In the short-term, economies shift preferentially into new activities that are related to ones they currently do. Such a tendency should have implications for the nature of an economy’s long-term development as well. We explore these implications using a dynamical network model of an economy’s movement into new activities. First, we theoretically derive a pair of coordinates that summarize long-term structural change. One coordinate captures overall ability across activities, the other captures an economy’s composition. Second, we show empirically how these two measures intuitively summarize a variety of facts of long-term economic development. Third, we observe that our measures resemble complexity metrics, though our route to these metrics differs significantly from previous ones. In total, our framework represents a dynamical approach that bridges short-and long-term descriptions of structural change, and suggests how different branches of economic complexity analysis could potentially fit together in one framework.
We analyze Ukraine's opportunities to participate in European value chains, using traditional gravity models, combined with tools from Economic Complexity Analysis to study international trade (exports) and Foreign Direct Investment (FDI). This toolbox is shown to be predictive of the growth and entry of new exports to the EU's Single Market, as well as foreign direct investments from the Single Market in Ukraine. We find that Ukraine has suffered from a decline of trade with Russia, which has led not only to a quantitative but also a qualitative deterioration in Ukrainian exports. Connecting to western European value chains is in principle possible, with several opportunities in the automotive, information technology and other sectors. However, such a shift may lead to a spatial restructuring of the Ukrainian economy and a mismatch between the geographical supply of and demand for labor.
We analyze how globalization affects the allocation of talent across competing teams in large matching markets. Focusing on amplified superstar effects, we show that a convex transformation of payoffs promotes positive assortative matching. This result holds under minimal assumptions on how skills translate into competition outcomes and how competition outcomes translate into payoffs. Our analysis covers many interesting special cases, including simple extensions of Rosen (1981) and Melitz (2003) with competing teams. It also provides new insights on the distributional consequences of globalization, and on the role of technological change, urban agglomeration, and taxation for the composition of teams.
In this paper, we develop a heterogeneous agent general equilibrium framework to analyze optimal joint policies of a lockdown and transfer payments in times of a pandemic. In our model, the effectiveness of a lockdown in mitigating the pandemic depends on endogenous compliance. A more stringent lockdown deepens the recession which implies that poorer parts of society find it harder to subsist. This reduces their compliance with the lockdown, and may cause deprivation of the very poor, giving rise to an excruciating trade-off between saving lives from the pandemic and from deprivation. Lump-sum transfers help mitigate this trade-off. We identify and discuss key trade-offs involved and provide comparative statics for optimal policy. We show that, ceteris paribus, the optimal lockdown is stricter for more severe pandemics and in richer countries. We then consider a government borrowing constraint and show that limited fiscal space lowers the optimal lockdown and welfare, and increases the aggregate death burden during the pandemic. We finally discuss distributional consequences and the political economy of fighting a pandemic.
We introduce quality differentiation into a Ricardian model of international trade. We show that (1) quality differentiation allows industrialized countries to be active across the full board of products, complex and simple ones, while developing countries systematically specialize in simple products, in line with novel stylized facts. (2) Quality differentiation may thus help to explain why richer countries tend to be more diversified and why, increasingly over time, rich and poor countries tend to export the same products. (3) Quality differentiation implies that the gains from inter-product trade mostly accrue to developing countries. (4) Guided by our theory, we use a censored regression model to estimate the link between a country’s GDP per capita and its export quality. We find a much stronger relationship than when using OLS, in line with our theory.
This paper constructed a simple model to illustrate the global supply chain profit sharing and industrial upgrading mechanism, from which it was found that the average profitability distribution in the different supply chain stages was determined by two main factors: (1) the average product of the labor in the firms at each production stage; and (2) the ratio of the output elasticity of capital to the output elasticity of labor in each stage. This paper also proposed a new industrial upgrading mechanism, the ‘inter-supply chain upgrading’, for supply chain firms. Rises in production complexity and increased factor intensity in each production stage were found to be the two essential conditions for the inter-supply chain upgrading. The empirical study results were found to be broadly consistent with the proposed theories.
This study focuses on the risk of automation and its interaction with training and the use of skills at work. Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013, the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts’ discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results.
The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities.
The degree to which modern technologies are able to substitute for groups of job tasks has renewed fears of near-future technological unemployment. We argue that our knowledge, skills and abilities (KSA) go beyond the specific tasks we do at the job, making us potentially more adaptable to technological change than feared. The disruptiveness of new technologies depends on the relationships between the job tasks susceptible to automation and our KSA. Here we first demonstrate that KSA are general human capital features while job tasks are not, suggesting that human capital is more transferrable across occupations than what job tasks would predict. In spite of this, we document a worrying pattern where automation is not randomly distributed across the KSA space – it is concentrated among occupations that share similar KSA. As a result, workers in these occupations are making longer skill transitions when changing occupations and have higher probability of unemployment.
When workers are displaced from their jobs in mass layoffs or firm closures, they experience lasting adverse labor market consequences. We study how these consequences vary with the amount of skill mismatch that workers experience when returning to the labor market. Using novel measures of skill redundancy and skill shortage, we analyze individuals’ work histories in Germany between 1975 and 2010. We estimate difference-in-differences models, using a sample in which we match displaced workers to statistically similar non-displaced workers. We find that displacements increase the probability of occupational change eleven fold, and that the type of skill mismatch after displacement is strongly associated with the magnitude of post-displacement earnings losses. Whereas skill shortages are associated with relatively quick returns to the counterfactual earnings trajectories that displaced workers would have experienced absent displacement, skill redundancy sets displaced workers on paths with permanently lower earnings.