We have daily prices for regular gasoline sold by all stations operating in the markets not subject to price ceilings. We control for the unit-cost of gasoline. Importantly we have variation in the carbon tax levied at each station. The variation in the carbon tax comes from two sources. First, the gross carbon tax (an amount of pesos per ton of carbon) is updated yearly in February with the previous year inflation, this gives us some time variation. The carbon tax is levied only on gasoline and the ethanol component of the fuel is exempt. The fuel sold at each station varies in the proportion of ethanol (it ranges from 2% to 10%). So the actual tax paid on each gallon of fuel varies depending on the proportion of ethanol. This gives us variation across stations during the same period. Importantly, as far as we know, the percent of ethanol mix is exogenous to the stations and it probably depends on the international price of sugar. But so far this is just a conjecture.
m1 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)| date+municipio|0|municipio, data=subset.reg)
m2 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+I(logcosto*integrada)| date+municipio|0|municipio, data=subset.reg)
m3 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)| date+municipio|0|municipio, data=subset.reg0)
m4 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+I(logcosto*integrada)| date+municipio|0|municipio, data=subset.reg0)
m5 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)| date+municipio|0|municipio, data=subset.reg1)
m6 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+I(logcosto*integrada)| date+municipio|0|municipio, data=subset.reg1)
#FE: DATE + CITY
screenreg(list(m1,m2,m3,m4,m5,m6),
custom.model.names = c("Gas","Gas","ACPM","ACPM","Extra","Extra"),
custom.coef.names = c("log(Tax)", "log(Cost)","VI","log(Tax)xVI","log(Cost)xVI"),
custom.note = "Market and time fixed effects",
digits = 4,stars = c(0.01,0.05,0.1), omit.coef = c("as.factor"))##
## ======================================================================================================================
## Gas Gas ACPM ACPM Extra Extra
## ----------------------------------------------------------------------------------------------------------------------
## log(Tax) 0.5302 *** 0.5313 *** 1.9425 ** 2.0311 ** 0.0245 0.0072
## (0.1687) (0.1697) (0.6812) (0.6720) (0.1668) (0.1755)
## log(Cost) 1.1153 ** 1.1203 ** 3.2917 ** 3.4318 ** 0.6780 0.6292
## (0.4003) (0.4043) (0.9911) (0.9689) (0.5216) (0.5587)
## VI 0.1782 * 0.3315 * 0.0606 ** 0.1440 *** -0.1824 -2.3880 ***
## (0.0928) (0.1585) (0.0229) (0.0282) (0.1665) (0.4209)
## log(Tax)xVI -0.0399 ** -0.0443 ** -0.0151 ** -0.0054 0.0296 0.0962 ***
## (0.0188) (0.0170) (0.0046) (0.0060) (0.0329) (0.0326)
## log(Cost)xVI -0.0145 -0.0146 ** 0.2062 ***
## (0.0171) (0.0042) (0.0387)
## ----------------------------------------------------------------------------------------------------------------------
## Num. obs. 755985 755985 37362 37362 100804 100804
## R^2 (full model) 0.6730 0.6730 0.3156 0.3156 0.8072 0.8089
## R^2 (proj model) 0.0482 0.0483 0.0286 0.0286 0.0894 0.0977
## Adj. R^2 (full model) 0.6723 0.6723 0.2960 0.2960 0.8041 0.8059
## Adj. R^2 (proj model) 0.0461 0.0462 0.0007 0.0007 0.0748 0.0833
## Num. groups: date 1595 1595 1033 1033 1571 1571
## Num. groups: municipio 17 17 6 6 17 17
## ======================================================================================================================
## Market and time fixed effectsm1 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+share| date+municipio|0|municipio, data=subset.reg)
m2 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+I(logcosto*integrada)+share| date+municipio|0|municipio, data=subset.reg)
m3 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+share| date+municipio|0|municipio, data=subset.reg0)
m4 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+I(logcosto*integrada)+share| date+municipio|0|municipio, data=subset.reg0)
m5 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+share| date+municipio|0|municipio, data=subset.reg1)
m6 = felm(logp~logimpocarb+logcosto+integrada+I(logimpocarb*integrada)+I(logcosto*integrada)+share| date+municipio|0|municipio, data=subset.reg1)
#FE: DATE + CITY
screenreg(list(m1,m2,m3,m4,m5,m6),
custom.model.names = c("Gas","Gas","ACPM","ACPM","Extra","Extra"),
custom.coef.names = c("log(Tax)", "log(Cost)","VI","log(Tax)xVI","Share","log(Cost)xVI"),
custom.note = "Market and time fixed effects",
digits = 4,stars = c(0.01,0.05,0.1), omit.coef = c("as.factor"))## Warning in chol.default(mat, pivot = TRUE, tol = tol): the matrix is either
## rank-deficient or indefinite##
## =======================================================================================================================
## Gas Gas ACPM ACPM Extra Extra
## -----------------------------------------------------------------------------------------------------------------------
## log(Tax) 0.5366 *** 0.5378 *** 2.2043 ** 2.2520 ** 0.0206 0.0033
## (0.1733) (0.1744) (0.5872) (0.5659) (0.1672) (0.1759)
## log(Cost) 1.1251 ** 1.1309 ** 3.7248 *** 3.8001 *** 0.6804 0.6316
## (0.4003) (0.4040) (0.9196) (0.8856) (0.5220) (0.5591)
## VI 0.1735 * 0.3497 ** 0.0799 * 0.1248 *** -0.1745 -2.3804 ***
## (0.0905) (0.1565) (0.0312) (0.0243) (0.1634) (0.4206)
## log(Tax)xVI -0.0384 * -0.0435 ** -0.0189 ** -0.0136 0.0278 0.0945 ***
## (0.0183) (0.0167) (0.0062) (0.0097) (0.0322) (0.0319)
## Share -0.3063 *** -0.3067 *** -0.3887 -0.3886 0.0590 0.0591
## (0.0905) (0.0908) (0.2753) (0.2754) (0.0421) (0.0412)
## log(Cost)xVI -0.0166 -0.0078 0.2062 ***
## (0.0165) (0.0062) (0.0388)
## -----------------------------------------------------------------------------------------------------------------------
## Num. obs. 755985 755985 37362 37362 100804 100804
## R^2 (full model) 0.6763 0.6764 0.3249 0.3249 0.8073 0.8091
## R^2 (proj model) 0.0580 0.0581 0.0417 0.0417 0.0901 0.0985
## Adj. R^2 (full model) 0.6757 0.6757 0.3055 0.3055 0.8042 0.8060
## Adj. R^2 (proj model) 0.0560 0.0561 0.0142 0.0142 0.0755 0.0840
## Num. groups: date 1595 1595 1033 1033 1571 1571
## Num. groups: municipio 17 17 6 6 17 17
## =======================================================================================================================
## Market and time fixed effects