Empirical business cycle studies using cross-country data usually cannot
achieve causal relationships while within-country studies mostly focus on the
bust period. We provide the first causal investigation into the boom period of
the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research
design, we show that credit expansion in private-label mortgages causes a
differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the
house-related industries in the high net-export-growth areas. Most
importantly, our unique research design enables us to perform the most
comprehensive tests on theories (hypotheses) regarding the business cycle. We
show that the following theories (hypotheses) cannot explain the cause of the
1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real
business cycle theory, the collateral-driven credit cycle theory, the business
uncertainty theory, and the extrapolative expectation theory.

Keywords: business cycle, financial crisis, credit supply, private-label
mortgages
JEL Classification: E32, G01, G21

Testing Business Cycle Theories: Evidence
from the Great Recession

Empirical business cycle studies using cross-country data usually cannot
achieve causal relationships while within-country studies mostly focus on the
bust period. We provide the first causal investigation into the boom period of
the 1999-2010 U.S. cross-metropolitan business cycle. Using a novel research
design, we show that credit expansion in private-label mortgages causes a
differentially stronger boom (2000-2006) and bust (2007-2010) cycle in the
house-related industries in the high net-export-growth areas. Most
importantly, our unique research design enables us to perform the most
comprehensive tests on theories (hypotheses) regarding the business cycle. We
show that the following theories (hypotheses) cannot explain the cause of the
1999-2010 U.S. business cycle: the speculative euphoria hypothesis, the real
business cycle theory, the collateral-driven credit cycle theory, the business
uncertainty theory, and the extrapolative expectation theory.

Keywords: business cycle, financial crisis, credit supply, private-label
mortgages
JEL Classification: E32, G01, G21

###### Contents

  1. 1 Introduction
  2. 2 Research Design
    1. 2.1 Operationalize the “Economic Base Theory”
    2. 2.2 OLS and Its Bias
    3. 2.3 Gravity Model-based Instrumental Variable
    4. 2.4 Data Implementation
  3. 3 Data Sources
    1. 3.1 Data for Net Export Growth
    2. 3.2 Data for Mortgages and House Prices
    3. 3.3 Data for Employment and Business
    4. 3.4 Local Economic Conditions
    5. 3.5 Counties Severely Affected by 2005 Hurricanes
    6. 3.6 Summary Statistics and Figures
  4. 4 Empirical Results: Supporting Credit Expansion
    1. 4.1 Total Employment Boom (00-06) and Bust (07-10)
    2. 4.2 The Housing Industry Channel
      1. 4.2.1 House-Related Employment
      2. 4.2.2 Residential Construction Employment Boom and Bust
      3. 4.2.3 Other House Employment Boom and Bust
      4. 4.2.4 Mortgage Employment Boom and Bust
    3. 4.3 Housing Industry Channel: Additional Evidence
      1. 4.3.1 BEA Construction Employment Boom and Bust
      2. 4.3.2 Residential unit Permit Boom and Bust
  5. 5 Empirical: Tests for Alternative Hypotheses
    1. 5.1 Speculation Euphoria Hypothesis
      1. 5.1.1 Credit Expansion Causes Speculation
      2. 5.1.2 Credit-Independent Speculation vs Pure Credit Expansion
      3. 5.1.3 Speculation Cannot Grow Without Credit Expansion
    2. 5.2 Real Business Cycle Theory
    3. 5.3 Two Variants in Spirit of Real Business Cycle Theory
      1. 5.3.1 Natural Disaster Hypothesis
      2. 5.3.2 Technology Shock Hypothesis in Construction Sector
    4. 5.4 The Collateral-Driven Credit Cycle Theory
    5. 5.5 The Business Uncertainty Theory
    6. 5.6 Extrapolative Expectation Theory
      1. 5.6.1 Irrelevance of Government Mortgage
      2. 5.6.2 Exclusion Restriction: Prior (91-99) vs. Boom (99-05)
  6. 6 Empirical: Robustness
    1. 6.1 Anti-Predatory-Lending States vs. Other States
    2. 6.2 Non-Recourse vs. Recourse States
    3. 6.3 Non-Judicial vs Judicial States
    4. 6.4 Sand vs Other States
    5. 6.5 State Capital Gain Tax
  7. 7 Conclusion
  8. 8 Figures and Tables
    1. A Appendix
      1. A.1 Gravity Model-based IV: US Imports
      2. A.2 Appendix for the Empirical Results Supporting Credit Expansion
        1. A.2.1 Broader House Employment Boom (00-06) and Bust (07-10)
        2. A.2.2 Exclusion Restriction: Test on PLMNJ Growth
      3. A.3 Appendix Figures and Tables

tempdima tempdima “Crises do not occur randomly, and, as a result, an
understanding of financial crises requires an investigation into the booms
that precede them.”

– Amir Sufi and Alan M. Taylor, Financial Crises: A Survey, 2022

##  1 Introduction

Finding the cause of the 1999-2010 U.S. business cycle is vital because the
U.S. ended up with the deepest recession since the Great Depression in the
1930s. In the bust period, the U.S. economy experienced widespread mortgage
defaults (Mayer, Pence, and Sherlund, 2009; Keys, Mukherjee, Seru, and Vig,
2010), massive failures in the banking industry (Bernanke, 2023), large
consumption drop (Mian, Rao, and Sufi, 2013; Kaplan, Mitman, and Violante,
2020), and huge unemployment rise (Hoynes, Miller, and Schaller, 2012; Mian
and Sufi, 2014). Identifying the cause and its major mechanisms is crucial for
understanding of the economic connections among credit, housing, banking, and
employment. It is also helpful for the design of regulatory frameworks and
macroeconomic policies that monitor the economy, avoid a similar recession,
and intervene in an early stage to heal the whole economy.

In the literature, there are multiple theories (hypotheses) trying to explain
the business cycle. Prevalent hypotheses include speculative euphoria
hypothesis (Kindleberger, 1978; Minsky, 1986), real business cycle theory
(Prescott, 1986), the collateral-driven credit cycle theory (Kiyotaki and
Moore, 1997), the business uncertainty theory (Bloom, 2009), extrapolative
expectation theory (Eusepi and Preston, 2011), and credit-drive household
demand hypothesis (Mian and Sufi, 2009; Schularick and Taylor, 2012). Each
theory (hypothesis) has found some pieces of supporting evidence. So far,
however, we have not gathered enough micro-style causal evidence to
distinguish which theory (hypothesis) captures the major origin of the
business cycle. The empirical difficulties are twofold. First, cross-country
empirical studies usually only achieve correlation because of large endogenous
differences in economic development, institutions, and culture. Second,
within-country studies mostly focus on the bust periods without digging into
the boom period, partially because of the difficulty in finding long-term
incentives that have persistent geographic divergence that potentially causes
the divergence in credit expansion. Prevalent theories and ample empirical
evidence, however, support geographic convergence within a country (Kim and
Margo, 2004).

To preview, by combining insights from regional economics and international
economics, we design a causal framework to locate the long-term incentive for
credit that has persistent geographic divergence. Our research design enables
us to find ample micro-style causal evidence for the “credit-driven housing-
dominant view”. Specifically, induced by net export growth, credit expansion
in private-label mortgages (non-jumbo) causes the 1999-2010 U.S. business
cycle. This business cycle is much stronger in the high net-export-growth
areas and housing-related industries experienced an amplified boom and bust
cycle (“housing industry channel”). Most importantly, our unique research
design empowers us to conduct the most comprehensive tests on the relevance of
prevalent theories (hypotheses) regarding business cycles. We find that other
hypotheses cannot explain the origin of this business cycle. We will describe
our research design, then major findings, and lastly, our contribution.

Research Design Empirical studies of the business cycle by cross-country data
face several challenges in identifying the cause and its primary channel.
First, credit supply and household demand can be driven endogenously by
institutional differences. For example, countries with higher prior economic
growth, better legal protection of creditors, and a stronger regulatory system
against financial fraud could have higher household demand that attracts
international credit. Alternatively, these countries could attract more
international funds that reduce interest rates and induce higher household
demand. Second, we need to achieve a consistent measure of household debt so
that such a measure can be comparable in the cross-section. For the largest
part of household debt, mortgages (around 70% in the US), the average maturity
is around 45 years in Sweden, 30 years in the USA, and 15 years in Germany
(Bernstein and Koudijs, 2021). This fact means the same debt-to-income ratio
due to the same amount of mortgage means relatively higher payback pressure
for households in Germany but much lower payback pressure for households in
Sweden. In a similar way, differences in the availability and level of social
welfare programs also present difficulties in achieving a consistent measure
of household leverage across countries. The above two difficulties prevent
many empirical studies from accomplishing causal evidence by cross-country
study (e.g., Mian, Sufi, and Verner (2017); Müller and Verner (2023)).

Within-country studies also face several challenges, though they can avoid the
above difficulties faced by cross-country studies. First, any identification
strategy requires cross-section differences in credit expansion, which in turn
needs the treatment variable to capture the incentives to the credit
supply.111To be precise, the ‘treatment variable’ here can refer to the
instrumental variable in an IV strategy, the running variable in a regression
discontinuity design, and the treatment variable in a difference-in-difference
design. For example, Di Maggio and Kermani (2017) uses the interaction between
anti-predatory lending laws and 2004 OCC preemption as the treatment variable
in a difference-in-difference setting. This requirement means the treatment
variable must have enough area coverage (US mainland), geographic variation,
and time coverage (99-10). Second, the geographic variation must persist over
the business boom period (1999-2005) since the credit supply expansion (in
corporate debt or in home mortgages) is a long-term financial decision. In
other words, the treatment variable must provide persistent incentives to
induce stronger credit expansion in certain areas or certain industries than
others throughout the entire boom period. Short-run shocks, such as weather
(e.g., rain, extreme temperature, and wildfires), are inadequate. Third,
beyond variable nature, shocks to the treatment variable are required to
constitute a solid identification strategy. Fourth, the underlying economic
theory (or story) shall explain why the treatment variable (incentive) can
induce stronger credit expansion in the boom period rather than in other
periods (such as the prior period). Such a theory (or story) requires a large
framework consist of the incentives of different types of participants, the
mortgage market structure, and the time-varying nature of them.

Our within-US research design addresses the above challenges in three parts.
In the first part, we operationalize the key idea of “economic base theory”
(Tiebout, 1962) and construct a treatment variable that captures the long-term
incentive of credit expansion: metropolitan exposure to net export growth of
manufacturing industries. The “economic base” (tradable sector) refers to the
economic activities that a local area provides for the areas outside, thus
bringing wealth to the local area. Most of the wealth will be reused locally
via a money multiplier effect. By this theory, the tradable sector growth is
the key driving force for local economic growth in the long term. Therefore,
credit expansion in mortgages would be stronger in areas with stronger growth
in tradable sector due to higher residual value of a mortgage given
default.222Please see the model by Li (2024) for more details. Ideal
measurement of the composite and growth of table sector requires census-style
data consisting of the accounting data of all firms, which is unavailable in
reality. Alternativly, following Li (2024), we proxy the composite (share)
with manufacturing employment data at the industry-by-metropolitan level. In
addition, we employ the substantial time series change (shift) in net export
growth in the U.S. as a proxy for the relative growth at the industry level.
Aggregating these two proxies (shift and share) can give us a good measure of
the relative growth of the local tradable sector across metropolitan areas. By
this construction, net export growth has enough area coverage (U.S. mainland)
and enough time coverage (92-09). In addition, its geographic variation comes
from the fact that the related manufacturing industries tend to cluster in
just a few locations. Further, the persistence feature of net export growth
comes from two dimensions. At the industry level, both increasing return to
scales at the industry level and comparative advantages across nations make
trade patterns persistent over time. At the individual level, job reallocation
across different industries or locations is very costly.

In the second part, our research design employs the gravity model-based
instrumental variable approach developed by Feenstra, Ma, and Xu (2019) as our
identification strategy. Feenstra, Ma, and Xu (2019) develop their IVs from a
general equilibrium model and their IVs can isolate the exogenous part of U.S.
imports and exports. Conceptually, their IVs capture the exogenous parts of
net export growth due to (1) increasing world demand reflected in US export
growth, (2) increasing world supply reflected in US import growth, and (3)
tariff changes. They also use high-dimensional fixed effects to remove the
potentially endogenous parts: (1) US industry-by-year supply-side shocks in
exports and (2) US industry-by-year demands-side shocks in imports, and (3)
pre-determined bilateral distance between U.S. and partner countries. They
construct IVs separately for exports and imports, and we combine them together
as an IV for net export growth.

In the third part of our research design, we rely on the model by Li (2024) to
explain (1) why our framework is consistent with the viewpoint that net export
growth cannot induce credit expansion in the prior period (1991-1999) and (2)
the intuition for the business cycle.

Intuition for the Business Cycle The basic intuition of the cross-metro
differential business cycle story in this paper can be illustrated in the
following Figure (1). Conceptually, U.S. metropolitan areas can be divided
into high and low net-export growth areas. Higher net export growth leads to
higher household income growth, higher employment growth, and higher
population growth gradually in the high net-export-growth area than in the low
net-export-growth area. These differences grant the mortgage borrowers in the
high net-export-growth area two advantages: (1) higher foreclosure price of
house given default 333In the empirical literature, there is ample evidence
that household income growth, employment growth, and population growth
(including migration) can push up housing demand and then housing price,
especially in the long term (Olsen, 1987). and (2) higher income growth in the
future that can be recoursed by lenders after default.

This paragraph explains why our framework is consistent with the viewpoint
that net export growth cannot induce credit expansion in the prior period
(1991-1999). A key legal requirement is that government-sponsored enterprise
mortgages cannot consider regional economic conditions (growth) (Hurst, Keys,
Seru, and Vavra, 2016) in setting up mortgage rates. But private-label
mortgages can. Since securitization innovation (notably the Copula approach by
Li (2000) (Salmon, 2012)), “global saving glut” (Bernanke, 2005; Bernanke et
al., 2007), mortgage market deregulation (Di Maggio and Kermani, 2017; Lewis,
2023), and political push (Mian, Rao, and Sufi, 2013) all occurred after 1999,
private-label mortgages maintained high mortgage rates between 1991 and 1999
and only had a small market share. On the contrary, government-sponsored
enterprise mortgages dominated the mortgage market with low rates due to
economies of scale and the government’s implicit guarantee.444Estimates show
that the spread between government-sponsored enterprise mortgages and
otherwise similar jumbo loans (purchased by private issuers) are, on average,
between 15-40 basis points between 1996 and 2006 (see Sherlund (2008) and its
summary of the literature). Therefore, Without aggregate credit expansion,
even high net-export-growth areas cannot undergo a business cycle before 1999.

However, documented by Justiniano, Primiceri, and Tambalotti (2022), there is
a huge credit expansion in the private-label mortgages that starts in 2003
summer.555Justiniano, Primiceri, and Tambalotti (2022) argues that this rate
drop likely reflects mispricing, as shown in the subsequent increasing default
rate. Using loan-level data and a regression model, they identify a sharp and
persistent decline in the spread between private-label mortgages and 10-year
treasury yield started in 2003 summer. Given such credit expansion, our story
predicts that private-label mortgages choose to increase strongly in high net-
export-growth areas because of the two above advantages in borrowers (higher
foreclosure price and higher borrower income growth). This differentially
stronger growth in private-label mortgages in high net-export-growth areas
eventually results in a much stronger boom and bust cycle in the house-related
employment.

Figure 1: Model Intuition

Findings We illustrate our major findings in four parts. In the first part, we
document two new empirical facts. There is a much stronger business
(employment) cycle for house-related industries in the high net-export-growth
metropolitan areas (HNEG areas) than in the low net-export-growth areas (LNEG
areas) between 1999-2010. Figure (2) shows this stronger local house-related
employment cycle. In the prior period (1992-2000), there is no difference in
the dynamics of house-related employment share in the working-age population
between the HNEG areas and the LNEG areas. However, in the boom period
(2000-2006) characterized by excess credit supply in private-label mortgages
(Justiniano, Primiceri, and Tambalotti, 2022), the increase in house-related
employment share is much stronger in the HNEG area (0.440%) than one in the
LNEG area (0.161%). From 2007 to 2010, the drop is also stronger in the HNEG
area (0.403%) than in the LNEG area (0.214%).

Figure 2: Refined House Employment Share of Working-age Population (1991-2011)

In contrast, the total employment share of working-age population only
experiences a differentially higher boom (2000-2006) the high net-export-
growth metropolitan areas (HNEG areas) (-0.077%) than in the low net-export-
growth metropolitan areas (HNEG areas) (-2.184%). for total employment. No
stronger bust (2007-2010) for total employment is found in these areas
(-5.477% in the HNEG areas and -5.616% in the LNEG areas). Figure (3) shows
these trends.

Figure 3: Total Employment Share (scaled by 1992 value) (1992-2011) across
Metropolitan Areas

In the second part, using the instrumental variable by Feenstra, Ma, and Xu
(2019), we provide the first causal evidence that the credit expansion in the
private-label mortgages (PLMs), rather than the government-sponsored
enterprise mortgages (GSEMs), causes the employment boom and bust across
metropolitan areas between 1999 and 2010. Intuitively, the gravity model-based
instrumental variable (GIV) captures the exogenous parts of net export growth
due to (1) rising world demand (supply) reflected in export (import) growth
and (2) tariff changes while controlling for US industry-by-year supply
(demand) shocks and pre-determined transportation cost. With this IV, we show
that credit supply expansion in the private-label mortgages (PLMs) causes a
much stronger cycle in house-related employment in the high net-export-growth
areas than in the low net-export-growth areas. We term this result as “housing
industry channel”, which emphasizes the much stronger employment cycle in
residential construction industry, other supporting industries, and the
mortgage industry.

Please note that “causal evidence” means that we only find an incentive (net
export growth) in the cross-section that induces credit expansion in private-
label mortgages to be much stronger in the high net-export-growth areas. In
contrast, We do not find the incentives that cause the aggregate credit
expansion between 1999 and 2005. For the causes of the aggregate credit
expansion, the literature have documented facts and evidence from financial
innovation in securitization (notably the Copula formula by Li (2000) (Salmon,
2012)), international capital flow (“global saving glut” mainly 2003-2007 by
Bernanke (2005); Bernanke et al. (2007)), mortgage market deregulation (2004
preemption of national banks from state anti-predatory lending laws by the
Office of the Comptroller of the Currency (Di Maggio and Kermani, 2017), 2005
Bankruptcy Abuse Prevention and Consumer Protection Act (Lewis, 2023)), and
political push (2002-2007 mortgage industry campaign contributions (Mian,
Sufi, and Trebbi, 2013)). Thus, our choice of 1999-2005 as the boom period
tries to include all major events documented above and our choice also matches
the mortgage boom period commonly used in the literature (see Griffin, Kruger,
and Maturana (2021) for a review).

In the third part, we take advantage of empirical design and test alternative
theories (hypotheses) regarding the business cycle. The first alternative one
is the “speculative euphoria hypothesis” by Kindleberger (1978); Minsky
(1986), which proposes that initial local economic growth can trigger bank to
loose lending standard that fuels speculation by borrowers. The speculation
eventually results in an unsustainable boom and ultimate crash. Due to the
irrelevance of government-sponsored enterprise mortgages, we only distinguish
speculation and credit expansion within the private-label mortgages (non-
jumbo). we use the “non-owner-occupied” home purchase mortgages as a measure
of speculation (Gao, Sockin, and Xiong, 2020) and “owner-occupied” ones as a
measure of pure credit expansion. We use three tests to address the
“speculative euphoria hypothesis”. Our first test shows that speculation
(99-05) is largely caused by pure credit expansion. we use the residuals from
the above regression as my measure of credit-independent speculation. Our
second test shows that, compared to the dominant impact of pure credit
expansion, credit-independent speculation only has a modest impact on the
house employment cycle. Our third test shows that, without credit expansion,
speculation does not respond to net export growth.

The second alternative theory is the famous “real business cycle theory” by
Prescott (1986), which states that business boom and bust are mainly driven by
shocks to the total productivity capacity of corporations. We provide three
pieces of evidence against this theory. First, we show that net export growth
causes a higher growth in the tradable employment in the high net-export-
growth areas in both the boom and the bust periods, rather than only in the
boom period. Second, commercial construction employment experiences neither a
stronger boom nor a stronger bust in the high net-export-growth areas. Third,
the debt (mainly home mortgages) in the household and nonprofit sector rather
than the corporate sector experiences a boom and bust cycle.

We address two variants of the real business cycle theory: the “natural
disaster hypothesis” and “technology shock hypothesis in construction sector”.
The “natural disaster hypothesis” proposes that good whether conditions might
drive the stronger boom and natural disaster might result in the stronger bust
in the high net-export-growth areas. We present two pieces of opposing
evidence. First, farm employment in the high net-export-growth areas
experiences neither a stronger boom (00-06) nor a sharper bust (07-10).
Second, manufacturing employment in these areas experiences a differentially
stronger growth in both the boom (00-06) and the bust (07-10) periods. The
“technology shock hypothesis in the construction sector” argues that the
technology advancement reduces the construction cost and other sectors
increase their building demand. then the mortgage supply only overreacts to
this shock and causes the boom and bust. We employ three pieces of opposing
evidence to address this hypothesis. First, unlike the residential
construction employment, the commercial construction employment experiences
neither a stronger boom nor a stronger bust in the high net-export-growth
areas (HNEG areas). Second, contrary to the trend in private-label mortgages
(non-jumbo), government-sponsored enterprise mortgages experience neither a
stronger boom (99-05) nor a stronger bust (05-08) in the HNEG areas. In other
words, credit-qualified households do not increase their demand to the
“declining cost of housing” argued by this hypothesis. Third, We do not find a
stronger boom (00-06) or a stronger bust (07-10) in manufacturing employment
in the HNEG areas.

The third alternative theory is the “collateral-driven credit cycle theory”
developed by Kiyotaki and Moore (1997). Their model highlights that a small
shock to real estate can be amplified by a feedback loop via collateral and
can ultimately result in large fluctuations. We offer two pieces of evidence
against such a hypothesis. First, the “collateral channel hypothesis” predicts
that the house (collateral) price increase (decrease) shall be followed by
mortgage amount increase (decrease). However, we show that private-label
mortgages (non-jumbo) increases at the same pace as the house price between
1999 and 2005 and decreases before the house price crash between 2005 and
2010. Second, the hypothesis predicts that the debt-to-GDP ratio by
corporations shall experience a much stronger boom and bust than households.
However, we show that debt (mainly home mortgages) by household and nonprofit
sector experiences a strong boom and bust cycle while no cycle can be found in
debt by the business sector.

The fourth alternative theory is the “The business uncertainty theory” Bloom
(2009), which emphasizes the uncertainty (that might rise from government
policies) can cause firms to pause investment and hiring. We present two
pieces of opposing evidence. First, we find commercial construction employment
does not experience a differentially stronger drop during the bust period
(07-10) in the high net-export-growth areas (HNEG areas). Additionally,
tradable sector employment continues to enjoy differentially stronger
expansion in the bust period (07-10) in the HNEG areas.

The fifth alternative theory, “extrapolative expectation theory” (Eusepi and
Preston, 2011), states that local economic growth can drive extrapolation
expectation in households that increases mortgage demand (and other
consumption), which might overshoot and eventually lead to a stronger business
cycle locally. Since government-sponsored enterprise mortgages (GSEMs) are
still cheaper than non-jumbo private-label mortgages (PLMNJs) for credit-
qualified households (Sherlund, 2008), the above theory would predict that
both GSEMs and PLMNJs show stronger growth in the high net-export-growth
metropolitan areas. However, we only see such a trend in PLMNJs rather than in
GSEMs. In addition, we use the evidence supporting exclusion restriction to
help address the above “extrapolative expectation theory”. We show that net
export growth impacts house employment only via its impact on PLMNJs, rather
than directly. Specifically, we show that in the prior period (91-99) without
credit expansion net report growth did not increase house employment.

We conduct various tests to show that our main conclusion is robust. First, we
consider the difference between states with and without the anti-predatory
lending law (APL). In January 2004, the Office of the Comptroller of the
Currency (OCC) preempted national banks from state-level anti-predatory
lending laws (Di Maggio and Kermani, 2017). We find that preempted states
experience neither a stronger boom nor a stronger bust in house-related
employment. After controlling the state difference in APL, our main conclusion
holds for all states. Second, we consider state-level differences in recourse
law (Ghent and Kudlyak, 2011). We do not find non-resource states experiencing
a stronger boom or a stronger bust in house-related employment. After
controlling for state-level differences in recourse law, our main conclusion
is robust for all states. Third, I investigate state-level differences in the
judicial requirement of foreclosure laws (Mian, Sufi, and Trebbi, 2015).
Though non-judicial states experience a stronger boom and a stronger bust in
house-related employment, our major conclusion still holds for all states.
Fourth, our major conclusion still holds after controlling sand-state dummy.
Fifth, our main conclusion is robust to the inclusion of state capital gain
tax as a control.

Contribution We illustrate our contribution to the literature in four parts.
First, we document two new empirical facts in the literature: (1) a stronger
boom in total employment and (2) a stronger boom and a stronger bust in
refined-house employment in the high net-export-growth metropolitan areas than
in the low net-export-growth ones in the U.S. from 1999 to 2010.

In addition, our paper makes unique contributions to the literature on the
causal impact of credit expansion on the U.S. 1999-2010 business cycle in five
dimensions. First, our paper explains the differential business cycles across
metropolitan areas (MSA) within the USA, a new dimension in the literature. In
contrast, most papers on business cycle focus on the cross-country differences
Mian, Sufi, and Verner (2017); Müller and Verner (2023) whereas some focus on
differences across states (Di Maggio and Kermani, 2017; Choi, Hong, Kubik, and
Thompson, 2016; Mian, Sufi, and Trebbi, 2015). Second, our unique method has
three major advantages. The first advantage is that we capture the long-term
incentive of credit expansion by building on the “economic base theory”. This
theory states that the tradable sector (for which we proxy it by net export
growth) determines the local economy in the long term. The second advantage is
that we can take advantage of the state-of-art instrumental variable by
Feenstra, Ma, and Xu (2019) from International Economics for causal inference.
The third advantage is that the IV approach by Feenstra, Ma, and Xu (2019) can
cover the entire period of credit boom period (99-05) consisting of most major
events in mortgage expansion. These events consist 2003-2007 “global saving
glut” by Bernanke (2005); Bernanke et al. (2007), financial innovation in
securitization (particularly the Copula formula by Li (2000) (Salmon, 2012)),
mortgage market deregulation (2004 preemption by the Office of the Comptroller
of the Currency (Di Maggio and Kermani, 2017), the 2005 Bankruptcy Abuse
Prevention and Consumer Protection Act (Lewis, 2023)), and political lobby
(2002-2007 mortgage industry campaign contributions (Mian, Sufi, and Trebbi,
2013)). In contrast, other causal studies mostly focus on one single event (Di
Maggio and Kermani, 2017; Lewis, 2023). The above three advantages enable us
to delve into the boom period preceding the bust period. In contrast, many
previous papers focus on the bust period only (Mian, Rao, and Sufi, 2013; Mian
and Sufi, 2014). Third and most importantly, we apply our method to test the
relevance of almost all major theories (hypotheses) on the business cycle,
including the speculative euphoria hypothesis, the real business cycle theory,
the collateral-driven credit cycle theory, the business uncertainty theory,
and the extrapolative expectation theory. No other paper can test as many
theories and hypotheses as us. Our extensive tests add knowledge to the cause
and the mechanism of the Great Depression and can advise future regulatory
designs aimed at preventing it from happening again. Fourth, we also take
advantage of a key legal constraint to distinguish the role of government-
sponsored enterprise mortgages and private-label mortgages. Most other papers
do not distinguish these two mortgages.666The only two exceptions that
separate the role of government-sponsored enterprise mortgages and private-
label mortgages are Justiniano, Primiceri, and Tambalotti (2022); Mian and
Sufi (2022), though they do not show the irrelevance of government-sponsored
enterprise mortgages to the differential business cycle across metropolitan
areas. Fifth, we show that housing-related employment is central to the
business boom and bust cycle (the housing industry channel).

These five dimensions distinguish our paper from other papers in the
literature. The most related paper is Di Maggio and Kermani (2017). They
exploit the 2004 preemption of national banks (rather than state-chartered
depository institutions and independent mortgage companies) from state
antipredatory-lending laws (APL) by the Office of the Comptroller of the
Currency (OCC). They find that, compared to non-APL states, preempted states
experienced stronger growth in national banks’ credit expansion, house price,
and nontradable employment in 2004-2006 but also stronger declines in these
outcomes subsequently. Our paper differs from their paper in ******** angles.
First, we explain the cross-metro differential business cycles while they
explain the cross-state differential business cycles. Table (A.7) shows that
our cross-metro results are still very strong after controlling for the anti-
predatory lending laws at the state level. Second, our empirical design builds
on the “economic base theory” so that net export growth captures the long-term
incentives of credit expansion in mortgages. One important advantage of our IV
is that it covers the boom period (1991-2005) with almost all major events
related to the mortgage market. In comparison, Their paper captures a single
and perhaps temporary legal change as the incentive, thus ignoring technology
innovation in securitization (Salmon, 2012), 2005 Bankruptcy Abuse Prevention
and Consumer Protection Act (Lewis, 2023), and other important changes
happened or started before 2004 (such as the international capital inflow
2003-2007 termed as “global saving glut” by Bernanke (2005)). Third, they and
we both provide causal evidence, but our IV strategy can do more.
Specifically, our IV strategy can decompose the household speculation (non-
owner-occupied private-label purchase mortgages) and show with three tests
that credit expansion is a necessary condition for speculation. Fourth and
most importantly, our research design and comprehensive data analyses allow us
to go further and test the relevance of major alternative hypotheses. In
contrast, their difference-in-difference research design can do little to test
alternative hypotheses with data from different periods. Fifth, we take
advantage of a key legal constraint to distinguish the roles of government-
sponsored enterprise mortgages and private-label mortgages whereas they use
the 2004 OCC’s preemption to separate national banks from other mortgage
institutions.

In spirit, our paper is similar to Mian, Sufi, and Verner (2020), which
studies the state-level differential deregulation in the banking sector in the
late 1970s and 1980s in the United States. They provide causal evidence that
credit expansions amplify the business cycle via the household demand channel:
credit expansions cause a differential higher rise in nontradable employment
and nontradable goods. Different from their period, our paper focus on
1999-2010 US business cycle. Two other papers focus on the business downturn
(2007-2009 or 2006-2009) and attribute the cause to the deterioration of house
prices in household balance sheets. Mian, Rao, and Sufi (2013) document that
the deterioration of household balance sheets significantly predicts the sharp
decline in U.S. consumption across areas between 2006 to 2009. Mian and Sufi
(2014) show that deterioration in household balance sheets predicts the sharp
decline in U.S. employment across areas between 2007-2009 mainly via the
“household demand channel”. Favara and Imbs (2015) exploit the 1994-2005
state-level branching deregulation and show that early-deregulating states
experience stronger growth in mortgage and house prices. However, their figure
4 shows that deregulation cannot explain well the house price growth from 2002
to 2005. Importantly, they do not study the period from 2005 to 2009.

Unlike our focus on the differential business cycle across metropolitan areas
within a country, most empirical business cycle papers focus on cross-country
differential business cycles. Schularick and Taylor (2012) use 14 countries
over the years 1870-2008 and show that credit growth is a powerful predictor
of the financial crisis. Mian, Sufi, and Verner (2017) use 30 countries from
1960 to 2012 and document that an increase in household debt predicts lower
GDP growth and higher unemployment in the medium term. Greenwood, Hanson,
Shleifer, and Sørensen (2022) employ 42 countries from 1950 through 2016 and
document that combination of rapid credit and asset price growth in the past
three years in either the nonfinancial business or household sector is
significantly associated with financial crisis in the next four years. Müller
and Verner (2023) use 117 countries between 1940 and 2014 to show that credit
expansion to the nontradable sector is systematically associated with
subsequent growth slowdown and financial crisis while credit expansion to the
tradable sector can systematically predict sustainable output and productivity
growth without a higher probability of financial crisis.

Third, we show that house-related industries dominate the business cycle.
Previous studies do not study the comprehensive list of housing-related
industries as we do, including the residential construction, other supporting
industries, and mortgage banker industries. Thus we emphasize the central role
of housing in the credit-induced business cycle.

##  2 Research Design

In this section, we illustrate our empirical research design, which is similar
to Li (2024), that can provide causal evidence for the business cycle. First,
we measure the fundamental incentive for mortgage credit by operationalizing
the central ideal of the “Economic Base theory” (Tiebout, 1962). Specifically,
we use metropolitan exposure to net export growth of manufacturing industries
(thereafter “net export growth”) as a proxy of the growth of local tradable
sector, which by theory is the fundamental force of long-term economic growth.
To overcome the endogeneity issue of OLS, we use an instrumental variable
approach from International Economics by Feenstra, Ma, and Xu (2019) as our
identification strategy.

###  2.1 Operationalize the “Economic Base Theory”

In this subsection, we operationalize the key idea of “economic base theory”
by using metropolitan exposure to net export growth of manufacturing
industries (thereafter “net export growth”) as a proxy of the growth of local
tradable sector. This net export growth measure addresses the requirement of
treatment variable in the introduction: enough area coverage (U.S. mainland),
enough time coverage (91-09), enough geographic variation, and a feature of
persistence.

Regional economics defines the “economic base sector” (tradable sector) as
economic activities that a local area offers for the areas beyond its
boundaries (Tiebout, 1962; Nijkamp, Rietveld, and Snickars, 1987). The
tradable sector brings wealth into the local area and majority of the money
will be circulated locally via a multiplier effect through nontradable sector.
By this theory, the economic base is the most important driving force for
local economic growth in the long term (Nijkamp, Rietveld, and Snickars, 1987;
Thrall, 2002; Ling and Archer, 2017). Therefore, the tradable sector growth
can predict demand-side factors that shape the long-term business growth, such
as employment growth, household income growth, and population growth.777Olsen
(1987) provides a survey on the demand factors of housing and business,
including the three factors mentioned above. By the above theory, credit
expansion would be stronger in areas with stronger tradable sector growth due
to at least two reasons: (1) higher foreclosure price of house and (2) higher
income growth that can be recoursed by lenders given default.

Perfect measurement of local tradable sector growth requires census-style data
to include the accounting data of all firms in the tradable sector, which is
not available. Instead, following Li (2024), we use the local employment
composite as a proxy for the composite (share) of local tradable sector.
Second, we use the time-series change in U.S. net export growth in
manufacturing industries as a proxy for the relative growth (shift) at the
industry level. Aggregating the above two proxies (share and shift) can
achieve a good measure of the relative growth of the local treatable sector
across metropolitan areas.888We acknowledge that the use of manufacturing does
not include several other factors in the tradable sector: college town,
retirement community, other tradable goods industries (e.g., natural
resource), and tradable services (e.g., information technology and medical
sector). To account for the work commuting within local areas, we aggregate
net export growth at the metropolitan level.

We implement the above approach in two steps in data. First, in industry $g$
in year $t$, net export measure is defined as
$\text{NetExp}_{g,t}=\frac{Export_{g,t}-Import_{g,t}}{Y_{g,91}}$, where
$Export_{g,t}$, $Import_{g,t}$, and $Y_{g,91}$ are US export, import and
domestic production value in industry $g$ in year $t$ (or 1991), respectively.
All terms are converted to 2007 US dollars. US domestic production in 1991
serves as the scaling factor. Year 1991 is chosen to avoid potential response
of domestic production to trade in later periods.999The same choice of
domestic production in 1991 as the denominator is taken by other papers like
Barrot, Loualiche, Plosser, and Sauvagnat (2022). Second, we use local
employment composite (share) to aggregate net export growth at the
metropolitan level across period:

$\triangle_{t_{1},t_{2}}\text{NetExp}_{m}=\sum_{g}\big{[}(L_{m,g,t_{0}}/L_{m.t_{0}})*(\text{NetExp}_{g,t_{2}}-\text{NetExp}_{g,t_{1}})\big{]}$
(1)

where $L_{m,g,t_{0}}$ and $L_{m.t_{0}}$ are the employment of industry $g$ and
total employment, respectively, in metropolitan area $m$ in year $t_{0}$. I
choose $t_{0}=t_{1}-1$ to make sure the employment share is pre-determined to
the trade measure so that all changes in net export growth
($\triangle_{t_{1},t_{2}}\text{NetExp}_{m}$) is driven by changes in trade
measures instead of employment composite. Year $t_{1}$ to $t_{2}$ is the
period of interest.

The above net export growth satisfies the requirements of treatment variable.
First, the net export growth has enough area coverage (U.S. mainland) and
enough time coverage (91-09). Second, its geographic variation arises from the
fact that the local tradable sector tends to cluster within several related
industries due to economies of scale. Internal economies of scale increase the
size of local manufacturing firms (The World Bank, 2009) while external
economies of scale attracts firms in the same and related industries to the
clusters (Krugman, Obstfeld, and Melitz, 2018; The World Bank, 2009). Internal
economies of scale include input purchase at a volume discount, fixed cost of
plant operating, and learning in operation. External economies of scale
consist specialization of suppliers, labor market pooling, and knowledge
spillovers. Third, the long-term reduction in transportation costs and
increased labor mobility in the last century strengthened the tradable sector
clustering. Fourth, many global events in the 1980s and 1990s promoting
international trade also added to the local industry clustering at the global
level. These events include reforms and opening in large emerging countries,
huge tariff reduction, the Dissolution of the Soviet Union in 1991, the
establishment of North American Free Trade Agreement (NAFTA) in 1994, and the
creation of World Trade Organization (WTO) in 1995. See World Trade
Organization (2007) for a review. Fifth, the persistence feature of net export
growth at the metropolitan level arises from three dimensions. At the industry
level, both comparative advantages (due to technology level or natural
endowment) and horizontal specialization (due to economies of scale) across
nations make trade patterns persistent over time. At the local area level,
clusters formation with huge costs is unlikely to change rapidly in a short
period of time. At the individual level, human capital accumulation and job
reallocation across industries are very difficult both locally and remotely.

###  2.2 OLS and Its Bias

In this subsection, we illustrate the potential bias from OLS regression
specification. we begin with OLS specification that relates the employment
growth to the growth of private-label mortgages (non-jumbo) (PLMNJ) at the
county level:

$\begin{split}\triangle_{00,06}TotalEmpShr_{c}=\beta*\triangle_{99,05}Ln(\text{Private-
Mort}_{c})+\gamma*Controls_{c}+\epsilon_{c}\end{split}$ (2)

Here, the dependent variable $\triangle_{00,06}TotalEmpShr_{c}$ is the change
of the total employment share in working-age population at county $c$ 00-06.
The independent variable $\triangle_{99,05}Ln(PLMNJ_{c})$ is the growth rate
of the dollar amount (07USD) of private-label mortgage (non-jumbo) (PLMNJ) at
county $c$ 99-05.

Omitted Variable Problems Potential omitted variables could bias $\beta$. For
example, the rapid net export growth and, hence, mortgage growth in 1999-2005
has been anticipated by employees in Silicon Valley so that house price and
total employment grow before 1999 to reflect such expectation. In this case,
$\beta$ could be biased downward because some of the effect of the mortgage
shows up in total employment growth in earlier period, reducing the total
employment growth 2000-2006.

###  2.3 Gravity Model-based Instrumental Variable

To overcome the endogenous concern regarding OLS specification, we use gravity
model-based instrumental variable introduced by Feenstra, Ma, and Xu (2019)
for net export growth. While they construct IV for exports and imports
separately, we combine both as an IV for net exports. For illustration, we
show the model of exports while leaving the model of imports in Appendix
Section A.1.

To form an instrumental variable for US exports at the industry-year level,
Feenstra, Ma, and Xu (2019) builds on the key idea that eight other high-
income countries’ exports can instrument for the US exports since they both
capture the world’s rising demand. In addition, they incorporate tariff
changes, which are commonly believed to be exogenous to foreign firms.
Further, this method corrects for the supply shocks in the home country by
employing a fixed effect to remove them.

To predict US export, the gravity model-based IV starts from a symmetric
constant-elasticity equation by Romalis (2007) for export:

${}\frac{X^{US,j}_{s,v,t}}{X^{i,j}_{s,v,t}}=\Bigg{(}\frac{w^{US}_{s,t}*d^{US,j}*\tau^{US,j}_{s,t}}{w^{i}_{s,t}*d^{i,j}*\tau^{i,j}_{s,t}}\Bigg{)}^{1-\sigma}$
(3)

In the formula above, $X^{US,j}_{s,v,t}$ is US export to country $j$ in
industry $s$ in product variant $v$ in year $t$. By the same notation,
$X^{i,j}_{s,v,t}$ represents export from country $i$ to $j$. $w^{US}_{s,t}$
and $w^{i}_{s,t}$ are the relative marginal cost of production in the industry
$s$ in the US and country $i$, respectively. $\tau^{US,j}_{s,t}$ and
$\tau^{i,j}_{s,t}$ are the ad valorem import tariff imposed by country $j$ on
exports from the US and country $i$, respectively. $d^{US,j}$ and $d^{i,j}$
are the bilateral distance and other fixed trade costs from US to country $j$
and from country $i$ to country $j$, respectively. Lastly, $\sigma$ is the
constant elasticity of substitution ($\sigma>1$).

The intuition of this gravity-style model is relatively straightforward.
Competing with country $i$, US exports to country $j$ are decreasing in the
ratio of bilateral distance, in the ratio of relative marginal cost, and in
the ratio of ad valorem total import tariff.

Assume that there are $N^{i}_{s,t}$ identical product varieties exported by
country $i$ to the country $j$ in the industry $s$ and year $t$, Feenstra, Ma,
and Xu (2019) re-arranges this equation, multiply both sides with
$N^{i}_{s,t}$, and sum over countries $i\neq US$:

$X^{US,j}_{s,v,t}*\sum_{i\neq
US}\big{[}N^{i}_{s,t}(w^{i}_{s,t}d^{i,j})^{1-\sigma}\big{]}=(w^{US}_{s,t}d^{US,j}\tau^{US,j}_{s,t})^{1-\sigma}*\sum_{i\neq
US}\big{[}N^{i}_{s,t}X^{i,j}_{s,v,t}(\tau^{i,j}_{s,t})^{\sigma-1}\big{]}$

As the above equation holds for any countries $i\neq US$, one can choose a set
of countries that have similar economic conditions (so that they are close
competitors of US exports) to make the prediction more accurate. Feenstra, Ma,
and Xu (2019) use the eight high-income countries used by Autor, Dorn, and
Hanson (2013).

Again, one can multiple $N^{US}_{s,t}$ (number of variants of products in US
exports) on both sides and denote the sectoral exports $X^{US,j}_{s,t}\equiv
X^{US,j}_{s,v,t}N^{US}_{s,t}$ and $X^{i,j}_{s,t}\equiv
X^{i,j}_{s,v,t}N^{i}_{s,t}$, then one can get

$X^{US,j}_{s,t}*\sum_{i\neq
US}\big{[}N^{i}_{s,t}*(w^{i}_{s,t}d^{i,j})^{1-\sigma}\big{]}=N^{US}_{s,t}*(w^{US}_{s,t}d^{US,j}\tau^{US,j}_{s,t})^{1-\sigma}*\sum_{i\neq
US}\big{[}X^{i,j}_{s,t}*(\tau^{i,j}_{s,t})^{\sigma-1}\big{]}$

With a few re-arrangements, one can get the formula for $X^{US,j}_{s,t}$:

$X^{US,j}_{s,t}=\frac{N^{i}_{s,t}*(w^{US}_{s,t}d^{US,j}\tau^{US,j}_{s,t})^{1-\sigma}}{\sum_{i\neq
US}\big{[}N^{i}_{s,t}*(w^{i}_{s,t}d^{i,j})^{1-\sigma}\big{]}}*\bigg{(}\sum_{k\neq
US}X^{k,j}_{s,t}\bigg{)}*\sum_{i\neq
US}\bigg{[}\frac{X^{i,j}_{s,t}}{\sum_{k\neq
US}X^{k,j}_{s,t}}*(\tau^{i,j}_{s,t})^{\sigma-1}\bigg{]}$ (4)

Note in the above formula, we multiply and divide by $\sum_{k\neq
US}X^{k,j}_{s,t}$ to prepare for the following regression setup. Now we can
take logs of the above equation and get the regression-style formula:

$\begin{aligned}
\ln\left(X^{US,j}_{s,t}\right)&=\underbrace{\ln\left(\sum_{k\neq
US}X^{k,j}_{s,t}\right)}_{\text{Term
0}}+\underbrace{\ln\left(N^{US}_{s,t}(w^{US}_{s,t})^{1-\sigma}\right)}_{\text{Ind-
Year FE:
}\alpha^{US}_{s,t}}+\underbrace{(1-\sigma)\ln\left(d^{US,j}\right)}_{\text{Importing-
country FE: }\delta^{US,j}}\\\
&+\underbrace{(1-\sigma)\ln\left(\tau^{US,j}_{s,t}\right)}_{\text{Term
1}}+\underbrace{(\sigma-1)\ln\left(\Bigg{\\{}\sum_{i\neq
US}\bigg{[}\frac{X^{i,j}_{s,t}}{\sum_{k\neq
US}X^{k,j}_{s,t}}(\tau^{i,j}_{s,t})^{\sigma-1}\bigg{]}\Bigg{\\}}^{\frac{1}{\sigma-1}}\right)}_{\text{Term
2:}(\sigma-1)\ln\left(T^{j}_{s,t}\right)}+\epsilon^{j}_{s,t}\\\ \end{aligned}$

(5)

Now, We can see that US exports to the country $j$ in the industry $s$ year
$t$ can be divided into six terms. “Term 0” represents the exports from eight
other high-income countries to the country $j$, which reflects the world
demand. The second term $\alpha^{US}_{s,t}$, which represents the US supply
shocks by industry and year, is potentially endogenous. We remove this term by
the US industry-year fixed effects. The third term $\delta^{US,j}$ reflects
the predetermined distance from the US to the destination market $j$ and all
other industry- and year-invariant trade costs. We remove it by importing
country fixed effects. “Term 1” is the tariffs on US exports imposed by
destination country $j$, which is out of control by US firms. I retain this
term to capture the shocks from tariffs. “Term 2” is the weighted average
tariffs on non-US exports charged by destination country $j$, which is out of
control of US firms. Intuitively, when this weighted average tariffs on non-US
exports rise, country $j$ will import more from the US as substitutions. I
retain this term to reflect this substitution effect. Lastly, the term
$\epsilon^{j}_{s,t}=-\ln\left(\sum_{k\neq
US}[N^{i}_{s,t}(w^{i}_{s,t}d^{i,j})^{1-\sigma}]\right)$ is unobserved and
remains in the regression error term.

After the above regression, we can construct predicted US exports that are
isolated from supply shocks and the predetermined distance:

$\ln\left(\widehat{X^{US,j}_{s,t}}\right)=\ln\left(\sum_{k\neq
US}X^{k,j}_{s,t}\right)+\hat{\beta_{1}}*\ln\left(\tau^{US,j}_{s,t}\right)+\hat{\beta_{2}}*\ln\left(T^{j}_{s,t}\right)$
(6)

###  2.4 Data Implementation

There are four detailed steps when implementing the above procedures in data
to get net export and its GIV at the metropolitan level across periods. First,
we estimate Eq (5) at the 6-digit HS industry level (5673 industries) and
derive predicted US export (isolated from supply shocks) by Eq (6). Second, we
aggregate predicted US exports across importing countries and crosswalk the
6-digit HS code (5673 industries) to the 4-digit revised SIC system (392
manufacturing industries) by the crosswalk with weights by Acemoglu, Autor,
Dorn, Hanson, and Price (2016). We end up with predicted US exports to the
world at the industry $g$ year $t$ level. We perform this aggregation by
$\widehat{X^{US}_{g,t}}=\sum_{s\in g}\sum_{j}\widehat{X^{US,j}_{s,t}}$. In a
similar fashion, we get predicted US imports from the world
$\widehat{M^{US}_{g,t}}$. Third, we derive the gravity model-based
instrumental variable for net export at the industry-year level

$\text{givNetExp}_{g,t}^{US}=\frac{\widehat{X^{US}_{g,t}}-\widehat{M^{US}_{g,t}}}{Y_{g,91}}$
(7)

where $Y_{g,91}$ is US domestic production in year 1991. Fourth, we use
employment data from County Business Pattern to aggregate $givNEP$ at the
metropolitan level across periods.

$\triangle_{t_{1},t_{2}}\text{givNetExp}_{m}=\sum_{g}\big{[}(L_{m,g,t^{\prime}_{0}}/L_{m.t^{\prime}_{0}})*(\text{givNetExp}_{g,t_{2}}-\text{givNetExp}_{g,t_{1}})\big{]}$
(8)

where $L_{m,g,t^{\prime}_{0}}$ and $L_{m.t^{\prime}_{0}}$ are the employment
counts of industry $g$ and total employment, respectively, in metropolitan
area $m$ in year $t^{\prime}_{0}$. Following Acemoglu, Autor, Dorn, Hanson,
and Price (2016), we choose $t^{\prime}_{0}=t_{1}-3$ to prevent potential
covariance due to data error between the dependent variable and the
independent variable.

Relevance Condition Conceptually, the gravity model-based IV captures the
exogenous part of net export growth due to (1) increasing world demand
reflected in net export growth by other eight high-income counties and (2)
tariff changes, after removing the US industry-year supply shocks. This
relevance condition of this GIV is satisfied because it starts from the
general equilibrium model by Romalis (2007) and is derived from specific
decomposition by Feenstra, Ma, and Xu (2019). We will test this condition in
data by robust F-statistics (Kleibergen and Paap, 2006) and efficient
F-statistics Olea and Pflueger (2013).

Exclusion Restriction We have exclusion restrictions at two levels. The first
level refers to the gravity model-based instruments by Feenstra, Ma, and Xu
(2019). They have already removed supply-side shocks via industry fixed-effect
in predicted US exports and demand-side shocks via industry fixed-effect in
predicted US imports. Thus, exclusion restriction holds for the gravity model-
based IV. The second level refers to our regression specification Eq (2),
where the exclusion restriction means that net export growth can only affect
house prices via private-label mortgages. Section 5.6.2 provides evidence
supporting such claim.

As with all instrumental variable estimates, our 2SLS estimates capture the
local average treatment effects on compilers (Imbens and Angrist, 1994). In
our setting, compilers are metropolitan-by-year observations that experience
more industry-by-year US net export to the world following increases in
gravity model-based predicted US net export.

##  3 Data Sources

I combine several datasets to study how credit expansion in mortgages causes a
stronger business boom and bust in the high-net-export-growth areas (HNEG
areas). The International trade and tariff data are new to the literature on
business cycles, enabling me to use the gravity model-based instrumental
variable (Feenstra, Ma, and Xu, 2019) as my identification strategy.

###  3.1 Data for Net Export Growth

Trade Flow Data International trade flow data are from the UN Comtrade
Database.101010The website of UN Comtrade Database is
https://comtrade.un.org/data/. This database contains bilateral imports and
exports data for detailed products recorded under the six-digit Harmonized
Commodity Description and Coding System (HS code). To convert trade value to
2007 USD dollar, I apply the Personal Consumption Expenditures Chain-type
Price Index by Federal Reserve in St. Louis.111111Federal Reserve in St. Louis
provides Personal Consumption Expenditures Chain-type Price Index at
https://fred.stlouisfed.org/series/PCEPI. To crosswalk these trade data from a
six-digit HS system to a four-digit SIC system, I use the crosswalk file and
revised SIC system (392 manufacturing industries) in Acemoglu, Autor, Dorn,
Hanson, and Price (2016).121212This crosswalk file is also available from
Prof. David Dorn’s website: https://www.ddorn.net/data.htm. The further
refined SIC system (392 manufacturing industries) and crosswalk file are
available from Acemoglu, Autor, Dorn, Hanson, and Price (2016). 131313To make
sure my calculation is correct, I calculate China’s exports to the US and
eight other high-income countries at the industry-year level from 1991 to 2007
and compare them to data provided by David Dorn’s website. The trade data is
within the section [D] Industry Trade Exposure via his data page
https://www.ddorn.net/data.htm. Correlations between my calculation and his
corresponding data are 0.9983 for China’s export to the US and 0.9973 for
China’s export to eight other high-income countries.

Tariff Data Bilateral tariff schedule data at five-digit SITC product level
between 1984 to 2011 is from Feenstra and Romalis (2014).141414The original
data are collected from the TRAINS, IDB databases, and various other
resources, with multiple cleaning steps and filling in missing values by other
resources. The data work is described in Appendix C in Feenstra and Romalis
(2014). To crosswalk tariff data from a five-digit SITC system to a six-digit
HS system, I follow the methods in Feenstra, Ma, and Xu (2019). Specifically,
I first use crosswalk files from the Trade Statistics Branch (TSB) of the
United Nations Statistics Division to convert the HS 2007 version to the HS
2002 version.151515The crosswalks files between different HS versions are
available from the UN Comtrade database:
https://unstats.un.org/unsd/trade/classifications/correspondence%2Dtables.asp.
Then I use a crosswalk from Feenstra, Lipsey, Deng, Ma, and Mo (2005) to match
each six-digit HS code to one five-digit SITC2. When one six-digit HS code is
matched to multiple SITC2 codes, I follow Feenstra, Ma, and Xu (2019) and use
the one with the highest value share.

Manufacturing Production Data I use the US 4-digit SIC manufacturing industry
total domestic production (vship) in 1991 as the denominator to scale the
trade value. Such data comes from NBER-Center for Economics Studies (NBER-CES)
Manufacturing Industry Database. 1991 is the first year in analysis so
production is unlikely to respond to trade change afterward.161616This choice
of scaling is also used by Barrot, Loualiche, Plosser, and Sauvagnat (2018).

Manufacturing Employment Data Employment data across detailed manufacturing
industries at the county-by-year level in the U.S. comes from County Business
Patterns (CBP) Database by U.S. Census.171717County Business Patterns Database
is available here: https://www.census.gov/programs-
surveys/cbp/data/datasets.html. Following the method by Acemoglu, Autor, Dorn,
Hanson, and Price (2016), I use manufacturing employment data to aggregate net
export growth and its IV at the metropolitan areas across periods. Refer to
Section 2.4 for implementation details.

###  3.2 Data for Mortgages and House Prices

Mortgage Data Detailed loan-level mortgage data are from the Home Mortgage
Disclosure Act (HMDA) database.181818The Consumer Financial Protection Bureau
(CFPB) provides 2007-2017 HMDA data”
https://www.consumerfinance.gov/data%2Dresearch/hmda/historic%2Ddata/. The
Federal Financial Institutions Examination Council (FFIEC) maintains 2017-2021
HMDA data at https://ffiec.cfpb.gov/data%2Dpublication/2021. CFPB offers links
of 1990-2006 HMDA to the National Archives at
https://github.com/cfpb/HMDA_Data_Science_Kit/blob/master/hmda_data_links.md.
In 1975, Congress enacted HMDA to improve public reporting of mortgage loans.
Any financial institution must report HMDA data to its regulator if it meets
certain criteria, such as a threshold for assets and whether the institution
has a home office or branch in a Metropolitan Statistical Area (MSA). This
annual database contains information on loan applications, borrower
demographics, lender identifiers, and loan specifics such as purpose, amount,
and location. The HMDA database provides near-universal coverage of the
mortgage market. Avery, Bhutta, Brevoort, Canner, Gibbs, et al. (2010) confirm
that in 2008, commercial banks filing HMDA carried 93% of the total mortgage
dollars outstanding on commercial bank portfolios.191919Although lenders with
offices only in non-metropolitan areas are not required to file HMDA, 83.2% of
the population lived in metropolitan areas in 2006 (Dell’Ariccia, Igan, and
Laeven, 2012).

We use the following filtering criteria for HMDA data. First, we keep
originated loans and delete applications that are denied, withdrawn, or not
accepted. Second, for loan types, we keep conventional and Federal Housing
Administration-insured (FHA-insured) loans and delete loans insured by the
Veterans Administration, Farm Service Agency, or Rural Housing Service. Third,
for loan purposes, we mainly use home purchase mortgages for most empirical
tests and add refinancing mortgages in some robustness tests. Fourth, for
occupancy types, we keep both non-owner-occupied and owner-occupied loans and
treat “not applicable” as owner-occupied.202020Based on the HMDA manual
(https://www.ffiec.gov/hmda/pdf/1998guide.pdf), this “not applicable”
occupancy very likely refers to a multifamily dwelling where the borrower
lives in. In terms of numbers, this “not applicable” occupancy is only around
3.5% of the number of non-owner-occupied loans and 0.59% of the number of
owner-occupied loans as of 2007.

We use the HMDA database to construct loan volume (number and dollar amount)
at the county-by-year level for government-sponsored enterprise mortgages
(GSEM) and private-label mortgages (PLM). Based on the HMDA examination
procedures, an institution is required to report the type of entities that
purchase the loans that are originated (or purchased) and then sold in the
same calendar year.212121See “Home Mortgage Disclosure Act Examination
Procedures” at
https://www.federalreserve.gov/boarddocs/caletters/2009/0910/09%2D10_attachment.pdf.
These procedures imply that the HMDA can potentially under-estimate the
mortgages that are sold as GSEM and PLM since the mortgages originated near
the end of the calendar year need some time to be sold. Nonetheless, this
potential underestimation can only bias my results to zero. I follow the
classification of PLMs and GSEMs by Mian and Sufi (2022).222222Mian and Sufi
(2022) group five categories as PLMs when a mortgage is sold: (1) to a
commercial bank, savings bank, or savings affiliation affiliate, (2) into
private securitization, (3) to an affiliate institution, (4) to a life
insurance company, credit union, mortgage bank, or finance company, and (5) to
other types of purchasers.

Conforming Loan Limits Data I obtain conforming loan limits by year and county
from Federal Housing Finance Agency.232323Before and in 2007, conforming loan
limits are set only at the national level:
https://www.fhfa.gov/AboutUs/Policies/Documents/Conforming%2DLoan%2DLimits/loanlimitshistory07.pdf.
From 2008 onward, conforming loan limits are set by year and by county:
https://www.fhfa.gov/DataTools/Downloads/Pages/Conforming%2DLoan%2DLimit.aspx.
Conforming loan limits are, in general, different for 1-unit, 2-unit, 3-unit,
and 4-unit dwellings in each year (and county). Since the HMDA data does not
include information on the number of units in a home between 1991 and 2009, We
use the 1-unit conforming loan limit for all mortgages. Thus, our conservative
measure of non-jumbo mortgages can help avoid a potentially upward bias in
results.

Consistent Counties Covered by HMDA Following the suggestion by Avery,
Brevoort, and Canner (2007), we infer counties that are consistently covered
by HMDA based on the scope of metropolitan areas defined and updated by the
U.S. Office of Management and Budget. Detailed steps are the same in Li
(2024). Consequently, We get 800 “HMDA consistent counties after 1996” and 712
“HMDA consistent counties after 1990”.

U.S. House Price Data We obtain the U.S. house price index data based on
repeated sales at the county and ZIP levels from the Federal Housing Finance
Agency.242424The data are available at
https://www.fhfa.gov/DataTools/Downloads.FHFA working paper Bogin, Doerner,
and Larson (2019) describes the construction of the index and tests its
accuracy via various methods.

Merge House Price and Mortgage Data For both figure and regression analysis
regarding house prices, I require that the counties are covered by both house
price data and mortgage data. The merged data set contains fewer counties
compared to mortgage data since house price data covers fewer counties.

###  3.3 Data for Employment and Business

Aggregate Debts for Sectors I obtain annual debt data for households, business
(corporate and non-corporate), and government (federal and local) from the
Federal Reserve.252525The debt data is available here:
https://www.federalreserve.gov/releases/z1/dataviz/z1/nonfinancial_debt/table/.
Such data also contains subcategories for household debt: mortgages, consumer
credit, and other liability.

BEA Employment Data I obtain annual employment data at the county level from
the U.S. Bureau of Economic Analysis (BEA) for analysis on total employment
because such data include both (1) wage and salary employment and (2)
proprietor employment (self-employment).262626The BEA employment data is
available at https://apps.bea.gov/regional/downloadzip.cfm. In the category
“Personal Income (State and Local)”, ”CAEMP25S” contains data from 1969 to
2000, while ”CAEMP25N” contains data from 2001 and onward. This coverage is
better than County Business Pattern employment data, which does not contain
self-employment not working in commercial establishments.

CBP Employment Data I get employment data in house-related and other
industries from the County Business Pattern (CBP) database in the U.S.
Census.272727County Business Patterns Database is available at:
https://www.census.gov/programs-surveys/cbp/data/datasets.html. To derive the
accurate number from ranges reported in CBP, I obtain the carefully imputed
CBP data from Eckert, Fort, Schott, and Yang (2020).282828Eckert, Fort,
Schott, and Yang (2020) provide final data, code, and detailed documentation
of their methodology in imputing CBP data at https://fpeckert.me/cbp/. For
industry classification, I follow Goukasian and Majbouri (2010) and Mian and
Sufi (2014). Please see detailed information in Table (1).

New Residential Unit Permits I get county-by-year new residential unit permit
data from the U.S. Census.292929New residential unit permits data is available
at: https://www.census.gov/construction/bps/index.html. To avoid reduced
sample size due to missing observations because of non-survey years for some
counties, I use the Census-imputed permit data.

###  3.4 Local Economic Conditions

IRS Household Income Data I obtain household income data at the county-by-year
level from the U.S. Internal Revenue Service (IRS).303030For 1989 to 2018, the
data is available at
https://www.irs.gov/statistics/soi%2Dtax%2Dstats%2Dcounty%2Ddata. The average
household income at the county level is the adjusted gross income divided by
the number of returns (households). The income is adjusted to the 2007 USD by
the Personal Consumption Expenditures Chain-type Price Index (PCEPI) from the
Federal Reserve Bank of St. Louis.

Local Control Variables I obtain Control variables at county and ZIP code
levels from U.S. Decennial Census Summary Files. Control variables in 1989 at
the county level are from 1990 (March) Census Summary File 1C and 3C, whereas
control variables at the ZIP level are from Summary File 3B.313131The 1990
U.S. Census Summary File 1 is available at
https://www.census.gov/data/datasets/1990/dec/summary-file-1.html and Summary
File 3 is available at https://www.census.gov/data/datasets/1990/dec/summary-
file-3.html.  Control variables in 1999 at the county level and the ZIP level
are from both 2000 (March) Census Summary File 1 and 3.323232The 2000 U.S.
Census Files are available at: https://www.census.gov/programs-
surveys/decennial-census/guidance/2000.html

###  3.5 Counties Severely Affected by 2005 Hurricanes

Following Li (2024), we delete twelve “deeply affected counties by 2005
Hurricanes” since they experienced unusual growth in mortgages due to
hurricane damage and subsequent government subsidies.333333I try my best to
present the most robust results. Since outliers only largely affect results in
regression but not the illustration in figures, I include these twelve
counties in the figures but remove them from regressions and summary
statistics. In 2005, three Category 5 hurricanes (Katrina, Rita, and Wilma)
caused enormous fatalities and damage (estimated $125 billion).343434These
“deeply affected counties” include Monroe County (FL, 12087), Cameron Parish
(LA, 22023), Jefferson Parish (LA, 22051), Orleans Parish (LA, 22071),
Plaquemines Parish (LA, 22075), St. Bernard Parish (LA, 22087), St. Tammany
Parish (LA, 22103), Vermilion Parish (LA, 22113), Hancock County (MS, 28045),
Harrison County (MS, 28047), Jackson County (MS, 28059), Stone County (MS,
28131).

###  3.6 Summary Statistics and Figures

Summary statistics of key variables are reported in Table (2) and (3),
separated into different periods. In the prior period, starting with 712 “HMDA
consistent counties after 1990”, we remove seven counties due to heavy impact
of the 2005 hurricanes described in section (3.5), ending up with 705
counties. Due to data availability, we have less number of observations for
refined house employment and housing supply elasticity. In the boom period and
bust period, beginning with 800 “HMDA consistent counties after 1996”, we
remove eight counties due to heavy impact of the 2005 hurricanes, resulting in
792 counties. Again, due to data availability, we have less number of
observations for many variables of employment growth and housing supply
elasticity.

First, we can see that the total employment and refined house employment share
experienced a clear boom and bust cycle. For example, while the mean of
annualized change across counties is only 0.029% in the prior period
(1992-2000), it is 0.042% in the boom period (1999-2005) and -0.089% in the
bust period (2007-2009).

Second, the government-sponsored enterprise mortgages (GSEM) play an less
important role in the boom period: its mean annualzed growth rate across
counties is 14.950% in the prior period but only 4.378% in the boom period.

Third, for the private-label mortgages (non-jumbo) (PLMNJ), it is interesting
that the mean of growth rates across counties is similar between the prior
period (17.072%) and boom period (16.978%). The similar mean values result
from the use of different base values.353535Specifically, the 1991 PLMNJ
dollar amount is used for the growth rate between 1991 and 1999 while the 1999
PLMNJ dollar mount is used for the growth rate between 1999 and 2005. The
similar mean growth rates disguise the much larger increase in absolute dollar
amount in private-label mortgages in the boom period . Figure (8) shows the
differential huge increase in dollar amount in PLMNJ in the boom period,
because time series values are scaled by 1991 dollar value only. Figure (8)
and (7) hint that the impact of private-label mortgages (non-jumbo) shall be
much larger than the government-sponsored enterprise mortgages in the boom and
bust cycle of refined house employment since the former ones do show a
differentially stronger increase in the high-net-export metropolitan areas.

Fourth, the net export growth rates have negative mean in the boom period
(with a mean of -0.218% and standard deviation of 0.20%) and in the prior
period (with a mean of -0.18% and standard deviation of 0.17%). This pattern
is also present in the gravity model-based net export growth rates.

We include control variables only in the starting year in each period in the
county $c$, which avoids any impact from net export growth during that period.
Control variables are employed to neutralize factors that may affect credit
expansion. Our basic controls include average household income, the number of
households, and the fraction of the labor force in the county $c$. Our housing
controls include the number of house units, house vacancy rate, housing supply
elasticity (Saiz, 2010), and fraction of renters in the occupied house units.
Our demographic controls include the percentage of the white population, the
fraction of population holding a Bachelor’s degree and above, and the count of
immigrants entering the U.S. between 1990 and 1999. We do not control local
industry employment share, which is partially and jointly determined by my
left-hand-side industry-level employment. The joint determinant relationship
means that local industry employment shares are very likely “bad controls”.

##  4 Empirical Results: Supporting Credit Expansion

Our main empirical tests provide direct evidence that credit expansion in
private-label mortgages (PLMs) instead of government-sponsored enterprise
mortgages (GSEMs) cause the 1999-2010 boom and bust in the business boom and
bust. To compare these two categories of mortgages under the same criteria, we
use the conforming loan limits to get the non-jumbo category of private-label
mortgage (PLMNJ) and remove the jumbo ones. we will focus on the comparison
between private-label mortgages (non-jumbo) (PLMNJs) and the government-
sponsored enterprise mortgages (GSEMs).363636First, jumbo loan borrowers are
usually not low-income households so credit expansion study shall focus on
non-jumbo loans. Second, the jumbo category of private-label mortgages is much
smaller in number when compared to non-jumbo ones. Third, including jumbo ones
only strengthens my results on private-label mortgages.

Based on the intuition described in the instruction, we predict that when the
funding cost declines private-label mortgages (non-jumbo) expand more in the
high net-export-growth areas in response to higher net export growth.

###  4.1 Total Employment Boom (00-06) and Bust (07-10)

Total Employment Boom (00-06)

Let us first focus on my main hypothesis: total employment boom (00-06) as a
result of credit expansion in response to net export growth across
metropolitan areas. First, I expect private-label mortgages (non-jumbo) would
experience stronger growth (99-05) in the high-net-export-growth metropolitan
areas. Consequently, the above credit expansion causes total employment to
grow stronger in the high-net-export-growth metropolitan areas.

Hypothesis 1 In cross-section, growth in private-label mortgages (non-jumbo)
(PLMNJs) in the boom period (1999-2005) causes the total employment boom
(2000-2006).

To test the total employment boom, I perform the following regression.

$\triangle_{00,06}TotalEmpShr_{c}=\beta*\triangle_{99,05}Ln(PLMNJ_{c})+\gamma*\bm{Controls_{c}}+\epsilon_{c}$
(9)

The left-hand-side dependent variable $\triangle_{00,06}TotalEmpShr_{c}$ is
the change of the total employment share in working-age population at county
$c$ 2000-2006, and the key independent variable
$\triangle_{99,05}Ln(PLMNJ_{c})$ is the growth rate of the dollar amount of
private-label mortgage (non-jumbo) (PLMNJ) at county $c$ 1999-2005.
$Controls_{c}$ indicates control variables at county $c$ in 1999. I use the
gravity model-based instrument variable
$\triangle_{99,05}\text{givNetExp}_{m}$ for net export growth
$\triangle_{99,05}Ln(PLMNJ_{c})$.

Since employment growth is usually lagged, we choose the period for employment
growth (00-06) to be a year later than the period for credit expansion
(99-05). To prevent excessive influence from outliers, total employment share
is winsorized at 0.5% and 99.5% level. Each regression uses analytical weight
as the natural logarithm of the number of house units in the starting year
1999. Logarithm instead of the absolute number of house units is chosen to
guard results from being dominated by a few super-populous counties. To take
into account that households might commute to work across counties within a
metropolitan area, I measure net export growth in the metropolitan
area.373737According to US Census, “the general concept of a metropolitan
statistical area is that of a core area containing a substantial population
nucleus, together with adjacent communities having a high degree of economic
and social integration with that core.” (https://www.census.gov/programs-
surveys/metro-micro/about.html) Furthermore, I cluster standard errors at the
metropolitan area (CBSA 2003 code) level.

Table (5) reports OLS, reduced-form, second-stage, and first-stage results in
panel A, B, C, and D, respectively. First, panel A shows the impact of PLMNJ
growth (99-05, annualized) on total employment growth (00-06, annualized) are
positive but insignificant. This result is consistent with our prediction that
some of the growth of PLMNJ is expected. Reduced-form estimates in panel B
show a significant and positive effect, since the gravity model-based
instrument captures the exogenous and unexpected part of net export growth.
The first-stage estimates in panel D are positive and significant at 1% level.
For the specification with full controls, clustered Kleibergen-Paap
F-statistic (Kleibergen and Paap, 2006) is 10.48, and the Montiel Olea-
Pflueger Efficient F-Statistic (Olea and Pflueger, 2013) is 10.47, both of
which are larger than 10. Thus, my estimates are very unlikely biased by a
weak instrumental variable. Panel C reports 2SLS estimates for equation (9).
Like reduced-form estimates, the 2SLS estimates are statistically significant
and quite stable across various specifications.

Let us turn our attention to economic meaning in 2SLS with full controls in
Table (8). According to column (5) with all controls, one standard deviation
in cross-sectional difference in annualized PLMNJ growth (99-05) results in
$8.057\%\times 0.094=0.757\%$ difference in annualized total employment share
growth (00-06) across metropolitan areas, translating into $4.540\%$
difference from 2000 to 2006. One standard deviation in cross-section
difference in annualized total employment share growth (00-06) is $0.950\%$,
translating into $5.701\%$ from 2000 to 2006. The two results mean that, one
standard deviation in PLMNJ growth (99-05) can explain
$4.540\%/5.701\%=79.71\%$ of one standard deviation in total employment share
growth (00-06).

The literature has shown that housing supply elasticity (Saiz, 2010) is a very
important determinant of mortgage growth. Our first-stage regression in Table
(8) verifies the above finding with coefficient being $-0.012$ and significant
at 1% level. However, the insignificant 2SLS estimate $0.001$ shows that
housing supply elasticity does not impact the total employment growth directly
after controlling for private-label mortgages. We provide a potential
explanation for this result. Recall that lower elasticity in the local housing
market means insufficient land supply for new buildings, thus attracting
mortgage growth given the higher expected housing price in the near future.
But lower elasticity also means that insufficient building supply cannot
provide enough cheap establishment buildings for business expansion. Thus the
total effect of housing supply elasticity on the total employment growth could
be near zero after controlling for private-label mortgages.

We also show that the employment boom is robust to the alternative measures of
total employment. In Appendix Table (A.2), we report OLS, reduced-form, first
stage, and second stage results for three measures of alternative total
employment in our context: wage and salary, nonfarm, and nonfarm private
employment. Wage and salary employees are likely more impacted by business
cycle than self-employed workers. Nonfarm employees are likely more influenced
by business cycle than farm employees. Nonfarm private employee are nonfarm
and non-government employees who are more vulnerable to business cycle than
nonfarm government workers. Again, to guard results from excessive influence
by outliers, alternative measures are all winsorized at 0.5% and 99.5% level.
The reduced-form and 2SLS estimates together show that the employment boom
results are very strong for the above three alternative measures, with
significant levels all at 1%. Specifically, the 2SLS coefficients ($0.081$,
$0.099$, and $0.111$) are also very close to the the coefficient ($0.094$) for
the total employment share growth.

Total Employment Bust (07-10) The employment bust (07-10) is quite different
from the employment boom (00-06) and is not stronger in the high net-export-
growth metropolitan areas. Conceptually, this happens because, in the boom
period, the high net-export-growth areas experience higher growth in tradable
sector, housing-related sector, and nontradable sector. However, in the bust
period, the high net-export-growth areas experience higher growth in tradable
sector but higher drop in housing-related sector and nontradable sector. Thus,
we do not find the total employment bust to have a differential trend in the
high net-export-growth areas. The results in the rest of paper will make the
above claim clear.

###  4.2 The Housing Industry Channel

Since the credit expansion in mortgages seem to dominant the 1999-2010 U.S.
business cycle, I propose a “housing industry channel”, which states that the
credit expansion in mortgages results in employment boom and bust primarily
through the house-related industries. I will show causal evidence for this
channel in this section. In this subsection 4.2, to make regression
coefficient more visible with digit, I multiple the employment share by 100
for all dependent variables.

####  4.2.1 House-Related Employment

This section provides causal evidence that growth in private-label mortgages
(non-jumbo) 1999-2005 causes the house-related industries employment boom
(2000-2006) and bust (2007-2010). For house-related industries, I use the
definition by Goukasian and Majbouri (2010). The complete definition of all
sub categories and finer SIC industries are provided in Table (1). I use
crosswalk files from US census to crosswalk these 1987 SIC codes to 2007 NAICS
codes. Then I process house employment data from the computed county business
pattern data by Eckert, Fort, Schott, and Yang (2020).383838Fabian Eckert
provides their computed county business pattern data at
https://fpeckert.me/cbp/.

Since the primary driving force is the private-label residential mortgages,
not all house-related industries experience the same boom and bust pattern. In
particular, commercial construction employment is likely not impacted as
significantly as residential construction employment. In addition, real estate
brokerage and management may not be impacted significantly because the
mortgage boom may only trigger a shift of renters to homeowners while the
entire real estate brokerage and management demand do not change. Due to these
reasons, I define “refined house-related industries” that include residential
construction, supporting industries, and mortgage banks and brokers. I expect
the boom and bust trend is stronger for “refined house-related industries” but
may be weaker for other house-related industries defined by Goukasian and
Majbouri (2010).

Refined House-Related Employment Boom (00-06) and Bust (07-10) I test the
refined house-related employment boom and bust in the following single
regression

$\begin{aligned}
\triangle_{00,06}\&\triangle_{07,10}RefinedHouseEmpShr_{c}&=\beta_{00,06}*\triangle_{99,05}Ln(PLMNJ_{c})\times
Dum_{00,06}+\beta_{07,10}*\triangle_{99,05}Ln(PLMNJ_{c})\times Dum_{07,10}\\\
&+\gamma_{00,06}*\bm{Controls_{c}}\times
Dum_{00,06}+\gamma_{07,09}*\bm{Controls_{c}}\times
Dum_{07,10}+\epsilon_{period,c}\end{aligned}$

(10)

Controls, weight, and standard errors are the same as Eq(9).

Table (7) reports OLS, reduced-form, second stage, and the first stage of the
stacked regression of refined house-related employment growth in the boom
period (00-06) and the bust (07-10) periods. First, the OLS coefficients in
panel A shows that the impact of PLMNJ growth (99-05) are significantly
positive in the boom period (00-06) and significantly negative in the bust
period (07-10), both of which are at 1% level. The similar trend applies to
reduced-form estimates in panel B and 2SLS estimates in panel C. Importantly,
the coefficient magnitudes in 2SLS are around four times of OLS results in the
specification with full controls in column (5), much lower than the average
nine in the top three finance journals (Jiang, 2017). The smaller coefficients
in the OLS regressions are consistent with our prediction in the research
design that part of net export growth is likely expected by local workers and
mortgage managers. First-stage estimates in panel D show the stable and strong
positive correlation between the PLMNJ growth and gravity model-based IV for
Net Export Growth (GIV-NEG), with large enough first-stage F-Statistics
(clustered Kleibergen-Paap F-statistic is 12.05, and the Montiel Olea-Pflueger
Efficient F-Statistic is 12.09). Therefore, my results are free from weak IV
concerns. Let us turn our attention to the coefficient equality test of impact
of PLMNJ growth in the boom and bust periods in the Table (6). For all
specifications through columns (1)-(4), the chi-square statistics are large,
and p-values are below 0.03, meaning the two coefficients are statistically
different. To sum up, induced by exogenous net export growth, the growth of
private-label mortgages (non-jumbo) (99-05) causes refined house-related
employment to experience a larger boom (00-06) and a larger bust (07-10) in
the high net-export-growth areas than the low net-export-growth areas.

In terms of economic meaning, one standard deviation in annualized PLMNJ
growth (99-05) in cross section causes annualized refined house employment
share to rise $8.137\%\times 0.382/100=0.031\%$ in boom (00-06) and to drop
$8.137\%\times-0.696/100=-0.057\%$ in bust (07-10). Since the lengths of the
boom and bust periods are different, I need to consider the time horizon: a
longer period of credit expansion results in a shorter period of bust period.
One standard deviation in six-year PLMNJ growth (99-05) causes refined house
employment share to rise $0.031\%\times 6=0.186\%$ 2000-2006 and to drop
$0.057\%\times 3=0.170\%$ 2007-2010. For annualized refined house employment
share, one standard deviation is $0.049\%$ in boom (00-06) and $0.085\%$ in
bust (07-10), translating into $0.049\%\times 6=0.294\%$ 2000-2006 and
$0.085\%\times 3=0.256\%$ 2007-2010. The two results mean that one standard
deviation in six-year PLMNJ growth (99-05) can explain
$0.186\%/0.294\%=63.53\%$ of one standard deviation in six-year refined house
employment boom (00-06) and $0.170\%/0.256\%=66.24\%$ of one standard
deviation in three-year refined house employment bust (07-10). To sum up, six-
year PLMNJ growth can explain $63.53\%$ refined house employment growth
2000-2006 and $66.24\%$ refined house employment drop 2007-2010.

However, the 2SLS estimates in column (4) in Table (6) show that housing
supply elasticity does not impact the refined house employment boom or bust
directly after controlling other factors, particularly the private-label
mortgages. Similar to our explanation above, although lower elasticity
attracts higher mortgage growth, lower elasticity also reduces cheap
establishment buildings for business expansion. Thus the total effect of
housing supply elasticity on the refined house employment growth could be near
zero after controlling for private-label mortgages.

####  4.2.2 Residential Construction Employment Boom and Bust

Since the growth of private-label mortgages (99-05) is the key driving, I
expect that residential construction employment experience both stronger boom
(00-06) and stronger bust (07-10) in the high net-export-growth areas.
However, there is no such trend in commercial construction employment. This
subsection provides causal evidence for the above two predictions.

I compare the residential construction employment boom and bust in the same
specification in Equation (10) except that the dependent variable is the
annualized residential construction employment growth in both boom (00-06) and
bust (07-10) periods. Controls, weight, and standard errors are the same.
Table (9) reports OLS, reduced-form, second stage, and the first stage of the
stacked regression of residential construction employment growth in the boom
period (00-06) and the bust (07-10) periods. First, panel A shows that the OLS
coefficients of PLMNJ growth (99-05) are significantly positive in the boom
period (00-06) and significantly negative in the bust period (07-10), both of
which are at the 1% level. Likewise, the reduced-form estimates in panel B and
2SLS estimates in panel C are all significant in the same directions.
Importantly, the coefficient magnitudes in 2SLS are around four to five times
of OLS results in specification with full controls in column (4), much lower
than the average nine in the top three finance journals (Jiang, 2017). The
smaller coefficients in the OLS regressions are consistent with our prediction
that part of net export growth is likely expected by local employees and
mortgage managers. First-stage estimates in panel D show a very robust and
positive correlation between the PLMNJ growth and gravity model-based IV for
Net Export Growth (GIV-NEG), with large enough first-stage F-Statistic
(clustered Kleibergen-Paap F-statistic is 11.75, and the Montiel Olea-Pflueger
Efficient F-Statistic is 11.78 in column (4)). Therefore, my results are free
from weak IV concerns. For the coefficient equality test of impact of PLMNJ
growth in the boom and bust periods in the Table (8), the chi-square
statistics are large, and p-values are below 0.04, meaning the two
coefficients are statistically different. To sum up, induced by net export
growth across metropolitan areas, the growth of private-label mortgages (non-
jumbo) (99-05) causes residential construction employment to grow
substantially in the boom period (00-06) but to drop significantly in the bust
period (07-10).

In terms of economic meaning, one standard deviation in annualized PLMNJ
growth (99-05) in cross-section causes annualized residential construction
employment share to rise $8.137\%\times 0.171/100=0.014\%$ in boom (00-06) and
to drop $8.137\%\times-0.261/100=-0.021\%$ in bust (07-10). Since the lengths
of the boom and bust periods are different, I need to consider the time
horizon: a longer period of credit expansion results in a shorter period of
bust period. One standard deviation in six-year PLMNJ growth (99-05) can
causes residential construction employment share to rise $0.014\%\times
6=0.084\%$ 2000-2006 and to drop $0.021\%\times 3=0.064\%$ 2007-2010. For
annualized residential construction employment share, one standard deviation
is $0.024\%$ in boom (00-06) and $0.085\%$ in bust (07-10), translating into
$0.024\%\times 6=0.146\%$ 2000-2006 and $0.036\%\times 3=0.109\%$ 2007-2010.
The two results mean that one standard deviation in six-year PLMNJ growth
(99-05) can explain $0.083\%/0.146\%=57.34\%$ of one standard deviation in
six-year residential construction employment boom (00-06) and
$0.064\%/0.109\%=58.30\%$ of one standard deviation in three-year residential
construction employment bust (07-10). To sum up, six-year PLMNJ growth can
explain $57.34\%$ residential construction employment growth 2000-2006 and
$58.30\%$ residential construction employment drop 2007-2010.

However, the 2SLS estimates in column (4) in Table (8) show that housing
supply elasticity does not impact the refined house employment boom or bust
directly after controlling other factors, particularly the private-label
mortgages. Although lower elasticity attracts higher mortgage growth, lower
elasticity also reduces cheap establishment buildings for business expansion.
Thus the total effect of housing supply elasticity on the refined house
employment growth could be near zero after controlling for private-label
mortgages.

####  4.2.3 Other House Employment Boom and Bust

Goukasian and Majbouri (2010) also provide a list of sub-industries that
support the house industries, for which I call “other house industries”. I
expect that other house industries employment experience both a stronger boom
(00-06) and a stronger bust (07-10) in the high net-export-growth areas. This
subsection provides causal evidence for this prediction.

I compare the other house industries employment boom and bust in a regression
that is the same as Equation (10) except that the dependent variable is other
house industries employment growth. Table (11) reports OLS, reduced-form,
second stage, and the first stage of the stacked regression of other house
industries employment growth in the boom period (00-06) and the bust (07-10)
periods. First, panel A shows that the OLS coefficients of PLMNJ growth
(99-05) are significantly positive in the boom period (00-06) and
significantly negative in the bust period (07-10), both of which are at 1%
level. Similar trends apply to reduced-form estimates in panel B and 2SLS
estimates in panel C. Importantly, the coefficient magnitudes in 2SLS are
between four to five times of OLS results across specifications, much lower
than the average nine in the top three finance journals (Jiang, 2017). The
relatively smaller coefficients in the OLS regressions are consistent with our
prediction that part of net export growth is expected by local employees and
mortgage bankers. First-stage estimates in panel D show a robust and positive
correlation between the PLMNJ and gravity model-based IV for Net Export Growth
(GIV-NEG), with large enough first-stage F-Statistic (clustered Kleibergen-
Paap F-statistic is 11.75, and the Montiel Olea-Pflueger Efficient F-Statistic
is 11.78 in column (4) with full controls). Therefore, my results are free
from weak IV concerns. For the coefficient equality test of impact of PLMNJ
growth (99-05) in the boom and bust periods in the Table (10), the chi-square
statistics are large, and p-values are below 0.03, meaning the two
coefficients are statistically different. To sum up, induced by net export
growth across metropolitan areas, the growth of private-label mortgages (non-
jumbo) (99-05) causes other house industry employment to grow substantially in
the boom period (00-06) but to drop significantly in the bust period (07-10).
Again, the 2SLS estimates in column (4) in Table (10) show that housing supply
elasticity does not impact the other house employment boom or bust directly
after controlling other factors, particularly the private-label mortgages.

In terms of economic meaning in the cross-section, one standard deviation in
annualized PLMNJ growth (99-05) causes annualized other house employment share
to rise $8.137\%\times 0.189/100=0.015\%$ in boom (00-06) and to drop
$8.137\%\times-0.350/100=-0.028\%$ in bust (07-10). Considering horizon
length, one standard deviation in six-year PLMNJ growth (99-05) causes other
house employment share to rise $0.015\%\times 6=0.092\%$ 2000-2006 and to drop
$0.028\%\times 3=0.085\%$ 2007-2010. For annualized other house employment
share, one standard deviation is $0.028\%$ in boom (00-06) and $0.085\%$ in
bust (07-10), translating into $0.028\%\times 6=0.174\%$ 2000-2006 and
$0.047\%\times 3=0.142\%$ 2007-2010. The two results mean that one standard
deviation in six-year PLMNJ growth (99-05) can explain
$0.092\%/0.174\%=53.05\%$ of one standard deviation in six-year other house
employment boom (00-06) and $0.085\%/0.142\%=60.10\%$ of one standard
deviation in three-year other house employment bust (07-10). To sum up, six-
year PLMNJ growth can explain $53.05\%$ other house employment growth
2000-2006 and $60.10\%$ other house employment drop 2007-2010.

####  4.2.4 Mortgage Employment Boom and Bust

I expect that mortgage industry employment can experience both a stronger boom
(00-06) and a stronger bust (07-10) in the high net-export-growth areas. This
subsection, however, provides evidence that the data noises in the County
Business Pattern likely make the boom and bust in the mortgage industry
employment not robust to controls. Further, I use private-label mortgage (non-
jumbo) boom (99-05) and bust (05-08) to show evidence that the mortgage
industry indeed experiences a stronger boom (99-05) and a stronger bust
(05-08) in the high net-export-growth areas.

First, I compare the mortgage industry employment boom and bust in a
regression that is the same as Equation (10) except that the dependent
variable is mortgage industry employment growth. Table (12) reports OLS,
reduced-form, second stage, and the first stage of the stacked regression of
mortgage industry employment growth in the boom period (00-06) and the bust
(07-10) periods. Only for specifications without controls, reduced-form,
first-stage, and second-stage regressions are all significant. Thus, my
analysis only provides suggestive but not robust evidence that the PLMNJ
growth likely triggers the mortgage industry employment boom and bust across
metropolitan areas.

Mortgage industry employment boom and bust give me a good chance to show that
the data noises in the County Business Pattern likely make my results
insignificant since the Home Mortgage Disclosure Act provides universal
coverage of mortgages in metropolitan areas. The mortgage dollar amount is a
better measure of the mortgage industry business boom and bust because such
measure covers both intensive margin (productivity change given fixed
employment) and extensive margin (business change associated with employment
change). I focus on private-label mortgages because the legal constraint rules
that only private-label mortgages (instead of government-sponsored enterprise
mortgages) can consider regional economic conditions. Given the credit
expansion during the 2000s (Justiniano, Primiceri, and Tambalotti, 2022), I
expect net export growth to induce private-label mortgages (non-jumbo) to show
a stronger boom (99-05) in the high net-export-growth areas, which eventually
leads to house prices stronger boom and bust in these areas. Eventually, the
stronger house price bust leads to a stronger bust (05-08) in private-label
mortgages (non-jumbo) in the same high net-export-growth areas.

I compare the dollar amount of private-label mortgages (non-jumbo) boom and
bust in a regression that is the same as Equation (10) except that the
dependent variable is the growth of private-label mortgages (non-jumbo). Table
(14) reports OLS, reduced-form, second stage, and the first stage of the
stacked regression of growth of private-label mortgages (non-jumbo) in the
boom period (00-06) and the bust (07-10) periods. First, reduced-form
estimates in panel B shows that the coefficients of net export growth (99-05)
are significantly positive in the boom period (00-06) and significantly
negative in the bust period (07-10). The same trend applies to the 2SLS
estimates in panel C. Importantly, the coefficient magnitudes in 2SLS are
between three to four times of OLS results across specifications, much lower
than the average nine in the top three finance journals (Jiang, 2017). First-
stage estimates in panel D show the stable and strong positive correlation
between the PLMNJ and gravity model-based IV for Net Export Growth (GIV-NEG),
with large enough first-stage F-Statistics (clustered Kleibergen-Paap
F-statistic is 17.78, and the Montiel Olea-Pflueger Efficient F-Statistic is
17.44). Therefore, my results are free from weak IV concerns. Let us turn our
attention to the coefficient equality test of net export growth in the boom
and bust periods in Table (13). For all specifications through columns
(1)-(4), the chi-square statistics are large, and p-values are below 0.01,
meaning the two coefficients are statistically different. To sum up, induced
by net export growth across metropolitan areas, private-label mortgage (non-
jumbo) businesses experience a stronger boom (99-05) and a stronger bust
(07-10) in the high net-export-growth areas.

In terms of economic meaning, one standard deviation in annualized net export
growth (99-05) in cross section causes annualized PLMNJ growth to rise
$0.200\%\times 10.851=2.174\%$ in boom (00-06) and to drop
$0.200\%\times-12.438=-0.249\%$ in bust (07-10). Since the lengths of the boom
and bust periods are different, I need to consider the time horizon: a longer
period of credit expansion results in a shorter period of bust period. One
standard deviation in six-year net export growth (99-05) causes PLMNJ growth
to rise $2.174\%\times 6=13.045\%$ 2000-2006 and to drop $0.249\%\times
3=7.477\%$ 2007-2010. For annualized PLMNJ growth, one standard deviation is
$8.137\%$ in boom (00-06) and $14.430\%$ in bust (07-10), translating into
$8.137\%\times 6=48.820\%$ 2000-2006 and $14.430\%\times 3=43.291\%$
2007-2010. The two results mean that one standard deviation in six-year net
export growth (99-05) can explain $13.045\%/48.820\%=26.72\%$ of one standard
deviation in six-year PLMNJ boom (00-06) and $7.4770\%/43.291\%=17.27\%$ of
one standard deviation in three-year PLMNJ bust (07-10). To sum up, six-year
net export growth can explain $26.72\%$ PLMNJ growth 2000-2006 and $17.27\%$
PLMNJ drop 2007-2010.

###  4.3 Housing Industry Channel: Additional Evidence

In addition to the above evidence of employment growth in three closely
related industries, I provide additional evidence to support the housing
industry channel: evidence of total construction employment and residential
unit permits.

####  4.3.1 BEA Construction Employment Boom and Bust

Since the growth of private-label mortgages (99-05) is the key driving force
of the 1999-2009 business cycle, I expect that general construction employment
experience both a stronger boom (00-06) and a stronger bust (07-10) in the
high net-export-growth areas. This subsection provides causal evidence for
this prediction.

To test BEA total construction employment boom and bust, I perform the same
regressions in Equation (10) except that the dependent variable is the growth
of total construction employment. Table (16) reports OLS, reduced-form, second
stage, and the first stage of the stacked regression of BEA construction
employment growth in the boom period (00-06) and the bust (07-10) periods.
First, panel A shows that the OLS coefficients of PLMNJ growth (99-05) are
significantly positive in the boom period (00-06) and significantly negative
in the bust period (07-10). The same trend applies to reduced-form estimates
in panel B and 2SLS estimates in panel C. Importantly, the coefficient
magnitudes in 2SLS are around three to four times of OLS results in
specification with full controls in column (5), much lower than the average
nine in the top three finance journals (Jiang, 2017). The smaller coefficients
in the OLS regressions are consistent with our prediction in the research
design that part of net export growth is likely expected by local employees
and mortgage bankers. First-stage estimates in panel D show the stable and
strong positive correlation between the PLMNJ and gravity model-based IV for
Net Export Growth (GIV-NEG), with large enough first-stage F-Statistics
(clustered Kleibergen-Paap F-statistic is 10.48, and the Montiel Olea-Pflueger
Efficient F-Statistic is 10.47). Therefore, my results are free from weak IV
concerns. As for the coefficient equality test of the impact of PLMNJ growth
in the boom and bust periods in Table (15), the chi-square statistics are
large, and p-values are below 0.05, meaning the two coefficients are
statistically different. Again, the coefficients of housing supply elasticity
are insignificant in both periods. To sum up, induced by net export growth,
the growth of private-label mortgages (non-jumbo) (99-05) causes construction
employment to experience a stronger boom (00-06) and a stronger bust (07-10)
in the high net-export-growth areas.

In terms of economic meaning, one standard deviation in annualized PLMNJ
growth (99-05) in cross-section causes annualized construction employment
share to rise $8.057\%\times 0.015=0.121\%$ in boom (00-06) and to drop
$8.057\%\times-0.022=-0.177\%$ in bust (07-10). Since the lengths of the boom
and bust periods are different, I need to consider the time horizon: a longer
period of credit expansion results in a shorter period of bust period. One
standard deviation in six-year PLMNJ growth (99-05) causes construction
employment share to rise $0.121\%\times 6=0.725\%$ 2000-2006 and to drop
$0.177\%\times 3=0.532\%$ 2007-2010. For annualized construction employment
share, one standard deviation is $0.216\%$ in boom (00-06) and $0.378\%$ in
bust (07-10), translating into $0.216\%\times 6=1.295\%$ 2000-2006 and
$0.378\%\times 3=1.134\%$ 2007-2010. The two results mean that one standard
deviation in six-year PLMNJ growth (99-05) can explain
$0.725\%/1.295\%=55.99\%$ of one standard deviation in six-year construction
employment boom (00-06) and $0.532\%/1.134\%=46.90\%$ of one standard
deviation in three-year construction employment bust (07-10). To sum up, six-
year PLMNJ growth can explain $55.99\%$ construction employment growth
2000-2006 and $46.90\%$ construction employment drop 2007-2010.

####  4.3.2 Residential unit Permit Boom and Bust

In the previous section 4.2.2, I provide evidence of residential employment to
support the housing industry channel. A natural prediction is that business
planning on future residential units also experience a boom and bust triggered
by growth in private-label mortgages (non-jumbo) in response to net export
growth. This subsection provides causal evidence for such a prediction.

To test residential unit permit boom and bust, I perform the same regressions
in Equation (10) except that the dependent variable is the growth of permit
value (imputed by the U.S. Census) growth.393939U.S. Census inputs some of the
unit permits for counties that are not required in certain years. Using the
imputation results by the U.S. Census avoids zero values of non-reporting
counties in certain years. Table (18) reports OLS, reduced-form, second stage,
and the first stage of the stacked regression of residential unit permit value
growth in the boom period (99-05) and the bust (05-09) periods. Due to its
forward-looking nature, residential building plans experienced earlier boom
(99-05) and earlier bust (05-09) ahead of employment boom (00-06) and bust
(07-10). First, OLS coefficients in panel A show that the impact of PLMNJ
growth (99-05) is significantly positive in the boom period (99-05) and
significantly negative in the bust period (05-09). The same trend applies to
reduced-form estimates in panel B and 2SLS estimates in panel C. Importantly,
the coefficient magnitudes in 2SLS are around two to four times of OLS results
in specification with full controls in column (5), much lower than the average
nine in the top three finance journals (Jiang, 2017). The smaller coefficients
in the OLS regressions are consistent with our prediction that part of net
export growth is likely expected by building companies. First-stage estimates
in panel D show a robust and strong positive correlation between the PLMNJ and
gravity model-based IV for Net Export Growth (GIV-NEG), with large enough
first-stage F-Statistics (clustered Kleibergen-Paap F-statistic is 11.74, and
the Montiel Olea-Pflueger Efficient F-Statistic is 11.80 in column (4)).
Therefore, my results are free from weak IV concerns. For the coefficient
equality test of the impact of PLMNJ growth in the boom and bust periods in
Table (15), the chi-square statistics are large, and p-values are below 0.01,
meaning the boom and bust coefficients are statistically different. Again, the
coefficients of housing supply elasticity are insignificant in both periods.
To sum up, induced by net export growth, the growth of private-label mortgages
(non-jumbo) (99-05) causes residential building applications to experience a
stronger boom (99-05) and a stronger bust (05-09) in the high net-export-
growth areas.

In terms of economic meaning, one standard deviation in annualized PLMNJ
growth (99-05) in cross-section causes annualized residential units permit
value to rise $8.137\%\times 0.713=5.801\%$ in boom (00-06) and to drop
$8.137\%\times-1.648=-13.409\%$ in bust (07-10). Since the lengths of the boom
and bust periods are different, I need to consider the time horizon: a longer
period of credit expansion results in a shorter period of bust period. One
standard deviation in six-year PLMNJ growth (99-05) causes residential units
permit value to rise $5.801\%\times 6=34.808\%$ 2000-2006 and to drop
$13.409\%\times 3=40.228\%$ 2007-2010. For annualized residential units permit
value growth, one standard deviation is $9.448\%$ in boom (00-06) and
$16.906\%$ in bust (07-10), translating into $9.448\%\times 6=56.688\%$
2000-2006 and $16.906\%\times 3=50.717\%$ 2007-2010. The two results mean that
one standard deviation in six-year PLMNJ growth (99-05) can explain
$34.808\%/56.688\%=61.40\%$ of one standard deviation in six-year residential
units permit value boom (00-06) and $40.228\%/50.717\%=79.32\%$ of one
standard deviation in three-year residential units permit value bust (07-10).
To sum up, six-year PLMNJ growth can explain $61.40\%$ residential units
permit value growth 2000-2006 and $79.32\%$ residential units permit value
drop 2007-2010.

##  5 Empirical: Tests for Alternative Hypotheses

In this section, we perform the most comprehensive tests on the business cycle
theories. We show that the following theories (hypotheses) cannot explain the
origin of the 1999-2010 U.S. business cycle:

###  5.1 Speculation Euphoria Hypothesis

In this section, we perform three tests to show that credit expansion is a
necessary condition for speculation and speculation plays a much less
important role than credit expansion. This section is designed to address the
concern from the “speculative euphoria hypothesis” that, even though credit
expansion happens in the first place, speculation by borrowers may also play a
central role by taking advantage of the credit expansion (Kindleberger, 1978;
Minsky, 1986). Since government-sponsored enterprise mortgages (GSEMs) do not
respond to net export growth (see Table (29)) and cannot explain the cross-
metro refined house employment cycle (see Table (19)), we only distinguish
speculation and credit expansion within the private-label mortgages (non-
jumbo). We use the “non-owner-occupied” home purchase mortgages to measure
speculation as (Gao, Sockin, and Xiong, 2020). In contrast, we use “owner-
occupied” home purchase mortgages as a measure of pure credit expansion.

To preview empirical tests, my first test shows that the cross-metropolitan
variation in speculation (99-05) is largely caused by pure credit expansion.
We use the residuals from the above regression as my measure of credit-
independent speculation, which is the part of growth rate of the speculation
(non-owner-occupied) that cannot be explained by the growth rate of pure
credit expansion (owner-occupied). My second test shows that, compared to the
dominant role of pure credit expansion, credit-independent speculation plays a
much less important role in explaining the refined house employment cycle.
Specifically, credit expansion explains 5.37 times the amount of refined house
employment growth explained by speculation . My third test focuses on the
prior period (91-99) where there is large difference in net export growth
across metropolitan areas but no credit expansion at aggregate level. We show
that, without credit expansion, speculation does not respond to the divergence
of local economic growth, proxied by net export growth.

Before going into regression analysis, it is useful to see the general trend
and magnitude of pure credit expansion vs. speculation across time in Figure
(9). First, in the boom period (99-05), the pure credit expansion (owner-
occupied home purchase) in absolute dollar amount is much higher than
speculation (non-owner-occupied home purchase). For example, in the peak year
of 2005 in the top-quintile metropolitan areas based on net export growth,
pure credit expansion is $67.0$ Billion (07USD) while the speculation is only
$13.9$ Billion (07USD). Growth of both time series are stronger in the high
net-export-growth areas. Such observations are consistent with my conclusion
for tests 1 & 2 discussed above. In addition, in the prior period, there is
not much difference in speculation (non-owner-occupied home purchase) between
the high and low net-export-growth quintile groups of metropolitan areas. This
observation is consistent with the conclusion in my third test above. Please
note that for ease of calculation and comparison, all dependent and
independent variables will not be annualized in this section.

####  5.1.1 Credit Expansion Causes Speculation

In this section, we show that cross-metropolitan variation in speculation is
largely caused by pure credit expansion. We use the following 2SLS
specification:

$\triangle_{99,05}Ln(PLMNJ\\_NonOwn_{c})=\underbrace{\beta*\triangle_{99,05}Ln(PLMNJ\\_Own_{c})}_{\text{Credit-
Induced
Speculation}}+\underbrace{\gamma*\bm{Controls_{c}}+\epsilon_{c}}_{\text{Credit-
Independent Speculation}}$

(11)

The left-hand-side dependent variable
$\triangle_{99,05}Ln(PLMNJ\\_NonOwn_{c})$ is the growth rate of the dollar
amount (07USD) of non-owner-occupied private-label mortgages (non-jumbo) at
county $c$ 99-05 and the key independent variable
$\triangle_{99,05}Ln(PLMNJ\\_Own_{c})$ is the growth rate of the dollar amount
(07USD) of owner-occupied private-label mortgages (non-jumbo) at county $c$
99-05. We use the gravity model-based instrumental variable
($\triangle_{99,05}\text{givNetExp}_{m}$) as IV for
$\triangle_{99,05}Ln(PLMNJ\\_Own_{c})$. Controls, weights, and standard errors
are the same as Eq (10).

Table (22) reports OLS, reduced-form, second stage, and the first stage of the
results. First, panel A shows the positive and significant impact of owner-
occupied private-label mortgages (non-jumbo) (PLMNJ) (99-05, An) as credit
expansion on the growth of non-owner-occupied PLMNJ as speculation. The
coefficients are quite stable across specifications and are significant at 1%.
A similar trend applies to the reduced-form estimates in panel B. First-stage
estimates in panel D show that the strong positive correlation between the GIV
and PLMNJ (owner-occupied) growth is quite stable across various
specifications. In column (4) with all control variables, the first-stage
clustered Kleibergen-Paap F-statistic is 11.00. And the Montiel Olea-Pflueger
Efficient F-statistics is 11.04. Thus, it is very unlikely that my estimates
are biased by weak instruments. The 2SLS estimates in panel C are
statistically significant at a one percent level and quite stable across
various specifications. The 2SLS estimates are very close to OLS estimates
throughout various specifications, where column (4) indicate a ratio of
$1.148/1.121=1.024$, much lower than nine. In terms of economic meaning in
2SLS with full controls, one standard deviation of 6-year PLMNJ (owner-
occupied, credit expansion) growth can cause $46.868*1.148=53.805\%$ increase
in 6-year PLMNJ (non-owner-occupied, speculation) growth, which is
$53.805/87.310=561.625\%$ of one standard deviation of PLMNJ (non-owner-
occupied) growth. Therefore, we can conclude that cross-metropolitan variation
in speculation is largely caused by by credit expansion.

####  5.1.2 Credit-Independent Speculation vs Pure Credit Expansion

In this section, we use the decomposition from the first test to get credit-
independent speculation. Then we show that, compared to the dominant role of
pure credit expansion measured by owner-occupied private-label mortgages (non-
jumbo), credit-independent speculation can only explain a tiny portion of
refined house employment cycle.

To be specific, we use the following regression specification:

$\begin{aligned}
\triangle_{00,06}\&\triangle_{07,10}RefinedHouseEmpShr_{c}&=\beta_{00,06}*\triangle_{99,05}Ln(PLMNJ\\_Own_{c})\times
Dum_{00,06}+\beta_{07,10}*\triangle_{99,05}Ln(PLMNJ\\_Own_{c})\times
Dum_{07,10}\\\ &\theta_{00,06}*\text{Credit-Independent Speculation (c,
99-05)}\times Dum_{00,06}+\theta_{07,10}*\text{Credit-Independent Speculation
(99-05)}\times Dum_{07,10}\\\ &+\gamma_{00,06}*\bm{Controls_{c}}\times
Dum_{00,06}+\gamma_{07,09}*\bm{Controls_{c}}\times
Dum_{07,10}+\epsilon_{period,c}\end{aligned}$

(12)

The first key independent variable $\triangle_{99,05}Ln(PLMNJ\\_Own_{c})$ is
the growth rate of the dollar amount (07USD) of owner-occupied private-label
mortgage (non-jumbo) (PLMNJ) as pure credit expansion at county $c$ 99-05.
This variable is instrumented by the gravity model-based instrumental variable
($\triangle_{99,05}\text{givNetExp}_{m}$). The second key independent variable
Credit-Independent Speculation is derived from the regression Eq (11), which
is the part of growth rate of the speculation (non-owner-occupied) that cannot
be explained by the growth rate of pure credit expansion (owner-occupied).

Table (23) reports OLS, reduced-form, second stage, and the first stage of the
results. First, panel A shows the OLS estimates of pure credit expansion and
credit-independent speculation are both significant on the boom and bust of
refined house employment. Panel B reports the reduced-form estimates and shows
that gravity-model-based net export growth explains the refined house
employment cycle. First-stage estimates in panel D show that the strong
positive correlation between the GIV of net export growth and the growth rate
of pure credit expansion. In column (4) with all control variables, the first-
stage clustered Kleibergen-Paap F-statistic is 11.17. And the Montiel Olea-
Pflueger Efficient F-statistics is 11.19. Thus, it is very unlikely that my
estimates are biased by weak instruments. The 2SLS estimates in panel C are
statistically significant and quite stable across various specifications. In
term of the economic meaning in the boom period (00-06), one standard
deviation of six-year pure credit expansion (owner-occupied) can cause
$46.868\%*0.397/100=0.186\%$ six-year refined house employment boom (00-06),
translating into $0.186\%/0.294\%=63.386\%$ of one standard deviation of the
latter. In comparison, one standard deviation of six-year growth of credit-
independent speculation can cause $69.331\%*0.050/100=0.347\%$ six-year
refined house employment boom (00-06), translating into
$0.347\%/0.294\%=11.810\%$ of one standard deviation of the latter. In the
bust period (07-10), one standard deviation of six-year pure credit expansion
can cause $46.868\%*0.373/100=0.175\%$ three-year refined house employment
bust (07-10), translating into $0.175\%/0.256\%=68.160\%$ of the one standard
deviation of the latter. In comparison, one standard deviation of six-year
growth of credit-independent speculation can cause
$69.331\%*0.042/100=0.029\%$ three-year refined house employment bust (07-10),
translating into $0.029\%/0.256\%=11.353\%$ of one standard deviation of the
latter. To sum up, the explanation power of the pure credit expansion is 5.37
times of the credit-independent speculation in the boom (00-06) and is 6 times
in the bust (07-10)

Therefore, we conclude that pure credit expansion can explain the majority of
the refined house employment cycle, while credit-independent speculation can
only explain a much smaller part. Results in test one and two together show
that home consumption demand (“owner-occupied”) induced by credit expansion
plays a much more important role than speculation (“non-owner-occupied”). This
conclusion is quite different from the view by Minsky (1986); Kindleberger
(1978).404040This conclusion only refers to the first mortgage market. Though
out of the scope of this paper, it might be the case that speculation by
investors plays an important role in the secondary mortgage market.

####  5.1.3 Speculation Cannot Grow Without Credit Expansion

In this subsection, using the prior period (1991-1999), we show that
speculation cannot respond to local net export growth without credit
expansion. Recall that in the test in Eq (11), we have shown that net export
growth causes the credit expansion that largely explains the growth the
speculation in the boom period (1999-2005). To further show that credit
expansion is a necessary condition for speculation, we focus on the prior
period (91-99), in which there is no aggregate credit expansion, and show that
even the net export growth cannot cause credit expansion. The financial
friction rises from the fact that most U.S. mortgages use 20% ratio of down
payment and borrow the rest 80% from lenders. Speculation with a mortgage is a
highly-leveraged and risky behavior, with a monthly payment requirement and a
high liquidity need at the sale. Without credit expansion characterized by low
mortgage rate, the high cost in mortgage rate deters most potential
speculation with a mortgage.

I perform the test by the following regression:

$\begin{aligned}
\triangle_{91,99}\&\triangle_{99,05}Ln(PLMNJ\\_NonOwn_{c})&=\beta_{91,99}*\triangle_{91,99}\text{NetExp}_{m}\times
Dum_{91,99}+\beta_{99,05}*\triangle_{99,05}\text{NetExp}_{m}\times
Dum_{99,05}\\\ &+\gamma_{91,99}*\bm{Controls_{c}}\times
Dum_{91,99}+\gamma_{99,05}*\bm{Controls_{c}}\times
Dum_{99,05}+\epsilon_{period,c}\end{aligned}$

(13)

The left-hand-side dependent variable
$\triangle_{91,99}\&\triangle_{99,05}Ln(PLMNJ\\_NonOwn_{c})$ is the stacked
growth rate of the dollar amount (07USD) of non-owner-occupied private-label
mortgages (non-jumbo) (PLMNJ) as a measure of speculation at county $c$ 91-99
and 99-05, respectively. The key independent variable, net export growth, is
instrumented by the gravity model-based IV in two periods.

Table (24) reports OLS, reduced-form, second stage, and the first stage of the
results. First, OLS estimates in panel A shows the net export growth is
positively correlated with the speculation in the boom period (99-05) but not
in the prior period (91-99). The same pattern shows up in the reduced-form
estimates in panel B. First-stage estimates in panel D and E show the strong
positive correlation between the net export growth and its GIV in both
periods. In column (5) with all control variables, the first-stage clustered
Kleibergen-Paap F-statistic is 74.02 in the prior (91-99) and 17.51 in the
boom (99-05). And the Montiel Olea-Pflueger Efficient F-statistics is 77.63
and 17.06. Thus, my estimates are unlikely biased by weak instruments. The
2SLS estimates in panel C show that net export growth causes speculation in
the boom period (99-05) but not in the prior period (91-99). This empirical
result is more consistent with the view that net export growth can cause
speculation only if there is aggregate credit expansion. Taken together, my
three tests are more consistent with the view that credit expansion is a
necessary condition for speculation and credit expansion plays a much larger
role than speculation. Thus, my three tests help address the concern from
“speculative euphoria hypothesis” by Kindleberger (1978); Minsky (1986).

###  5.2 Real Business Cycle Theory

Real business cycle theory (Prescott, 1986) predicts that business cycles are
mainly driven by shocks to the total productivity capacity of corporations. In
our study, there is a possibility that the stronger positive technology shocks
in the boom and stronger negative technology shocks in the bust to
corporations in the high net-export-growth areas mainly drive the employment
cycle. To address this concern, we show three pieces of evidence against the
predictions by the real business cycle hypothesis. First, we show that net
export growth causes differential higher growth in the tradable employment in
both the boom and the bust in the high net-export-growth areas. Second,
commercial construction employment experiences neither a stronger growth in
the boom nor a stronger drop in the bust in the high net-export-growth areas.
Third, the debt (mainly home mortgages) in the household and nonprofit sector
instead of corporate sector experiences a substantial boom and bust.

First, if the real business cycle hypothesis mainly explains the employment
cycle, it implies that the tradable sector employment shall experience
differentially higher growth in the boom period (99-05) and differentially
stronger drop in the bust period (07-10) in the high net-export-growth areas.
However, reduced-form and 2SLS estimates in Table (25) show that tradable
sector employment in the high net-export-growth areas continues to show a
differentially stronger growth in the bust period (07-10). Thus the real
business cycle hypothesis cannot explain at least the bust period for tradable
employment growth.

Second, since technology shocks trigger business boom and bust, the real
business cycle hypothesis implies that commercial real estate construction
shall experience a differentially higher growth in the boom period (99-05) and
a differentially sharper drop in the bust period (07-10) in the high net-
export-growth areas. In sharp contrast, OLS, reduced-form, and 2SLS estimates
in Table (26) show that commercial construction employment experiences neither
a differentially higher growth in the boom period (99-05) nor a sharper drop
in the bust period (07-10) in the high net-export-growth areas. Thus the real
business cycle hypothesis cannot explain either the boom or the bust period