California's Cost of Living & Interstate Migration (Thesis #8)
- C&C
- Nov 24, 2020
- 33 min read
Updated: Feb 25, 2021
California is expensive.
How expensive?
Very expensive.
Yes. Bar none, California is extremely expensive. Mortgage payments, rental payments, grocery prices and multilevel taxes (sales, gas, property, estate, payroll, income, etc.) add up to one hell of a bill. Has it always been so expensive?
Point blank, no. California has not always been so expensive. In fact, prior to the 1990s, California was a relatively cheaper place to live than many other states.
Californians today pay approximately 30% to 120% more for homes, rentals and groceries. People say that the high costs cause people to leave. Alternatively, people say that a combination of high tax rates and political hostility cause people to leave.
In addition to high costs and hefty tax burdens, other widespread problems like droughts, fires, homelessness, poverty and cultish cultural conformity factor into California migration trends. While it is difficult to quantify the impact of these additional variables, California is losing residents to interstate migration. People who covered the most recent migration wave rightly confirmed that many residents are moving to states like Texas and Nevada, states noted to be exceptionally tax friendly. [1]
"More People Are Leaving Californian Than Ever Before..."
"Growing Number Of Californians Opting To Leave The State..."
"California Exodus..."
"How Liberal Politics, COVID-19 and a High Cost of Living are Fueling a New California Exodus."
Each story reads the same way. More people are leaving than coming. Political, economic or cultural considerations incentivize migration, but to an uncertain degree. Each author limits their analysis to data from the last decade.
Their analysis is flawed in two major respects. One, they do not quantify how much more expensive California is or show how it changed over time relative to other states. In order to understand the economic incentives to leave (or stay), we have to quantify the costs of living and see how they affected each migration wave. Two, they do not compare the recent migration trend with any other period. In order to grasp the unique nature of migration trends now, we have to compare it to some other period. In this case, Internal Revenue Service (IRS) data goes as far back as 1990. This essay identifies key California migration trends from 1990-2018 and calculates the discount value that fueled migration.
There were three significant waves of migration: (1) 1992-1997, (2) 2003-2008 and (3) 2016-2018. Over the 28-year period, California lost a net of 4.36 million residents who represented $73 billion in taxable income. All variables aside, interstate migration negatively affected California state revenues.
Outline:
The essay builds up to a detailed analysis of migration trends through an evidence-based lens. Sections 1 to 5 describe the economic conditions that inform migration trends. Sections 6 to 9 comb through the migration data with those economic conditions in mind.
1) Thrice: "location, location, location"
2) Consumer Price Index (CPI)
3) The CPI in Dollars and Cents
4) Home Values
5) Rental Prices
6) IRS Migration Data
7) Migrant AGI
8) California-Texas Data
9) Summary
10) Questions For Further Discussion
11) Footnotes
12) Figures
13) Appendices A-E
Thrice
Anything said three times in a row ought to be taken seriously. Your teacher repeats the same line three times- take note, that will be on the test. Your friendly politician slings the slogan, "jobs, jobs, jobs!" Jobs a-comin'. Your auntie saw you do something shameful, *tisk, tisk, tisk.* Think about what you've done. Your local real estate agent explains why this 1500 ft^2 shack from 1974 costs $500,000, "location, location, location."
The value of a home is largely dependent upon... location.
Beautiful mountain ranges, beaches and (golden) foothills inform California's title, "The Golden State." Photos, songs and films circulate and support the title. In culture, California's mystique approaches the level of myth not unlike the Hanging Gardens of Babylon. Does this fable-like mystique cloud the history of California's real estate? Data show that California's natural beauty- its location- was not always so expensive to enjoy.
We have to confront this assumption that California's natural beauty- the one constant- is the sole reason for its high costs. It contributes to the California Premium, but is not the sole reason for it. So, let us dispatch with it.
A: California's natural beauty is neither better nor worse than it was in the past.
B: It was once cheaper to live in California.
C: Therefore, California's natural beauty alone does not explain why it is more expensive than other states.
A location's value can be derived from its proximity to grocery outlets or city centers. It could be defined by crime rates or school district ratings. All told, an appraisal of a location's value is dependent on an individual's priorities, but certain location-related properties are consistently appraised the same. An old cottage with a wonderful view is more expensive than a castle next to a sewage processing plant. Therefore, California's real estate value increased (generally) due to restrained developments of civil amenities in the vicinity of natural beauty (and temperate weather).
*Case in point: The city of San Francisco prohibits the construction of buildings exceeding three stories high (except in one district). The edict retains visibility of beautiful views and conserves an architectural heritage. Necessarily, the edict also reduces the dwelling supply. That is, taller buildings would otherwise be constructed absent the edict. This artificially inflates home values. San Francisco is the most expensive city in the Nation.
Similar restrictive zoning polices and architectural guidelines are commonplace throughout California. The effects are consistent. California is the most expensive state in the Union.*
Consumer Price Index: Changes Over Time
Consumer Price Index (CPI) data show that California was not always so expensive. In fact, up until the mid-1980s it was cheaper to live in California relative to many other states. In fact, until just a few years ago, Massachusetts' CPI was consistently higher than California's CPI. Figure A shows that California's CPI began to diverge from the other states in the 1990s. This divergence is trending wider and wider, year after year.
*The CPI figure here is based on the San Francisco Bay Area.*
Figure B shows the CPI-based discount value (or premium) to live in California. A negative percentage is a discount and a positive percentage is a premium. The mid-1980s is the only period during which there is CPI parity among the states. Before the 1980s, it was cheaper to live in California. After the 1980s, it was more expensive to live in California.
In 1950, California's CPI (San Francisco Bay Area) is 8.6%, 10% and 8.6% lower than Massachusetts (Boston), Illinois (Chicago) and Missouri (St. Louis), respectively.
In 1981, California's CPI (San Francisco Bay Area) is 1.1% lower than Massachusetts (Boston) and 0.9%, 0% and 0.8% higher than Illinois (Chicago), Texas (Dallas) and Missouri (St. Louis), respectively. Throughout the 1980s, California's CPI jockeyed up and down around zero with the other states. This is a special stat to note. Only a few years in the the 1960s come close to showing consistent CPI parity among the states as shown in the 1980s.
But over the next 38 years, California's CPI separated from the other states. As of 2019, California's CPI (San Francisco Bay Area) is 4.7%, 18.7%, 20.2% and 22.3% higher than Massachusetts (Boston), Illinois (Chicago), Texas (Dallas) and Missouri (St. Louis), respectively.
California's expensiveness is discussed like it is ancient history, but this is misleading. It is a relatively new phenomenon.
In the next section, we use the federal government's subsistence formula to put the abstract CPI figures into material terms. By doing this, we can see what the CPI means in dollars and cents. This clarifies the growing premium to live in California and how it changed over time.
The CPI in Dollars and Cents
The Department of Defense (DoD) generates what it calls the Basic Allowance for Housing (BAH)- a voucher to supplement a soldier's cost of living. The DoD allocates the BAH per zip code. Using the DoD BAH formula, we can compare the cost of living among zip codes. This is helpful, because it puts the indexed cost of living into material terms.
The Defense Travel Management Office (DTMO) estimates that an Army Second Lieutenant with no dependents living in the 94016 area (San Francisco) receives $4,101 a month, an additional $49,212 of non-taxed income per year. The rate falls slightly to $3,501 in the 94088 area (Sunnyvale), $3,123 in the 94501 area (Oakland) and falls a lot more in the 94571 area (Vallejo) to $2,244. [2][3]
The California Bay Area is the most expensive metropolitan area in the Nation. O-1 soldiers based in San Francisco receive $1,089 (36%), $1,752 (75%) and $2,139 (109%) more BAH than O-1 soldiers based in New York City, Seattle and Chicago, respectively. On an annual basis, O-1 soldiers based in San Francisco make $13,068, $21,024 and $25,668 more in BAH than O-1 soldiers based in New York City, Seattle and Chicago, respectively.
*Honorable mentions: 02108 Boston, MA- Beacon Hill, $2,958.00, 96814 Honolulu, HI $2,655.00, 20005 Washington D.C.- Logan Hill, $2,298.00*
Has it always been this way?
The earliest year of available DTMO BAH calculations is 1998. The calculations show the Bay Area was not as divergent as it is in 2020. In 1998, an O-1 soldier based in San Francisco received $827.92 in BAH. He received $179.92 (27.8%), $253.79 (44.2%) and $199.07 (31.7%) more BAH than O-1 soldiers based in New York City, Seattle and Chicago, respectively. The divergence in the CPI readings is confirmed in the DTMO BAH calculations. Additionally, the DTMO calculations show that from 1998-2020, the basic costs of living in California became exceedingly more expensive relative to other states.
Home Values
Zillow data show that California mid-tier home values grew $412,000 (260%) from $158,000 in 1996 to $570,000 in 2020 (Figure C). On average, they were never less than 40% more expensive than national home values. And from 2004-2008 and 2013-2020, they were over 120% more expensive than national home values. (Appendix A)
California's expensiveness was not always evident. The Federal Reserve of St. Louis' (FRED) Home Price Index (HPI) shows that California homes were actually cheaper than the national average up until 1980. And it was not until the mid-1980s that California home values became more expensive than the national average.
The HPI indicates three California housing bubbles: (1) 1988-1994, (2) 2002-2008 and (3) 2013-2020. The height of each bubble occurred in 1990, 2006 and 2019. In contrast, California's home values converged with the national average from 1996-1997 and approached convergence from 2009-2012. These dates are important to keep in mind, because historic highs and lows, or even 5-year highs and lows, can have novel effects on consumer behavior and, as a result, migration trends. [4][5][6][7][8]
Rental Prices
Home values are important, but millions of people rent every year (roughly 5.9 million in CA). Zillow mid-tier rental pricing data from 2014 to 2020 show that Californian rental prices were more than 20% higher than national prices every year (Figure E). In just 7 years, California rental prices increased $500 (25%). In 2020, a Californian pays a 33% premium for a rental.
So, quick recap. According to the BLS, California's CPI is approximately 20% higher than other states. According to the DMTO, California is approximately 100% more expensive than other states. According to Zillow, Californians pay a 120% premium for mid-tier homes and a 32% premium for mid-tier rentals. According to FRED, there is a 33% premium for (all-tier) California homes.
IRS Migration Data
Internal Revenue Service (IRS) data tracks the migration of people (and their Adjusted Gross Income- taxable income) to and from California. The data is derived from individual income tax returns. Any year-over-year change in address from one state to another constitutes a migration. The data is split into two categories: exempt and return. Exempt represents individuals and return represents households. [9]
The net flow of exempt status people to and from California was net negative from 1990 to 2018. The total in-flow was 11,648,558 and the total out-flow was 15,018,738. Over the 28-year period, California lost a net of 3,370,180 exempt status people to out-migration. (Figure F)
From 1990 to 2018, there were two significant waves, one lesser wave and one spike. From 1992 to 1998 and 2004 to 2009, the out-flow swelled well above the in-flow. In the first major wave, the out-flow rose from 571,000 to 675,000 (18%) and the in-flow fell from 448,000 to 353,000 (-27%). The net out-flow increased from 123,000 to 322,000 (162%). Much of the same occurred in the second major wave.
In the subsequent minor wave from 2011 to 2015 there is much less separation between in- and out-flow. Indeed, the third wave is more like the years in which the gap between in- and out flow narrowed from 1998-2002 and 2009-2015. The narrowness does not continue.
The latest spike from 2016 to 2018 is another period in which separation between in- and out-flow widens. In fact, 2017 was the largest migratory year on record with 750,000 out-flow migrants and 571,000 in-flow migrants, totaling 1,321,000 migrants.
The net flow of households to and from California was net negative from 1990 to 2018. The total in-flow was 6,677,855 and the total out-flow was 7,667,215. Over the 28-year period, California lost a net of 989,360 return status households to out-migration. (Figure G)
The total flow of individuals and households to and from California was negative from 1990 to 2018. The total in-flow was 18,326,413 and the total out-flow was 22,685,953. Over the 28-year period, California lost a net of 4,359,540 exempt and return status individuals and households to out-migration. (Figure H)
Figure H shows that with respect to net in- and out-flow (Figure H), the minor wave from 2011 to 2015 was inconsequential. Instead, the spike in 2017 was consequential. In total, the net out-flow of individuals and households swelled and curled from (1) 1992-1997, (2) 2003-2008 and (3) 2016-2018.
Initially, the data looks rather curious. Each out-flow swell correlates positively with good economic times and each out-flow curl correlates negatively with good economic times. Does migration correlate with recovery and growth or bust and recession?
First, we must define what characterizes a good or bad economic climate. Usually, an economy is described in one of three ways: recessionary, stagnant ("secular") or expansionary. Simply, the economy is either shrinking, not growing, or growing. From 1990-2018, FRED highlights three recessions: (1) 1990-1991, (2) 2000-2001 and (3) 2008-2009. But this traditional measure of growth, like the Gross Domestic Product (GDP), does not obviously indicate the experience of the economic climate on the ground. (Appendix E).
What other basic economic measurement rises and falls with presumably good and bad economic times?
Total gross unemployment rises and falls given the economic climate. When the economy is good, unemployment is low. When the economy is bad, unemployment is high.
Usually, the unemployment rate is a better measure of economic well-being, because it measures the unemployed as a proportion of the whole participating workforce. In our case, total gross unemployment is a better measurement than the unemployment rate, because it captures the total amount of people involved. And in the study of migration, gross sums are vitally important.
*To see me tinker with economic indicators as a guide for economic health, see Appendix B*
The relationship between unemployment and migration over the 28-year period is dynamic. We know this because, the correlation between unemployment and migration flips from positive to negative. For this reason, the correlation between the two variables over the whole period is cancelled out, appearing meagre at first glance.
The correlation between total unemployment and net-migration over the whole 28-year period is -0.187. But from 1990-2008, total unemployment correlates positively with net migration by 0.63- a moderate correlation. This indicates that as unemployment rises, net negative migration rises. From 2008-2018, total unemployment correlates negatively with net migration (negatively) by -0.802. This indicates that as unemployment rises, net negative migration falls. The relationship between unemployment and migration flipped (Figure I).
This is surprising. I expected out-flow migration to increase significantly as the California Premium rose and the incentive to leave increased. The additional macroeconomic condition of high total unemployment should also mean that more people were seeking employment. With virtually no barriers to migrate within the United States, people should be more willing and able to migrate for work. This did not occur. In fact, unemployment rose and net migration fell near to zero. Amazingly, fewer total people migrated in the height of the Great Recession than in any other period within the 28-year span.
The post-2009 recovery period is by far the most interesting period with respect to migration. It is the largest and most erratic. Three of the lowest total migration years occurred within the 10-year span including 2009-2011 and 2014-2015. In contrast, two of the highest years of migration occurred in the 10-year span from 2016-2018. (Figure J)
Figure J shows just how erratic the period from 2008-2018 was. The year-over-year rate of change averaged 14.5% and bounced from 7.5% to 4%, to 15%, to 5%, to 35% to 20%, all within 10 years. In contrast, the year-over-year rate of change from 1990-2008 averaged 2.5% and never went above 6%. In no previous period was total migration so choppy and irregular. Additionally, BLS national migration data shows that total migration steadily declined from 2007-2019. So, while national migration fell, Californian migration rose and became more erratic. (Figure K)
Migrant AGI
Before there is any analysis on the flow of migrant Adjusted Gross Income (AGI), one thing must be clear. The California tax base grew 250% from 402 billion to 1.4 trillion. Of the 26 years shown in Figure L, five years grew negatively (1992-1993; 1999-2001; 2006-2007; 2011-2012). The rest of the period saw positive growth in excess of 5% (1993-1997; 2002-2006; 2013-2014), 10% (1997-1999; 2010-2011; 2012-2013) and even 15% (2011-2012). With this background clearly set, we may continue to assess migrant AGI.
Annually, about 4.5% of total AGI is subject to migration. Out-flow AGI averages 1.92% of the total. In-flow AGI averages 1.52% of the total. As the state's AGI increases, so too does gross and proportionate in- and out-flow AGI. (Figure M)
The net flow of AGI to and from California was net negative from 1990 to 2018. The total in-flow was $310 billion and the total out-flow was $384 billion. Over the 28-year period, California lost a net of $73 billion in AGI to out-migration. (Figure N)
There are four distinct periods during which out-flow AGI greatly outweighed in-flow AGI: (1) 1992-1996, (2) 2001-2007, (3) 2012-2014 and (4) 2016-2018.
Net migrant AGI is negative in every year except for two (2000-2001 & 2011-2012). Figure O shows that the net out-flow of AGI decreases from 1993-1998 and 2005-2012. Conversely, net out-flow of AGI increases significantly from 2000-2006 and, but for a blip from 2013-2016, from 2012-2018.
*In Figure O, the years 2001 and 2012 are net positive*
Figure P displays both the net flow of persons and AGI. There are three periods characterized by three distinct economic profiles: (1) 1992-2000, (2) 2000-2012 and (3) 2012-2018. By profile I mean the economic standing of migrants based on AGI and ancillary economic elements like: the California Premium, rental vacancy rates and homeownership rates. (Appendix C).
The first wave is the biggest period of net out-flow on record. It occurred when the California Premium first manifested for a sustained period of time. As shown in Figure D, home values soared to unprecedented levels. Not only did the California HPI premium average 20% from 1987-1994, but the premium reached 28% in 1990 (during the recession). In contrast, the premium averaged a mere 2.35% from 1980-1987. Based on these migration figures, the premium had a larger effect on the those who represented less AGI during this period. (Figure Q) [10]
The effect may well have been an opportunity to cash in. CPI data indicates that from 1986-1997 the cost of living in California was cheaper than NY, on par with Illinois, but more expensive than Texas and Missouri. For the first time, Californians were pressed with exorbitantly high home values as well as higher costs of living relative to other states.
Figure Q shows the break down of AGI per migrant. It indicates that during the first wave, migrants leaving California were slightly less well off than migrants coming to California. At this time, California attracted people who represented slightly more AGI.
Vacancy rates show that the new economic conditions were intolerable to renters. From 1986-1995, rental vacancy rates soared. In fact, they nearly doubled from 4.6% in 1986 to 8.5% in 1995 (Figure R). This indicates that renters comprised a large portion of migrants. Outpriced, migrants left California for a 20% discount elsewhere (per the HPI reading). This is especially true of the three peak years of out-flow from 1992-1995.
Homeownership rates confirm this assessment. The homeownership rate slid between 54%-56% from 1988-1999. This swing does not sufficiently account for the swings in rental vacancy rates or the increase in net out-flow. Instead, it is more likely that the in-flow migrants and not the would-be-renters made up the increase in homeownership rates.
What does this all mean? The first period of migration is contextualized by high vacancy rates, a relatively higher CPI than historically experienced, unprecedentedly high home values, minimal homeownership rates, high net out-flow and less than average AGI per mover.
...
The second migration wave begins in earnest in 2001. Per Figure P, the first half of the wave (2001-2005) is characterized by slightly better off migrants leaving California. The second half is characterized by a one to one exchange of similarly well-off in- and out-migrants (2005-2008). This is indicated by Figure Q, which shows out-flow AGI per migrant was above in-flow AGI per migrant.
CPI data shows that the cost of living in California fell across the board from 2001-2006 (Figure B). In that same timeframe mid-tier home values doubled (Figure C). FRED HPI data shows that home values marched up 35% from a modest 5% premium in 2001 to an incredible 40% premium in 2006 (Figure D). BLS survey data shows that from 2000-2006, a disproportionate number of movers migrated in the search for homeownership (likely taking advantage of the favorable lending environment precipitating the housing bubble) (Figure T). FRED data shows that California Homeownership rates continued to march from 55% in 1996 to 60% in 2006- the highest rate in California's history (Figure S). FRED data also shows vacancy rates going from a low of 4% in 2000 to a high of 7.5% in 2009 (Figure R).
What does this all mean? The economic profile of migrants to and from California during the second wave was fairly balanced. In huge numbers, people left rentals nation-wide with aspirations to own a home. While the cost of living in California did not shoot up, home values did. In that context, slightly cheaper costs of living and cheaper home values in other states were attractive. People of similar economic standing either bet on a rising California housing market or washed their bets for a fresh start on a home in another state.
...
The third wave is erratic, but one thing is consistent. Out-flow migrants represent way more AGI than in-flow migrants. The out-flow of total migrants is minimal, but the amount of AGI that each migrant represents is huge (Figure U). In fact, the margin between in-flow and out-flow is the highest on record. From 2012-2015 the AGI per out-flow migrant was 11% higher than in-flow migrants. For context, prior to 2012 the average difference between in-flow and out-flow migrant's AGI was 0.85%.
The third wave is marked by California's advance to ever higher costs of living. Notably, CPI rose nation-wide, but paled in comparison to California. The cost of living in California became three times larger than other states (Figure B). FRED data shows that from 2013-2020 California home values settled 35% above the rest of the nation (Figure D). Likewise, Zillow data shows that mid-tier home values nearly doubled in value, reaching prices upwards of 120% higher than those in other states (Figure C). From 2014-2020, rental prices increased by 33%. In 2020, a mid-tier Californian renter pays 30% more than his peers in other states (Figure E).
The amount of people seeking to own a home was cut in half (Figure T). In contrast, people seeking cheaper housing grew to all-time highs from 2009-2011, stabilizing in 2015 (Figure U). Despite the desire for cheaper housing and despite increased costs of living in California, real estate, rental and leasing earnings rose to all-time highs- doubling in less than 10 years (Figure V). The trifecta of decreased homeownership rates, decreased vacancy rates and increased real estate, rental and leasing earnings implies that in-flow migrants are not seeking to buy a home in California. People are unwilling to invest in real estate and take on long-term debt. People are willing to rent instead.
What does this all mean? People are paying for flexibility. This is understandable. The recovery from the Great Recession was slow. Loan delinquency rates across the board were sky high. And finally, delinquency rates are on the mend (Figure W).
Unlike the previous two migration waves, the third wave is clearly set apart by the exodus of richer people. A threshold of reasonable prices was crossed. But for one year (2015-2016), in-flow AGI does not come close to matching out-flow AGI. That said, many people are still willing to move to California despite the historically high prices. High wages, cultural gravity, and great weather could explain why people are willing to pay exorbitant prices.
That said, the Covid-19 kerfuffle will stress test just how much residents are willing to pay to enjoy these features.
The Texas Narrative
People say that Californians are moving to Texas in troves. The narrative goes that people are fleeing high taxes and political hostility. Indeed, income tax rates in California are the highest in the Nation and the conservative's vote is meaningless in most state and district elections. Texas is one of nine states that does not have a state income tax and it routinely votes conservative. The narrative makes sense, but does the data prove it to be true?
*Does a person fleeing taxes have to be rich? No. The California income tax, gas tax, sales tax, payroll tax, etc. are set up to impact every single income bracket. This gives reason for every Californian to consider a move elsewhere. (Figure X)*
Migration between California and Texas favored Texas. From 1990 to 2018, the total in-flow from Texas (to California) was 1,504,240 and the total out-flow to Texas was 2,278,762. Over the 28-year period, California lost a net of 774,522 residents to Texas. Migration from California to Texas constituted 17.8% of net losses. (Figure Y)
Migration to Texas grew over the years. From 1990-2005, Texas was the destination for 8% of all California migrants. From 2006-2018, Texas was the destination for 11% of all California migrants. From 1990-2009, Texas was the state of origin for 7% of all migrants to California. From 2010-2018, Texas was the state of origin for 9% of all migrants to California. Migration to and from Texas grew as a share of total migration.
But for one year (2000-2001), the flow of AGI favored Texas from 1990 to 2018. The total in-flow of AGI from Texas was $25.8 billion. The total out-flow of AGI to Texas was $38 billion. Over the 28-year period, California lost a net of $12.2 billion in AGI to Texas. That means net AGI to Texas constituted 16.6% of total net AGI loss.
The balance of migration and AGI clearly favors Texas, but per migrant comparison favors California. From 1996-2009, migrants from Texas represented between 7%-18% more AGI per person than migrants going to Texas. In contrast, from 2009-2018, migrants to Texas represented between 1%-18% more AGI per person. In fact, AGI per migrant to Texas doubled from 2007-2018 ($15,000-$30,000). Since the Great Recession, more Californians are choosing Texas as their new home and those migrants represent more AGI.
Figure AA shows the difference between migration to Texas and other states. Californians migrating to Texas represented less AGI per person compared to California migrants going to other states. In 2007, Californians going to Texas represented 20% less AGI than Californians who moved to other states. In 2014, the dip was even more significant growing to 35%. California migrants going to Texas are not wealthier than California migrants going to other states.
From 1992-2008, migrants from Texas represented a slightly higher AGI per migrant compared to migrants from elsewhere. From 2009-2018, migrants from Texas started to represent less AGI than migrants from elsewhere. In 2015, the difference between them dropped significantly to 20%. This drop is the trend. In the last decade, migrants from Texas represented less AGI than migrants from other states.
While California-Texas migration increased, there is nothing spectacular about migrants' economic profiles going to or coming from Texas. (Joe Rogan notwithstanding)
Summary
People assume that California has always been expensive. If we narrow our view of history to the recent past, that statement is true. But as far back as the statistics take us, California was not always so expensive.
The cost of living was comparable across the nation in the 1980s. In fact, California would not stand alone as the most expensive state in the union until 2015. Today, Californians pay approximately 30% to 120% more for simple necessities like food and shelter. This is the California Premium.
There were three major waves of migration: (1) 1992-1997, (2) 2003-2008 and (3) 2016-2018. In total, California lost a net of 4,359,540 individuals and households to out-migration representing a net of $73 billion in taxable income (Figures O&M).
Over the years, out-flow migrants progressively represented more AGI than in-flow migrants.
While nothing in this period showed evidence of economic law, California consistently lost people and taxable income to interstate migration.
Looking Forward:
Covid-19 can change California for many years to come. Major California-based corporations are proclaiming that work-from-home policies may be permanent. Rental prices in San Francisco are in a free fall as workers ditch unnecessary expenses (Appendix C). The daily costs of living are not going down. State legislators are trying to raise tax rates on income and levy taxes on wealth world-wide. It is imperative that the state understands what conditions are intolerable to the larger palates of people (rich and poor), so that it may avoid an exodus of valued residents.
Consider the website that directs Bay Area residents to options elsewhere:
In a follow-up article, I will do a literature review of interstate migration studies. I hope to create an economic model that takes into account multiple opportunity costs, including: employment opportunities, wages, rental prices, home values, effective tax rates and CPI disparities. The model should provide an aggregate opportunity cost making it easier to see what the discount value is to leave. Hopefully, the model helps identify what economic conditions at home and elsewhere provoke people to migrate.
Questions for Further Discussion:
1) In California, does dwelling construction keep pace with population growth? If yes, then what other factors contribute to the excessively high cost of living?
2) The State grew by 9.83 million despite a net loss of 3.37 million to out-migration. Foreign immigration accounted for 4.4 million of the gain, leaving at least 5.43 million to internal growth. What do the birth rates look like for California generation zero, one, two and so forth?
3) Are the minimum wage hikes alleviating poverty? Do minimum wage hikes correlate with reduced unemployment? Better yet, do they correlate with worker participation? When we compare that data with other states or countries that did not raise minimum wages, do we find a discrepancy? Is there a notable difference in resident retention, cost of living and out-migration?
-A number of data points would help in that discovery including: dwelling construction, construction rates, permit applications, foreclosures, personal income, bankruptcy filings and on and on.
Footnotes
1) CBSLA Staff. "Growing Number of Californians Opting to Leave the State." CBS: LA. September 23, 2020.
Marlene Lenthang. "More people are leaving California than ever before, driven out by worsening wildfires, politics and the skyrocketing cost of living." Dailymail. September 13, 2020.
Lauren Hepler. "California Exodus: An online industry seizes COVID-19 to sell the Red State Dream." Calmatters. September 24, 2020. Updated September 29, 2020. https://calmatters.org/economy/2020/09/anti-california-dream-moving-industry/
Tony Bizjak. "How liberal politics, COVID-19 and a high cost of living are fueling a new California exodus." Sacramento Bee. October 23, 2020.
2) BAH Calculator. The Defense Travel Management Office. Estimates for 2020. https://www.defensetravel.dod.mil/site/bahCalc.cfm
3) Out of curiosity, let’s see what an Army officer living in SF is paid. With basic pay of $3,287.10 a month, taxed at about 20%, the sum is $2,629.68 plus BAH at an untaxed $4,101 equaling $6,730.68. The annual salary of the officer living in S.F. is $80,768.16. When that officer moves to Tracey, 59 miles away, he earns $52,688.16. The DoD recognizes the tremendous costs borne by the residents of SF and, to a lesser extent, the greater Bay Area.
This data would further substantiate the impetus to leave the Bay Area while maintaining employment.
*Remember, an Army officer must attain at least a bachelor’s degree, which correlates with a higher median income than those who do not have a bachelor’s degree.*
4) While local markets vary, the general experience of the Nation is synchronized. The correlation between Zillow CA and US mid-tier home value is 0.986. Therefore, we can rest assured that the housing market is open. Where legal barriers to migration are low, economic principles tend to prevail. The implications are borne in the data.
5) The HPI is calculated using millions of property sales postings and home refinancing data spanning the entire United States. Because the index is not controlled for a certain percentile of home values, it represents the whole housing market, quarterly.
6) Despite their differences in methodology, both indexes track the same general trends in the housing market. The correlation between Zillow CA mid-tier home value index and FRED CA HPI is 0.999. Likewise, the correlation between Zillow US mid-tier home value index and FRED US HPI is 0.995. The correlation is very strong. In terms of trends, the two indexes confirm and support each other. That said, they tell a slightly different story.
7) From 1988 to 1994, the California HPI averaged 50 points (30%) above the national HPI. In 1990, the difference reached an apex of 64 points (39%). From 2000 to 2009, the California HPI averaged 138 points (44%) above the national HPI. In 2006, the difference between them reached an apex of 270 points (73%). From 2013 to 2020, the California HPI averaged 186 points (49%) above the national HPI. In 2019, the difference between them reached an apex of 226 points (51%). Therefore, over the 45-year period, California's HPI indicated premium reached apex heights in 1990, 2006 and 2019.
8) The two HPIs converged once from 1996 to 1997 and approached convergence from 2009 to 2012. In the first case, the difference between the two HPIs slid between 2-4%. In the second case, the difference between the two HPIs slid between 19-20%. Therefore, by the time HPIs converged (relatively) for a second time, the California Premium was already baked in well above historical precedent.
9) Residents who were not legally permitted to reside in the U.S. pay Federal taxes using an Individual Taxpayer Identification Number or a stolen Social Security Number. In either case, the resident alien pays taxes and receives tax credits as if he or she was legally recognized. Employers who hire unauthorized residents are subject to Federal fines and prison time.
The IRS tracks the migration of those unauthorized residents who pay taxes. So, this data shows that Federal tax-paying residents (a blanket term) are, on a net basis, leaving California and taking their taxable income with them.
Chief Counsel Memorandum POSTN-122111-10 (June 21, 2010).
10) If net AGI excessively exceeds net migration, then those who migrate out are richer on average than those who migrate in. If net AGI flows at the same pace with net migration, then those who migrate in and out are equally situated on average. If net AGI does not keep up with net migration, then those who migrate out are poorer on average than those who migrate in- in macro terms.
Figures
A) U.S. Bureau of Labor Statistics. "Consumer Price Index." Division of Consumer Prices and Price Indexes. https://www.bls.gov/cpi/data.htm
B) Ibid.
C) Zillow Research. "Zillow Home Value Index (ZHVI): California." Zillow. https://www.zillow.com/research/data/
"Zillow Home Value Index (ZHVI): A smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range."
D) U.S. Federal Housing Finance Agency, All-Transactions House Price Index for California [CASTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CASTHPI, October 19, 2020.
E) Zillow Research. "Zillow Observed Rent Index (ZORI): California." Zillow. https://www.zillow.com/research/data/
The CA figure is the average of the following city-metropolitan areas: Los Angeles, San Francisco, Riverside, San Diego, Sacramento, San Jose, Fresno, Bakersfield, Ventura and Stockton.
"Zillow Observed Rent Index (ZORI): A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is once again weighted to reflect the rental housing stock. "
F) The U.S. Internal Revenue Service. "SOI Tax States- Migration Data." U.S. Population Migration Data.
G) Ibid.
H) Ibid.
I) U.S. Bureau of Labor Statistics. "California Unemployment." Local Area Unemployment Statistics.
J) The U.S. Internal Revenue Service. "SOI Tax States- Migration Data." U.S. Population Migration Data.
K) U.S. Census Bureau, "Table A-5. Reason for Move (Specific Categories): 1999-2019." Current Population Survey, Annual Social and Economic Supplement 1948-2019.
L) IRS. "Migration Data."
M) Ibid.
N) Ibid.
O) Ibid.
P) Ibid.
Q) Ibid.
R) U.S. Census Bureau, Rental Vacancy Rate for California [CARVAC], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CARVAC, November 15, 2020.
S) U.S. Census Bureau, Homeownership Rate for California [CAHOWN], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CAHOWN, November 8, 2020.
T) U.S. Census Bureau, "Reason for Move." Current Population Survey, Annual Social and Economic Supplement 1948-2019.
U) Ibid.
V) Board of Governors of the Federal Reserve System (US), Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks [DRSFRMACBS] & Delinquency Rate on Loans Secured by Real Estate, All Commercial Banks [DRSREACBS] Delinquency Rate on Credit Card Loans, All Commercial Banks [DRCCLACBS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DRSFRMACBS, November 15, 2020.
W) Federal Reserve Bank of St. Louis and U.S. Bureau of Economic Analysis, Real Estate, Rental and Leasing Earnings in California [CAEREA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CAEREA, November 9, 2020.
X) State of California Franchise Tax Board Newsroom. “Standard deductions, exemption amounts, tax rates, and doing business thresholds updated for 2019.” Last updated: 07/14/2020. Accessed October 19, 2020.
Y) IRS, Tax Stats.
Z) Ibid.
AA) Ibid.
Tables
Table 1 is a small survey of DTMO BAH estimates for major cities in the United States and California. Rest assured that there is no short changing as the zip codes for New York City, Los Angeles, Seattle, Chicago and Nashville correspond to "good" neighborhoods. Nonetheless, San Francisco, Sunnyvale (San Jose region) and Oakland (above) are more expensive than any of the other cities, including the California Beverley Hills area.
DoD, DTMO BAH 2020 Estimates per Zip Code.
Appendix A
California's Swift Housing Market
The Zillow data is controlled for middle-class homes. The index considers neither the homes of the rich, nor the homes of the especially poor. This is helpful, because the index focuses on the common experience.
California home values changed rapidly and significantly over the 24-year period. Figure 1 shows that year-over-year rate of change reached above 17.5%, six times. Those six years represent significant flux (boom and bust) including: (1) 1997-2005, (2) 2007-2009, (3) 2009-2010 and (4) 2012-2013.
Even if the index is adjusted for inflation using the Consumer Price Index (CPI) (based on the 2019 dollar), the finding is still impressive (Figure 2). Note the mammoth of growth during the "housing bubble" (2003 to 2007). Through the lens of the CPI adjusted Zillow index, home values grow more slowly in the last decade compared to the previous decade. CPI adjusted, mid-tier home values still doubled from $258,000 to $547,000, a difference of $289,000. That difference represents a gain of 120%. This is still a significant gain.
Appendix B
Gauging the Health of the Economy
So, how do we know if a year or prolonged period is economically "good" or "bad?"
Is it like obscenity: I know it when I see it? Not really. In that case, subjectivity might distort the common experience. On a case by case basis, subjectivity is deeply moving, but it is also not inherently representative of a time. Similarly, one industry is not the whole economy.
Is it plainly visible in market indexes like the Dow Jones, S&P 500 or NASDAQ? Not entirely. In that case, large capital markets might distort the common experience. While a vitals check on the major market indexes like the Dow Jones provides useful information about capital markets (and expectations), it seldom tells the whole story (beyond guidance and profits). For this reason, relying solely on capital markets indexes is not enough to characterize the state of the economy.
Luckily, statistics are collected every day, week, month, quarter and year to provide an essence of the economic experience. This data legitimizes the comparison between market performance and its participants. Data like the unemployment rate, Gross Domestic Product and the Consumer Price Index are commonly used as near-absolute indications of good or bad economic times, and critically, how the economy relates to the common experience. Alone, these indicators are helpful and simple, but they are not unimpeachable.
Are there multidimensional economic activity indexes that capture more about an economic climate than just one data point?
The Coincident Economic Activity Index (CEAI) is a composite index. It is made up of three factors: (1) nonfarm payroll employment, (2) the unemployment rate, and (3) wage and salary disbursements plus CPI adjusted proprietors' income. The CEAI measures how many people are employed (labor) and how much money those who are employed earn. (Figure 1)
Effectively, the CEAI measures personal income across a region. This is helpful, because the GDP can continue to rise even though the unemployment rate rises and personal income falls. In the case of the CEAI, stagnation or recession (like in 2020) equates to a poor economic climate. Given the graphic below (Figure O), the CEAI is too neatly tied to macro capital markets, so it obscures the experience of the many. That said, 2020 appears even more alarming as the CEAI drops at a rate never seen before. Nonetheless, it alone does not tell us enough about the 28-year period in question.
Federal Reserve Bank of Philadelphia, Coincident Economic Activity Index for California [CAPHCI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CAPHCI, November 5, 2020.
Federal Reserve Bank of Philadelphia. "State Coincident Indexes." Accessed November 5, 2020.
We need a measurement that is more attuned to the housing market and employment figures. After all, we want to see if migration, the housing market and the general economic climate are strongly interconnected. There is one such index.
The Leading Index (LI) is a multidimensional economic activity index that builds off the CEAI. In addition to payroll and employment guidance, the LI includes a composite score of four economic variables: housing permits (construction), initial unemployment claims, Institute of Supply Management (ISM) manufacturing survey scores, and the treasury bond yield market. (Figure 2) In their own way, each of these factors is relevant to migration.
Federal Reserve Bank of Philadelphia, Leading Index for California [CASLIND], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CASLIND, November 5, 2020.
"The leading index for each state predicts the six-month growth rate of the state's coincident index. In addition to the coincident index, the models include other variables that lead the economy: state-level housing permits (1 to 4 units), state initial unemployment insurance claims, delivery times from the Institute for Supply Management (ISM) manufacturing survey, and the interest rate spread between the 10-year Treasury bond and the 3-month Treasury bill."
Figure M shows the data from Figure L in a condensed form making the trends easier to see. Does migration have a positive relationship with the economic climate? Is there more net negative out-flow migration in the good times or in the bad times?
With respect to the CEAI and the LI, my calculations do not show a meaningful correlation between the health of the economy (as represented in the composite indexes) and migration. Nevertheless, the graphs jump at you with the suggestion. (See Figure M)
The LI data roughly corresponds positively with net migration data. This is apparent when we demarcate a healthy and non-healthy economy by way of average. What is above average is healthy. What is below average is unhealthy.
The LI average is 1.62. The average demarcates the higher and lower years of economic well being. The lower than average years are: (1) 1989-1993, (2) 2001-2002, (3) 2006-2010 and (4) 2017-2018. The higher than average years are: (1) 1988-1989, (2) 1993-2001, (3) 2002-2005, (4) 2011-2017 and (5) 2018-2020. This arrangement shows that there are periods in which the economy is healthy and out-migration is high.
Figure 4 demonstrates where general market health correlates with net migration. In the two recovery periods from 1990-1993 and 2002-2005, the LI and net migration rise concomitantly. However, as the economy shows continued health from 1993-2000, net migration slows. Indeed, 2000 was the lowest year of net migration after which net migration and the health of the economy rise.
The LI and net out-flow rise and fall together from 2000-2008. However, when the LI is recovering from 2009-2012, net migration diverges and remains historically low. Indeed, the years from 2008-2014 constituted the longest continuous stretch of low net migration. The third wave of net migration from 2015-2018 correlates negatively with the LI, rising when the LI falls and falling when the LI rises. This may have something to do with the consistently low homeownership and rental vacancy rates from 2008-2018.
Besides the first year (1992) of increased out-flow, the first wave from 1992-1997 occurred during a higher than average LI period. The second wave from 2003-2008 occurred during a higher than average LI period. The third wave occurred from 2016-2018 during an otherwise above average LI period, besides one blimp in the year 2017-2018.
The LI is an exceptional measurement of economic activity regarding housing, employment, manufacturing and capital markets. Housing permits data show how much people are willing to invest in long-term projects that are capital intensive. This indicates a favorable market in which people hold liquidity (money for investment). Likewise, construction related employment is forecasted to rise or fall just as the number of dwellings is forecasted to beat or lag behind dwelling dilapidation rates.
Separating initial unemployment claims from total unemployment and the unemployment rate, captures the immediacy of market pressures and the novelty of one quarter, year or period.
The ISM survey is itself considered a strong market activity index focused on the intermediary steps of production that the GDP does not capture. For that reason the ISM survey represents economic activity and forecasts it out.
Treasury bond interest rates and bond yields have long been regarded as indicators of macroeconomic health, especially in their relationship with equities on the New York Stock Exchange (NYSE). The rule of thumb is: when markets are risky and volatile, bond prices rise concomitantly with demand for secure and stable financial instruments.
The LI is a great economic activity index that is especially fitting for this topic. It captures the immediacy of market pressures in the labor market. It incorporates a measure of large capital markets. And it includes a constant measurement of housing construction and manufacturing production.
Karol Kopp, "ISM Manufacturing Index." Investopedia.com. November 2, 2020. https://www.investopedia.com/terms/i/ism-mfg.asp. Accessed November 5, 2020.
"The ISM manufacturing index is a composite index that gives equal weighting to new orders, production, employment, supplier deliveries, and inventories. Each factor is seasonally adjusted."
1) Dow Jones Industrial Average
2) S&P 500
3) NASDAQ
Appendix C
Crowding
United States Census Bureau (USCB) data shows that the California housing market was extraordinarily crowded from 1980-2000 (and beyond, but the dataset does not go beyond 2000). Crowding is defined as more than one person per room. Severe crowding is defined as more than 1.5 persons per room.
*No, I do not know why the Census Bureau decided to denote people using decimal notation. https://www.census.gov/data/tables/time-series/dec/coh-crowding.html*
In 1990, 2 million of the 10.4 million occupied housing units were crowded or severely crowded. Not only is does that constitute 19.4% of all of California's occupied housing units, but also 31.2% of all crowded and severely crowded housing units Nation-wide.
In 2000, 2.8 million of the 11.5 million occupied housing units were crowded or severely crowded. Not only is does that constitute 24.3% of all of California's occupied housing units, but also 31.3% of all crowded and severely crowded housing units Nation-wide.
U.S. Census Bureau, Rental Vacancy Rate for California [CARVAC], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CARVAC, November 8, 2020.
Appendix D
Changes in the Bay Area
Home values continue to rise in California (See Figure C&D). Rental prices also continue the rise, but are slowing down (See Figure E). In the San Francisco Bay Area, rental prices are in a free fall.
According to Apartment List, San Francisco (SF) rental prices fell 20.4% in the last year. The discrepancy between the two indexes (Zillow and Apartment List) is accounted for in what they measure. The Zillow index measures mid-tier priced rentals and Apartment List measures all rentals. So, when the two indexes measured rental prices the State over, it is not so surprising that the two have varied results. In this case, Apartment List recorded a 4.6% drop in rental prices across the State, but Zillow’s mid-tier index recorded an increase of 1.8%. Since Zillow covers rentals within the 40 to 60 percentiles, the discrepancy lies in either or both rentals in the low and high tier price ranges. (Note)
Many of the largest corporations in the Bay Area announced that their employees are not required to work at their headquarters. Corporations like Facebook, Google, Airbnb, Twitter, Slack, Dropbox and Square are largely operating with their employees working from home. Twitter, Slack, Dropbox and Square took the additional step and announced that their employees can work from home as long as they like (even if a vaccine was discovered, disseminated, etc.). (Note) (Note)
If the population density of the Bay Area (7.15M as of 2010) was not enough to persuade residents to find housing elsewhere during the pandemic, then work from home policies were. The flight from the Bay Area was so large that Facebook announced pay-cut policies for those who leave to cheaper areas. Indeed, a recent survey discovered that 44% of employees in 35 companies (most of whom are based in the Bay Area, CA) are willing to take a pay cut if they are permitted to work from home permanently.
(NOTE)
*For more on the prospective effects of Covid-19 on housing in the Bay Area see: Bay Area Council Economic Institute. “Housing and Transportation in a Post-Pandemic Bay Area.” Accessed October 20, 2020.
http://www.bayareaeconomy.org/housing-and-transportation-are-two-of-the-regions-biggest-challenges-how-will-they-evolve-in-a-post-covid-19-bay-area/
NOTE: Joey Hadden, Laura Casado, Tyler Sonnemaker, and Taylor Borden. “20 major companies that have announced employees can work remotely long-term.” Business Insider. October 12, 2020. Accessed October 19, 2020.
https://www.businessinsider.com/companies-asking-employees-to-work-from-home-due-to-coronavirus-2020
NOTE: Juliana Kaplan. “A survey finds more than two-thirds of companies may be working from home forever.” Business Insider. June 20, 2020. Accessed October 19, 2020.
https://www.businessinsider.com/survey-over-two-thirds-of-companies-work-from-home-forever-2020-6
NOTE: Avery Hartmans. “From pay cuts to permanent work-from-home, here's how Silicon Valley companies are thinking about the future of work.” Business Insider. October 15, 2020. Accessed 19, 2020.
https://www.businessinsider.com/silicon-valley-future-of-work-port-coronavirus-apple-amazon-facebook-2020-10
NOTE: PYMNTS. “PERSONNELDropbox To Let Employees Work From Home Permanently.” PYMNTS.com. October 13, 2020. Accessed October 19, 2020.
https://www.pymnts.com/personnel/2020/dropbox-let-employees-work-from-home-permanently/ 1
NOTE: Tekla S. Perry. “Will Relocating Engineers Embrace a Pay Cut?” IEEE Spectrum. September 24, 2020. Accessed October 19, 2020.
https://spectrum.ieee.org/view-from-the-valley/at-work/tech-careers/will-relocating-engineers-embrace-a-pay-cut
Appendix E
Gross Domestic Product Comparison
GDP is a basic measure of economic activity. It can also serve as a measure of real development. That is, the development of products that increase the value of land.
When we compare GDP growth among the states (as we did for CPI), we find that California's GDP grew exceedingly more. California's GDP grew 4500% from 1963-2019. Illinois' GDP grew 2140% and New York's GDP grew 2300%. Flying well above them all, however, is Texas' growth of 6200%. Still, California's GDP is 70% larger than Texas' GDP in 2019- 3.13 trillion and 1.84 trillion. California's economy stands alone in terms of size and this compounded over time.
Bureau of Economic Analysis: SAGDP2S
Appendix F
Median Household Income Does not Explain CA Expensiveness
Figure 1 shows that household income does not correlate strongly with the California Premium writ large. The New England states on down to Richmond, VA are highly concentrated areas of high income households. Yet, in terms of the common experience, the cost of living and housing costs are much less than those in California. This indicates that people are willing to pay a premium to live in California despite equitable pay and significant discounts elsewhere.
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