The following graph shows the supply and demand curves for Airbnb rentals in the hypothetical economy of Hosttown in 2010, two years after Airbnb launched; the equilibrium quantity of rentals was 80 rooms per day, and the equilibrium price was $140 per room. At that time, Hosttown was enforcing tax regulations on the market for hotels, but it hadn't yet initiated a tax arrangement on room rentals through Airbnb. On the following graph, use the green rectangle (triangle symbols) to indicate the tax revenue the government could have collected in 2010 if it had levied a $15perroom tax on Airbnb rentals. (Note: You will not be graded on your placement of this area on the graph.) 200 Demand2010 Supply 2010 190 Potential Tax Revenue 180 170 160 PRICE (Dollars per rental) 150 140 130 120 110 100 0 20 40 60 80 100 120 140 160 180 2 RENTALS (Rooms per day)Realizing that there is a great potential for increased tax revenue, government officials in Hosttown began discussing how they could align Airbnb rentals with hotel stays from a tax perspective. Fastforward to 2018, at which time Hosttown has finally made tax arrangements with Airbnb to levy a $15perroom tax on rentals. However, now the market conditions have changed. More hosts have now entered the Airbnb market, and awareness of this hotel alternative has increased demand. The following graph shows the demand and supply curves for Airbnb rentals in 2018. Use the green rectangle (triangle symbols) to illustrate the area representing the revenue raised by a $15perroom tax. Then use the black point (cross symbol) to shade the area representing the deadweight loss generated by this tax. 200 Demand2018 H 190 1&0 _ Tax Revenue suPp'yzm a 160 Deadweight L055 150 __ Tax Wedge 140 -----+ PRICE (Dollars per rental) 130 120 110 100 l l | | | l l | 0 20 40 60 80 100 120 140 160 180 200 RENTALS (Rooms per day) The demand for Airbnb rooms has not only shifted to the right, but it has also become relatively more V elastic since 2000. Comparing the market for Airbnb rentals for the two years is complicated by the fact that the graph depicts three changes: total demand increases, total supply increases, and the slope of the demand curve changes. To isolate the effect of elasticity on deadweight loss and government revenue, consider the following scenario: Comparing the market for Airbnb rentals for the two years is complicated by the fact that the graph depicts three changes: total demand increases, total supply increases, and the slope of the demand curve changes. To isolate the effect of elasticity on deadweight loss and government revenue, consider the following scenario: Suppose the government wants to estimate the tax revenues from room rentals for 2030, and economic models predict two different scenarios (A and B), each with a different demand curve (labeled Demand/1. and DemandB, respectively, on the following graph). Use the objects to the right of the graph to help you determine the potential deadweight loss and revenues generated by the same $15 tax in 2030 under each scenario and enter these values into the following table. (Note: You will not be graded on your placement of any of the objects on the graph.) PRICE (Dollars per rental) 200 190 180 170 160 150 140 130 120 110 100 20 40 Supply 30 l l l l 60 80 100 120 140 RENTALS (Rooms per day) | 160 | 180 | 200 Tax Revenue 3-4} Deadweight Loss Deadweight Loss Tax Revenue Scenario (Dollars per day) (Dollars per day) A 75 2,550 B 150 2,400 Under scenario A, demand is relatively less V elastic, and the tax results in a smaller V deadweight loss and higher V government revenue than under scenario B. This suggests that, all other things being equal, the government should tax industries with a relatively lower V elasticity of demand if it wants to minimize deadweight loss