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
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.
Treasuries and corporate bonds are two types of investments ruled by interest rates. The interest rate on corporate bonds will always be higher due to the increased risk of default. Treasuries are less likely to default because of the guaranteed safety in investing with a world power like the United States. If the country does end up in a bind, they could also just print out money to guarantee a return, whereas a corporation does not have that same ability. While Treasuries and corporate bonds will not have the same interest rate 99.99% of the time, any movements in the fed funds rate will affect both investments in the same manner.
The interest rate on a Treasury is assigned by the FOMC, and they are usually divided into three categories. Treasury Bills have the shortest maturity of less than a year and do not pay a fixed interest rate. Instead they are issued through a bidding process at a discount from par. Treasury notes have maturity between one and 10 years, and have a fixed-interest rate set by the Federal Reserve. Treasury bonds have a maturity of more than 10 years, and also have a fixed interest rate. As the interest rate rises, the price of treasuries fall consistent with other types of bonds. Because the return on these investments is so low, they’re normally seen as the safest type of investment and one of the best places to store money in order to avoid market risk.
Corporate bond rates have a higher interest rate than Treasuries, but depending on the issuer can still be seen as a safe investment. The interest rate and yield corporate bonds offer is dependent on their rating. Ratings higher than BBB can be considered investment grade and will have relatively low interest rates, with the lowest coming from any issuer with a rating of AAA (equal to that of the U.S government). Ratings lower than BB are seen as sub-investment grade and have high interest rates. Like any other bond, when interest rates fall the price of the bond will rise and vice versa. This means that corporate bonds are still subject to any fluctuation in the interest rates set by the Fed, even though they are not a direct reflection of Treasuries. Corporate bonds are inherently riskier because they do not have the guarantee of the U.S Government and will always have a higher interest rate even if the issuer has a low default risk
In his article, “What Is the New Normal for U.S. Growth,” John Fernald argues that the United States could plausibly see a decline in average GDP growth to a range of 1.5-1.75%, which is well below historic growth rates. He examines trends in demographics, education, and productivity to determine inputs into his calculation of expected GDP growth rate, in which GDP growth = growth in worker hours + GDP per hour. He uses the labor force growth rate, especially in relation to the overall population growth rate, to determine the growth in worker hours. Labor force growth is expected to be low due to a consistently low overall population growth rate as well as the aging of baby boomers so that they retire and leave the work force. The Congressional Budget Office predicted the labor force growth rate to be .5% per year for the next decade, so Fernald translates this to project a .5% per year growth in worker hours. To determine GDP per hour, Fernald examines past fluctuations in productivity growth. He argues that productivity growth cycles between strong and weak, or normal, periods, and thus compares the U.S.’s current low productivity growth rate to that of the last era of low productivity growth (1973-1995). If the U.S. returns to its 1973-95 pace, then GDP growth would be about 1.75%. However, Fernald also argues that it is very possible for productivity growth to be less than that of 1973-95 because educational attainment has plateaued and thus education does not contribute as much to productivity growth as it would have in the past. With this scenario, Fernald calculates GDP growth to be closer to 1.5%.
Fernald then argues that the primary reason for this relatively low expected GDP growth rate is demographics, and that a workers hours growth rate of .5% would have made growth in the 1973-95 period just as slow as the current rate. He then acknowledges that due to uncertainty about future productivity, it is very possible that his predicted rate of GDP growth underestimates actual growth, particularly if there is a new wave of productivity-improving technology as was the case during past productivity booms. Despite this possible inaccuracy, Fernald’s estimated GDP growth rate has strong implications for future economic conditions and possible policy changes. People can expect slower growth in wages, sales, and tax revenue, as well as lower interest rates. However, Fernald also argues that it is possible to improve productivity and thus improve these rates by encouraging innovation, improving infrastructure, and providing better (and possibly cheaper) education.
While this is an interesting argument, it is very oversimplified, especially when examining the role productivity plays in his equation for the GDP growth rate. He fails to discuss the effect of productivity growth, particularly technological innovation, on the growth rate of worker hours, as technology advances that replace workers’ jobs or make them more efficient could lead to a slower rate of worker hours growth. However, this improved technology taking the place of workers does not necessarily lead to slower GDP growth, as the work is still getting done. In this case, he would have underestimated the GDP growth rate. Therefore, I think he should restructure his equation for GDP growth to include a more nuanced interpretation of productivity, as well as change his argument so that demographic is not the sole determinant of the growth in worker hours.
- Arbitrage. With perfect foresight and no risk premium the return one-year bond should be equal to the interest rate on a six-month bond bought today, reinvested in a six-month bond bought six months from now. (1+i1)(1+i2)=(1+yr) reflects compounding. Now rates are quoted as one-year rates, so if the six-month rate is quoted as 0.6% then the actual return is (1+x)(1+x)=1.006 so sqrt(1.006)=(1+x)=1.0029955 or 1.003. That is, half the one-year rate is a very close approximation when rates are low. So if the one year rate is 1.0082, then the implied return for the second six months is 1.0083/1.003 = 1.0052. That is, markets expect the 6-month interest rate six months from now to be 1.04%. In other words, the FOMC will continue boosting rates at a steady pace.
- Here is what has happened since 1990 to long rates. I used the average of 20-year and 30-year bond rates, in part because there are years in which only one or the other was available. Just to check, I downloaded the excel spreadsheet as an .xml file (Excel 2004 format), imported into Stata, and regressed interest rate = constant + a*date + b*date2. That’s the trend line in the graph. (I forgot to convert the date sequence to give the proper x-axis labels. The series starts Jan 2, 1990 and runs to yesterday.)
- I also calculated the implied yield on long bonds, using the same approach as in (1) above, by comparing the yields on 7 year bonds and 10 year bonds, and 20 year bonds versus 30 year bonds. That graph shows that markets have built in somewhat higher yields.
- Is it sensible that over the next 30 years inflation will never top 2%? In other words, if I were to factor in a risk premium, what would change?
- Back out from that what markets “believe” growth will be 20-odd years from now.
- What questions does that raise? (Many, I trust!)
For your reference I’ve written about this on Autos and Economics.
under construction (duh!)
“Long” Treasuries yield a scant 2.6% over 30 years. Basic theory suggests this should should equal expected growth rate plus inflation. If a rise in the CPI of 2% per annum is the Fed’s target, then do investors really expect that to never be attained? or attained but with real growth bouncing around at 1% pa? Now we can – will! – poke around with data using FRED, and refine this query. Then we can link it to a simple growth model. That will lead to a set of topics revolving around productivity, aging, and saving for the future. Out of those can come a wide array of research topics for a capstone project.
This course will have several components. One is to do a research paper on a topic tied to the course theme. (I can consider other topics, if you can present a compelling case.) That will require you to explore the literature on a topic, and present what is known and what we economists would like to know.
A second component is to explore data. How is the US economy doing? Criteria, metrics, evaluation … learning how to locate data and set forth a concise, coherent argument is another part of what we will do. For that I will assign topics for blogs and class presentations.
Third, we will have policy proposals for you to critique. If you but scour the news you should find plenty.
The latter part of the term will evolve to incorporate your paper topics. Early on we will read a couple papers, particularly Robert Gordon and then several on technology; review the basic growth model, and explore the concept of “demographic dividend(s)” in the context of the US economy and countries such as India and China.
draft of Oct 28, 2016
Note: I have left material from previous capstones “up” but this term will be very different in content and approach.
Clements, Benedict, Kamil Dybczak, Victor Gaspar, Sanjeev Gupta, and Mauricio Soto. 2015. “The Fiscal Consequences of Shrinking Populations.” SDN/15/21. International Monetary Fund.
What is “fiscal sustainability”? They provide a very specific approach.
How do they use “solow”?
What is the “demographic dividend”?
We will continue our discussion of intergenerational transfers and overlapping generations issues. To make it concrete, we’ll look at the US Social Security programs.
- FAQs: http://ssa.gov/oact/ProgData/fundFAQ.html#&a0=-1
- Current SS system stats: http://ssa.gov/pubs/EN-05-10003.pdf
- Summary of program and issues: http://ssa.gov/OACT/TRSUM/tr15summary.pdf
- The full Trustees Report: http://ssa.gov/OACT/TR/2015/tr2015.pdf
- Wikipedia: See in particular for the history of the system: https://en.wikipedia.org/wiki/Social_Security_(United_States)
A report by the Census Bureau titled, Fertility of Women in the United States: 2012 describes the characteristics of mothers by variables such as race, location, and age. The factor that this post will focus on is educational attainment.
One measure that the report uses is completed fertility for women aged 40-50. Since most women are done having children by around 40, this table shows how many children a typical woman has had in her lifetime. This data is separated by education cohort. (I can’t figure out how to insert excel graphs into the post. So refer to table 2 on the pdf linked at the bottom) The table shows that as education increases, births per 1,000 women decreases. When you transform the data to births per woman, you can see that women with no high school diploma have on average 0.9 more children than women with professional or graduate degrees.
…fertility affects future growth…
Another interesting chart is figure three. For each five year age range starting at 15, the graph shows how birth rates change with education. For women under 30 years old, those with a high school diploma or less have much higher birth rates than the other levels. However, once you pass age 30, women with Bachelor’s degrees have the highest fertility rates.
Thus, women with little education tend to have more children and have them earlier in life compared to those with more education.
As a followup to our class conversation on summer jobs on Friday, here’s a link to an item on “What happened to summer jobs?” on the Forbes Modeled Behavior site.
This paper attempts to:
- compare GDP per capita level and growth across 17 advanced countries over 1890-2013.
- compare the level and growth of the main components (TFP, capital intensity, working time, and employment rate) of GDP per capita in order to see how they contribute to the GDP per capita difference.
- test the convergence hypothesis of GDP per capita and its components over different sub-periods.
The second half of this paper focuses on convergence. This is the hypothesis that economies with lower per capita income will tend to grow faster than the ones with higher per capita income. There are mainly 3 types of convergence: absolute convergence, conditional convergence, and club convergence. One of the two approaches to convergence is sigma-convergence. It refers to the reduction of dispersion of levels of income across economies with time.
Through data presentation and analysis, it yields the following results:
- All countries have at least one huge growth in GDP per capita in the 20th century, but in a staggered manner.
- Almost all countries have faced a huge decline in GDP per capita growth.
- The GDP per capita leadership shifted over years.
- Overall convergence among advanced countries.
- GDP per capita convergence to the leadership position is not always happening.
- Employment rates and hours worked did not contribute to the overall convergence process.
The paper claims that its analysis’ originality is that “it is presented over a long period, on a large set of countries, with data reconstituted in purchasing power parity and on the basis of, as much as possible, consistent assumptions.” However, most of the results it yields are simple observations such as “all countries experienced at least one big wave of GDP per capita growth during the 20th Century, but in a staggered manner” and “all most all countries have suffered, during the last decades of the period, from a huge decline in GDP per capita growth.” I think the paper could develop more into the implications of these observations. Also, since this is a time-series analysis, endogeneity and dual causality might present and I think the paper should address a bit more on that. For example, the paper talks about the impact of institutions on the components of GDP per capita. Is it possible that those components shape the institutions as well?