This is Blog Author's Typepad Profile.
Join Typepad and start following Blog Author's activity
Join Now!
Already a member? Sign In
Blog Author
Recent Activity
Kelvin: Although we don’t have information on respondents’ professions from the survey, it has a set of questions on how feasible it is for a respondent to work from home. We can use that information to do more analysis on some of those issues.
Hi Ed, Thanks for your comment. As you say, the blog post focuses on the impact on the bond market as well as the counterfactual impact on employment and investment if this market were to become dysfunctional. As we discuss in more detail in the paper, though the primary effect of the facilities announcement was to reduce the corporate bond default risk premium, default probabilities also declined upon the introduction of the facilities. The equilibrium implications are worth considering. To the extent that these interventions encourage dealers to resume intermediation of corporate bonds, the expectation of intervention should be a net positive. On the contrary, in order to conclude that the facilities contribute to a debt bubble, you would have to find evidence that the facilities are distorting prices and issuance decisions significantly. By pricing the PMCCF at a market price plus a penalty rate, and by buying a market index, the facility design attempts to limit those distortions. Even the “fallen angel” segment, which conceivably could benefit most from market access, has issued bonds at market prices in the public debt markets, without accessing the facility. Furthermore, we haven’t seen firms changing bond characteristics -- for example, by shifting issuance to below 5 year maturity -- to specifically target facility eligibility.
Hi Brian. Thanks for your comments. The TBTF reforms “worked” in the sense that average funding subsidies are statistically lower post-reform relative to pre-reform. However, if your point is that implicit subsidies are not, on average, statistically zero during the post-reform period, that’s also true.
In reply to Svein Olnes: Thanks for your interest. We’re aware of Lewis’s post and his different take on the relative confidence a payee can have for determining the validity of a dollar bill versus a digital currency. As we tried to explain in our post, and others have observed as well, dollars have readily identifiable and well-known security features that allow holders to establish them as authentic in the moment, while a digital currency must be verified through a third-party.
Gary: Thank you for your comment. You are correct that there is a wage Phillips curve, mapping slack in economic activity (or more narrowly, unemployment) into nominal wage growth. This is indeed the relationship originally uncovered by A.W. Phillips. Economists also talk about a price Phillips curve, which maps slack—or more narrowly, in the New Keynesian tradition, measures of marginal costs—into price inflation. As we discuss in more detail in the paper, the wage Phillips curve seems to be alive and well, as you have also found. It is the price Phillips curve that we find has become flatter after 1990. To put it differently, in the transmission from labor market slack to wage and price inflation, the first link remains solid, but the second one has become much weaker.
Sean: Thank you for your very good question. We agree that payment methods may have changed with the pandemic. Our analysis is able to take account of this change. Our estimates are coming from comparing states that have just reopened with states that are about to reopen. Unless payment methods change differentially at the time of state reopening, changes in payment methods will not affect this comparison.
Robert: Thanks for your comment. The objective of our post is to simply understand who may be eligible for the mortgage and student debt relief measures offered under the CARES Act. Our August 6 post* provides some color on the changes in balance reduction seen during this time, and indeed the forbearance and waiving of interest had a direct impact on the repayment rates. *
Alan: Thank you for your interest and comments. 1. Does the households with children category include single parent households with children? Authors' response: Yes 2. The composition of households without children also includes the young as well as older cohorts, including retires. Having said that, probably skewed toward the older cohorts given the 30% response reporting a decline in income even though only 19% reporting household job loss. That likely reflects asset income, which also leaks into households with children. Authors' response: The group of households without children does indeed consist of a mix of those groups. Our (weighted) sample is nationally representative. It is important to note that beside job losses many households experience income losses due to a decline in working hours and cuts in earnings. We are collecting new data on non-labor income so we hope to have more insight into any declines in asset income in the future. 3. I would like to see the breakdown by race for the single parent households, given the high percentage of black single parent households. Authors' response: Of the single parent w/kids households, 71.8% are female, 35.3% are non-white and 29.2% are female & non-white. Unfortunately we are unable to show all estimates separately for each subgroup of single parents because of small sample sizes. 4. The race breakdown would also be interesting for the income breakdown. Authors' response: Agreed, but again sample sizes are not big enough to separate out and obtain precise enough statistics on non-whites.
Gabriele, thank you for your comment. We think of "identity" in a broad way. You are correct that it is possible to transact with Bitcoin while remaining anonymous. Nevertheless, to transact with Bitcoin any user must obtain a public-private key pair, which corresponds to that person’s identity within the system. Regarding your second point, whether a currency can be counterfeit is only one aspect of what makes it secure. There are many well documented cases of people losing their Bitcoins because they were not able to keep their private key secret.
Jessica, thank you for your comment. As you note, it is common in central bank and academic circles to use the term ‘digital currency’ for any system that allows for electronic payments, include Fedwire. This is also the way we think of digital currency in this blog post. Our main point also holds for the more narrow definition you prefer, as the example of Bitcoin should make clear.
Hi Jon. Thank you for your comments. We broadly agree with you and have expressed similar views in a previous LSE blog.
Julia: The role of the portfolio balance channel is a complicated debate in monetary theory, and our blog post was an effort to add some context by reviewing Japan’s experience with YCC. As we noted, it is not clear how effective YCC will be, relative to asset purchases, in allowing the BoJ to reach its inflation target. In addition, one could argue that Japan’s experience does not help settle the portfolio balance debate since YCC followed purchases on a scale not undertaken by other major central banks.
Ram: Thank you for your question. The intuition behind a negative natural real interest rate is that real interest rates ought to be negative in order to reach the “efficient” level of output and employment.*** Recall that low real rates stimulate consumption, and hence demand. The fact that the natural real rate of interest is lower than the actual real rate of interest suggest that there is a demand deficiency: consumption and demand should be higher than what they currently are in order to reach the “efficient” level of output. Depending on whether the model interprets the shocks that hit the economy over the past few quarters as, broadly speaking, demand or supply shocks, the natural real rate of interest can be positive or negative. Note that while in the mean projections the natural rate of interest is negative, there is much uncertainty around these projections, reflecting the uncertainty about the nature of the Covid-19 shock in the model. *** This post discusses the definition of the natural rate of interest in more detail:
Leslie: First of all, thanks a lot for your comments and your interest in our work. On the first point, we want to emphasize two key features of our model. First, we consider a two-region model (consisting of the U.S. economy and a set of emerging economies), where these two regions are interlinked through both international trade and cross-border lending. Goods produced in the U.S. and those produced in emerging economies are imperfect substitutes. In this respect, from the emerging economies’ point of view, demand for their goods from the U.S. is not infinitely elastic – the volume of exports is determined jointly by demand and supply. Second, we assume dominant currency pricing. This means that export prices are set in dollars, and sticky in the short-term. For this reason, the expenditure-switching effect of currency depreciation is much weaker than in the classical Mundell-Fleming framework (which assumes producer currency pricing). On your second point, the risk premium is only partly determined exogenously. It is important to note that the risk premium also reacts endogenously to the state of private-sector balance sheet conditions in the domestic economy. A monetary policy tightening in response to a risk premium shock, to the extent that it pushes down economy-wide asset prices and asset valuations of private sector banks and firms, will cause a further increase in the risk premium in these economies. As we note in the text, for the typical borrowing firm a currency depreciation raises the value of just a fraction of its liabilities (since part, but not all, of the average firm’s debt is in dollars), but a monetary tightening adversely affects the value of all of its assets – thus still weakening its balance sheet, despite the presence of foreign-currency liabilities, and thereby prompting a further rise in the risk premium.
Ric: We formatted the graphs in such a way to best compare the size of the discontinuity rather than the overall scale of the graphs, but the graphs of the discontinuities for ages 15-34 and 35-59 have slightly different scales. In fact, the number of cases per capita for 35-59 year olds is somewhat higher than that for 15-34 year olds on average and at the border. Furthermore, out of the 400 German counties included in the analysis, in 301 counties the number of cases per capita for the older group exceeds that for the younger group. While the COVID-19 epidemic in Germany indeed was concentrated among the young to a greater extent than in multiple other countries, Germany as a whole does not appear to be an exception to the medical community consensus that you cited.
Dino: We focused on the case of Germany because there are smaller differences across the border as both sides have the same central government. Looking at Spain vs. Portugal (or Northern Ireland vs. Ireland, where BCG-vaccinating Ireland has more cases per capita than its non-vaccinating neighbor) would introduce more confounding factors into the analysis.
Daniel: Thank you for your question. When you have multiple jobs and lose one of them (or experience reduced hours at either or both), you can file a partial loss claim. Whether or not you qualify for unemployment benefits still depends on your state’s eligibility criteria, and so there isn’t much difference there in most states. However, the weekly amount you will receive conditional on qualifying depends on the amount you are still earning. Again, the exact amount you will get varies across states. How do such job losses factor in the official statistics? The payroll survey reports the net change in number of jobs. Let’s take a person with two jobs and suppose that a person loses both of them and does not find another work. This would count as two jobs lost on net in the payroll (establishment) survey data. The employment and unemployment rate statistics in the household survey, however, are based on the number of people, not of jobs. Therefore, in the example above, the person would count as one unemployed.
Frank: Thank you for the question. Continuing claims refer to unemployed workers that are currently collecting unemployment insurance benefits. Therefore, while initial claims is a proxy for people that lose their job, continuing claims is aimed at capturing the evolution of the stock of unemployed workers. The relationship between continuing claims (the insured unemployment rate) and the “true” unemployment rate is also a bit murky in general. First of all, continuing claims does not capture people that are unemployed but do not qualify for benefits, although this discrepancy would likely be much smaller this time, since the CARES Act relaxed eligibility criteria. Second, there are outflows from continuing claims that are not related to going back to work but are simply due to benefit exhaustion. However, this is not relevant now, as it will be some time before recently laid-off workers start seeing their benefits expire. On the other hand, there is one way in which changes in continuing claims are a more accurate indicator of changes in the labor market than initial jobless claims: that is that they reflect net, rather than gross changes—i.e., when someone in that group finds a job, that is reflected in the continuing claims numbers (they go down), but not in the initial claims numbers. Consistent with this idea, the weekly change in continuing claims is typically well below the level of weekly initial claims. We hope this is helpful.
Fred: Thank you for your comments. The effect of this pandemic on labor force participation—both in terms of magnitude and persistence—is clearly an open question. As you suggest, it may be a negative effect (as in #1 and #3 above) or a positive effect (as in #2 above). At any rate, if this does end up discouraging some people out of the workforce, that might adversely affect the level of employment, but it would probably have a downward effect on the unemployment rate (and an upward effect on wages). Only time will tell. --------------------- Phil: Thanks for your comment. Your stool comparison is an interesting one. On the other hand, there may well be **more** impetus to rebuild this time, because migrating to an unaffected place (like Houston after Katrina) is simply not an option. To illustrate this, there was a spike of about 150,000 in Texas’ population in 2006—75,000 in Harris County (Houston) alone—which appears to reflect outmigration from Louisiana. This is unlikely to happen this time, at least to anywhere near the same extent. --------------------- Buddy: Thanks for your comment. This is an excellent point. It is true that New Orleans was hit much harder than the rest of the state, especially in terms of flooding, which was the major problem. However, jobless claims—the only really solid economic series with recent information on COVID19—is only available at the state level. It is true that the New Orleans metro saw a steeper percentage job loss, and took even longer to come back. However, there was a good deal of variation across Louisiana, and there is likely to be a good deal of variation during this pandemic across the U.S. At any rate, while it is unclear if Louisiana or New Orleans is the better comparison, that should become clearer in the weeks ahead as we get more regional economic data. Our main point, though, is that comparisons to these types of natural disasters are more informative than comparisons to the 2008-09 recession.
In reply to YD: To scale the WEI to GDP growth, we compute the average WEI over a quarter and report the predicted values from a regression of GDP growth on the “quarterly WEI” and a constant.
In reply to Woodward: Regular updates are posted at We anticipate the WEI being available from FRED in the near future.
In reply to Leahey: We are aware of the Homebase labor market data. We are continuing to evaluate potential series and may refine the index in the coming weeks.
Thanks for your interest. We anticipate making this data public shortly.
Thanks for your question. Existing work both within the Federal Reserve System and outside shows results consistent with this finding. The Atlanta FED wage tracker has been showing similar results for a while. Here is a link to one such article: The New York Times also ran a similar story recently relating this to minimum wage hikes. Here: The authors look at this in a slightly different way, specifically they link same people over a 1-year window and study their wage growth. They find that wage growth at the bottom has always been higher, but this is also to be expected because low wages also reflects transitory factors (as discussed in more detail in the post). The earnings measure includes base salary or wage, and tips, bonuses, and overtime compensation. But it does not capture fringe benefits or stock options.
Ted: thank you very much for your questions. On your first question, in the associated working paper we controlled for the level of subprime lending in 2002, and the results we report in this post still hold. Also, the initial level of subprime lending is not correlated with subsequent home price appreciation. Regarding your second question, in the paper we show that the results hold at a zip-code level. We also note that most of the variation in home price appreciation in our sample period was at the county level and that the variation in home price appreciation across zip codes within counties was relatively small. Finally, thank you for your note on the footnote of the subprime map! We have updated it.