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Shane: Thank you for your comment. The Consumer Financial Protection (CFPB) provides a few resources that may help. Repairing a credit score takes time, and the CFPB does list best credit practices (pay bills on time, maintain low credit utilization, etc.), warning signs of fraudulent credit repair services, and even templates for writing letters to the credit bureaus. Some consumers go through a credit repair company, but these companies may mislead their customers. In September 2016, the CFPB issued a consumer advisory on how credit repair services were charging excessive (and sometimes illegal) fees. A list of credit counseling agencies approved by the federal government to provide counseling to individuals before they can file for bankruptcy can be found here: Consumers also may dispute information with Experian, Transunion, and Equifax directly on the credit bureaus’ websites. The CFPB provides free templates for disputing incorrect credit information with either a credit company or a bank and offers step-by-step guides on how to fill out such letters. For an example, see this link: Many large cities also offer vetted, lower cost credit counseling services. New York City, for example, offers these through the Department of Consumer Affairs Office of Financial Empowerment.
Rebecca: Thank you very much for your comment. We hope to be able to look further into the considerations that you identify.
Sabeth: Thank you for your comment. It is correct that agency MBS are treated differently from Treasury securities in various regulations affecting commercial banks. These differences are among the reasons why purchases of Treasuries and purchases of MBS may have somewhat different cross-sectional effects, as discussed in our post.
Bob: Thank you for your comment. We agree with your statement that “A prohibitive tariff will stop ALL trade and cause a zero trade balance.” However, even if we know the trade balance at the end points, it doesn’t mean we can predict how it will evolve if we were to raise tariffs from where we stand now to some non-prohibitive level. As we point out in the concluding statement in the blog post, “While we cannot predict the size of the trade deficit, what seems clear from our analysis is that higher import tariffs will reduce both imports and exports.”
Rajat: Thank you for your comment. As you point out, exchange rate movements can affect exports. However, this was not the focus of our analysis. Instead, we were trying to identify how changes in import tariffs on intermediate inputs in China would affect China’s exports to the U.S. We do this by looking at differential effects—essentially comparing Chinese exporters to the U.S. that imported inputs subject to a big drop in import tariffs with those that experienced a small change in import tariffs for their inputs. All of the Chinese exporters were exposed to the same exchange rate changes but they differed in their exposure to input tariff liberalization. The Chinese firms in industries that experienced the largest fall in input tariffs were the ones that experienced the relatively largest boost in exports to the U.S.
Alan: Thank you for your interest in the blog post and comment. The trend has been driven by healthcare and in recent years by energy and technology (using the Fama and French 12 industry portfolios to define industries where technology refers to business equipment). However, we see less of a trend and a smaller percentage of negative EBITDA firms on a value-weighted basis. We also don’t see changes in the percentage of public firms with negative EBITDA as related to the tax law changes.
Yaw and Eric: Thank you for bringing these points to our attention. The post has been corrected.
Nat: Thank you for your comment. The primary dealers report information to the Fed on their trading activity in Treasury securities, broken down by security type and maturity bucket. The Fed publishes this information in aggregated form, available at the following link (scroll down to “Statistical Releases”):
Shawn: Thank you very much for reading the blog and for the very interesting question. According to my model, an increase in risk-free rates and risk premia, such as the one you describe in your question, would lead prime MMFs with relatively low default costs to increase their risk taking. However, the effect on prime MMFs with high default costs would be ambiguous because the increase in risk-free rates would lead them to take more risk, but the increase in risk premia (if it reflects an increase in underlying risk, as I assume in my model) would push them to take less risk. As a consequence, the effect on the overall industry is ambiguous. It depends both on the magnitude of the risk-free rate rise relative to the surge in risk premia and on the relative size of the two groups of funds (more specifically, on the distribution of default costs in the industry). Finally, as you rightly point out, keep in mind that the current environment is very different from the one I study in my paper, especially because of the new SEC regulation of the MMF industry, which requires all prime MMFs to adopt a system of liquidity gates and redemption fees and institutional prime MMFs to move from a stable NAV to a floating NAV.
Wilson: Thanks for your comment. While liquidity in the bond market may be an issue (though it is unclear in which way it would bias our results), we obtain a similar conclusion when we rely on CDS spreads. As for the data request, we are not at liberty to share proprietary data, but it can be accessed via subscription to Bloomberg and Markit.
Cornelius: Thank you for reading our post and for your comments. To the extent that there was a “subsidy,” the widening in the rating gap could be a sign of its reduction, consistent with rating agencies’ announcements of a decline in their expectation of government support in the event of financial distress/failure. However, the absence of a similar widening in bond and CDS spreads is not consistent with such a reduction. It would be interesting to extend the analysis to the next tier of institutions, as you suggest, but data availability limits our ability to implement this idea.
Thank you for your questions. There were dealers who borrowed from both programs, although borrowing via the TOP and the regular TSLF were not perfect substitutes because of differing borrowing intervals. Information on borrowings by program, dealer, and date is available here:
Thank you both for your comments. In response to Yaw’s question about possibly changing collateral value, the program required that collateral be valued daily by the clearing bank. Adjustments to collateral levels might then need to be made to maintain the designated margin amounts. For additional details on the program’s terms and conditions, see:
Toggle Commented Feb 26, 2018 on Options of Last Resort at Liberty Street Economics
Thanks for your comment, and interesting question. HELOCs are the only type of debt that have persistently declined since the recession. However, a close look at the HELOC balances in North Dakota reveals that the pattern is indeed different – HELOC balances have increased in North Dakota since the last peak, and ND is among only a small handful of states (including ND, SD, PA, & WV) that have HELOC balances that are more than 10% higher than their previous peak. By contrast, there are 40 states that are still more than 5% below their 2008Q3 peak. We hope to release some new analysis on HELOC use in the coming months, so please keep an eye out for it!
Thanks very much for your comment. Regarding the first part, it is difficult to isolate all the drivers of changes in jumbo mortgage supply; of course one cannot rule out that the stock market would have some effect on bank risk appetite. Regarding the second part, we track availability and pricing for mortgages with a constant loan-to-value ratio. Although rising home values would increase equity for existing homeowners, it shouldn’t necessarily lead to reduced interest rate spreads holding the loan-to-value ratio at origination constant. However, if lenders perceive that the likelihood of a home price crash is lower now than a few years ago, because of the strong economy and housing market, that could lead to lower interest rate spreads as we observe in the data. One could argue that lenders should be cautious in extrapolating recent home price trends: as the 2007-2010 period illustrated, the more home prices increase, the further they could potentially fall.
kd: Thanks for your interest. “Banked” means a person or household has a checking account regardless of how they access the account, so someone with an account but without remote access (via phone or internet) would still be considered banked. In answer to your other question, the 2015 FDIC survey we cited shows that 36.9 percent of banked households in metropolitan areas (principal city) accessed their account primarily online versus 27.4 percent in non-metro areas (Appendix Table B.8, p. 82 ). The survey may also have separate numbers for metro versus rural. Here’s the link:
Thank you for your comment. We very much agree with your interpretation: the fact that Treasuries are used as collateral is likely to be a key factor in what we call the liquidity convenience yield. Having said that, in this work we only want to point out, and quantify, the relationship between the rise in the convenience yield on the one hand and the secular decline in Treasury yields on the other. Speaking of which, a number of other readers pointed out to us that the timing of our post was curious in light of the fact that yields have risen of late (and spreads have narrowed). To this we would like to point out that our analysis in the Brookings paper did not imply that we expected the safety and liquidity premiums to remain high, and Treasury yields low, forever, but rather that liquidity and safety are important drivers of Treasury yields. As financial conditions ease, and the supply of safe assets increases, those premiums may well come down and yields correspondingly increase.
JAFD: Thanks for your comment. Our use of a ten-mile cutoff in defining a banking desert follows this paper: Though the ten-mile metric seems standard, we see your point and we’re already considering experimenting with a lower cutoff. While lowering the cutoff could alter our finding of essentially no correlation between the share of state population that is unbanked and the share of banking desert “dwellers,” it wouldn’t change the fact from the FDICs Financial Exclusion Survey that “inconvenient location” ranks far below other reasons (high fees, inadequate savings, and mistrust of banks) as reasons unbanked household cite for not having a checking account. That finding, as much as our own (on the location of banking deserts mostly in deserts and rural areas and the zero correlation just mentioned), informs our conclusion that the notion of “banking deserts” and emphasis on proximity, may distract attention from those other, evidently more important, reasons why households decide to go without a checking account. We also agree that local knowledge and soft information matter for credit availability, which is a topic that we discussed in our previous post: Thanks again for the comment and reading our post!
Ron: Thank you for your comment. Our results are obtained from analysis based solely on our data (described in the blog post) and do not reflect any political influence. We have a related paper that presents first causal analysis on this question using another data source and obtains very similar results. It also finds that for-profit college attendance leads to substantially higher default rates relative to public college attendance (Armona, Chakrabarti, Lovenheim, FRBNY Staff Report 811, April 2017). Similar results were obtained in other (independent) research by David Deming, Claudia Goldin and Lawrence Katz (Journal of Economic Perspectives 26(1), 2012).
Paul: Thanks for your question. Unfortunately, we do not have data on student employment, nor do we have data on time of day the classes were taken, so we cannot disaggregate our analysis by these indicators. What we do see though is that four-year for-profit students are much more likely to default than their counterparts in not-for-profit and public schools. While two-year for-profit students are also more likely to default than community college students after their late 20s, the gap is considerably lower than the four-year gap. This is because the community college students also have high default rates (unlike four-year public school students). However, our data do not allow us to understand conclusively the specific reasons why the default rates of community college students and four-year for-profit students are relatively similar.
Mary: Thanks for the comment and suggestion - we have been monitoring student and auto loans for some time but have not formally investigated the role of securitization directly. While securitization plays a role in auto loan issuance, student loans are predominantly issued (and guaranteed) by the federal government, with private loans issuance being mostly restricted to borrowers with high credit scores.
Mary: Thanks for the comment and suggestion - we have been monitoring student and auto loans for some time but have not formally investigated the role of securitization directly. While securitization plays a role in auto loan issuance, student loans are predominantly issued (and guaranteed) by the federal government, with private loans issuance being mostly restricted to borrowers with high credit scores.
Ennio: There definitely is a strong relationship between earnings and degree attainment [Card (1999, 2001); Oreopoulos and Salvanes (2011); McMahon (2009); Oreopoulos and Petronijevic (2013); Heckman, Humphries, Veramendi (2016)]. This is part of the reason that the loan default rate is so much lower for those who attended private nonprofit and public four-year programs, and for those who attained BA degrees (compared to dropouts). The results by major at selective colleges may be more surprising, showing only slightly higher defaults for arts/humanities majors. This reflects the fact that the employment rate is very high for all students who attended a selective college, irrespective of major, and suggests that it is employment status more than major that determines loan repayment for that select group of students.
In reply to James Chen: Thank you for your comment. You are correct that the one-month volatility risk premium is only a short-dated measure of expected returns. Our upcoming post on Wednesday will discuss the term structure of implied volatility and risk premia for longer maturities.
Ryan, thanks for the comment and question. I agree that household deleveraging and tighter credit supply following the GFC likely were among the factors behind the weakness of consumer spending during the recession and afterwards. Those factors also have been cited in commentary about consumer spending weakness, including speeches by New York Fed President Dudley, and in academic research (for example, a number of papers by Atif Mian and Amir Sufi). Nevertheless, as documented in the post, discretionary services spending in this cycle remains subdued compared to previous cycles, even after the deleveraging process appears to have been completed. So other factors, including continued slow productivity growth that I cite in the post, probably have been contributors. How much to assign to these various factors requires more research.