It Takes a Regime Shift: Recent Developments in Japan through the Lens of the Great Depression

A recent paper from Christy Romer (via Greg Mankiw).

The Miracle of Microfinance? Evidence from a Randomized Evaluation

From Esther Duflo, Abhijit Banerjee, Rachel Glennerster, Cynthia G. Kinnan: 

ABSTRACT: This paper reports on the first randomized evaluation of the impact of introducing the standard microcredit group-based lending product in a new market.  In 2005, half of 104 slums in Hyderabad, India were randomly selected for opening of a branch of a particular microfinance institution (Spandana) while the remainder were not, although other MFIs were free to enter those slums.  Fifteen to 18 months after Spandana began lending in treated areas, households were 8.8 percentage points more likely to have a microcredit loan.  They were no more likely to start any new business, although they were more likely to start several at once, and they invested more in their existing businesses.  There was no effect on average monthly expenditure per capita.   Expenditure on durable goods increased in treated areas, while expenditures on “temptation goods” declined.  Three to four years after the initial expansion (after many of the control slums had started getting credit from Spandana and other MFIs), the probability of borrowing from an MFI in treatment and comparison slums was the same, but on average households in treatment slums had been borrowing for longer and in larger amounts.  Consumption was still no different in treatment areas, and the average business was still no more profitable, although we find an increase in profits at the top end.  We found no changes in any of the development outcomes that are often believed to be affected by microfinance, including health, education, and women’s empowerment. The results of this study are largely consistent with those of four other evaluations of similar programs in different contexts.

Worker Flows Over the Business Cycle: the Role of Firm Quality

Lisa Kahn and Erika McEntarfer have an interesting paper on worker flows, firm quality, and the business cycle. They define firm quality as average pay (and their findings are robust to using other sensible definitions).

Here’s one of their key graphs:

This graph shows that while all firm types shrink the size of their workforce in recessions (i.e. growth is negative), net job growth declines more for good (high paying) firms. This is because they have more separations in recessions than low-quality firms. High wage firm hiring doesn’t fall by as much as it does for low wage firms, but this difference in hiring is not large enough to overcome the difference in separations.

Here’s how they conclude:

In this paper, we use employer-employee matched U.S. data to study net and gross worker flows over the business cycle as a function of firm quality. We find that low-quality firms fare relatively better in the recession; their growth rates shrink by less. This is because separation rates at low-wage firms fall by more. It looks as though high-quality firms are more likely to make layoffs in an economic downturn, while still keeping up a modest amount of hiring. This set of results is consistent with the need for low-quality firms to continually replenish their stock of workers in boomtimes when they lose their workforce to high-quality firms, while in busts they can grow, relative to high-quality firms. In contrast, high-quality firms grow relatively faster in boomtimes and experience relatively more separation in busts. As we have said, these findings are consistent with the Moscarini Postel-Vinay poaching model described above, while we provide ancillary evidence that labor demand explanations cannot be driving our results.

Furthermore, this set of facts is suggestive of two important implications for workers matching in recessions. First, low-quality firms may have an easier time attracting and retaining high-quality workers in a recession. We might therefore see that among workers matching in recessions, workers will be overqualified, relative to the firms that hire them. Second, relatively speaking, low-quality firms have an easier time retaining workers in recessions, since, as we have shown, they shrink less quickly. Therefore a worker matching to a low-quality firm in a recession is likely to stay there for longer; he or she will have less of an opportunity to make a job-to-job transition to a high-quality firm. In our data, we can look at both of these effects directly and we do so in Kahn and McEntarfer (2013).

While previous research has emphasized match quality may decline in recessions due to a lack of workforce reallocation (Barlevy 2002), our evidence here suggests an additional sullying effect. The types of jobs workers get stuck in are more likely to be low-quality. This is evident in our finding that, relatively speaking, low-quality firms have an easier time growing in the bust, while high-quality firms want to reduce the size of their workforce. One interpretation of our results is that the reduced ability to move on to better matches caused by a recession has a greater impact on workers in low-quality firms compared to those in high-quality firms. These results have implications then for the costs of recessions, both in the short- and long-run. These results also have important implications for the literatures on the differential impact of recessions of workers. For example, that entering the labor market in a recession (Kahn 2010, Oreopoulos, von Wachter and Heisz 2010) or being displaced from a long-term job in a recession (Davis and von Wachter 2010) has particularly long-lasting, negative wage impacts, could potentially be explained by these workers spending more time in low-quality firms.

Does Entrepreneurship Pay? The Michael Bloombergs, the Hot Dog Vendors, and the Returns to Self-Employment

From an interesting paper by Ross Levine and Yona Rubinstein:

We find that the incorporated self-employed earn much more per hour and work many more hours than salaried and unincorporated workers.  After conditioning on standard Mincerian characteristics, the incorporated self-employed have median residual hourly earnings that are 25% greater and median residual hours worked that are 15% greater than their salaried counterparts. We also find that the median unincorporated individual earns less per hour than his salaried counterpart and much less than a comparable incorporated worker. This helps explain earlier findings concerning the negative pecuniary returns to self-employment: the incorporated earn more than salaried workers, the unincorporated earn less, and there are more unincorporated than incorporated individuals.

The higher earnings of the incorporated self-employed partially reflect returns to individual traits and partially the returns to activities associated with incorporation. Individuals that at some point in their lives incorporate tend to earn about 33% more on average (and 20% more at the median) as salaried workers than comparable salaried workers that never incorporate: some people have traits associated with both higher earnings, regardless of employment type, and a greater tendency to incorporate. Nevertheless, even when controlling for individual effects, the average individual enjoys a 14% boost in residual hourly earnings when switching from salaried to incorporated self-employment (while the median person receives a 4% increase).

Furthermore, the distribution of the residual hourly earnings of the self-employed, especially the incorporated, has much fatter tails than that of salaried workers. For example, people that are successful when they are incorporated (90th-percentile of the residual hourly earnings distribution of the incorporated) tend to enjoy 70 percent more earnings than their earnings as successful salaried workers (90th-percentile of the residual hourly earnings distribution of the salaried). Entrepreneurship offers the possibility of comparably enormous positive returns.

In particular, we find that (1) incorporated individuals are more educated and more likely to come from high-earning, two parent families than salaried workers, and (2) even as teenagers, people that incorporate later in life tend to score higher on learning aptitude tests, exhibit greater self-esteem, and engage in more aggressive, illicit, and risky activities than those that do not. Along most of these dimensions, the unincorporated are on the other end of the spectrum, with values lower than salaried workers. This helps account for the puzzling observation that self-employed and salaried workers have similar traits: aggregating the incorporated and unincorporated masks crucial differences about the traits of people that sort into each sub-category of self-employment.

We also discover that several cognitive and noncognitive traits are more important for shaping the pecuniary returns to incorporated self-employment than they are for influencing the success of salaried and unincorporated individuals. Learning aptitude, self-esteem, and aggressive, risk-taking traits—which are all measured when individuals are teenagers—are especially, positively associated with being a highly successful incorporated business owner later in life. While a diverse body of research argues that traits related to self-esteem induce individuals to try self-employment (e.g., Zukerman 1994; Nicolaou, et al. 2008), our work suggests that such traits yield large pecuniary returns. These findings are consistent with research documenting nontrivial returns to noncognitive traits (Bowles et al. 2001; Heckman and Rubinstein, 2001; Heckman et al. 2006; Heckman, 2000). [...]

Note, we do not evaluate the causal impact of incorporation on earnings. Rather, we assess the pecuniary returns from self-sorting into incorporation, the cognitive and noncognitive traits underlying this sorting, and how these traits differentially shape the returns to various employment activities. The nature of entrepreneurs and the returns to entrepreneurship are inextricably connected.

Large Variation in Hospital Billing: Three Preliminary Takeaways from New U.S. Data

The NYTimes has an interesting article on variation in hospital billing. In addition to highlighting substantial dispersion for the same procedure even within local areas (e.g. “a hospital in Livingston, N.J., charged $70,712 on average to implant a pacemaker, while a hospital in nearby Rahway, N.J., charged $101,945.”), it also cited the release of awesome Medicare price and billing data from CMS.

New Data: The data provide the average amount each hospital in the US charged Medicare for every DRG (Diagnosis-related group) and how much Medicare actually paid in 2011. I merged this hospital-DRG level data with zipcode level tax return data from the IRS to link these hospital prices and payments to local incomes (the IRS data are from 2008. Presumably average zipcode incomes are highly persistent).

Here are three preliminary takeaways from an initial look at this data.

1. There’s substantial variation in the amount hospitals charge for a given procedure in the US. The first figure shows that the amount hospitals charge for a DRG varies widely from 50% to 3X relative to the average amount all hospitals in the US charge for that DRG. While there are local price, quality, demographic, etc. differences, this is pretty substantial variation.
2. Medicare typically pays about 30% of the amount hospitals charge, but there’s also wide variation in this amount. Interestingly, hospitals in zipcodes with higher average incomes tend to charge more, but Medicare pays hospitals in these areas an even lower share of the charged amount.
3. The amount hospitals charge for a given procedure (relative to the mean US charge for that procedure) increases with local income. Figure 3 shows the average ratio between hospital charges for a given DRG to the nation mean for 50 AGI bins. There are likely quality differences and other important omitted variables that affect outcomes, but the simple correlation appears to be quite strong. Roughly speaking, going from a zipcode that has mean AGI of $40-50K to one with average AGI of $80-$100K is associated with an ~ 50% increase (from .8 to 1.2 or 1.3) in the amount Hospitals charge. It may be the case that part of the story is that hospitals in richer areas have to offset higher labor and rental costs so they charge higher prices (based on mechanisms related to Bamoul’s cost disease). In addition, higher relative prices of healthcare and higher incomes both affect the demand for healthcare. I am investigating these explanations more in depth/more formally and will hopefully be able post about this more soon.

Where the Jobs for the Young Are and Aren’t

David Leonhardt has a nice post with this graphic on regional variation epop among young adults aged 25-34.

Corporate Profits as a Share of GDP

 

Note these are corporate profits after taxes.