We’re looking at the Hoshi & Ito paper and then will ask whether the US is different.
The core item (using lower case to indicate per GDP, eg bonds B/y = b) is their equation (1), though here I use gamma γ as more natural for GDP growth.
We can then add the surplus (deficit) term (g-t). Again, that is in per GDP terms.
Now they define sustainability relative to private domestic financial assets “a”. A starting point though is to ask what level g-t needs to be to stabilize b at a constant level. What do the numbers look like for the US?
Is it reasonable in the US context (or the Japanese) to hold g constant, barring explicit policy changes? how about t?
What seem reasonable levels for “r” and “γ” in the US context? What is “b”?
What is “net debt”? Does it matter?
Finally, Japan is not a federal system; local, prefectural and national are more-or-less one big pot of debt. How about the US?
Now it would be many hours of work (3-4 hours, once data are in hand?) to do a simplified simulation for the US along the lines of what they have done. We can however do crude back-of-the-envelope calculations. When I did that for Japan many years ago I got more-or-less the same answer, but at that point debt levels were much lower so the fiscal adjustment required was smaller. So I think we’ll learn a lot from doing simple calculations for the US.
Social Security is at the center of many current debates, with many politicians and citizens arguing that the program will become unsustainable in the near future. When looking at the program, it is important to look at the past figures and the present to get an understanding of the future of the Social Security Program.
One useful way to look at the change of the program overtime and the effects of the changing workforce population is by comparing the ratio of beneficiaries to workers from the past and present. For the past few decades, there have been approximately 3.3 workers per beneficiary; however, according to future projections, by 2030, there will only be approximately 2 workers per beneficiary.
According to statistics provided by the Social Security Administration, with the average worker benefit at around $1,000/ month, each worker needs to provide around $300/month in order to meet these needs; however, with future ratio projections at 2 workers per beneficiary, each worker will need to provide $500/month in the future to continue to meet the needs of the beneficiary, under current standards.
This is largely a result of the “Baby Boomer” generation starting to retire, resulting in an increased retired population, with a smaller workforce. As illustrated in the figure below, the increased fertility in the 1950’s and 60’s resulted in the baby boomer generation; Due to their current retirement and the decrease in the workforce due to the slowing of fertility rates from 1970’s onward, we are experiencing a decrease in the worker to beneficiary ratio, thus creating a dilemma with the Social Security Program.
It is important to realize that some form of change is necessary moving forward in order to save the program. Furthermore, it is important to realize that this issue is not specific to Social Security, but that the change in demographics affects many other government sponsored programs, such as Medicare.
Work Cited: https://www.ssa.gov/policy/docs/ssb/v70n3/v70n3p111.html
Real estate usually makes up 15%-18% of America’s GDP. This number is broken down into 3%-5% from “Residential Investment” and 12%-13% from “Consumption Spending on Housing Services”. There is no doubt that this industry is an integral part of our economy, which is why it can be used as an indicator for the economy as a whole. In 2008, the industry completely collapsed, with home values sharply declining after the subprime mortgage crisis. For years after this collapse, new construction rates were cut in half and the future of the industry looked bleak. In 2007, 776,000 new houses were sold. By 2011, this number had dropped to 306,000 new houses. But have prices followed this trend?
This FRED graph shows the Housing Price Index. Clearly, prices collapsed along with new construction rates, but prices are now above their previous highs. Because of this strong price growth, some speculate that the industry is over producing and that supply is now outpacing demand. This speculation is hit and miss, because real estate as a whole looks pretty good, but some markets are definitely becoming saturated. A prime example of this is Salt Lake City. SLC has gone through extreme growth in construction and prices, while vacancy rates have dropped to an all time low. Because SLC’s market has been so promising, developers have loaded the pipeline for new constructions. According to Colliers International, SLC’s new supply will outpace demand by the end of 2017. So by mid 2018 we will most likely see prices drop and vacancy increase. Since 2011, the hotspots for real estate have been mostly located in the south, in warmer climates, and in harbor cities. Primary markets have had prohibitively high pricing, which explains why many firms and families are relocating to cities like: Tampa, Orlando, San Fran, San Jose, Seattle, Houston, etc. The following infographic gives a good outlook of relative prices by region.
Within the next few years, cities with low rents and home values will experience growth, while primary markets will remain low growth/ high price. As you can see from the infographic, most of the locations that I mentioned are in a lighter shade (lower price). In today’s technological environment, service based companies can operate from pretty much anywhere. Firms are capitalizing on this advantage by relocating to states like Indiana. For example, Salesforce.com, a growing tech behemoth, recently moved to downtown Indianapolis. Indy rents are less than half of comparable rents in NYC, so why not move there? Firms relocating to these low-rent cities will be the main driver of real estate in the coming years. Their employees will need housing, which will boost new construction rates and increase housing prices. Secondary market growth and primary market stability in the real estate industry will expand our economy as long as the US continues to add new jobs.
In 2015, 65.1 million people received benefits from the Social Security Administration and this number is constantly increasing. As we’ve talked about in class, the current demographic challenges facing our nation are daunting. It is a fact that a dwindling workforce must continue to produce in order to provide income for the expanding dependent population. Because of the dangerous momentum affecting the proportion of workers to dependents, GDP growth needs to return early post-war levels in order to maintain current Social Security benefits. Of course, many scholars are highly skeptical that this will ever happen. Increasing the tax burden on a smaller workforce would be crippling. Decreasing Social Security benefits is another possibility, though the likelihood of this occurring seems slim as well. Despite the small monthly sum that Social Security represents, people are extremely dependent on this income. In 2014, 61% of beneficiaries received at least half of their income from the program.
Social Security primarily consists of two programs, Old-Age and Survivors and Disability Insurance program, and Supplemental Security Income program. The latter benefits the neediest, as well as the blind and disabled of all ages, and 8.3 million people received support in 2015. In 2015, the OASDI Trust Funds collected $920.2 billion in revenues. 86.4% was from taxed income, 3.4% came from taxed Social Security benefits, and the final 10.1% was from interest on government bonds held by the funds. In 2015, it was projected that there are 2.8 workers contributing to Social Security for each beneficiary of the program. This number will fall to 2.1 in 2037, a far cry from the level in 1955 which was a ratio above 8. This is a testament rate at which the population is aging. In fact, growth in payments to retirees have tripled that of disabled workers over the last 40 years.
As Professor Smitka mentioned in class, the current Social Security program is unsustainable in the long term. The Social Security Trust Fund, established in 1983, has been essential towards funding the program. Social Security’s income has surpassed its costs since the eighties, but will only do so until 2019. However, given the current tax rates and benefit plans, the trust fund will most likely be empty by 2034, and it is projected that only 79 cents for every dollar of today’s benefits will be available. Unless there is some sort of reform, this is the reality.
Data from: www.ssa.gov
The graphs accompanying this post had some issues when they were uploaded to the site. You can view them at:
As I stated in my comment in the previous blog post, I was surprised that healthcare was not among the top two expenditures on the table. This is probably because I have heard so much about how rising healthcare costs, especially when it comes to pharmaceuticals in the United States, has been hurting people’s retirement savings. For example, according to this time article (linked below), a study found that a healthy 65-year-old couple could be spending $400,000 on health care in their remaining years. This is a lot of money to be spending – as a result, a majority of people are stressed about their retirement savings, and this could have detrimental effects overall on the economy. Karen mentioned how baby boomers will start retiring soon, so retirement is an important economic issue to look at in the coming years. According to the same article, “nearly half of boomers report having zero retirement savings. And a rising percentage say that Social Security will be a major source of retirement income.”
While this is surprising to hear, looking at FRED and health expenditures per capita, we can see that there has been a dramatic increasing trend since the 2000’s – this graph only goes till 2013, so if the trend has continued (which I’m assuming it has) then healthcare per capita is probably double the amount it was in 2000 (from 4,000 to about 8,000 in U.S. dollars). This suggests that healthcare will continue to be a large source of expenditure for individuals – and has a good chance at remaining in the top three expenditures for retirees – especially if we remember the graph we looked at in class on Thursday, where expenditures rose with age in the United States as opposed to going down in other countries.
Though health deteriorates with age, and we can expect to spend more on health care as we get older, I wonder what types of policies and programs could help the aging population (especially retirees) decrease the cost of healthcare to where it is more affordable.
One of the things that intrigued me in class this week was thinking about the spending habits of those in retirement age. During a person’s lifetime, they’re theoretically preparing for retirement and planning their financial future. With the Baby Boomer generation starting to leave the workforce, it’s important to know their spending habits in order to predict how the economy will change as we depend on their consumption habits for economic growth in lieu of their work. According to TIME, the majority of retiree’s spending goes to their homes since they overwhelmingly choose to maintain their mortgage as opposed to paying it off.
The Bureau of Labor Statistics put together a helpful profile of spending patterns for older Americans and looked at the changes from previous surveys. In 2014 older households (55 or older) made up 41.5% of the Consumer Expenditure Survey with an average household income of $58,528 for the group as a whole. Average income was higher for 55-64 which would make sense since they’re still working instead of relying on their retirement savings like hose in the above 75 group. Housing continued to be the largest expenditure overshadowing the second largest, Transportation (33% compared to 17%).
While these figures may not seem very shocking, it paints a grim picture for the future since retirees are not remaining productive members of society. Even as they spend, their habits are skewed towards things like housing which do not have any grand returns to the economy. As retirees become a larger group in society, those who are working-age will have to make up for lost economic growth and decreases in consumption.
The long-term visualization of home prices in the U.S. tells an interesting tale of overall domestic growth, labor force demographics, and mortgage industry changes.
Beginning in 1975, housing prices rise fairly steadily. A number of industry reforms aimed at providing credit to underprivileged demographics may have aided this growth (most taking place in the 70s.). However, labor force demographics are likely the main drivers, as baby boomers entering the labor force created higher demand for housing following 1980s economic expansion.
Home price growth was relatively flat throughout the early 90s, but seemed to increase exponentially from 1995-2006. Early home price growth in this period may be attributed to baby boomers moving from urban to suburban residential communities as they started families. Homeowner demographics also changed as easier access to credit allowed for lower income individuals to be accepted for a mortgage. The securitization of mortgages allowed mortgage-lending institutions to supply the loan and sell the debt, removing the risk associated with lending to less qualified mortgage applicants.
With more “qualified” mortgage applicants, demand soared until 2008. Once home prices had plateaued, lower income individuals could not refinance their homes using higher market values. As a result, many defaulted on loans and walked away from their debt obligations. Housing supply was in a large glut, leading to a collapse in home prices (and in the prices of mortgage-backed securities), and the ensuing recession.
As we’ve already discussed the Case-Shiller index and mortgage metrics in order to assess the health of the housing market, I would like to take a slightly different approach: affordability. In order to gauge affordability in the housing market today, I will analyze two different ratios—the ratio of price to income, and the ratio of price to rent. We will define the ratio of prices to income as the ratio of house prices to median household incomes compared to their long-run average. Likewise, we will define the price to rent ratio as the ratio of house prices to annual private-sector rents compared to their long-run average.
The Economist has pulled price information for properties from Zillow in order to illustrate these ratios for some of the nation’s biggest cities. I will focus on the nation’s two biggest metro populations: New York and Los Angeles, and the US average, for simplicity’s sake. As you can see in the figure below, the price to income ratio for all three of these locations is higher than the long-run average (represented by the horizontal red line = 100), indicating that homes today in these areas are less affordable compared to income than the historical average, especially in LA and New York. In terms of the price to rent ratio, we see from the price to rent graph that New York and the US average is below its long-run average, however, LA is above its long run average. This illustrates that the price to rent ratio in the US, on average, is on-par with fair value, however, in some metro areas, like LA, home prices are much less affordable than annual private-sector rents, in terms of long-run averages.
The two graphs demonstrate the growing disparity between housing markets in the US, in terms of geography. Even though home prices in the US, on average, are close to fair value; some metro areas like LA are no where near the average long-run affordability levels when compared to income.
The National Transfer Accounts project is spearheaded by Ronald Lee and Andrew Mason. It has compiled data on income and consumption by age, as well as demographic data, for an array of countries. Note that the focus below is on labor income, and is age-specific rather than aggregate [since different ages have different populations]. You can download their data in excel.
Above I’m also putting several of the Japanese population pyramids drawn from their National Institute of Population and Social Security Research. Below is the simple framework we developed in class. Note that in a developing country, or any time in human history before 1900, child mortality was high. So this is the framework for thinking about the target number of surviving children a family (or mother) would desire. With 50% mortality, a woman would have to aim for 4 children to have a 50% chance of one boy surviving. Having 6-8 children would thus not be unusual. Oh, and it’s not as though women haven’t been able to control fertility until the advent of modern contraceptives. In case you lack imagination, you can even written guides for past centuries from many societies.
We need to pull this back into the US context; more on that on Thursday. Note the very different profiles of Mexico and the US at older ages. Note the gap between labor income and consumption. Think about the age profile of the US [I did NOT put up a graph on that – a blog post topic for one of you. Because fertility is very close to 2.0 over a woman’s child-bearing years, our profile is NOT like that of Japan!
Housing starts have historically indicated a strong real estate market, which indicates a strong economy. As you see from the FRED graph, the 2008 crash brought the US to almost 1/6th of the US’s peak in housing starts. We are still recovering in 2017, but gradually. January 2017 housing starts exceeded forecasts, and analysts are expecting a 3% growth in February 2017.
It is unlikely that the US will experience a development boom like one from the 70’s or 80’s. Increased government regulation along with more cautious investment capital will make any serious development speedup unlikely. Since 2015, America has been tiptoeing around 1.2 million new units a year after steady growth from 2010-2015.
Economic policy (low rates) is probably helping the gradual growth, but the #1 cause of growth is the labor market. Job growth leads to housing start growth. Some are speculating that recent changes in the political environment may have a positive effect on housing starts due to new jobs along with jobs coming back to America. Also, if the Federal Reserve increases interest rates, like they have been planning on doing, it is likely going to negatively affect development.
Another interesting feature of the graph is the lack of any serious drop after the 2000 Dot-com bubble popped. That recession barely had an effect on the housing market while the 2008 subprime mortgage crisis is still affecting real estate growth nine years later. It could be that America is stagnating and will experience a similar European style economic slowdown. Hopefully not, though.
Graph posted by the Prof
Note that within FRED you can Edit a graph; one of the options is to add a series so that you can (as I did) divide the first one by it using the formula field (eg, a/b).
Courtesy of the graphs to Prof. Smitka who made them in excel using data from CoreLogic – thank you (Apologies if they’re blurry – I couldn’t make them any better than this as they are pictures)
The data for mortgage performance can be found at http://www.corelogic.com/ for anyone who wants the specifics of numbers. You have to sign up with your e-mail in order to download the documents, but they’re a data/consulting company so they don’t send you annoying emails every day.
The recession in 2008 reminds us of why mortgage performance is an important indicator of the economy. During the recession, a lot of subprime loans were being pushed on people who had bad or nonexistent credit. These borrowers did not have income, yet they were still being pressured to get what are sometimes referred to as “liar loans.” This was working as long as house prices were going up because people would take out new mortgage to replace old mortgage. So, for example, in the 1990’s house prices were pretty stable, so this was not disastrous. While there were slight increases and peaks, 2008 was when it really took a hit and home prices dropped and plummeted. This meant that someone who bought a house at the prime time could have had his/her home worth 40% less than the value they thought. As a result, people stopped paying their mortgages because it did not make sense to. The reasoning was that you can’t sell off the house and be able to pay the loan off, so it was a better option to just let the banks repossess your house instead of paying the mortgage.
Though I do not have data from earlier than 2008, it is important background to keep in mind when looking at mortgage performance. The data that is being presented here is from 2010 onward on a quarterly basis (Q1, Q2, Q3, Q4). On the graph “Mortgage Equity,” net homeowner equity has been steadily rising since 2010 (the red line), indicating a more positive trend. Basically, it signifies the value of ownership of the house that represents the current market value (without any remaining mortgage payments). Mortgage debt outstanding (the green line) has stayed relatively constant, though there has been a slight increase since 2013-Q2. In the second graph, “Corelogic Loan-to-Value ratios,” we can see that all values have been steadily decreasing since 2010 as well. These all seem to be good signs.
Overall, mortgages are performing much better today than they did 9 years ago when the recession hit, which is expected. I wonder about the state of home prices as well, and whether this would look different if we were looking at a regional basis (e.g. would we expect mortgage performance to be different in california vs. oklahoma (i’m assuming so), and if so, what kind of difference and how much?) Looking forward to those posts soon.
The labor force participation rate is the percentage of people in the civilian noninstitutionalized (not in prison or the military) population, aged 16 and older, who are either working or actively seeking work. This rate has been gradually decreasing since the early 2000s, but appears be leveling off around 62.5% (Graph 1). There are two possible explanations for this decline: either the labor force is shrinking due to poor government policy and/or the dwindling effects of the 2008 Recession, or the participation rate is simply growing more slowly than the population as a whole. Based on Graphs 2 and 3, it appears that the latter explanation is most likely.
According to Graph 2, the labor participation rate of people aged 25-54 is relatively constant at around 82%, while that of people aged 55 and up appears to hover around 40%. This indicates that, while there was a slight decline over time, these participation rates are mostly steady and do not decline enough to account for the large drop in overall civilian participation rate. However, as apparent by Graph 3, the population of people not in the labor force who are 65 years and over has grown rapidly in recent years. This trend is likely due to baby boomers, a significant portion of the population, leaving the work force and retiring. Thus, it is safe to assume that a large part of the decline in the civilian participation rate is due to the rapid decrease in baby boomers in the work force, which has been declining faster than the population has been growing. This conclusion is supported by a 2014 report by the Congressional Budget Office, which found that of the 3 percent drop in the labor force participation rate from 2007 to 2013, half (1.5%) of the percentage point drop is due to the aging of the population, while 1% of the drop is from temporary business cycle factors and only .5% is due to policy decisions. It is not hard to imagine this trend continuing into more recent years.
Further, it is likely that the main reason the labor force participation rate was so much higher before the early 2000s is that baby boomers made the population of working people under 65 much greater, so it more than counteracted the declining participation rate of people older than 65. Based on these conclusions, is it safe to assume that we will never reach such high labor force participation rates again unless there is another baby boom?
A multitude of useful construction data was released last week. In addition to overall construction spending, public construction spending, and total housing starts, it is perhaps equally as telling to examine construction per capita, and construction as a percentage of GDP. These metrics are necessary to assess real estate development within a slightly more realistic context. Construction is a very cyclical industry, and periods of high economic growth can be associated with both high and low levels of construction. Similarly, strong construction performance does not necessarily translate to strong economic performance. In this way, examining raw construction numbers alone can be deceptive. As one can see from the graph below, private development projects contributed as much as 5.1% to annual GDP growth in 2006. Coinciding with the economic downturn in 2008 and 2009, construction, as a percentage of GDP, experienced a precipitous decline of its own. It bottomed at 3.5% in 2011, but has recovered only to about 4.2%. This metric has only been kept since 2005, so there isn’t an effective way of comparing recent trends with reliable historical data. All we can tell is that private construction expenditures have not only failed to recover to previous highs in nominal terms, but also has not returned to its previous proportion of the country’s economic output. It is important to note that nonresidential private spending has, in fact, surpassed previous levels, though residential development has come nowhere close. Additionally, one will notice that public construction has remained largely stagnant since 2011, and has similarly remained below peak levels.
Furthermore, it is revealing to consider construction within the context of population growth. Equivalent levels of construction at two distinct points can be differentiated by examining construction per capita. This gives a better indication of overall economic health. 1.205 Billion dollars were spent on total construction in February of 2006, which, following the economic collapse of 2008, has only recovered to about 1.189 billion in November and December of 2016. FRED does not have any record of construction spending per capita, so comparisons must be calculated manually. The population was approximately 325,268,000 in December, which means that there was $3.655 spent on construction per capita. The population in February of 2011 was 311,203,000, resulting in $3.86 of total construction per capita. This gap is even more conspicuous when considering that greater construction spending occurred in a smaller economy with a smaller population. This could have many implications. Of course, it is well known that the availability of subprime mortgages contributed to greater home construction before the crash. However, it is also true that demographic pressures to economic growth may slow real estate development as well. American workforce participation is historically low, and the proportion of working age people to the general population continues to dwindle due to aging baby boomers. In any case, both of these numbers show us that development has not quite recovered to its previous high.
Full employment is usually defined as any level of employment under 5%. Full employment means that all eligible workers regardless of skill level are in jobs. Economists believe that unemployment falls until it reaches the “natural rate” where everyone is employed and new hires only occur when people leave their current jobs for higher wages at other positions. The U.S economy has been in a state of full employment because the last unemployment numbers have come in at 4.7%. Usually full employment results in a period of inflation as a result of an increase in disposable income which drives up prices. The U.S economy is currently in a low inflationary period which contradicts the idea of full employment, but due to the recent recession, the current low inflation period can be seen as a result of that.
Full employment does not mean 0% employment, as previously mentioned, different types of unemployment can still exist. Structural unemployment is a result of a skills gap. Jobs are available but people do not have the necessary skills to fill these positions. This is more likely with large/sudden technological changes. Frictional unemployment is the amount of unemployment that results from workers being in between jobs but are still in the labor force. This is a result of misinformation and is seen as temporary unemployment. Finally, voluntary employment results from a person’s conscious effort to remain unemployed so they are no longer counted as being part of the workforce.
Because full employment does not translate to 0% unemployment, there is usually reason to believe that the unemployment numbers may be misstated. Unemployment numbers also seem understated from the changes in part-time and full-time employment. Firms hiring part-time workers do lower the unemployment rate, but these workers may not be happy in their current position and still want to work towards a full-time position that is not available. This questioning of the unemployment rate was even more prevalent in our latest presidential cycle where the current President stated that unemployment numbers were fake and too low, and even ventured to (falsely) state that unemployment may be as high as 42%. Unemployment may not be as low as 4.8% because of an increase in voluntary employment from the recent recession as well as structural unemployment stemming from the loss in manufacturing jobs and increase in technology heavy roles.
Overall, the construction industry has experienced far from rapid growth in 2016. With oil prices low, major capital projects from firms involved in the energy and power industry saw low demand, correlating to a lackluster year within the greater engineering and construction sector. Total construction spending growth from a year ago, including residential and nonresidential, has lagged, hitting the lowest level of year over year growth since 2012. Specifically, business fixed investment growth, which includes investment in property, plant and equipment, has reflected the pessimism of today’s construction environment, remaining very soft over the past year. A major trend impacting the residential, particularly single-family, construction industry has been demand. Millennials, with their urban attraction and debt-aversion, have much preferred renting to home owning.
It’s hard to not mention President Trump’s $1 trillion infrastructure spending plan when discussing public construction. Uncertainty surrounding magnitude and time table make this hard to predict, given that it took Congress over 10 years to pass its current long-term funding plan, which amounted to 1/3 the size of the proposed plan and half the time horizon. One part of Trump’s infrastructure plan that has really excited the construction industry is collaboration. The promotion of public-private partnerships might give the construction industry the boost it needs to return to its pre-recessionary glory. Regardless of the exact magnitude or timeline, policy makers on both sides of the aisle agree that significant investment into the crumbling infrastructure of the US is desperately needed, and quickly. The $124 billion municipal construction industry has experienced lackluster performance in the past 5 years, with only 0.3% of annual growth. Funding for many public projects has stalled amongst economic concerns, and growth in the industry is projected to remain modest for the next 5 years.
Long-run CPI trend
Figure 1: CPI for all items and all items less food and energy
This graph shows the long-run CPI trends since the 1950’s. Specifically, after 1955, when the reorientation of the economy following WWII was complete. There seems to be a steadily rising trend throughout the years as observed in the graph (See Figure 1). However, if you narrow the scope of the graph, you can see that there was a great deal of variation between and within years. It is important to keep that in mind though the long-term trend seems to be smooth up-wards trend.
The CPI is a “measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services” (bls.gov), so it makes sense that the CPI would continue to increase as time goes on because the price of the goods and services that people tend to buy also increases – especially if we’re considering prices in the mid-1900’s. However, it is important to keep in mind purchasing power – though the value of goods and services may have increased, it does not necessarily mean that the average household cannot afford the goods and services because the value of the dollar and their purchasing power also fluctuates.
Interestingly, adding the line, “All items less food and energy” gets rid of some of the volatility in the graph and gives us a cleaner look at the trend over the years. When reintroducing energy as a line, one can see the variation that is present (See Figure 2). However, the upward trend pattern over time is still present. This may be because energy prices tend to fluctuate more so than other items. Though food and beverages also adds more variation to the graph (See Figure 3), it does not seem to have as much volatility as energy.
Figure 2: CPI with energy as a line
Figure 3: When introducing food and beverages as a line
The Prof: Log Scale
Wednesday’s release of the Consumer Price Index (seasonally adjusted for All Urban Consumers) revealed inflation of .3% in December of 2016, leaving annual 2016 inflation at 2.1% before seasonal adjustment. This data release signals year over year price growth not seen since 2013 and a significant increase from the meager .7% inflation in 2015.
It is essential when interpreting the CPI to understand the portfolio that the index represents, and how subjective inclusion of different goods can affect the perceived signal. The 2015 reweighting of CPI components lists Housing at 42%, Transportation at 15%, and Food and Beverages at 15% of the index composition. The remaining 28% is composed of education and communication, recreation, medical care, and apparel. However, although food/beverages and transportation amount to 30% of the CPI portfolio, large potions of those subcategories are stripped from the data in order to exclude volatile energy and grain prices. This “core inflation” is named CPI For All Urban Consumers (Less Food and Energy), and is the version predominantly used by the Fed and other market actors in order to measure standardized price growth over time (although other indices not published by the Bureau of Labor Statistics are often favored).
Stripping out data from volatile external markets may provide a long-run inflation signal, but the exclusion of energy and food data will undoubtedly blur our short-run vision. Regarding Figure 1, we can observe that core inflation has remained between 3% and approximately 0.7% year over year since mid-2006 with a slow and steady decline through the recession, while total CPI has ranged from above 5.5% all the way down to -2% year over year. Looking at Figure 2, you can see the index separation between total and core inflation beginning in late-2014, showing the impact of energy price collapses across domestic prices. Although energy itself is not a subcategory of the CPI, energy consumption prices are included slightly in housing (gas, power utilities, etc.), but energy makes up a considerable and volatile portion of the transportation subcategory. Viewing Figure 3, notice the total index prices of energy and transportation, and how volatility in energy markets heavily impacts transportation costs (keep in mind, this is 15% of the CPI). Figure 4 shows the monthly percent changes in energy and transportation prices and gives light to the volatility caused by external markets. It should be noted that the prices of cars, tires, maintenance, and other non-energy factors keep these two indices from total correlation.
Although core inflation provides a steady observation of general price growth in the United States, the visualization of volatility in the transportation subcategory is enough to understand that short-run inflation should not be analyzed purely by the prices of clothes, movie tickets, and Benadryl. Although FED members and economists have labeled energy market volatility as purely transitory, that volatility has significant impacts in the prices of overall consumption in the United States.
Try to build a simple Excel simulation of our growth model, streamlined. We can embed it in a full production function, but just use the following for simplicity:
- Y0 = AK0α – output is a function of capital
- I0 = S0 = sY0 – investment is equal to savings
- K1 = K0 (1-δ) + I0 – capital grows with investment less depreciation
You can then calculate Y1, and create a bunch of rows. How fast does it converge? It’s clearer if you set (say) A = 10, with (say) K0=1 and depreciation not too much higher than savings so that the capital stock grows rather than shrinks, say s=8% and δ=12%. What you choose for α alpha doesn’t really matter, so say 0.33. [A=10 helps keep initial savings greater than initial depreciation so that the capital stock grows]
Of course you gain the ability to tweak it if you add in L labor with a growth rate (1+gl) relative to the previous period (row!). Ditto letting TFP “A” growth at (1+ga). You can start with these set to zero, but then let labor grow. But if you add L, you want to focus on Y/L not total output Y.
For many people, mortgage rates are the most important rates they will consider in their entire lives. Because of this, mortgage rates need to be understood and analyzed. This post delves into correlations that mortgage rates share with other interest rates and the maximum percentage of income that people should dedicate to mortgage payments. Looking at the 30 year mortgage rate graph from FRED doesn’t show much. One can see a huge spike in the rates during the early 1980’s, which can be attributed to high levels of inflation during that time. The closest comparable interest rate is the 10 year treasury rate. The rates almost perfectly match. If one compares the 30 year mortgage rate to a 1 year treasury rate, there is a much greater variance, but the general trends still remain. An important question becomes why the mortgage rate so closely follows the 10 year treasury rates. The answer comes from the average time the mortgage owner actually owns the mortgage. In other words, on average the mortgage is paid off in 10 years. For this reason, the 10 year treasury rate serves as a very accurate measure of the mortgage rate.
One of the more important facts homeowners need to know about mortgage rates is that a lower mortgage rate is better than a higher interest rate. This comes from the monthly payment the homeowner would pay. A lower interest rate means there is less interest to pay to the lender, so the monthly payments are lower. Similarly, a higher interest rate means the homeowner has to pay more interest compared to lower interest rates per month. Another important decision to make on a mortgage is whether to have a fixed-rate mortgage or an adjustable mortgage rate. A fixed-rate means the interest rate remains the same throughout the term of the mortgage, while an adjustable interest rate means the interest rate changes occasionally over the term of the mortgage. What are the advantages and disadvantages of both? When should a homeowner decide to use one over the other? Fixed-rate mortgages are most effective when the mortgage rate is low, while adjustable rates may be a better decision at high mortgage rates. After all, there shouldn’t be many people happy with paying 15% interest on a mortgage for 30 years. An adjustable rate would allow that to potentially fall over the 30 years and save the homeowner a boatload of money.
People can find mortgage calculators all over the internet that allow them to quickly determine how much they would pay every month for a given house price and mortgage rate. This allows a homeowner to determine the maximum mortgage a lender would give. The general cut-off for mortgages is a monthly payment no greater than 28& of monthly income. Most lenders would feel uncomfortable lending money to potentially higher risk homeowners, especially after the Great Recession. Overall, mortgage rates play a huge role in economy and should be understood thoroughly.
We frequently see claims that a lower tax rate – particularly of corporate or personal income taxes – will work miracles. That is seldom the case.
First, the stated tax rates are not the realized tax rates. While on paper someone with $500,000 in income is paying a 39.9% rate, the rate on the first $450,000 in income is lower: even without deductions the average rate drops to 29%. With the normal array of deductions for mortgages and this and that it will be lower. Yes, as economists we look at marginal rates. But is someone in this tax position working for an hourly wage, where they can choose to put in another hour a day and thereby boost their income, but aren’t doing so because they just don’t earn enough? (Working 10-hour days and 6-day weeks gives 3000 hours a year, so they’re earning $160 an hour before tax, $100 an hour after income tax. [There are other taxes.] So dropping taxes to 30% so that they earn $117 an hour will lead them to work 3200 hours??)
The example above is for individuals. If your income is high, though, you’re unlikely to be earning all of this as a wage. Everything else – dividends, gains on shares – are taxed at lower rates. Unfortunately W&L doesn’t pay me with stock options… Anyway, the ability to shield income is far greater for large corporations, many of which pay no tax at all. Cutting taxes from zero to zero provides no incentive effect.
…there’s not much room for a boom…
Second, for companies the incentive is presumably to expand their business. Cutting taxes provides a higher return on investment. So does lowering interest rates. But we’ve already performed the latter experiment many times over, we have a lot of data on how that affects business investment. The answer is that on the margin the impact is almost nil. Most businesses have a hurdle rate of return that they use, one that surveys show is infrequently adjusted. The bottom line, robust across work in different time periods and different countries with several different methodologies, is that expectations affect investment, but that small changes in the cost of capital from shifts in tax rates and interest rates do not.
Exploring the latter is a potential Econ 398 Capstone project.
Now relative to the recent distant past there is room for improvement. As we’ve just read, that distant past was also one of higher real and nominal growth rates. In addition, the US economy is approaching its full potential (full employment, high capacity utilization). There’s not much room for a boom.
There are tax rates that are high, particularly for power workers. Even on minimum wage you need to pay social security and medicare taxes. You may lose benefits if you go back to work, even part-time at a low wage, if you’re on disability or receive certain other benefits. That can result in an effective tax of over 100%. Those in the next administration operate in a world where they never encounter such taxes.