dependent variable. Columns 2 and 3 report Tobit-type of estimates for (3). The “standard” Tobit estimates in column 2 account more appropriately for the censorship of the dependent variable. Now the dependent variable is the log of imports plus one dollar, implying that av=1. However, as discussed before, all coefficient values, are very sensitive to the choice of av used to avoid truncation. Column 3 goes one step further and reports the ET-Tobit estimates that also estimates the value of av that fits best the data. Column 4 and 5 report estimates for the outcome and the selection equations of the Heckman selection model. Finally, column 5 reports estimates when applying the Poisson Pseudo Maximum Likelihood (PPML) model recommended by Santos Silva and Tenreyro (SS-T) (2006) to deal with heteroskedastic errors in log-linear gravity models.

# Table 7 here

Most coefficients are significant with reasonable magnitudes and expected sign across estimation methods. Among the standard gravity variables, distance is negative and statistically significant, while a colonial relationship (statistically significant) and common official language (statistically significant) are both positive. The coefficient of the applied tariff is negative and significant except in the last column for the PPML, confirming the negative impact tariffs have on imports. A possible explanation for the sign reversal when using the PPML method lies on the fact that the dependent variable is in level rather than logarithmic form, giving more weight to extreme observations and, the loss in precision being attributable to giving probably too much weight to very noisy data at the level at which product categories are defined.

We find that all estimated coefficients for the count of internationally harmonized standards in each category of electronics goods are positive and significant across all columns. The results are less homogenous for non-harmonized variables. Although coefficients for non-harmonized standards in category 3 are all positive, the nonharmonized standards coefficients in categories 1 and 2 have different signs across estimation methods. However, when non-harmonized standard coefficients are positive, their magnitude is always smaller than corresponding coefficients for harmonized standards<sup>25</sup> , as confirmed more formally when carrying out hypothesis tests (not reported here)<sup>26</sup> .

> 25 The single exception is the coefficient for category 1 in column 5, but it is the selection equation of the Heckman selection model.

> 26 As standards harmonization seems to reduce the informational asymmetries between producers and buyers, the effect should be larger for products that suffer the most from these asymmetries. A possible avenue for future extension of this research is to test this hypothesis using variables of technical complexity for our defined categories as proxies of the scope for informational asymmetry, as suggested by an anonymous referee. A similar identification challenge was faced by Kugler and Verhoogen (2008), who use the ratio of total industry advertising and R&D to total industry sales for the US firms from the 1975 U.S. Federal Trade Commission (FTC) Line of Business Survey as a measure of the scope for quality differentiation.

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