Many workers value purpose and meaning in their jobs alongside income, and firms need to align these preferences with profit goals. This paper develops a dynamic model in which firms invest in ”purpose” to enhance job meaning and motivate effort. Workers, who differ in productivity, choose both productive and socialization effort, gaining utility from income and meaning. Purpose accumulates over time through firm investment and interacts with socialization to generate meaning, which boosts productivity. Firms invest in purpose only insofar as it raises profits. We characterize the unique equilibrium, including steady state and transition dynamics. Meaning and purpose rise with the importance workers place on meaning and with firm’s patience, but fall with depreciation and socialization costs. The relationship with workers’ share of output is non- monotonic. We also show that some intermediate level of heterogeneity in skills is best for performance. Compared to a worker-owned firm, profit-maximizing firms underinvest in purpose, highlighting a misalignment between firm incentives and worker preferences. The model provides insight into when and why firms adopt purpose-driven practices and underscores the role of diversity in fostering meaning at work.

Technological advances in advertising enable firms to contact new customers faster and to better target those most likely to buy their products. To study the aggregate implications, we develop a framework of demand as a network, where heterogeneous consumers dynamically become “aware” of differentiated products. With their adver- tising choices, firms can affect the rate at which their networks expand (“contacting”) and the probability with which they match with high valuation consumers (“targeting”). When calibrating the model to the advent of digital advertising in the United States, we find an increase in aggregate productivity due to improved consumer-firm match quality. Moreover, while both contacting and targeting intensified over this period, firms did not sufficiently increase their targeting investment relative to the social optimum.

We provide a macroeconomic framework to study monetary and fiscal policies for AI. Advances in AI expand firms’ ability to automate production. While higher automation boosts productivity and potential output, it also reduces workers’ share of income. Since workers have a high propensity to consume, advances in AI may depress aggregate demand and lead to a slump. Expansionary monetary policy can convert an AI slump into an AI boom, but in doing so it faces two challenges. In the short run, AI worsens the inflation-employment trade off faced by the central bank. In the medium run, monetary policy may be constrained by the zero lower bound, since weak demand lowers the natural rate. Employment subsidies and cuts in labor taxes can usefully complement monetary policy, by reducing firms’ cost of labor and inflation, as well as supporting workers’ income and aggregate demand.

What role did the Republic of Letters play in Europe’s transition to sustained innovation? We combine a corpus of digitized correspondence within the Republic of Letters with European aristocratic genealogies, historical postal routes, and a database of notable individuals to trace the diffusion and consequences of Enlightenment correspondence between 1600 and 1850. We first show that the Republic spread, in part, through aristocratic kinship networks: aristocrats connected to already participating peers entered earlier, and their probability of entry declined sharply with network distance. To isolate a causal channel, we exploit changes in postal distances along pre-existing kinship paths to already-inoculated aristocrats, while controlling directly for local postal access. We then aggregate this variation to European grid cells and estimate the effect of exposure to the Republic on the rise of applied science, innovation, and economic activity. Cells instrumented into the Republic experienced a near- doubling in applied scientists and inventors roughly three decades after first contact, with no pretrends and effects concentrated in scientific and technical correspondence rather than religion or philosophy. These findings suggest that the Republic of Letters helped reshape the geography of innovation before industrialization.

This paper explores the effect of road rehabilitation on violent conflict using a novel, rich dataset of road rehabilitation projects in the Democratic Republic of Congo. The country received massive external investments in transport infrastructure rehabilitation under conditions of endemic conflict, often with the explicit objective of supporting peacebuilding objectives. The paper finds that investments in road rehabilitation deter violence, which decreases significantly by around 5 to 10 percentage points after the completion of road rehabilitation. However, another significant finding, based on large-scale machine learning analysis of remote sensing data of road quality over time, is that the peace dividend of infrastructure investments is perish- able: violence increases again as roads progressively deteriorate. Improved durability and systematic maintenance of roads are thus necessary to extend the “peace dividend” of road investments.

Forced displacement is an important policy challenge, yet forecasting is hindered by sparse, annually observed flow data and reporting delays. This article proposes a forecasting method for country outflows and dyadic flows tailored to this sparse data setting. We combine slow-moving structural predictors with high-frequency text-based signals, compress high-dimensional news into low-dimensional topic representations via Latent Dirichlet Allocation to mitigate overfitting, and estimate a stacked ensemble of gradient-boosted trees that captures non-linear origin–destination interactions while making optimal use of the available data. We further apply conformal prediction to construct statistically valid prediction intervals for bilateral flows. Analyzing the text component yields that destination-specific search intensity of migration terms is a central predictor of subsequent dyadic displacement flows.

Long-run economic growth depends on the international diffusion of frontier technologies. Using Brazilian data, we identify a channel through which tariff cuts slow this diffusion: they weaken foreign firms’ incentives to transfer technology to domestic producers. Exploiting variation in import tariffs across origin countries within narrowly defined industries, we find that tariff reductions lead to fewer technology transfers and fewer citations to foreign technology, with the largest declines occurring among firms located near previous technology recipients. To interpret these findings, we develop a growth model in which foreign firms choose between exporting goods and transferring technology, with learning from exports being less efficient than learning from transferred technologies, as informed by the empirical evidence. Trade liberalization shifts learning from transferred technologies to imported goods, raising welfare in the short run but slowing diffusion and productivity growth. An optimal subsidy to technology transfers substantially amplifies the welfare gains from trade liberalization.

We study similarity in the complete set of one-shot 2×2 games with payoffs from {1, 2, 3, 4} without replacement. Similarity is defined geometrically via a neighborhood structure on games and continuity of behavior, and is applied to both theoretical rules (e.g., Nash equilibrium, level-k reasoning) and experimental data. This produces a partition of the games into (theoretical or empirical) similarity classes. We run a large-scale experiment in which each subject plays all 78 games within our class without feedback. We find that empirically inferred similarity classes diverge sharply from those predicted by Nash equilibrium and dominance reasoning. Instead, the empirical similarity classes align closely with the theoretical classes of a level-k variant, with deviations reflecting fairness and efficiency concerns. At the individual level, subjects’ play can be classified according to primary and secondary rules, conforming with either level-k variant (0 ≤ k ≤ 5) or a fairness and efficiency-based heuristic. The main insights extend to strategic settings beyond our 2 × 2 games.

We exploit a novel opportunity to study the dynamics of wage inequality and labor market competition over the course of economic development. Our context is Lithuania, where two decades of sustained growth and labor market tightening coincided with a substantial decline in wage inequality. We first fit a two-way fixed-effects model with worker and firm heterogeneity and document that the compression of the variance of firm fixed effects has been the main source of the fall in inequality. Guided by a standard dynamic monopsony model, we then estimate firms’ labor supply elasticities and show that labor market competition has increased over the same period. Finally, we construct a shift-share instrument and provide evidence that new job opportunities created by the accession to the European Union in 2004 contributed to the fall in inequality through their impact on labor market competition in Lithuania.

Historically, contract enforcement between multinationals and host governments relied on military power. In the late 1960s, Western Great Powers (WGP) – U.S., U.K., France – sharply reduced military interventions, increasing expropriation risk in weak-institution countries. We study the effect of this unanticipated global shift using microdata from the petroleum industry. Firms headquartered in WGP responded by delaying (“backloading”) production by 2-4 years, converging to the delays already exhibited by other multinationals. Backloading resulted in annual revenue losses of one quarter billion US$ per country, largely offset by higher government rent-shares. These patterns are consistent with the formation of self-enforcing agreements.

Family-friendly policies aim to help women balance work and family life, encouraging them to participate in the labor market. How effective are such policies in increasing fertility? We answer this question using a search model of the labor market where firms make hiring, promotion, and firing decisions, taking into account how these decisions affect workers’ fertility incentives and labor force participation decisions. We estimate the model using administrative data from Spain, a country with very low fertility and a highly regulated labor market. We use the model to study family-friendly policies and demonstrate that firms’ reactions result in a trade-off: policies that increase fertility reduce women’s participation in the labor market and lower their lifetime earnings.

Financial crises are characterized by depressed asset prices, tight financial constraints, and misallocation of resources. Standard policy responses—such as asset purchases and low interest rates—are generally intended to alleviate these symptoms. This paper distinguishes between two types of crises that appear similar but differ fundamentally in their underlying mechanisms: fire-sale crises, where productive firms are forced to sell assets; and demand-freeze crises, where productive firms are unable to purchase assets. While both lead to similar observable outcomes, they have contrasting general equilibrium effects and may call for different policy interventions. Notably, conventional policies can be counterproductive in demand-freeze crises, as they may exacerbate financial constraints and further distort resource allocation. Empirical evidence on the pattern of capital reallocation among U.S. firms suggests that demand-freeze crises are, in fact, more common.

We document that U.S. hurricanes striking close to Election Day trigger larger public spending responses and sustained population inflows than comparable hurricanes occurring between elections. Exploiting quasi-random variation in hurricane timing, we show that electoral incentives shape post- disaster policy with lasting spatial consequences. A quantitative spatial equilibrium model implies that eliminating these electoral timing distortions would raise aggregate welfare by 0.025%, but the aggregate gain masks an 18:1 asymmetry in per-capita stakes between losers and gainers. This distributional asymmetry rationalizes the persistence of these electoral distortions.

We study the impact of incomplete consumption risk-sharing on land misallocation in rural economies. We develop a general equilibrium model of land cultivation choices, where heterogeneous agricultural households face idiosyncratic output shocks and insure themselves by participating in a risk-sharing arrangement. Incomplete insurance distorts households’ land cultivation choices, leading them away from maximizing expected incomes and resulting in land misallocation. Using the latest ICRISAT panel data from rural India, we quantify the losses attributable to limited risk-sharing. Completing insurance markets leads to output and welfare gains of 19% and 29%, respectively. Improving the functioning of consumption insurance markets can yield gains comparable to those achieved by removing distortions in factor markets.

We analyze the problem of locating a public facility on a line in a society where agents have either single-peaked or single-dipped preferences. We focus on the domain introduced by Alcalde-Unzu et al. (2024), in which the type of preference of each agent is public information, but the location of her peak or dip, as well as the rest of the preference are unknown. We characterize all strategy-proof and type-anonymous social choice rules on this domain. The first characterization identifies the additional constraints that type- anonymity imposes on the class of strategy-proof rules described in Alcalde-Unzu et al. (2024). The second one generalizes existing results in a two-step procedure as follows: In the first step, the rule computes the median of the reported peaks together with a fixed set of locations (Moulin, 1980) leading to either a single alternative or a pair of contiguous alternatives. In the second step, applied only when the first step yields a pair, we use a double-quota majority method to select between the two alternatives of the pair (Moulin, 1983). Finally, we establish that these two characterizations are equivalent.

High-skilled migration programs exist around the world in the hope that immigrants complement native workers, allow firms to grow, and boost innovation. We study the effect of one such program by exploiting the 2016 extension of the Optional Practical Training (OPT) program, which significantly prolonged the work authorization period for international STEM graduates. Using a synthetic difference-in-differences approach, we find that the policy successfully increased the local supply of high-skilled immigrants in exposed Commuting Zones. This local inflow stimulated firm creation and the demand for native high-skilled workers. The program might have also boosted innovation in certain sectors and startup investment, especially in Commuting Zones hosting top-ranked universities, where, overall, the effects tend to be larger.

Firms’ innovation outcomes depend on their ability to attract and retain talented inventors. What market frictions prevent the sorting between firms with high innovation potential and high-productivity inventors? How does this sorting impact aggregate innovation, growth and welfare? We address these questions both empirically and theoretically. Empirically, we show that firms facing strong competition in the product market employ more productive inventors, while less productive inventors tend to be allocated in concentrated industries. Theoretically, we embed a frictional labor market for inventors into an endogenous-growth model of strategic innovation. In line with the data, the model predicts that high-productivity inventors are disproportionately employed in firms that operate in competitive industries. We then use the model to quantify the growth and welfare implications of this inventor sorting. Our results show that matching frictions in the market for inventors impede the allocation of high- productivity inventors to firms with high implementation intensity, and are responsible for a 32% loss in economic growth. Industrial policies that subsidize R&D spending relax these frictions by boosting inventor productivity, helping high-quality inventors reallocate to firms with high implementation incentives. Under optimal subsidies, growth increases as much as 74 basis points, closing most of the gap in missing growth caused by frictions in the market for inventors.

The carbon rebound effect, a phenomenon that usually accompanies improvements in energy efficiency, is frequently observed in households. To mitigate the household carbon rebound effect, the removal of fossil fuel subsidies has emerged as a potential strategy. This paper aims to systematically simulate the consequences of removing such a subsidy to inform evidence-based policy-making. We develop an analytical framework that facilitates a detailed investigation of changes in the prices of various goods and services consumed by residents after the removal of fossil fuel subsidies, as well as the main sources of household carbon rebound effect (CRE). Moreover, we examine the extent to which the removal of fossil fuel subsidies can counteract the rebound effect. Our findings indicate that rebound-driven fossil fuel consumption is the primary contributor to the total CRE. Meanwhile, consumption in the ‘residence’ category is the dominant source of the indirect CRE. We further show that removing all fossil fuel subsidies could totally offset the CRE in Chinese households. A notable urban-rural heterogeneity is observed. The subsidy removal for oil and natural gas yields the strongest mitigation effect on CRE in urban regions, whereas removing the oil subsidy proves most effective in rural areas. These results affirm the effectiveness of policies to remove fossil fuel subsidies. We conclude that reforming fossil fuel subsidies is a powerful tool to counteract the CRE, offering valuable insights not only for China but also for other developing countries with similar household consumption patterns.

Innovative startups are frequently acquired by large incumbents. Such acquisitions have recently come under scrutiny, as policymakers suspect that incumbents might acquire startups just to “kill” their ideas. However, acquisitions also provide an incentive for startup creation, and have ambiguous effects on incumbents’ own innovation. This paper assesses the net effect of these forces. To do so, we build an endogenous growth model with heterogeneous multi-product firms and startup acquisitions, and calibrate its parameters to match micro-level evidence from the United States. Our calibrated model implies that taxes on startup acquisitions lower the startup rate, but increase incumbent innovation as well as the implementation rate of startup ideas. Banning killer acquisitions, a policy that appears desirable in partial equilibrium, yields virtually no welfare gains in general equilibrium. The optimal policy instead imposes high taxes on startup acquisitions (reducing their frequency by more than half) and raises consumption-equivalent welfare by 0.48%.

We use CPI micro data for nine euro area countries to document new evidence on consumer price stickiness in the euro area during the 2021-2024 inflation cycle. In 2022, the monthly frequency of price changes reached 12%, compared with an average of 8% over 2010–2019, roughly a four- percentage-point increase; it then fell quickly in 2023 and more slowly in 2024, ending close to its pre-pandemic level. The decline in the frequency of price changes was faster for food and non- energy industrial goods (NEIG) than for services, where frequencies remained elevated in 2024. The overall frequency rose mainly because there were more price increases, while the magnitude of the average size of the price increases or decreases changed only marginally during the surge. Products with a larger imported-energy cost share responded more strongly, and hazard-rate evidence shows that the probability of price adjustments increases with the gap between actual and optimal prices, consistent with state-dependent pricing and a steepening of the Phillips curve. To illustrate the implications of this state dependence, a macro model suggests that peak inflation would have been almost 1 percentage point lower if the frequency had not responded to the inflation surge.