monopolistic_competition_tariff: Tariff Simulation With A Monopolistic Competition Pricing...

In trade: Tools for Trade Practitioners

Tariff Simulation With A Monopolistic Competition Pricing Game

Description

Simulate the effect of tariffs when firms play a Monopolistic Competition game and consumer demand is either Logit or CES

Usage

monopolistic_competition_tariff(
  demand = c("logit", "ces"),
  prices,
  quantities,
  margins,
  mktElast = NA_real_,
  mktSize,
  tariffPre = rep(0, length(quantities)),
  tariffPost = rep(0, length(quantities)),
  priceOutside = ifelse(demand == "logit", 0, 1),
  labels = paste("Prod", 1:length(quantities), sep = "")
)

Arguments

demand	A character vector indicating which demand system to use. Currently allows “logit" or “ces" .
prices	A length k vector product prices. Default is missing, in which case demand intercepts are not calibrated.
quantities	A length k vector of product quantities.
margins	A length k vector of product margins. All margins must be either be between 0 and 1, or NA.
mktElast	A negative number no greater than -1 equal to the industry pre-tariff price elasticity. Default is NA .
mktSize	A positive number equal to the industry pre-tariff market size. Market size equals total quantity sold,including sales to the outside good.
tariffPre	A vector of length k where each element equals the current ad valorem tariff (expressed as a proportion of the consumer price) imposed on each product. Default is 0, which assumes no tariff.
tariffPost	A vector of length k where each element equals the new ad valorem tariff (expressed as a proportion of the consumer price) imposed on each product. Default is 0, which assumes no tariff.
priceOutside	price of the outside good. Default 0 for logit and 1 for ces. Not used for aids.
labels	A k-length vector of labels.

Details

Let k denote the number of products produced by all firms. Using price, and quantity, information for all products in each market, as well as margin information for at least one products in each market, monopolistic_competition_tariff is able to recover the slopes and intercepts of a Logit demand system. These parameters are then used to simulate the price effects of an ad valorem tariff under the assumption that the firms are playing a monopolisitcally competitive pricing game

Value

monopolistic_competition_tariff returns an instance of class TariffMonComLogit , depending upon the value of the “demand” argument.

References

Simon P. Anderson, Andre de Palma, Brent Kreider, Tax incidence in differentiated product oligopoly, Journal of Public Economics, Volume 81, Issue 2, 2001, Pages 173-192. Anderson, Simon P., and André De Palma. Economic distributions and primitive distributions in monopolistic competition. Centre for Economic Policy Research, 2015.

See Also

bertrand_tariff to simulate the effects of a tariff under a Bertrand pricing game.

Examples

## Calibration and simulation results from a 10% tariff on non-US beers "OTHER-LITE"
## and "OTHER-REG"
## Source: Epstein/Rubenfeld 2004, pg 80

prodNames <- c("BUD","OLD STYLE","MILLER","MILLER-LITE","OTHER-LITE","OTHER-REG")
price    <- c(.0441,.0328,.0409,.0396,.0387,.0497)
quantities   <- c(.066,.172,.253,.187,.099,.223)*100
margins <- c(.3830,.5515,.5421,.5557,.4453,.3769)
tariff <- c(0,0,0,0,.1,.1)

names(price) <-
 names(quantities) <-
 names(margins) <-
 prodNames


result.logit <- monopolistic_competition_tariff(demand = "logit",prices=price,quantities=quantities,
                                margins = margins,
                                 tariffPost = tariff, labels=prodNames)

print(result.logit)           # return predicted price change
summary(result.logit)         # summarize merger simulation

result.ces <- monopolistic_competition_tariff(demand = "ces",prices=price,quantities=quantities,
                                margins = margins,
                                 tariffPost = tariff, labels=prodNames)

print(result.ces)           # return predicted price change
summary(result.ces)         # summarize merger simulation

trade documentation built on Aug. 24, 2022, 9:06 a.m.