Knowledge that Transforms

We study a model of the rise and fall of illiberal democracies. Voters value both liberty and economic security. In times of crisis, voters may prefer to elect an illiberal government that, by violating constitutional constraints, offers greater economic security but less liberty. However, violating these constraints allows the government to manipulate information, in turn reducing electoral accountability. We show how elements of liberal constitutions induce voters to elect illiberal governments that remain in power for inefficiently long—including forever. We derive insights into what makes constitutions stable against the rise of illiberal governments. We extend the model to allow for illiberal governments to overcome checks and balances and become autocracies. We show that stronger checks and balances are a double-edged sword: they slow down autocratization but may make it more likely. We discuss the empirical relevance of our theoretical framework and its connection to real world examples.

We develop a model of inter-temporal and intra-temporal price discrimination by monopoly airlines to study the ability of different discriminatory pricing mechanisms to increase efficiency and the associated distributional implications. To estimate the model, we use unique data from international airline markets with flight-level variation in prices across time, cabins, and markets and information on passengers’ reasons for travel and time of purchase. The current pricing practice yields approximately 77% of the first-best welfare. The source of this inefficiency arises primarily from private information about passenger valuations, not dynamic uncertainty about demand. We also find that if airlines could discriminate between business and leisure passengers, total welfare would improve at the expense of business passenger surplus. Also, replacing the current pricing that involves screening passengers across cabin classes with offering a single cabin class has minimal effect on total welfare.

Industrialization experiences differ substantially across countries. We use a benchmark model of structural change to shed light on the sources of this heterogeneity and, in particular, the phenomenon of premature deindustrialization. Our analysis leads to three key findings. First, benchmark models of structural change robustly generate hump-shaped patterns for the evolution of the industrial sector. Second, heterogeneous patterns of catch-up in sectoral productivities across countries can generate variation in industrialization experiences similar to those found in the data, including premature deindustrialization. Third, differences in the rate of agricultural productivity growth across economies can account for the majority of the variation in peak industrial employment shares.

We explore how business groups use internal labour markets (ILMs) in response to changing economic conditions. We show that following the exit of a large industry competitor, group-affiliated firms expand, and gain market share by increasing their reliance on the ILM to ensure swift hiring, especially of technical managers and skilled blue collar workers. The ability to take advantage of this shock to growth opportunities is greater in firms with closer access to their affiliates’ human capital, as geographical proximity facilitates employee relocations across units. Overall, our findings point to the ILM as a prominent mechanism making affiliation with a business group valuable at times of change. For the ILM to perform its role in the face of industry shocks, group sectoral diversification must be combined with geographical proximity between affiliates.

We propose a simple model of racial bias in hiring that encompasses three major theories: taste-based discrimination, screening discrimination, and complementary production. We derive a test that can distinguish these theories based on the mean and variance of workers’ productivity under managers of different pairs of races. We apply this test to study discrimination at a major U.S. retailer using data from 48,755 newly hired commission-based salespeople. White, black, and Hispanic managers within the same store are significantly more likely to hire workers of their own race, consistent with all three theories. For black–Hispanic pairs, productivity variance is lower for same-race pairs than cross-race pairs, implying that screening discrimination dominates. For white–Hispanic pairs, mean productivity is higher for same-race pairs, indicating a combination of screening discrimination and complementary production. For white–black pairs, biased hiring implies the presence of discrimination, but productivity results suggest the effects of the three forms of discrimination offset one another.

We investigate the bank lending channel of negative nominal policy rates from an empirical and theoretical perspective. For the empirical results, we rely on Swedish data, including daily bank-level lending rates. We find that retail household deposit rates are subject to a lower bound (DLB). Empirically, once the DLB is met, the pass-through to mortgage lending rates and credit volumes is substantially lower and bank equity values decline in response to further policy rate cuts. We construct a banking sector model and use our estimate of the pass-through of negative policy rates to lending rates as an identified moment to parameterize the model and assess the impact of negative policy rates in general equilibrium. Using the theoretical framework, we derive a sufficient statistic for when negative policy rates are expansionary and when they are not.

We present a model of online content sharing where agents sequentially observe an article and decide whether to share it with others. This content may or may not contain misinformation. Each agent starts with an ideological bias and gains utility from positive social media interactions but does not want to be called out for propagating misinformation. We characterize the (Bayesian–Nash) equilibria of this social media game and establish that it exhibits strategic complementarities. Under this framework, we study how a platform interested in maximizing engagement would design its algorithm. Our main result establishes that when the relevant articles have low-reliability and are thus likely to contain misinformation, the engagement-maximizing algorithm takes the form of a “filter bubble”—creating an echo chamber of like-minded users. Moreover, filter bubbles become more likely when there is greater polarization in society and content is more divisive. Finally, we discuss various regulatory solutions to such platform-manufactured misinformation.

We develop an economic model of transitions in and out of employment. Heterogeneous workers switch employment status when the net benefit from working, a Brownian motion with drift, hits optimally chosen barriers. This implies that the duration of jobless spells for each worker has an inverse Gaussian distribution. We allow for arbitrary heterogeneity across workers and prove that the distribution of inverse Gaussian distributions is partially identified from the duration of two non-employment spells for each worker. We estimate the model using Austrian social security data and find that dynamic selection is a critical source of duration dependence.

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level unconfoundedness and provide conditions under which aggregate balancing statistics—group-level averages of functions of treatments and covariates—are sufficient to eliminate differences between groups. Building on these results, we re-interpret commonly used linear fixed-effect regression estimators by writing them in the Mundlak form as linear regression estimators without fixed effects but including group averages. We use this representation to develop Generalized Mundlak Estimators that capture group differences through group averages of (functions of) the unit-level variables and adjust for these group differences in flexible and robust ways in the spirit of the modern causal literature.

In disseminating information, policymakers face a choice between broadcasting to everyone and informing a small number of “seeds” who then spread the message. While broadcasting maximises the initial reach of messages, we offer theoretical and experimental evidence that it need not be the best strategy. In a field experiment during the 2016 Indian demonetisation, we delivered policy information, varying three dimensions of the delivery method at the village level: initial reach (broadcasting versus seeding); whether or not we induced common knowledge of who was initially informed; and number of facts delivered. We measured three outcomes: the volume of conversations about demonetisation, knowledge of demonetisation rules, and choice quality in a strongly incentivised policy-dependent decision. On all three outcomes, under common knowledge, seeding dominates broadcasting; moreover, adding common knowledge makes seeding more effective but broadcasting less so. We interpret our results via a model of image concerns deterring engagement in social learning, and we support this interpretation with evidence on differential behaviour across ability categories.