In Lieu of the Thursday February 29t in-class assignment, please answer the following questions. Due no later than Friday March 8" midnight. 1. Use the data provided to you for the most recent consistent year available (2022), estimate an airline service demand and supply relationship. For demand, include airline ticket price and household income as variables determining the quantity of trips/flights. For supply. Include ticket price and revenues as variable determining quantity supplied. Assume the observed 2022 ticket price and trips/flights are an equilibrium. 2. Suppose there is a $25 per ticket tax imposed on airline travel. What would be the new ticket price and quantity? How much of this tax would be paid by consumers and how much would be paid by producers? 3. Suppose that household income is expected to increase by 10% in 5 years. Show the effect on a graph assuming all other variables remain constant. Derivation of Demand from Known Elasticities: Example Price elasticity of demand: Eqs=-0.20 Income elasticity of demand: ,= 0.90 Current Price P = $100 Current Consumption Q = 4,000 Current Income | = 510 With the above information can estimate a linear demand curve: Q%= a + bP + l We know -0.20 = dQ%/dp * p/q 0Q%/dp represents coefficient b which is the impact of price on Q So: -0.20 = b*(100/4,000) so b = -8 We know 0.90 = 8Q/d! * I/q 0Q\"/d1 represents coefficient which is the impact of income on Q So: 0.90 = c*(10/4,000) so c = 360 We know Q%= a + bP + l So: a=Q%-bP-cl a =4,000 (-8 * 100) (360 * 10) = 4,000 +800 3,600 = 1,200 Estimated demand curve is: Q%= 1,200 - 8P + 360I Can use the same basic approach for supply Your airline data is below A demand curve can be estimated from the data below. I~ . . b 2y . ' T ?u&sf/'o ns * Annual U.S. Domestic Average Itinerary Fare in Current and Constant Dollars BTS reports average fares based on domestic itinerary fares. Itinerary fares consist of round-trip fares, unless the customer does not purchase a return trip. In that case, the one-way fare is included. Fares are based on the total ticket value, which consists of the price charged by the airlines plus any additional taxes and fees levied by an outside entity at the time of purchase. Fares include only the price paid at the time of the ticket purchase and do not include fees for optional services, such as baggage fees. Averages do not include frequent-flyer or \"zero fares.\" Constant 2022 dollars are used for inflation adjustment. Inflation-Adjusted (2023 constant dollars*) Unadjusted (current dollars) Percent Change Percent Change From Cumulative Average From Previous Cumulative Average Fare Previous from 1995 Year Fare ($) Year (%) from 1995 (%) ($) Year (%) (%) 1995 583 292 1996 536 -8.0 -8.0 277 -5.3 -5.3 1997 544 1.5 6.7 287 3.8 -1.7 1998 576 6.0 -1.1 309 Te 5.8 1999 591 2.5 1.4 324 47 10.8 2000 598 1.3 2.7 339 4.7 16.0 2001 550 -8.0 -5.6 321 -5.4 9.7 2002 528 -4.1 -9.4 312 -2.6 6.9 2003 521 -1.3 -10.6 315 1.0 7.9 2004 491 -5.7 -15.7 305 -3.2 4.5 2005 478 2.7 -17.9 307 0.6 5.2 2006 495 3.6 -15.0 329 6.9 12.4 2007 477 -3.8 -18.2 325 -1.0 11.3 2008 489 2.6 -16.1 3486 6.5 18.5 2009 440 -10.1 -24.6 310 -10.4 6.2 2010 468 6.5 -19.6 336 8.3 15.0 2011 492 49 -15.6 364 8.3 245 2012 496 0.9 -14.9 375 3.0 28.3 2013 501 1.0 -14.0 384 2.5 31.5 2014 209 1.8 -12.7 396 3.2 35.7 2015 486 -4.4 -16.6 379 -4.3 29.8 2016 449 -7.7 -23.0 355 6.5 21.3 2017 430 -4.2 -26.2 347 2.1 18.8 2018 423 -1.7 -27.4 350 0.7 19.6 2019 419 -1.0 -28.1 352 0.8 20.6 2020 343 -18.0 -41.1 292 -17.0 0.0 2021 344 0.3 -40.9 307 5.0 5.1 @ 14.1 326 378 23.2 29.4 2023 380 -3.1 -34.7 380 0.6 302 SOURCE: Bureau of Transportation Statistics * Rate calculated using Bureau of Labor Statistics Consumer Price Index. Note: Percent change based on unrounded numbers AIRRPMTSID11 Frequency: Annual Air Revenue Passenger Miles, Thousands Annual, Seasonally observation_date Adjusted 2000-01-01 58,989,398 2001-01-01 55,463,991 2002-01-01 54,688,746 2003-01-01 56,238,657 2004-01-01 62,559,344 2005-01-01 66,323,871 2006-01-01 67,518,412 2007-01-01 70, 180,760 2008-01-01 68,515,423 2009-01-01 64,976,792 2010-01-01 67,398,554 2011-01-01 68,872,666 2012-01-01 69,286, 159 2013-01-01 70,694,626 2014-01-01 72,488,061 2015-01-01 75,724,498 2016-01-01 78, 129,578 2017-01-01 80,824,790 2018-01-01 84,788,903 2019-01-01 88,453, 133 2020-01-01 31,888,336 2021-01-01 57,709,614 2022-01-01 79,470,953 2023-01-01 #NIA Data from all U.S. air carriers Flight Quantity This data is collected by the U.S. Department of Transportation, Bureau of Transportation Statistics (BTS). U.S. Bureau of Transportation Statistics, Air Revenue Passenger Miles [AIRRPMTSID 11], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred. stlouisfed.org/series/AIRRPMTSID11.Table 1-38: Average Length of Haul, Domestic Freight and Passenger Modes (miles) 1960 1965 1970 1975 1980 1985 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2005 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Freight Air camer N N N Z z ? N 1,307 1,496 1,478 1,580 1,565 1 1,441 1,115 1,105 1 1,241 1,218 1,218 1,220 1,245 1,162 1,157 1,150 1,174 1,153 1,161 1,165 1,166 1,202 1,217 1,212 1,270 1,298 1,308 Class Ira 461 503 515 616 726 751 763 817 843 842 851 835 835 862 902 893 906 919 918 4 917 73 990 1,006 1,020 1,021 1,033 1,046 1,032 1,037 1,043 Coastwise (water) 1,496 1,501 1,509 1,362 1,915 1,972 1,605 1,705 1,762 1. 1,652 32 1,525 1,380 1,261 1,279 1,251 1,228 1,219 1,248 1,269 1,233 1,126 1,108 1,116 1,170 1,163 1,119 1,032 993 1,003 1,003 1,018 1,073 1,084 1,065 1,152 1,138 Lakewise (water) 522 494 506 530 536 535 508 514 508 74 563 577 282 297 330 358 868 8322 8985-22 Internal (water) 8 86 : 2 2 $85922 289-22 $9 922 286272 Intraport (water) N N Crude (oil pipeline) 325 300 N Petroleum products (oil pipeline) 269 335 357 N N N Passenger Air carrier, domestic, scheduled 583 614 678 698 7:30 806 872 878 883 885 895 906 917 922 927 928 944 Commuter rail 8 8 8 Amtrak' NA 218 miles pert lightFRED Graph Observations Federal Reserve Economic Data Link: https://fred.stlouisfed.org Help: https:/fredhelp.stlouisfed.org Economic Research Division Federal Reserve Bank of St. Louis Frequency: Annual Real Median Household Income in the United States, 2022 CPI-U-RS Adjusted observation_date Dollars, Annual 2000-01-01 $67,470 2001-01-01 $66,360 2002-01-01 $65,820 2003-01- $65,860 2004-01-01 $65,760 2005-01-01 $66,780 2006-01-01 $67,520 2007-01-01 $68,610 2008-01-01 $66,280 2009-01-01 $65,850 2010-01-01 $64,300 2011-01-01 $63,350 2012-01-01 $63,350 2013-01-01 $65, 740 2014-01-01 $64,900 2015-01-01 $68,410 2016-01-01 $70,840 2017-01-01 $72,090 2018-01-01 $73,030 2019-01-01 $78,250 2020-01-01 $76,660 2021-01-01 $76,330 2022-01-01 $74,580Table 1: Estimated Price Elasticities of Passenger Demand Route/Market level National level Supra-national level Short-haul Long-haul Short-haul Long-haul Short-haul Long-haul Intra N America -1.5 -1.4 .0.9 8'0- -0.7 0.6 Intra Europe 2.0 -2.0 -1.2 -1.1 0.9 -0.8 Intra Asia -1.5 -1.3 0.8 -0.8 -0.6 0.6 Intra Sub -0.9 0.8 -0.5 -0.5 -0.4 -0.4 Saharan Africa Intra S America 1.9 1.8 -1.1 1.0 0.8 0.8 Trans-Atlantic -1.9 -1.7 -1.1 -1.0 -0.8 -0.7 Trans-Pacific -0.9 0.8 -0.5 0.5 0.4 .0.4 Europe-Asia -1.4 1.3 -0.8 -0.7 -0.6 -0.5 Table 2: Estimated income elasticities of passenger demand Route / Market Short-haul Medium-haul Long-haul Very long-haul level US 1.8 1.9 2.0 2.2 Developed 1.5 1.6 1.7 2.4 economies Developing 2.0 2.0 2.2 2.7 economies National level Short-haul Medium-haul Long-haul Very long-haul US 1.6 1.7 1.8 2.0 Developed 1.3 1.4 1.5 2.2 economies Developing 1.8 1.8 2.0 2.5 economies Source for Table 1 and Table 2. Mark Smyth and Brian Pearce. "Air Travel, Measuring the responsiveness of air travel demand to changes in prices and incomes." IATA Economics Briefing No 9. April 2008Price Elgsticit _of Dem: for U.S. Air Travel 163 4.34%, and the estimated price elasticity of demand would be -3.64. Therefore, the four samples produce equivalent results, indicating the robustness of the results. Conclusions The application of the QEM under different sample designs enables achievement of the main objective of the present study, estimation of the arc price elasticity of demand for the air transportation of passengers on domestic routes in the U.S. Among the sample designs analyzed, the selection of observations with a minimum differential price growth of 10% in one quarter aver the previous quarter gives a point estimate(of -0.72 and a 95% confidence interval estimate of -0.62 to-0.83.7 requirement that observations be supported by data on changes in the supply of air- line seats on the route leads to an additionally justified and well-aligned estimate of the price elasticity of demand: -0.70 (- 0.64; -0.76). It is noteworthy that samples 3 and 4, based on the exceptional period of the COVID-19 impact and a supply shock from the withdrawal of an aircraft model, yield an equal value, making the result considerably robust. Demand for passenger air transport on U.S. domestic routes is inelastic and is approximately -0.70. Comparison with results from other methods is of limited value due to the hetero- geneous estimation procedure used. The literature offers a broad variety of results. Jung and Fujii (1976) found a price elasticity of demand in the interval of -1.77 to -3.15; interVISTAS (2007) estimated the price elasticity of demand at the route level to be in the range of -1.2 to -1.5 (the national market level =-0.8). Gundelfinger Casar (2017) found a valuc of -0.62. Oum et al. (1990) reported cstimates in the interval from -0.8 to -2.1. Brons et al. (2002) estimated the average price elasticity of demand at -1.146 (values from -3.20 to 0.21). Smyth and Pearce (2008) estimated the elasticity of demand at the route level between -1.2 to -1.5. Richard (2009) quan- tified a clearly inelastic demand and Chi et al. (2010) found that "city-pair markets range from 1.2% to 1.5% for 2000 and from 2.5% to 3.3% for 2005" (p. 89). The Department for Transport (2017) in a United Kingdom (UK) environment reflects an elasticity of -0.6 (UK business travel =-0.2, UK leisure travel =-0.7). In summary, our estimate is not outside the range found in the scientific litera- ture, although it is in its lower range. However, we believe that there are two impor- tant factors that support the inelasticity of passenger demand for air travel. The first is that the share of spending on airfare has been declining as a percentage of total consumer income, and this decline implies lower price sensitivity. The second is that the share has also declined as a proportion of total travel expenditure due to the relative cheapening of airfares since airline liberalization, with similar consequences (Department for Transport, 2017; Pearce, 2008). In conclusion, we believe that this research applies a methodological approach that provides a more accurate approximation of the true value of the elasticity of demand in air passenger transport beyond regression analyses (Brons et al., 2002). Moreover, this study also rightly rules out the reliability of estimates made with- out data support that point to the existence of a supply shock only, Estimating the price elasticity of demand during the epidemic period is almost impossible due to @ Springer CA. Gallet, H. Doucouliagos/Annals of Tourism Research 49 (2014) 141-155 153 routes, yet significantly lower when air fare is included in a dynamic specification of demand. Other features, such as most measures of income, as well as the method used to estimate demand, have less influence the income elasticity. There are several benefits to now having a better understanding of how changes in consumer inco he_demand for air travel. For instance, given that-we-find-for-our-preferred IMRA that the income elasticity)is1.186 on baseline domestic routes, yet 1.546 on international routes,)ceteris paribus, this \"Trtch Tiore volatile to changes in in ~Assuch, dur- ing periods of rising income, the distribution of demand will shift away from domestic towards inter- national travel, whilst the opposite holds during periods of falling income. Knowing this, airlines can better adjust marketing strategies in response to income shocks. Also, given that we find the baseline income elasticity falls when air fare is included in a dynamic specification of demand, controlling for these features of demand has an impact on air travel forecasts. For instance, assuming the correct specification of demand should be dynamic and include air fare, then failure to do so would lead to upward bias in air travel forecasts in the presence of positive income shocks. Indeed, with the baseline income elasticity falling from 1.186 to 0.633 when air fare is included in a dynamic specification of demand, a 10 percent increase in income is predicted to increase air travel by 11.86 percent in the for- mer, yet only 6.33 percent in the latter. Not only does this have a sizeable impact on forecasts, but fail- ure to include air fare in a dynamic specification of demand would also lead one to conclude air travel is less mature than it is in reality, thus affecting strategic planning decisions. Lastly, our MRA results are of specific benefit to researchers in several ways. First, while our results suggest both air fare and income should be included in the specification of air travel demand, the choice of income measure matters less. Second, the multiple MRA results show the literature is free of publication selection bias, and so there is no preferential reporting of income elasticities. This stands in sharp contrast to the situation in many other areas of economics and business literature. Third, based on the results, future primary research may wish to consider why some factors influence income elasticities, whilst others do not. For instance, it would be useful to further examine contem- poraneous differences in income elasticities across regions of the world, to see whether or not our fail- ure to identify specific regional differences is simply a historical artifact of the literature. Also, although our literature review indicates that North American income elasticities have slowly increased over time, ceteris paribus, it would be interesting to examine in greater detail what is driving this trend. As markets increasingly mature worldwide, it may be that future studies estimate lower income elasticities of demand. References Alperovich, G., & Machnes, V. (1994). The role of weaith in the demnand for international air travel. Journal of Transport Economics and Policy, 28(2), 163-173. Anderson, |. E., & Kraus, M. (1981). Quality of service and the demand for air travel, Review of Economics and Statistics, 63(4), 533-540. Angrist, J. D., & Pischke, ].-S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton, NJ: Princeton University Press. Bechdolt, B. V. Jr., (1973). Cross-sectional travel demand functions: US visitors to Hawaii, 1961-70. Quarterly Review of Economics and Business, 13(4), 37-47. Bhadra, D. (2004). Air travel in small communities: An econometric framework and results. Journal of the Transportation Research Forum, 43(1), 19-37. Behbehani, R., & Kanafani, A. (1980). Demand and supply models of air traffic in international markets. Manuscript. Boeing (2013). Current market outlook: 2013-2032. Seattle: Boeing. Britto, R., Dresner, M., & Voltes, A. (2012), The impact of flight delays on passenger demand and societal welfave. Transportation Research Part E, 48(2), 460-469. Brons, M., Pels, E., Nijkamp, P., & Rietveld, P. (2002). Price elasticities of demand for passenger air travel: A meta-analysis, Journal of Air Transport Management, 8(3), 165-175. Brown, ., & Watkins, W. (1968). The demand for air travel: A regression study of time-series and cross-sectional data in the U.S. domestic market. Highway Research Record, 213, 21-34, Cameron, A. C., Gelbach, J. B., & Miller, D. L.. (2008). Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics, 90(3), 414-427. Castelli, L., Pesenti, R., & Ukovich, W. (2003). An airline-based multilevel analysis of airfare elasticity for passenger demand. Manuscript, Chi, J., & Baek, J. (2012). A dynamic demand analysis of the United States air-passenger service. Transportation Research Part E: Logistics and Transportation Review, 48(4), 755~761. Table 5. Jan-Dec U.S. Scheduled Domestic Passenger Airlines Revenue, Expenses and Profits Reports from 25 airlines in 2022 ( millions of dollars) % of YTD 2022 Revenue or Jan-Dec Jan-Dec 2021-2022 Expense 2021 202 Change % Change Total Operating Revenue Passenger Fares (scheduled/charter) 71,363.1 117, 108.2 45,745.0 64. 10 71.46 Cargo 1,221.7 1,327.4 105.6 8.65 0.81 Baggage 4,312.2 5,451.8 1, 139.6 26.43 3.33 Reservation Changes 610.4 873.9 263.6 43. 18 0.53 Transport-Related* 22,383.6 29,226.3 6,842.7 30.57 17.83 Other** 7,445.9 9,899.1 2,453.2 32.95 6.04 Total Operating Revenue*** 107,337.0 163,886.6 56,549.7 52.68 100.00 Operating Expense Fuel 17,354.7 34,641.1 17,286.4 99.61 22.06 Labor 41,868.6 49,325.5 7,456.9 17.81 31.41 Rentals 9,398.4 8,847.7 -550.7 -5.86 5.63 Depreciation & Amortization 7,626.0 7,462.9 -163.1 -2.14 4.75 Landing Fee 2,922.3 2,932.7 10.4 0.36 1.87 Maintenance Materials 2,066.2 2,645.6 579.4 28.04 1.68 Transport-Related* 16,905.6 22,219.1 5,313.5 31.43 14.15 Other* * * * 21,365.0 28,962.1 7,597.1 35.56 18.44 Total Operating Expense 119,506.7 157,036.7 37,530.0 31.40 100.00 Profits or Losses Operating Profit -12,169.7 6,849.9 19,019.7 -156.29 N/A Operating Margin# (%) -11.3 4.2 15.5 N/A N/A Nonoperating Income/(Expense)## 12, 164.5 -4194.296 -16,358.8 -134.48 N/A Pre-Tax Income -5.2 2,655.6 2,660.8 -51,186.99 N/A Income Tax Benefit/(Expense) 385.4 -808.9 -1, 194.3 -309.86 N/A Other Income/(Expense) 2.8 0.0 -2.8 0.00 N/A Net Income 383.0 1,846.8 1,463.8 382.16 N/A Net Margin### (%) 0.4 1.1 0.8 N/A NIA Source: Bureau of Transportation Statistics, Form 41; Schedules P1.2 http://www.transtats.bts.gov/Fields.asp?Table_ID=295 and P6 http://www.transtats.bts.gov/Fields.asp?Table_ID=291Price elasticity of supply Transportation based studies Bruce Schaller (1999), \"Elasticities for Taxi Cab Fares and Service Availability,\" Transportation, Vol. 26, 1999, pp. 283-297. e The price elasticity of supply for taxi service was estimated to be equal to 1.0. D. Coyle, J. DeBacker, Prisinzano R. Energy Econ., Vol. 34 (2), 2012, pp. 195-200. e Estimating the supply and demand of gasoline using tax data, the price elasticity of supply for gasoline is estimated to be 0.290. J.J. Evans (1988) The Elasticity of Supply of Transport, Maritime Policy & Management, 15:4, 309-313, DOIL: 10.1080/03088838800000008. e Although the elasticity of supply for transportation in the maritime industry can vary considerably depending on season, supply chain issues, etc., average supply elasticity is virtually constant with a value of about 0.5 in the region studied. You may want to do a quick internet search for more airline specific airline supply elasticity estimates, but I was unable to find specific estimates. For the purposes of estimating a supply curve, can use the average of the three estimates above. Also assume that the revenue elasticity of supply is 1.0 (a 1% increase in revenues leads to a 1% increase in the quantity supplied. PSS eeas