the essential air service program: a cost-effectiveness study

12-706: Civil Systems Investment Planning and Pricing

The Essential Air Service Program A Cost-Effectiveness Study

Aditya Chaganti [email protected]

A Summary of the Statistics Used for the Study The data collected for this study has been put together from four primary sources:

• A proximity dataset (proximity.pdf) that details the distance of an EAS serviced airport to the closest large, medium, small, or non-hub airports. For purposes of convenience, this dataset will be referred to as the ‘proximity dataset’ through the rest of the study.

• A dataset (071101nonalaska_v2.htm) that details subsidies and airplane model information for every EAS community. This dataset will be referred to as the ‘subsidy dataset’ through the rest of the study.

• A dataset that details enplanements for commercial years 2003 and 2004, and the percent change in enplanements for the top 2000 airports (sorted based on passenger volume) in the country. This dataset will be referred to as the ‘enplanement dataset’ through the rest of the study.

• A DOT website on average fares for the top 500 airports in the US over the period 1993-2014. This dataset will be referred to as the ‘fares dataset’ through the rest of the study.

The Enplanements Dataset Table: Enplanement Statistics for EAS Communities (2004) Classified Basing on Airplane Seating Capacity Total $691,239 (All Values in U.S

Dollars)

Average Five Percentile Ninety Five Percentile

Maximum Minimum

9 Seat 7,727 Insufficient Data Insufficient Data 18,578 1,219 19 Seat 6,059 788 13,479 22,321 338 30 Seat 6,790.7 Insufficient Data Insufficient Data 12,472 2,630 34 Seat 9,370.3 2,557 17,018 17,018 2,557 37 Seat 34,533 Insufficient Data Insufficient Data Table: 2004 Enplanement Statistics for the Top 2000 Airports (Sorted Based on Passenger Volume)

Average Maximum Minimum 5th Percentile

95th Percentile

Enplanements 364,700 41,123,857 1 2 734,921 The Proximity Dataset Data analyzed from this dataset consists of ranges characterized by the previously mentioned statistical parameters of the distances of the EAS airports from L/M Hub, Small Hub, and Non-Hub airports.

L/M Hub Airports Small Hub Airports Non-Hub Airports

The Subsidy Dataset Data analyzed from this dataset consists of ranges characterized by the previously mentioned statistical parameters of the annual subsidies received by each of the EAS communities in order to implement the EAS act.

Proximity (Miles)

Average 136.5

Five Percentile 53 Ninety Five Percentile 280

Maximum 319

Minimum 39

Proximity (Miles)

Average 101.2


Maximum 231

Minimum 33

Proximity (Miles)

Average 228.9


Maximum 724

Minimum 32

Table: Subsidy Statistics Classified Basing on Airplane Seating Capacity for EAS Communities Total $100.1

Million (All Values in Dollars)

Average Five Percentile

Ninety Five Percentile

Maximum Minimum

9 Seat 501,041.8 Insufficient Data

Insufficient Data 735,660 303,554

19 Seat 981,736.4 519,858 1,532,891 2,373,320 487,004 30 Seat 967,041.3 Insufficient

Data Insufficient Data 1,696,977 247,122

34 Seat 1,051,021 547,532 1,504,929 1,504,929 547,532 37 Seat 1968830 Insufficient

Data Insufficient Data Insufficient Data Insufficient Data

The Fares Dataset Table: Fare Statistics (Averaged over the Year of 2004) for all Airports in the Fares Dataset Average Maximum Minimum 5th Percentile 95th Percentile Fare per Trip 415.79 1454.80 62.00 223.97 687

Table: Fare Statistics (2004) for EAS Airports Classified Basing on Airplane Seating Capacity Total Average 442.4

Average Five Percentile


Maximum Minimum

9 Seat 410.5 Insufficient Data

Insufficient Data 457.4 348.7

19 Seat 448.3 302.1 641.9 857.0 226.0 30 Seat 490.1 Insufficient

Data Insufficient Data 572.9 422.6

34 Seat 406.6 311.1 508.2 508.2 311.1 37 Seat 491.7 Insufficient

Data Insufficient Data Insufficient Data Insufficient Data

Cost-Effectiveness and Putting the Subsidy in Context It is important noting that airports have been classified on the basis of the carrier capacity of the EAS subsidized air plane that serves a particular city. This was done to avoid generalization, and in order to account for differences in airplane capacities, and their plausible effect on the subsidies and fares. Further, cost effectiveness and subsidy data from two aircrafts, one with a capacity of 37, and another listed as 30+ (small datasets) have been ignored. The summary of the data for EAS airports is as follows: Table: 9-Seaters: Subsidy/Fare, Cost Effectiveness (& Corresponding Subsidy Fare Values) Subsidy/Fare Ratio Cost effectiveness

(Subsidy/Enplanement Ratio) Corresponding Subsidy/Fare Ratio

Average 0.3 161.4 - Maximum 0.4 249.0 Fare Not Listed Minimum 0.1 39.6 0.1 Table: 30-Seaters: Subsidy/Fare, Cost Effectiveness (& Corresponding Subsidy Fare Values) Subsidy/Fare Ratio Cost effectiveness


Average 0.3 164.2 - Maximum 0.6 322.0 0.6 Minimum 0.2 76.7 0.2

Table: 19-Seaters: Subsidy/Fare, Cost Effectiveness (& Corresponding Subsidy Fare Values) Subsidy/Fare Ratio Cost effectiveness


Average 0.6 316.2 - Maximum 4.0 2122.8 - Minimum 0.1 29.5 - Five Percentile

0.1 51.6 0.1


1.7 797.67 2.2

Table: 34-Seaters: Subsidy/Fare, Cost Effectiveness (& Corresponding Subsidy Fare Values) Subsidy/Fare Ratio Cost effectiveness


Average 0.4 165.8 Maximum 1.2 529.9 Minimum 0.1 47.1 Five Percentile

0.1 47.1 0.1


1.2 529.9 1.2

The subsidy-fare ratio gives a measure of the proportion of the total passenger fair that is being paid for by the government, and gives a clear indication of the Government’s estimate of the degree of incentive that air carries and passengers need to be offered in order to connect a particular community to hubs. In general, an increasing subsidy fare ratio results in increasing subsidy paid per enplanement. When plotted together on a graph [Appendix-A] with the Subsidy/Fare on the x-axis, and subsidy/enplanement on the y-axis, this trend is confirmed to be a fairly linear relationship with a best fit coefficient of 0.9. Therefore, it would be fair to state that the primary factor that should influence the government subsidies to EAS communities is the estimated annual enplanements from an airport, and the corresponding estimated cost effectiveness of implementing a certain subsidy. It is also worth noting that given that the correlation is not a perfect fit, there are ancillary factors affecting the overall cost effectiveness that must be considered. These could be factors such as the size of the aircraft, or the size of the target population being catered to that also affect the cost effectiveness. However, no clear inference can be drawn about the given the data, and research into the same would help arrive on optimally accurate estimates of subsidies. Cost of Flights taken via the EAS Program-Analysis of Costs to the Government and Passengers The primary aim of the EAS program is to facilitate improved connectivity in small communities in the U.S with the objective of commercial air connectivity to the nearest hub airport. As is evident from the analysis on cost effectiveness, the government provides subsidies (whose magnitude is plausibly defined by the estimated enplanements from a community) to the airlines, which would then convince carriers to fly from these communities, thus ensuring connectivity to the closest hub airport. (Small, Medium or Large) The three primary stakeholders directly affected by the implementation of the program are as follows:

• The Government. • Passengers from EAS communities who would otherwise drive to the nearest hub. • The employees hired under the banner of the EAS program. This involves airplane staff, and people

working at EAS airports on services to EAS subsidized flights. • Airport authorities.

The two primary stakeholders however, are the government and passengers. Therefore, a cost analysis was deemed necessary to estimate the total cost incurred by the two stakeholders of the program. Cost of Flights to Individual and all Enplaned passengers in the year 2004 The total cost incurred by a single passenger (per enplanement) is a sum of the direct and indirect costs.

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡 𝑜𝑓 𝐹𝑙𝑦𝑖𝑛𝑔 = 𝐷𝑖𝑟𝑒𝑐𝑡 𝐶𝑜𝑠𝑡𝑠 + 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝐶𝑜𝑠𝑡𝑠 The direct costs in this case, are taken to be the fare paid by each passenger to purchase a ticket on an EAS subsidized flight.

Assumptions-Direct Costs

• Direct costs are taken to be the price of the EAS subsidized flight ticket purchased by a passenger. Taxi rides to and fro from the airport are not considered.

• There are no additional mandatory charges incurred by a passenger during travel. Other costs such as the costs incurred on food, comfort or entertainment are considered to be outside the gamut of this study, as they are a function of individual choice.

• The fare dataset consists of fare values taken from a single airport, but the set of values are assumed to be representative of a range of fare values that are a function of business/leisure traveler ticket reservations at different points in time, given that each airport fare is plausibly influenced to a large extent by the purchase decisions of business/leisure travelers from that particular area.

The indirect costs were calculated based on the passenger’s time value of money. In order to monetize the cost of time, the time spent was divided into waiting time, and travel time. Further, in order to diversify the mix of passengers and reach a more precise estimate, all passengers are assumed to be in either one of two categories:

• Business Travelers - Passengers travelling on business. • Leisure Travelers – Passengers traveling for personal reasons.

Assumptions-Indirect Costs • The average speed of an airplane flying under the EAS program is 350 Miles per Hour. • Average waiting time at airports for both business and leisure travelers is 60 minutes. • There are 52 weeks in a year.

Business Travelers

§ Business travelers earn an average of $60,000/year. § Travel time savings are valued at a 100%. § Waiting time savings are valued at a 100%. § Waiting time compensation factor = 1.2 § Business travelers are assumed to book their ticket either a week in advance, or a month in

advance. The fare for booking a week in advance is assumed to be the ninetieth percentile value of the fares dataset, that is, $566. The fare for booking a day in advance is assumed to be the maximum value of the fares dataset, that is, $857.

§ Business travelers work eight hours a day for five days a week.

Leisure Travelers § Leisure travelers earn an average of $30,000/Year. § Travel time savings are valued at 70%. § Waiting time savings are valued at a 100%. § Leisure travelers are assumed to book their ticket either three months in advance, or a year in

advance. The fare for booking three months in advance is assumed to be the sixtieth percentile value of the fares dataset, that is, $453. The fare for booking a year in advance is assumed to be the minimum value of the fares dataset, that is, $226.

§ Leisure travelers work eight hours a day for five days a week. Model

• The direct cost estimates have been explicitly specified as part of the assumptions for each of business, and leisure travelers.

• Indirect cost estimates were arrived upon basing on proximity ranges, calculated separately for Large/Medium Hub airports, and Small Airports. In cases where the maximum and minimum values were significant outliers, the fifth and ninety fifth percentile range was considered. The proximity ranges used are as follows: § Large/Medium Hubs:

Lower Bound: 77 Miles (Five Percentile) Upper Bound: 558 Miles (Ninety Five Percentile)

§ Small Hubs: Lower Bound: 39 Miles Upper Bound: 319 Miles

• Calculations were made separately for business and leisure travelers, before adding lower bounds and

upper bounds of each to arrive on a range of final estimates. • Using these ranges for distances of travel, and the speed of the airplane assumed, travel time per

enplanement was calculated. • Travel Cost was arrived upon using the following equation

𝑇𝑟𝑎𝑣𝑒𝑙 𝐶𝑜𝑠𝑡 = 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒 𝑆𝑎𝑣𝑖𝑛𝑔𝑠 𝑥 𝑊𝑎𝑔𝑒 𝑝𝑒𝑟 ℎ𝑜𝑢𝑟 𝑥 𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒

• Using an average waiting time of 1.5 hours per enplanement, the waiting cost was calculated as follows:

𝑊𝑎𝑖𝑡𝑖𝑛𝑔 𝐶𝑜𝑠𝑡= 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑊𝑎𝑖𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 𝑥 𝑊𝑎𝑖𝑡𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 𝑐𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 𝑂𝑛𝑙𝑦 𝑓𝑜𝑟 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑡𝑟𝑎𝑣𝑒𝑙𝑒𝑟𝑠

𝑥 𝑊𝑎𝑔𝑒 𝑝𝑒𝑟 𝐻𝑜𝑢𝑟 𝑥 𝑇𝑟𝑎𝑣𝑒𝑙 𝑇𝑖𝑚𝑒 𝑖𝑛 ℎ𝑜𝑢𝑟𝑠

• The total indirect costs are therefore a sum of the travel cost per enplanement, and the waiting time per enplanement.

• These are then added to the direct cost ranges specified, and multiplied with enplanements (Business or Leisure, base case being 50% of each) in order to arrive on a lower and upper bound cost estimate for each of business, and leisure travelers. Similar calculations are then carried out for both business and leisure passengers for Small Hub airports. (All the ranges of these values are accounted for in the appendix) The total costs were calculated for each of the two airport categories by adding the ranges of estimates for business and leisure passengers. The ranges of the total cost estimates were found separately for large/medium hub airports, and small airports.

Total Cost over a range of mixes, and incomes of Business and Leisure Travelers Quantities Varied

• The base case for the passenger mix was 50% each of business, and leisure passengers. This was varied over 90% of leisure passengers (10% Business Passengers), and 30% Leisure Passengers (70% Business Passengers). The 90% value was used keeping in mind travel patterns during festive periods such as the Christmas/New Year period when most passengers are likely to be travelling for personal, rather than business purposes.

• Business and leisure earnings were varied over +20%/-20% around the base case. Estimates for the total cost of the program to passengers vary as follows [List of results in Appendix-B] Large/Medium Hubs

Table: Upper Bound Mean (All Values in Millions)

Mean

% Leisure 467.8 Business Earnings/Year 468.6 Leisure Earnings/Year 470.4

Small Hubs


Mean

% Leisure 459.5 Business Earnings/Year 462 Leisure Earnings/Year 460.6

Table: Lower Bound Mean (All Values in Millions)

Mean % Leisure 276.4 Business Earnings/Year 277.1 Leisure Earnings/Year 278.3



Analysis and Final Estimate As is evident from the ranges above, costs vary in the range of a couple of millions basing on the destination of travel, with small hubs in general being a cheaper option than large hubs. Small Hubs are significantly closer with respect to average proximity, and the travel time savings resulting from that lead to savings for passengers. However, a real world scenario would be one where the mix of passengers flying to large and small hubs from these EAS airports is not a 100% biased towards either, and the scenario assumed would therefore represent two extremes where the total annual enplanements either fly to Large/Medium Hubs in the first case or Small Hubs in the second. Therefore, the lower bound of the costs to passengers through the EAS Program would be the lowest value of the means obtained from the ranges of the sensitivity analysis on costs of Small Hub Airports, and the upper bound would be the highest of the means obtained from the ranges of the sensitivity analysis of the Large/Medium Hub airports. Thus, the estimated cost to passengers from the program is in the range of $275-$470 Million, or $400/enplanement-$680/enplanement. (Total Enplanements=691239) Costs to the Government in the year 2004 Costs to the government are the subsidies that it provides to air carriers, which results in subsidized fares incurred by the passengers. Model • The lower and Upper Bounds of direct costs for each of business and leisure passengers (Fares as explained

previously) were multiplied by the total enplanements for each to arrive on a range of total direct costs. • The lowest (Minimum) and highest (95th Percentile-19 Seaters) subsidy/fare ratios are then multiplied with

the corresponding lower and upper bounds of direct costs (fares), thus establishing two extreme cases within which the cost incurred by the government must fall. Direct Costs (All values in Million $) Business Leisure Range of Direct Costs Subsidy Fare

Ratio Government Subsidy

Lower Bound

195.62 78.11 273.7 0.1 27.4

Upper Bound

296.20 156.57 452.8 0.6

271.66

Therefore, government subsidy is in the range of $27.4 Million-$769.7 Million for the entire program, or $39.6/Enplanement-$1113.7/Enplanement. (Estimates considering mix of passengers, and salary ranges)

Results and Analysis It is important to note that these costs are entirely dependent on the mix of business and leisure travelers, given that it only considers direct costs. Costs vary significantly from the lowest to highest possible estimate, the highest representing an extremely cost ineffective alternative. However, there are only three instances of a subsidy-fare ratio greater than one, and ignoring these, the highest subsidy fare ratio would be 0.6. The government costs in this, more plausible case are expected to be in the range of $27.4 Million-$271.7 Million, or $39.6/Enplanement-$393/Enplanement. Government expenditure on the program therefore, is estimated to between 10%-approximately 60% of total expense (Including indirect costs) incurred by passengers enplaning, on an annual basis. Alternative Methods of Analysis A more complex analysis could be performed by plotting a distribution of costs to passengers and the government over a number of randomly selected airports that offer EAS subsidies. Further, this analysis could also consider costs over any further connecting flights taken from the destination to other airports to determine an optimal mix of routes that could be served as part of the program, as an improvement on the current program. Counterfactual of Passenger Costs without the EAS Program The objective now, is to analyze the magnitude of benefits that passengers accrue due to the implementation of the EAS program, against a baseline case where they would have to drive to their preferred Hub/Non-Hub destination. Note that Non-Hub airports are also considered in cost analysis here, as there is no restriction like in the case of the EAS which only connects passengers to Hub airports.

Assumptions The model used is similar to the one used in the estimation of passenger costs of the program. The following are additional/alternative set of assumptions made to analyze the passenger cost of driving, instead of flying.

• The waiting time is assumed to be zero, since using a personal car would entail minimal/null waiting time.

• The average speed of a car over the journey is 60MPH, the average of the night time speed limit (55MPH), and the day time speed limit (65MPH) on most highways.

Model

• The model, in this analysis remains very similar to that followed in the previous analysis. However, the average direct cost/mile associated with automobile travel is taken as 56.2 cents, the 2004 estimate, taken from the source provided. (Office of Assistant Secretary for Research and Technology, Bureau of Transportation Statistics , United States Department of Transportation,)

• Proximity ranges for Large/Medium hubs are the same as taken previously, in addition to which the minimum and maximum proximity values for Non-Hubs were also considered.

• Further, in addition to the mean values of the ranges, the change of the mean value (resulting from variation of parameters), from the corresponding mean values computed as part of the analysis of the costs of flying were compared. Large/Medium Hubs


Mean/ (Change)

% Leisure 330.5(-137.4) Business Earnings/Year 329.8(-138.8) Leisure Earnings/Year 329(-141.4)

Small Hubs


Mean/ (Change)

% Leisure 188.2(-271.3) Business Earnings/Year 188.2(-273.8) Leisure Earnings/Year 187.4(-273.2) Non- Hubs


Mean /(Change)


Using a method of analysis like mentioned previously, the driving costs for passengers are estimated to be in the range of $19.4 Million-$330.5 Million, or $28/Trip-$478/Trip. [All results in Appendix-C] Two important observations can be drawn out of the calculations above:


Mean/ (Change)

% Leisure 45.6 (-230.8) Business Earnings/Year 45.5 (-231.6) Leisure Earnings/Year 45.4 (-232.9)


Mean/ (Change)

% Leisure 23 (-252.1) Business Earnings/Year 23 (-254) Leisure Earnings/Year 23 (-252.1)


Mean /(Change)


• Car travel to Large/Medium Hub airports is the most expensive of the three options, followed by travel to Small airports, and Non-Hub Airports. Looking at this data alone, it seems justifiable that the program connects hub airports, as the costs incurred on driving to these are significantly higher than those incurred on driving to Non-Hub airports.

• Driving costs are far less (the negative values indicating savings through driving) than flying as part of the EAS program. This is because of the exponentially higher direct costs incurred by flying, which offsets any gains with respect to the indirect costs, given that direct costs/enplanement are far greater than the indirect costs/enplanement, as observed in the model. However, the number of enplanements listed suggests that people are willing to travel by air, albeit at a higher cost, for factors other than the direct and indirect costs incurred. The objective now, must be to reduce the gap between the costs incurred on flying versus any alternative means of transport (In the case of this study, travel by car) in order to improve the EAS program.

Estimation of the Life Years Saved due to the Implementation of the EAS The risk comparison according to estimates published by the Pipeline and Hazardous Materials Safety Administration, PHMSA (January 1, 2004) is as follows: Motor Vehicle Risk= 1.3 Deaths per 100 million vehicle miles Air Carriers Risk=1.9 Deaths per 100 million aircraft miles Assumptions

• Each life saved saves 35 life years. • An average airplane considered in the PHMSA estimate has a capacity of 300, and an average

occupancy of 75%. • Cars surveyed by the PHMSA have an average occupancy of 3.

Model

• The estimates for motor vehicle, and air carriers death risk are given on a Deaths/100 Million vehicle miles basis. It wouldn’t be correct to directly compare these two estimates owing to the fact that the average occupancy of the two varies significantly. Therefore, the units need to be normalized with respect to the occupancy. Further, it would also be convenient to convert these to a per mile unit. The conversion is done as follows:

𝐷𝑒𝑎𝑡ℎ 𝑅𝑖𝑠𝑘 𝑝𝑒𝑟 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟 𝑚𝑖𝑙𝑒 =𝐷𝑒𝑎𝑡ℎ 𝑅𝑖𝑠𝑘 𝑝𝑒𝑟 100 𝑚𝑖𝑙𝑙𝑖𝑜𝑛 𝑣𝑒ℎ𝑖𝑐𝑙𝑒 𝑚𝑖𝑙𝑒𝑠

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑂𝑐𝑐𝑢𝑝𝑎𝑛𝑐𝑦 𝑥 100000000

• The lower bound for the proximity range was taken as the minimum proximity value for small hubs, and

the corresponding upper bound was taken as the 95th percentile of the large/medium hub proximities, ignoring one maximum outlier.

• The lower and upper bounds of the total passenger miles were then found by multiplying the proximity ranges with the total enplanements under the EAS program.

• Each of the two death risks of cars and airplanes (expressed on a per passenger mile basis) are then multiplied with the lower and upper bounds of the passenger miles travelled to obtain estimates of death risks under the EAS program.

𝐷𝑒𝑎𝑡ℎ𝑠 = 𝐷𝑒𝑎𝑡ℎ 𝑅𝑖𝑠𝑘 𝑝𝑒𝑟 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟 𝑚𝑖𝑙𝑒 𝑥 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟 𝑚𝑖𝑙𝑒𝑠

• The difference between anticipated deaths between cars and airplanes is then found, to establish the fatality benefits. It is found that the fatality benefits as a result of implementation of the EAS are positive, that is, airplanes are safer than cars.

• The total subsidy under the EAS program is then divided by the product of the fatality benefits, and the life years saved per life saved.

𝐶𝑜𝑠𝑡 𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑛𝑒𝑠𝑠 =𝑇𝑜𝑡𝑎𝑙 𝑆𝑢𝑏𝑠𝑖𝑑𝑦

𝐹𝑎𝑡𝑎𝑙𝑖𝑡𝑦 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑥35

Findings The cost effectiveness was found to be in the range of $25.1 Million/Life Year Saved for the lower bound of proximity, and $1.8 Million/Life Year Saved for the upper bound of proximity. These values suggest that the EAS program does save life years, but invests anywhere between 2%-25% of the total subsidy (Depending on the proximity of the airport) on saving one life year. No qualitative critique can be made on these values without

a perspective on the Government’s risk tolerance with respect to social costs, but the values do seem very high. However, it is also important to note that the primary aim of this program is not to improve transport safety, but boost connectivity. Therefore, subsidies (as shown previously) are likely to be dependent on factors such as fares incurred by passengers, and any other benefits are to be treated as a positive byproduct resulting from the implementation of the primary objective. [Tabular Calculations-Appendix-D]

Improvements to the Existing Program Three proposed improvements to the existing model of the program are as follows:

• Reducing waiting time for business passengers from the previously assumed 90 minutes, down to 45 minutes.

• Reducing the waiting time of leisure travelers from the previously assumed 90 minutes to 60 minutes. • Improving the air fleet, so average flight speeds are 500 MPH, as compared to the previously assumed

350 MPH, in order to reduce travel times. It must be noted that the preference given to business travelers with respect to direct costs is due to the higher value they associate with the time lost during a wait. Optimization for reducing overall passenger costs, thus requires a certain degree of prioritization. Model The model used is the same as the one used while calculating passenger costs of the program. Findings Incorporating the three stated improvements leads to an estimated total annual passenger cost (of the modified program) in the range of $246.4 Million-$462 Million (or $383/Enplanement-$668/Enplanement). These represent annual average passenger savings of 20.3 million, or $30/Enplanement. [Appendix E(a)] The modifications, therefore, result in significantly improved savings from the EAS programas a result of shortened travel, and waiting times.

Alternatives to the EAS Program The alternative scenario considers a situation where a mini-bus is used to ferry passengers from the small community to the hub destination. This estimation uses the same model as that used to estimate passenger costs under the EAS program. While direct cost incurred per mile is assumed to be the same as that used for cars, the case considers the mini-bus to have an average speed of 45 MPH. Further, the waiting time is assumed to be zero. The results are as follows: [Appendix E(b)] Large/Medium Hubs


Mean


Small Hubs


Mean

% Leisure 210.9 Business Earnings/Year 209.5 Leisure Earnings/Year 209

The estimated cost to passengers from the program is in the range of $25.6 Million-$368.1 Million ($39/trip$532.5/trip), representing annual average saving of $175.6 Million, or $254/trip.





Executive Summary: A Study on the efficiency the Essential Air Services Program, and Possible Alternatives

In the year 2004, the federal Government, as part of the EAS program, spent approximately a $101 Million towards subsidies granted to various EAS communities, in order to allow them to attract air carriers to connect the community with the closest Hub airports. The same year also saw close to 700,000 passengers from EAS communities take advantage of the program and fly on EAS subsidized flights. This number represents success with respect to the primary objective of the EAS program. However, given the large costs incurred, it is important to analyze the cost effectiveness, and benefits of the program to the two primary stakeholders involved, the government and passengers. Thus, a stakeholder benefit, and cost-effective analysis was carried out. The following were the main findings that emerged:

§ The estimated cost to passengers flying as part of the EAS program are in the range of $275-$470 Million, or $400/enplanement-$680/enplanement. (Total Enplanements=691239). 372.5

§ The cost to the government is estimated to be in the range of $27.4 Million-$271.7 Million, or $39.6/Enplanement-$393/Enplanement. Government expenditure on the program therefore, is estimated to between 10%-approximately 60% of total expense (Including indirect costs) incurred by passengers enplaning, on an annual basis.

§ The driving costs for passengers are estimated to be in the range of $19.4 Million-$330.5 Million, or $28/Trip-$478/Trip, relatively less than the alternative of flying as part of the EAS program

§ Car travel to Large/Medium Hub airports is the most expensive of the three options, followed by travel to Small airports, and Non-Hub Airports. Looking at this data alone, it seems justifiable that the program connects hub airports, as the costs incurred on driving to these are significantly higher than those incurred on driving to Non-Hub airports.

§ Driving costs are far lower than flying costs as part of the EAS program. This is because of the exponentially higher direct costs incurred by flying, which offsets any gains with respect to the indirect costs, given that direct costs/enplanement are far greater than the indirect costs/enplanement, as observed in the model.

§ The cost effectiveness of lifesavings, against a baseline case of driving was found to be in the range of $25.1 Million/Life Year Saved for the lower bound of proximity of the closest airport, and $1.8 Million/Life Year Saved for the upper bound of proximity of the closest airport. These values suggest that the EAS program does save life years, but invests anywhere between 2%-25% of the total subsidy (Depending on the proximity of the airport) on saving one life year.

From the findings above, it is evident that a significant number of people have benefited from the program, inspite of the higher costs as compared to a baseline case of driving to the airports. This indicates a willingness to incur signficntly higher costs for shorter travel times, and plausibly other factors such as comfort, and convenience. The popularity of the EAS program, and its ability to meet its stated objectives therefore, warrant its continued presence. However, the model of the program is not without its faults. The sulutions explored are as follows:

§ Modification of the existing program with an emphasis on reduced waiting, and travel time. The waiting time for passengers travelling on business purposes was taken to be 45 minute, and all other passengers were assumed to have had to wait for an hour. Further, the model assumed an airplane speed of 500 MPH, that is 150 MPH faster than that assumed for the base case. Incorporating the three stated improvements leads to an estimated total annual passenger cost (of the modified program) in the range of $246.4 Million-$462 Million (or $383/Enplanement-$668/Enplanement). These represent annual average passenger savings of 20.3 million, or $30/Enplanement.

§ The second alternative considered replacing the EAS system with a mini-bus system used to ferry passengers from the small community to the hub destination. While direct cost incurred per mile is assumed to be the same as that used for cars, the case considers the mini-bus to have an average speed of 45 MPH. Further, the waiting time is assumed to be zero. The estimated cost to passengers from the program is in the range of $25.6 Million-$368.1 Million ($39/trip-$532.5/trip), representing annual average saving of $175.6 Million, or $254/trip.

Incorporating these stated improvements/alternatives to the program results in savings of a significant magnitude, as is evident from above. The popularity of the EAS program, as is evident from the statistics above, means that it cannot be rescinded. However, the Department of Transportation must consider scaling back its expenditure on the EAS program, while investing in other, more cost-effective alternatives.

Appendix-A

Appendix-B

Calculations of Indirect Costs of Passengers Flying to Large/Medium Hub Airports Table: Travel Cost per Enplanement

Distance (Miles)

Speed (mph)

Travel Time (Hours) Travel Cost ($/Enplanement)

Five Percentile 77.0 350.0 0.2 6.3 Ninety Five Percentile 558.0 350.0 1.6 46.0

Table: Waiting Cost per Enplanement

Distance (Miles)

Waiting Time (Hours)

Waiting Cost ($/Enplanement)

Five Percentile 77 1.5 51.9 Ninety Five Percentile 558 1.5 51.9

Table: Total Indirect Costs per Enplanement

Total Indirect Costs ($/Enplanement)

Five Percentile 58.3 Ninety Five Percentile 97.9 The ranges of total income incurred by passengers flying across to hubs, considering varied ranges for each of % Mix of Passengers, Business, and Leisure Incomes are as follows:

Large/Medium Hubs

Figure-1: Total Passenger Cost Ranges-Upper Bound

P.T.O

Figure-2: Total Passenger Cost Ranges-Lower Bound

These ranges are summarized below:

Table: Upper Bound Ranges (All Values in Millions)

Minimum Maximum Mean

% Leisure 380.9 554.7 467.8 Business Earnings/Year 457.7 479.6 468.6 Leisure Earnings/Year 460.8 479.9 470.4

Table: Lower Bound Ranges (All Values in Millions)


% Leisure 206.4 346.4 276.4

Business Earnings/Year 268.8 285.4 277.1 Leisure Earnings/Year 270.8 285.9 278.3

Small Hubs


P.T.O


These ranges are summarized below:






% Leisure 375 543.9 459.5 Business Earnings/Year 449.1 474.9 462

Leisure Earnings/Year 452.5 468.7 460.6

Appendix-C Counterfactual driving calculations The ranges of total income incurred by passengers driving, considering varied ranges for each of % Mix of Passengers, Business, and Leisure Incomes are as follows.

Results Large/Medium Hubs Figure: Lower Bound Range of Total Cost to all Passengers




Minimum Maximum Mean (Change [Car-Airplane])

% Leisure 298 362.9 330.5 (-137.4) Business Earnings/Year 320.4 339.2 329.8 (-138.8) Leisure Earnings/Year 322.1 335.8 329 (-141.4)



% Leisure 41.1 50.1 45.6 (-230.8) Business Earnings/Year 44.2 46.8 45.5 (-231.6)

Leisure Earnings/Year 44.5 46.3 45.4 (-232.9) Small Hubs





% Leisure 169.9 206.4 188.2 (-271.3) Business Earnings/Year 183 193.3 188.2 (-273.8) Leisure Earnings/Year 184.5 190.3 187.4 (-273.2)



% Leisure 20.8 25.2 23 (-252.1) Business Earnings/Year 22.4 23.7 23.1 (-254) Leisure Earnings/Year 22.5 23.5 23 (-252.1)

Non- Hubs


P.T.O







% Leisure 123.1 150.3 136.8


Appendix-D

Proximity Enplanements

Total Passenger Miles

Airplane Death Risks

Car Death Risks

Fatality Benefits

Subsidy /LifeYearSaved

Lower Bound 39 691239 26958321 0.002 0.11 0.11 25115206.05 Upper Bound 558 691239 385711362 0.03 1.67 1.63 1755363.864

Appendix-E (a)

The following are the ranges, and estimates associated with the improved program, and travel to various hubs, varying the same parameters as those mentioned in Appendix-B. Large/Medium Hubs


P.T.O




% Leisure 370.3 533.2 451.8 Business Earnings/Year 443.5 459.7 451.6 Leisure Earnings/Year 444.8 459 451.9



% Leisure 199.3 331.5 265.4 Business Earnings/Year 258.7 271.2 265

Leisure Earnings/Year 259.8 271.1 265.5

Small Hubs


P.T.O







% Leisure 366 525.5 445.8 Business Earnings/Year 440.4 456.1 448.3

Leisure Earnings/Year 438.8 453.1 446

Appendix-E (b) The following are the ranges, and estimates associated with the improved program, and travel to various hubs, varying the same parameters as those mentioned in Appendix-B.

Large/Medium Hubs


P.T.O




% Leisure 324.4 409.9 367.2 Business Earnings/Year 354.4 381.8 368.1 Leisure Earnings/Year 362 372.8 367.4



% Leisure 44.8 56.6 50.7

Business Earnings/Year 48.9 52.7 50.8 Leisure Earnings/Year 50 51.4 50.7

Small Hubs


P.T.O




% Leisure 186.1 235.6 210.9 Business Earnings/Year 202.9 216.1 209.5 Leisure Earnings/Year 203.7 214.2 209



% Leisure 22.7 28.8 27.8


the essential air service program: a cost-effectiveness study

Engineering

proximity dataset data

subsidy dataset data

enplanement dataset

fares dataset

eas airports

percentile maximum minimum

percentile enplanements

enplanements dataset