Posted by: Josh Lehner | July 29, 2015

Oregon Labor Force Participation

Along with the latest employment report came the news that Oregon’s labor force participation rate fell to at least a four decade low. We have good data going back to 1976 and June’s LFPR of 60.3% is the lowest in this data. However, much of the decline in the past 15 years was expected. Given that LFPR includes all Oregonians 16 years and older, aging Baby Boomers were always going to pull the measure down.

The graph below shows the actual LFPR in red compared with a demographically-adjusted LFPR which takes participation rates in 2000 (arguably the last time the U.S. economy was operating at full capacity) and then adjusts for demographic changes. As such the gray line is a reasonable approximation for what the LFPR in Oregon would be if the economy was firing on all cylinders. What truly matters for the health of the economy is the difference between the red and gray lines. This is the so-called participation gap our office uses in the Total Employment Gap. This gap is what I am most worried about economically as the expansion continues. To what extent will this gap close? I also highlight the last benchmarked data point (the most recently revised data, essentially.) Data since then shows the LFPR plummeting further. With the Oregon economy on the upswing and job growth at full-throttle, it’s hard for me to see such large declines in the LFPR. We shall see what revisions bring next year…


Since about two-thirds to three-quarters of the LFPR decline since 2000 is due to changes and trends among the youngest and oldest populations, I want to focus on just the so-called prime working age folks, those between ages 25 and 54 years old. Participation rates are down among this group, which is the biggest potential issue in terms of future growth as fewer workers generally equals lower productive capacity overall. The chart below shows the relative changes for this population and the reason they’re not in the labor force over the past decade or so. This also adjusts for demographics, so even though there are more 25-34 year olds today with the Millennials, they are enrolled in higher education at an even higher degree. I have ordered the reasons why these Oregonians are not in the labor force from most likely to least likely to return, from left to right.


The results here are a mixed bag, not surprisingly. The youngest group (25-34) has seen less LFPR for reasons that are fairly easy to reverse: school, weak economy and staying at home with the kids (an overall upward trend as our office’s report showed.) Changes among the oldest group (45-54) appear to be harder to reverse, although a stronger economy can and will pull some of these workers back into the labor force. Side note on the ill or disabled group, this reflects survey responses not actual disability claims and awards. If you look at age-adjusted SSDI there was an uptick during the Great Recession but history has shown it is not likely to be a permanent trend. The 35-44 year old group lies somewhere in between the others, although certainly skewing more toward the easy to reverse end with more discouraged workers and an increase in staying at home with the kids. Not that transitioning back into the workforce is easy, but provided the right opportunity exists — and it should in a stronger labor market — these individuals will at least be tempted to return.

All told the bulk of the LFPR decline is due to basic demographics. As such it is expected to continue to fall over the coming decade as well. What truly matters is the participation gap. This does mean that at least some of the LFPR decline is economic-related and should be cyclical to a certain extent.

In terms of the outlook there are three reasonable scenarios. First, the truly pessimistic scenario is that all of the LFPR decline is permanent. The economic-related decline is now structural and not cyclical due to the initial lackluster recovery. Those Oregonians who dropped out of the labor force will not return in the future. Our office does not believe this will happen, but, unfortunately, it does remain a distinct possibility. Second, a somewhat pessimistic yet reasonable view would be that Oregon does not see any uptick in the LFPR. Rather it moves sidewise during the expansion, similar to the 2005-08 period. Under such a scenario, the participation gap will narrow and maybe even close however not quickly. Finally, our office’s baseline is that Oregon will see another percentage point or so increase in the LFPR, following the gains in 2014. A stronger economy with more plentiful and better paying jobs will pull workers back into the labor force, at least somewhat. Not enough to overcome the aging demographics, but some of those prime working age adults will return.

For more see the Oregon Employment Department’s report on LFPR.

Posted by: Josh Lehner | July 27, 2015

Oregon Household Formation, 2015

Just an update on household formation in Oregon. This is particularly of importance for the housing market, as each new household needs a place to live. With vacancy rates low for both rental and ownership properties — particularly in Bend and Portland — strong growth in the number of households translates directly into new home construction (be it single family or multifamily.) Beyond household formation, by all accounts overall population growth is continuing to pick up as well. Much of this due to migration flows returning but clearly births are picking up as well, with a sample size of 1 :) .


And yes, I do worry about the pace of home construction relative to demand, particularly from a housing affordability perspective. See the recent post on home prices across the 50 largest MSAs, or the four part series on housing in Portland. Also, see this post for a more complete discussion on household formation in Oregon.

Posted by: Josh Lehner | July 24, 2015

Bend and Central Oregon, 2015 Update

Once a year the Governor’s Council of Economic Advisors gets out of Salem for our meetings and heads somewhere in our beautiful state. In recent years this has included Ashland/Medford, Astoria, Hood River, McMinnville, and Pendleton. We usually meet with local businesses and officials and also tour a business. This year (today) the group returns to Bend. A place the group last visited at the depths of the Great Recession in 2009. I’m still out on leave and unable to attend (diapers call) but pulled together a graphical update for the meetings in terms of Bend, and Central Oregon more broadly.
Keep reading for regional update

Posted by: Josh Lehner | July 21, 2015

Careful with the Data, Cost of Living Edition

The two most commonly used cost of living measures these days are the BEA’s regional price indexes and the C2ER’s cost of living index. The BEA figures are relatively new and have only been around a couple years at this point. They provide data at the metro and state level for just a few broad categories: all items, goods, rents and other services.

The C2ER data (formerly ACCRA) has been around a long time and provides much greater detail when it comes to the cost of various items, all the way down to the price of a men’s dress shirt or a T-bone steak. I know because I used to do this pricing survey at my previous job. I drove to 5 different stores in the area and priced dozens of products to provide a local estimate of cost. This makes the C2ER data the best choice at the local level, no question. However the issues arise in that the C2ER data only covers areas that are priced, thus not making it representative of other, or larger areas. Unfortunately they roll up the local prices into statewide measures. Even though C2ER uses a population-weighted average, it is still not representative if your state does not have good coverage in terms of the underlying cities that are, you know, actually used in the population-weighted average calculation.

All of which brings me to the point and the title of the post. Be careful with your use of data! I have seen this specific example a few times over the years in various reports (both government and consulting, e.g.) that use the state level C2ER data. It reared its head again the other day with the ranking of “best states to make a living in” which generated lots of public comments and questions across the web.

For comparison purposes, below I show both the BEA and C2ER measures for Oregon and Washington.


The BEA measure shows Oregon just below the U.S. average and Washington just above. However the C2ER data shows Oregon significantly above the U.S. average and Washington just above. What’s going on here? My suspicion is it has to be the underlying cities used to generate a statewide average. If you look at the C2ER data for just Portland vs Seattle, it shows Portland is certainly above the U.S. but 1-2% lower than Seattle. So if the 2 largest cities are so close, how can the state differences be so large? In reality, they can’t. It’s not realistic to assume that living in Pendleton is more expensive than Tri-Cities to such a degree that it overwhelms the similarities in the states’ largest cities to make the statewide average nearly 25% different. C2ER data is not useful at the state level, however it is the best, most detailed data available at the city level.

To summarize, be careful with your data. It’s always helpful to know how your data is constructed and how best to use it. Of course there are times you must use the data you have, not the data you wish you had. However in this case there is a clear alternative that makes for better use at the state level.

Posted by: Josh Lehner | July 15, 2015

Portland Timbers and Argentina

I’ve been taking a closer look at exchange rates recently, given the strong U.S. dollar appreciation in the past year — by extension the Oregon dollar as well — along with the ongoing events in Europe and potential Grexit (Greece exiting the Euro.) I should have more on this in the near future, however here’s something fun.

The Portland Timbers have a handful of players from Argentina on their roster and have been heavily linked with adding another Argentine this summer. There are lots of reasons players would want to play in MLS and for the Timbers and plenty of reasons teams would like players from Argentina as well. (Full disclosure: I am a season ticket holder.) Besides the simple fact that Argentina has great players — all of the Argentines on the Timbers are regular starters and/or contributors —  there are other factors are play here as well including network effects and exchange rates.

In short, network effects in this case would be the simple fact that because Portland already has players from one country on the team, it makes it easier to sign additional players from the same country (provided the current players like it, of course.) Individual relationships between players, in addition to having similar backgrounds and assimilating to American culture all play a part on the player side. From a team perspective, having international scouts you’ve worked with before and doing business with same league, teams and/or agents is likely easier the fourth or fifth time as well.

Exchange rates are also likely playing a role here, even as they are not commonly discussed. One U.S. dollar goes a long way these days in Argentina, following two major economic crises in the country in the past 15 years. Textbook economics says a strong U.S. dollar makes imports inexpensive to U.S. consumers and U.S. exports expensive to foreign buyers. This works in Portland’s and MLS’ favor when it comes to bringing in players from elsewhere. Players are, essentially, a type of import in this case.


Per the Timbers website, Diego Valeri was first loaned to the Timbers in January 2013, and the club then purchased his contract in August 2013. Maxi Urruti came to MLS in 2013 as well, first to Toronto FC then in a trade to Portland. Gaston Fernandez and Norberto Paparatto both signed in early 2014. Of course the top reason these players are on the team is the simple fact that they’re good players. However, network effects and the exchange rate have helped in allowing a team like the Timbers attract and retain such talent.

Posted by: Josh Lehner | July 14, 2015

Quasi-Hiatus, Part Deux

As you may have noticed, blogging has been light lately and for personal reasons, just as it was about two years ago, it will continue to be so in the weeks and months ahead as me and my family adjust and welcome another addition.

While the typical family in the U.S. has averaged just under 2 kids for the past 30 years, the most common household size is 4 — two adults and two children. Less than 1 in 20 (19%) married couple families have 3 or more children (family size of 5 or larger) and only 5% have 4 or more children. I come from a family of 4, my wife from a family of 6, so I guess we’ll see where we end up ourselves…


One important duty when it comes to kids and families is choosing names. The Social Security Administrations’ baby name database is a fascinating tool that helps you explore names over time and their relative popularity. Luckily, my wife and I have had a relatively easy time choosing names and popularity was not a factor in our decision, although that doesn’t mean I didn’t put together graphs and analyze trends! Below are the 3 names we were strongly considering for our daughter and their relative popularity. It’s fascinating to see the big swings. As you might have guessed the two blue lines were the names of our grandmothers — popular back in the early 1900s and rising in popularity today as parents name their children after their grandmother.


Anyway, just some fun data and graphs. I’ll be back in the office working on the upcoming forecast in the near future. However, overall blog activity will be lower. I have a few posts ready to go in the coming weeks, but beyond that we shall see.


Posted by: Josh Lehner | July 7, 2015

Graph of the Week: Housing Costs

This edition of the Graph of the Week highlights the relationship between home prices and new home construction across the 50 largest metros in the country. (Original graph idea from Jed Kolko, formerly of Trulia.) It comes out of some work Tim Duy and I did recently in a different venue but is important to highlight. In essence, expensive housing markets do not build much new construction. Portland, along with other popular cities like Denver and Seattle, are right on the middle of the curve, essentially indicating that we’re adding just enough housing to keep affordability at a somewhat manageable level. These regions are keeping their heads above water, but just barely.

Find an interactive version of the graph here.


In talks I have been framing this as the choice we face. Either the region builds enough homes and apartments to keep affordability from getting worse, or watch as the region slides into the unaffordable category. I realize many already find Portland unaffordable. Using the benchmark of 30% of income spent on housing as the threshold, 37% of homeowners spend more on their mortgage and 52% of renters do likewise (2013 ACS data.) A relatively new line of research, new to me at least, is focusing on 45% of income spent on housing and transportation costs as affordable. To the extent that a household can afford to spend more on rent given they live close to work, or have good public transit or other mode options, this makes sense. Similarly, housing is generally more inexpensive on the outskirts of the metro, but transportation costs are generally higher as well given longer commutes. The worst problems arise when both categories are high for a given household.

Finally, such a high level look leaves to the side many different and important facets of the housing discussion, from land use policy and zoning to neighborhood character and gentrification. All of these are important topics should Oregonians want to have the conversations, but also lie largely outside the realm of economics or supply and demand. However supply and demand goes a long way toward explaining today’s level of housing affordability as discussed previously (HERE and HERE, e.g.)


Posted by: Josh Lehner | June 29, 2015

Oregon Severe Recession Update

Unlike the nation, unfortunately, Oregon’s Great Recession does have a modern peer: the early 1980s. Our office has documented the similarities numerous times over the years, from the unemployment rate to total jobs and from housing starts to manufacturing jobs and the like. Below are a few updates to the comparison and add in our new measure of the Total Employment Gap.

First, total employment in Oregon returned to pre-recession levels following the Great Recession 3 months ahead of the early 80s recovery, or nearly 7 years after job losses started (83 months overall today vs 86 months back in the 80s.) Similarly the unemployment rate and even the U-6 measure of labor market utilization followed very similar paths to the 1980s. In early 2015 both measures were actually a bit below their comparative figures nearly 30 years ago.


However, while the topline economic data are nearly identical, it does not mean everything is the same of course. In particular two, related, differences arise: population growth and labor force participation.

In both cases, population growth slowed considerably from pre-recession rates. However one very big difference emerges in the fact that Oregon lost population in both 1982 and 1983. This is the only time Oregon has lost population in the state’s modern history. Former state economist Tom Potiowsky has a great story of moving to Oregon in 1983. Following graduate school in Colorado, he accepted a job offer from Portland State. U-Haul did not charge him anything for his trailer. The outbound traffic from Oregon was so large and in such demand, the company let him bring a trailer into the state for free, since they could turn around and rent it to someone else moving out. In essence, Tom was doing the company a favor. That did not happen this time. Overall population growth has been much stronger today than back in the 1980s, both in growth rate terms and in total number of new Oregonians. From 1980 to 1987, Oregon added 59,800 individuals for an increase of 2.3% overall. From 2007 to 2014, Oregon has added 221,400 individuals for an increase of 5.9%. That’s nearly 3.7 times as many individuals for 2.6 times the growth rate.

One place such differences show up in the economic data is in the total employment gap which combines the unemployment gap, the labor force participation gap and the underemployment gap. Today, Oregon’s total employment gap is 3.5-4 percentage points higher than back in the late 1980s, even as most topline economic measures are the same.


The reason for the difference is the labor force participation gap, as both the unemployment and underemployment gaps are identical to the 1980s. It’s hard to pinpoint the exact nature of the differences however a few possibilities exist.

One, the fact that the state did not lose population has to play some factor given that at least some of the early 1980s population loss was due to workers leaving in search of job opportunities elsewhere. Two, the Baby Boomers are at vastly different points in their working careers today (retiring, or nearing retirement) compared with the early 80s (just finishing college or still in their 30s.) This impact on the overall labor force participation rate over time is immense. Third, the labor market simply is not as strong today as back in the mid- or late-80s.

I think all 3 possibilities play important roles here and certainly are not mutually exclusive. The nature of the business cycle this time is completely different than back in the 1980s. Not only did the Fed cut interest rates back then to revive the economy (after choking off inflation with a Fed funds rate of about 12%, mortgage rates at 18%), but the generational impact of the Boomers entering into their prime working years in the late 80s and early 90s was a considerable boon to the economy. Today we’re dealing with the exact opposite situation. The Fed was cutting interest rates heading into the crisis and has held them low since and the Boomers are exiting their prime working years.


On the somewhat bright side, the early 1980s has helped our office in our forecasting of the recovery. Since about mid-2009, Oregon jobs, income and tax revenue has largely tracked our expectations, even if the severity of the crisis caught us off-guard.

Regardless of the exact differences and unlike the nation, Oregon has seen such an economy before. Jobs and unemployment are identical today and nearly 30 years ago. However even after accounting for demographic differences, a labor force participation gap remains today. Our office expects a stronger economy, with larger wage gains to draw more workers back into the labor market over the coming year or two. All told, we expect the total employment gap to close, or for Oregon to reach full employment, in about 2 more years. Oregon is growing strongly today, however the damage from the Great Recession is still not fully healed.

Posted by: Josh Lehner | June 25, 2015

Oregon Personal Income, 2015q1

The U.S. Bureau of Economic Analysis released the latest state income estimates this week, including revisions to 2014 data. Similar to our state’s overall economy and labor market, the data show strong growth that outpaces the nation. Again, the economy is on the upswing, during which time Oregon typically grows significantly faster than the average state. So it’s no surprise to see Oregon’s personal income gains over the past year ranked second best among the 50 states. Wages grew 2nd fastest as well over the year.


Relative to our office’s forecast, the results are somewhat of a mixed bag. Wages were a bit above forecast, non-wage income a bit below, however in aggregate, personal income in 2015q1 was 0.15% below forecast. By far the largest error component here were transfer payments which came in nearly 1 percent below forecast. The largest shift in transfer payment patterns was in Medicaid, which grew strongly in 2014 following the rollout of the ACA (aka Obamacare) and tapered off in 2015, at least in terms of growth rates. All else effectively cancels each other out and the end result is not much different from our outlook.

As detailed last month, Oregon’s relative income measures continue to improve along with the economy. The graph below is updated to include actual 2015q1 income data and not just our office’s estimates. Oregon’s per capita income is back to pre-Great Recession levels relative to the nation (about 90-91%.) Oregon’s average wage shows even larger gains in recent years. Of course both remain below the national average.


Posted by: Josh Lehner | June 22, 2015

Saving the Kicker

Say what you will about Oregon’s unique kicker law — good, bad, indifferent — but it is enshrined in the state constitution and it is the law. When our office’s revenue forecast is off by 2% or more, everything above the forecast is returned to Oregon taxpayers (key point: including that first 2%.) We also pay the kicker out the same way we take the revenue in, based on one’s tax liability. So the more one earns, the larger one’s kicker. Our office included the following table in our presentation to the legislature to both illustrate this point and to provide estimates of what the typical taxpayer should expect, based on the latest forecast. The specific numbers will change when our office certifies the kicker in another couple of months.


While the above details the expected kicker across the income distribution, an interesting question is how much of it will be spent. This exact question was asked at the most recent legislative hearing and forecast release. While our office did not have a specific number at that time, we do have an estimate now based on some recent research. First, however, a bigger point that we did make at the hearing. Lower income taxpayers and households spend a higher fraction of their income than do high income households. Similarly, savings rates are much higher for high-income households than for low-income households. So giving $100 to different taxpayers at different points along the income spectrum result in different numbers in terms of spending.

One potential issue along these lines is the difference between average propensity to consume (think of savings rates vs spending rates) and the marginal propensity to consume/spend — if someone were to hand you $20 today, how much would you spend? Given that kickers occur on an infrequent basis, it’s hard to argue that taxpayers plan on receiving them in advance, or build them into their household financial plans. Milton Friedman’s permanent income hypothesis certainly plays a role here. As such, recent research on the marginal propensity to consume across the income distribution helps shed light on this question. The estimates* of spending the kicker are shown below.


Such findings may be a surprise. Just 15% of the kicker is expected to be spent, at least pretty quickly. In the paper, different model results peg the figure between 10 and 20%. Of course these numbers vary across the income spectrum as well, with those in the bottom half spending about three-quarters of their kicker while those at the top spending less than 10% of theirs.

However a few additional things are worth mentioning. First, all $473 million (the final figure will differ) will be returned to taxpayers via a credit on next year’s tax return. Much of it will go towards savings and/or paying down debt, most likely. Second, given that the kicker will be a credit that results in a larger refund or a smaller final payment, and not a check in the mail around the holiday season, how does this change consumer behavior? We don’t know, but it does seem to imply a smaller spending share, particularly for those now owing a smaller final payment. For a middle-income household sending in a final payment of $150 instead of $300, how much of the $150 kicker will they spend? Behavioral economics in general suggest not much. Humans react significantly different to losses than to gains. Finally, with all that being said, much of the kicker will be eventually spent over time. This research indicates the kicker may not pack much of a punch initially, however over a longer period, the kicker will certainly be factored into household finances and spending.

* The model results in the paper do not explicitly lay out the marginal propensity to consume by quintile, but rather by various segments of the population (bottom 60%, top 20%, top 10%, etc.) The results shown in the table are my own estimates based on the published research, but made to ensure they sum to the total and published figures. As such, it is unlikely that both the bottom 20% and second 20% have the exact same MPC, however I am unable to determine the differences given the published results. This detail is unlikely to significantly impact the overall findings.

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