Mortgage Regulation
“Regulating Household Leverage,” by Anthony A. DeFusco, Stephanie Johnson, and John Mondragon. October 2017. SSRN #3046564.
The Dodd–Frank Wall Street Reform and Consumer Protection Act of 2010 mandated that lenders evaluate a borrower’s “ability to repay” (ATR) when originating a mortgage. However, Congress created a class of mortgages called “qualified mortgages” (QM) that are automatically deemed to satisfy the ATR rule. This designation includes all mortgages eligible for Fannie Mae and Freddie Mac guarantees. Thus, in practice, the ATR rule only affects loans with principals above $453,100 in 2018—so-called “jumbo” loans. The ATR rule as implemented by the Consumer Financial Protection Bureau (CFPB) required non-QM recipients to have a debt-to-income ratio (DTI) no greater than 43%.
How did lenders respond to the rule? This paper compares the interest rates on jumbo loans before and after the QM rule. Rates increased by 0.10 to 0.15 percentage points per year for DTI above 43%, or 2.5%–3% relative to rates before the rule. In addition, the quantity of high–DTI jumbos was reduced by 15% (2% of all jumbo loans). So lenders increased prices and rationed credit.
Had this rule been in place before the housing bust, would it have decreased the number of defaults? The authors estimated the relationship between DTI and default probability in a sample of loans originated between 2005 and 2008. While higher DTIs are generally associated with increased default probabilities, there was no difference in probability of default for those jumbo loans in the regions just above and below the 43% cutoff. This suggests that the policy would not have improved mortgage performance had it been in effect during 2005–2008.
CAFE Standards
“Environmental Protectionism: The Case of CAFE,” by Arik Levinson. Working paper, Georgetown University. August 2017.
A recurring theme in economists’ evaluations of regulation is that incumbent firms use regulation to raise the costs of their competitors. This paper searches for that phenomenon in 2007’s tightening of the Corporate Average Fuel Economy (CAFE) vehicle fuel efficiency standard on automobiles.
Historically the standard was uniform: a sales-weighted average of 27.5 miles per gallon for all cars. The revised standard, effective with the 2011 model year, varied by the “footprint” of the vehicle. The largest cars needed to get 28 mpg while the smallest cars needed 36 mpg in 2012.
The author of this paper, Arik Levinson, notes that domestic cars are larger than imports, thus a CAFE standard that grants larger vehicles less stringent fuel economy requirements benefits U.S. manufacturers. “The switch to footprint-based standards in 2012 granted the average U.S.-assembled vehicle an extra 0.62 mpg, and cost the average imported vehicle 0.68 mpg, for an overall difference of 1.3 mpg,” he writes. Given the fine of $55 per vehicle per mpg, the effective tax on imports is $71.50 per vehicle.
Antitrust in Europe
“Is EU Merger Control Used for Protectionism? An Empirical Analysis,” by Ann Bradford, Robert J. Jackson Jr., and Jonathon Zytnick. July 2017. SSRN #3003955.
Another policy arena in which regulation is alleged to increase rivals’ costs is antitrust. Anecdotes suggest that the European Union uses its antitrust regulation to advantage European producers over U.S. firms seeking greater economies of scale through merger. For instance, in 2001 the EU blocked General Electric’s acquisition of Honeywell even though the U. S. Justice Department had approved the acquisition. The EU also stopped proposed mergers by Boeing, Time Warner, and UPS.
To see if the anecdotes do indeed reflect a larger pattern by the EU, the authors of this paper examine the universe of proposed mergers from 1990 through 2014 (5,000 cases). After controlling for the usual explanations of antitrust concerns, the authors found no effect on the incidence or intensity of merger challenges by the EU if the acquiring firm was non-EU. For that time period at least, the EU wasn’t using antitrust as a form of protectionism.
Economics of Energy Booms
“Who Wins in an Energy Boom? Evidence from Wages, Rates, and Housing,” by Grant D. Jacobsen. May 2017. SSRN #2972681.
How has the increase in oil and gas production from hydraulic fracturing changed the economic fortunes of people living in the rural areas where that extraction takes place? In this paper Grant Jacobsen offers some estimates of these effects.
He defines an energy boom area as a non-metropolitan area (NMA) in which annual gas and oil revenues were at least $500 million greater in 2011 than in 2006. Under this definition, 10% of NMAs were energy boom areas. Forty percent of NMAs had some energy production and 50 percent had none.
Jacobsen compares various outcomes in boom and non-boom areas. In boom areas, population increased by 5.7%, wage rates by 7%, house values by 12.5%, and rents by 5%. Wages went up across occupations—even those not related to oil and gas—because the labor supply proved less elastic than demand. And he found “no evidence that the boom increased the cost of rent when measured as a percentage of household income.”
Jacobsen concludes: “The results indicate that there are many monetary ‘winners’ from energy development in local communities and very few losers. An implication of the results is that bans on drilling have negative monetary consequences for a large share of local residents.”
Nudges and Electricity Pricing
“Default Effects and Follow-On Behavior: Evidence From an Electricity Pricing Program,” by Meredith Fowlie, Catherine Wolfram, C. Anna Spurlock, Annika Todd, Patrick Baylis, and Peter Cappers. June 2017. NBER #23553.
An important distinction between behavioral and traditional neoclassical economic analysis is the former’s emphasis on “default effects,” the tendency of people to remain in their original state of affairs. The most famous real-world example of this is the tendency of individuals to save more in employer-sponsored 401k retirement-savings plans if they are enrolled automatically in the plans but have the option to opt out, relative to saving when employees are automatically not enrolled in a plan but have the option to opt in.
Traditionally, electricity prices faced by consumers have not varied over time even though the marginal cost of production is higher on a summer afternoon than during a spring or fall night. Even though the installation of “smart” electric meters now allows consumer electricity prices to vary by time, 95% of U.S. residential customers pay time-invariant electricity prices.
Regulation has published the results of how electricity consumers react to dynamic pricing from some pilot programs. (See “Moving Forward with Electricity Tariff Reform,” Fall 2017.) But how would consumers respond under different scenarios in which consumers have the option to opt in or opt out of different price-variant regimes? That is what this paper explores.
It examines a Sacramento, CA electricity pricing experiment over the years 2011–2013 in which 174,000 households were randomly assigned to five groups:
- A control group that paid a traditional time-invariant price, in this case 9.38¢ per kilowatt hour for their first 700 kWh of consumption and 17.65¢ per kWh afterward.
- A second group that could opt into time-of-use (TOU) pricing. That pricing was 27¢ per kWh on weekdays 4–7 p.m., and 8.46¢ per kWh for the first 700 kWh of off-peak consumption and 16.6¢ per kWh for off-peak consumption above 700 kWh.
- A third group that was assigned to the same TOU pricing but participants could opt out.
- A fourth group that could opt into critical peak pricing (CPP) of 75¢ per kWh 4–7 p.m. on 12 critical days between June 1 and September 30, with prices at other times the same as the TOU groups.
- A fifth group that was assigned to a CPP/TOU scheme like the fourth group, but participants could opt out.
The authors’ findings reflect behavioral economists’ discovery that initial assignment matters. Only 20% of the consumers assigned to the two groups that required opt-in to the TOU or CPP/TOU plans actually opted in. Yet over 90% of those who were assigned to TOU or CPP/TOU stayed in those programs and did not opt out.
The effects of higher prices on consumption did vary by whether the customers were assigned or volunteered. Complacent consumers who were assigned to TOU or CPP/TOU but did not opt out decreased their consumption by about 10% given the higher prices, while those who actively opted in decreased their consumption by about 25%.
However, the complacent customers assigned to TOU or CPP/TOU had an aggregate reduction in electricity consumption that was twice as large in TOU and three times larger in CPP/TOU as compared to consumers in the opt-in groups. Such savings made the programs cost-effective overall. In contrast, in the opt-in CPP/TOU program, costs equaled benefits, while the opt-in TOU program was not cost effective.
