Knowledge that Transforms

Abstract I propose a model of skill-replacing routine-biased technological change (SR-RBTC). In this model, technology substitutes for the use of skill in routine tasks, in contrast to standard RBTC models, which assume that technology replaces the workers themselves. The SR-RBTC model explains three key trends that are inconsistent with standard RBTC models: (1) why specifically middle wages declined even though workers in routine occupations are dispersed across the entire bottom half of the wage distribution, (2) why middle wages stopped declining while technological change continued, and (3) why there is no substantial decline in the average wage of workers in routine occupations. I derive two new testable predictions from the model: a decrease in the return to skill and a decrease in skill level in routine occupations. I use an interactive fixed-effects model to confirm both predictions. Since SR-RBTC violates the ignorability assumption required by standard decomposition methods, I introduce a “skewness decomposition” to show that SR-RBTC is the main driver of bottom-half inequality trends.

Abstract Business dynamism—the process of firm entry, growth and exit—lies at the heart of modern endogenous growth models. While productivity differences have traditionally been seen as the main driving forces of business dynamism, a growing body of evidence suggests that customer acquisition is at least as important. In light of this evidence, we propose a novel endogenous growth model in which innovating firms must first acquire customers to sell their products. Estimating our model with aggregate and firm-level data, we find that expansions of firms’ customer bases (market sizes) boost their incentives to innovate and shift resources towards high-growth businesses (“gazelles”). Combined, these effects explain over 40% of aggregate growth and substantially change predictions about the efficacy of growth policies. Finally, we document support for key model predictions using firm-level micro-data.

Abstract What are the effects of supply-side climate policies in the oil market? We use global company-level data to estimate the impact of 84 reforms of production taxes between 2000 and 2019 on oil production, exploration, and discoveries. We find that higher taxes primarily reduce companies’ exploration expenditures and oil discoveries, and also reduce short-term production of unconventional oil. We then quantify the implications for the oil market using a short- and medium-term dynamic model extending until the end of the century. Imposing a global climate royalty surcharge of 20 percentage points on oil producers reduces average annual emissions from oil by 5–7% in the first 5 years, and 9–20% in the medium term. If only OECD countries adopt this policy, 47–73% of the total emission reductions would be offset by increased production in non-OECD countries in the medium term.

Abstract For many technologies and behaviours, an agent’s benefit from adopting depends on his contacts adopting, and the benefit to his contacts of adopting depends on their contacts adopting. This paper examines how the architecture of these connections shapes the success or failure of the diffusion of innovations. We start with a standard model of diffusion with the key addition that some agents can coordinate their decisions. This captures the idea that people often talk and make decisions together with friends or family to adopt technologies. We show that insularity of connections, that is, the extent to which agents tend to concentrate their connections to a narrow set of other agents, determines contagion. However, whether insularity helps or hinders depends on the technology being diffused. For technologies that are valuable even without many contacts adopting, we find insular connections hinder adoption, but for technologies that are valuable only when many contacts adopt, insular connections facilitate adoption.

Abstract This article combines personnel records of the U.S. government with census data to study how exposure to greater female representation at work can persistently reduce intergenerational gender gaps in labour market outcomes. Exploiting city-by-department variation in the sudden expansion of female employment during World War I, we find that daughters of civil servants exposed to female co-workers are more likely to work later in life. This intergenerational effect operates through exposed fathers and extends beyond the public sector, reducing the earnings gap by 12%. Consistent with a broader shift in attitudes towards working women, exposure to female co-workers also made male civil servants more likely to marry working women. We show that cities exposed to larger increases in female federal workers saw persistently higher female labour force participation in both the public and the private sector. Increasing gender representation within the public sector can thus have broader labour market implications.

Abstract The seminal paper by Baron and Ferejohn (1989) leaves significant gaps in our understanding of open rule bargaining. We aim to fill these gaps by providing a fresh analysis of open rule bargaining. Our approach relies on an appealing class of stationary equilibria. In this class, we show that delays tend to be longer and allocations tend to be less egalitarian than originally predicted by Baron and Ferejohn. Our results shed new light on the efficiency and fairness implications of using an open vs. closed rule in legislatures and of bargaining processes in general.

Abstract Discrete choice data allow researchers to recover differences in utilities, but these differences may not suffice to identify policy-relevant counterfactuals of interest. In fact, in the case of dynamic discrete choice models, only a narrow set of counterfactuals are point-identified. In this paper, we explore how much one can learn about counterfactual outcomes of interest within this framework. We focus on the partial identification of counterfactuals, while allowing for (mild) model restrictions that can gradually shrink the identified set. We derive bounds for low-dimensional objects (such as average welfare) as arguments of optimization programmes, along with a uniformly valid inference procedure. Furthermore, we develop new and tractable computational tools and algorithms suitable for dealing with high-dimensional problems like this. Finally, we illustrate in Monte Carlos, as well as an empirical exercise of firms’ export decisions, the informativeness of the identified sets, and we assess the impact of (common) model restrictions on results.

Abstract I study linear panel data models with predetermined regressors (such as lagged dependent variables) where coefficients are individual-specific, allowing for heterogeneity in the effects of the regressors on the dependent variable. I show that the model is not point-identified in a short panel context but rather partially identified, and I characterize the identified sets for the mean, variance, and CDF of the coefficient distribution. This characterization is general, accommodating discrete, continuous, and unbounded data, and it leads to computationally tractable estimation and inference procedures. I apply the method to study lifecycle earnings dynamics among U.S. households using the Panel Study of Income Dynamics (PSID) dataset. The results suggest the presence of unobserved heterogeneity in earnings persistence, implying that households face varying levels of earnings risk which, in turn, contribute to heterogeneity in their consumption and savings behaviours.

Abstract When a policy prioritizes one person over another, is it because they benefit more, or because they are preferred? This paper develops a method to uncover the values consistent with observed allocation decisions. We estimate how much each person benefits from an intervention, and then reconcile the allocation with (i) the welfare weights assigned to different people; (ii) heterogeneous treatment effects of the intervention; and (iii) weights on different outcomes. We demonstrate this approach by analyzing Mexico’s PROGRESA anti-poverty programme. The analysis reveals that while the programme prioritized certain subgroups—such as indigenous households—the fact that those groups benefited more implies that the programme did not actually assign them a higher welfare weight. We also find evidence that the policy valued outcomes differently from households. The PROGRESA case illustrates how the method makes it possible to audit existing policies, and to design future policies that better align with values.

We assess the relationship between the racial diversity of medical providers and racial health disparities in the use of preventive care and in patient outcomes. We use unique data from the Military Health System, where we observe providers as patients so that we can identify their race, and where moves across bases change exposure to provider race in a plausibly exogenous fashion. We consider patients with four chronic, deadly, but manageable illnesses, where the relationship with the provider may have the most direct impact on health. We find striking evidence that provider racial diversity leads to reduced disparities in maintenance of preventive care and mortality.