https://macropaperwarehouse.com/authors/daron-acemoglu/Daron AcemogluHugo -- gohugo.ioen-ushttps://macropaperwarehouse.com/papers/automation-and-rent-dissipation-implications-for-wages-inequality-and-productivity/https://macropaperwarehouse.com/papers/automation-and-rent-dissipation-implications-for-wages-inequality-and-productivity/<p>Acemoglu and Restrepo examine the effects of automation in economies where labor market distortions cause some workers to earn rents—wages above their opportunity cost or outside option. The central question is how the interplay between automation and these distortions shapes wages, inequality, and productivity. The paper makes three contributions: a theoretical framework identifying a rent dissipation mechanism, reduced-form empirical evidence using US data from 1980 to 2016, and a general equilibrium quantification of automation&rsquo;s aggregate effects.</p>https://macropaperwarehouse.com/papers/online-business-models-digital-ads-and-user-welfare/https://macropaperwarehouse.com/papers/online-business-models-digital-ads-and-user-welfare/<p>Acemoglu, Huttenlocher, Ozdaglar, and Siderius develop a two-sided platform model to study the welfare consequences of digital advertising as an online business model. The platform intermediates between a firm selling a horizontally differentiated product and a continuum of users who derive utility from both entertaining content and informative signals about product quality embedded in ads. Users have a two-dimensional type: a sophistication dimension (sophisticated with probability lambda, naïve with probability 1-lambda) and a product-quality dimension (high quality with prior probability q). The central departure from the standard informational-advertising literature is that sophisticated users hold the correct model of the ad signal process, while naïve users underestimate the false-positive rate — the probability that a low-quality product generates a positive ad signal (phi_0). Naïve users perceive this false-positive rate to be phi_{0,N} = omega_N * omega_P * phi_0, where omega_N &lt;= 1 captures inherent naïveté and omega_P &lt;= 1 captures failure to understand personalized targeting, so phi_{0,N} &lt; phi_0. The equilibrium concept is Berk-Nash equilibrium (Esponda and Pouzo 2016), meaning all agents are Bayesian given their subjective model.</p>