Diana Farrell

The Online Platform Economy: Has Growth Peaked?

In this report the JPMorgan Chase Institute assembled one of the largest samples of participants in the Online Platform Economy to date: over 240,000 de-identified individuals who earned income between October 2012 and June 2016 from one or more of 42 different platforms. Our findings point to several dimensions of how the growth in online platform participation has slowed. First, growth in participation in the Online Platform Economy peaked in 2014 and has slowed since then. Second, while monthly earnings on capital platforms increased by 34 percent between June 2014 and June 2016, they decreased on labor platforms by 6 percent. Third, turnover in the Online Platform Economy is high: one in six participants in any given month is new, and more than half of participants exit within 12 months. Fourth, employed, higher-income, and younger participants are more likely to exit the Online Platform Economy within a year. Finally, non-employed individuals are more likely than the employed to participate in labor platforms but represent a decreasing share of participants as the unemployment rate drops. In sum, growth in online platform participation is highly dependent on attracting new participants or increasing attachment of existing participants. As outside options improve, recruiting and retaining platform workers might become increasingly difficult.

Going the Distance: Big Data on Resident Access to Everyday Goods

In this report, we measure the distance between consumers’ homes and the merchants at which they shop. Leveraging over 197 million credit and debit card transactions from more than 581 million de-identified customers in 15 cities, we analyzed credit and debit card transactions in Detroit and New York for the second quarter of each year in the 2013-2016 period. We organize our results into five findings. First, in 2016, residents of Detroit and New York made 71.6 percent and 56.8 percent of their transactions outside of their 20-minute neighborhood, respectively. Second, distances between residents and their chosen merchants dropped in both cities between 2013 and 2016. Third, in 2016, the distance between residents and their chosen merchants was longest for low-income residents and shortest for high-income residents in both cities. Fourth, between 2013 and 2016, the distance between residents and their chosen merchants fell for low-income residents in both Detroit and New York. Fifth, between 2013 and 2016, access to necessities like pharmacies, grocery stores, and non-durable providers improved the most. Overall, we explored the time costs of local, commercial activity, and the data revealed that access to retail is a complex issue that can be better addressed by understanding where and how often people actually shop. We find that significant variation in the distance between residents and their chosen merchants exists across product types, which is critical information for local policymakers and other stakeholders.

Mortgage Modifications after the Great Recession: New Evidence and Implications for Policy

In the aftermath of the Great Recession, various mortgage modification programs were introduced to help homeowners struggling to make their monthly mortgage payments remain in their homes. We use mortgage data at the individual borrower level, joined to credit card spending and deposit account data, to investigate the relative importance of changes in monthly mortgage payments and long-term mortgage debt on default and consumption. We first quantify the variation in payment reduction offered by these modification programs and then use the variation in payment and principal reduction experienced by program recipients to estimate the impact of payment and principal reduction on default and consumption. First, we find that payment reduction for borrowers with similar payment burdens varied by two to three times across different modification programs. Borrowers with a high mortgage payment to- income (PTI) ratio received more than twice the payment reduction from HAMP compared to the GSE program. Borrowers with a low mortgage PTI ratio received three times the payment reduction from the GSE program compared to HAMP. Second, a 10 percent mortgage payment reduction reduced default rates by 22 percent. Third, for borrowers who remained underwater, mortgage principal reduction had no effect on default. This suggests that “strategic default” was not the primary driver of default decisions for these underwater borrowers. Fourth, for borrowers who remained underwater, mortgage principal reduction had no effect on consumption. Finally, default was correlated with income loss, regardless of debt-to-income ratio or home equity. Mortgage default closely followed a substantial drop in income. This pattern held regardless of pre-modification mortgage PTI or loan-to-value ratio, suggesting that it was an income shock rather than a high payment burden or negative home equity that triggered default. These findings suggest that mortgage modification programs that are designed to target substantial payment reduction will be most effective at reducing mortgage default rates. Modification programs designed to reach affordability targets based on debt-to-income measures without regard to payment reduction or target a specific LTV ratio while leaving borrowers underwater may be less effective at reducing defaults. Furthermore, policies that help borrowers establish and maintain a suitable cash buffer that can be used to offset an income shock could be an effective tool to prevent mortgage default. Both high and low mortgage PTI borrowers experienced a similar income drop just prior to default, suggesting that even among those borrowers with “unaffordable” mortgages, it was a drop in income rather than a high level of payment burden that triggered default.

Shedding Light on Daylight Saving Time

In this report, the JPMorgan Chase Institute measures the impacts of Daylight Saving Time (DST) on consumer spending. Using a sample of over 380 million daily credit and debit card transactions, made by over 2.5 million de-identified customers, we compared consumer spending in Los Angeles, a city that observes DST, to that of Phoenix, a city that does not observe DST. We organize our results into three findings. First, the onset of DST in Los Angeles increases daily card spending per capita by 0.9 percent, while the end of DST reduces daily card spending per capita by 3.5 percent. Second, in Los Angeles, DST is more likely to be associated with changes in daily card spending per capita directed towards goods rather than services. Third, daily card spending per capita in Los Angeles drops significantly more during the work week in response to the end of DST. This report indicates that economic impact of DST is not uniform; the impact on a given city is an empirical question. It is also important to note that there are other reasons to support DST beyond the impacts on consumer spending.