Knowledge that Transforms

We study the investment effects of market integration on renewable energy expansion. Our theory highlights that market integration not only improves allocative efficiency by gains from trade but also incentivizes new investment in renewable power plants. To test our theoretical predictions, we examine how recent grid expansions in the Chilean electricity market changed electricity production, wholesale prices, generation costs, and renewable investments. We then build a structural model of power plant entry to quantify the impact of market integration with and without the investment effects. We find that the market integration in Chile increased solar generation by around 180%, saved generation costs by 8%, and reduced carbon emissions by 5%. A substantial amount of renewable entry would not have occurred in the absence of market integration. Our findings suggest that ignoring these investment effects would substantially understate the benefits of market integration and its important role in expanding renewable energy.

We develop a new approach to estimating the causal effects of treatments or instruments that combine multiple sources of variation according to a known formula. Examples include treatments capturing spillovers in social or transportation networks and simulated instruments for policy eligibility. We show how exogenous shocks to some, but not all, determinants of such variables can be leveraged while avoiding omitted variables bias. Our solution involves specifying counterfactual shocks that may as well have been realized and adjusting for a summary measure of non-randomness in shock exposure: the average treatment (or instrument) across shock counterfactuals. We use this approach to address bias when estimating employment effects of market access growth from Chinese high-speed rail construction.

We postulate a continuous‐time heterogeneous agent model with a financial sector and households to study the nonlinear linkages between aggregate and financial variables. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk . This risk makes the economy transition between a high‐leverage region and a low‐leverage region, which, in turn, creates state dependence in impulse responses: the same shock starting from the high‐leverage region gets propagated and amplified more than when the shock arrives when leverage is low. State dependence in impulse responses generates a time‐varying aggregate precautionary savings motive that, by moving the risk‐free rate, justifies the leverage level of the financial sector in each region. Finally, we illustrate the usefulness of neutral networks to solve for the nonlinear perceived law of motion of the model, and the importance of household heterogeneity in driving its quantitative properties.

We show that foreign capital liberalization reduces capital misallocation and increases aggregate productivity for affected industries in India. The staggered liberalization of access to foreign capital across disaggregated industries allows us to identify changes in firms' input wedges, overcoming major challenges in the measurement of the effects of changing misallocation. Liberalization increases capital overall. For domestic firms with initially high marginal revenue products of capital (MRPK), liberalization increases revenues by 23%, physical capital by 53%, wage bills by 28%, and reduces MRPK by 33% relative to low MRPK firms. The effects of liberalization are largest in areas with less developed local banking sectors, indicating that inefficiencies in that sector may cause misallocation. Finally, we propose an assumption under which a novel method exploiting natural experiments can be used to bound the effect of changes in misallocation on treated industries' aggregate productivity. These industries' Solow residual increases by 3–16%.

We develop a class of tests for time‐series models such as multiple regression with growing dimension, infinite‐order autoregression, and nonparametric sieve regression. Examples include the Chow test and general linear restriction tests of growing rank p . Employing such increasing p asymptotics, we introduce a new scale correction to conventional test statistics, which accounts for a high‐order long‐run variance (HLV), which emerges as p grows with sample size. We also propose a bias correction via a null‐imposed bootstrap to alleviate finite‐sample bias without sacrificing power unduly. A simulation study shows the importance of robustifying testing procedures against the HLV even when p is moderate. The tests are illustrated with an application to the oil regressions in Hamilton (2003).

As search frictions in the market for a consumer product decline, buyers are able to locate and access more and more sellers. In response, sellers choose to design varieties of the product that are more and more specialized in order to take advantage of the heterogeneity in buyers' preferences. I find conditions on the fundamentals of the market under which the increase in specialization exactly offsets the decline in search frictions. Under these conditions, the extent of competition and the extent of price dispersion remain constant over time even though search frictions are vanishing. Buyer's surplus and seller's profit, however, grow over time at a constant endogenous rate, as the increase in specialization allows sellers to cater better and better to the preferences of individual buyers.

We combine personnel records of the United States federal bureaucracy from 1997 to 2019 with administrative voter registration data to study how ideological alignment between politicians and bureaucrats affects turnover and performance. We document significant partisan cycles and turnover among political appointees. By contrast, we find no political cycles in the civil service. At any point in time, a sizable share of bureaucrats is ideologically misaligned with their political leaders. We study the performance implications of this misalignment for the case of procurement officers. Exploiting presidential transitions as a source of “within‐bureaucrat” variation in political alignment, we find that procurement contracts overseen by misaligned officers exhibit greater cost overruns and delays. We provide evidence consistent with a general “morale effect,” whereby misaligned bureaucrats are less motivated to pursue the organizational mission. Our results thus help to shed some of the first light on the costs of ideological misalignment within public organizations.

This paper theoretically and empirically investigates factional arrangements within the Chinese Communist Party (CCP), the governing political party of the People's Republic of China. Using detailed biographical information of political elites in the Central Committee and provincial governments, we present a set of new empirical regularities within the CCP, including systematic patterns of cross‐factional balancing at different levels of the political hierarchy and substantial faction premia in promotions. We propose and estimate an organizational economic model to characterize factional politics within single‐party nondemocratic regimes and its economic implications.

R&D investment reduces current profits, so short‐term pressure to hit profit targets may distort R&D. In the data, firms just meeting Wall Street forecasts have lower R&D growth and subsequent innovation, while managers just missing receive lower pay. But short‐termist distortions might not quantitatively matter if aggregation or equilibrium dampen their impact. So I build and estimate a quantitative endogenous growth model in which short‐termism arises naturally as discipline on conflicted managers and boosts firm value by about 1%. But short‐termism reduces R&D, and the social return to R&D is higher than the private return due to standard channels including knowledge spillovers and imperfect competition. So at the macro level, short‐termist distortions slow growth by 5 basis points yearly and lower social welfare by about 1%.