Meet with labor unions, law firms, and organizations dedicated to worker rights to learn about summer internship and full-time opportunities. NEW this year: Engage in informal chats with ILR alumni to explore careers in the labor movement, hear directly about the work, and build meaningful connections.

Ben Scuderi Market Definition Bias in Studies of (Labor) Market Power Abstract: This paper demonstrates two distinct and quantitatively important biases introduced by using an “incorrect" definition of market boundaries when attempting to make inferences about labor market power. The first source of bias, long recognized in the antitrust literature, stems from mismeasurement of relative firm size: the same firm will appear artificially dominant when markets are drawn too narrowly and artificially competitive when they are drawn too broadly. We derive a novel second source of bias, which we term elasticity bias, that generates statistical attenuation of estimates of key parameters that govern model-based conclusions about the size and distribution of markdowns across employers and markets. In simulations calibrated to Brazilian administrative data, we show that the second channel is an order of magnitude more important than the first. Further, we show that market definition bias can be large in empirically-relevant cases where the relative rate of misclassification may be modest, as with administrative labor-market boundaries such as industry/occupation-region cells adopted by virtually all existing studies. We propose an alternative network-based procedure for defining labor market boundaries that extends the algorithm of Fogel and Modenesi (2022). Drawing upon the empirical strategy of Felix (2022), we show that relative to using administrative market definitions, using network-based market definitions yields estimates with 40% larger markdown dispersion and overturns several qualitative conclusions about which workers are harmed by monopsony power. Finally, we propose a simple diagnostic that allows practitioners to pick among off-the-shelf classifications when using a data-driven one is infeasible.

Mary Ann Bronson