Dear Statalists, my sample is an unbalanced worldwide panel data sample of around 2000 firms in the period 2001-2015. The dependent variable is the first-time adoption of a firm-level corporate governance policy. More specifically, the dependent variable equals 1 for the firm-year observation in which a firm adopts the policy for the first time and 0 for the firm-year observations before the first adoption. If a firm never adopts the policy during the period of analysis, the variable is coded as 0 for all available years. Also, I remove a firm from the sample after it adopts the policy. Thus, the dependent variable is truncated.
As the main independent variable is at the country-level (i.e., a proxy of the country’s regulatory pressure), I was thinking of using a multilevel logit, with firm (level 1) and country (level 2) as clusters.
First, I have run the unconditional means model, to examine to what extent each level of the analysis explains the variance in the dependent variable (between-level analysis) (Raudenbush & Bryk, 2002; Aguinis et al., 2013). However, because of the specific features of the dependent variable (a truncated variable that takes the values of 0 until the event occurs), the variance partition coefficient of the cluster ‘firm’ tends to 0. In other words, the unconditional means model suggests not to include the cluster ‘firm’. Thus, the idea was not to use such a model, and prefer a logit model, adjusting the standard errors to consider both the cluster ‘country’ and the cluster ‘firm’ (using the Stata command vcemway, see Gu and Yoo, 2019). Would you agree with this approach?
Thank you in advance for your help


Code:
melogit DEPVAR  || country_cluster: || firm_cluster: