Differentiated products demand systems are a workhorse for understanding the price effects of mergers, the value of new goods, and the contribution of products to seller networks. Berry, Levinsohn, and Pakes (1995) provide a flexible random coefficients logit model which accounts for the endogeneity of prices. This article reviews and combines several recent advances related to the estimation of BLP-type problems and implements an extensible generic interface via the PyBLP package. Monte Carlo experiments and replications suggest different conclusions than the prior literature: multiple local optima appear to be rare in well-identified problems; good performance is possible even in small samples, particularly when "optimal instruments" are employed along with supply-side restrictions.

We delineate a general framework for incorporating many types of micro data from summary statistics to full surveys of selected consumers into Berry, Levinsohn, and Pakes (1995)style estimates of differentiated products demand systems. We extend recommended practices for BLP estimation in Conlon and Gortmaker (2020) to the case with micro data and implement them in our open-source package PyBLP. Monte Carlo experiments and empirical examples suggest that incorporating micro data can substantially improve the finite sample performance of the BLP estimator, particularly when using well-targeted summary statistics or "optimal micro moments" that we derive and show how to compute.

We provide the first analysis of workers' on-the-job networking activity following their firm's credit deterioration. Using high-frequency networking on LinkedIn, we show that workers initiate more connections immediately following adverse credit shocks. We propose a simple model in which workers are driven by concerns about both unemployment and reduced future prospects at their firm. Consistent with this model and distinct from prior work, we find that the stronger response of high-value workers is magnified when the firm is far from bankruptcy. We further show that elevated networking activity is associated with departures and diminished profitability in following years, consistent with on-the-job networking being a source of fragility for firms.