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Ira S: Race can affect outcomes in many ways, including ones you mention. We are simply reporting the relationship between race and outcomes.
Curious George: There actually isn’t much variation by *lender* in the student loan space, since more than 90 percent of the outstanding student loan balance is federal loans (thus the lender, for virtually all, is the federal government). There is some variation in which firms service the loans. Our data don’t reveal lenders or servicers.
In reply to Kenneth Dreifus: Thanks for your comment. The main reason we selected those two sectors—retail trade and leisure & hospitality—was that they tend to be the lowest-wage industries and thus most susceptible to an increasing minimum wage. To illustrate this, at the national level, the average hourly wage of a manufacturing worker was $24.80 in 2014, versus $17 in retail trade and just under $14 in leisure & hospitality. Clearly, there are many manufacturing workers making less than that, but we wanted to gauge the effect on those industries with the largest exposure. Still, it is true that, as you suggest, manufacturers would be much more flexible about location than retailers or restaurants. And it may well be the case that the manufacturing sector has seen some employment effect from the minimum wage hikes. This is an area of research that we intend to pursue going forward. However, bear in mind that the fact that people may not cross state lines for a cup of coffee does not, by any means, rule out the potential for adverse employment effects—restaurants, hotels, and retailers can invest in automation, cut back on more labor-intensive services, and implement other such labor-reducing policies … or they may simply go out of business.
Jeffrey: Many thanks for your comments on this post. In terms of the spatial unit of this analysis, we focus on metropolitan areas as they are a good approximation of local labor markets. So, yes, when we refer to Fairfield, CT, in this post it is the Fairfield County metro area, not the town.
In reply to Or-el: Thanks for your interest in our post and thanks for your question. In response, we did not make any inflation adjustment to earnings nor do we believe this is a concern for a few reasons: 1) There are no broad measures of inflation at available at such a localized level. 2) Even if such measures were available, CPI inflation typically does not vary much geographically—even across widely different metro areas in the United States, let alone adjacent counties. 3) This analysis is based on where people work, and it is likely that at least some people who work on the New York side of the border live in Pennsylvania and vice versa. So even if the inflation rates were slightly different, any effects would be muted.
Jeffery: Thank you for your interest and your thoughtful questions. In this analysis, we focused only on employment and earnings, not the number of establishments. But I think it is almost certain that the USVI did indeed lose a larger proportion of such establishments, after the storms, than Puerto Rico. If so, the same question remains: why? Insofar as disruptions in the USVI (positively) affected Puerto Rico, it’s important to remember that PR’s economy and population are about 30x as large as the U.S. Virgin Islands’, so it’s unlikely that anything going on in the USVI would have any significant direct effect on Puerto Rico. As for the notion that some of the high-paying construction jobs may be going to workers imported from the mainland, that is indeed a possibility in both USVI and Puerto Rico—and that would cause *both* the post-hurricane earnings and employment levels to be somewhat overstated. However, to be included in the islands’ job and income counts, a worker has to be on the payroll of a local employer; contract workers from the mainland are not included. And finally the policy ratcheting up the minimum wage in the U.S. Virgin Islands was implemented long before Irma and Maria, so the storms would not have been a factor in that decision.
Peter: One key advantage of our transition rates is they measure the flow into delinquency -- thus the non-dischargeability is not an issue. It is true that the transition rate on student loans has not improved with the recovery, which is surprising especially with the multitude of repayment plans available to borrowers now. We are currently working on analyses on this topic (stay tuned!).
Stefan: Thank you for your comment and for reading our blog. The effective fed funds rate can print below interest on reserves (IOR) because government-sponsored enterprises (GSEs) are eligible to lend funds in the federal funds market, but are not eligible to earn IOR.
Wojciech: Thank you for your questions and for your interest in our work. Interest on reserves (IOR) has not served as a hard minimum rate at which all institutions are willing to lend funds because some institutions are eligible to lend funds in the federal funds market but are not eligible to earn IOR, such as the government-sponsored enterprises (GSEs). Clicking on “fed funds lending” in our interactive map shows that lending in this market could be done by two types of entities: banks and GSEs such as Federal Home Loan Banks (see https://libertystreeteconomics.newyorkfed.org/2013/12/whos-lending-in-the-fed-funds-market.html for details). The rates at which these entities are willing to lend are different because banks are eligible to earn IOR while GSEs are not. In an environment with abundant reserves, banks might have an incentive to borrow at rates below IOR from an entity that does not earn IOR to then hold the funds in their reserve account and earn IOR on those funds. This would reflect arbitrage activity rather than a real funding need and could lead the effective fed funds rate (EFFR) to print below IOR (see https://www.federalreserve.gov/econresdata/feds/2015/files/2015047pap.pdf for additional detail). In addition to IOR eligibility, other factors including the impact on their balance sheet influence the rates some banks are willing to pay for fed funds, which may limit on the rate a bank is willing to pay.
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.