Christopher Tonetti

How much of past economic growth is due to automation, and what does this imply about the effects of A.I. in the coming decades? We perform growth accounting using a task-based model for key sectors in the U.S. economy. Historically, TFP growth is driven primarily by improvements in capital productivity. At the task level, capital productivity has grown approximately 4 percentage points per year faster than labor productivity in the U.S. private business sector, and substantially more in some industries. The main benefit of automation is therefore that we use rapidly-improving machines instead of slowly-improving humans on an increasing share of tasks. Looking to the future, we develop an endogenous growth model in which the production of both goods and ideas is endogenously automated and calibrate the model based on our historical accounting. Automation leads economic growth to accelerate, but the acceleration is remarkably slow because of the prominence of weak links, i.e., an elasticity of substitution among tasks substantially less than one. Even when most tasks are automated by rapidly-improving capital, output is constrained by the tasks performed by slowly-improving labor.

We study consumer demand for savings, life insurance, annuities, and long-term care insurance using novel survey data and a structural life-cycle model. We document that individuals perceive substantial insurance nonpayment risk, and these beliefs predict ownership. Embedding elicited beliefs into an incomplete-markets model alongside additional real-world insurance features, we match empirical patterns of low participation. Relative to a no-insurance benchmark, access to existing imperfect insurance reduces median wealth by 16% and generates a modest 0.6% welfare gain. Eliminating nonpayment risk would substantially increase insurance ownership, yield a further 11% decline in median savings, and generate an additional 1.7% welfare gain.

We study optimal policy when markups reflect compensation for risk instead of market power. Although markups correctly capture the private cost of risk, they are socially inefficient. This calls for a subsidy, as in the market-power perspective. However, uninsurable risk also leads entrepreneurs to dynamically overaccumulate too large a share of wealth to self-insure. Therefore, an income effect makes relatively impoverished workers oversupply labor. In the long run this effect dominates and it is optimal to tax labor and reduce aggregate output, in sharp contrast to the common wisdom derived from the market-power perspective.

We develop a nonparametric method that identifies marginal treatment effects (MTEs) using survey data on subjective expectations instead of instruments. Subjective treatment probabilities rank people by their latent propensity to be treated if they forecast treatment the same way. Combined with expected treatment-contingent outcomes, the ranking identifies the full MTE function if those expectations are correct on average. Both results hold under common forms of reporting error. We apply the method to study childbirth and female labor supply in Denmark. Estimated MTEs are roughly constant across ranks of fertility beliefs, so there is little selection into childbirth on unobserved determinants of work after birth. The survey-based average treatment effect on the treated nearly equals the event-study estimate from administrative data at every horizon, evidence that the identifying assumptions hold. Furthermore, eliciting beliefs under a counterfactual policy measures its effect before implementation, accounting for how the policy changes selection into treatment.

When women plan for life after childbirth, they form beliefs about work, childcare, and how their careers will unfold. These expectations shape key decisions but are formed under deep uncertainty. We use a 2019 state-contingent survey of 11,000 Danish women linked to administrative data to compare pre-birth beliefs to realized outcomes. Mothers accurately anticipate their eventual return to work but underestimate the duration of the career interruption. This miscalibration stems from two belief errors—about partner leave and own labor supply—which interact and persist even among second-time mothers, with implications for labor supply, planning, and policy design.

In many search markets, offers must be accepted or rejected before their quality is known. We build a search and matching model in which suppliers privately choose offer quality and searchers choose costly due diligence before accepting. How carefully searchers screen determines the mix of high- and low-quality offers. This composition channel is absent when information and offer quality are exogenous. In the static model, improving low-quality offers makes accepting one less bad, so searchers screen less carefully and suppliers post more low-quality offers. The mix worsens enough that more searchers remain unmatched and their welfare falls. Raising the value of remaining unmatched actually reduces the number of unmatched searchers, because searchers screen more carefully and suppliers post more high-quality offers. In the dynamic economy the composition channel survives, and a classic market tightness force can reinforce or overturn it. A planner who treats information and offer quality as exogenous can subsidize entry where a tax is optimal.

We estimate the present discounted value of earnings for a particular worker having a job at a particular firm, using a nonparametric statistical model that nests many rich structural models. We assume a stationary Markov structure conditional on a vector of idiosyncratic states, where states determine payoffs and transitions. We use rich employer-employee matched data from Denmark, cluster workers and firms into types, define a parsimonious set of state variables, and estimate type- and state-specific payoffs and transition probabilities directly from their empirical counterparts. We then compute values by iterating over a Bellman equation. We decompose the value into its components, including earnings on the current job, the probability of staying or moving to a new job of a particular type and the earnings growth associated with staying or moving, and transitions to nonemployment with associated payoffs. We also compare the distribution of job value changes upon job-to-job moves to the wage change distribution and show how patterns of worker and firm sorting differ when using values instead of wages.

Cognitive decline may lead older Americans to make poor financial decisions. Preventing poor decisions may require timely transfer of financial control to a reliable agent. Cognitive decline, however, can develop unnoticed, creating the possibility of suboptimal timing of the transfer of control. This paper presents survey-based evidence that wealthholders regard suboptimal timing of the transfer of control, in particular delay due to unnoticed cognitive decline, as a substantial risk to financial well-being. This paper provides a theoretical framework to model such a lack of awareness and the resulting welfare loss.

We study how innovation and technology diffusion interact to endogenously determine the shape of the productivity distribution and generate aggregate growth. We model firms that choose to innovate, adopt technology, or produce with their existing technology. Costly adoption creates a spread between the best and worst technologies concurrently used to produce similar goods. The balance of adoption and innovation determines the shape of the distribution; innovation stretches the distribution, while adoption compresses it. On the balanced growth path, the aggregate growth rate equals the maximum growth rate of innovators. While innovation drives long-run growth, changes in the adoption environment can influence growth by affecting innovation incentives, either directly, through licensing of excludable technologies, or indirectly, via the option value of adoption.

We study how opening to trade affects economic growth in a model where heterogeneous firms can adopt new technologies already in use by other firms in their home country. We characterize the growth rate using a summary statistic of the profit distribution—the mean-min ratio. Opening to trade increases the profit spread through increased export opportunities and foreign competition, induces more rapid technology adoption, and generates faster growth. Quantitatively, these forces produce large welfare gains from trade by increasing an inefficiently low rate of technology adoption and economic growth.

Data is nonrival: a person's location history, medical records, and driving data can be used by any number of firms simultaneously. Nonrivalry leads to increasing returns and implies an important role for market structure and property rights. Who should own data? What restrictions should apply to the use of data? We show that in equilibrium, firms may not adequately respect the privacy of consumers. But nonrivalry leads to other consequences that are less obvious. Because of nonrivalry, there may be large social gains to data being used broadly across firms, even in the presence of privacy considerations. Fearing creative destruction, firms may choose to hoard data they own, leading to the inefficient use of nonrival data. Instead, giving the data property rights to consumers can generate allocations that are close to optimal. Consumers balance their concerns for privacy against the economic gains that come from selling data to all interested parties.

Older wealthholders spend down assets much more slowly than predicted by classic life-cycle models. This paper introduces health-dependent utility into a model with incomplete markets in which preferences for bequests, expenditures when in need of long-term care (LTC), and ordinary consumption combine with health and longevity uncertainty to explain saving behavior. To sharply identify motives, it develops strategic survey questions (SSQs) that elicit stated preferences. The model is estimated using these SSQs and wealth data from the Vanguard Research Initiative. The desire to self-insure against long-term-care risk explains a substantial fraction of the wealthholding of many older Americans.

Older Americans, even those who are long retired, have strong willingness to work, especially in jobs with flexible schedules. For many, labor force participation near or after normal retirement age is limited more by a lack of acceptable job opportunities or low expectations about finding them than by unwillingness to work longer. This paper establishes these findings using an approach to identification based on strategic survey questions (SSQs), purpose-designed to complement behavioral data. These findings suggest that demand-side factors are important in explaining late-in-life labor market behavior and need to be considered in designing policies aimed at promoting working longer.

There exists an extensive literature estimating idiosyncratic labor income processes. While a wide variety of models are estimated, GMM estimators are almost always used. We examine the validity of using likelihood based estimation in this context by comparing the small sample properties of a Bayesian estimator to those of GMM. Our baseline studies estimators of a commonly used simple earnings process. We extend our analysis to more complex environments, allowing for real world phenomena such as time varying and heterogeneous parameters, missing data, unbalanced panels, and non-normal errors. The Bayesian estimators are demonstrated to have favorable bias and efficiency properties.

The least productive agents in an economy can be vital in generating growth by spurring technology diffusion. We develop an analytically tractable model in which growth is created as a positive externality from risk taking by firms at the bottom of the productivity distribution imitating more productive firms. Heterogeneous firms choose to produce or pay a cost and search within the economy to upgrade their technology. Sustained growth comes from the feedback between the endogenously determined distribution of productivity, as evolved from past search decisions, and an optimal, forward-looking search policy. The growth rate depends on characteristics of the productivity distribution, with a thicker-tailed distribution leading to more growth.

Will fast growing emerging economies sustain rapid growth rates until they catchup to the technology frontier? Are there incentives for some developed countries to free-ride off of innovators and optimally fall-back relative to the frontier? This paper models agents growing as a result of investments in innovation and imitation. Imitation facilitates technology diffusion, with the productivity of imitation modeled by a catch-up function that increases with distance to the frontier. The resulting equilibrium is an endogenous segmentation between innovators and imitators, where imitating agents optimally choose to catch-up or fall-back to a productivity ratio below the frontier.

Using data on repurchase agreements by primary securities dealers, we show that three classes of securities (Treasury securities, securities issued by government-sponsored agencies, and mortgage-backed securities) can be formally ranked in terms of their collateral values in the general collateral (GC) market. We then show that GC repurchase agreement (repo) spreads across asset classes display jumps and significant temporal variation, especially at times of predictable liquidity needs, consistent with the "safe haven" properties of Treasury securities: These jumps are driven almost entirely by the behavior of the GC repo rates of Treasury securities. Estimating the "collateral rents" earned by owners of these securities, we find such rents to be sizable for Treasury securities and nearly zero for agency and mortgage-backed securities. Finally, we link collateral values to asset prices in a simple no-arbitrage framework and show that variations in collateral values explain a significant fraction of changes in short-term yield spreads but not those of longer-term spreads. Our results point to securities' role as collateral as a promising direction of research to improve understanding of the pricing of money market securities and their spreads.

Individuals face significant late-in-life risks, prominently including the need for long-term care (LTC). Yet, they hold little long-term care insurance (LTCI). In this paper we use a structural model and a purpose-designed dataset to understand the determinants of insurance demand. We distinguish between a fundamental lack of desire to insure, crowd out from existing insurance, and unmet demand due to poor products available in the market. The model features individual-specific non-homothetic health-state-dependent preferences over normal consumption, consumption when in need of long-term care, and bequests, which are estimated using strategic survey questions. To account for differences between the modeled and measured insurance products, we study not only individuals holdings of LTCI, but also their stated demand for an idealized product that mirrors that in the model. We find that many individuals would purchase LTCI and receive a large consumer surplus if it were a better product, while many others do not want to purchase even high-quality actuarially fair LTCI due to the values of their heterogeneous state-dependent preferences, their demographics, and their financial situation.

Wealth, though crucial for modeling economic behavior and understanding well-being, is difficult to measure in surveys. This paper introduces a new, comprehensive account-by-account approach for eliciting asset holding. This approach is implemented in the Vanguard Research Initiative, a panel of wealthholders designed to yield high-quality measurements for a large sample of older Americans with significant financial assets. Because survey responses are linked to administrative account balances, this paper can show that the approach yields precise, unbiased estimates. Having accurate and dense data on the wealth of wealthholders provides sharper inferences on wealth management behavior as well as on relationships between wealth and economic behavior than is possible in leading datasets.

This paper develops a estimation procedure to construct a panel data set of consumption, income, and other covariates. We construct a Bayesian imputation algorithm that uses the full information from the likelihood of data contained in both the PSID and CEX and provides information about the uncertainty surrounding imputed values. We treat individual consumption in the PSID and CEX as latent variables and draw from the posterior distribution of agents' consumption given information contained in both data sets. This extremely high dimensional posterior distribution imposes sampling challenges that are overcome by exploiting derivatives of the posterior distribution using a Metropolis Adjusted Langevin Algorithm.

How do geography and other barriers to the free flow of information shape the rate of knowledge diffusion? To address this question, we develop a model of product discrete choice with Bayesian learning on a social network. Estimating this model using monthly data on the cholesterol-drug prescription decisions of over 50,000 U.S. physicians during January 2000 through December 2010, we find that the evolution of product choice efficiency is highly responsive to network structure changes, particularly targeted friction reductions that strengthen the strongest bilateral links.