swiss economics

Impact of Discontinuance 

of Saturday Delivery for Letters and Flats

Final Report

Prepared for the U.S. Postal Regulatory Commission

February 2014

ISSN 2235-1868

Dr. Urs Trinkner

Andreas Haller
Swiss Economics, final report for PRC, page 1/65

Acknowledgments

Swiss Economics has been commissioned by the U.S. Postal Regulatory Commission to conduct this 

research on the plan of the United States Postal Service to discontinue the delivery of letters and 

flats on Saturdays while maintaining parcels delivery. Swiss Economics is an independent economic 

think tank.

Swiss Economics would like to acknowledge the valuable input and feedback from the Postal Regulatory Commission staff. We also thank the United States Postal Service for providing the Postal 

Regulatory Commission the data necessary to perform this research. 

This report has been authored by the project team at Swiss Economics. Any errors or omissions 

herein remain the responsibility of the project team.
Swiss Economics, final report for PRC, page 2/65

Contents

List of Figures .......................................................................................................................................... 4

List of Tables ............................................................................................................................................ 5

1. Executive Summary..................................................................................................................... 6

2. Introduction ................................................................................................................................ 11

2.1 Overview.......................................................................................................................... 11

2.2 Discontinuance of Saturday Delivery .......................................................................... 11

2.3 Methodology ................................................................................................................... 12

2.4 Structure of the report.................................................................................................... 13

3. Status quo: USPS processes and costs .................................................................................... 14

3.1 Overview of the postal value chain.............................................................................. 14

3.2 Actual costs of USPS....................................................................................................... 15

4. Plan 5+ scenarios ........................................................................................................................ 18

5. A model to compute route and access times in delivery...................................................... 21

5.1 Model................................................................................................................................ 22

5.1.1 Bottom-up model to calculate route time differentials................................... 22

5.1.2 Composition of delivery costs ........................................................................... 22

5.1.3 Calibration of the model..................................................................................... 23

5.1.4 Saturday delivery ................................................................................................ 24

5.1.5 Remaining delivery days (Monday to Friday) ................................................ 24

5.1.6 Sample Selection.................................................................................................. 24

5.2 Data................................................................................................................................... 24

5.2.1 Data per delivery point....................................................................................... 25

5.2.2 Data per route ...................................................................................................... 26

5.2.3 Volume and street time data.............................................................................. 26

5.3 Simulation results and calibration to USPS data ........................................................ 28

5.3.1 Simulation............................................................................................................. 28

5.3.2 Calibration of route and access times ............................................................... 29

5.3.3 Calibration of office time .................................................................................... 32

6. Financial impact analysis .......................................................................................................... 33

6.1 Impact of plan 5+ on delivery........................................................................................ 33

6.1.1 City carriers .......................................................................................................... 34

6.1.2 Rural carriers........................................................................................................ 39

6.1.3 Other carrier costs ............................................................................................... 39

6.1.4 Summary............................................................................................................... 39

6.2 Impact of plan 5+ on other processes ........................................................................... 40

6.3 Overall impact on profits and unit costs ..................................................................... 41

6.3.1 Profitability boundaries ...................................................................................... 41

6.3.2 Change in profitability differentials for selected demand scenarios............ 42

7. References ................................................................................................................................... 44
Swiss Economics, final report for PRC, page 3/65

8. Appendix A: Methodological details ..................................................................................... 45

8.1 Step 1: Modified delivery schedules ........................................................................... 45

8.2 Step 2: Impact on cost and days to delivery............................................................... 45

8.3 Step 3: Impact on profitability...................................................................................... 46

8.4 Remark on net costs........................................................................................................ 47

8.5 Deviations from the original scope of the project....................................................... 47

8.6 Alternative methodologies ............................................................................................ 48

8.6.1 Assessment criteria.............................................................................................. 48

8.6.2 Profitability cost vs. NPV ................................................................................... 48

8.6.3 Cost modeling: bottom-up modeling vs. econometric analysis .................... 48

9. Appendix B: Cost allocation .................................................................................................... 49

10. Appendix C: Model calibration—estimation output ........................................................... 50

11. Appendix D: Business process analysis, consumer response ............................................. 54

11.1 Collection ......................................................................................................................... 54

11.1.1 Scenario................................................................................................................. 54

11.1.2 Plan 5 approach ................................................................................................... 54

11.1.3 Plan 5+ approach ................................................................................................. 54

11.2 Sorting (Processing)........................................................................................................ 55

11.2.1 Scenario................................................................................................................. 55

11.2.2 Plan 5 approach ................................................................................................... 55

11.2.3 Plan 5+ Approach ................................................................................................ 56

11.3 Delivery............................................................................................................................ 57

11.3.1 Scenario................................................................................................................. 57

11.3.2 Plan 5 approach ................................................................................................... 57

11.3.3 Plan 5+ Approach ................................................................................................ 59

11.4 Transport.......................................................................................................................... 59

11.4.1 Scenario................................................................................................................. 59

11.4.2 Plan 5 approach ................................................................................................... 59

11.4.3 Plan 5+ Approach ................................................................................................ 61

11.5 Demand effects: impact of quality and demand response ....................................... 61

11.5.1 Plan 5 Approach .................................................................................................. 61

11.5.2 Plan 5+ Approach ................................................................................................ 62

11.6 Further issues: Peak load and impact on service level ............................................. 62

11.6.1 Peak load............................................................................................................... 62

11.6.2 Service ................................................................................................................... 62
Swiss Economics, final report for PRC, page 4/65

List of Figures

Figure 1: USPS’ value chain .......................................................................................................................14

Figure 2: Distinction of route time, access time and load time .............................................................21

Figure 3: Illustration of shortest path simulation for an illustrative route ..........................................22

Figure 4: Shift of delivery volumes to primarily Monday.....................................................................23

Figure 5: Number of valid x/y coordinates per route.............................................................................25

Figure 6: Mean volume per delivery day as percentage of yearly average daily volume ................27

Figure 7: Time Series Total Volume..........................................................................................................27

Figure 8: Shortest path results ordered by size .......................................................................................28

Figure 9: Histogram shortest path: US vs. Switzerland ........................................................................28

Figure 10: Histogram linear distance........................................................................................................29

Figure 11: Time series total volume (status quo vs. adjusted/Plan 5+) ................................................33
Swiss Economics, final report for PRC, page 5/65

List of Tables

Table 1: Daily delivery patterns for status quo and plan 5+ and impact of plan 5+ in delivery.........7

Table 2: Estimated direct cost savings in delivery ....................................................................................8

Table 3: Break-even volume response ........................................................................................................9

Table 4: Financial effects of plan 5+ compared to plan 5..........................................................................9

Table 5: Overall USPS figures for FY 2012 ...............................................................................................15

Table 6: Attribution of the USPS’ operating expenses to processes and products .............................16

Table 7: Volume, revenue and allocated fixed costs per product group .............................................16

Table 8: Profitability per product group ..................................................................................................16

Table 9: Discontinued processes in plan 5 ...............................................................................................18

Table 10: Assumed changes per modified delivery schedule ...............................................................19

Table 11: Variables in DOIS .......................................................................................................................26

Table 12: Performance of proxies ..............................................................................................................30

Table 13: Frequency of route types ...........................................................................................................31

Table 14: Calibration by delivery type .....................................................................................................32

Table 15: Daily delivery patterns for status quo and plan 5+ and impact of plan 5+ in delivery.....34

Table 16: Delivery scenario ........................................................................................................................35

Table 17: Avoided costs on Saturdays ......................................................................................................37

Table 18: Incremental costs on Mondays .................................................................................................38

Table 19: Incremental cost on Fridays ......................................................................................................38

Table 20: Financial effect of additional overtime hours .........................................................................39

Table 21: Estimated direct cost savings in delivery ................................................................................40

Table 22: Estimated direct cost savings in collection, sorting, and transport .....................................40

Table 23: Break-even volume response ....................................................................................................41

Table 24: Demand effects............................................................................................................................42

Table 25: Financial effects of plan 5+ as compared to plan 5.................................................................42

Table 26: Attribution of USPS cost segments to processes ....................................................................49

Table 27: Attribution of USPS products to product groups ..................................................................49

Table 28: Share of USPS costs per product group and process .............................................................49

Table 29: Variable and fixed costs per piece ............................................................................................49

Table 30: Regression output with shortest path as proxy......................................................................50

Table 31: Regression output with USPS sequence path as proxy .........................................................50

Table 32: Regression output with Base Mileage......................................................................................50

Table 33: Regression output with no proxy (constant) ..........................................................................51

Table 34: Regression output with dummies for delivery methods ......................................................51

Table 35: Regression output for “foot” routes.........................................................................................52

Table 36: Regression output for "Park & Loop" routes ..........................................................................52

Table 37: Regression output for “dismount” routes ...............................................................................52

Table 38: Regression output for “curb” routes ........................................................................................53

Table 39: Regression output for “other” routes.......................................................................................53

Table 40: Regression output for office hours ...........................................................................................53

Table 41: Estimated cost savings in outgoing mail processing operations..........................................56

Table 42: Estimated cost savings in delivery ...........................................................................................59

Table 43: Estimated cost savings in transportation.................................................................................60

Table 44: Estimated demand response and financial effects .................................................................62

Table 45: Lags caused by plan 5 ................................................................................................................63
Swiss Economics, final report for PRC, page 6/65

1. Executive Summary

Overview

Delivery costs are the largest segment of total costs incurred by the United States Postal Service

(USPS). These costs comprise 38 percent of total operating costs. Accurately assessing how unit 

delivery costs behave is crucial to properly attribute costs to products. This report presents the 

application of a model for estimating the relationship between the cost of city carrier delivery and 

the number of delivery points receiving mail, as well as the volume of mail to be delivered. This 

model uses data from the Postal Service that allow the identification of the geographic location of 

all delivery points served by each delivery route, the volume delivered on the route each day, and 

the time spent on the route by the carrier. The model simulates each route, determining the shortest 

linear distance to serve all delivery points receiving mail.

The simulation can be modified to estimate the cost impact of a variety of possible scenarios that 

would alter the number and location of addresses served by a route on a given day including shifts 

in weekly or seasonal mail volumes, redesign of routes to shift addresses from one route to another, 

or switching to centralized mailbox delivery from delivery to each address. Additional research 

could also lead to refinements in measuring the relationship between delivery mode (e.g., foot or 

motorized) and delivery costs.

The discontinuance of Saturday delivery—a plan announced by USPS in 20131—is likely to have the 

greatest impact on city carrier delivery costs. Therefore, an analysis of the estimated impact of 

discontinuing Saturday delivery of letters and flats is provided in this report as an illustration of 

how the model can be applied.

After running a baseline simulation to calibrate the model, the routes are re-simulated under the 

assumption that only addresses receiving parcels are served on Saturdays. Using the results of the 

re-simulation, the lengths and times are recalculated for each route, and compared to the status quo 

to measure the effects of the change on delivery costs.

This report adopts and modifies, as appropriate, previous Postal Service and U.S. Postal Regulatory 

Commission (PRC) analysis of the impact on other operations (collection, sorting, transportation) of 

discontinuing Saturday delivery. The results of these analyses are combined with the results of the 

delivery analysis to develop an overall estimate of the net impact. The results are presented as a 

range, with the low estimate of net savings reflecting more conservative assumptions and the high 

estimate reflecting the greatest likely potential savings.

Discontinuance of Saturday delivery: Plan 5, Plan 5+ and methodology

A first plan of USPS to discontinue Saturday delivery and deliver letters, flats and parcels five times 

a week was filed in 2010 (“plan 5”). The main difference in the adapted plan of 2013 is that Saturday 

delivery for parcels would be maintained. It is therefore referred to as “plan 5+.” In contrast to plan 

5, plan 5+ was never officially filed by USPS. This study focuses on assessing the impact of plan 5+ 

and applies information from plan 5 as far as considered appropriate. 

 

1 On February 6, 2013, the Postal Service announced plans to discontinue the street delivery of letter and flat mail on Saturdays, while continuing parcels delivery on Saturdays, and the delivery of mail addressed to PO Boxes. Post Offices 

would remain open on Saturdays. Once fully implemented, the USPS expects cost savings of approximately USD 2 billion. The implementation of the new delivery schedule was prohibited by congress. As a consequence, the Postal Service decided to delay the implementation and it did not file the plan for regulatory review by PRC. 
Swiss Economics, Report for PRC, Seite 7

Two possible scenarios of plan 5+ are analyzed. “Plan 5+ high” is designed to represent the upper 

bound of the savings estimates, whereas “plan 5+ low” indicates the lower bound. Plan 5+ high is 

based on adapted sorting and transport processes that were projected for plan 5. In contrast, “plan 

5+ low” is much closer to the status quo, implying lower savings. 

In both scenarios, letters and flats are not delivered on Saturdays. With only parcels remaining, this 

leads to significant changes in Saturday street times. It also affects Mondays, when the workload is 

significantly increased by the need to deliver letter and flat volumes no longer delivered on Saturdays. Based on earlier USPS assumptions, mail volume for delivery on Mondays would be about 

twice the volume of Wednesdays or Thursdays under plan 5+. On Saturdays, the parcels delivered 

are less than 1% of the daily average of mail pieces. The differences in delivery patterns between the 

status quo and plan 5+ are shown in Table 1. A value of 126.6% indicates that on that day, volumes 

are 26.6% higher than on an average weekday. 

Table 1: Daily delivery patterns for status quo and plan 5+ and impact of plan 5+ in delivery

Status quo Plan 5+ Impact in delivery

Monday 126.6% 195.7% Major impact on load times likely to increase existing overtime hours

Tuesday 100.6% 109.6%

Wednesday 92.7% 92.7%

Thursday 94.0% 94.8%

Friday 93.6% 116.2% Impact on load times may increase existing overtime hours

Saturday 92.5% 0.7% Major impact on route, access and load times; reduction of existing overtime

The last column of Table 1 indicates the major financial impact of plan 5+ in delivery: Sharply reduced street times on Saturdays and an increase in workload on Mondays and Fridays, which is 

likely to increase existing carrier overtime hours, especially on Mondays. To estimate these effects, 

the adapted street times and increases of overtime hours are calculated in a model of street delivery 

activity. For all other direct effects, the information on plan 5 is analyzed and where necessary corrected. To compute the overall effect on USPS profits, the direct effects are then complemented with 

an illustrative assessment of potential indirect effects arising from consumers responding to the service changes by sending less mail.

Bottom-up delivery model

To compute the effects of plan 5+ on delivery, Swiss Economics used a bottom-up delivery model. 

For each carrier route and delivery day, the model computes a proxy of the time necessary to access 

all recipient mailboxes that require delivery. First, the model was applied for a random subsample 

of over 16,000 routes. Next, the model was calibrated and validated with actual USPS street times 

and base mileages. The model was then run for all FY 2012 Saturdays using actual parcels volumes. 

While the model can also be used to estimate the effect of increased coverage of addresses on weekdays, but based on experience with the model it would likely have a very small impact and would 

require a very substantial increase in the time required to run the model. Using the results of the 

model, savings on Saturdays and increases in overtime on Mondays and Fridays can be calculated. 

The savings on Saturdays can be computed both as avoided costs from plan 5+ relative to the status 

quo and incremental costs of plan 5+ relative to plan 5.

Direct effects of plan 5+ high and low

Based on the model, the following estimates on direct effects on USPS’ operational delivery cost 

emerge.
Swiss Economics, Report for PRC, Seite 8

For city carriers, the direct savings are USD 634 million before overtime effects, assuming that the 

cost for load times for letters and flats are shifted from Saturdays to other weekdays with no change 

in productivity. If however the plan 5 assumption of higher productivity on Mondays is applied, 

then the savings are USD 892 million. The more conservative estimate of USD 634 million is taken 

for plan 5+ low, and the higher value of USD 892 million for plan 5+ high. 

In terms of overtime, the street time calculations for Friday, Saturday and Monday for FY 2012 translate into an upper bound of incremental overtime costs of USD 378 million. The recent American 

Postal Workers Union contract allows greater use of workers with more flexible work schedules.

This may allow USPS to handle peak loads with fewer overtime hours. If all overtime hours were 

managed this way, zero additional paid overtime hours could occur. The assumptions for plan 5+ 

high and low are therefore zero and USD 378 million incremental costs, respectively.

For rural carriers, no such savings or incremental costs relative to plan 5 are assumed, as rural carriers are paid by the number of various workload elements (e.g., pieces, miles, and delivery points) 

each route requires. Similarly, plan 5 estimates for incremental express delivery costs are adopted.

For administrative “indirect” carrier costs, the approach used by USPS for plan 5 is applied, leading 

to an estimated additional savings of USD 265 million (plan 5+ high) and USD 238 million (plan 5+ 

low).

In total, the estimated direct savings of plan 5+ in delivery range between USD 0.9 and 1.7 billion.

Table 2 provides an overview and compares with plan 5. The elements that differ from plan 5 are 

highlighted in red.

Table 2: Estimated direct cost savings in delivery

For the processes other than delivery (collection, sorting and transport), the plan 5+ high scenario

assumes the same adaptations are made as in plan 5. This is possible because the plan 5 operational 

changes are compatible with plan 5+. Under plan 5, sorting of mail items including parcels is continued on Friday night; therefore, parcels would arrive in delivery offices in time to be delivered on 

Saturday mornings. As a consequence, savings arise mainly from the discontinuation of Saturday 

dispatch from Post Offices, inducing fewer mail items requiring sorting and transport during the 

weekends. Based on earlier analysis of plan 5, the corresponding savings are estimated at about USD 

290 million. In the plan 5+ low scenario it is assumed that Saturday dispatch continues as in the 

status quo, and consequently there are no savings. However, this would result in faster end-to-end 

delivery times, which should mitigate the reduction in volumes sent by mailers in response to the 

service changes. 

In total, the estimated direct effects of plan 5+ range from USD 0.9 billion for plan 5+ low to 2 billion 

for plan 5+ high, as shown in Table 4.

M USD Plan 5 Plan 5+ high Difference Plan 5+ low Difference

City carrier direct street time 1'162 892 (270) 634 (528)

City carrier direct in-office costs 102 102 - - (102)

City carrier adjustment for Saturday Express Mail (7) (7) - (7) - 

City carrier adjustment for cost of overtime hours - - - (378) (378)

Rural carrier direct costs 341 341 - 341 - 

Rural carrier EMA savings 85 85 - 85 - 

Rural carrier adjustment for Saturday express mail (1) (1) - (1) - 

Indirect cost for city and rural carriers 305 265 (40) 238 (67)

Total delivery savings 1'987 1'677 (310) 912 (1'075)
Swiss Economics, Report for PRC, Seite 9

Overall financial impact

The yearly impact on USPS’ profits is computed as the combination of the direct effects of cost reductions, and indirect effects of volume changes as customers respond to the changed level of service.

If consumers respond to plan 5+ by sending fewer mail items, the resulting loss of revenue will 

partially offset savings. The quantification of demand effects is not part of this project. Instead, profitability boundaries are calculated and illustrative demand scenarios are evaluated.

For the computation of profitability boundaries, the magnitude of reduced mail demand that would 

equally offset savings (i.e., the break-even point) from plan 5+ is calculated. Table 23 presents the 

results of the calculation for FY 2012. The implementation of plan 5+ high with estimated direct 

savings of about USD 2 billion is therefore profitable as long as volume losses do not exceed 7.5% 

on average. With estimated direct savings of about USD 900 million for plan 5+ low, an average 

volume loss of 3.5% would offset savings.

Table 3: Break-even volume response

Plan 5+ high Plan 5+ low

Break-even average volume response -7.5% -3.5%

In addition, the net effect of plan 5+ high and low on the USPS’ yearly profits is evaluated for illustrative demand assumptions. The demand responses are assumed to be smaller in plan 5+ low compared to plan 5+ high because the effects on quality are less severe in the low scenario compared to 

the high scenario. The results are shown in Table 4. In plan 5+ high, indirect effects caused by the 

assumed volume loss of 2.20% on average would reduce profits by about USD 570 million. In plan 

5+ low, consumers respond less sharply, leading to a decrease of USD 287 million. For plan 5 with 

illustrative volume losses of 2.22% on average, the indirect effects are slightly smaller than in plan 

5+ high. This somewhat counterintuitive result is caused by a negative contribution of market dominant parcels in FY 2012.

Table 4: Financial effects of plan 5+ compared to plan 5

M USD Plan 5 Plan 5+ high Plan 5+ low

Direct effects (direct avoided cost) 2'276 1'966 912

Savings Collection/Sorting 120 120 -

Savings Transport 170 170 -

Savings Delivery 1'987 1'677 912

Indirect effects for FY2012 (lost contribution) (571) (573) (287)

Average volume response -2.22% -2.20% -1.10%

Foregone revenue* (1'234) (1'169) (585)

Avoided cost* 663 596 298

Total Savings 1'705 1'393 625 

*For Plan 5, adapted to FY2012 figures
Swiss Economics, Report for PRC, Seite 10

The illustrative demand scenarios indicate plan 5+ net savings ranging between USD 0.6 and 1.4

billion. This compares to the expected savings of USD 1.7 billion for plan 5. A final assessment would 

require additional details of the plan and its calculations, and market research on consumers’ response to the plan. The estimated impact on USPS finances, which measures the impact on producers’ surplus, may be complemented with an assessment of the impact on consumers’ surplus to estimate the overall economic effects.

Additional applications

The model can be modified to evaluate a variety of possible scenarios that would alter the number 

and location of addresses served by a route on a given day, as well as a more general examination 

of the behavior of street delivery costs. The study currently used by the USPS and PRC to estimate 

the volume variability of city carrier street time costs predates many recent operational changes. It 

is currently being reviewed, and the USPS is collecting data on parcel delivery costs to be used in 

combination with data on regular delivery and mail collection to develop an updated analysis of 

total street time costs. The model presented in this report may prove useful in validating the results 

of that analysis and providing additional insight.

To respond to recent volume declines and financial difficulties, the USPS may also consider potential 

changes in delivery operations other than (or in addition to) reduced delivery frequency. Possible 

changes could include converting routes where deliveries are made to the door into curbline routes, 

or delivering to centralized neighborhood locations instead of delivering to each address. The model 

used in this report could be modified to evaluate the likely impacts of these types of operational 

changes.
Swiss Economics, Report for PRC, Seite 11

2. Introduction

2.1 Overview

Delivery costs are the largest segment of total costs incurred by the Postal Service. These costs 

comprise 38 percent of total operating costs. Accurately assessing how unit delivery costs behave is 

crucial to properly attribute costs to products. This report presents the application of a model for 

estimating the relationship between the cost of city carrier delivery and the number of delivery 

points receiving mail, as well as the volume of mail to be delivered. This model uses data from the 

Postal Service that allow the identification of the geographic location of all delivery points served 

by each delivery route, the volume delivered on the route each day, and the time spent on the route 

by the carrier. The model simulates each route, determining the shortest linear distance to serve all 

delivery points receiving mail.

The discontinuance of Saturday delivery —a plan recently announced by USPS and further 

explained below—is likely to have a substantial impact on city carrier delivery costs. Therefore, an 

analysis of the estimated impact of discontinuing Saturday delivery of letters and flats is provided 

in this report as an illustration of how the model can be applied.

To provide a more complete analysis of the effects of discontinuing Saturday delivery of letters and 

flats, the report adopts and modifies, as appropriate, previous Postal Service and PRC analysis of 

the impact on other operations (collection, sorting, transportation) of discontinuing Saturday 

delivery. The results of these analyses are combined with the results of the delivery analysis to 

develop an overall estimate of the net impact. The results are presented as a range, with the low 

estimate of net savings reflecting more conservative assumptions and the high estimate reflecting 

the greatest likely potential savings.

2.2 Discontinuance of Saturday Delivery

On February 6, 2013, the Postal Service announced plans to discontinue the street delivery of letter 

and flat mail on Saturdays, while continuing parcels delivery on Saturdays, and the delivery of mail 

addressed to PO Boxes. Post Offices currently open on Saturdays will remain open on Saturdays. 

The USPS expects to achieve cost savings of approximately USD 2 billion annually when fully implemented. Congress responded in March 2013 by prohibiting the implementation of the new delivery schedule. Consequently, the Postal Service Board of Governors decided on April 9 to delay the 

implementation of its new delivery schedule until legislation is passed that provides the authority 

to implement a new schedule. Therefore, the USPS has not yet filed a plan for regulatory review by 

PRC. In anticipation of the USPS’ postponed plan, the PRC commissioned Swiss Economics SE AG 

(Swiss Economics) to perform an analysis to better understand the background of how the USPS’ 

plan 5+ would affect delivery, mail processing, and transportation costs. The main results of the 

analysis are summarized in this report.
Swiss Economics, Report for PRC, Seite 12

2.3 Methodology

The analysis assesses the long-term impact of selected modified USPS delivery schedules on cost 

and quality, and calculates boundaries for induced demand effects under which the delivery schedules are still profitable for the USPS. In addition, bottom line effects on the USPS’ profits are calculated for illustrative demand assumptions.

The analysis has been carried out in the following steps. Details are provided in Appendix A.

– Step 1: Definition of modified delivery schedules

Based on the known details of plan 5+ and plan 5, definition of modified USPS delivery schedules that are to be analyzed (modified compared to the status quo). 

– Step 2: Impact on cost and quality

Quantification of impact on cost and quality of the selected modified delivery schedules. To 

this end, a business process analysis along the postal value chain is performed to identify the 

relevant cost and quality effects. Delivery is the largest cost of the USPS, and the most affected 

by plan 5+. Therefore, the analysis centers on the application of a new approach to estimating 

the effects on delivery costs. The effects of the new delivery schedules on delivery street time 

will be assessed by a bottom-up model, which is the core of this study. As no detailed USPS 

filing is available, many other effects are – as far as appropriate – based on the USPS’ plan 5 

filing. The analysis focuses on direct effects and results in estimates of avoided cost, incremental cost, as well as effects on delivery time. Indirect effects are accounted for in Step 3.

– Step 3: Impact on USPS’ profitability

Computation of the impact of the modified delivery schedules on the USPS’ profitability. The 

difference in yearly profits ∆π (or net financial effect of modified delivery schedule) is computed as the net impact of direct effects and indirect effects. Direct effects relate to avoided or 

incremental costs due to changes of processes (e.g., savings from not delivering letters on 

Saturdays). Indirect effects result from mailers’ responses to the adjusted services. If mailers

respond to a five-day delivery schedule by sending less mail, then postage that is not sold 

anymore constitutes foregone revenue, and the reduction in variable costs resulting from the 

decrease in volume are the avoided volume costs. The following summarizes the computations, a technical derivation can be found in the appendix. 

∆π = ⏟avoided process cost – incremental cost 

direct effects on process costs

− ⏟foregone revenue + avoided volume costs 

indirect effects from demand response

The direct effects are calculated in Step 2. Indirect effects (difference of avoided volume costs

and foregone revenue) must be considered whenever a modified delivery schedule has an 

effect on mail and/or parcels demand. The exact quantification of demand effects is beyond 

the scope of this project. We will therefore compute boundaries of maximum revenue losses 

such that the net effect is zero and compute the net effects for illustrative demand assumptions that are based on earlier USPS market research on plan 5.
Swiss Economics, Report for PRC, Seite 13

2.4 Structure of the report

This report summarizes the results of the three steps above and is structured as follows.

Section 3 presents an overview of the postal value chain and current USPS process and product 

costs. 

Section 4 defines the details of the modified delivery schedules that are analyzed. Two variants of 

plan 5+ are analyzed: A “high” scenario representing an upper bound of the savings estimates, and 

a “low” scenario that gives a lower bound representation. Plan 5+ high is based on processes that 

were foreseen for plan 5 by the USPS, whereas plan 5+ low is much closer to the status quo. For both 

scenarios however, a bottom-up delivery model is required to compute the effect of plan 5+ on the 

time USPS’ carriers can save when delivering parcels only on Saturdays.

Section 5 presents the bottom-up delivery model. The model is run for a subsample of real USPS 

routes. It is then calibrated to actual USPS street time data for 2012, confirming that the model provides an accurate fit of actual street times.

Section 6 then presents the analysis of the financial effects of plan 5+ on the USPS. The delivery 

savings are based on the model from Section 5. The results show that the savings are likely to be 

lower than expected by the USPS. Whereas the USPS estimated savings of about USD 2 billion for 

plan 5+, the calculations indicate savings between USD 0.6 and 1.4 billion.

Section 1 summarizes the report.

Appendices A (methodology), B (cost allocation), C (model calibration) and D (business process 

analysis) can be found in Sections 8 to 11.
Swiss Economics, Report for PRC, Seite 14

3. Status quo: USPS processes and costs 

3.1 Overview of the postal value chain

The postal value chain can be segmented into the four main processes

– collection,

– processing (also referred to as sorting),

– transportation,

– delivery, and

– overhead.

Collection takes many forms. The traditional retail channels are post offices and mail drop boxes. 

Larger customers also use customized acceptance units, e.g., bulk mail entry units at processing 

facilities, detached entry units at the mailer’s location, direct collection by delivery carriers or electronic acceptance (click and ship). Processing is done in several main steps. Outgoing primary mail 

involves a first run where the mail is sorted to the first three to five digits of the postal ZIP Code, 

and incoming primary mail involves sorting to the five-digit ZIP Code. Incoming secondary mail

consists of delivery point sequencing (DPS) where mail is sorted to the walk sequence per route. 

Delivery is done by mail carriers who first complete the sorting of the residual mail that is not yet 

in walk sequence before physically delivering the mail to mailboxes on their routes. Transport involves dispatch at collection facilities to sorting infrastructures, transport in between sorting facilities, and the distribution to delivery units. In this typology, overhead includes all other costs that 

cannot be attributed to one of these four processes, e.g., marketing.

USPS mail operations are highly integrated. For example, a postmaster will lead a post office that is 

involved in mail collection and delivery. In addition to window services, the postmaster will organize local delivery, and the post office is used for both operations. Similarly, there are bulk mail entry 

units (BMEU) in most processing and distribution centers, with joint organization and overhead.

The main processes and the attribution of USPS facilities is stylized in Figure 1.

Figure 1: USPS’ value chain

Source: Swiss Economics

Transport

Delivery

Collection

Sorting

Overhead

Other centers 

with BMEU

Mailboxes

P.O. Boxes

Processing & 

Distribution

Centers with 

BMEU

Post 

Offices/

DDU

Mail

drops

Clients

Other centers
Swiss Economics, Report for PRC, Seite 15

3.2 Actual costs of USPS

In this chapter, USPS’ total operating costs are distributed to processes and product groups and 

differentiated between variable and fixed components. As a direct consequence of the USPS’ integration, various cost components are shared among processes, which will lead to a larger share of 

overhead costs as compared to other postal operators.

Table 5 shows fiscal year 2012 USPS operating costs of USD 67.7 B, which is about USD 2.5 B higher 

than operating revenue. For purposes of this analysis, this represents all costs except the statutorily

mandated Retiree Health Benefits Fund payment and an adjustment to workers’ compensation liabilities. Including these non-operating expenses, the net loss was considerably higher. For the remaining part of the analysis, it is assumed that these other expenses are non-variable costs. Therefore, the analysis focuses on operating expenses.

Table 5: Overall USPS figures for FY 2012

Source: PRC Annual compliance report, fiscal year 20122

USPS organizes its accounts among the cost segments C/S 1 to C/S 20. These are further divided into 

sub-segments. For example, segment 3.3.1 reflects the cost for administrative clerks of segment three 

(C/S 3). Table 26 of Appendix B shows how the cost segments have been allocated to the four processes described above and to overhead. Because of the high integration of the USPS, portions of 

some cost segments reflect costs related to more than one process. Therefore the allocation performed here is indicative only.

The USPS further subdivides the costs of every segment into “attributable” (to products) and 

“other,” which is also called “institutional.” Based on information from the PRC and the USPS’ computations for plan 5, attributable costs can be roughly considered as variable and the other costs as 

fixed. The USPS then allocates the attributable costs of every segment to its products identified by a 

product number. The products have been grouped by Swiss Economics to largely shape-based product groups according to Table 27 from Appendix B. Although most of these product groupings are 

intuitive, it should be noted that Packages consists of only those parcel-shaped pieces that remain 

classified as market dominant products. The competitive product group includes all products classified as competitive, such as Express Mail and bulk Parcel Post. The assignment of products to 

product groups is presented in the Appendix.

The distribution of cost segments to processes and products to groups leads to the allocation of total 

operating costs shown in Table 6. Delivery is by far the most costly process—more than collection, 

sorting and transportation together. The corresponding shares are provided in Table 28 from Appendix B. In terms of cost variability a much smaller share of delivery costs are attributable compared to the other processes. This can be seen in the last row of Table 6.

 

2 http://www.prc.gov/Docs/86/86931/2012_ACD_Web-REVISED-5-7-13.pdf M USD Expenses Revenue Difference

Operating 67'697 65'248 (2'449) Net Operating Loss

Other 13'456 -

Total 81'153 65'248 (15'905) Net Loss
Swiss Economics, Report for PRC, Seite 16

Table 6: Attribution of the USPS’ operating expenses to processes and products

Source: Swiss Economics

In the PRC’s Annual Compliance Determination (ACD) report for fiscal year 2012 and the corresponding background information from Docket No. ACR2012, volume and revenue data have been 

reported for USPS products, leading to the volumes and revenues per product group as shown in 

Table 7. Column five computes the share of attributable cost distributed to each product group. 

Applying the same shares to allocate fixed costs on product groups results in the values in column 

6. These are taken as the basis to compute fully distributed costs (FDC) as shown in Table 8.

Table 7: Volume, revenue and allocated fixed costs per product group

Source: Swiss Economics

Based on this information, it is straightforward to compute variable and fixed costs per piece (Table 

29 of Appendix B), as well as contribution, fully distributed costs and profits per piece (Table 8). The 

contribution can be seen as economic profit per piece, whereas the profits per piece are arbitrary 

accounting profits. Overall however, the USPS lost an average of about 2 cents per piece delivered 

in FY 2012 (based on operating costs only).

Table 8: Profitability per product group

Source: Swiss Economics

M USD FY2012 Collection Sorting Transport Delivery Overhead Sum

Letters 1'007 5'640 1'070 6'128 3'887 17'733

Flats 345 2'416 599 2'759 1'536 7'655

Packages 138 699 528 389 365 2'119

Periodicals 9 1 867 238 746 460 2'402

Competitive 363 1'990 2'133 1'010 1'236 6'732

Others 731 613 1'197 784 976 4'300

Attributable Costs 2'675 12'224 5'767 11'816 8'459 40'941

Institutional Costs 1'772 1'198 863 13'842 9'081 26'756

Total Operating 4'448 13'422 6'630 25'658 17'540 67'697

Cost Variability 60.2% 91.1% 87.0% 46.1% 48.2% 60.5%

K USD Volume Revenue [USD] Attributed Share Allocated

Costs [USD] Attributed Fixed Costs [USD]

Letters 119'810'779 36'000'932 17'733'009 43% 11'589'150

Flats 29'209'976 9'599'102 7'655'348 19% 5'003'042

Packages 911'250 2'013'129 2'118'511 5 % 1'384'522

Periodicals 6'741'351 1'723'228 2'401'602 6 % 1'569'532

Competitive 2'259'651 9'607'416 6'732'259 16% 4'399'770

Others 3'087'361 6'304'192 4'300'222 11% 2'810'348

Sum 162'020'368 65'248'000 40'940'952 100% 26'756'364

USD Avg. Price Marginal Contribution Fully Distrib. Calc. 

Costs Costs (FDC) Profit

Letters 0.30 0.15 0.15 0.24 0.06

Flats 0.33 0.26 0.07 0.43 (0.10)

Packages 2.21 2.32 (0.12) 3.84 (1.64)

Periodicals 0.26 0.36 (0.10) 0.59 (0.33)

Competitive 4.25 2.98 1.27 4.93 (0.67)

Others 2.04 1.39 0.65 2.30 (0.26)

All 0.40 0.25 0.15 0.42 (0.02)
Swiss Economics, Report for PRC, Seite 17

The results show that packages and periodicals have a negative contribution, i.e., the average price 

per piece is lower than marginal costs. Therefore, the USPS loses money with every additional piece 

sent and may need to increase prices. In contrast, the other products have a positive contribution. In 

terms of accounting profits, all products except letters are unprofitable. However, the figures are 

based on a very simplified allocation of fixed costs to product categories (according to the respective 

share of attributable costs) and therefore may be distorted.

Later in the analysis, contribution per piece (per product) will be mirrored in the computation of

indirect effects caused by changed consumer demand. The positive contribution of letters and flats 

means that if consumers respond to the service changes of plan 5 or plan 5+ by sending fewer of 

these items, then the contribution is lost for every lost item, reducing the positive impact of direct 

cost savings. However, for parcels, fewer items translate to an increase of profits, ceteris paribus, as 

long as the USPS does not increase its prices above marginal costs.
Swiss Economics, Report for PRC, Seite 18

4. Plan 5+ scenarios

As noted in Section 2, detailed information is available for plan 5 only. Very limited information has 

been published for plan 5+. Nevertheless, to facilitate analysis, the specific changes that would likely 

occur if plan 5+ were implemented are identified. These assumptions are compared to the status quo 

and plan 5.3 Plan 5 is similar to plan 5+ except that the delivery of parcels would have been discontinued as well. The information on plan 5 is taken from Docket No. N2010-1 with emphasis on the 

advisory opinion as well as witnesses Neri and Bradley. Table 9 illustrates the proposed operational 

changes of plan 5.

Table 9: Discontinued processes in plan 5

Mon Tue Wed Thurs Fri Sat Sun

Collection in Post Offices X X X X X X -

Dispatch in Post Offices X X X X X Discontinued -

Sorting (Processing) X X X X X (X) X

Delivery X X X X X Discontinued -

Source: Swiss Economics based on N2010-1, advisory opinion and Neri

The USPS would have discontinued two major processes on Saturdays: Saturday delivery of letters, 

flats and parcels, and the dispatch of mail from Post Offices on Saturdays (that is, no transport of 

mail items collected on Saturdays to sorting centers). It is important to note that the USPS planned—

despite discontinuing letters and parcels delivery on Saturday—to continue sorting on Friday night 

and dispatching the mail to the delivery units Saturday morning:

“Periodicals, First-Class flat mail, standard flat mail, Priority Mail, other flat mail and parcels will be worked Friday night and dispatched early Saturday morning to the delivery units. This will include all P.O. box mail and may 

include any available street-addressed mail. Processing and dispatching of some street addressed mail to delivery 

units on Saturday will allow us to more fully utilize existing transportation capacities and advance available work 

to the carriers for the following Monday.”4

Therefore, under plan 5, mail and parcels would have arrived in the delivery units early Saturday 

morning, but would not have been delivered until Monday. In other words, plan 5+ with its parcel 

delivery on Saturdays appears essentially compatible with the sorting and transport architecture of 

plan 5.

Based on this understanding of plan 5 there are two viable scenarios to calculate possible cost savings in plan 5+:

 Scenario 1 “plan 5+ low”: All processes except for delivery remain the same in plan 5+ as 

they are today. The discontinuation of delivery of letters and flats on Saturdays will lead to 

longer average end-to-end delivery times for all items previously delivered on Saturdays.

 Scenario 2 “plan 5+ high”: All processes from collection to transport except delivery are adjusted in the identical way as in plan 5. The restructuring of mail processing induces higher 

average end-to-end delivery times as compared to plan 5+ low. Mail items collected on Saturdays would not arrive before Tuesday.

 

3

In the original scope of work, the re-engineered processes according to the USPS’ filing would have been analyzed on 

cost and quality differentials. As no filing took place, the necessary details are not known. Business process re-engineering is outside the scope of the project. Therefore, any deviation from the status quo or plan 5, where extensive information is available, is critical.

4 Docket No. N2010-1, USPS-T-4, DIRECT TESTIMONY OF FRANK NERI ON BEHALF OF THE UNITED STATES 

POSTAL SERVICE, p. 9.
Swiss Economics, Report for PRC, Seite 19

Compared to the status quo, Scenario 1 features cost differentials in the delivery process only. Scenario 2 features cost differentials in all other processes. Thereby, the cost differentials in the collection, sorting and transport processes between the scenarios largely correspond to the cost differentials calculated for plan 5.

Evaluation between scenario 1 and 2 would require a detailed analysis of the current business processes and their reorganization under the new scenario. As the detailed plans of USPS are not available, this is beyond the scope of this study. Cost savings for scenarios in between scenarios 1 and 2 

in the collection process, sorting and transport can be expected to be lower than the savings in scenario 2. Therefore, scenario 1 “plan 5+ low” can be viewed as a lower bound on cost savings and 

scenario 2 “plan 5+ high” as an upper bound on cost savings.

Table 10 gives an overview of the adaptations of business processes under plan 5, plan 5+ high and 

plan 5+ low, and how they compare. A detailed discussion along the processes collection, sorting, 

transport, and delivery can be found in Appendix D.

Table 10: Assumed changes per modified delivery schedule5

Status quo Changes in plan 5 Assumed changes in 

plan 5+ high 

Assumed changes in 

plan 5+ low

Collection Post offices: 

Open 6 days 

Collection boxes: 

6 days

Post offices: No change.

Collection boxes: Elimination of retrieval on 

Saturday.

As plan 5 As status quo

Sorting Sorting 7 days a week() Elimination of all Saturday outgoing mail processes (excluding Express Mail).6As plan 5 As status quo

Transport Transport 7 days a 

week

Elimination of Saturday transport for mail 

collected in post offices/collection boxes to 

processing facilities.

Less transport on Sundays due to elimination 

of all Saturday outgoing mail processes.

As plan 5 As status quo

Delivery 6 days home delivery 

of letters, flats, packages

6 days P.O. Box delivery of letters, flats, 

packages 

No home delivery of 

letters or flats and 

packages on Saturdays

[Continued delivery of 

Express Mail and Saturday P.O. Box delivery]

No home delivery of 

letters or flats on Saturdays 

[Continued delivery of 

Express Mail, Saturday 

P.O. box delivery and 

package delivery]

As plan 5+ high

Volume 

Scenario

FY2012 Volume response consideredAs plan 5, but no response for parcelsAs plan 5+ high, but reduced response for letters and flats

Source: Swiss Economics.

 

5 Based on USPS press releases, USPS filing of plan 5 (Docket No. N2010-1), and Swiss Economics analysis.

6 Sorting Friday night remains unchanged.
Swiss Economics, Report for PRC, Seite 20

The remaining chapters estimate the impact of plan 5+ high and low on the USPS’ profits.

As Table 10 reveals, much of the analysis had already been done when plan 5 was filed. The major 

new element from plan 5+ that needs careful consideration is the standalone delivery of parcels on 

Saturdays. Delivery is the focus of this study. Chapter 5 presents the delivery model and describes 

how it can be used to compute street times under different delivery scenarios. The model is then 

applied to estimate the financial effects of plan 5+ low and plan 5+ high in Chapter 6.
Swiss Economics, Report for PRC, Seite 21

5. A model to compute route and access times in delivery

As highlighted in Table 6, delivery is by far the process that accounts for the highest overall costs. 

As seen in Table 10, delivery is also the process likely to be the most affected by plan 5+. 

Delivery carriers typically spend the first hours of the day in the office to prepare the mail for the 

route (“office time”). Once finished, the carrier leaves the office and delivers the mail along the route. 

The time spent leaving the route to access a recipient mailbox, is referred to as “access time.” “Load 

time” is the time required to insert the mail into the mailbox. The time on the route itself is referred 

to as “route time” (Cohen and Chu, 1997). Route and access times constitute a significant cost driver 

for postal services. The distinction of route, access and load time is illustrated in Figure 2. The sum 

of the three components is referred to as “street time” by the USPS. For city carriers, street time 

accounts for more than 75 % of overall delivery time.7

Figure 2: Distinction of route time, access time and load time

Source: Swiss Economics based on Felisberto et al. (2006).

While load time is essentially variable with respect to the number of mail items delivered, access 

and route times are quasi-fixed costs. For a given delivery point, access time is variable with the first 

mail item, after which it is fixed. For a given (independent) route section, route time is variable with 

the first mail item for that section; thereafter, it is fixed up to the most remote delivery point receiving mail. In countries with high volumes per capita such as the U.S., route times can be considered 

as effectively constant, and access times as mostly constant (cf. Haller et al., 2014), which is also 

confirmed below in Section 5.1.3.

Discontinuing letter delivery on Saturdays while maintaining parcels delivery means that 

 on Saturdays, route times are fundamentally different and much more variable, as the probability that a given household gets a (parcel) delivery is reduced sharply (leading to different 

routes every Saturday); 

 from Monday to Friday, the probability that a household is served is increased slightly, leaving route times mainly constant and increasing access times to a small extent only. In the 

analysis, it is assumed that route and access remain constant from Monday to Friday.

In particular, the assessment of the first (Saturday) effect requires a model to predict the changes to

route and access times. Such a prediction can be obtained from an application of the model presented by Trinkner et al. (2012) and Haller et al. (2014). The model computes bottom-up route and 

access times for different delivery schedules, and allows letter and parcel volumes to be taken explicitly into account. Route and access times can then be transformed into costs and normalized to 

 

7 Source: Swiss Economics based on DOIS data from Section 5.2.3.

Access time

Route time

Load time

Post

Office
Swiss Economics, Report for PRC, Seite 22

fit the yearly delivery costs of the USPS. This allows for the estimation of the effects of the modified 

delivery schedules on delivery costs and unit cost. The time differentials can also be used to estimate 

the need for overtime hours on days with increased workload (in particular, Mondays). 

The application of the methodology is explained in more detail below. The methodology to assess 

the cost impact of modified delivery schedules is described for Saturday delivery and for the remaining delivery days. 

5.1 Model

5.1.1 Bottom-up model to calculate route time differentials

In the model, the location of the delivery center is fixed, but the number of delivery days and/or the 

percentage and location of delivery points to be served can be varied. To determine the route costs 

as a function of the distribution of the households around the delivery center, the delivery process 

is treated as a routing problem (minimizing the total route time to deliver all the mail to the served 

delivery points) and solved by numerical methods (hereafter referred to as “shortest path”). To keep 

the model tractable, it is assumed that the mail deliverer can move freely in the area, i.e., we abstract 

spatial obstacles and roads. This approach does not deliver route costs directly. Instead, it computes 

linear distances which can serve as proxies for the real route costs. The model therefore requires 

calibration to determine effective route times and/or distances. Once calibrated, the model allows 

comparisons of delivery costs across various delivery plans or Universal Service Obligations (USO)

definitions and letter volumes. Figure 3 illustrates the result of the applied shortest path simulation 

for route 7 (ZIP-Code 11793). 

Figure 3: Illustration of shortest path simulation for an illustrative route

This simulation model has been applied successfully to Switzerland on behalf of Swiss Post. The 

model could on average explain 95.36 % of Swiss Post’s route times per delivery region, with high 

values independent of the specific delivery areas (cities, agglomerations, rural, mountain valleys). 

Calibration can take place either by actual (measured) route times or actual distances, or both.

5.1.2 Composition of delivery costs

As introduced and illustrated above, street time can be separated into route time, access time and 

load time. Route and access time are simulated in the model. The load time as well as work performed in the delivery office are estimated empirically based on DOIS data.

200 400 600 800 1000

200

400

600
Swiss Economics, Report for PRC, Seite 23

5.1.3 Calibration of the model

Before the new delivery costs can be calculated, the model needs to be adapted to the U.S. situation 

and calibrated accordingly, using USPS baseline delivery costs. To calibrate the model, it is run to 

simulate actual delivery routes. The resulting modeled distances are then regressed on the actual 

delivery times, while controlling for mail volumes. This calibration also allows an assessment of the 

integrity of the model.

The calibration exercise requires (1) defining the actual delivery routes, i.e., which points of delivery 

are served, (2) defining the measure of delivery time, and (3) defining the level of disaggregation in 

distinguishing the type of delivery, i.e., is there one type of delivery per route or should the types 

also be distinguished within a route.

For requirement (1), points of delivery served on a specific delivery day for each route are needed

Figure 4 reveals significant changes in the pattern of mail volumes on Saturday and Monday expected for plan 5, and therefore plan 5+ as well. These patterns are confirmed for FY 2012 figures 

based on evaluations of DOIS data from Section 5.2.3. Table 15.

Figure 4: Shift of delivery volumes to primarily Monday

Source: Cigno (2011), Docket No. N2010-1 Advisory Opinion.

Accounting for daily variations in the specific delivery points served would require conducting iterated simulations for every route, consuming important computation time. Alternatively, the 

routes can be treated as fixed on regular delivery days, assuming that all points of delivery per route 

are served on every delivery day. This implies that the model needs to be simulated only once for 

each route.

Experience from previous model applications suggests that the marginal gains in the integrity of the 

calibration are small compared to the large additional computation requirements, which would 

mean—given the time constraints in the project—a much smaller sample of routes could be analyzed 

(c.f. 5.1.6). It is therefore assumed that the route calibrations are fixed on regular delivery days.

The assumption of fixed routes on regular delivery days was validated for 230 routes which were

randomly chosen from DOIS data. For each route the simulation model was run with random assignment of actual mail volumes on households for two Mondays in FY 2012. In this setup, a household has to be served if it receives at least one mail item. The simulations confirmed that the assumption of fixed routes on non-Saturdays is reasonable. On average, the percentage deviations of 

the fixed route length compared to the stochastic route lengths amount to only 0.019% and from 460

observations only 25 showed deviations above 1 % of fixed route lengths.

0%

5%

10%

15%

20%

25%

30%

35%

Mon Tues Wed Thurs Fri Sat

FY2009 percentages of total volume by day of the week

FY 2009 Volume

Five Day volume

FY 2009 volume 
Swiss Economics, Report for PRC, Seite 24

5.1.4 Saturday delivery

Continuing Saturday parcel delivery will lead to different and more variable routes on Saturdays. 

The model is used to calculate these new routes. New routes are based on which delivery points are 

served on Saturdays. A delivery point is served if it receives at least one parcel. To determine the 

parcel distribution across delivery points, the actual parcel volumes on a given Saturday are randomly assigned to the addresses of a route.8 That is, if there were n parcels on a given Saturday, then 

n addresses of a route are randomly drawn. The delivery points served are then the points which 

have at least one address receiving a parcel.9 Delivery points are calculated for every Saturday in FY 

2012. The new parcel-only Saturday routes are then simulated. Using this method, the parcel-only 

Saturday routes for FY 2012 are simulated under the new delivery regime.

5.1.5 Remaining delivery days (Monday to Friday)

Discontinuing Saturday letter delivery will affect volumes on the remaining delivery days. The 

model allows simulating the effects of increased letter volumes on the actual route times on Mondays, as well as any change that may occur from Tuesday to Friday. On Mondays, many letters will 

be delivered that were formerly delivered on Saturday (cf. Figure 4 based on USPS assumptions, 

“Double-Mondays”), making it more likely that a point of delivery has to be served on a given delivery day. This tends to lengthen the routes. 

To do this within the model would require using a stochastic process of letter distribution as in the 

Saturday parcel delivery, and conducting a number of repeated simulations. This is time consuming 

and we expect the effects of increased volumes on route times to be small. Therefore, this aspect is 

ignored here.

The second, more important channel through which delivery times are affected is load time, which 

is mainly variable and independent of Saturday delivery (exception: possible overtime surcharges 

on “Double-Mondays”). 

5.1.6 Sample Selection

The model is computationally intensive and there are 224,000 routes in the U.S., which makes it 

extremely time-consuming to apply the model to all routes. To get an accurate estimate of the cost 

effects it is sufficient to consider a sample of all routes. The accuracy of the estimate of course increases in the size of the sample. For the calculation of delivery costs for parcel delivery on Saturdays, repeated simulations are needed. The number of simulations increases the accuracy of the cost 

estimate per route. However, running more simulations per route reduces the size of the sample 

that is feasible.

Based on predicted simulation times, a random sample of 20,000 city routes was chosen (for more 

information see below). 

5.2 Data

The following data were provided by the PRC: 

– Data from the USPS’ Address Management System (AMS) for a random sample of 19’958 city 

carrier routes, containing delivery points, coordinates with additional information for each

delivery point; 

 

8 This is as accurate as possible, as we do not have any information on address level.

9 A delivery point can have several addresses.
Swiss Economics, Report for PRC, Seite 25

– Data from the USPS’ Delivery Operations Information System (DOIS), containing office and 

street times as well as volume data for city carrier routes for every delivery day of FY 2012. In 

addition, DOIS information for the type of delivery and base mileage for each route was available.

The data is described in more detail below.

5.2.1 Data per delivery point

The analysis is based on data for 19,958 routes that were randomly drawn by the PRC from AMS 

data provided by the USPS. The PRC used ArcGIS software to identify the coordinates of each address. Per route, for every delivery point the following data was available: coordinates, USPS sequence number, vacancy, delivery type (e.g., curb line), and others.

Prior to the simulations, a series of steps were performed to validate and prepare the data:

– Validation of the structure of all files/routes, adaptations where necessary;

– Consistency check, e.g., number of delivery points, sequence numbers, check of random single coordinates in Google Maps, and others; 

– Deletion of delivery points with or without invalid or incomplete coordinates;

– Transformation of longitude/latitude coordinates into metric coordinates; therefore, the 

origin for every route was set at the bottom left of the delivery points served by that route;

– Classification of routes into “ok”, “validate”, “and exclude.” Exclusion means the number of 

routes with 100 or less delivery points and routes with a spanned area of zero. Similarly, 

routes with too large a spanned areas and/or suspect USPS sequence were marked as “validate.” In total, 16,572 routes could be classified as “ok.”

Figure 5 illustrates the distribution of the 19,958 sample routes. Routes are ordered with low number 

of delivery points first.

Figure 5: Number of valid x/y coordinates per route 

Source: Swiss Economics based on PRC data of FY 2012.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

#delivery points

Routes in increasing order
Swiss Economics, Report for PRC, Seite 26

5.2.2 Data per route

For FY2012, DOIS data for 141,436 routes were provided by the USPS. For each route and delivery 

day, the DOIS data provides information on office hours, out-of office hours, and the number of 

parcels, letters and flats. Details on the variables are in the next section. In FY 2012, there were 303 

delivery days.

From the 16,572 routes in the AMS data classified as “ok,” 16,274 could be identified in DOIS data. 

With 303 delivery days in FY2012, a sample with 4.9 million observations was used for our calibration exercise. Calibration per delivery type is only partially possible because from the 16,274 routes 

from AMS and DOIS data, there are 2,251 routes for which it was not possible to recover information 

on the delivery type.

The next section gives an overview of the DOIS data. Section 5.3 presents the results of the calibration.

5.2.3 Volume and street time data 

The provided FY2012 DOIS data contains the variables as listed in Table 11 for 141,436 routes.

Table 11: Variables in DOIS

Variable Example Category

ServiceDate 1/18/2012

RouteZIPCode 1108

RouteNumber 001

ActualOfficeHoursAmount 1.58

ActualStreetHoursAmount 6.02

TotalCasedLettersPiecesAmount 207 Letters

TotalCasedFlatsPiecesAmount 1041 Flats

TotDeliveryPtSequenceMailAmt 0 Letters

TotalFlatSeqMailPieceAmt 721 Flats

TotalSequencedMailPiecesAmt 0 Letters

TotalParcelsPiecesAmount 3 Parcels

With 303 delivery days in FY2012 and 141,436 routes in the dataset, this results in 42 million data 

points. From the 16,572 routes in the AMS data classified as “ok,” 16,274 could be identified in DOIS 

data. For each of these 16,274 routes there are observations for 303 delivery days. The 77 observations with negative values in either hours or volume variables were excluded. In the following, all 

analysis is based on that sample. The sample size is 4.9 million. 

The following variables were defined:

Flats = Totalflatseqmailpieceamount + Totalcasedflatspiecesamount

Letters = Totalcasedletterspiecesamount + Totdeliveryptsequencemailamt 

 + Totalsequencedmailpiecesamount

Parcels = TotalParcelsPiecesAmount

Based on these definitions the volume patterns shown in Figure 6 and Figure 7 are observed in our 

sample.
Swiss Economics, Report for PRC, Seite 27

Figure 6: Mean volume per delivery day as percentage of yearly average daily volume

Source: Swiss Economics based on DOIS data of 2012

These patterns are consistent with information from PRC as shown in Figure 4. 

Figure 7: Time Series Total Volume

Source: Swiss Economics based on DOIS data of 2012

From Figure 6 it can be observed that Mondays have the highest volumes in all product categories. 

On average, Monday’s letter volumes are 128 % of the average volume of all days. The other days, 

Tuesday to Saturday, tend to have similar volumes. Figure 7 shows the volumes per product for FY 

2012. The peak in Monday’s volume is again well observable. Letters and flats do not exhibit strong 

seasonal patterns. Parcels have peak volumes in the weeks before Christmas.

All routes in the sample have letters throughout the year. From 16,274 routes, 546 routes do not have

any parcels during FY 2012.

0%

20%

40%

60%

80%

100%

120%

140%

Monday Tuesday Wednesday Thursday Friday Saturday

Letters Flats Parcels

 -

 100

 200

 300

 400

 500

 600

 700

0

10'000

20'000

30'000

40'000

50'000

60'000 (000) (000)

letters flats parcels (right scale)
Swiss Economics, Report for PRC, Seite 28

5.3 Simulation results and calibration to USPS data

Based on the available data, route and access times are simulated and calibrated. In addition, office 

times are calibrated. These results are the basis of the financial effects as computed in Section 6.2. 

5.3.1 Simulation 

Linear distances with shortest path algorithm

The model described in chapter 5.1 was applied to the data described in chapter 5.2. That is, the 

16,274 routes in the dataset were simulated in the model. This results in proxies for route and access 

time in the status quo (2012). In 7 % of all routes, the standard algorithm failed to converge in a 

reasonable time horizon and a greedy algorithm10 was used instead. 

Figure 8: Shortest path results ordered by size 

Source: Swiss Economics.

Figure 9: Histogram shortest path: US vs. Switzerland

Source: Swiss Economics.

 

10 The "greedy" algorithm moves from one point to the nearest unvisited neighbor.

0

10000

20000

30000

40000

50000

60000

0 2000 4000 6000 8000 10000 12000 14000 16000 18000
Swiss Economics, Report for PRC, Seite 29

Linear distances with USPS sequence number

The AMS data contains information on the sequence of delivery points for each route applied by the 

USPS and walked by the carriers. In order to validate the shortest path results, a second proxy for 

the length of the routes was constructed by simply totaling the linear distances between the delivery 

points along the provided sequence. This proxy is referred to as “sequence path” in the following. 

Figure 10 shows two histograms of the sequence paths. The histogram on the left is the histogram 

over all sequence paths. There are some implausibly large values, which indicate that there might 

be some errors in either the sequence in the AMS data or the coordinates assigned to the delivery 

points. 

The histogram on the right is a clean version, excluding all distances above 80,000 for better comparability with the “shortest path” distribution above. The histogram reveals similar patterns. 

Figure 10: Histogram linear distance

Source: Swiss Economics.

5.3.2 Calibration of route and access times

For each sample route, street hours, office hours, and volume data are available for all 303 FY 2012

delivery days. In addition, route information is available on type of delivery (e.g., curb) and base 

mileage. This forms an excellent base for calibrating the model. 

Two calibration exercises are performed. First, calibration is done on a global level without reflecting 

different delivery types (e.g., curb or foot). All routes are treated the same, and actual street times 

on the simulated shortest paths and observed volumes are regressed. Second, the calibration is performed differentiating the delivery methods, i.e., running the regression of actual street times on 

simulated shortest paths, observed volumes, and the type of delivery.

In the global calibration, different proxies for route length are used and the model fits are compared. 

Results show the simulated shortest paths perform equally well as the reported base mileage in 

explaining actual street times. Based on these findings, it is concluded that the simulation model 

forms a valid base for calculating route times for alternative delivery scenarios, in particular, Saturday delivery of parcels only.
Swiss Economics, Report for PRC, Seite 30

Global calibration street time

To validate and calibrate the model the following regression was run.

11

actual street hours = β0shortest path + β1letters + β2flats + β3parcels.

As shown in the details of Table 30 of Appendix C, the model provides an accurate fit.. As expected, 

all coefficients are highly significant and the signs of the coefficients are all positive. Overall, the 

model is able to explain 87.24 % of all variation in actual street hours. 

The same regression was run once with sequence path instead of the shortest path, once with a 

constant instead of the shortest path, and once with base mileage instead of the shortest path. The 

results show how well the simulated route length performs compared to other proxies of route distances. Table 31 to Table 33 from Appendix C present the results of these regressions. The variable 

Base Mileage is only available for a subset of the sample, so the sample for the Base Mileage regression is about 1.1 million data points (“delivery days”) smaller than the standard sample (4.9 million).

Table 12 provides an overview of the results. In terms of quality of fit, the model with base mileage 

and shortest path perform equally well, and both do better than the other specifications. The variable 

Base Mileage measures the actual miles per route. This implies that the simulated distances perform 

equally well in explaining street hours as actually measuring the effective miles per route. The simulation model is therefore “as good as it can get.” Moreover, the shortest path algorithm has two 

advantages: it is available for all routes (not the case for base mileage), and it is possible to simulate 

alternative volume and delivery point scenarios, which is of particular value for this study.

Table 12: Performance of proxies

Proxy β0 Coefficient β0t-Value Adj. R2

Linear distance shortest path 0.00014 981.45 87%

Linear distance sequence path USPS 0.00000592 265.84 85%

Base Mileage 0.1288 937.74 87%

Benchmark: Constant 5.567 3492.95 6%

Calibration by delivery type

The initial calibration did not differentiate between the USPS’ methods of delivery. The USPS identifies five types of delivery: 

– Curb: Carrier uses a vehicle and can deliver mail without leaving (dismounting) the car for 

more than 50 % of delivery points.

– Dismount: Carrier uses a vehicle and can deliver mail without leaving (dismounting) the car 

for less than 50 % of delivery points.

– Park & Loop: Carrier uses a vehicle, but parks it at a selected locations; then delivers by foot. 

– Foot: Carrier delivers mail by foot.

– Other: No information available. 

In our sample, delivery methods for around 14,000 routes could be identified. 2,251 routes could not 

be classified and are tagged as “Missing.” Table 13 gives an overview of the number of routes per 

delivery type in the sample.

 

11 Regressions were run with log(.) and quadratic specifications. These were not superior to this simple linear model.
Swiss Economics, Report for PRC, Seite 31

Table 13: Frequency of route types

Delivery Method Number of Routes Share

Curb 3842 24%

Dismount 2200 14%

Park & Loop 7131 44%

Foot 771 5%

Other 80 0%

Missing 2251 14%

Total 16275 100%

To account for the delivery method in the calibration, there are generally two approaches: (a) include dummies for the delivery method and run a global regression, or (b) run the regressions separately for the delivery types. 

a) Include delivery dummies in global regression

The results for the first approach, a global regression with delivery dummies, are reported in Table 

34 of Appendix C. Compared to the regression without dummies, the model fit increases to 91.5 %. 

However, the approach with additive dummies is not intuitive. One cannot expect that the delivery 

method has a fixed effect on the delivery time. Rather, one would expect that speeds differ by deliver 

type, that is, the beta coefficients of “shortest path” should differ by delivery type. To reflect this

reasoning, the regressions are run separately by delivery type below.

b) Separate regression per delivery type

As mentioned above, a total of 2,251 routes do not have a specified delivery type. For the remaining 

14,000 routes, the regression is also run separately by delivery type. Table 35 to Table 39 of Appendix 

C show the results. Table 14 provides an overview.

The calibration at the delivery method level shows a better fit for dismount, park & loop, and curb 

and a poorer fit for foot and “other” compared to the global calibration over all delivery types. The 

coefficients for shortest path, i.e., the speeds, of the different delivery methods seem reasonable. The 

motorized delivery methods (curb, dismount, and park & loop) exhibit higher speeds than delivery 

by foot. However, the highest speed is found for the category “other.” There is no further information on what type of delivery methods the category “other” contains.

The coefficients for flats, letters, and parcels show some variation between the different delivery 

types. Within each delivery method, the volume coefficients seem reasonable except for the delivery 

methods “other” and “foot.” Specifically, the coefficients for letters and flats are of a similar order,

and the coefficients for parcels are higher than the coefficients for letters and flats. In the model, this 

interprets flats and parcels featuring similar load times and parcels having significantly higher load 

times. For the delivery methods “other” and “foot,” Flats are estimated to have much lower load 

times than letters. This seems inconsistent with reasonable expectations. 

Comparing the load time coefficients between delivery methods, one would expect that load times 

are not delivery method specific, i.e., one would expect to observe similar coefficients in all regressions. For the methods curb, dismount, and park & loop, this is to some extent the case. “Foot” and 

“other” exhibit significantly different coefficients than the other delivery methods. However, “foot”

and “other” account for only 6 % of all routes for which the delivery methods are known (see Table 

13). The category “other” might consist of very heterogeneous delivery methods, which could explain the coefficients.
Swiss Economics, Report for PRC, Seite 32

Discussion

Looking at the results of the regressions by delivery type in Table 14 (lower half), the delivery methods do not show stark inconsistencies except for the delivery methods “other” and “foot.” The coefficients differ by delivery method, which implies some error when calibrating on a global level ignoring the delivery methods. 

However, the errors made with the global approach without dummies seem not to be excessively 

large. This is shown at the top of Table 14 by comparing the two global regressions with and without 

dummies. R2is reduced to a minor extent in the benchmark regression without dummies, but the 

“shortest path” coefficient is more consistent with the individual coefficients by delivery type. Moreover, the global approach without dummies implies a calibration of load times that is independent 

of the delivery method. Compared to the individual regressions by delivery type, the global approach allows using the entire sample.

For these reasons, the subsequent calculations are based on the global calibration without delivery 

dummies.

Table 14: Calibration by delivery type

Proxy β0 N Coefficient β0

(shortest path)

t-Value Adj. R2

Benchmark: Global without dummies4.9 M 0.00014 981 87%

Global with dummies 4.9 M 0.00009 682 91.5%

Foot 0.2 M 0.00012 161 82%

Park and Loop 2.1 M 0.00019 806 89%

Dismount 0.7 M 0.00013 370 88%

Curb 1.1 M 0.00012 629 91%

Other 0.02 M 0.00028 81 79%

5.3.3 Calibration of office time

To estimate the effect of volumes on office time, it is assumed that only cased items cause office 

time and that all such costs are variable. Therefore, the following model is estimated:

office time = β1casedletters + β2casedflats + β3parcels.

The regression results are summarized in Table 40 of Appendix C. All coefficients are highly significant and seem consistent with expectations because cause more parcel office time than letters and

flats.
Swiss Economics, Report for PRC, Seite 33

6. Financial impact analysis

This chapter estimates the financial impact of plan 5+ high and low resulting from changes in costs

and speed of delivery. In terms of direct effects, delivery costs are of primary concern. The impact 

analysis for home delivery is reported in Section 6.1. It is based on the calibrated bottom-up delivery

model from Section 5. Results for processes other than delivery are summarized in Section 6.2 (details cf. Appendix D). Indirect effects from reduced mail demand that is caused by longer end-toend delivery times (details also in Appendix D) are analyzed in Section 6.3 as part of the discussion 

of the overall impact of plan 5+ on USPS profits.

6.1 Impact of plan 5+ on delivery

Under plan 5+, parcels are still delivered on Saturdays, while letters and flats are not. Compared to 

plan 5, plan 5+ requires an approach to estimate delivery times for separate parcel delivery on Saturdays.

Plan 5+ shifts letter and flat volumes away from Saturday, causing new, reduced route and access 

times on Saturday (parcels only). The shift of letters and flats to other weekdays will impact route 

and access times to a limited extent only. Shifts of office time and load time, however, may cause 

overtime work that requires higher compensation per hour. For the shifts of letters and flats, the 

USPS’ assumptions from plan 5 are applied; that is, 25% of mailpieces from Saturday are shifted to 

Fridays, and 75 % to Mondays (or the next delivery day when a Monday is a holiday). 

This leads to the delivery patterns illustrated in Figure 11. The red dotted line shows the daily volumes that would have been delivered in plan 5+ assuming FY 2012 volumes. The figure reveals that 

the differences among delivery days are much larger than the status quo. Volumes are particularly 

high on Mondays. 

Figure 11: Time series total volume (status quo vs. adjusted/Plan 5+) 

These patterns can also be seen in Table 15. In FY 2012, average actual Monday volumes were 127

% of the average daily volume. The days with minimum volumes were Saturday and Wednesday, 

both around 93 % of average. The shift from Saturday letters and flats to Fridays and Mondays (or 

Tuesdays, if Monday is a holiday) increases the differences sharply, ranging from 196 % on Mondays to 1 % on Saturday (Parcels only). This means that on Mondays, delivery volumes would be

about twice the size as compared to Wednesday or Thursday. Table 15 confirms the finding of the 

PRC as shown in Figure 6.

0

10'000

20'000

30'000

40'000

50'000

60'000

70'000

80'000

90'000

100'000 (000)

total volume adj. total volume
Swiss Economics, Report for PRC, Seite 34

Table 15: Daily delivery patterns for status quo and plan 5+ and impact of plan 5+ in delivery

Status quo Plan 5+ Impact in delivery

Monday 126.6% 195.7% Major impact on load times likely to increase existing overtime hours

Tuesday 100.6% 109.6%

Wednesday 92.7% 92.7%

Thursday 94.0% 94.8%

Friday 93.6% 116.2% Impact on load times may increase existing overtime hours

Saturday 92.5% 0.7% Major impact on route, access and load times; reduction of existing overtime

Table 15 also indicates the major financial effects of plan 5+ on delivery. These are: 

– Sharply reduced route and access times on Saturdays;

– A shift of load times from Saturdays to Mondays and Fridays, likely to increase existing overtime hours of carriers, in particular on Mondays (while decreasing on Saturdays);

The USPS’ segments its plan 5 delivery cost calculations into city delivery savings (which is further 

segmented into street time and in-office time savings), rural carrier time savings, and indirect savings. The distinction between city carriers and rural carriers is meaningful as the two types of carriers face fundamentally different pay schedules: City carriers are essentially paid by the hour, while 

rural carriers are paid based on workload, e.g., pieces delivered. Therefore, USPS’ savings would 

be—as in plan 5—mainly on the city carrier side. As a consequence, the analysis below distinguishes 

the same cost categories as those from plan 5 (details cf. Appendix D), and special emphasis is given 

to city carriers. 

6.1.1 City carriers

Approach and assumptions

City carrier savings can be calculated relative to the status quo (avoided cost) or relative to the savings estimates of the USPS/PRC from plan 5 (incremental costs). Relevant details on the cost saving 

estimates of the USPS/PRC from plan 5 can be found in Section 11.3 in Appendix D. With the model 

presented in Section 5, both avoided and incremental route and access times can be calculated. 

If current costs from the status quo are corrected based on avoided route and access times on Saturdays, the underlying assumption is that productivity for shifted load times is independent of the 

weekday. The load time costs for 1,000 mailpieces on Saturdays translates to the same load time 

costs for 1,000 additional mailpieces on Mondays. This differs from the assumption made by USPS 

for plan 5. The USPS argued that on Mondays, average productivity is already higher, and therefore 

the load costs of additional 1,000 mailpieces on Mondays would be lower than the original cost on 

Saturdays. Applying the second approach of computing and applying the incremental route and 

access time on Saturdays for plan 5+, as compared to plan 5, reflects the productivity assumptions

from PRC’s review of plan 5.

The first approach represents a cautious approach and ideally fits to the plan 5+ low scenario, which 

aims at finding the lower bound of the plan 5+ savings. The second approach is in line with the plan 

5+ high scenario (upper bound).

In both approaches, however, additional overtime hours must also be considered. Based on new 

contracts with labor unions, the USPS may be able to apply flexible workforce models, reducing the 

need for overtime hours. Consistent with the idea of providing lower and upper bounds for plan 5+ 

savings, it is assumed in plan 5+ high, that USPS can effectively handle overtime, whereas in plan 

5+ low it cannot (leading to incremental overtime costs).
Swiss Economics, Report for PRC, Seite 35

Table 16 summarizes the delivery scenarios and the corresponding major assumptions.

Table 16: Delivery scenario

Scenario Plan 5 Plan 5+ high Plan 5+ low

No home delivery of Letters/Flats and Packages 

on Saturdays, 75% 

shifted to Mondays, 25% 

to Fridays

[Continuation of delivery 

of Express Mail and Saturday post office box delivery]

No home delivery of Letters/Flats on Saturdays. 

Volume shifts for Letters 

and Flats as plan 5.

[Continuation of delivery 

of Express Mail, Saturday 

post office box delivery 

and package delivery]

As plan 5+ high

City carrier implementation Plan 5 Plan 5+ high Plan 5+ low

Street time

Route and access time None on Saturdays Incremental delivery cost 

as compared to plan 5

Avoided delivery costs 

as compared to status 

quo

Load time/productivity For shifted Saturday volumes, application of 

higher Monday productivity 

As plan 5 (reflected in incremental approach)Constant productivity for 

shifted Saturday load 

time (all other costs other

than route and access 

costs are transferred 1:1 

to Mondays/Fridays)

Office Time

Fixed costs Saturdays fixed costs can 

be saved

Saturdays fixed costs can 

be saved

Saturday Fixed costs remain

Variable costs No costs avoided (shift) No costs avoided (shift) No cost avoided (shift)

Street + Office Time

Overtime Additional overtime 

hours can be managed 

with flexible workforce

Additional overtime 

hours cannot be managed with flexible workforce (surcharge of $20 

per hour)

Street time as referred to by the USPS includes route, access, and load time (cf. Figure 2). Based on 

the bottom-up model from Section 5, route and access time can be calculated and distinguished from

load time.

To calculate cost differentials of plan 5+ in delivery, the following simulations are performed:

– For Saturdays, the new routes for parcel delivery are simulated by applying the model shown

in Section 5. This gives an estimate for the new Saturday delivery hours. The difference between the actual delivery hours and the simulated “new” delivery hours constitute USPS’ 

time savings on Saturdays in the first approach (avoided costs relative to status quo). These 

can be broken down into route and access times on the one hand, and load time on the other. 

The load time for letters and flats is shifted to other days, showing the effect on route and 

access times is the primary concern. The new route and access times can also be used to compute incremental costs in the second approach (relative to plan 5). The sum of all three components can be used to assess the impact on overtime hours, which are more costly than regular hours. 
Swiss Economics, Report for PRC, Seite 36

– On Mondays and Fridays, additional time is needed to deliver the mail previously delivered 

on Saturdays. Additional time per route is computed based on the empirical results on variable time per mailpiece from above (beginning of Section 6.1). Once additional time is determined, the effects on overtime can be calculated. 

In both simulations, the effects on overtime are accounted for. From DOIS, the actual working hours 

for every day and route are known. The new working hours on Fridays, Saturdays, and Mondays 

are calculated as the actual working hours in FY2012 plus/minus the additional/reduced working 

hours from the volume shifts as shown above. Overtime is then defined as total working hours minus 8 hours: 

overtimei = max{(hoursi + ∆hoursi

) − 8, 0},

where i ∈ {Friday, Saturday, Monday}.

Street time: Cost differentials on Saturdays

Saturday parcel delivery routes are simulated in the bottom-up model for all of FY2012.

12 It is assumed that a delivery point is served on a Saturday if it receives at least one parcel. To determine 

the parcel distribution across delivery points, the actual parcel volumes on a given Saturday are 

randomly assigned to the addresses of a route.13 That is, if there were n parcels on a given Saturday, 

then n addresses of a route are randomly drawn. The delivery points served are then the points 

which have at least one address receiving a parcel.14 This is done for every Saturday in FY2012 and 

the new Saturday routes are then simulated. 

It is assumed that the volume of parcels remains the same as in FY 2012 after the change in delivery 

regime because the level of service of parcel delivery remains unchanged. AMS data provide information on the number of addresses per deliver point. From DOIS data, the number of parcels per 

delivery day is known. The random assignment of parcels to addresses does not imply that all delivery points have the same probability of receiving at least one parcel because some delivery points 

have more than one address. Delivery points with more addresses are more likely to receive a parcel 

on a given Saturday.

The results from the model calibration in section 5.3.2 and 5.3.3 allows simulated Saturday route 

distances and volumes to be converted into working hours and then into financial costs. These are 

then compared to the actual FY 2012 Saturday delivery costs to quantify the costs savings.

The new street and office hours on Saturdays are computed as

street hours = ⏟0 .0001402 ∗ sh ortest pat h

route and access time

+ ⏟0 .014067 ∗ parcels 

load time

.

office hours = .0084455 ∗ parcels.

As introduced above, we are primarily interested in changes on route and access times to estimate 

avoided fixed costs of plan 5+. The net savings are the difference in route and access times multiplied 

by the average piggy-backed hourly rate of USD 59.42, which corresponds to the hourly costs of city 

carriers including labor and vehicle use. The factor was provided by the PRC. 

 

12 There are 53 Saturdays in FY 2012.

13 This is as accurate as possible as we do not have any information on address level.

14 A delivery point can have several addresses.
Swiss Economics, Report for PRC, Seite 37

All other costs shift to other days. Assuming constant productivity along labor days, the shifts are 

relevant if these cause increased or decreased overtime work, the latter may be the case for Saturdays. Therefore, total street hours and office hours per route are calculated to estimate the effects on 

overtime costs. The calculations assume a work day of eight hours and an overtime compensation 

premium of an additional USD 13.92 per hour. 

The resulting avoided cost (reduced route and access time, reduced overtime) from discontinued 

Saturday delivery of letters and flats is shown in Table 17. Avoided costs for all city carriers are 

obtained from scaling up the estimated avoided costs in our sample with a factor of 8.69. This factor 

inflates our sample of 16,274 routes to represent the 141,469 total city routes in the system.

Table 17: Avoided costs on Saturdays

Route and 

access time

Load time Office hours Total hours Overtime

Status quo hours 1'750'150 3'314'94115 1'477'631 6'542'722 229'319

Plan 5+ hours 521'837 194'885 117'004 833'725 1'195

Plan 5+ hours as % of Status quo 30% 6% 8% 13% 1%

Difference Status quo/plan 5+ (hours) -1’228’313 -228'124

Avoided costs (sample, USD) -72'986'382 -3'175'486

Avoided costs (all city carriers, USD) -634'466'665 -27'604'328

Incremental costs compared to plan 5 

(all city carriers, USD) 269'546'784 144'602

Therefore, compared to the status quo, route and access times are USD 634 million lower under plan 

5+. Compared to plan 5, the corresponding incremental cost is USD 270 million.

16

In terms of overtime, less overtime is needed and USD 28 million can potentially be saved on Saturdays if overtime was performed by the carrier (as assumed in plan 5+ low). 

Street time: Incremental costs on Fridays and Mondays

In line with the USPS’ assumption from plan 5, it is assumed that 25% of letter and flat volumes of 

Saturdays shift to Fridays and 75% shift to Monday. Assuming that the routes on Friday and Monday are not redesigned even with the additional volumes, the calibrated model in Section 5.3.2 can 

be used to calculate the new working hours on Fridays and Mondays. Specifically, the additional 

hours are calculated as

∆streethoursFriday = 0.0017725 ∗ 0.25 ∗ lettersSaturday + 0.0015453 ∗ 0.25 ∗ flatsSaturday

∆streethoursMonday = 0.0017725 ∗ 0.75 ∗ lettersSaturday + 0.0015453 ∗ 0.75 ∗ flatsSaturday

As DOIS provides data on every delivery day, the volume shifts are done for every Saturday in FY

2012 for each route. Thus, the entire FY 2012 volume shifts are simulated under the new delivery 

regime.

 

15 DOIS does not differentiate access and load times. The reported load time here is an estimate from the calibrated 

model.

16 521k additional hours for parcels delivery times 59.42 times 8.69.
Swiss Economics, Report for PRC, Seite 38

The same is done for the office hours by using the coefficients of the office hours regression, i.e.,

∆officehoursFr = 0.0034844 ∗ 0.25 ∗ casedlettersSaturday + 0.0025288 ∗ 0.25 ∗ casedflatsSaturday

∆officehoursMo = 0.0034844 ∗ 0.75 ∗ casedlettersSaturday + 0.0025288 ∗ 0.75 ∗ casedflatsSaturday

The resulting incremental overtime costs on Mondays and Fridays from discontinued Saturday delivery of letters and flats are provided in Table 18 and Table 19. Incremental overtime costs for all 

city carriers are obtained from scaling up the estimated avoided costs in the sample with the same

factor of 8.69 used to scale up avoided costs on Saturdays. 

Table 18: Incremental costs on Mondays

Street hours Office hours Total hours Overtime

Status quo (hours) 4'671'268 1'416'890 6'088'158 485'701

Plan 5+ (hours) 6'665'237 2'145'018 8'810'255 3'049'811

Plan 5+ hours as % of Status quo 143% 151% 145% 628%

Difference Status quo/plan 5+ (hours) 2'564'110

Incremental costs overtime (sample, USD) 35'692'411

Incremental costs overtime (all city carriers, USD) 310'272'196

Table 19: Incremental cost on Fridays

Street hours Office hours Total hours Overtime

Status quo (hours) 5'054'918 1'405'321 6'460'239 268'865

Plan 5+ (hours) 5'794'411 1'676'716 7'471'127 1'059'192

Plan 5+ hours as % of Status quo 115% 119% 116% 394%

Difference Status quo/plan 5+ (hours) 790'327

Incremental costs overtime (sample, USD) 11'001'349

Incremental costs overtime (all city carriers, USD) 95'634'131

Overtime

A shift of 75% of Saturday’s letter and flat volumes to Mondays leads to a considerable workload 

on Mondays. To a lesser extent, workloads on Fridays also increase. Based on the distinction of route 

and access times on the one hand and load time on the other, we are able to compute the effects on 

the daily workhours per route. If these exceed eight workhours, then overtime may be required. 

Table 17 to Table 19 above show the calculated upper bounds of required overtime work in the 

status quo and in plan 5+. The upper bound in workhours is calculated assuming constant productivity per piece, independent of the weekday. The resulting increases in overtime can then be translated into increased overtime costs.

The recent APWU contract allows greater use of workers with more flexible work schedules. This 

may allow the USPS to handle peak loads with fewer overtime hours. If all overtime hours were 

managed this way, zero additional paid overtime hours would occur. As the scenario plan 5+ high

aims at providing an upper bound in terms of saving, it is assumed that additional overtime hours 

can be fully managed with this flexible work force (resulting in zero additional costs). In contrast, 

in plan 5+ low, it is assumed that additional overtime hours are required of the carrier in place, i.e.,

a surcharge on the daily rate is incurred. Assuming an hourly surcharge of USD 13.92 results in

incremental overtime costs of USD 378 million, as shown in Table 20. 
Swiss Economics, Report for PRC, Seite 39

Table 20: Financial effect of additional overtime hours

[USD] Additional 

overtime hours

Cost effect in 

plan 5+ high [USD]

Cost effect in 

plan 5+ low [USD]

Friday (25% of letters and flats from Saturday) 6'870'268 0 95'634'131

Saturday (no letters and flats) -1'983'070 0 -27'604'328

Monday (75% of letters and flats from Saturday) 22'289'669 0 310'272'196

Total 27'176'868 0 378'301'999

Direct in-office costs

In plan 5, the USPS/PRC assume that the fixed part of Saturday direct in-office costs can be saved 

and the variable part is transferred one-to-one to the rest of the week. Consequently, in plan 5+low

with continued Saturday parcel delivery, it is assumed that the fixed part of in-office costs still accrue so there are no savings for in-office costs. For plan 5+ high, it is assumed that parcel delivery 

does not involve any fixed in-office time; therefore, the fixed part of Saturday in-office costs are 

saved even with parcel delivery taking place demonstrating in-office cost savings in plan 5+ high 

are the same as in plan 5.

Express items

For express items, the values from the USPS/PRC are taken (USD 7 million incremental costs), implying methodically a separate distribution channel for Express Mail.17

6.1.2 Rural carriers

As rural carriers are paid on the basis of workload elements such as the number of pieces delivered, 

route miles, and addresses, only Saturday hours associated with non-volume-related elements may 

be eliminated. It is therefore assumed that there is no distinction between plan 5 and 5+. Total savings are estimated to be about USD 450 million.

6.1.3 Other carrier costs

Carriers induce some administrative costs. These other or indirect carrier costs are calculated as in 

plan 5, using the method presented in Library Reference USPS-LR-N2010-6. The different carrier 

labor costs in plan 5+ high and low scenario lead to slightly reduced indirect carrier costs, totaling 

USD 265 million in plan 5+ high and USD 238 million in plan 5+ low.

6.1.4 Summary

Table 21 provides an overview of the calculated incremental effect of plan 5+ in delivery as compared to plan 5. The changes as compared to plan 5 are highlighted in red.

 

17 An integrated computation would require daily express volumes per route that were not available.
Swiss Economics, Report for PRC, Seite 40

Table 21: Estimated direct cost savings in delivery

Source: Swiss Economics.

6.2 Impact of plan 5+ on other processes 

For the processes other than delivery (collection, sorting, and transportation) it is assumed that in 

the plan 5+ high scenario, the same operational changes are made as in plan 5. This is possible because the plan 5 processing architecture is compatible with plan 5+, as sorting of mail items including parcels is continued Friday night. Therefore, parcels would arrive at delivery offices in time for 

delivery on Saturday mornings. As a consequence, savings arise mainly from the discontinuation of 

Saturday dispatch from post offices, leading to fewer mail items that require sorting and transport 

during the weekends. The corresponding savings of about USD 290 million that were estimated by 

the PRC are reported in Table 22. In Appendix D, the processes and the planned adaptations and 

assumptions are described in greater detail.

In the plan 5+ low scenario, it is assumed that Saturday dispatch continues as in the status quo; 

therefore, no adaptations take place in these processes compared to the status quo, and there are no 

savings. However, continuing the dispatch would lead to faster end-to-end delivery times, implying 

a somewhat reduced secondary effect of consumer response.

Based on the assumptions on the adaptations in the processes other than delivery, the cost savings 

displayed in Table 22 emerge.18 Details can be found in Appendix D.

Table 22: Estimated direct cost savings in collection, sorting, and transport

 

18 Collection and sorting are not declared separately in plan 5; therefore, they are taken together here as well. M USD Plan 5 Plan 5+ high Difference Plan 5+ low Difference

City carrier direct street time 1'162 892 (270) 634 (528)

City carrier direct in-office costs 102 102 - - (102)

City carrier adjustment for Saturday Express Mail (7) (7) - (7) - 

City carrier adjustment for cost of overtime hours - - - (378) (378)

Rural carrier direct costs 341 341 - 341 - 

Rural carrier EMA savings 85 85 - 85 - 

Rural carrier adjustment for Saturday express mail (1) (1) - (1) - 

Indirect cost for city and rural carriers 305 265 (40) 238 (67)

Total delivery savings 1'987 1'677 (310) 912 (1'075)

M USD Plan 5 Plan 5+ high Plan 5+ low

 Collection and Sorting/Processing 120 120 -

Clerks and Mailhandlers 3 4 3 4 -

Supervisor 2 0 2 0 -

Equipment Maintenance 1 3 1 3 -

Custodial 0 0 -

Post Office 5 3 5 3 -

 Transport 170 170 -

Air 6 2 6 2 -

Highway 1 3 1 3 -

Box Routes 3 5 3 5 -

Vehicle Service Drivers 5 9 5 9 -

 Total Other Processes 289 289 

 Delivery (from Section 6.1) 1'987 1'677 912

 Total Direct effects 2'276 1'966 912 
Swiss Economics, Report for PRC, Seite 41

6.3 Overall impact on profits and unit costs

Based on the calculations above, it is now possible to assess the impact of plan 5+ high and 5+ low 

on USPS profitability. The difference in yearly profits ∆π is computed as the difference between the

direct effects of savings and the indirect effects of mailers’ demand response (details cf. Appendix 

A):

∆π = ⏟avoided process cost – incremental cost 

direct effects on process costs

− ⏟foregone revenue + avo ided volume costs 

indirect effects from demand response

Direct effects relate to avoided or incremental costs due to changes of processes (e.g., savings from 

not delivering letters on Saturdays) described above (Section 6.1 for delivery and 6.2 for other processes). 

Indirect effects are induced by mailers’ responses to the adjusted services; that is, if plan 5+ has an 

impact on demand for postal items sent. If mailers send less mail, variable costs can be saved, and 

revenue is lost. The exact quantification of demand effects is beyond the scope of this project. Nevertheless, to provide context and an indication of the sensitivity of profits to secondary effects, we 

compute boundaries of maximum revenue losses such that the net effect is zero, and compute the 

net effects for illustrative demand assumptions that are based on earlier USPS market research on 

plan 5 (details cf. Appendix A).

6.3.1 Profitability boundaries

When computing profitability boundaries, it is asked how much mail demand can decrease such 

that plan 5+ is just profitable (break-even). If this exercise results in unrealistically high volume 

losses that cannot be expected due to the effects on quality from plan 5+, then it is likely that plan 5+ 

increases the USPS profitability, ceteris paribus.

Table 23 presents the results of this break-even calculation for FY 2012. The calculation assumes that 

plan 5+ will not affect parcel demand (because parcels are still delivered on Saturdays) and that the 

relative size of the demand effects between First-Class Mail products and other products is as documented in Table 44. The calculation of the corresponding revenue forgone and attributable costs 

avoided is based on USPS figures from FY 2012. (See summary in Section 3.2.)

Table 23: Break-even volume response

Plan 5+ high Plan 5+ low

Break-even average volume response -7.5% -3.5%

The implementation of plan 5+ high with estimated direct savings of about USD 1.96 billion is profitable as long as volume losses do not exceed 7.5% on average. For plan 5 low with estimated direct 

savings of about USD 912 million, an average volume loss of more than 3.5% would offset these 

savings and result in a net loss (i.e., reduced overall USPS profitability).
Swiss Economics, Report for PRC, Seite 42

6.3.2 Change in profitability differentials for selected demand scenarios

Finally, overall net effects of plan 5+ high and 5+ low on USPS’ yearly profits are computed for an

illustrative demand scenario.

19

To estimate the indirect effects, illustrative demand scenarios of consumers are evaluated (i.e., scenarios of possible mailer responses to plan 5+). The demand scenarios are evaluated against the cost 

and volume structure of the USPS in FY 2012 (as summarized in Section 3.2), adjusted to include the 

direct effects of plan 5+. For plan 5+, no market research is available. For plan 5, however, some 

market research was commissioned by the USPS and prepared for filing in 2010 (details in Section 

11.5 from Appendix B). The methodology of this research was subject to a technical discussion and 

resulted in some modifications by the PRC. The doubts about the accuracy of the resulting figures

remain, however. The demand effects as shown in Table 44 should, therefore, be viewed only as an 

illustrative example of potential net effects of plan 5 on the USPS’ yearly profits. As shown in Table 

24 (details in Section 11.5.2), the demand responses are assumed to be reduced in plan 5+ low compared to plan 5+ high because the effects on quality are less severe in the 5+ low scenario compared 

to the 5+ high scenario. End-to-end delivery times for items collected on Saturdays are faster if this 

mail is processed right away and not after Monday (which would be the case in the high scenario). 

However, end-to-end delivery times for pieces originally delivered on Saturdays remain lower. For 

illustrative purposes, it is, therefore, assumed that volume responses are lowered by 50 % in plan 5+ 

high. 

Table 24: Demand effects

Status quo Plan 5 Plan 5+ high Plan 5+ low

Volume 

scenario

FY2012 Volume response consideredAs plan 5, but no response for parcelsAs plan 5+ high, but reduced response 

This illustrative demand scenario leads to the results shown in the lower part of Table 25. In plan 5+ 

high, indirect effects caused by the assumed 2.2% volume loss in average would reduce USPS’ profits by about USD 570 million. In plan 5+ low, mailers respond less sharply, leading to a decrease of 

USD 287 million. For plan 5 with illustrative volume losses of 2.22% in average, the indirect effects 

are slightly smaller than in plan 5+ high. This somewhat counterintuitive result is caused by a negative contribution of parcels in FY 2012 (cf. Table 8).

Table 25: Financial effects of plan 5+ as compared to plan 5

 

19 Technically, equation (1) from Appendix A is evaluated for specific demand scenarios x

j..J,1

.

M USD Plan 5 Plan 5+ high Plan 5+ low

Direct effects (direct avoided cost) 2'276 1'966 912

Savings Collection/Sorting 120 120 -

Savings Transport 170 170 -

Savings Delivery 1'987 1'677 912

Indirect effects for FY2012 (lost contribution) (571) (573) (287)

Average volume response -2.22% -2.20% -1.10%

Foregone revenue* (1'234) (1'169) (585)

Avoided cost* 663 596 298

Total Savings 1'705 1'393 625 

*For Plan 5, adapted to FY2012 figures
Swiss Economics, Report for PRC, Seite 43

If the illustrative volume losses of 2.2% and 1.1% respectively are realistic, then the total savings of 

plan 5+ ranges between USD 0.6 and 1.4 billion. The range is somewhat lower than USPS’ expected 

savings of USD 2 billion. The underlying USPS calculations are not known, and it is, therefore, not 

possible to identify where the differences lie.
Swiss Economics, Report for PRC, Seite 44

7. References

Cohen, R.; Chu, E.H. (1997). “A Measure of Scale Economies for Postal Systems.” In Managing Change in the Postal and 

Delivery Industries, edited by M. A. Crew and Paul R. Kleindorfer. Boston, MA: Kluwer Academic Publishers. 

Cremer, H., Grimaud, A.; Laffont, J.-J. (2000). The Cost of Universal Service in the Postal Sector. In: Current Directions in 

Postal Reform, ed. by Michael A. Crew and Paul R. Kleindorfer, Kluwer.

Felisberto, C.; Finger, M.; Friedli, B.; Krähenbühl, D.; Trinkner, U. (2006). “Pricing the last mile in the postal sector.” In: 

Crew, M A; Kleindorfer, P R. Progress toward liberalization of the postal and delivery sector. New York, N.Y., US, 249-

266. ISBN 978-0-387-29743-9.

Haller, Andreas; Jaag, Christian; Trinkner, Urs (forthcoming 2014). “Calculating the Net Cost of Home Delivery Obligations.” In: The Role of the Postal and Delivery Sector in a Digital Age, edited by M.A. Crew and T. Brennan. Cheltenham, Edward Elgar, pp. 227 – 239.

Jaag C. & U. Trinkner & J. Lisle & N. Waghe & E. Van Der Merwe, 2011. "Practical Approaches to USO Costing and Financing," Competition and Regulation in Network Industries, Intersentia, vol. 12(2), pages 108-108, June.

Kenny, Ch. (2005) Questioning the Monopoly-Supported Postal USO in Developing Countries in M. Crew and P. Kleindorfer Progress Toward Liberalization of the Postal and Delivery Sector New York: Springer NERA. 2004. Economics of 

Postal Services: Final Report, A report to the European Commission.

Panzar, J. (2000). A Methodology for Measuring the Costs of Universal Service Obligations. Information Economics and 

Policy 12(3), 211-220.

Postal Regulatory Commission (2011). Advisory Opinion on Elimination of Saturday Delivery

Trinkner, Urs, Bernd Holznagel, Christian Jaag, Helmut Dietl, Andreas Haller (2012). Möglichkeiten eines gemeinsam definierten Universaldienst Post und Telekommunikation aus ökonomischer und juristischer Sicht. Study on behalf of 

Deutscher Bundestag.
Swiss Economics, Report for PRC, Seite 45

8. Appendix A: Methodological details

The steps performed in this study are summarized in Section 2.3. In the following, further details 

are provided.

8.1 Step 1: Modified delivery schedules

The modified delivery schedules that are to be analyzed in detail are defined and specified. Plan 5+ 

is specified in detail and assumptions are taken on how the rest of the value chain is organized 

compared to the status quo and the known plan 5.

8.2 Step 2: Impact on cost and days to delivery

Plan 5 and 5+ impact unit costs of mail and parcels; quality of service may also be affected. The 

impact may be different in each element of USPS’ value chain. It is, therefore, foreseen to conduct—

where necessary—a value chain analysis to understand how the relevant processes are affected by 

the modifications (thereafter referred to as business process analysis).

Collection, sorting, transport and delivery are the main processes of the postal value chain. In the 

first place, modifying a delivery schedule has an impact on delivery itself. Upstream processes may,

however, be impacted as well.

The exercise has already been done for plan 5 after the USPS’ 2010 filing. It is built as much as possible on these calculations and respective PRC findings. A new exercise is necessary where plan 5+ 

deviates from plan 5. The analysis focuses on direct effects and results in avoided cost, incremental 

cost, and effects on delivery time. Indirect effects are accounted for in Step 3, requiring knowledge 

on cost shares per process that are variable (with mail demand/the number of letters).

The most crucial process in terms of cost differentials will be home delivery. On the one hand, there 

are cost savings from plan 5+ of discontinuing Saturday delivery for letters and flats, and on the 

other, there are the costs of a stand-alone parcel delivery organization on Saturdays. The assessment 

of the net effects requires a model to predict the changes on route and access times (following the 

distinction established by Cohen and Chu, 1997, see Figure 1). 

In this framework, discontinuing letter delivery on Saturdays means that:

 on Saturdays, route times are fundamentally different and much more variable, as the probability that a given household gets a delivery is reduced sharply (leading to different routes 

every Saturday); 

 from Monday to Friday, the probability that a household is served is increased slightly, leaving route times mainly constant and increasing access times to a small extent. In the analysis,

it is assumed that route and access remain constant from Monday to Friday.

To predict the effects of the USPS’ plans on Saturday route and access times, the delivery model 

from Trinkner et al. (2012) and Haller et al. (2014) is applied. The model computes route and access 

times for different delivery schedules with a bottom-up approach. Route and access times can then 

be transformed into cost and normalized to fit the yearly delivery costs of the USPS, allowing the 

estimation of the effects of the modified delivery schedules on delivery and unit cost. The time differentials can also be used to either estimate the need for overtime hours on days with increased 

workload (in particular, Mondays). 

Cost effects other than route and access times are based on the existing analysis and methodologies 

from the PRC and USPS respectively (2010 discussion).
Swiss Economics, Report for PRC, Seite 46

8.3 Step 3: Impact on profitability 

Step 3 extends the analysis to be able to assess the impact of the modified delivery schedules on the 

USPS’ profitability. This would require thorough analysis of demand effects that come along with a 

reduction of Saturday delivery. As this is beyond the scope of this project, two alternative calculations are performed. In Step 3a, boundaries of demand effects are estimated such that the new delivery plans are still profitable. In Step 3b, the yearly profitability differentials per delivery schedule

are estimated for illustrative demand scenarios, assuming that the new plans would be already fully 

operational. The net effects on yearly profits π compute as:

∆π = (R

1 − C1 ⏟ )

Profit before implementation 

of modified delivery schedule

− (R

0 − C0 ⏟ )

Profit before in 

status quo of USPS

.

Rearranging yields: 

∆π = (C

0 − C1 ⏟ )

net effect 

on cost

−(R

0 − R1 ⏟ )

net effect 

on revenue 

. (1)

Extending for processes i and products j yields:

∆π = ∑ C

i,0

(x

j..J,0

)− C

i,1

(x

j..J,1

)

I ⏟i = 1 

per process i, 

avoided cost (C0>C1) 

or incremental cost (C0<C1) 

− ∑ R(x

j,0

)− R(x

j,1

)

J

⏟j = 1 

per product j, 

forgone revenue (R0>R1) or 

additional revenue (R0<R1)

. (2)

That is, 

∆π = avoided cost – incremental cost − foregone revenue + additional revenue. (3)

It is noted that effects on the cost side are computed per process; whereas, net revenue effects are 

calculated per product category. See Jaag et al. (2011) for a more detailed treatment. 

The cost functions per process C

i...I,1

(x

j..J

) have been assessed in Step 2. An idea on the demand 

effects x

j..J,0

-x

j..J,1 per product is needed to calculate the net profit effects. It is noted that these are 

not only relevant to compute the right-hand side (net effect on revenues), but also for assessing the 

net cost effects (left-hand side). The first effect is direct; the second indirect. To compute the indirect 

effect, cost elasticities or variable costs of all affected products need to be determined. 

Assuming no additional revenue, equation (3) can be rearranged as follows to separate direct and 

indirect effects: 

∆π = ⏟avoided process cost – incremental cost 

direct effects on process costs

− ⏟foregone revenue + avoided volume costs 

indirect effects from demand response

. (4)

The USPS and PRC have distinguished these two effects in their analysis of plan 5. This analysis will 

make the same differentiation, allowing basing the calculations, as far as considered appropriate, on 

existing data. Therefore, the analysis below will differentiate according to direct effects on process 

costs and indirect effects induced by the demand response of consumers. 

It is assumed that in plan 5+, only demand of letter and flat products are affected. In plan 5, demand 

for parcels is affected as well. The demand assumptions are based on an earlier analysis of plan 5.
Swiss Economics, Report for PRC, Seite 47

Step 3a: Profitability boundaries

As the demand effects are not known in detail, boundaries for induced demand effects under 

which the delivery schedules are still profitable are calculated. Formally: 

min( x

j..J,0

) s.t. ∆π = 0.

If these boundaries lead to induced volume losses that can be considered as unrealistically high, 

then it is very likely that the corresponding delivery schedules reduce the USPS’ yearly profit. 

Step 3b: Profit differentials for selected demand scenarios

In addition, equation (2) will be evaluated for selected demand scenarios x

j..J,1

that are based on 

quantitative analysis made publicly available by the USPS in its filing of plan 5. The results indicate 

per modified delivery schedule the yearly impact on USPS’ profits.

8.4 Remark on net costs

According to the profitability cost approach pioneered by Panzar (2000) and Cremer et al. (2000), 

the “net cost of the USO” N is the difference in profits in a competitive environment without USO 

π

1 and with USO (status quo) π0

:

N

Profitability Cost ≡ π1 − π0

.

The profit in both scenarios results from the difference in revenue R and cost C. Therefore, 

N

Profitability Cost = (R1 − C1 ⏟ )

Profit without USO

− (R

0 − C0 ⏟ )

Profit with USO

.

This can be rearranged in terms of avoided cost and foregone revenues: 

N

Profitability Cost = (C0 − C1 ⏟ )

Avoided 

Cost

−(R

0 −R1 ⏟ )

 Foregone 

 Revenue

. (5)

If a modified delivery schedule is not feasible due to the US USO, then equations (1) and (5) are 

equal:20

∆π = N

Profitability Cost

.

Therefore, if plan 5 or plan 5+ are not feasible because of USO constraints, then the results of Step 

3b above may qualify as (a component of) net costs of the USO. For such a classification, a detailed 

analysis of the legal framework, other USO dimensions and potential benefits of the USO would be 

required.

8.5 Deviations from the original scope of the project

Compared to the original scope of the project, the steps above induce the following changes:

Reductions: 

 A somewhat less detailed analysis of cost and quality effects in collection, sorting and 

transport; 

 Focus on long-term effects (as if the delivery schedule would be operational already) and 

differences to status quo plan 5. 

 

20 Technically, this is limited to the special case where no other plans exist where one or several universal service obligations are binding. If there are other service modifications that are profitable but not feasible because of the USO, then 

the net costs are higher and the net costs of plan 5+ are one element of the net costs. 
Swiss Economics, Report for PRC, Seite 48

Extensions:

 Comparative analysis to plan 5;

 Two plan 5+ scenarios to indicate upper and lower bound of savings;

 Analysis of profitability boundaries;

 Evaluation of selected demand scenarios.

The scope of the project is therefore broadened, while some of the calculations are of less detail. This 

is observed against the background that the USPS has not filed plan 5+, meaning that the relevant 

details of the plan are not known (in particular, re-engineered processes in sorting, transport and 

delivery). The core part of the work remains the same however; namely, the computation of Saturday route and access times under plan 5+. 

8.6 Alternative methodologies

8.6.1 Assessment criteria 

The methods and model should meet the following criteria: 

1. Accurate prediction of empirical data;

2. Accurate predictions of future date;

3. Efficiency;

4. Refutability, enabling estimation of the degree of confidence in the model; and

5. Simplicity.

8.6.2 Profitability cost vs. NPV

The main alternative to the profitability cost approach undertaken would be the computation of the 

net present value (NPV) of the modified delivery schedules, implying discounted cash flow protections into the future. 

Under certain conditions, the profitability cost and NPV approach converge. This is why the profitability cost approach has a solid background in economic theory. Profitability cost approach can be 

seen as a conversion of NPV to yearly economic profit differences. NPV, however, needs much more 

information, although less informative to the reader as compared to yearly profit differentials. 

This may also be the reason why the profitability cost approach is preferred by regulators in the US, 

Europe and elsewhere. 

8.6.3 Cost modeling: bottom-up modeling vs. econometric analysis

Econometric methods to predict future changes in cost are powerful if the empirical data contains 

the technologies applied. For example, if a data set includes different types of post offices, then 

econometric techniques such as frontier analysis can be used to identify the production function. If 

such data is not available, then other techniques such as bottom-up modeling must be applied. 

For delivery plans 5+ and plan 5, no empirical data exists to analyze the impact on street delivery 

times when packages only are delivered on Saturdays. Here, empirical studies of cost elasticities are 

of little value. An application of the model from Trinkner et al. (2012), in particular, when considering its accurate predictions of Swiss route times, appears reasonable as accurate measures route

changes and access times can be expected. For other cost components such as load or office time,

however, econometric methods are well suited as long as the underlying technology/processes are

not affected by plan 5 or plan 5+.
Swiss Economics, Report for PRC, Seite 49

9. Appendix B: Cost allocation

Table 26: Attribution of USPS cost segments to processes

Source: Swiss Economics.

Table 27: Attribution of USPS products to product groups

Source: Swiss Economics.

Table 28: Share of USPS costs per product group and process

Source: Swiss Economics.

Table 29: Variable and fixed costs per piece

Source: Swiss Economics,

Process

Collection

Sorting

Transport

Delivery

Overhead 1.1; 1.2; 2.3; 2.5.2; 2.5.5; 2.5.6; 2.5.8; 11.1.1; 11.1.2; 11.2; 11.3; 12.1; 12.2; 12.3; 13.3; 13.4; 13.5; 15.1; 15.3; 16.2; 

16.3.1; 16.3.3; 16.3.4; 16.3.5; 16.3.6; 17.1; 18.1.1; 18.1.2; 18.1.3; 18.1.4; 18.2.1; 18.2.2; 18.2.3; 18.2.4; 18.2.5; 18.2.6; 

18.2.7; 18.3; 19.1.1; 19.1.2; 20.4; 20.5; 20.6

Attributed USPS Cost Segments

2.2; 3.2; 3.3.1; 3.3.2; 4.1; 13.1; 16.1.1; 16.1.2; 16.1.3; 20.3

2.1.1; 2.1.2; 2.5.3; 3.1; 3.3.3; 16.3.2; 20.1

14.1.1; 14.1.1; 14.1.2; 14.1.3; 14.1.4; 14.2

2.4.1; 2.4.2; 2.4.3; 2.5.4; 2.5.7; 6.1; 6.2.2; 6.2.3; 7.1; 7.2; 7.3; 8.1; 10.1; 10.2; 10.3; 13.2.1; 13.2.2; 13.2.3; 13.6; 13.7; 

15.2; 20.2

Product group

Letters

Flats

Packages

Periodicals

Competitive

Others

175

51; 52; 54; 55; 56; 57; 58; 61; 62; 73; 74; 76; 185; 

Attributed USPS product numbers

3; 4; 8; 9; 21; 25; 125; 130

14; 22; 23; 26; 42; 44

19; 27; 41; 43

31; 32

Share FY 2012 Collection Sorting Transport Delivery Overhead Sum

Letters 1.5% 8.3% 1.6% 9.1% 5.7% 26.2%

Flats 0.5% 3.6% 0.9% 4.1% 2.3% 11.3%

Packages 0.2% 1.0% 0.8% 0.6% 0.5% 3.1%

Periodicals 0.1% 1.3% 0.4% 1.1% 0.7% 3.5%

Competitive 0.5% 2.9% 3.2% 1.5% 1.8% 9.9%

Others 1.1% 0.9% 1.8% 1.2% 1.4% 6.4%

Attributable Costs 4.0% 18.1% 8.5% 17.5% 12.5% 60.5%

Institutional Costs 2.6% 1.8% 1.3% 20.4% 13.4% 39.5%

Total Operating 6.6% 19.8% 9.8% 37.9% 25.9% 100.0%

USD Variable costs per piece Fixed costs

Collection Sorting Transport Delivery Overhead per piece

Letters 0.01 0.05 0.01 0.05 0.03 0.10

Flats 0.01 0.08 0.02 0.09 0.05 0.17

Packages 0.15 0.77 0.58 0.43 0.40 1.52

Periodicals 0.01 0.13 0.04 0.11 0.07 0.23

Competitive 0.16 0.88 0.94 0.45 0.55 1.95

Others 0.24 0.20 0.39 0.25 0.32 0.91

All 0.02 0.08 0.04 0.07 0.05 0.17
Swiss Economics, Report for PRC, Seite 50

10. Appendix C: Model calibration—estimation output 

Table 30: Regression output with shortest path as proxy

Table 31: Regression output with USPS sequence path as proxy

Table 32: Regression output with Base Mileage

parcels .014067 .0000509 276.60 0.000 .0139673 .0141666

flats .0015453 3.58e-06 431.71 0.000 .0015383 .0015523

letters .0017725 1.20e-06 1475.09 0.000 .0017702 .0017749

shortestpath .0001402 1.43e-07 981.45 0.000 .0001399 .0001405

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 1967040784905936 40.0951169 Root MSE = 2.2617

Adj R-squared = 0.8724

Residual 25095709.64905932 5.11538065 R-squared = 0.8724

Model 171608368 4 42902092 Prob > F = 0.0000

F( 4,4905932) = .

Source SS df MS Number of obs = 4905936

parcels .0188513 .000055 343.05 0.000 .0187436 .018959

flats .0018329 3.87e-06 473.24 0.000 .0018253 .0018405

letters .002302 1.15e-06 1997.83 0.000 .0022997 .0023042

sequencepath 5.92e-06 2.23e-08 265.84 0.000 5.88e-06 5.97e-06

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 1967040784905936 40.0951169 Root MSE = 2.4562

Adj R-squared = 0.8495

Residual 295967114905932 6.03284167 R-squared = 0.8495

Model 167107367 4 41776841.6 Prob > F = 0.0000

F( 4,4905932) = .

Source SS df MS Number of obs = 4905936

parcels .0122998 .0000578 212.67 0.000 .0121864 .0124132

flats .0016159 4.33e-06 372.77 0.000 .0016074 .0016244

letters .0015731 1.46e-06 1074.63 0.000 .0015702 .001576

BaseMileage .1288333 .0001374 937.74 0.000 .128564 .1291026

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 1564076263881922 40.291285 Root MSE = 2.2461

Adj R-squared = 0.8748

Residual 19584410.23881918 5.0450345 R-squared = 0.8748

Model 136823215 4 34205803.9 Prob > F = 0.0000

F( 4,3881918) = .

Source SS df MS Number of obs = 3881922
Swiss Economics, Report for PRC, Seite 51

Table 33: Regression output with no proxy (constant)

Table 34: Regression output with dummies for delivery methods

_cons 5.567108 .0015938 3492.95 0.000 5.563984 5.570231

parcels .0028247 .00003 94.17 0.000 .0027659 .0028835

flats -.0004545 2.19e-06 -207.48 0.000 -.0004588 -.0004502

letters .0004372 8.23e-07 531.36 0.000 .0004356 .0004388

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 9147684.64905935 1.86461594 Root MSE = 1.3248

Adj R-squared = 0.0588

Residual 8610154.414905932 1.75504968 R-squared = 0.0588

Model 537530.194 3 179176.731 Prob > F = 0.0000

F( 3,4905932) = .

Source SS df MS Number of obs = 4905936

d_other 1.64266 .0134671 121.98 0.000 1.616265 1.669055

d_park 2.8745 .001924 1494.02 0.000 2.870729 2.878271

d_foot 1.52995 .0042583 359.28 0.000 1.521603 1.538296

d_dismount 2.019309 .0028564 706.95 0.000 2.013711 2.024907

d_curb 1.325841 .0027092 489.39 0.000 1.320531 1.331151

parcels .0090431 .000042 215.12 0.000 .0089607 .0091255

flats .0006845 3.09e-06 221.64 0.000 .0006784 .0006905

letters .0014196 1.02e-06 1386.03 0.000 .0014176 .0014216

shortestpath .0000903 1.32e-07 682.43 0.000 .0000901 .0000906

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 1967040784905936 40.0951169 Root MSE = 1.8441

Adj R-squared = 0.9152

Residual 16683797.84905927 3.40074319 R-squared = 0.9152

Model 180020280 9 20002253.3 Prob > F = 0.0000

F( 9,4905927) = .

Source SS df MS Number of obs = 4905936
Swiss Economics, Report for PRC, Seite 52

Table 35: Regression output for “foot” routes

Table 36: Regression output for "Park & Loop" routes

Table 37: Regression output for “dismount” routes

parcels .0053295 .0002447 21.78 0.000 .00485 .005809

letters .0019427 4.22e-06 460.27 0.000 .0019344 .001951

flats .0005996 9.33e-06 64.25 0.000 .0005813 .0006179

shortestpath .000121 7.50e-07 161.25 0.000 .0001195 .0001225

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 6264950.93233308 26.8527051 Root MSE = 2.1898

Adj R-squared = 0.8214

Residual 1118733.45233304 4.79517474 R-squared = 0.8214

Model 5146217.48 4 1286554.37 Prob > F = 0.0000

F( 4,233304) = .

Source SS df MS Number of obs = 233308

parcels .0108376 .0000778 139.25 0.000 .0106851 .0109902

letters .0020529 2.03e-06 1010.45 0.000 .002049 .0020569

flats .00181 5.90e-06 306.57 0.000 .0017984 .0018216

shortestpath .0001901 2.36e-07 806.68 0.000 .0001896 .0001905

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 88654919.22150766 41.2201602 Root MSE = 2.108

Adj R-squared = 0.8922

Residual 9557305.322150762 4.44368337 R-squared = 0.8922

Model 79097613.8 4 19774403.5 Prob > F = 0.0000

F( 4,2150762) = .

Source SS df MS Number of obs = 2150766

parcels .0127172 .0001107 114.83 0.000 .0125001 .0129342

letters .0016146 2.91e-06 554.05 0.000 .0016089 .0016204

flats .001852 .0000101 182.78 0.000 .0018321 .0018718

shortestpath .0001334 3.60e-07 370.75 0.000 .0001327 .0001341

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 26716737.5664922 40.1802579 Root MSE = 2.2079

Adj R-squared = 0.8787

Residual 3241273.46664918 4.87469652 R-squared = 0.8787

Model 23475464 4 5868866 Prob > F = 0.0000

F( 4,664918) = .

Source SS df MS Number of obs = 664922
Swiss Economics, Report for PRC, Seite 53

Table 38: Regression output for “curb” routes

Table 39: Regression output for “other” routes

Table 40: Regression output for office hours

parcels .0129971 .0000934 139.20 0.000 .0128141 .0131801

letters .0013999 1.92e-06 729.08 0.000 .0013961 .0014036

flats .0013175 5.98e-06 220.38 0.000 .0013058 .0013292

shortestpath .0001281 2.03e-07 629.81 0.000 .0001277 .0001285

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 45066703.81161430 38.8027722 Root MSE = 1.8824

Adj R-squared = 0.9087

Residual 4115371.191161426 3.54337787 R-squared = 0.9087

Model 40951332.6 4 10237833.1 Prob > F = 0.0000

F( 4,1161426) = .

Source SS df MS Number of obs = 1161430

parcels .0119014 .0004275 27.84 0.000 .0110634 .0127393

letters .0003437 .0000146 23.52 0.000 .0003151 .0003723

flats .0049844 .0000535 93.18 0.000 .0048795 .0050892

shortestpath .0002874 3.54e-06 81.09 0.000 .0002805 .0002944

actualstre~t Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 543180.973 18925 28.7017687 Root MSE = 2.4406

Adj R-squared = 0.7925

Residual 112700.731 18921 5.95638345 R-squared = 0.7925

Model 430480.242 4 107620.06 Prob > F = 0.0000

F( 4, 18921) =18068.02

Source SS df MS Number of obs = 18925

.

totalparcelspiecesamount .0084455 .0000172 491.62 0.000 .0084118 .0084792

totalcasedflatspiecesamount .0025288 1.08e-06 2350.96 0.000 .0025267 .0025309

totalcasedletterspiecesamount .0034844 3.37e-06 1034.18 0.000 .0034778 .003491

actualofficehoursamount Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 182023564905936 3.7102718 Root MSE = .81204

Adj R-squared = 0.8223

Residual 3235051.34905933 .65941612 R-squared = 0.8223

Model 14967304.7 3 4989101.56 Prob > F = 0.0000

F( 3,4905933) = .

Source SS df MS Number of obs = 4905936
Swiss Economics, Report for PRC, Seite 54

11. Appendix D: Business process analysis, consumer response

All subchapters are structured equally. First, the plan 5 approach is summarized based on the publicly available information of Docket No. N2010-1; then the approach for plan 5+ for the high and 

low scenarios are described; finally, corresponding results are provided.

11.1 Collection

11.1.1 Scenario

Status quo Changes in plan 5

[based on USPS 2010 

filing N2010-1]

Assumed changes in 

plan 5+ high

Assumed changes in 

plan 5+ low

Collection Post offices: 

Open on 6 days 

Collection boxes: 

6 days

Post offices: No change

Collection boxes: Elimination of retrieval on 

Saturday 

As plan 5 As status quo

11.1.2 Plan 5 approach

All mail is still accepted on Saturdays. With the exception of Express Mail, retail customers’ mail 

accepted on Saturday will, however, not be processed until the following Monday: Outgoing mail 

from retail facilities, with the exception of Express Mail, will not be picked up and dispatched on 

Saturdays. (For respective savings see sorting and transport.) Letter drops (blue collection boxes) 

will not be emptied on Saturdays, except in instances where local managers determine the need for 

capacity reasons. 

The USPS calculates savings for no longer emptying letters drops. The USPS, however, expects the 

workhour savings of eliminating Saturday letter drop collection to be less than its current full costs. 

This is due to capacity issues which might require extra collection during the weekend and heavier 

collection volumes on Mondays, which may require additional Monday collections. For these reasons, after eliminating the 4.832 M workhours21 associated with Saturday collections, one-half of 

those hours, 2.416 M were added back to cover the time that may be necessary to perform limited 

collections during a weekend or early collections on Mondays, or Tuesday after a holiday.22

It is noted that in the filing, the USPS and PRC did not treat collection as a separate process. Post 

office operations were considered as part of mail delivery processing. The corresponding savings 

are, therefore, included in Table 41 from Section 11.2 (sorting). 

11.1.3 Plan 5+ approach

High scenario: As of plan 5, included in section below (sorting).

Low scenario: As of today, no savings. 

 

21 The workhours are taken from DOIS data for Saturday city delivery collections’ operations during August and September 2009 and scaled up to yearly hours.

22 See Library Reference USPS-LR-N2010-1/3 p. 6.
Swiss Economics, Report for PRC, Seite 55

11.2 Sorting (Processing)

11.2.1 Scenario

Status quo Changes in plan 5

[based on USPS 2010 

filing N2010-1]

Assumed changes in 

plan 5+ high

Assumed changes in 

plan 5+ low

Sorting Sorting every day (7 

days)

Elimination of all Saturday outgoing mail processes (excl. Express).23As plan 5 As status quo

11.2.2 Plan 5 approach

The USPS assumes no clearance would take place on Saturdays, shifting the sorting workload to 

Mondays. According to USPS official Neri24, the most significant change to mail processing in the 

planned five-day delivery environment is the elimination of all Saturday outgoing mail processes 

except outgoing Express Mail operations. Incoming operations will generally continue on Saturdays. It is further assumed that the USPS would continue sorting Friday night even though no delivery takes place on Saturdays. Importantly in this context, witness Neri states that parcels will be 

worked Friday night and dispatched early Saturday morning to the delivery units.25

Based on these assumptions, USPS calculates the savings in mail processing labor costs that would 

result from eliminating the processing of outgoing mail on Saturday and shifting the sorting workload. It first estimates the costs that would be avoided if outgoing processing were eliminated on 

Saturdays, and then adds the costs that would be incurred by the need to isolate post office box 

addressed mail from other mail on Friday to support delivery of post office box addressed mail on 

Saturday.

To estimate mail processing savings the USPS distinguishes between direct distribution operations 

and “allied” operations. Direct distribution operations include manual and automated processing 

of letters, flats, and mixed mail sorting, as well as supervisor and indirect time. Operations that are 

not direct distribution operations are categorized as “allied operations” since they support more 

than one distribution operation. They consist of loading mail in bulk form, generally out of larger

containers into other containers, or vice versa, and moving the mail from the dock to sorting operations, or from sorting operations to the dock. Allied operations also involve some prepping of mail 

for sorting or for dispatch.

For direct distribution operations, a productivity analysis approach is taken by the USPS. The 

productivities on Mondays compared to Saturdays are from 10 to 35% higher for automated operations, and from 25 to 35% higher for manual operations. The USPS argues that the percent of labor 

hours saved on Mondays in these operations will equal the difference in productivities between 

Saturdays and Mondays.

The USPS’ estimates of the percentage of variable costs in allied operations range from 10 to 100%

for different operations. These estimates are based on operational experience. The estimates for each 

group, when added together, imply that 38% of outgoing processing labor hours is fixed on any 

 

23 Sorting Friday night remains unchanged.

24 Docket No. N2010-1, USPS-T-4, DIRECT TESTIMONY OF FRANK NERI ON BEHALF OF THE UNITED STATES 

POSTAL SERVICE, p. 8.

25 Docket No. N2010-1, USPS-T-4, DIRECT TESTIMONY OF FRANK NERI ON BEHALF OF THE UNITED STATES 

POSTAL SERVICE, p. 9, lines 13-15.
Swiss Economics, Report for PRC, Seite 56

given day of the week, including Mondays. As a consequence, 62 % of those operations feature volume variability.

Table 41 provides an overview of the USPS’ savings estimates.

The PRC questions the estimated cost savings related to labor hours savings of clerks and 

mailhandlers as these are significantly higher than in previous studies. 

To corroborate the estimates for the direct distribution operations, the PRC assumes that 25% of 

Saturday volumes falls on Fridays and 75% are shifted to Mondays. The saved hours are then calculated by dividing new volumes by either average weekly productivities or day-specific productivities. In the first case, the saving are 1% of workhours, in the latter 7% of workhours are saved. 

However, it remains an open question whether new Monday and new Friday volume levels would 

exhibit either of the historical productivities modeled above, or some other productivity. However, 

the PRC accepts, with caution, the USPS’ analysis of direct distribution operations.

For the allied operations, the PRC takes a volume variability approach and finds volume variability 

for mail processing labor costs of about 94%. This implies that about 6% of these costs are fixed. As 

USPS’ estimate of fixed costs is around 38%, the PRC questions the USPS calculated cost savings in 

allied operations. In total, the PRC estimates workhours savings of $1.5 million which is $1.2 million 

less than USPS’ estimate resulting in a difference in costs savings of $56 million, cf. Table 41.

Table 41: Estimated cost savings in outgoing mail processing operations

M USD USPS PRC Difference

Clerks and Mailhandlers 90.11 33.73 56.39

Supervisor 19.50 19.50 -

Equipment Maintenance 13.33 13.33 -

Custodial 0.13 0.13 -

Post Office 53.00 53.00 -

Total 176.07 119.69 56.39

11.2.3 Plan 5+ Approach

High scenario: The sorting processes are assumed to be adapted in the exact same way as in plan 

5.

It could be argued that variable costs of sorting a mailpiece is independent of volumes per day. Both 

the USPS and PRC have followed a different approach and assume higher productivity for Mondays 

because of higher volumes. If productivity measures include fixed costs, then increased productivity 

is a natural effect, and would not necessarily lead to changes in variable costs per piece. It is not 

known how the USPS is measuring productivity. We are forced to follow the approach of the USPS 

or PRC, respectively. As the discussion above on their approaches reveals, it is key to determine 

how the shift in volumes affects the productivity of sorting on the remaining days and to distinguish 

variable and fix costs in the sorting process properly. 

For estimating the costs savings of direct distribution operations and allied operations, we follow 

the more conservative approach of the PRC. 

The assumed plan 5+ high savings are those approved by the PRC for plan 5, i.e., USD 120 million. 

Low scenario: As the USPS would continue to sort on Saturdays, no savings from volume shifts 

occur.
Swiss Economics, Report for PRC, Seite 57

11.3 Delivery

11.3.1 Scenario

Status quo Changes in plan 5

[based on USPS 2010 

filing N2010-1]

Assumed changes in 

plan 5+ high

Assumed changes in 

plan 5+ low

Delivery 6 days home delivery 

of letters, flats, packages

6 days P.O. box delivery of Letters, Flats, 

packages 

No home delivery of 

letters/flats and packages on Saturdays

[Continuation of delivery of Express Mail and 

Saturday post office 

box delivery]

No home delivery of 

letters/flats on Saturdays 

[Continuation of delivery of Express Mail, 

Saturday post office 

box delivery and package delivery]

Assumption that additional overtime hours 

can be managed with 

flexible workforce

As plan 5+ high

Assumption that additional overtime hours 

cannot be managed 

with flexible workforce

11.3.2 Plan 5 approach

In plan 5, on Saturdays only, Express Mail and P.O. boxes are delivered. 

The USPS’ segments its calculations into city delivery savings (which is further segmented into street 

time and in-office time savings), rural carrier time savings, and indirect savings. The USPS and the 

PRC disagree on the level of potential cost savings of the city delivery street time and of the indirect 

costs. 

City Carrier Time Savings

The USPS’ calculation of city carrier time savings starts with Delivery Operations Statistical Information System (DOIS) data on city delivery carrier hours and delivered volume. Hours and volumes 

are apportioned to delivery days and hours are broken down into in-office time and street time. 

Street time is further classified into network travel, delivery activities, and delivery support. Network travel is the amount of time it takes the carrier to travel between delivery segments on the 

route. Delivery activities include the time it takes to leave the route and reach the receptacle, fingering the mail, and loading the mail into the receptacle. Delivery activities support is time needed for 

returning to vehicles and refilling satchels. The USPS estimates the hours that would be saved in 

each component by eliminating Saturday delivery and then multiplies these savings with an adjusted average carrier wage rate. 

For in-office time, the operational analysis of the USPS suggest that on Saturdays, 34% of office time 

is fixed per route and 66% can be considered variable.26 Given these numbers, the calculation of 

office time savings is then straightforward. The fixed office time is saved and the variable office time 

is transferred to the rest of the week. Therefore, the USPS estimates that 34% of Saturday office hours 

are saved in the five-day delivery regime. This corresponds to costs savings of $261 million.

With respect to street time savings, USPS assumes that 100% of Saturday’s network travel time and 

90% of both Saturday’s delivery activities and delivery support time are saved. The 90% estimate is 

 

26 See Library Reference USPS-LR-N2010-1/3 at 3, Table 1.
Swiss Economics, Report for PRC, Seite 58

based on historical data of Tuesday delivery times after a Monday holiday. The USPS argues that 

Mondays in a five-day environment are similar to Tuesdays after Monday holidays. The USPS finds 

that all but 9.4% of incremental post-holiday volume is absorbed on post-holiday Tuesdays. It assumes then that Saturday volume would be absorbed at the same rate when delivered on Mondays 

in a five-day delivery environment. The calculation of cost savings is then again straightforward. In 

total USPS estimates costs savings of $1690 million based on 41,3 M hours saved, implying an adjusted hourly wage rate of about USD 40,8.

Next to these savings, the USPS estimates the extra costs incurred for continuation of Saturday Express Mail to be $7 million27

.

With respect to in office time, the PRC agrees on the classification of fixed and variable time carrier 

activities. However, it raises some doubts concerning capacity adjustments; amely, they criticize the 

USPS’ assumption that routes will not be rebalanced in the face of large transfers of volume from 

Saturday to Monday as this would imply that there is a great deal of excess capacity in current 

routes. Rebalancing routes on Mondays would imply that some of the fixed costs are also transferred 

to Mondays. Adjusting for that, the PRC estimates cost savings of $102 million.

The PRC doubts the consistency of the estimated street time delivery savings. USPS’ comparison of 

post-holiday Tuesdays productivities leads to an incremental productivity which is much larger 

than average productivity on Mondays, or average weekly productivity and even substantially 

larger than the incremental productivity estimate of the PRC comparing Mondays and Saturdays. 

Furthermore, the PRC points out that delivered volume during the remainder of the week after a 

Monday holiday shows productivity declines. That is, average weekly productivity of a Monday 

holiday week and regular weeks do not significantly differ. The PRC also doubts that the higher 

volumes on Mondays could be absorbed without capacity adjustments. The PRC comes to a much 

lower estimate of street time delivery savings. They estimate cost savings to be $1’162 million.

Rural Carrier Savings 

As rural carriers are paid essentially by the piece count of mail that they deliver, the variable costs 

of Saturday delivery are transferred one-to-one to the rest of the week because the volume is assumed to remain constant. Only Saturday hours associated with non-volume-related workload and 

the associated evaluation factors would be saved by eliminating Saturday delivery. However, there 

would be a shift in the labor mix accompanying five-day delivery which affects costs because replacement carriers would be eliminated under five-day delivery, all weekday hours would be paid 

at the higher regular carrier wage rate. This is accounted for by using an adjustment factor.28

The PRC agrees on this approach to estimate rural carrier savings. The cost savings estimates are 

listed in Table 42.

Indirect Savings

The USPS estimates indirect cost savings by examining indirect costs, element-by-element, and then 

assessing how costs would be affected by eliminating Saturday delivery. Because volumes remain 

constant, the USPS estimates that only some indirect costs, such as supervision and vehicle maintenance, change. No changes to other indirect costs, such as building maintenance and depreciation, 

or vehicle depreciation, are expected.

 

27 See Library Reference USPS-LR-N2010-1/3.

28 For details on the calculation see Docket No. N2010-1, USPS-T-6, DIRECT TESTIMONY OF MICHAEL D. BRADLEY 

ON BEHALF OF THE UNITED STATES POSTAL SERVICE, p. 21ff.
Swiss Economics, Report for PRC, Seite 59

The PRC widely agrees on that approach, but comes to different cost savings estimates in their analysis.

Table 42: Estimated cost savings in delivery

M USD USPS PRC Difference

City carrier direct route and access time 1690 1162 528

City carrier direct in-office costs 261 102 159

City carrier adjustment for Saturday Express Mail -7 -7 0

Rural carrier direct costs 341 341 0

Rural carrier EMA savings 85 85 0

Rural carrier Adjustment for Saturday Express Mail -1 -1 0

Indirect carrier costs 378 305 73

Total 2'747.00 1'987.00 760.00

11.3.3 Plan 5+ Approach

Cf. Section 6.1. 

11.4 Transport

11.4.1 Scenario

Status quo Changes in plan 5

[based on USPS 2010 

filing N2010-1]

Assumed changes in 

plan 5+ high

Assumed changes in 

plan 5+ low

Transport Transport every day Elimination of Saturday transport for mail 

collected in post offices 

/ collection boxes to 

processing facilities.

Less transport on Sundays due to elimination 

of all Saturday outgoing mail processes.

As plan 5 As status quo

11.4.2 Plan 5 approach

In plan 5, no Saturday transport for mail is needed anymore. Further, the USPS assumes savings on 

Sundays because transportation volumes are lower. 

The USPS calculates transportation savings separately for air and surface. Surface transportation is 

further broken down into purchased highway transportation, box route transportation, and vehicle 

service driver activities Vehicle Service Drivers (VSD). For highway transportation and VSD, the 

USPS claims that there is sufficient truck capacity to move the diverted weekend mail without inducing added trips. This implies that under the new regime, weekend savings do not shift to Monday. With air and box route transportation contracts, the USPS recognizes added costs during weekdays to move mail that would have otherwise been delivered or processed on the weekend. 
Swiss Economics, Report for PRC, Seite 60

For highway transportation, the baseline costs of Saturday and Sunday transportation are constructed using USPS Transportation Cost Surface System (TCSS) for each contract type.29 Then,

based on an operational analysis, the USPS estimates the reduction in transport capacity measured 

in cubic foot miles (CFM) when discontinuing Saturday outgoing operations. These estimates range 

for Saturday from 20 to 60% and for Sunday from 50 to 80% depending on the contract type.30 The 

USPS states that there is not a one-to-one relationship between CFM and transportation cost. Therefore, capacity variability estimates are used to estimate cost savings. The capacity variability estimates range from 70 to 91%.

31 The costs savings in highway box route transportation are then the 

product of the reductions in required CFM multiplied by capacity variability estimate times the 

baseline costs.

For box route transportation, operational analysis of the USPS experts suggests that 68.1% of box 

route costs are route related. USPS operation experts find that 100% of box route transportation on 

Saturdays can be saved in the 5-day delivery regime. This implies that under the new delivery regime, 68.1% of box route costs fall apart. On Sunday, there are no box route transports.

For VSD (transportation provided by USPS and not contracted) there are two types of direct costs 

savings: labor cost savings and fuel cost savings. The USPS first constructs total VSD driver hours 

of Saturday transportation by using TACS data base. Operational experts estimate that 42% can be 

saved on Saturdays. The costs savings estimate is then 42% of total Saturday VSD costs. For Mondays, it is assumed that no additional costs occur. 

For air transportation, it is assumed that the volumes transported on the week in the current regime 

will be transported on Tuesdays. There arise costs savings as the rates on Tuesdays are lower than 

the weekend tariffs.

The PRC disagrees with the way surface transportation costs are estimated. Specifically, it questions 

the argument of sufficient capacity during the remainder of the week to absorb all additional volume 

without incurring additional costs. If this were the case, the PRC argues that this indicates excess 

capacity in the regime today, which could be reduced to save costs without adjusting Saturday delivery. The PRC instead suggests an approach which uses elasticities of the number of trips to cubic 

feet of volume to calculate the additional number of trips required during the remainder of the week 

to absorb the shifted volumes. These additional costs are then added to the savings calculated by 

the USPS, which lead to a $207 million lower estimate of cost savings in highway transportation.32

Table 43 shows an overview of the costs savings estimates of the PRC and USPS.

Table 43: Estimated cost savings in transportation

M USD USPS PRC Difference

Air 62.35 62.35 -

Highway 220.22 12.68 207.54

Box Routes 35.15 35.15 -

Vehicle Service Drivers 59.32 59.32 -

Total 377.04 169.50 207.54

 

29 For details see Docket No. N2010-1, USPS-T-6, DIRECT TESTIMONY OF MICHAEL D. BRADLEY ON BEHALF OF 

THE UNITED STATES POSTAL SERVICE, p. 35f.

30 For details see Docket No. N2010-1, USPS-T-6, DIRECT TESTIMONY OF MICHAEL D. BRADLEY ON BEHALF OF 

THE UNITED STATES POSTAL SERVICE, p. 42.

31 For details see Docket No. N2010-1, USPS-T-6, DIRECT TESTIMONY OF MICHAEL D. BRADLEY ON BEHALF OF 

THE UNITED STATES POSTAL SERVICE, p. 44.

32 For details, see Docket No. N2010-1 Advisory Opinion, p. 99.
Swiss Economics, Report for PRC, Seite 61

11.4.3 Plan 5+ Approach

High Scenario: Based on the evidence in plan 5, it cannot be expected that all volume shifted from 

the weekend to the remainder of the week can be absorbed without any capacity adaptations unless 

there exists overcapacities in the current regime. However, if this were the case, savings arising from 

reducing these overcapacities should not be accrued to the change in the regime of Saturday delivery. In fact, there are two consistent scenarios: either the capacity is fixed, or capacity can be 

adapted. If capacity can be adapted, then there should be savings on Saturday and Sundays, but 

also additional costs on Mondays. We, therefore, follow the approach of the PRC in calculating the 

cost saving potentials in transport and assume savings equaling USD 169.5 M.

Low Scenario: No savings can be achieved as processing is still fully operational. 

11.5 Demand effects: impact of quality and demand response

Status quo Changes in plan 5

[based on USPS 2010 

filing N2010-1]

Assumed changes in 

plan 5+ high

Assumed changes in 

plan 5+ low

Volume 

scenario

FY2012 Volume response consideredAs plan 5, but no response for parcelsAs plan 5+ high, but reduced response

Reductions in services reduce demand certeris paribus. In plan 5, demand for letters and parcels 

are reduced, whereas in plan 5+ only demand for letters is affected. 

11.5.1 Plan 5 Approach

The USPS estimated $201 million in forgone revenue (USPS-T-7 at 17-18) based on a prediction of 

mailers’ volume response to the implementation of five-day delivery. The prediction is based on 

quantitative market research. Overall, the USPS estimates that the loss of volume would be 1.242 B 

pieces or 0.7%, leading to a revenue loss of USD 456 M and of avoided attributable costs of USD 255 

M (USPS-T-9 at 2). USPS’ estimates are shown in Table 44. 

The USPS provided a confidence interval of between a net loss of USD 576 M and a net gain of USD 

114 M, the latter being rather unrealistic. (Service decreases should not lead to an increase of volumes when prices remain constant.) In market research methodology,, consumer’s best estimates of 

own volume responses were decreased (multiplied) by likelihoods provided by the same respondents. The PRC did not consider it appropriate to deflating best estimates by an expected value function. As a consequence, the likelihood correction was not accepted, leading to revenue foregone of 

USD 587 M. This translates to an average volume response of 2.3%. 

The USPS aggregated flats and parcels. The individual effects were reviewed, as well as the details 

of the calculations from PRC-N2010-1-LR6. The analysis of the estimates made a correction necessary. According to the USPS’ filing, the calculations assumed for consumer parcels that First-Class

Mail parcels would behave the same way as Priority Mail. Thereby, Priority Mail is positively affected from plan 5 because it is still delivered on Saturdays; therefore, some letters sent by FirstClass Mail is shifted towards Priority Mail. This will not be the case for first class parcels, as these 

are no longer delivered in plan 5. An analysis of the corresponding consumer segment analysis (also 

available in PRC-N2010-1-LR6) revealed a predicted decrease of First-Class Mail parcels sent by 

consumers by 14% with likelihood correction and 21% without correction. Taking the corresponding 

value for the aggregation with other customer segments (businesses etc.) leads to an estimated decrease of 6.5% of parcels. Flats are estimated to decrease by 4.2%. The corrections by Swiss Economics are highlighted in red. 
Swiss Economics, Report for PRC, Seite 62

Table 44: Estimated demand response and financial effects

Source: PRC-N2010-1-LR6, extended by Swiss Economics

11.5.2 Plan 5+ Approach

High scenario: With the exception of parcels, it is assumed that the volume responses occur as 

indicated in the second to last column of Table 44. For parcels, it is assumed that these remain constant in a plan 5+ environment. 

Low scenario: In the low scenario, the effects on quality are less severe as in the high scenario; endto-end delivery times for items collected on Saturdays are faster if this mail is processed right away 

and not after Monday (which would be the case in the high scenario). However, end-to-end delivery 

times for pieces originally delivered on Saturdays remain lower. Therefore, it is assumed that the 

volume responses from Table 44 are lowered by 50%. 

11.6 Further issues: Peak load and impact on service level

11.6.1 Peak load

Peak load issues can be expected to remain the same as for plan 5 (high scenario), or they may be 

reduced in case of the low scenario. In collection, no peak load issues must be expected as there are 

no changes. In sorting, there may be some minor issues on Mondays to process the dispatched 

mail from Saturday and Mondays. In transport, no peak load issues are expected because of rather 

low capacity utilization. In delivery, the workload on Mondays increases sharply (doubled as compared to Wednesday) (see Table 15), forcing either a reorganization of routes or the use of overtime. Based on USPS’ information for plan 5, it expects no need for route reorganizations. Therefore, the effect of plan 5+ on overtime is assessed in detail in Section 5.

11.6.2 Service

The main effects of discontinuing Saturday delivery for letters and parcels are longer end-to-end 

delivery times (from the time of collection to the time of delivery). According to the advisory opinion, of Docket No. N2010-1, about 25% of all letters, flats and parcels will be delivered with a lag of 

at least one day (see Table 45). Service

Volume 

change %

Volume 

change #

Revenue 

foregone

Cost 

avoidable

Net income 

change

Volume 

change %

Net income 

change

First-Class Mail: -1.2% -974.64 -407.87 -205.58 -202.29 -3.4% -595.91 

Single-Piece Letters & Cards -1.9% -593.9 -259.5 -156.6 -102.8 -3.9% -213.1 

Presort Letters & Cards -0.7% -350.1 -119.6 -41.0 -78.6 -3.0% -324.9 

Flats -0.9% -30.6 -28.8 -8.0 -20.9 -4.2% -58.0 

Parcels -6.5% -1.3 

Standard mail -0.3% -267.3 -28.4 -41.4 13.0 -1.7% -14.0 

Regular 0.1% 93.0 20.9 13.6 7.3 -0.6% -29.9 

Nonprofit -2.7% -360.3 -49.3 -55.0 5.7 -7.4% 15.9 

Periodicals 0.0% -0.9 -1.8 0.1 -1.9 0.7% -8.4 

Regular -0.4% -24.5 -6.6 -8.2 1.6 -0.1% 0.3 

Nonprofit 1.4% 23.6 4.8 8.2 -3.4 3.7% -8.7 

Express mail -4.5% -2.1 -39.4 -24.7 -14.8 -2.9% -9.6 

Priority mail 0.4% 3.2 22.0 16.9 5.1 3.2% 39.2 

Total -0.70% -1'241.7 -455.6 -254.7 -200.9 -2.32% -588.7 

 USPS estimates PRC/SE correction 
Swiss Economics, Report for PRC, Seite 63

The lags cause some decreases in mail demand which have been discussed in Section 11.5. 

Table 45: Lags caused by plan 5

Product % of pieces with lag of at least one day

First class mail 25.8%

Priority 25.4%

Package services 26.1%

Source: Swiss Economics based on N2010-1, Advisory Opinion
Swiss Economics, Report for PRC, Seite 64

Swiss Economics SE AG

Stampfenbachstrasse 142

CH-8006 Zürich

T: +41 (0)44 500 56 20 

F: +41 (0)44 500 56 21

office@swiss-economics.ch

www.swiss-economics.ch swiss economics