Nicholas Bloom, Erik Brynjolfsson et al. IZA: What Drives
Differences in Management? Partnering with
the Census we implement a new survey of "structured" management
practices in 32,000 US manufacturing plants. We find an enormous dispersion of management practices
across plants, with 40% of this variation across plants within the same firm.
This management variation accounts for about a fifth of the spread of
productivity, a similar fraction as that accounted for by R&D, and twice as
much as explained by IT. We find evidence for four "drivers"
of management: competition, business environment, learning spillovers and human
capital. Collectively, these drivers account for about a third of the
dispersion of structured management practices.
Derek Thompson,
The Atlantic: So, Where Are All Those Robots? Lots of people think that the robots are coming to steal everybody’s
jobs. But another story is emerging from several recent papers and columns by
economists and economic writers. Instead of a world without work, they say, there is currently more
evidence for a world with too much work—and not enough humans to do it all.
Rather than high-flying investment in machines and similarly high unemployment,
there is strangely low investment and happily low joblessness. How can
anybody say robots are killing jobs when the killer is nowhere to be seen and
the supposed victim isn’t even dead?
Jonathan M.V.
Davis, Sara B. Heller, NBER: Rethinking the Benefits of Youth Employment
Programs: The Heterogeneous Effects of Summer Jobs. This
paper reports the results of two randomized field experiments, each offering
different populations of youth a supported summer job in Chicago. In both
experiments, the program dramatically reduces violent-crime arrests, even after
the summer. It does so without improving employment, schooling, or other types
of crime; if anything, property crime increases over 2-3 post-program
years. To explore mechanisms, we implement a machine learning method that
predicts treatment heterogeneity using observables. The method identifies a
subgroup of youth with positive employment impacts, whose characteristics
differ from the disconnected youth served in most employment programs. We find
that employment benefiters commit more property crime than their control
counterparts, and non-benefiters also show a decline in violent crime.
Erling Barth, Sari
Pekkala Kerr, Claudia Olivetti, NBER: The Dynamics of Gender Earnings
Differentials: Evidence from Establishment Data. We use a unique match between the 2000 Decennial
Census of the United States and the Longitudinal Employer Household Dynamics
(LEHD) data to analyze how much of the increase in the gender earnings gap over
the lifecycle comes from shifts in the sorting of men and women across high-
and low-pay establishments and how much is due to differential earnings growth
within establishments. We find that for the college educated the increase is substantial and, for the most
part, due to differential earnings growth within establishment by gender. The
between component is also important. Differential mobility between
establishments by gender can explain 27 percent of the widening of the pay gap
for this group. For those with no college, the, relatively small,
increase of the gender gap over the lifecycle can be fully explained by
differential moves by gender across establishments. The evidence suggests that,
for both education groups, the between-establishment component of the
increasing wage gap is due almost entirely to those who are married.
David Autor,
Andreas Ravndal Kostol, Magne Mogstad, Bradley Setzler, NBER: Disability
Benefits, Consumption Insurance, and Household Labor Supply.We comprehensively assess these missing margins in
the context of Norway's DI system, drawing on two strengths of the Norwegian
environment. First, Norwegian register data allow us to characterize the
household impacts and fiscal costs of disability receipt by linking employment,
taxation, benefits receipt, and assets at the person and household level.
Second, random assignment of DI applicants to Norwegian judges who differ
systematically in their leniency allows us to recover the causal effects of DI
allowance on individuals at the margin of program entry. Accounting for the total effect
of DI allowances on both household labor supply and net payments across all
public transfer programs substantially alters our picture of the consumption
benefits and fiscal costs of disability receipt. While DI allowance causes a
significant increase in household income and consumption on average, it has
little impact on income or consumption of married applicants because spousal
earnings responses (via the added worker effect) and benefit substitution
entirely offset DI benefit payments among those who are allowed relative to
those who are denied.
Yannis
Paschalidis, Harvard Business Review: How Machine Learning Is Helping Us
Predict Heart Disease and Diabetes. In an ongoing effort with Boston-area hospitals, including the Boston
Medical Center and the Brigham and Women’s Hospital, we found that we could predict hospitalizations
due to these two chronic diseases about a year in advance with an accuracy rate
of as much as 82%. This will give care providers the chance to intervene
much earlier and head off hospitalizations. Our team is also working with the
Department of Surgery at the Boston Medical Center and can predict readmissions
within 30 days of general surgery; the hope is to guide postoperative care in order
to prevent them.
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