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Many thanks for your comment, AG. You raise a good point on the potential influence of intraday liquidity buffers. While we didn’t explore this in our most recent analysis, this could represent an interesting question for future analysis. Relatedly, you might like to see the Liberty Street Economics post “Stressed Outflows and the Supply of Central Bank Reserves” (2/20/19), which analyzes one-day stress horizons for LCR cash outflows (it does not cover RLEN/MOL, however). https://libertystreeteconomics.newyorkfed.org/2019/02/stressed-outflows-and-the-supply-of-central-bank-reserves.html
Lloyd: The Haver codes for the profits are below. It is the sum of these two series. Nonfinancial Corporate Business: Profits After Tax (SAAR, Bil.$) - BNCPAT@USECON Corporate Profits with IVA & CCAdj: Rest of World (SAAR, Bil.$) - YCPR@USECON
Thank you for your comments. In regard to David Fawer’s comment, our analysis includes all four-year colleges that received federal Title IV funding (NCES, U.S. Department of Education, IPEDS -- https://nces.ed.gov/ipeds/). To understand heterogeneous effects by selectivity of college attended, we match this dataset with Barron’s rankings and classify colleges in the top three Barron’s tiers as “selective” and rest of the colleges, including those not in Barron’s, as non-selective. The results reported in the blog post indeed show interesting differences by college selectivity. In general considering all Title IV four-year colleges, we study different cuts in the data by college type to understand whether labor market outcomes differed between students from different types of colleges and between boom and bust (after controlling for several time invariant characteristics and a rich set of observable characteristics). In regard to Phil’s comment, we have released previous posts that look at student debt ( https://libertystreeteconomics.newyorkfed.org/2016/09/the-changing-role-of-the-community-college-and-for-profit-college-borrowers.html )and default ( https://libertystreeteconomics.newyorkfed.org/2017/11/who-is-more-likely-to-default-on-student-loans.html ) of students by college type. The last chart and the corresponding discussion in the latter blog looks at the role of family background in student loan default. In a related blog post ( https://libertystreeteconomics.newyorkfed.org/2017/11/are-student-loan-defaults-cyclical-it-depends.html ), we found that default dynamics vary by the phase of the business cycle (as revealed through analysis of the Great Recession period and the boom that preceded it) and college type matters critically here too. In regard to Ian Wilder’s comment, this is a good question but our current datasets do not allow us to convincingly answer this question. The data used in the post allow us to see debt originations but not debt repayments. We have another dataset that allows us to identify debt payments but not earnings. While this latter dataset does not include comprehensive earnings data, it does help us to get at another measure of well-being, homeownership. You may be interested in a previous blog post ( https://libertystreeteconomics.newyorkfed.org/2017/04/diplomas-to-doorsteps-education-student-debt-and-homeownership.html ) on student debt and homeownership that casts some light to your question.
Giulio: The after tax rest of the world profits come from table 10 from the GDP press release (https://www.bea.gov/system/files/2019-06/gdp1q19_3rd_1.pdf)
Thanks to all readers who posted comments. We respond to a number of queries in this note. As for what we assume about the earnings path of a typical worker, we estimate total wages over one’s entire working career using various statistical techniques, which we outline in some detail on our earlier study (https://www.newyorkfed.org/research/current_issues/ci20-3.html). Essentially, we model lifetime earnings profiles where earnings vary by age from 22 to 64 for college graduates and 18 to 64 for high school graduates, and use these figures to calculate the college wage premium values that factor into our rate of return analysis. This is different than the average wages we cite in the first chart in our post, which is meant to provide only a rough estimate of how the average college wage premium has evolved over time, and is not used in our rate of return estimates. We do publish estimates of average early- and mid-career wages earned by college graduates overall and by major on our website the Labor Market for Recent College Graduates (https://www.newyorkfed.org/research/college-labor-market/college-labor-market_compare-majors.html).
Thanks to all readers who posted comments. We respond to a number of queries in this note. Unfortunately, we don’t have any projections for the cost of college in future years. As for the current cost of tuition, $32,000 for a bachelor’s degree may seem low (our data come from the College Board, and do not include community colleges), but there are some reasons why our estimates are not as high as one might expect. - First, they don’t include room and board, which, as we note in the post, are not considered an out-of-pocket cost of college from an economic perspective. - Second, our computations are an average among both public and private schools, and tuition at public institutions is less than a third of that at private institutions. - Third, our figures represent the net price, as opposed to the sticker price, meaning they deduct financial aid received by the average student.
Matthew: If you are interested in more detail on household credit, FRBNY’s latest Quarterly Report on Household Debt and Credit reveals that total household debt rose by $124 billion to reach $13.67 trillion in the first quarter of 2019—an increase of 0.9 percent, compared to a rise of 0.2 percent in the fourth quarter of 2018. This past quarter, balances climbed by 1.3 percent on mortgages, 0.5 percent on auto loans, and 2.0 percent on student loans, while total credit card balances fell by 2.5 percent. https://www.newyorkfed.org/microeconomics/hhdc The Federal Reserve’s May 2019 Financial Stability Report offers a consolidated view of business and household debt, summarizing the vulnerabilities to financial stability arising from private borrowing. The May report also has more analysis on corporate borrowing. https://www.federalreserve.gov/publications/2019-may-financial-stability-report-borrowing.htm
NOTE: In addition to the reader comments posted here, we received an offline question that we wanted to answer publicly: Q. Are the costs in the table total costs or incremental costs? Are you saying that the new tariffs will cost the average household $831 per household per year in addition to the $414 per household per year cost that they were already paying? A. The $414 number in the upper panel of the table is the annualized total cost of all of the new tariffs imposed in 2018, e.g., including the steel tariffs, solar panels, washing machines, and all three tranches of the China-specific ones. The aggregate numbers come from our recent study [https://www.nber.org/papers/w25672.pdf]. To put these costs in perspective, in the blog post we divided the annualized monthly number by the number of households in 2018 reported by the Census (equal to 127.6 million as reported in the note to the table). The total cost number includes both the deadweight loss and the added tax payments to the U.S. government. Additional tax payments could, in principle, be rebated to households but the $133 per household deadweight loss is an efficiency loss that represents a real loss to the United States. The lower panel of the table uses the estimates from our paper to infer the total cost of the tariffs, taking account of the new 15% tariff on $200 billion of imports from China, which raised U.S. tariffs on these products from 10% to 25%. So the $831 total household cost is the total household cost of all tariffs that were raised in 2018 and this year.
In reply to tanstaafl: Thank you for your comment on our blog. While you are correct that our analysis is based on a partial equilibrium framework, another study [https://www.nber.org/papers/w25638] analyzed the costs of the 2018 tariffs for the United States in a general equilibrium framework and produced very similar numbers to ours. Thus, we do not think the choice of partial vs. general equilibrium analysis makes a large difference for the conclusions. In both of these studies, import demand is a function of domestic production, so the analysis does implicitly take tradeoffs between domestic and foreign production into account. As mentioned in the blog post, the efficiency loss arises whether U.S. purchasers switch to other foreign sources or domestic sources. We do, however, agree that we have not taken the costs of foreign retaliation into account in this analysis. These foreign tariffs may well be another source of loss for the United States.
Thanks you for your question. The immigration data include those from German enclaves in Eastern Europe.
Dear readers, Thank you for your interest in the post. I will address a number of the points raised in your comments here. First, some definitions: QE, or “quantitative easing,” is a type of unconventional monetary policy. In the context of this blog post, it describes the Federal Reserve’s large-scale asset purchase programs (LSAPs). Mortgage-backed securities (MBS) are collateralized claims on payments from one or more mortgages. Agency MBS are MBS originated by government-sponsored enterprises (GSEs). Regarding our findings: We see some evidence that the financing conditions for firms and households, and hence real economic activity, would have been worse absent the Federal Reserve’s actions. Further, we find that, after QE3, counties in the upper tercile of the MBS exposure distribution have higher employment growth (by around 50 basis points per quarter) than counties in the lower tercile. The effect is not only statistically significant but also economically significant. Regarding the challenge of establishing causality: As we mention in the blog post, it is inherently quite difficult to tease out what exactly the effects of a given macroeconomic policy are. However, we believe our evidence shows that, while QE could have had many effects on many different outcomes, it likely had a positive effect on employment. We went to great lengths in our study to avoid falling into analytical traps. For instance, our paper carefully documents and controls for observable differences between high-MBS counties and low-MBS counties. Altogether, even though we don’t know what growth would have been absent QE, we believe our findings are indicative of a positive effect of QE on real economic activity. -- Stephan Luck
We thank Yaw for his useful comments. Regarding the first comment, what we report in the post is the origination mortgage balance and not the home price. If the average loan-to-value ratio of mortgages was higher following the Great Recession, then a flat or rising mortgage balance for first-time buyers wouldn’t be inconsistent with falling home prices. We examined whether there was a difference in the share of first-time buyers and repeat buyers in bubble and non-bubble states and found that pre-Great Recession there was a higher share of repeat buyers in the bubble states compared to the post-Great Recession period. Regarding the second comment, student debt is positively correlated with income because larger student debt indicates a longer period of attending post-secondary education and a greater likelihood of obtaining a post-secondary degree (both of which are associated with higher income). At the same time, student debt has been increasing across age cohorts. Repeat buyers tend to have higher income and to be older than first-time buyers, which would have opposite effects on the amount of student debt they hold. The data indicate that the income effect is stronger than the age effect in this case resulting, on net, in higher levels of student debt for repeat buyers.
We thank Joe for his useful comment. A duration constant scorecard has the advantage that the sustainability results for each cohort are based on the same amount of time. We chose to present all of the information for each cohort and to remind readers that the sustainability results for the most recent cohorts will continue to evolve with time.
Kristos: Each respondent in the SCE is asked to report a subjective probability of losing his or her job in the next twelve months, with answers necessarily bounded from 0 to 100. We then divide these reported percentages into bins. For example, per the upper panel of the first chart, just over half of the sample provided an answer in the range from 0 to 10 percent for the probability of job loss in the next twelve months. A response of precisely 10 percent is placed into the next higher bin (10-20 percent).
Thomas: Thanks for your comment. We are indeed working on a review article that will discuss these links with the post-Keynesian literature.
Jen: Thanks for your question – this chart depicts the percent of balances that became 90 or more days past due during each quarter.
NOTE: In addition to the reader comments posted here, we received some off-line questions that we wanted to answer publicly: Q. Why are your balance-weighted 90+ days past due (dpd) rates higher than those widely circulated in the industry? A. The 90+ dpd rates widely circulated in the industry stop including debt in that rate once it has been “charged off” on the lenders’ books. Our rates are different because we use the credit bureau data to show the status of household debt from the borrower’s perspective. Thus our measure of 90+ dpd debt includes “severely derogatory “ balances that loan servicers continue to report to credit bureaus (even after that debt has been “charged off” on the lenders’ books). A large share of 90+ dpd auto debt is reported as severely derogatory, and much of it remains on borrowers’ credit reports for years after it becomes severely derogatory. This also helps to explain why the 90+ dpd delinquency rate (the stock of borrowers or debt that is 90+ dpd in this period) is higher than the delinquency transition rate (the share of debts or borrowers that was current or less than 90+ dpd last period, but is 90+ dpd this period).
Q. How do you get the 7 million delinquent borrowers figure? A. The first thing to note is that this figure refers to borrowers rather than debt. In the Quarterly Report we report balance-weighted delinquency rates, either as transition rates or stock delinquency rates. The blog focuses on people, and the percentage of people with a seriously delinquent or severely derogatory auto loan on their credit reports is higher (7.88% of the 89 million people with at least one auto loan in 2018Q4) than the percentage of debt that is 90+ dpd (4.47% in 2018Q4; the average borrower with a seriously delinquent or severely derogatory loan has less than the average amount of auto debt). The time series of borrowers with 90+ dpd auto debt is now attached to the post.
Carl: This is a very interesting question and may well explain some of the growth in the popularity of auto loans, but whether a driver intends on using the vehicle for rideshare apps is not denoted on credit reports and not something we are able to observe. It’s worth noting again that the loan origination volume we observe in our data has been commensurate with the sales volume of new and used autos.
Tom: It would depend on the how the lender classifies their lender type and the loan description they use to report to the credit bureau, but we don’t see this much detail, unfortunately.
Thank you for the excellent question. China does indeed have a very high household savings rate, both in absolute terms, and relative to other countries. This is a key reason why a relatively small proportion of households in the survey data (according to the author of the working paper we cite in the blog post) report liabilities exceeding their assets, and why risks to financial stability are a longer term watch point. Of course, desired household savings rates in China are high for a variety of reasons, including population aging, gaps in the social safety net, the need to pay for education and housing, and financial repression. Households could respond to higher debt service costs through a combination of lowering savings or lower consumption, providing a “cushion” against missing payments on their financial obligations, but also weighing on growth. Household savings have been heavily invested in property, so a decline in property values could also lower consumption spending through wealth effects. Finally, in China a fairly large amount of business activity is recorded under the household sector, suggesting that higher debt levels eventually could weigh on economic activity more broadly.
Thank you for your question. As we mention in the post, there have been two main drivers of the downward trend in U.S. manufacturing jobs. First, technological developments have enabled manufacturers to produce more with fewer workers; for example, by using robots or other computer-assisted technologies. Second, greater globalization has allowed the U.S. to import more labor-intensive manufactured goods from countries with lower labor costs. As to the reasons behind the partial rebound in manufacturing jobs since 2010, that remains an open question. Some of the factors you mention may very well be at play, such as rising labor and other costs abroad.
Thank you for your feedback. We agree it’s interesting to think about the alternatives when no formal bankruptcy process, such as chapter 11, exists. One alternative is an out-of-court settlement, such as an exchange offer. Evidence shows that the direct costs can be lower for exchange offers compared to chapter 11. The possibility you suggest -- that creditors seize assets on an individual basis --- would likely result in significant litigation over claim priority, resulting in delays and legal expenses. Moreover, these alternatives may not be feasible for a large, complex firm such as Lehman. In any case, these are complicated issues, and beyond the scope of our current focus.
Since the washing machine tariff was assigned to February, then the graph will show an increase between January and February. There would only be an increase in March if there were another new tariff in March. The reason that graph shows an increase in June and July is because there were new tariffs assigned in June (a second wave of the steel tariffs) and in July (the first tranche of the China tariffs). I hope this helps clarify the points in Figure 1.
Thank you for your comment. Because the prices in the PPI are the prices reported around the middle of the month (https://www.bls.gov/ppi/ppifaq.htm), we decided to assign the tariff change to the following month if the date of implementation was after the 15th and to the current month if the tariff was before then. For example, the washing machine tariffs were implemented on January 22 so we assigned this tariff change to the month of February.