Feodora Teti (ifo Institute)
Many studies use tariffs to measure changes in trade policy. This paper shows that standard sources for tariffs suffer from substantial measurement error due to misreporting and the resulting false imputation: Countries fail to report tariffs every year and missing data are more prevalent for preferential than for most favored nation (MFN) tariffs. WITS, the main data provider for tariffs, falsely interpolates missing preferential tariffs with MFN tariffs. This practice leads to artificial spikes in bilateral time series data and, hence, induces massive measurement error. I introduce a new global tariff dataset at the six-digit product level for 197 countries and 30 years that combines five different sources for tariffs and proposes a new interpolation algorithm taking the misreporting into account. Lastly, I show using gravity that correcting for the messy data increases the estimates of the trade elasticity by 2.89 times.
Lecture Hall (A-032)