This research empirically establishes and interprets the hypothesis that the relationship between population aging and inventive activity is hump-shaped. We estimate a reduced form, hump-shaped relationship in a panel of 33 OECD countries over the period 1960–2012, as well as in a panel of 248 NUTS 2 regions in Europe over the period 2001–2012. The increasing part of the hump may be associated with various channels including the acknowledgement that population aging requires inventive activity to guarantee current and future standards of living, or the observation that older educated workers are more innovative than their young peers. The decreasing part may reflect the tendency of aging societies to lose dynamism and the willingness to take risks.

The development of artificial intelligence (AI) does influence human jobs but not necessarily in a negative way. Although labor force participation rates and firms’ job vacancies for human labor decline, the unemployment rate may be lower than that in an economy without AI. In an economy with heterogeneously skilled workers, the invention of AI usually has a negative effect on the skilled labor market but a positive effect on the unskilled labor market. The overall unemployment rate may decline as AI develops.

We devise an endogenous growth model in which agents’ utility depends not only on current consumption but also on the pleasure of anticipated future consumption. We consider the case in which agents derive satisfaction from their own anticipatory feelings—inward-looking or internal anticipation—and the case in which agents derive utility from anticipation of other people’s future consumption—outward-looking or external anticipation. We characterize the effects of introducing a forward-looking consumption reference on the dynamics of the economy. Whereas the inward-looking economy features transitional dynamics, the outward-looking economy does not. The distortions caused by the externality in the economy with external habits can be corrected by subsidizing income at a time-varying rate or by means of a tax on consumption at a decreasing rate. We contrast the equilibrium dynamics of our specification to the more standard specification of the habit formation consumption reference point. Numerical simulations supplement the theoretical analysis.

Large productivity gaps across sectors persist and the process of structural transformation is stagnant in many developing economies. This wedge between observed and optimal labor allocations reflects the presence of institutional and market frictions, which impose costs on the optimal reallocation of labor from low- to high-productivity sectors. Using a panel of cross-country sector-level data, I estimate a dynamic panel error correction model that captures the dynamics of sectoral labor flows. The model estimates provide a new set of stylized facts on the dynamics of the structural transformation process and a measure of the magnitude of frictions facing labor flows. In addition, I analyze the contribution of labor regulations and reforms to the pace at which labor flows across economic sectors. Results suggest that policy reforms need to steer between the goal of easing job creation and destruction, while supporting labor supply incentives to reallocate through strong social nets, labor protection, and risk sharing.

This paper investigates the long-run effects of immigration on wages and welfare in a model with endogenous technology choice (ETC) where firms are allowed to choose their optimal skill intensity from a menu of available technologies. I embed the ETC framework into the Auerbach and Kotlikoff model (1987) that features a large set of overlapping generations, a rich collection of population dynamics, and a social security system. I calibrate the model to match with the U.S. data and evaluate the effect of ETC with the help of two experiments. In the first experiment, I increase the share of high-skilled immigrants and compare the wage and welfare predictions of the model with ETC to a standard model where the skill intensities in production technology are fixed. In the standard model, since the skill intensities are constant, increase in the supply of high-skilled labor leads to a decrease in high-skilled wages and an increase in low-skilled wages. On the other hand, in the model with ETC, negative supply-side effects are counterbalanced by an increase in the intensity of the more abundant high-skilled labor, leading to a smaller decrease in their wages. The discrepancy between wage predictions of these two models is also reflected in the welfare: while the model with ETC predicts an increase in both high- and low-skilled natives’ welfare, the standard model would predict a decrease in the welfare of the high skilled and a larger increase in the welfare of the low skilled. In the second experiment, I examine the effects of an increase in low-skilled immigration and find that in this case, since the initial production technology is low-skilled intensive, the ETC effects are smaller. These results imply that if ETC is ignored, both in the short run and long run, wage and welfare analyses of immigration will be incomplete, and even misleading.

One of the most important advantages of an inflation target is that it helps to reduce uncertainty about future inflation. However, this confidence may be undermined if actual inflation continuously deviates from the target level. We examine how inflation uncertainty relates to the presence of an inflation target and deviations of inflation from the targeted level. Inflation uncertainty is quantified by means of an unobserved components stochastic volatility model that allows to distinguish between permanent and transitory inflation uncertainty. While long-term inflation appears largely stable in most economies, the short-term inflation uncertainty is found to be time-varying. Most notably, short-term inflation uncertainty is high if inflation rates are below the target level. This is particularly relevant for economies which are currently confronted with the presence of persistently low-inflation rates. Our findings suggest that announcing higher inflation targets as it is currently discussed may be costly in terms of provoking higher inflation uncertainty.

This paper presents a multi-country version of the Ramsey growth model with cross-country technological interdependence. The results rationalize several stylized facts about growth and convergence. First, individual countries tend to converge toward country-specific balanced growth paths rather than steady-state equilibria. Second, an economy that accounts for a smaller share of the world technology distribution harnesses the “advantages of backwardness” to catch up at a faster speed. Third, countries grow at different rates during the phase of transitional dynamics. However, technological interdependence creates a force toward cross-country convergence in the growth rate and stability of world income distribution in the long run. Finally, cross-country differences in structural characteristics and initial conditions lead to divergences in the level of income per capita.

Dynamic spatial theory has been a fruitful approach to understanding economic phenomena involving time and space. However, several central questions still remain unresolved in this field. The identification of the social optimal allocation of economic activity across time and space is particularly problematic, not been ensured yet in economic growth. Developing a monotone method, we study the optimal solution of the spatial Ramsey model. Under fairly general assumptions, we prove the existence of unique social optimum. Considering a numerical implementation of our algorithm, we study the role played by capital mobility in the neoclassical growth environment. With capital irreversibility and economic openness, space allows for transitional dynamics. Moreover, in this context, capital mobility is beneficial as well in terms of social welfare and inequality. We also consider an application of our method to an extension of the spatial Ramsey model for optimal land-use planning.