VOTE FORWARD et al v. DEJOY et al

Filing 16

MOTION for Preliminary Injunction by AMY BOLAN, AARON CARREL, COLORADO ORGANIZATION FOR LATINA OPPORTUNITY AND REPRODUCTIVE RIGHTS, INDERBIR SINGH DATTA, DANTE FLORES-DEMARCHI, PAUL HUNTER, SEBASTIAN IMMONEN, KATHRYN MONTGOMERY, SEAN MORRISON, PADRES & JOVENES UNIDOS, LINDA ROBERSON, MARTHA THOMPSON, VOCES UNIDAS DE LAS MONTANAS, VOTE FORWARD, GARY YOUNG (Attachments: #1 Memorandum in Support, #2 Exhibit Index, #3 Exhibit USPS OIG Report (Aug. 2020), #4 Exhibit USPS, Postal Operations Manual (Excerpts), #5 Exhibit USPS OIG Report (May 2020), #6 Exhibit USPS, Mandatory Stand-Up Talk: All Employees, #7 Exhibit Leaked USPS Powerpoint, #8 Exhibit DeJoy Testimony - House Oversight Hearing (Aug. 24, 2020), #9 Exhibit DeJoy Testimony - Senate Hearing (Aug. 21, 2020), #10 Exhibit Statement of DeJoy - House Oversight Comm. (Aug. 24, 2020), #11 Declaration Professor Justin Grimmer, #12 Exhibit USPS, Congressional Briefing (Aug. 31, 2020), #13 Exhibit Chart of States Where Voters' Mail-In Ballots Are Impacted By Defendants' Delays, #14 Exhibit USPS Letter to Pa. (July 29, 2020), #15 Declaration Eitran D. Hersh, #16 Exhibit House Oversight Hearing on USPS Operations - Transcript (Aug. 24, 2020), #17 Exhibit Senate HSGAC Hearing on USPS Operations - Transcript (Aug. 21, 2020), #18 Declaration Aaron Carrel, #19 Declaration Martha Thompson, #20 Declaration Kathryn Montgomery, #21 Declaration Sebastian Immonen, #22 Declaration Amy Bolan, #23 Declaration Inderbir Singh Datta, #24 Declaration Scott J. Forman of Vote Forward, #25 Declaration Alex Sanchez of Voces Unidas De Las Montanas, #26 Text of Proposed Order)(Duraiswamy, Shankar)

EXHIBIT 9

UNITED STATES DISTRICT COURT
FOR THE DISTRICT OF COLUMBIA
VOTE FORWARD, AARON CARREL,
VOCES UNIDAS DE LAS MONTAÑAS,
COLORADO ORGANIZATION FOR
LATINA OPPORTUNITY AND
REPRODUCTIVE RIGHTS, and PADRES
UNIDOS,

Civil Case No. 1:20-cv-02405

Plaintiffs,
v.
LOUIS DEJOY, in his official
capacity as the Postmaster General; and the
UNITED STATES POSTAL SERVICE,
Defendants.
DECLARATION OF JUSTIN GRIMMER
I.

Statement of Inquiry
1.

On July 10, 2020 the USPS announced a policy change that limits the number of

Extra and Late trips. I have been asked to provide a preliminary assessment of how the policy
change will affect the arrival time of ballots mailed in the upcoming election.
II.

Qualification
2.

I am a Professor of Political Science at Stanford University in Stanford California.

I also hold the titles of Senior Fellow at the Hoover Institution and Co-Director of the
Democracy and Polarization Lab. I first joined the Stanford Faculty in 2010 as an Assistant
Professor. I was promoted to Associate Professor in 2014 and I held a courtesy appointment in
the department of Computer Science from 2016-2017. From 2017-2018 I was an Associate
Professor in the Department of Political Science and the College at the University of Chicago. I
received my Ph.D. in Political Science from Harvard University in 2010.

1

3.

In my scholarly research I develop and apply new statistical methods to study US

elections, political communication, the US Congress, and social media. I have taught courses for
graduate students on fundamentals for statistical analysis, “Math Camp”, along with graduate
courses on applying machine learning methods to social science problems “Model Based
Inference” and the quantitative analysis of text data in “Text as Data”. At the undergraduate
level I have taught “Introduction to Machine Learning”. My research and writing on quantitative
methods have been published in Political Analysis, the Journal of the American Statistical
Association, Proceedings of the National Academy of Science, and the Proceedings of the Annual
Meeting of the Association for Computational Linguistics. My CV is attached as Exhibit 1 to
this Declaration.
III.

Preliminary Estimate of the Probability a Letter is Delayed
4.

I have used the USPS provided PowerPoint dated August 31, 2020, attached as

Exhibit 2 (the "August 31 PowerPoint") to make a preliminary assessment of the effect of the
USPS policy change limiting the number of Extra and Late Trips. A formal and conclusive
analysis of the causal effect of the policy change on the probability that a ballot is delayed is not
possible here, in part because I have not been provided with the underlying data set that provides
the relevant data about how the intervention affected the number of Extra and Late Trips, the
number of On-Time Trips, and the likely distribution of ballots across these types of trips with
and without the policy change. This analysis is limited to the data provided by the USPS in the
August 31 PowerPoint.
5.

While the August 31 PowerPoint provides only plots and not the underlying data

points, I was able to extract the relevant numbers from the charts through a close analysis of
Slides 4, 5, and 6. I extracted the numbers first on Slide 4, which provides the total number of
On-Time Trips, Late Trips, and Extra Trips. To extract the numbers, I took a screenshot of the
2

plot on Slide 4 and imported that screenshot into Preview. The photo editing tools in Preview
provide a tool that acts as a ruler, which I used to align with the appropriate axis and then
provide an approximation of the numerical value for On-Time, Late, and Extra Trips on each
day. Determining the number of On-Time Trips was more straightforward. This is because the
left-hand axis on Slide 4 and the corresponding grey-lines make obtaining the numerical values
relatively easy. Unfortunately, the right-hand axis (corresponding to Extra and Late Trips) does
not correspond to the light-grey lines, which creates a chart that can be misleading unless
assessed carefully. I overcame this using the editing tools in Preview. Using the screenshot of
the plot, I oriented each point to the right-hand axis using the Preview tools and determined the
approximate numerical value by using the Preview tools to measure the vertical distance of each
point from the closest axis label. This yielded an approximate number of Extra and Late Trips.
6.

I validated the numbers extracted from this procedure using the USPS provided

information on Slides 5 and 6 of the August 31 PowerPoint. On Slide 5 the USPS provides the
average number of daily Late Trips before and after the new policy and on Slide 6 the USPS
provides the average number of daily Extra Trips made before and after the new policy. (I refer
to the period before the date when the new policy on Late and Extra Trips was instituted, i.e., on
July 10, 2020, as the “pre-policy period” and the period after the policy was instituted as the
“post-policy period.”)
7.

Using my procedure to extract numbers of Late Trips from Slide 4, I estimated the

USPS averaged 44,900 Late and Extra Trips in the pre-policy period and averaged 13,514 Late
and Extra Trips in the post-policy period. Using the daily average numbers provided on Slides 5
and Slides 6, the USPS estimated 45,171 Late and Extra Trips in the pre-policy period and

3

12,271 Late and Extra Trips in the post-policy period. This is a small difference (2%),
indicating that the procedure for extracting information from Slide 4 was valid.
8.

I performed the same process on Slide 5 and Slide 6 to obtain the total daily

number of Late and Extra Trips. Specifically, I took a screenshot of Slide 5 and Slide 6 and then
used the Preview photo editing tools to orient the points along the axis and then obtain the
numerical values. My daily estimates using this procedure correspond to the estimates reported
on the USPS slides. My extracted numbers indicate an average of 4,317 daily Late Trips, while
the USPS reports an average of 4,193 daily Late Trips in the pre-policy period, a difference of
124 trips or 3%. In the post-policy period, my extracted numbers indicate a daily average of
1,090 Late Trips, while the USPS reports a daily average of 1,147 Late Trips, a difference of 57
trips per day (5% difference). Similarly, for Extra Trips my extracted numbers provide an
average of 2,305 daily Extra Trips in the pre-policy period, and the USPS reports a daily average
of 2,260 Extra Trips in the pre-policy period: a difference of 45 trips or a 2% difference. In the
post-policy period, my extracted numbers indicate a daily average of 577 Extra Trips, and the
USPS reports a daily average of 606 Extra Trips: a difference of 29 daily trips or a 5%
difference.
9.

Based on the numbers provided on Slide 5 of the August 31 PowerPoint, the

USPS averaged 4,193 daily Late Trips in the pre-policy period, and in the post-policy period it
averaged 1,147 Late Trips: a decline of 3,046 fewer daily Late Trips. Similarly, in the pre-policy
period, the USPS averaged 2,260 daily Extra Trips, but in the post-policy period, the postal
service averaged 606 daily extra trips, a decline of 1,654 daily Extra Trips. Based on my
analysis of the pre-policy period, the average number of Extra Trips or Late Trips was stable
over this time period. Similarly, in the post-policy period, the average number of Extra Trips or

4

Late Trips was also stable. Altogether, this implies an average weekly decline of 32,900 Extra
or Late Trips in the post-policy period.
10.

Using the numbers extracted from Slide 4, I determined that the number of On-

Time Trips increased. The additional On-Time Trips, however, do not ameliorate the shift away
from Extra or Late Trips, because any letter that would have been transported with an Extra or
Late Trip will necessarily experience an at least one-day delay waiting for an On-Time Trip the
next day. In the pre-policy period, the average weekly number of On-Time Trips was
approximately 244,600, and in the post-policy period, the average number of weekly On-Time
Trips was approximately 273,600. This implies an increase of approximately 29,000 On-Time
Trips per week. While this corresponds closely to the weekly decrease in the number of Extra
and Late Trips: 32,900, the additional On-Time Trips does not, and cannot, ameliorate the delay
from the letters not being transported with an Extra or Late Trip.
11.

To maintain the USPS on-time rates in the pre-policy period, approximately

15.5% of all weekly trips in the pre-policy period were Extra and Late Trips. In the post-policy
period, after the reduction of the 32,900 Extra or Late Trips per week, Extra and Late Trips
comprised only approximately 4.7% of all weekly trips.
IV.

Complexities and Limitations
12.

My estimates are based on aggregate level data extracted from the August 31

PowerPoint and rely upon assumptions that the information in these slides are informative about
how ballots will be processed through the mail. I might reach a different conclusion if I was
provided data on how On-Time, Late, or Extra Trips were used to serve particular kinds of
mail—in particular first class mail.

5

V.

Conclusion
13.

In this preliminary assessment of the USPS policy change limiting Extra or Late

Trips I have demonstrated that the USPS has made a major reduction in their Extra and Late
Trips. In particular, the USPS policy has caused an average weekly decline of 32,900 Extra or
Late Trips in the post-policy period.
I declare under penalty of perjury that the foregoing is true and correct.
Executed this 7th of September, 2020:

_________________________________
Justin Grimmer

6

EXHIBIT 1

Justin Grimmer
Contact
Information

Department of Political Science
Stanford University
Encina Hall West
616 Jane Stanford Way
Stanford, CA 94305
Office: 212

Voice: [phone]
email: [e-mail]

Employment

Stanford University
Assistant Professor, Department of Political Science. 2010-2014.
Associate Professor, Department of Political Science. 2014 - 2017. 2018.
Associate Professor (by courtesy), Department of Computer Science. 2016-2017.
Professor, Department of Political Science. 2018 - Present
Hoover Institution
Senior Fellow. 2018-present
University of Chicago
Associate Professor, Department of Political Science and the College. 2017-2018.

Education

Harvard University Department of Government
Ph.D Political Science, 2010
A.M. Political Science, 2009
Wabash College,
A.B. Mathematics and Political Science 2005
Summa cum laude, Distinction in Mathematics and Political Science Comprehensive Exams

Books

Representational Style in Congress: What Legislators Say and Why It Matters. Cambridge University Press, 2013.
The Impression of Influence: Legislator Communication, Representation, and Democratic Accountability. With Sean Westwood and Solomon Messing. Princeton University Press. 2014.
Text as Data: How to Make Social Science Inferences Using Language. With Margaret E Roberts
and Brandon Stewart. (Under Contract, Princeton University Press)

Publications

“The Durable Differential Deterrent Effect of Strict Photo Identification Laws” with Jesse Yoder.
Political Science Research and Methods. 2020.
“Political Cultures”. with Lisa Blaydes. Political Science Research and Methods. 2020.
“Obstacles to Estimating Voter ID Laws’ Effect on Turnout”. with Eitan Hersh, Marc Meredith,
Jonathan Mummolo, and Clayton Nall. Journal of Politics. 2018. 80 (3).
“Mirrors for Princes and Sultans: Advice on the Art of Governance in the Medieval Christian and
Islamic Worlds” with Lisa Blaydes and Alison McQueen. Journal of Politics. 2018. 80 (4).
“Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with
Ensemble Methods” with Solomon Messing and Sean J. Westwood. Political Analysis 2017. 25(4).
413-434.

“Discovery of Treatments from Text Corpora” with Christian Fong. In Proceedings of the Annual
Meeting of the Association for Computational Linguistics (ACL 2016) Berlin, Germany
“Money in Exile: Campaign Contributions and Committee Access” with Eleanor Neff Powell. Journal of Politics. 2016. 78(4). 974-988.
“Measuring Representational Style in the House: The Tea Party, Obama, and Legislators’ Changing
Expressed Priorities” in Data Analytics in Social Science, Government, and Industry Edited Volume
from Cambridge University Press. 2016.
“TopicCheck: Interactive Alignment for Assessing Topic Model Stability” North America Chapter
of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT).
Jason Chuang, Molly Roberts, Brandon Stewart, Rebecca Weiss, Dustin Tingley, Justin Grimmer,
and Jeffrey Heer. 2015.
“We’re All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work
Together” Part of Symposium on “Formal Theory, Causal Inference, and Big Data” PS: Political
Science & Politics , 2015. 48(1), 80-83
“Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations.” Advances in Neural Information Processing Systems Workshop on Human-Propelled Machine
Learning. Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart,
Margaret E. Roberts, Forough Poursabzi-Sagdeh, Justin Grimmer, Leah Findlater, Jordan BoydGraber, and Jeffrey Heer. 2014.
“Congressmen in Exile: The Politics and Consequences of Involuntary Committee Removal” with
Eleanor Neff Powell. The Journal of Politics, 2013. 75 (4), 907–920
“Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional
Representation”. American Journal of Political Science, 2013. 57 (3), 624–642.
“Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political
Documents” with Brandon Stewart. Political Analysis, 2013. 21 (3), 267–297.
“Evaluating Model Performance in Fictitious Prediction Problems”. Discussion of “Multinomial
Inverse Regression for Text Analysis” by Matthew Taddy. Journal of the American Statistical
Association 2013.108 (503) 770-771
“Elevated Threat-Levels and Decreased Expectations: How Democracy Handles Terrorist Threats”
with Tabitha Bonilla. Poetics, 2013. 41, 650-669.
- Special issue on topic models in the social sciences
“How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on
Constituent Credit Allocation” with Solomon Messing and Sean Westwood. American Political
Science Review, 2012. 106 (4), 703–719.
“General Purpose Computer-Assisted Clustering and Conceptualization” with Gary King. Proceedings of the National Academy of Sciences, 2011. 108 (7), 2643-2650.
“An Introduction to Bayesian Inference Via Variational Approximations” Political Analysis, 2011.
19(1), 32–47.
- Included in Political Analysis virtual issue on Big Data in Political Science
“Approval Regulation and Endogenous Provision of Confidence: Theory and Analogies to Licens-

ing, Safety, and Financial Regulation” with Daniel Carpenter and Eric Lomazoff. Regulation and
Governance. 2010. 4(4) 383-407.
“A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate
Press Releases” Political Analysis, 2010. 18(1), 1–35.
- Included in Political Analysis virtual issue on Bayesian methods in Political Science
Working Papers

“Causal Inference with Latent Variables” with Christian Fong (Revise and Resubmit)
“How to Make Causal Inferences Using Texts” with Naoki Egami, Christian Fong, Margeret E.
Roberts, and Brandon Stewart (Under Review)
“A Women’s Voice in the House: Gender Composition and Its Consequences in Committee Hearings”. with Pamela Ban, Jaclyn Kaslovsky, and Emily West (Under Review)
“Partisan Enclaves and Information Bazaars: Mapping Selective Exposure to Online News” with
Matt Tyler and Shanto Iyengar. (Revise and Resubmit).
“Who Put Trump in the White House? Explaining the Contribution of Voting Blocs to Trump’s
Victory” with Will Marble. (Under Review)
“The Effect of Identifying Constituents on Representative-Constituent Communication” with Monica
Lee.
“The Limited Effect of Presidential Public Appeals” with Annie Franco and Chloe Lim (Under
Review).
“Changing the Subject to Build An Audience: How Elected Officials Affect Constituent Communciation” with Annie Franco and Monica Lee (Under Review)
“The Unreliability of Measures of Intercoder Reliability, and What to do About it”. with Gary King
and Chiara Superti.

Reviews and
Other Writing

Review of Cyberwar: How Russian Hackers and Trolls Helped Elect a President Public Opinion
Quarterly. 2019. 83, 1.

Honors and
Awards

2018. Wabash College Jeremy R. Wright Young Alumnus Distinguished Service Award
2015. Political Methodology section emerging scholar award. Awarded to a young researcher, within
ten years of their degree, who is making notable contributions to the field of political methodology.
2015. School of Humanities and Sciences Dean’s award for achievement in teaching.
2014. The Richard F. Fenno, Jr. Prize. Awarded to the best book in legislative studies published
in 2013.
2013. Political Analysis Editor’s Choice Award for an article providing an especially significant
contribution to political methodology.
2012. School of Humanities and Sciences Dean’s award for achievement in the first years of teaching
at Stanford.
2011. Warren Miller Prize. Awarded for the best paper published in Political Analysis in 2010.

2010. Senator Charles Sumner Prize. Awarded by the Harvard Government faculty for the best
dissertation from the legal, political, historical, economic, social, or ethnic approach, dealing with
any means or measures tending toward the prevention of war and the establishment of universal
peace.
2010. Robert H. Durr award, for the best paper presented at the 2009 Midwest Political Science
Association meeting applying quantitative methods to a substantive problem.
2010. Certificate of Distinction in Teaching, Gov 2010: Qualitative and Quantitative Research
Design.
2008. John T. Williams Prize. Awarded by the Society for Political Methodology for best dissertation
proposal.
2005. Phi Beta Kappa, Wabash College.
2005. John Maurice Butler Prize. Awarded to the senior who, by vote of the Wabash College faculty,
has highest achievements in scholarship and character.
2005. N. Ryan Shaw II Political Science Award. Awarded to the outstanding senior political science
major.
2005. George E. Cascallen Prize in Mathematics. Awarded to the outstanding senior Mathematics
major.
Fellowships and
Grants

2013-2016. Stanford University Victoria Schuck Faculty Scholar in the School of Humanities and
Sciences.
2013-2014. Stanford University, United Parcel Service Endowment Fund Grant, “Infrastructure
Spending in American Cities”.
2013-2014. National Fellow, Hoover Institute.
2012-2013. Faculty Fellow, Institute for Research in the Social Sciences.
2011-2013. Visiting Fellow, Hoover Institute.
2010. Dirksen Center Congressional research award, for “It’s the Flow Not the Stock: Congressional
Staff and Their Influence on Policy Outcomes” (with Matt Blackwell).
2009-2010. Center for American Political Studies (CAPS) dissertation completion fellowship.
2009. Eliot Dissertation Completion Grant. A competitive, merit-based Graduate School of Arts
and Sciences fellowship for the Social Sciences (declined).
2008-2009. CAPS dissertation research fellowship.
2005-2006. National Science Foundation Graduate Research Fellowship, Honorable Mention.

Software and
Patents

Patent Number: US 8,438,162 B2 Method and Apparatus for Selecting Clusterings to Classify
a Predetermined Data Set (with Gary King)
Patent Number: US 9,519,705 B2 Method and Apparatus for Selecting Clusterings to Classify

a Data Set. (with Gary King)
Consilience: Software for Understanding Large Volumes of Unstructure Text (with Merce
Crosas, Gary King and Brandon Stewart) (consilience.com).
Implements a general purpose methodology to facilitate discovery in large collections of texts
textEffect (CRAN)
Implements text as intervention method introduced in Fong and Grimmer (2016).
“arima: ARIMA time series models” in Kosuke Imai, Gary King, and Olivia Lau “Zelig:
Everyone’s Statistical Software”. 2006.
Invited
Presentations
and Workshops
(Last 3 years)

Department of Political Science and Economics. ITAM, Mexico City. 2016
Department of Political Science. University of California, San Diego. 2016
Digital and Computational Knowledge Initiative. Wesleyan University. 2016
New Directions in Computational Social Science and Data Science. University of California, Berkeley. 2016
Atlantic Causal Inference Conference. New York University. 2016.
Interdisciplinary Seminar in Quantitative Methods. University of Michigan. 2016.
Department of Political Science. Columbia University. 2016
Department of Political Science. University of California-Berkeley. 2017
Department of Political Science. Duke University. 2017
Department of Sociology. University of California-Berkeley. 2017
Center for Statistics and Social Science. University of Washington. 2017
Big Data and Human Behavior Speaker Seriers. University of Southern California. 2017
Text as Data Workshop. University of California, Merced. 2017.
International Conference for Computational Social Science. Keynote. Cologne, Germany. 2017
Text as Data Workshop. Washington University, St. Louis. 2017.
Amazon. Seattle. 2017.
Facebook Artificial Intelligence. New York. 2017.
Text as Data Workshop. University of Copenhagen. 2017.
American Politics Workshop. University of Notre Dame. 2017.
Department of Political Science. Northwestern University. 2018.
Methods Workshop. Northwestern University. 2018.
Methods Workshop. Department of Political Science. Yale University. 2018.
Methods Workshop. Department of Political Science. Texas A&M University. 2018.
MIDAS Interdisciplinary Seminar Series. University of Michigan. 2019.
American Politics Workshop. Department of Political Science. UC Berkeley. 2019.
American Politics Workshop. Department of Political Science. New York University. 2019.
Summer Institute in Computational Social Science. Princeton University. 2019.
Empirical Implementations of Theoretical Models. Emory University. 2019.
Southern California Methods Workshop. UC Riverside. 2019.
Data Science Institute. Columbia University. 2019.
Department of Politics and CSDP. Princeton University. 2019.
Text as Data Workshop. US Census Bureau. 2019.
TextXD Keynote Address. UC Berkeley. 2019.
Department of Political Science. University of North Carolina. 2020.
Institute for Advanced Study. Princeton University. 2020

Professional and Reviewer for American Political Science Review, American Journal of Political Science, Journal of
Departmental
Politics, Journal of the American Statistical Association, Proceedings of the National Academy of
Service
Sciences, British Journal of Political Science, Political Analysis, State Politics and Policy Quarterly,

Public Opinion Quarterly, Journal of Public Economics, Legislative Studies Quarterly, Congress
and the Presidency, Journal of Political Communication, Political Science Research and Methods,
Research and Politics, American Politics Research, Political Behavior, Journal of Information Technology & Politics, Journal of Information Science, Journal of Artificial Intelligence Research, Evaluation and Program Planning, National Science Foundation, Journal of Social Structure, Sociological
Methodology, Cambridge University Press, Oxford University Press, Social Forces, Chapman & Hall
(CRC Press), North American Chapter of the Association for Computational Linguistics: Human
Language Technologies (NAACL HLT), Association for Computational Linguistics Annual Conference (ACL), Social Science Computer Review, Swiss National Science Foundation
Co-Director, Democracy and Polarization Lab. 2018-Present
Chair, Omnibus Faculty Search Committee. 2018
Organizer Text as Data. 2019. (TADA2019)
Editorial Board Member, Political Analysis (2014-2015)
Co-Editor, Political Analysis Letters (2014-2018)
Editorial Board Member, Journal of Politics (2015-Present)
Graduate Admissions Committee, 2010-2011
Omnibus Faculty Search Committee, 2011-2012
Award Committee, Warren Miller Prize, 2012-2013
Award Committee, Fenno Prize, 2014-2015
Methods Curriculum Committee, 2013-2014
Undergraduate Curriculum Committee, 2013-2014, 2014-2015
Policy and Planning Committee, 2014-2016, 2018-Present
Director of Undergraduate Studies, 2015-2016.
Co-organizer: Stanford Conference on Computational Social Science. June 1st, 2012.
Section Chair for Legislative Campaigns and Elections. MPSA, 2013. Program Committee: Neural Information Processing Systems (NIPS), Computational Social Science Workshop, 2011, Topic
Modeling Workshop 2013

EXHIBIT 2

CONGRESSIONAL BRIEFING:
Transportation & Service
Performance Updates
August 31, 2020

Transportation Performance
Data Through 8/29/20

2

Transportation Analysis Overview
Service Impacts: USPS transportation and logistics professionals manage an average flow of over 390 million mail pieces
daily throughout the Postal Service network, which includes 285 processing facilities and about 35,000 retail locations.
Postal Service facilities are linked by a complex transportation network that depends on the nation’s highway, air, rail, and
maritime infrastructures. The success of each system affects the success of others. If surface transportation departs late or
unscheduled trips are added, the connection between processing facilities, post offices, airlines, and others become
misaligned, impacting downstream operations and hindering efforts to meet service performance.
Financial Impacts: In FY 2019, the Postal Service spent over $550 million extra in transportation to mitigate delays that
occurred in the network:
• $266 million in extra trips;
• $130 million in overtime;
• $14 million in late trips; and
• $140 million in air freight mitigation
Effectively aligning operational plans and a timely, consistent transportation network will improve service and reduce cost.
For more information, please reference the Office of Inspector General Audit Report Number 20-144-R20

3

Transportation Summary
Trips on Time vs. Late and Extra Trips (Weekly)
35000

300000

30000

250000

Number of Trips on Time

200000
20000
150000
15000
100000
10000

50000

0

Number of Late and Extra Trips

25000

5000

7-Jun

14-Jun

21-Jun

28-Jun

5-Jul

Number of Trips On Time

12-Jul

19-Jul

Number of Late Trips

26-Jul

2-Aug

9-Aug

16-Aug

23-Aug

0

Number of Extra Trips

Trips on time references left axis. Late and extra trips references right axis

Source: SV - Surface Visibility

4

Late Trips Analysis

Source: SV - Surface Visibility

5

Extra Trips Analysis

Source: SV - Surface Visibility

6

Service Performance
Data Through 8/26/20

7

50%

75%

70%

65%

First-Class Mail
USPS Marketing Mail

All scores for current week-to-date (week of 8/22) are through 8/26. USPS Marketing Mail score for current week-to-date does not include Saturation Mail as that data is available after the end of the week i.e. on 9/1
8/22/2020

8/15/2020

8/8/2020

8/1/2020

7/25/2020

7/18/2020

7/11/2020

7/4/2020

7/1/2020

6/27/2020

6/20/2020

6/13/2020

6/6/2020

5/30/2020

5/23/2020

5/16/2020

5/9/2020

5/2/2020

4/25/2020

4/18/2020

4/11/2020

4/4/2020

4/1/2020

3/28/2020

3/21/2020

3/14/2020

3/7/2020

2/29/2020

2/22/2020

2/15/2020

2/8/2020

2/1/2020

1/25/2020

1/18/2020

1/11/2020

1/4/2020

1/1/2020

12/28/2019

12/21/2019

12/14/2019

12/7/2019

11/30/2019

11/23/2019

11/16/2019

11/9/2019

11/2/2019

10/26/2019

10/19/2019

10/12/2019

10/5/2019

10/1/2019

9/28/2019

COVID-19

California Wildfires

Pitney Bowes Cyber Attack

Tropical Storm Imedla
Houston Roof Collapse

9/14/2019
9/21/2019

55%

Hurricane Dorian

60%

9/7/2019

Rochester Mercury Spill

80%

8/31/2019

85%

8/24/2019

8/17/2019

On-Time Score

Official Scores
52 Week Trend

100%

95%

90%

Periodicals
8

Presort First-Class Mail
Score Breakdown – Processing vs Last Mile
• Baseline Period: 3/14 - 7/3
• Current week-to-date: 8/22 - 8/26

OVERALL

OVERALL

100%

5.30%

0.28%

LOWER
THAN
BASELINE

HIGHER
THAN
8/1 – 8/7

LAST MILE

5.58%
2.21%

PROCESSING
LOWER
THAN
BASELINE

LOWER
THAN
BASELINE

0.00%

On-Time Score

-1.00%
90%
-1.50%
85%
-2.00%
80%

Last Mile Impact

-0.50%

95%

-2.50%
-3.00%

75%

Overall Score

Processing Score

1 Extra Day

Last Mile Impact

Last Mile Impact represents the score decrease caused by time spent in the last mile (from last processing scan to delivery); Processing score represents service performance from USPS possession to last processing scan
measured against the service expectation; Overall score represents service performance from USPS possession to delivery (i.e. includes the last mile) measured against the service expectation; 1 Extra Day represents the
overall score if the mailpiece had 1 extra day to meet service expectations; Scores are NOT weighted and may NOT match the official scores in slide 1 which are weighted.

9

Single Piece First-Class Mail
Score Breakdown – Processing vs First & Last Mile
• Baseline Period: 3/14 - 7/3
• Current week-to-date: 8/22 - 8/26

OVERALL

HIGHER
THAN
8/1 – 8/7

100%

PROCESSING

4.26%
4.37%

OVERALL

LAST MILE

FIRST MILE

3.28%

0.42%

0.56%

LOWER
THAN
BASELINE

LOWER
THAN
BASELINE

LOWER
THAN
BASELINE

LOWER
THAN
BASELINE

0.00%
-0.50%
-1.00%
-1.50%

90%

-2.00%
85%

-2.50%

Last Mile Impact

On-Time Score

95%

-3.00%

80%

-3.50%
75%

-4.00%

Overall Score

Processing Score

1 Extra Day

First Mile Impact

Last Mile Impact

First Mile Impact represents the score decrease caused by time spent in collection; Last Mile Impact represents the score decrease caused by time spent in the last mile (from last processing scan to delivery); Processing
score represents service performance from USPS possession to last processing scan measured against the service expectation; Overall score represents service performance from USPS possession to delivery measured 10
against the service expectation; 1 Extra Day represents the overall score if the mailpiece had 1 extra day to meet service expectations; Scores are NOT weighted and may NOT match the scores on slide 1 which are weighted.

USPS Marketing Mail
Score Breakdown – Processing vs Last Mile
• Baseline Period: 3/14 - 7/3
• Current week-to-date: 8/22 - 8/26

OVERALL

OVERALL

100%

0.10%

0.40%

LOWER
THAN
BASELINE

HIGHER
THAN
8/1 – 8/7

LAST MILE

0.49%
8.26%

PROCESSING
LOWER
THAN
BASELINE

LOWER
THAN
BASELINE

0.00%

On-Time Score

-1.00%
90%
-1.50%
85%
-2.00%
80%

Last Mile Impact

-0.50%

95%

-2.50%
-3.00%

75%

Overall Score

Processing Score

1 Extra Day

Last Mile Impact

Last Mile Impact represents the score decrease caused by time spent in the last mile (from last processing scan to delivery); Processing score represents service performance from USPS possession to last processing scan
at the destination plant measured against the service expectation; Overall score represents service performance from USPS possession to delivery (i.e. it includes the last mile) measured against the service expectation;
1 Extra Day represents the overall score if the mailpiece had 1 extra day to meet service expectations; Scores are NOT weighted and may NOT match the official scores in slide 1 which are weighted.

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Periodicals
Score Breakdown – Processing vs Last Mile
• Baseline Period: 3/14 - 7/3
• Current week-to-date: 8/22 - 8/26

OVERALL

OVERALL

100%

0.60%

2.29%

LOWER
THAN
BASELINE

HIGHER
THAN
8/1 – 8/7

LAST MILE

2.88%
7.83%

PROCESSING
LOWER
THAN
BASELINE

LOWER
THAN
BASELINE

-1.00%

95%

-2.00%

90%

-3.00%
-4.00%

85%

-5.00%
80%

-6.00%

Last Mile Impact

On-Time Score

0.00%

-7.00%

75%

-8.00%

70%

-9.00%

65%

-10.00%

Overall Score

Processing Score

1 Extra Day

Last Mile Impact

Last Mile Impact represents the score decrease caused by time spent in the last mile (from last processing scan to delivery); Processing score represents service performance from USPS possession to last processing scan
at the destination plant measured against the service expectation; Overall score represents service performance from USPS possession to delivery (i.e. it includes the last mile) measured against the service expectation;
1 Extra Day represents the overall score if the mailpiece had 1 extra day to meet service expectations; Scores are NOT weighted and may NOT match the official scores in slide 1 which are weighted.

12