Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Dina
Pomeranz*, Assistant Professor of Business Administration at Harvard
Business School and faculty affiliate at NBER, J-PAL, BREAD, CEPR, and IGC.
She will be presenting work entitled *Can Audits Backfire? Evidence from
Public Procurement in Chile**.* Please find the abstract below and on the
website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Can Audits Backfire? Evidence from Public Procurement in Chile
Abstract: The government is the biggest buyer in the economy of most
countries. At the same time, the public procurement process if often
thought to be fraught with waste and corruption. For this reason, many
governments try to promote the use of online auctions instead of direct
contracting by public entities. We analyze the impact of audits aimed at
reducing such malpractice in public procurement on public entities'
subsequent procurement practices in Chile. For identi cation, we exploit a
scoring rule of the national auditing agency, which allows for regression
discontinuity analysis. Surprisingly, the audits lead to a shift away from
online auctions and towards higher use of the less transparent and more
discretionary modality of procurement through direct contracting. The share
of the value of total purchases through direct contracts increases by about
7 percentage points, at the expense of the use of public auctions.
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Heidi
Williams*, Assistant Professor of Economics at MIT. She will be presenting
joint work with Bhaven Sampat entitled *Do Patents Affect Follow-on
Innovation? Evidence from the Human Genome**.* Please find the abstract
below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Do Patents Affect Follow-on Innovation? Evidence from the Human
Genome
Abstract: We investigate whether patents on human genes have affected
follow-on scientific research and product development. Using administrative
data on successful and unsuccessful patent applications submitted to the US
Patent and Trademark Office, we link the exact gene sequences claimed in
each application with data measuring follow-on scientific research and
commercial investments. Using this data, we document novel evidence of
selection into patenting: patented genes appear more valuable — prior to
being patented — than non-patented genes. This evidence of selection
motivates two quasi-experimental approaches, both of which suggest that on
average gene patents have had no effect on follow-on innovation.
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Marie-Abele
Bind*, Post-Doctoral Fellow at the Harvard University Center for the
Environment. She will be presenting work provocatively entitled *Valid and
Informative p-values from Big Data, Illustrated in an Epigenomic Cross-Over
Experiment**.* Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view
previous Applied Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Valid and Informative p-values from Big Data, Illustrated in an
Epigenomic Cross-Over Experiment
Abstract: A common issue that arises with current analyses of epigenomic
data is the repeated use of statistical tests. For example, consider 17
people in a randomized experiment measuring the results of exposure to two
treatment conditions (e.g., clean air and ozone), with measurements at
484,531 epigenome locations, where the aim is to find the locations with an
epigenetic effect (i.e., of clean air versus ozone). Here, we describe the
use of randomization-based tests to obtain a Fisher exact p-value that is
valid whatever the correlational structure of the data from the epigenomic
locations. The power of the resultant test to detect real differences,
however, requires the careful a priori selection of the single test
statistic.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Chris
Winship*, Diker-Tishman Professor of Sociology at Harvard University,
and *Ethan
Fosse*, Post-Doctoral Researcher in the Department of General Education at
Harvard University. He will be presenting work entitled *Bounding Analyses
of Age-Period-Cohort Models**.* Please find the abstract below and on the
website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Bounding Analyses of Age-Period-Cohort Models
Abstract: For at least 80 years researchers in a wide variety of fields
have sought to uniquely identify age, period, and cohort (APC) effects,
even though an infinite number of solutions exist due to perfect linear
dependency. In this paper we introduce a new approach for identifying APC
effects based on bounding feasible regions of the parameter space.
Depending on the location of the solution line in the parameter space,
minimal constraints on the direction and magnitude of the linear trends can
lead to substantively meaningful conclusions. Furthermore, bounds can be
derived from mechanism-based modelsthat specify the processes by which one
or more of the linear effects affect the outcome of interest, even when
such models are misspecified. Unlike previous methods, our approach is
based on applying theoretically-relevant and empirically-derived
constraints only on those components of the APC effectsthat are
unidentified. To illustrate the usefulness of bounding analyses of APC
effects, we examine trends in verbal ability and perceived well-being. In
contrast to previous research, we find strong overall effects for period
and cohort forboth outcomes. We conclude with a discussion of Bayesian
interpretations ofbounding analyses as well as guidelines for further
research on APC effects.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
