Working Papers

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.

Additional readings:

• Q&A with the authors
• Op-ed in VoxEU

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.