Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Trade costs contribute to price differentials across geographically separated regions. However, when using price differential data, the identification of distance-elastic trade costs depends on how producers set prices in remote markets. To address this problem, we first empirically demonstrate that a variable markup model is more relevant than a constant markup model to describe the data variation. We then adopt a nonhomothetic preference framework to consider pricing-to-market and self-selection bias to pin down the distance effect. If these factors are not accounted for, the distance elasticity of trade costs is small. However, by incorporating these mechanisms, our empirical analysis using micro-level data reveals that the distance effect is significantly large, suggesting that the price of geographic barriers to regional trade is high.