heaven in the ‘rapture’
(Denver Post, April 10, 2005). A popular series of novels set during the ‘tribulation,’the Left
Behind series (authored by Tim LaHaye and Jerry B. Jenkins), had sold a total of 65 million
copies as of 2007. This …gure includes the 16 books making up the series as well as a number
of related titles (source: http://www.leftbehind.com). The tenth title in the series was the
best-selling …ction title in the United States in 2001, while a May, 2001 poll found that 9
percent of U.S. adults had read at least one of the books in the series (Forbes, 2004).
2
assets on household balance sheets (Bucks and others, 2006). Housing has the
additional bene…t of being location-speci…c, which is exploited in the paper’s
identi…cation strategy. It also has speci…c liquidity characteristics compared to
other widely-held household assets, allowing the paper’s …ndings to shed light
on the second empirical question identi…ed above. This paper therefore focuses
on the house price index published quarterly by the O¢ ce of Federal Housing
Enterprises Oversight (OFHEO) for 363 Metropolitan Statistical Areas (MSAs).
The second challenge is …nding suitable proxies for end times beliefs that are
exogenous to the dependent variable and therefore o¤er convincing identi…cation of a causal relationship. The identi…cation strategy employed in this paper
exploits the panel dimension of the house price data, focusing on the interaction
between two exogenous variables. The …rst is a geographically-varying proxy
for the share of those holding ‘end times’beliefs, and the second a time-varying
proxy for the intensity of the beliefs. Controlling for area and time …xed e¤ects,
any relationship between the resulting interaction term and house prices should
then re‡ect a causal one. The exogeneity of the population share of ‘believers’
relies on the fact that population characteristics change only slowly over time,
so that the proportion of the population that holds ‘end times’beliefs is, at …rst
approximation, constant over the short- to medium-term, and hence uncorrelated with within-area variation in house prices over the ten-year time horizon
studied here. The exogeneity of the ‘belief intensity’proxy relies on the fact that
the information set available to ‘believers’(including the eschatological framework through which information is …ltered) is common across areas. In both
cases, the actual data proxies used strengthen this claim of exogeneity, since the
proxy for the geographically-varying proportion of believers is available for only
one relevant year, and the proxy for the belief intensity measure is taken from
an internet source which is by de…nition common across areas.
To proxy for the proportion of believers in premillenialism in each area, the
paper uses the share of evangelical Protestants, derived from the 2000 Religious Congregations and Membership Study, obtained from the Association of
Religion Data Archive (ARDA). While premillenialism is most strongly associated with the most conservative, or ‘fundamentalist’, evangelical denominations, polling evidence suggests that its view of the ‘end times’is the majority
one for evangelicals as a whole. For instance, a 1999 poll for Newsweek found
that around 71 percent of evangelical Protestants believed that the world would
end exactly as prophesied in the Bible, compared to only 28 percent of nonevangelical Protestants and just 18 percent of Catholics (Newsweek, October 24,
1999). Figure 1 shows the geographical distribution of evangelical Protestants
across MSAs.
[Figure 1 about here]
To proxy for the intensity of ‘end times’beliefs this paper employs the Rapture Index (RI), a well-known “prophetic speedometer”published on a popular
premillenialist website.3 The RI embodies what Grenz (2004) describes as “the
3 The RI’s creator, Todd Strandberg, describes it as a “prophetic speedometer.
The
higher the number, the faster we’re moving towards the occurrence of pre-tribulation
3
dispensationalist admonition to read the newspaper in one hand and the Bible
in the other,”interpreting current events in light of premillenialist beliefs –notably with respect to Israel and the Middle East, but also with respect to Russia
and Iran (who are interpreted as modern manifestations of biblical entities), the
European Union and United Nations (associated with ‘world government’and
hence the Antichrist), and natural disasters (seen as precursors of the tribulation). The index consists of an unweighted sum of 45 subcomponents which are
each scored from 1 to 5 depending on the scorer’s subjective assessment of the
current degree of ‘prophetic activity’ with respect to each category. Figure 2
presents a snapshot of the index in December, 2007, including the 45 categories.
The index is updated regularly in response to domestic and world events. For
instance, following the events of 9/11 – that were interpreted by some fundamentalists and premillenialists as “indications that Christ’s return is imminent”
(Kilde, 2004) – the Rapture Index reached a record high. Figure 3 shows the
evolution of the index over time.
[Figures 2 & 3 about here]
As a …rst step, the paper conducts an event study centered on 9/11. Comparing house price growth in the second quarter and fourth quarter of 2001
(immediately before and after the attacks), there is a clear di¤erential e¤ect on
areas depending on their population share of evangelicals. Controlling for area
and time …xed e¤ects, there is a marked increase in house price growth in areas
with large shares of evangelicals in the fourth quarter, compared to the second
quarter. By contrast, no di¤erential e¤ect is visible in 2000.
The paper then uses a ten-year panel of annual data (1997-2006) covering all
363 MSAs to test whether the interaction of the two proxies has any impact on
house prices. The preferred speci…cation analyzes the relationship in lagged …rst
di¤erences, with geographic and time …xed e¤ects and some economic controls.
In line with the event study evidence, the paper uncovers a positive and highly
statistically signi…cant e¤ect (with a t-statistic of around 15). A one standard
deviation change in the Rapture Index in MSAs with the average share of evangelicals is estimated to generate a 0.8 percentage point increase in house prices
(compared to MSAs with no evangelicals). To place this in context, the average
annual real increase in prices over the period was 3.4 percentage points.
This result is then subjected to a battery of robustness checks. First, the
baseline regression is run without lags and in levels rather than …rst di¤erences.
Second, the RI is interacted with State dummies to focus on within-State interactions. Third, the share of evangelicals is interacted with a series of macroeconomic variables, in case the RI is simply picking up cyclical or macroeconomic
factors. Fourth, the baseline regression is run for a number of di¤erent subsamples (with the sample cut across areas, time periods and area characteristics).
Fifth, the baseline regression is run using di¤erent data proxies for house prices
and the share of ‘believers’. Finally, the paper attempts to control for possible
rapture.” http://www.raptureready.com/rap2.html. The RI has featured in Time magazine (July 1, 2002), and appears to enjoy some currency, with the website receiving more than 250,000 visits per month in 2003 (Christianity Today, March 2003:
http://www.christianitytoday.com/ct/2003/marchweb-only/3-24-43.0.html.).
4
patterns of correlation, temporal and spatial, either in the dependent variable
or in the error term. The results remain signi…cant in all speci…cations.
This positive e¤ect may appear counterintuitive. According to the basic asset pricing equation, the price of an asset should re‡ect the expected product
of its return and the stochastic discount factor (SDF) (Campbell, 2000). An
increase in the expected probability of the world ending should lower the SDF,
and therefore have a negative impact on house prices in areas with a large share
of evangelicals (assuming some trading frictions across areas). However, housing’s particular asset characteristics –speci…cally its relative illiquidity –could
account for the observed positive interaction e¤ect. In particular, illiquidity
allows agents to commit to a particular consumption stream when their preferences are time-inconsistent (Harris and Laibson, 2002; Laibson, 1997; Strotz,
1956). Illiquid assets that facilitate commitment will therefore trade at a higher
price, re‡ecting the premium placed on the ability to commit (Kocherlakota,
2001). The appendix outlines a simple 3-period model with time-inconsistent
preferences, based on Kocherlakota (2001), in which such a commitment premium is shown to increase in response to a higher perceived probability of the
world ending, with the e¤ect stronger in areas with higher shares of ‘believers’
(Proposition 1 in the appendix).
For premillenialists, a potential source of time inconsistency is the tension
between the belief that the ‘end times’are imminent, and Biblical injunctions
to act prudently in the time remaining. For instance, ‘end times’writers quote
Jesus’s parable (Luke 19: 12-26) cautioning his followers to “occupy till I come.”
The parable describes a nobleman who entrusts his savings to his servants ahead
of a foreign trip, and on his return praises those servants who have invested the
money and are able to return it with interest, while castigating the servant who
simply holds the original sum for safe-keeping. The parable has been interpreted
by end times writers as indicating the need to behave wisely in all spheres of life
(including the economic) even while expecting Jesus’s return.4 Hence, although
believers may initially intend to behave as suggested by the parable, and save
for the future regardless of their beliefs about the end times, they may later be
tempted to renege on this commitment, in response to their end times beliefs,
and save less. This temptation is less easy to act upon if agents save using
relatively illiquid assets, such as by paying into a 401(k) account or by tying
themselves to large monthly mortgage payments.
This account of the positive interaction e¤ect uncovered in the data is not
the only potential explanation. Others include the possibility that concerns
about the ‘end times’prompt a general ‡ight to (perhaps safer) real assets such
as housing. However, the general equilibrium model outlined in the Appendix
provides a number of additional predictions which can be taken to the data to
shed light on the relative merit of the particular channel proposed here. In particular, the model makes two predictions concerning borrowing behavior that
can be tested against data on mortgage applications (using the Home Mortgage
Disclosure Act (HMDA) Loan Application Registry data collected and aggre4I
am grateful to Paul Boyer for bringing this point to my attention.
5
gated to the MSA level by Dell’Ariccia, Igan and Laeven, 2008).5 The model
predicts that (1) the volume of mortgage lending (the total number of loans)
among believers falls, and (2) the average loan size increases, as the perceived
probability of the world ending increases. Both predictions are supported by
the data, lending further credence to this channel.
This paper touches on a number of previously unrelated literatures. It contributes to a growing literature on the economic e¤ects of religion (Iannaccone,
1998 provides an early survey, while recent contributions include those by Barro
and McCleary, 2003; Scheve and Stasavage, 2006; and Guiso, Sapienza and Zingales, 2003). The results are also in line with an earlier empirical literature
that found that fear of a nuclear holocaust adversely a¤ected savings during the
Cold War, consistent with it depressing the discount factor (Russert, Cowden,
Kinsella and Murray, 1994; Russert and Slemrod, 1993; Russert and Lackey,
1987; and Slemrod, 1986). The empirical results contribute to a growing literature on recent house price trends that stress non-standard psychological factors (Shiller, 2007) and geographical heterogeneity (Gyurko, Mayer and Sinai,
2006). The results also provide evidence consistent with the existence of a commitment premium on illiquid assets. This paper can therefore be thought of as
a positive response to Kocherlakota’s (2001) argument that the prediction of a
commitment premium provides the key means of testing whether models with
time-inconsistent preferences are valid.6
The rest of the paper is organized as follows. Section 2 provides an overview
of the paper’s empirical strategy and the data employed. Section 3 presents the
9/11 event study. Section 4 gives the panel estimation results for the house price
data, including the baseline results, robustness checks and an interpretation of
the results. Section 5 tests the model’s additional predictions using the mortgage
application data, while section 6 o¤ers some conclusions.
2
2.1
Empirical Strategy and Data
Basic Speci…cation
The basic relationship we are interested in identifying is given as:
ln pit = b (
i
L (! t )) + BL (Xit ) + "it
(1)
5 The author is grateful to the authors for sharing this data. See Dell’Ariccia, Igan and
Laeven (2008) for details of the variables.
6 Other work in this area has provided some evidence in support of time-inconsistent preferences. Angeletos and others (2001) argue that a calibrated consumption model with this
preference speci…cation can better account for observed phenomena such as consumptionincome comovement and the relatively low share of liquid assets in total household assets.
Huang, Liu and Zhu (2006) …nd evidence that agents who are subject to self-control problems
(similar to having time-inconsistent preferences) are more likely to invest in human capital,
which they argue is a commitment asset. They also …nd that holdings of two di¤erent commitment assets – pensions and education – appear to be positively correlated, as predicted
by the model. However, Kocherlakota (2001) argues that average aggregate returns on these
same two commitment assets appear to be above the return on liquid assets, contradicting
the theory.
6
where pit gives the house price index in area i at time t, i gives the population
share of believers, ! t gives the time-varying probability attached to the world
ending by believers, L () is a general lag function and Xit gives a vector of controls (including area and time …xed e¤ects and area-speci…c linear time trends).
Equation (1) with b 0 can be derived from the general equilibrium model with
time-inconsistent preferences outlined in the Appendix (equation 52).
2.2
Data Proxies
This paper uses house price data from repeated sales at the level of Metropolitan
Statistical Areas (MSAs) available from the O¢ ce of Federal Housing Enterprise
Oversight.7 The data is based on conventional conforming mortgage transactions obtained from Freddie Mac and Fannie Mae and is available at a quarterly
frequency, although annual averages are used for most of the analysis. The principal advantage of this data over other similarly constructed and widely-used
indices (such as the Case-Shiller index) is that it is available for a large and
disaggregated set of geographical areas.8 As a robustness check the baseline
speci…cation is also estimated using the Case-Shiller data, albeit with coverage
therefore limited to only 196 observations over 20 MSAs. The baseline is also
estimated using real house price indices, de‡ated by MSA-speci…c consumer
price indices obtained from the Bureau of Labor Statistics. Figure 4 presents
the average of the change in log house prices across MSAs (using the OFHEO
data and weighted by 2000 populations) for 1996-2006.
[Figure 4 about here]
Data on the share of various religious denominations is obtained from the
2000 Religious Congregations and Membership Study, which provides estimates
of adherents at the county level for 149 Christian denominations (including
Latter-day Saints), two groups of independent Christian churches, and nonChristian congregations including Jews and Muslims.9 Denominations were
coded as Evangelical Protestant denominations following Campbell (2006); in
addition, independent (charismatic and non-charismatic) churches were also
coded as Evangelical.10 As a robustness check two alternative evangelical measures are compiled and compared to the preferred measure: the ARDA’s own
coding, and a narrower measure including only those denominations which explicitly profess to believe in premillenialism (based on the description of individ7 See
Table A1 for information on all variables and data sources employed in the paper.
individual house prices within each MSA are aggregated geometrically to arrive
at the overall index. See Calhoun (1996) for an overview of the underying methodology and
data.
9 A description of the data is available at
http://www.thearda.com/Archive/Files/Descriptions/RCMSCY.asp. See Jones and others
(2002) for more information.
1 0 Campbell follows the ARDA researchers’own coding of White Evangelical denominations,
with the exception of the Community of Christ (see the Appendix to Campbell, 2006). We
also include the two independent churches groupings in our preferred evangelical measure
following Mead, Hill and Atwood (2005, p. 318) who note that ‘the theology and doctrine
of [these churches] ... vary according to the beliefs of the pastor, but in general they may be
termed conservative evangelical.’
8 Implicitly,
7
ual denominations in Mead, Hill and Atwood and a reading of their statements of
faith and other information on their individual websites). The broader measure
seems preferable since the polling and anecdotal evidence suggests that premillenialist beliefs are widely held among the broader evangelical population, and
not only among the relatively small ‘fundamentalist’and other denominations
for which premillenialist beliefs are central to their faith.
Data on the Rapture Index was assembled from several internet sources that
provide archived information on past values of the index or information on its
current value. Only a subset of the observations of the index and its subcomponents is available (the index is updated approximately weekly). However,
because each update of the index provides information on maxima and minima
for several past years alongside the current value of the index and its subcomponents, we were able to reconstruct annual observations covering 1995-2006 by
taking the midpoint between the reported annual low and high as a proxy for
the annual average.
Demographic data at the county level – including the population shares of
Hispanics and non-Hispanic whites and blacks (respectively sH , sW and sB ) –
is obtained from 2000 census data and aggregated to the level of MSAs. Annual
data on per capita personal income and population at the MSA level is obtained
from the Bureau of Economic Analysis. Annual unemployment data at the MSA
level is obtained from the Bureau of Labor Statistics. Macroeconomic controls
are obtained from the Federal Reserve Board and the IMF’s World Economic
Outlook dataset.
2.3
Empirical Strategy
Taking (1) to the data requires two principal steps. First, a …rst di¤erence
transformation is applied to deal with the potential non-stationarity of the house
price series. Second, the vector of …rst di¤erenced controls is modeled to include
time and area …xed e¤ects, so that the interaction term of interest is strictly
exogenous to the remaining variation in the dependent variable. Finally, a
simple one-year lag structure is assumed for both the interaction term and the
controls. The baseline empirical speci…cation is then given by (2):
ln pit = b (
i
!t
1)
+
i
+
t
+ Zi;t
1
+ uit
(2)
Area and time …xed e¤ects are given by i and t , respectively, while Zit gives
the remaining (…rst di¤erenced) controls and uit is assumed iid.
2.4
Extensions to the Baseline Model
House prices are likely to vary in response to a wide set of economic and social
conditions, many of which are excluded from our baseline set of controls. In
particular, both our main proxies are likely to be correlated with other variables,
so that the estimated interaction term could be picking up the e¤ect of these
omitted variables. There is also a danger that the baseline results could somehow
8
be an arti…ce due to the assumed dynamic structure, or that any signi…cance
level attached to the estimated e¤ect could be over-estimated due to failing to
adequately capture spatial or temporal correlation patterns.
A number of extensions to the baseline empirical model are therefore also
estimated. First, (2) is estimated without lags (in …rst di¤erences and levels):
ln pit
ln pit
= b(
= b(
! t ) + i + t + Zit + uit
! t ) + i + t + Xit + uit
i
i
(3)
(4)
where Xit includes State-speci…c time trends to capture the widely di¤erent time
pro…le of house prices in di¤erent parts of the U.S. Second, a set of interaction
terms between State dummy variables Ds and the RI is included, to counter
concerns that the share of evangelicals in each MSA could be acting as a proxy
for any other factors that vary signi…cantly across geographic areas:
X
ln pit = b ( i
!t 1 ) +
(Ds
! t 1 ) + i + t + Zi;t 1 + uit (5)
s
Third, a number of additional interactions with a set of macroeconomic
variables m are included, to control for any correlation between the RI and
general macroeconomic conditions:
ln pit = b (
!t
i
1)
+ d(
mt
i
1)
+
i
+
t
+ Zi;t
1
+ uit
(6)
Finally, the paper presents estimates of a number of speci…cations that attempt
to deal with problems caused by spatial or temporal correlation. One set of
speci…cations simply relaxes the iid assumption with respect to uit : assuming
a panel AR(1) process, allowing for clustering (within MSAs to capture serial
correlation or across State-year clusters to capture spatial correlation), or by
explicitly specifying and estimating a spatial correlation structure.
A second set of speci…cations attempts to control for spatial and temporal
correlation by modifying equation (2). First, a one period lag of ln pit is
introduced:
ln pit =
ln pi;t
1
+ b(
!t
i
1)
+
i
+
t
+ Zi;t
1
+ uit
(7)
The introduction of the endogenous lagged dependent variable renders standard …xed e¤ects estimation inconsistent, so (7) is estimated using panel GMM
techniques. Second, a spatial lag model is estimated via MLE:
0
1
X
ln pit = @
wij ln pj;t A + b ( i
! t 1 ) + i + t + Zi;t 1 + uit (8)
j
where wij gives the element of the spatial weight matrix for areas i; j. Finally, a
matching exercise is run where each area is matched with its closest geographic
neighbor, and (2) is then di¤erenced across the two members of each unique
pair i; j:
(ln pit
ln pjt ) = b (
i
j)
!t
1+
9
i;j + t +
(Zi;t
1
Zj;t
1 )+ei;jt
(9)
where i;j denotes the combined …xed e¤ect for the unique area pair i; j and
ei;jt the corresponding error term.11
2.5
Sample and Descriptive Statistics
Table 1 presents summary statistics for the principal variables used in the paper.
The baseline sample consists of 10 years of data for the 363 MSAs de…ned in the
O¢ ce of Management and Budget Bulletin number 07-01 (December, 2006).12
[Table 1 about here]
3
9/11 Event Study
A Time/CNN poll in 2002 found that interest in the ‘end times’ increased
markedly in the wake of the 9/11 attacks: more than one-third of respondents
claimed to be paying more attention to how the news might relate to the end of
the world, and almost one-quarter believed that the Bible predicted the attacks
(Time, July 1, 2002). Some 59 percent claimed to believe that the account of
the ‘end times’in the Bible’s book of Revelation would come true (compared to
40 percent in response to a similar question in 1999). This up-tick in interest is
also re‡ected in a spike in internet tra¢ c to the Rapture Index site (some eight
million visitors in the immediate aftermath of 9/11 according to Time), while
the index itself concurrently reached its all-time high.
Hence, one means of analyzing the impact of ‘end times’beliefs on behavior
is to analyze the evolution of house prices in the run-up to and immediate
aftermath of the attacks. An advantage of this exercise is that it does not
rely explicitly on the Rapture Index to proxy for end times beliefs, but rather
exploits the broader evidence that the attacks were interpreted by many as a
sign of the ‘end times.’ One can also exploit the quarterly frequency of the
OFHEO data, focusing on the period immediately surrounding the attacks by
comparing the behavior of house prices in the second and fourth quarters of
2001. As an additional comparison, one can also compare the behavior of prices
in the same two quarters in 2000. Of course, 9/11 had a wide-ranging economic
impact via a number of channels, making it hard to disentangle the e¤ect of
‘end times’beliefs, so that the results of this exercise should be taken as merely
suggestive.
To eliminate common time or area e¤ects, the one-quarter change in the log
house price index is …rst regressed on period and MSA dummies. Figure 5 then
1 1 This exercise is somewhat similar to Black (1999) and related studies that test parental
valuation of school quality by comparing house prices close to school district boundaries (eliminating geographically-speci…c omitted variables that vary continuously rather than discretely
in the region of the boundary). In this study the matching is coarser, re‡ecting the larger
unit of analysis (MSAs versus individual homes). This exercise results in 253 unique matched
pairs with mean distance 60.8 miles. Note that to the extent that serial correlation enters
via common regional house price cycles, then this speci…cation will minimize serial as well as
spatial correlation.
1 2 However, house price data is unavailable for the Hinesville-Fort Stewart, GA MSA prior
to 2001, so that our baseline sample size is 3,625 observations.
10
plots the …tted relationship between the residuals obtained from this …rst stage
and the share of evangelicals in each MSA.13
[Figure 5 about here]
Figure 5 suggests that 9/11 had a di¤erential impact on house price growth
across MSAs depending on the proportion of evangelicals in their population.
Between the second and fourth quarters of 2001 there was a statistically significant upward shift in house price growth for areas with a high share of evangelicals, compared to areas with a low share. This pattern is not discernible in
2000.14
4
Panel Estimation
4.1
Baseline Speci…cation and Macroeconomic Interactions
This section presents the main results as well as some robustness checks with
respect to speci…cation and omitted variables. Table 2 presents the principal
panel regression results. The baseline speci…cation (equation (2), shown in column I) is in …rst di¤erences and includes as controls the MSA unemployment
rate and the change in (log) MSA per-capita income and the change in (log)
population. All explanatory variables, including the interaction term, are lagged
one period. The regression includes year dummies and MSA …xed e¤ects. The
interaction term is highly statistically signi…cant (with a t-statistic of around
15) and is also economically signi…cant: a one standard deviation change in the
Rapture Index for an MSA with the average share of evangelicals in the population, other things being equal, would lead to a 0.8 percentage point increase
in house prices compared to an MSA with no evangelicals.
[Table 2 about here]
Columns II and III assess whether the imposed dynamic structure is giving
‡attering results, by presenting results for speci…cations without lags: column II
retains the …rst di¤erenced speci…cation (equation 3) while column III presents
results for a speci…cation in levels (equation 4; this speci…cation also includes
state-speci…c deterministic time trends to help control for the large geographical
variations in house price growth over the period; note that the unemployment
rate is included in levels in all speci…cations). Column IV replicates column I but
models the geographic e¤ects as random disturbances rather than parameters
to be estimated (GLS random e¤ects). Columns V and VI are also estimated
via GLS but additionally drop the year dummies (column VI also includes a
deterministic time trend to control for the upward trend in house price growth
over the period apparent in Figure 4). The estimated interaction term is stable
across speci…cations, and remains highly statistically signi…cant. Note that
the estimated e¤ect of increases in the Rapture Index, for MSAs with a zero
1 3 One
obtains almost identical results using the raw data rather than residuals.
p-value of the test that the slope coe¢ cient is the same in quarters 2 and 4 in 2001
is 0.000; for 2000 the p-value is 0.538.
1 4 The
11
evangelical population, is negative in columns V and VI, which is also in line
@pN
com
with the theory since it is demonstrated in the appendix that @!
0.15
Column VII includes State dummies interacted with ri 1 (as in equation
5) and hence estimates the within-State interaction e¤ect. This controls for the
fact that the share of evangelicals varies considerably across States along with
other factors that might be driving the estimated interaction e¤ect (particularly
if the Rapture Index were itself correlated with some other time-varying factor
whose e¤ects might di¤er across di¤erent types of areas, such as a common
macroeconomic shock). Because a considerable amount of information is lost
by looking only at within-State variation, one would expect the estimated e¤ect
to be smaller. However, it remains statistically signi…cant at the 1 percent level
even when this large quantity of information is dropped.
While the results of Table 2 appear to be robust across speci…cations, a potential criticism of the results is that the change in the Rapture Index over time
might be proxying for other factors, such as the macroeconomic environment,
that might impact on house prices in di¤erent MSAs according to their demographic or economic characteristics in a way that would mimic the interaction
e¤ect we uncover in the data. Table 3 therefore jointly estimates interaction
e¤ects for both the Rapture Index and a range of macroeconomic variables to
see whether the former e¤ect stands up to the inclusion of the latter (equation
6). The variables (GDP growth, in‡ation, the national unemployment rate, the
change in the world oil price and the average federal funds rate) are included
both individually (columns I-V) and jointly (column VI). Finally, column VII
includes the share of evangelicals interacted with a linear deterministic time
trend, to proxy for any macroeconomic factors (or …nancial variables, such as
a loosening of lending standards associated with mortgage securitization) that
may have changed monotonistically over time and might have di¤erential e¤ects
on di¤erent areas (perhaps depending on some variable correlated with the share
of evangelicals).
[Table 3 about here]
Only the inclusion of the federal funds rate has any major e¤ect on the coe¢ cient estimate of the interaction term (column V), but the latter remains
highly statistically signi…cant. Quantitatively, using the results in column VI
and comparing one standard deviation changes in each time-varying variable,
only changes in the federal funds rate have a larger di¤erential impact on house
prices according to the share of evangelicals in the population. Only the interaction e¤ect with respect to growth (and only in column VI) has a higher
t-statistic than the interaction e¤ect with respect to the Rapture Index. When
a deterministic time trend is included (interacted with the share of evangelicals,
in column VII) the point estimate for the interaction coe¢ cient with respect to
1 5 Proposition 2 in the Appendix proves this result in the region of ! = 0, although the result
would appear to be more general. The empirical evidence should be interpreted with some
caution since there only 10 independent observations of ri 1 and many other factors will
cause mean house prices to change over time, not only changes in ri. On the other hand, the
negative coe¢ cient is robust to the inclusion of macroeconomic controls, as for the inclusion
of the time trend in column VI.
12
the Rapture Index is reduced somewhat, but remains highly statistically significant.16
4.2
Alternative Samples and Data Sources
The baseline results could be driven by outliers rather than re‡ecting a widely
occurring pattern. Table 4 tests for this possibility by re-running the baseline
speci…cation for ten subsamples. The …rst two subsamples (I and II) bifurcate
the sample by time period, breaking it up into 1997-2001 and 2002-06. The
interaction e¤ect appears to have strengthened over time (which is in keeping
with the apparently increasing salience of ‘End Times’ beliefs) but is highly
statistically signi…cant in both subsamples. Columns III-VI present respective
results dropping one of each of the four census divisions (North East, Midwest,
South and West). One might expect that dropping the South would decrease
the estimated interaction e¤ect if it were spurious, since the South di¤ers significantly from the rest of the country in its share of evangelicals but also along a
number of other dimensions for which the share of evangelicals might be acting
as a proxy. In fact, the coe¢ cient estimate is higher when the South is excluded.
The results remain highly statistically signi…cant in all four samples.
[Table 4 about here]
The share of evangelicals tends to be somewhat higher in MSAs with high
black populations and much lower in MSAs with high Hispanic populations.
Hence, the interaction e¤ect might be picking up some idiosyncrasies relating
to racial composition or heterogeneity. Columns VII-IX therefore drop some
MSAs which are outliers in terms of their racial composition or are particularly
racially diverse, based on 2000 census data. Column VI restricts the sample to
the most racially homogeneous MSAs (which are all majority-white), including
only those where whites account for more than 90 percent of the population,
which reduces the sample size from 363 to 73 MSAs. The estimated interaction
e¤ect is somewhat reduced but remains statistically signi…cant at the 1 percent
level even in this restricted and homogeneous sample. Columns VIII and IX drop
two groups of outliers, MSAs whose (respectively) black or Hispanic population
is greater than 10 percent, and again the results are robust. Finally column
X drops MSAs with shares of evangelicals above the 90th percentile, in case
the results are skewed by variation among this small group of MSAs with a
particularly high share of evangelicals. In fact the opposite result holds, with
the interaction e¤ect somewhat stronger in the sample with relatively low shares
of evangelicals.17
1 6 The author also included interaction terms with respect to the yield di¤erential between
10-year and 3-month treasury bills (to capture the yield curve), the change in the (log) gold
price, and the OECD’s composite leading indicator for the U.S., in case the Rapture Index is
simply a measure of ‘bad news’and therefore acting as a proxy for forward-looking economic
indicators (sources: Fedeal Reserve, IMF’s WEO database; OECD). The inclusion of the yield
di¤erential has a similar quantitative impact on the coe¢ cient of interest to including the
federal funds rate; the other variables have essentially no impact.
1 7 This result might have been expected given the result from dropping the South (column
V), the region with the highest share of evangelicals.
13
A second concern is that the baseline results could be particular to the data
proxies chosen. The event study in Section 3 provides some evidence that the
positive interaction e¤ect does not depend on the use of the Rapture Index to
capture variations in the intensity of ‘end times’beliefs. Tables 5 and 6 present
evidence using alternative proxies for the share of believers and house prices,
respectively. Table 5 presents data on the baseline coding of adherents as evangelical and two alternatives: the ARDA’s own coding and a narrower de…nition
that focuses speci…cally on denominations whose doctrinal beliefs appear to
explicitly include premillenialism. The ARDA evangelical de…nition is almost
identical to the baseline, and hence yields the same results. The narrower de…nition yields a higher coe¢ cient estimate (as one might expect) which remains
highly statistically signi…cant.
[Table 5 about here]
The underlying data coverage of the OFHEO house price index could be
biasing the results. For instance, the index does not cover all sales, particularly
at the high end, and includes re…nancing as well as actual sales, so could incorporate biased valuations. Table 6 (columns I and II) compares results from the
Case-Shiller index and the OFHEO index. The sample coverage is limited to
the 20 MSAs for which Case-Shiller indices are available, giving a total of only
196 observations (5 percent of the OFHEO sample). The two indices are highly
correlated (the correlation coe¢ cient for log changes is .96). The signi…cance
level associated with the interaction e¤ect drops to 10 percent for the CaseShiller index, although the point estimate increases somewhat. The principal
reason for the drop in signi…cance appears to be the smaller sample size: the
signi…cance level using the OFHEO index for the same sample is also sharply
reduced (to the 5 percent level).18
[Table 6 about here]
A related concern is the use of nominal house prices in the empirical work,
whereas real prices may be of greater interest.19 MSA-speci…c consumer price
indices to use as de‡ators are available from the BLS for only 27 MSAs. Columns
III and IV compare results for real and nominal indices for this limited sample.
Columns V and VI compare results using nominal and real house price changes
for all 363 MSAs, where the CPI from the nearest of the 27 MSAs covered by the
BLS index is used to proxy for the missing de‡ators.20 The results are robust
in both cases.
4.3
Correcting for Temporal and Spatial Correlation
A further criticism of the results is that they might over-state the statistical
signi…cance of the estimated e¤ect because they fail to adequately take into
1 8 When the non-lagged speci…cation (as in Table 2, column II) was run for both samples,
the signi…cance pattern was reversed, with the interaction e¤ect using the OFHEO index
signi…cant only at the 10 percent level, but at the 5 percent level using the Case-Shiller index.
1 9 In the model in the Appendix, the e¤ect of interest is on real prices, with the period 1
consumption good as the numeraire).
2 0 All distances are calculated using MSAs’ latitude and longitude, where these are
population-weighted means of the latitude and longitude of each constituent county.
14
account correlation patterns in the data, either over time or across areas. Table 7 presents several attempts to control for serial correlation, by estimating,
respectively, a FE model with MSA-speci…c AR(1) residuals or with residuals
clustered at the MSA level, and the dynamic price growth equation (7). Column I presents results from the AR(1) model. The coe¢ cient estimate on the
interaction e¤ect is somewhat reduced but remains statistically signi…cant at
the 1 percent level. Column II presents results for the baseline FE model with
residuals clustered at the MSA level; reported standard errors are somewhat
higher, but the e¤ect remains highly statistically signi…cant.21
[Table 7 about here]
Columns III-VI present estimates of the dynamic price equation (7). Since
the lagged dependent variable violates the exogeneity assumption required for
FE estimation, these speci…cations are estimated via consistent GMM (Arellano
and Bond’s (1991) …rst di¤erenced estimator).22 Columns III and IV present
results from the one-step and two-step Arellano-Bond estimator (all results are
presented in …rst di¤erences).23 The speci…cation tests proposed by Arellano
and Bond (1991) –both the Arellano-Bond test for AR(2) residuals and Hansen’s
over-identi…cation test –suggest that the orthogonality conditions required for
consistency are not met. To overcome this, columns V and VI present results
from applying GMM to a speci…cation in second di¤erences, where the additional di¤erencing should minimize residual serial correlation. The speci…cation
tests suggest that the residual correlation problem is eliminated by seconddi¤erencing, and the Arellano-Bond estimators (both one-step and two-step)
meet the conditions for consistency. The estimated interaction e¤ect remains
positive and statistically signi…cant even in these second-di¤erenced speci…cations (where a signi…cant amount of information has been discarded).24
Table 8 presents the results of robustness checks with respect to spatial correlation in house price growth. As a …rst pass, column I presents results with
residuals clustered jointly by State and year (this requires the MSA …xed e¤ects
to be dropped, and the equation therefore includes the share of evangelicals and
State dummies). The estimated interaction e¤ect is essentially unchanged and
remains statistically signi…cant at the 1 percent level. Columns II-IV present
2 1 Bertrand et al. (2004) argue that clustering is a superior technique for addressing panel
autocorrelation than …tting a model with AR(1) errors.
2 2 Estimation is undertaken using the xtabond2 Stata command developed by Roodman
(2006). I focus on contemporaneous e¤ects from the exogenous variables (rather than oneperiod lags, as in the baseline) since any lagged impact should already be captured via the
lagged dependent variable.
2 3 Note that the baseline speci…cation (equation 2) is estimated in …rst di¤erences (the
dependant variable is the change in log house prices). When we refer to …rst di¤erences in the
context of the GMM estimates, we are referring to …rst di¤erences with respect to this baseline,
and hence second di¤ erences (in terms of log house prices). When we come to estimate the
baseline in second di¤erences (columns V and VI) to eliminate residual serial correlation, the
results from the GMM estimates are therefore derived from third di¤ erences.
2 4 The author also estimated both …rst- and second-di¤erenced speci…cations using Blundell and Bond’s (1998) system GMM estimator, but speci…cation tests suggested that the
results remained inconsistent even with the second-di¤erenced speci…cation and so they are
not reported here.
15
results from a matching exercise, in which each MSA is matched with its nearest
geographic neighbor and the baseline speci…cation is then di¤erenced across each
pair to eliminate common geographic e¤ects (equation 9). Column II presents
results for the full matched sample; column III and IV present results for more
limited subsamples with ‘better’(closer) matches. Despite a signi…cant loss of
information in this exercise (re‡ected in much lower R2 s and reduced coe¢ cient
estimates), the estimated interaction e¤ect remains positive and statistically
signi…cant. Finally, columns V and VI present results from MLE estimation of
speci…cations with, respectively, a spatial lag (equation 8) and spatially correlated errors. The models assume that spatial correlation is inversely related to
the distance between MSAs for concurrent observations (and zero otherwise).25
In both speci…cations the estimated interaction e¤ect is somewhat reduced, but
remains positive and highly signi…cant.
[Table 8 about here]
4.4
Interpretation
The results presented above suggest that ‘end times’ beliefs have a signi…cant
e¤ect on asset holding decisions. Anecdotal evidence lends support to this view.
Kilde, 2004, presents historic evidence of these beliefs leading people to make
economically signi…cant decisions, including selling all their possessions (the
“Millerites” of the 1840s) or risking death in confrontation with the authorities (the followers of David Koresh in 1993). Contemporary press accounts
include examples of people selling their houses or cutting short their education (Christian Science Monitor, February 18, 2004).26 Keister (2005) provides
evidence from the 1979-2000 National Longitude Survey of Youth that conservative Protestants accumulate less overall wealth in early adulthood than others,
even controlling for demographic and economic factors (including those, such as
educational attainment, fertility behavior, job market status and income, that
might themselves be a¤ected by religious beliefs).27
2 5 Formally, if observations are ordered by year and then by MSA identi…er (the HinesvilleFort Stewart, GA MSA is dropped to give a balanced panel and simplify the calculations,
giving 10 362 = 3620 observations), then the spatial weights matrix W is a 3620 3620
symmetric matrix comprising 100 362 362 submatrices, where the symmetric matrix w gives
its diagonal submatrix elements
and 0 matrices make up its1other elements. The matrix w
0
! 1
362
X
has elements fwr;c g = @
dr;c1
dr;c1 j r 6= c; 0 j r = cA where dr;c gives the distance
c=1
in miles between MSA r and MSA c (i.e. the spatial weights matrix is row-normalized so
that the non-zero elements of each row sum to one). In the spatial lag model, the dependent
variable is directly related to that of neighboring observations, while in the spatial error model
only the error terms are related. In both cases, the spatial autocorrelation coe¢ cient is given
by . Both speci…cations also include year dummies and MSA …xed e¤ects. Estimation is
undertaken using spatreg and associated Stata commands (Pisati, 2001).
2 6 According to an April, 13, 1998 report in the New York Times, it was also possible
to obtain “rapture insurance” up until relatively recently, when pressure from within the
insurance industry (rather than a lack of demand) halted the practice.
2 7 Keister argues that the direct e¤ect of denominational membership on wealth accumulation – distinct from indirect e¤ects via the demographic and economic channels discussed in
16
Nevertheless, the positive price e¤ect uncovered in the data is at …rst glance
surprising, since the most simple asset pricing model would predict that more
aggressive discounting behavior should reduce the net present value of the income stream associated with any asset, lowering its price. I conjecture that
the positive e¤ect could re‡ect a response to time inconsistency in believers’
preferences, created by the tension between their end times beliefs and Biblical injunctions to behave reasonably even while anticipating Jesus’s return.
It is well established that time inconsistency raises the value of commitment,
potentially generating a premium on illiquid assets that help agents to commit (Laibson, 1997; Kocherlakota, 2001). In fact, Kocherlakota (2001) argues
that testing for a commitment premium is a means of testing the validity of
models incorporating time-inconsistent preferences. My results can therefore be
interpreted as o¤ering some support to this class of models.
In the appendix I outline a simple 3-period model, based on that of Kocherlakota, that illustrates this conjecture. The model incorporates two types of
agents: believers (whose preferences are time inconsistent) and non-believers
(whose preferences are not). The model adopts a simple islands formulation,
with agents homogeneous within islands. Trade in liquid assets is possible across
islands, but trade in illiquid (commitment) assets is not. The latters’illiquidity
also means that they are also non-tradable in the intermediate period, allowing
the believers to commit to their …rst-best consumption path over all three periods. In equilibrium an endogenous fraction of the believers choose to follow a
commitment strategy (holding the illiquid assets), while the remainder hold liquid assets. The commitment strategy delivers the …rst-best consumption pro…le
(given asset prices); to equalize utility across the two strategies, commitment
assets must then deliver a lower return, or trade at a premium. Islands can
be aggregated to generate heterogeneous areas to mimic the MSAs in the data.
Then the model predicts a positive interaction e¤ect as in equation (1) with
b 0 (Proposition 1).
This explanation remains only a conjecture, and further research focusing on
individual-level savings data is required to verify if it is a convincing explanation.
However, one bene…t of the simple general equilibrium model outlined above
is that it makes a number of additional predictions with respect to borrowing
behavior that can be tested against mortgage data. Rejecting the null hypothesis
of zero e¤ect on mortgage borrowing is of course only a …rst step toward assessing
the model’s validity; however, if it were to fall at this …rst hurdle one would
doubt its usefulness.
5
Evidence from mortgage data
The model’s …rst prediction relates to the total number of loans. A mortgage
transaction typically re‡ects two o¤setting asset transactions on the household’s
balance sheet: the purchase of a real asset, and the acquisition of a …nancial
the text – might arise from Biblical injunctions against hoarding or from generous charitable
giving or ‘tithing’to the church, but does not look at the role of ‘end times’beliefs.
17
liability. In terms of the model, the liquid long term asset can be thought of as
a claim on future income, so the model’s proxy for mortgage borrowers is the
share of agents following the commitment strategy (selling the liquid asset to
buy the illiquid asset). In the appendix it is demonstrated that this share falls
as the subjective probability of the ‘end times’increases (for ! in the region of 0;
Proposition 3).28 The mechanism driving the result is simple: an increase in !
leads to a decline in the period 3 consumption of the believers who choose not to
commit. However, overall consumption of these agents is essentially …xed. Hence
a lower average consumption level requires there to be more of these agents, and
hence fewer believers following the commitment strategy. We should therefore
expect to see the volume of mortgage lending decline in response to an increase
in the Rapture Index in areas with high shares of evangelicals. This is not a
trivial prediction, since alternative explanations for the relationship between
house prices and the interaction term (for instance, that the interaction term is
correlated with some other variable associated with a boom in lending activity
that drives up prices) might generate the opposite prediction.
The second prediction relates to the average loan-to-income ratio, which is
predicted to increase – for believers – as the subjective probability of the ‘end
times’ increases (for ! in the region of 0; Proposition 4).29 The logic of this
result is the inverse of the previous result: since the share of believers choosing to
commit has decreased, then, given that the real stock of commitment assets does
not change, the per-borrower value increases. Hence, we should see an increase
in the average mortgage size in response to an increase in the Rapture Index
in areas with high shares of evangelicals. Given that we have already observed
rising house prices in response to this interaction term, an increase in the loan to
income ratio would not be a surprising result. However, this nevertheless o¤ers
a useful additional test of the model since the mortgage data is independent
of the house price data and also because the measured e¤ect on house prices
could be due to a correlation between the interaction term and some unobserved
component of household income, whereas the model speci…cally predicts an
increase in household leverage.
To test these additional predictions, I use data on individual loan applications from the Home Mortgage Disclosure Act (HMDA) Loan Application Registry collected and aggregated to the MSA level by Dell’Ariccia, Igan and Laeven
(2008). As with the main panel results, identi…cation rests on entering the interaction term
! on the right hand side. In terms of dependent variables, I use
the ratio of the number of loan applications to the total population within each
MSA as a proxy for testing Proposition 3 and the mean of the loan to income
ratio of loans originated within each MSA for testing Proposition 4. For the
latter I use two di¤erent measures: the simple unweighted mean (the average of
the loan/income ratio for each loan) and the weighted mean (total loans to total
income). Both dependent variables are transformed into …rst di¤erences. Data
2 8 See Appendix. In K01 the relationship holds for all values of !; the addition of islands
with non-hyperbolic agents ( < 1) leads to changes in the relative consumption levels across
islands (h) that can o¤set this e¤ect somewhat.
2 9 Again, for
= 1 (so that h = 1) this result will hold for all values of !. See Appendix.
18
are available for 360 MSAs covering 1997-2006. Results are presented in Table
9. Each equation follows the speci…cation of Table 2, column I with respect to
the right hand side, and includes MSA …xed e¤ects and year dummies. Column
I presents results for the volume of loan applications; columns II and III present
results for the loan to income ratio (using the unweighted and weighted mean,
respectively). The results are in line with the model’s additional predictions:
the coe¢ cient on the interaction term carries the predicted sign and is highly
statistically signi…cant in each case. Taken together with the main results, these
additional results, testing some auxiliary predictions using a di¤erent dataset,
o¤er some preliminary support for my conjecture.
[Table 9 about here]
6
Conclusions
This paper has attempted to achieve two objectives. First, to test whether
‘end times’ beliefs have a measurable impact on consumer behavior. Second,
to shed light on theories of portfolio choice with time inconsistent preferences,
based on my conjecture that those with ‘end times’beliefs might face a tension
between their belief that the world could end soon and Biblical injunctions to
behave reasonably in the meantime that could generate time inconsistency in
their preferences. The paper focuses on house prices, and my identi…cation
strategy relies on interacting two exogenous variables: a geographically varying
proxy for the population share of believers in each area, and a time-varying
measure of believers’subjective probability of the world ending, and restricting
my attention to within-area and within-time period variation by including area
and time …xed e¤ects.
I test for an e¤ect using a 10-year panel of 363 U.S. Metropolitan Statistical
Areas (MSAs) and …nd a statistically and economically signi…cant positive e¤ect
that is robust across subsamples, speci…cations and alternative data sources. In
particular, the e¤ect is robust to specifying the estimation equation in levels or
…rst di¤erences, assuming one-period lags or contemporaneous e¤ects, dividing
the sample by area or time period, focusing only on within-State variation in
the interaction term, including macroeconomic variables interacted with the
share of believers proxy, using alternative data proxies, running speci…cations
with temporal or spatial lagged dependent variables, and correcting for spatial
or temporal correlation in the error term’s variance-covariance matrix. I also
identify a positive interaction e¤ect using an event study centered on 9/11.
To account for this positive e¤ect, I conjecture that believers in Biblical
prophecy face a tension between their eschatological beliefs and other aspects
of their religious values that stress the need to behave reasonably even while
anticipating the end times. This could generate time inconsistency in their preferences. In the appendix I outline a model with time inconsistent preferences,
adapted from Kocherlakota (2001) to enable it to be taken to the data, that
can replicate the positive interaction term identi…ed in the empirical work. The
bene…t of outlining a formal model in this way is that it can generate auxil-
19
iary predictions that can then be taken to data on mortgage lending as a …rst
attempt at falsifying the hypothesized channel. In fact, the evidence from the
mortgage data supports the model’s additional predictions.
This paper re‡ects only a …rst attempt to gauge the economic relevance
of ‘end times’ beliefs. The e¤ect I identify could turn out to be particular
to the data and sample employed, and the hypothesized channel is simply a
conjecture. Future research could usefully focus on alternative data sources,
notably individual level data on savings behavior and portfolio choice, to assess
both whether the e¤ect identi…ed here is more widespread, and to provide further
insights into the likely channels. Assessing the broader quantitative signi…cance
of the results is beyond the scope of this paper. An open question for future
research is whether ‘end times’ beliefs could help to explain other phenomena
in the U.S. economy, such as the historically low savings rate.
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the United States 2000: An Enumeration by Region, State and County
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22
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23
7
Appendix: Model, Comparative Statics, Propositions 1-4 with Proofs.
This appendix outlines a general equilibrium model which is su¢ ciently simple to o¤er an analytic solution but can also be taken to the data. Its starting point is the three-period model with both commitment and liquid assets
and consumers with either hyperbolic (time-inconsistent) or exponential (timeconsistent) preferences outlined in Kocherlakota (2001).30 The three periods
represent the current period and the two relevant decision horizons: the short
term and the long term. The key modi…cation from Kocherlakota (2001; appendix, henceforth K01) is the coexistence of the two types of agents in a single
economy, modeled by assuming a collection of island economies with immobile
consumers but with inter-island trade in consumption and the liquid asset.31
The illiquid, or commitment, asset is assumed to be nontraded across islands to
allow its price to vary across regions. Subsets of islands can be aggregated into
regions, corresponding to the MSAs in our dataset, that include both types of
consumer. The average price of the commitment asset in each region –proxied
for by housing assets in our dataset –will then vary depending on the degree of
time-inconsistency in the preferences of the hyperbolic consumers (as in K01)
and also the share of each type of consumer in the population of the region.
The economy consists of a number of geographically distinct island economies
whose total mass of population is unity. The set of agents within each island i
is denoted mi (where mi also denotes its mass). All the agents within each mi
are identical and are of one of two types, either ‘believers’(denoted superscript
B) or non-believers (denoted superscript N ). These islands
can be aggregated
X
into larger regions M r : mi 2 M r with mass M r =
mi , containing both
mi 2M r
types of consumers, corresponding to the MSAs in our dataset. The proportion
r
of agents
, while a proportion
X in region r that are of type B is denoted
r r
M
of all the agents in the economy as a whole are of type B. Agents
r
live for three periods and in period 1 receive an endowment of income of value
1 as well as endowments of assets yielding 1 in each of periods 2 and 3. Period
3 assets consist of two types: a liquid asset b13 and a commitment asset acom
which is illiquid in two senses: it cannot be traded in period 2 and also cannot
be traded across islands. The period 2 asset b12 and the liquid period 3 asset
b13 can be traded across islands. In addition, the liquid period 3 asset can be
retraded in period 2. The commitment asset makes up a proportion k of the
3 0 This paper also assumes log utility to arrive at an analytic solution, as in the model
presented in the Appendix to Kocherlakota (2001).
3 1 Laibson, Repetto and Tobacman (1998) simulate a more complex model with both types
of agent in a single economy. This paper adopts the islands formulation (where each island
has homogeneous consumers) since in our model (which is kept extremely simple in order
to yield analytical results), if the two types of agents existed in a single economy with no
barriers to trading the commitment asset, then the price of the commitment asset would be
identical regardless of the share of the hyperbolic consumer in the economy (assuming an
interior solution).
24
total period 3 asset endowment in each island.
The model di¤ers from that of K01 in two key regards. First, K01 has
a single economy consisting of identical agents who have either hyperbolic or
non-hyperbolic preferences. Second, in K01 the commitment asset is tradable
across all agents in period 1. The introduction of two types of agent and the
island formulation is the simplest way of introducing geographical heterogeneity
in agent types. The assumption that acom is non-tradable across islands is
required to deliver di¤erent average prices for the commitment asset across
regions. Given that we are using housing as a proxy for the commitment asset,
then this assumption requires that each agent owns property in only one small
area, and does not move between areas. One could conjecture that qualitatively
similar results would obtain if there was rather some …nite cost of trading the
commitment asset across islands.
Utility of the two types in period 1 is given as
U1B = U1N = ln c1 +
[ln c2 +
ln c3 ]
(10)
while utility in period 2 is given as
U2B
U2N
= ln c2 +
= ln c2 +
(1 !) ln c3
ln c3
(11a)
(11b)
where the believers place a probability ! on the world ending.32 The believers’
preferences in period 1 re‡ect their desire to behave responsibly regardless of
their belief that the world is likely to end soon: hence, they discount the future
similarly to other agents. Their period 2 preferences re‡ect the temptation
to allow their discounting behavior to be guided by their ‘end times’ beliefs,
implying a discount factor (1 !).
Agent j’s decision problems in periods 2 and 1 are given below:
Period 2
j
Agent j enters period 2 with liquid wealth Wliq
= q23 bj13 + bj12 that can be
j
= q23 ajcom . Then,
traded freely with all agents and committed wealth Wcom
following K01, DP2 can be written as:
max [ln c2 +
ln c3 ]
cj2 ;cj3 ;bj23
(12)
subject to
j j
cj2 + q23
b23
cj3
bj23
j
= Wliq
=
bj23
0
+
(13)
q231 Wjcom
(14)
(15)
where
= (1 !) for the believers and otherwise. The solution to DP2
can be written as c2 (Wliq ; Wcom ) ; c3 (Wliq ; Wcom ).
3 2 In addition to the substantive chages from K01 detailed in the text, we also normalize the
total endowment of each island (and the overall economy) in each period to 1, and have the
non-hyperbolic discount factor equal between periods 1-2 and 2-3.
25
Period 1
Agent j’s period 1 decision problem (DP1) can be written as:
h
i
j
2
max
ln
c
+
ln
(c
(W
;
W
))
+
ln
(c
(W
;
W
))
(16)
liq
com
liq
com
2
3
1
j
j
j
j
c1 ;acom ;b12 ;b13
subject to
cj1 + pcom ajcom + q12 bj12 + q13 bj13
j
Wliq
j
Wcom
bj12 ; bj13 ; ajcom
=
1 + pcom k + q13 (1
=
q23 bj13 + bj12
q23 ajcom
=
k) + q12 (17)
0
(18)
(19)
(20)
Equilibrium
Equilibrium is de…ned in terms of consumption, asset holdings and prices.
Equilibrium satis…es the following three conditions: …rst, cj2 ; cj3 solves DP2
j
j
given q23 , Wliq
= q23 bj13 , and Wcom
= q23 ajcom ; second, cj1 ; bj12 ; bj13 ; ajcom solves
DP1 given q12 , q13 , q23 , and pcom ; and third, markets clear so that:
Z
cjt dj = 1; t = f1; 2; 3g
(21)
Z
bj12 dj = 1
(22)
Z
Z
bj23 dj =
bj13 dj = 1 k
(23)
Z
ajcom = kmi 8i
(24)
j2mi
The principal di¤erence between this model and K01 is seen in the last set of
market clearing conditions for the commitment asset (24), which hold for each
island individually (while the market clearing conditions for the liquid bonds
hold for the economy as a whole).
Solution
The solution to DP2 for the type B agents, following K01, is given below:
c2 (Wliq ; Wcom )
c3 (Wliq ; Wcom )
Wliq + Wcom
; Wliq
1 + (1 !)
Wliq + Wcom
= q231 max (1 !)
; Wcom
(1 !)
=
min
As in K01, the nonconcavity of c2 generates asymmetrical equilibrium allocations for the type B consumers. As with K01, the equilibrium is …rst guessed
and then veri…ed:
Guessing:
26
De…ne ( ; h) as the solution to
0
(1
)h
0 = ln @
1
h 1
(1 !) 1+ 1+
(1
ln
0
k
(1
+ ln (1
k )
!)
1+
= h
1+
+
2
1 k
1 k
+
1
A
!)
(1 + )
2 1 k
1 k
(1
)
1+ (1 !)
+
(1 + (1 !))
(1 + )
ln
(1
!) 11+k + !
(25)
(26)
Equation (25) de…nes the share of hyperbolic consumers that hold the
liquid asset in each island, while equation (26) de…nes the period 1 consumption
of the representative hyperbolic consumer, h. In K01 an interior solution for
is guaranteed (setting = 1 gives this result). For a solution to exist in the
augmented model with < 1 requires that33
!
1+
1+
k
(1 + ) (1
)
ln
+ ln (1 !) ln
> ln
1 + (1 !)
1 k
1 + + 2 11 kk
Given
and h, de…ne asset prices as
q12
=
1+
q13
= pN
com =
h
1+
1+
(1
!)
1
(27)
2
pH
com
=
q23
=
(1
q13
q12
)
k
1
2
k
1
h 1
(1
!)
1+
1+
(1
h
!)
(28)
(29)
(30)
Within each island with hyperbolic agents (of mass mi ), then a mass mi
receive the following allocation
cj1
= h
(31)
1+
=
q12 1 + (1
cj2
= bj12
cj3
= bj13 = bj23 =
ajcom
=
2
0
!)
h
(1 !)
1+
q13
1 + (1
(32)
!)
h
(33)
(34)
3 3 This expression is obtained by setting
> 0. When there are a comparatively large
number of N types ( is low) then the e¤ective demand for the type B’s liquid asset b13 is
high, driving up the price and lowering the return. This crowds out B type agents wishing to
hold the liquid asset, requiring a lower as more choose the commitment asset. However, the
market incompleteness that prevents direct trade in acom requires that > 0.
27
) mi receive
while a mass (1
cj1
= h
(35)
cj2
bj12
=
cj3
= ajcom =
bj13
= bj23 = 0
=
q12
h
(36)
2
pH
com
h
(37)
(38)
Consumption allocations for the non-hyperbolic consumers are given by (39)
- (41):
1
1
h
cN
1
=
cN
2
= bj12 =
cN
3
= bj13 + ajcom = bj23 + ajcom =
(39)
1
q12 1
h
(40)
2
1
q13 1
h
(41)
0 requires that34
Finally, the condition bN
13
cN
3
k
0
(42)
Verifying the Solution
The veri…cation that this set of allocations and prices constitutes an equilibrium follows K01. The type N agents have time consistent preferences, so that
their problem DP2 can be collapsed into DP1 (recall that there is no uncertainty,
3 4 This condition will be met in equilibrium since the N types consume more than their
endowment (1) in period 3, and are therefore net purchasers of b13 . To see this note that,
rewriting the N types’budget constraint, one obtains:
cN
2
1 q12 + cN
3
which implies that, if cN
1, then cN
2
3
resource constraint to yield:
cN
2
1 q13 =
(h 1)
1
1. To see that cN
2
1=
cB
2
1
0
1, one can rewrite the period 2
1
where cB
2 gives the (weighted) average period 2 consumption of the B types. This implies
that, if the B types consume above their endowment (on average) in period 2, then the N
types must consume below their endowment. In fact, period 2 consumption is given by:
cN
2
cB
2
=
=
q12
q12
1
1
1+
h
q12
1+
!
(1
!)
q12
which implies that the N type’s consumption in period 2 is below their endowment. Hence
cN
1 and bN
0.
3
13
28
so the problems at the two time periods are identical if the time preference parameter is stable). Standard arbitrage conditions require that pN
com = q13 .
Then DP1(N) is given by:
(c1 ; c2 ; c3 ) 2 arg max
(c1 ;c2 ;c3 )
ln c1 +
ln c2 +
2
ln c3
(43)
subject to
c1 + q12 c2 + q13 c3
1 + q12 + q13
(44)
which yields (39) –(41) given the period 1 aggregate resource constraint and the
35
de…nition of h. Next, I show that pH
Substitution among equations
com > q13 .
(28), (29) and (25) then yields (45).
ln pH
com
ln (q13 ) =
ln (1
!) +
(1 + )
ln
1+
1+
(1
!)
>0
(45)
The commitment premium can therefore be decomposed into three separate
terms: a positive e¤ect from the inadequate period 3 (far future) consumption,
ln (1 !), a negative term from the excessive period 2 (near future) consumption, ln 1+ 1+
(1 !) , and the relative weight placed on consumption in each
period, (1+ ) . Note that equation (45) is identical to that in K01, which indicates that the presence of non-hyperbolic consumers in other island economies
and the tradability of the liquid assets do not alter the main result. This is
not surprising, since with log utility the ratio of the asset returns (or equivalently prices) is related only to the di¤erent e¤ective discount factors for the
two groups of hyperbolic consumers (those that commit, by holding acom and
do not commit, by holding b13 , respectively), via the indi¤erence relation that
equates utility across the two strategies. However, note that the introduction of
non-hyperbolic consumers gives the hyperbolic consumers an additional opportunity to trade current and future consumption, leading to higher consumption
in period 1 for these agents compared to K01 (equation (??))36 .
It is easy to show that markets clear and that cj2 = c2 ; cj3 = c3 8j. The rest
of the proof directly follows that in K01 and is therefore not reproduced here.
Solution in ; h space
Totally di¤erentiating (25) with respect to ; h yields the following relationship along the line de…ned by the equation in ; h space:
dh
j eq (25) > 0
d
(46)
3 5 This condition is required for the later stages of the veri…cation of the solution, detailed
in Kocherlakota (2001).
k(pH
com q13 )
1+ 1+q
3 6 Rewriting the expression for h in terms of asset prices, we have h =
12 +q13
.
k(pH
com q13 )
1+
1+q12 +q13
Then it is clear that h > 1 since pH
com > q13 . To see the intuition, note that the ‘believers’
that buy the commitment asset sell their liquid assets b13 to other ‘believers’ (since they
cannot trade the commitment asset with agents in other islands). This depresses the price of
the liquid assets, encouraging the ‘non-believers’ to buy these assets in exchange for current
consumption. In e¤ect, the ‘non-believers’lend indirectly to the ‘believers’seeking to commit.
29
while di¤erentiating the same equation with respect to !; h yields:
0
1
1
h 1
(1 !) 1+ 1+
(1 !)
dh
!
j eq (25) = h @
h (1 + )A
d!
(1 !)
(1 + (1 !))
(47)
dh
Hence d!
j (25) is negative for ! ! 0 and positive for ! ! 1 (conversely,
d
d! j (25) is positive for ! ! 0 and negative for ! ! 1).
Totally di¤erentiating (26) with respect to ; h yields the following relationship along the line de…ned by the equation in ; h space:
dh
j eq (26) < 0
d
(48)
while di¤erentiating the same equation with respect to !; h yields:
dh
j eq (26) > 0
d!
(49)
Figure A1 illustrates the equilibrium and comparative statics for changes in
!.
[Figure A1 about here]
Proofs of propositions 1–4
@ 2 ln
pr
com
q
13
Proposition 1:
0
@!@ r
Proof. Aggregating across islands within geographical regions r (corresponding
to the MSAs in the data) yields:
prcom
ln
@ 2 ln
prcom
q13
prcom
q13
@!@
pH
com
=
=
r
r
(1
r
1
(q13 )
ln (1
!
!) (1 +
r
!) +
(1
dpN
(50)
(1 + )
!))
ln
0
1+
1+
(1
!)
(51)
(52)
com
= dqd!13
0 in the region of ! = 0
Proposition 2: d!
@q13
Proof. To show that @!
0, note that the primary e¤ect of an increase
in ! will be to reduce the average consumption of the B types in period 3,
and to increase the consumption of N types. To persuade the N types to shift
consumption from earlier periods requires a higher return on their period 3
assets, and hence a lower price. To see this algebraically, one can rewrite the
period 3 aggregate resource constraint to give:
"
!#
1
q13
1 + (1 !)
2
q13 =
(1
h) + h
+ (1
)
(53)
pcom
1+
30
(1 !)
13
The main e¤ect of an increase in ! will be to decrease pqcom
and 1+ 1+
.
Since these e¤ects will tend to dominate e¤ects via and h, then the e¤ect on
13
q13 will be negative. Substituting pqcom
using (45) yields:
"
1 !#
1+
(1 !) (1 + )
2
q13 =
(1
h) + h
+ (1
)
1 + (1 !)
1 + (1 !)
(54)
@q13
dq13
13
For ! = 0, @q
=
=
0,
and
is
therefore
given
by:
@h
@
d!
dq13
j [! = 0] =
d!
0
1+
Proposition 3: Proportion of believers following the commitment
strategy declines with increases in ! in the region of ! = 0
Proof. The proportion of believers following the commitment strategy is given
d
by (1
). The proposition is therefore that d!
0 for ! ! 0. Note that:
dh
j [eq (25) ; ! = 0] =
d!
(1
k) (1 + ) < 0
(55)
dh
d!
j (26) 0, then reference to Figure A1 immediately establishes that
0 in equilibrium (in the region of ! = 0).37
Proposition 4: The loan to income ratio for believers following the
commitment strategy increases in the region of ! = 0
Proof. The loan to income ratio is given by:
Since
d
d!
pcom (acom
loan
=
income
1
k)
=
2
h
(56)
0 for ! ! 0. Consider a series of ‘isoThe proposition is therefore that dd!h
h’ lines plotted in ; h space (a series of downward sloping lines where those
located above and to the right correspond to a higher value of the product h).
We know that the movement to a new equilibrium involves a downward shift in
the line plotting equation (25) and an upward shift in the line plotting equation
(26). Then a su¢ cient condition for dd!h
0 is that the iso- h lines have a
steeper (negative) slope than the lines plotting (26). These slopes are given by:
dh
d
dh
d
2
j [eq (26) ; ! = 0] =
(1 + ) (1
1
1 k
j [ h = c; ! = 0] =
(1
)
k )+
2
(1
k)
(57)
(58)
3 7 For the model in K01 ( = 1), the proofs for Propositions 2 and 3 hold for all values of !.
Since h = 1, then Proposition 2 implies Proposition 3. Proposition 2 clearly holds: equation
(25) simpli…es to:
1
ln
and
d
d!
=
(1
)
1+ (1 !)
= ln
k
1
k
+
0.
31
1
ln
1+
(1
1+
!)
Then the su¢ cient condition for
d h
d!
2
1
1
k
(1 + ) (1
0 is given by:
(1
)
k )+
2
(1
k)
0
(59)
and hence:
(1 + ) (1
k )+
which clearly holds.
32
2
(1
k)
0
(60)
Tables and Figures
Table 1.
Table 1. Descriptive Statistics
Variable
Mean
Std. Dev.
Min
Δln(house price index)
0.06
0.04
-0.07
Δln(population)-1
0.01
0.01
-0.07
Δln(p.c. income)-1
0.04
0.02
-0.42
Max
0.29
0.10
unemployment-1
0.21
1.2
30.1
sB
5.0
2.1
(2000 Data)
0.10
0.11
0.00
0.48
sH
0.09
0.14
0.00
0.94
0.76
0.17
0.17
0.13
(Annual Data)
3.3
10.3
0.05
0.01
0.97
0.57
-15.0
21.0
sW
evang
Δri-1
growth-1
3.2
1.2
0.8
4.5
inflation-1
2.5
0.8
1.6
3.4
unemployment-1
5.0
0.6
4.0
6.0
Δoil price-1
15.2
26.2
-32.1
57.0
fed funds-1
3.9
3,625 Observations (363 MSAs)
1.8
1.1
6.2
33
Table 2.
evang*Δri-1
Δln(population)-1
Δln(p.c. income)-1
unemployment-1
evang*Δri
Δln(population)
Δln(p.c. income)
unemployment
evang*ri
ln(population)
ln(p.c. income)
unemployment
evang
I
.00446***
(.000307)
1.44***
(.145)
.196***
(.0590)
-.0104***
(.000832)
Table 2. Panel Regression Results
II
III
IV
.00494***
(.000320)
1.30***
(.100)
.269***
(.0655)
-.00397***
(.000414)
.00371***
(.000277)
1.15***
(.156)
.0839***
(.0317)
-.0118***
(.000932)
.00618***
(.000391)
.537***
(.0425)
.631***
(.0694)
.00555***
(.00120)
-.0883***
(.00693)
Δri-1
V
.00510***
(.000345)
1.26***
(.102)
.278***
(.0577)
-.00182***
(.000412)
VI
.00503***
(.000335)
1.35***
(.0988)
.323***
(.0549)
-.00258***
(.000386)
VII
.00139***
(.000526)
1.47***
(.140)
.182***
(.0548)
-.00699***
(.000789)
-0.0860***
(.00663)
-0.00258***
(.000105)
-.0862***
(.00667)
-.00148***
(.000114)
.00577***
(.000300)
.41
3,625
363
RE
No
No
No
.62
3,625
363
FE
Yes
No
Yes
year
2
R (within)
Obs.
MSAs
Estimation Technique
Year Dummies
State Dummies*year
State Dummies*Δri-1
.50
.50
.93
.47
.31
3,625
3,624
3,987
3,625
3,625
363
363
363
363
363
FE
FE
FE
RE
RE
Yes
Yes
Yes
Yes
No
No
No
Yes
No
No
No
No
No
No
No
Dependent variable is Δln(house price index) except column III: ln(house price index).
Robust Standard Errors in parentheses. Sig. level: 1 percent: ***; 5 percent: **; 10 percent: *.
All regressions also contain a constant term.
2
For comparison, estimating column I without the interaction term yields (within) R =.47.
34
Table 3.
evang*Δri-1
evang*growth-1
Table 3. Robustness to addition of Macroeconomic Variables
I
II
III
IV
V
VI
VII
.00461*** .00477*** .00407*** .00489*** .00293*** .00277*** .00410***
(.000329) (.000316) (.000310) (.000344) (.000345) (.000542) (.000486)
-0.00403*
-0.0157***
(.00241)
evang*inflation-1
(.00298)
.0187***
.0290***
(.00434)
evang*unemployment-1
(.00821)
-.0174***
.0167*
(.00469)
(.00997)
evang*Δoil price-1
.000416***
(.000128)
evang*fed funds-1
.0130***
(.00199)
-.000388**
(.000263)
.0233***
(.004729)
evang*year-1
Δln(population)-1
1.45***
1.43***
1.45***
1.43***
1.44***
(.146)
(.144)
(.144)
(.144)
(.144)
Δln(p.c. income)-1
.194***
.196***
.200***
.195***
.205***
(.0589)
(.0593)
(.0596)
(.0592)
(.0599)
unemployment-1
-.0104*** -.0104*** -.0104*** -.0104*** -.0103***
(.000832) (.000833) (.000832) (.000832) (.0656)
2
R (within)
.50
.50
.50
.50
.50
Obs.
3,625
3,625
3,625
3,625
3,625
MSAs
363
363
363
363
363
Dependent variable is Δln(house price index) for all specifications.
Robust SEs in parentheses. Sig. level: 1 percent: ***; 5 percent: **; 10 percent: *.
All regressions contain a constant term, year dummies and MSA Fixed Effects.
35
1.44***
(.145)
.198***
(.0592)
-.0103***
(.0654)
.50
3,625
363
-.00188
(.00191)
1.44***
(.145)
.198***
(.0592)
-.0103***
(.000832)
.50
3,625
363
Table 4.
evang*Δri-1
Δln(population)-1
Δln(p.c. income)-1
unemployment-1
2
R (within)
Obs.
MSAs
Subsample
evang*Δri-1
Δln(population)-1
Δln(p.c. income)-1
unemployment-1
2
Table 4. Subsample Robustness Checks
I
II
III
IV
.00208***
.00666***
.00450***
.00575***
(.000316)
(.000523)
(.000365)
(.000343)
.934***
1.39***
1.35***
1.55***
(.136)
(.260)
(.148)
(.160)
.115***
.139*
.206***
.221***
(.0370)
(.0826)
(.0633)
(.0711)
-.00610***
-.0131***
-.0102***
-.00723***
(.00108)
(.00325)
(.000865)
(.000827)
.43
.43
.48
.57
1,810
1,815
3,175
2,725
362
363
318
273
1997-2001
2002-2006
Not NE
Not Midwest
VI
VII
VIII
IX
.00300***
.00311***
.00435***
.00330***
(.000282)
(.000630)
(.000459)
(.000300)
1.44***
1.72***
1.63***
1.40***
(.150)
(.311)
(.189)
(.155)
.123*
.131***
.277***
.113*
(.0640)
(.0747)
(.0436)
(.0659)
-.00916***
-.0132***
-.00982***
-.0125***
(.00105)
(.00187)
(.000919)
(.00117)
.43
.49
.52
.46
2,825
730
2,320
2,775
283
73
232
278
sW≥.90
sB≤.10
sH≤.10
Not West
V
.00771***
(.000872)
1.52***
(.223)
.241***
(.0467)
-.0133***
(.00112)
.52
2,150
215
Not South
X
.00513***
(.000485)
1.56***
(.158)
.210***
(.0648)
-.0105***
(.000881)
.51
3,255
326
evang<90pctile
R (within)
Obs.
MSAs
Subsample
Dependent variable is Δln(house price index) for all specifications.
Robust SEs in parentheses. Significance level: 1 percent: ***; 5 percent: **; 10 percent: *.
All regressions contain a constant term, year dummies and MSA Fixed Effects.
sJ denotes 2000 population shares of group J (B: Black; H: Hispanic; W: White).
36
Table 5.
Table 5. Different Evangelicals Measures
Preferred Measure
ARDA Measure
Strict Premillenialists
Summary Statistics
Mean
.172
.173
.0398
Standard Deviation
.129
.129
.0226
Correlations
Preferred Measure
1.00
ARDA Measure
1.00***
1.00
Strict Premillenialists
.421***
.422***
1.00
Estimated Effects
Interaction Coefficient
.00446***
.00502***
.00662***
(.000307)
(.000307)
(.00189)
Interaction Coefficient estimated using specification as in Table 2, Column I.
Table 6.
Table 6. Different House Price Indices
I
II
III
IV
V
VI
Case-Shiller
OFHEO
Real
Nominal
Real
Nominal
(20 MSAs) (20 MSAs) (27 MSAs) (27 MSAs) (363 MSAs) (363 MSAs)
Summary Statistics
Mean
0.08
0.08
0.05
0.07
0.03
0.06
Standard Deviation
0.06
0.05
0.05
0.05
0.04
0.05
.96***
.98***
.98***
Correlations
Estimated Effects
Interaction Coefficient
.00575*
.00700**
.00907*** .00820***
.00445***
.00424***
(.00343)
(.00306)
(.00244)
(.00241)
(.000312)
(.000303)
2
R (within)
.50
.61
.56
.63
.45
.49
Observations
196
263
3,540
Interaction Coefficient estimated using specification as in Table 2, Column I.
All variables in log first differences.
37
Table 7.
evang*Δri-1
Δln(population)-1
Δln(p.c. income)-1
unemployment-1
evang*Δri
Δln(population)
Δln(p.c. income)
unemployment
Δln(house price index)-1
Table 7. Controlling for Serial Correlation
I
II
III
IV
.00326***
.00446***
(.000387)
(.000430)
.781***
1.44***
(.0887)
(.170)
.136***
.196***
(.0197)
(.0603)
-.00513***
-.0104***
(.000752)
(.00141)
.00192***
.00195***
(.000337)
(.000382)
.488***
.587***
(.119)
(.113)
.0933***
.154***
(.0380)
(.0364)
-.00322*** -.00373***
(.000801)
(.00109)
.585***
.434***
(.0847)
(.0982)
2
evang*Δ ri
2
Δ ln(population)
2
Δ ln(p.c. income)
Δunemployment
2
Δ ln(house price index)-1
2
R (within)
Obs.
MSAs
Clusters
Arellano Bond AR(1)
Arellano Bond AR(2)
Hansen over-id test
.33
3,262
363
n.a.
n.a.
n.a.
n.a.
V
VI
.00110***
(.000334)
.315***
(.0923)
.0511**
(.0242)
-.000635
(.000731)
-.136**
(.0540)
n.a.
2,535
363
n.a.
-6.66***
.39
6.80
.000958***
(.000316)
.307***
(.0932)
.0442**
(.0206)
-.000711
(.000706)
-.173***
(.0489)
n.a.
2,535
363
n.a.
-6.68***
-.43
6.80
.50
n.a.
n.a.
3,625
2,898
2,898
363
363
363
363
n.a.
n.a.
n.a.
-6.48***
-4.66***
n.a.
2.66***
2.66***
n.a.
51.0***
51.0***
FE
Cluster
GMM
GMM
GMM
GMM
FE
(MSA)
(1 step)
(2 step)
(1 step)
(2 step)
AR(1)
Estimation Technique
2
Dependent variable is Δln(house price index) except columns V and VI: Δ ln(house price index).
Robust Standard Errors in parentheses. Sig. level: 1 percent: ***; 5 percent: **; 10 percent: *.
All regressions contain year dummies and a constant term.
Column I: ρ = .56.
GMM estimation uses collapsed instrument list to reduce bias due to overfitting with small samples.
GMM results are for first-differenced variables (in addition to any differencing already applied).
Standard errors from 2 step GMM results incorporate Windmeijer’s (2005) finite-sample correction.
38
Table 8.
evang*Δri-1
Δln(population)-1
Δln(p.c. income)-1
unemployment-1
evang
D.evang*Δri-1
D.Δln(population)-1
D.Δln(p.c. income)-1
D.unemployment-1
2
R
Obs.
MSAs/ MSA pairs
Clusters
ρ
2
LM χ (1)
Table 8. Controlling for Spatial Correlation
I
II
III
IV
.00519***
(.000646)
.783***
(.0908)
.263***
(.0839)
-.00328***
(.000543)
-.0414***
(.0103)
.00256*** .00163***
.00139**
(.000616) (.000514) (.000644)
.723***
.557***
.518***
(.0940)
(.0866)
(.0990)
.0980***
.0682**
.0853***
(.0297)
(.0312)
(.0277)
-.00519*** -.00425*** -.00323***
(.000703) (.000806) (.000942)
.55
.12
.08
.08
3,625
2,520
1,890
1,260
363
253
190
127
500
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
OLS
Cluster
(St/Yr)
FE
Matched
Difference
V
.00312***
(.000245)
1.35***
(.120)
.158***
(.0413)
-.00893***
(.000695)
VI
.00384***
(.000305)
1.38***
(.131)
.173***
(.0485)
-.00927***
(.000824)
n.a.
n.a.
3,620
3,620
362
362
n.a.
n.a.
.685
.668
1571.4
1313.4
(p=0.000) (p=0.000)
MLE
FE
MLE
Spatial
Matched Spatial Lag
Error (FE)
Difference
(FE)
FE
Matched
Difference
Estimation Technique
≤75th
≤median
percentile
None
(62 miles) (42 miles)
Distance criterion
n.a.
n.a.
n.a.
Dependent variable is Δln(house price index) except columns II-IV: D.Δln(house price index).
D.x defined as xa-xb, for matched MSA pair {a,b} and any variable x.
Robust Standard Errors in parentheses. Sig. level: 1 percent: ***; 5 percent: **; 10 percent: *.
All regressions contain year dummies and a constant term (and State dummies, column I).
Distance criterion refers to distance between matched MSAs.
Row-standardized weights matrix used for columns V and VI.
2
2
All reported R 's are within R , except column I.
39
Table 9.
Table 9. Mortgage Data
I
II
III
evang*Δri-1
-.000403***
.00666***
.00907***
(.000105)
(.00132)
(.000847)
Δln(population)-1
.124***
1.77***
1.74***
(.0375)
(.410)
(.303)
Δln(p.c. income)-1
0.00212
.0297
0.00902
(.00833)
(.105)
(.0630)
unemployment-1
-.000448**
-.00778***
-.00741***
(.000186)
(.00251)
(.00140)
2
R (within)
.72
.19
.30
Obs.
3,600
3,600
3,600
MSAs
360
360
360
Dependent Variable
Δ(loan apps/ popn.)
Δ(loan/inc. ratio, unweighted)
Δ(loan/inc. ratio, weighted)
Robust SEs in parentheses. Significance level: 1 percent: ***; 5 percent: **; 10 percent: *.
All regressions contain a constant term, year dummies and MSA Fixed Effects.
40
Derived from 2000 Religious Congregations and Membership Study, as
detailed in text.
Subjective "Prophetic Speedometer" giving perceived probability of end
times occuring soon.
Population shares obtained from 2000 census data at the county level
and aggregated to the MSA level
Available at the MSA level, Annual Data
Available at the MSA level, Annual Data
Available at the MSA level, Annual Data
Annual average
Share of Evangelicals (three
definitions)
Rapture Index
Share of hispanics and non-hispanic
blacks and whites (2000)
Per capita personal income
Population
Unemployment Rate
Fed Funds Rate
GDP Growth
CPI Inflation
Unemployment Rate
Change in WEO Oil Price
Number of Mortgage Loan
Applications
Average Mortgage Loan Size
31
MSA level data from the Home Mortgage Disclosure Act (HMDA) Loan
Application Registry
MSA level data from the Home Mortgage Disclosure Act (HMDA) Loan
Application Registry
(National level)
Repeat-sales based house price index available for 20 large MSAs
Dell'Ariccia, Igan and Laeven (2008)
Federal Reserve Board of Governors (Table H15)
IMF World Economic Outlook (WEO) database
IMF World Economic Outlook (WEO) database
IMF World Economic Outlook (WEO) database
IMF World Economic Outlook (WEO) database
Dell'Ariccia, Igan and Laeven (2008)
Bureau of Labor Statistics (http://www.bls.gov/lau/home.htm)
Bureau of Economic Analysis (http://www.bea.gov/regional/reis/)
Bureau of Economic Analysis (http://www.bea.gov/regional/reis/)
Bureau of the Census (http://factfinder.census.gov)
http://www.brunching.com/toys/rapture-original.txt (1995-1997).
http://web.archive.org/web/*/http://www.raptureready.com (19982004). http://www.raptureready.com/rap2.html (official website,
2005-06).
Association of Religion Data Archive (http://www.thearda.com).
Available from http://www2.standardandpoors.com.
Table A1 Variable Definitions and Sources
Definition
Source
Repeat-sales based house price index derived from data relating to
2007Q2 release, obtained from OFHEO
conventional conforming mortgage transactions (Fannie Mae and Freddie
Mac).
As above, deflated using MSA-specific CPI available for 27 MSAs.
CPI from Bureau of Labor Statistics (http://www.bls.gov/cpi/).
Case Shiller House Price index
OFHEO House Price Index (real)
Variable
OFHEO House Price Index (nominal)
Table A1
Figure 1.
Evangelicals: Share of MSA Population
Less than 10 percent
15-25 percent
10-15 percent
More than 25 percent
Source: 2000 Religious Congregations and Membership Study, 2000 Census
31
Figure 2.
1. False Christs
2. Occult
3. Satanism
4. Unemployment
5. Inflation
6. Interest Rates
7. The Economy
8. Oil Supply/Price
9. Debt and Trade
10. Financial unrest
11. Leadership
12. Drug abuse
13. Apostasy
14. Supernatural
15. Moral Standards
16. Anti-Christian
17. Crime Rate
3
2
2
3
3
2
4
4-1
3
5
4
2
4
1
3
3
4
18. Ecumenism
4
35. Date Settings
19. Globalism
3
36. Volcanoes
20. Tribulation Temple 2
37. Earthquakes
21. Anti-Semitism
4
38. Wild Weather
22. Israel
5
39. Civil Rights
23. Gog (Russia)
5
40. Famine
24. Persia (Iran)
5
41. Drought
25. The False Prophet 3
42. Plagues
26. Nuclear Nations
5
43. Climate
27. Global Turmoil
4
44. Food Supply
28. Arms Proliferation 4
45. Floods
29. Liberalism
4
30. The Peace Process 3+1 Rapture Index 159
31. Kings of the East 4
Net Change unch
32. Mark of the Beast 3
33. Beast Government 4
Updated Dec 3, 2007
34. The Antichrist
2
2004 High 157 2005 High 161 2006 High 163 2007 High 163
2004 Low 135 2005 Low 143 2006 Low 151 2007 Low 154
Record High 182
24 Sept 01
Record Low 57
12 Dec 93
Source: http://www.raptureready.com/rap2.html.
32
2
4
5
5
3
3
5
3
3
5
5
Figure 3.
120
130
140
150
160
170
End Times Beliefs: the Rapture Index
1996
1998
2000
Source: see text.
33
2002
2004
2006
Figure 4.
0
.05
.1
.15
Change in log House Price Index (2000 pop. weighted)
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Source: Office of Federal Housing Enterprise Oversight.
34
Residual Change in log House Price Index
-.005
0
.005
Figure 5.
9/11 Effect
0
.2
Share of Evangelicals
.4
Fitted Relationship
2000q2
2001q2
2000q4
2001q4
Source: OFHEO, 2000 Religious Congregations and Membership Study.
35
.6
Figure A1.
1.03
1.02
Eq (24)
1.01
h
Effect of increase in ω
1
Eq (25)
0.99
iso-hθ line
0.98
0.97
0.67
0.69
θ
36
0.71
0.73