The size distribution of innovations revisited: An application of extreme value statistics to citation and value measures of patent significance

This paper focuses on the analysis of size distributions of innovations, which are known to be highly skewed. We use patent citations as one indicator of innovation significance, constructing two large datasets from the European and US Patent Offices at a high level of aggregation, and the Trajtenberg [1990, A penny for your quotes: patent citations and the value of innovations. Rand Journal of Economics 21(1), 172–187] dataset on CT scanners at a very low one. We also study self-assessed reports of patented innovation values using two very recent patent valuation datasets from the Netherlands and the UK, as well as a small dataset of patent licence revenues of Harvard University. Statistical methods are applied to analyse the properties of the empirical size distributions, where we put special emphasis on testing for the existence of ‘heavy tails’, i.e., whether or not the probability of very large innovations declines more slowly than exponentially. While overall the distributions appear to resemble a lognormal, we argue that the tails are indeed fat. We invoke some recent results from extreme value statistics and apply the Hill [1975. A simple general approach to inference about the tails of a distribution. The Annals of Statistics 3, 1163–1174] estimator with data-driven cut-offs to determine the tail index for the right tails of all datasets except the NL and UK patent valuations. On these latter datasets we use a maximum likelihood estimator for grouped data to estimate the tail index for varying definitions of the right tail. We find significantly and consistently lower tail estimates for the returns data than the citation data (around 0.6–1 vs. 3–5). The EPO and US patent citation tail indices are roughly constant over time, but the latter estimates are significantly lower than the former. The heaviness of the tails, particularly as measured by value indicators, we argue, has significant implications for technology policy and growth theory, since the second and possibly even the first moments of these distributions may not exist.     

Market liquidity as dynamic factors

We use recent results on the Generalized Dynamic Factor Model (GDFM) with block structure to provide a data-driven definition of unobservable market liquidity and to assess the complementarity of two observed liquidity measures: daily close relative spreads and daily traded volumes for a sample of 426 S&P500 constituents recorded over the years 2004-2006. The advantage of defining market liquidity as a dynamic factor is that, contrary to other definitions, it tackles time dependence and commonness at the same time, without making any restrictive assumptions. Both relative spread and volume in the dataset under study appear to be driven by the same one-dimensional common shocks, which therefore naturally qualify as the unobservable market liquidity shocks.     

The multi-state latent factor intensity model for credit rating transitions

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model.     

Economies in Transition: How Important Is Trade Openness for Growth?*

We investigate the effect of trade openness on economic growth in transition countries using a transparent statistical methodology that leads to data‐driven case studies. In particular, we employ synthetic control methods in a panel of transition economies and compare GDP growth in treated (that is, open) countries with growth in a convex combination of similar but untreated (that is, closed) countries. We find that trade liberalization tends to have a positive effect on the pattern of real GDP per capita. One of our most robust results shows that making the transition without opening up to trade considerably hampers growth.     

The heterogeneous impact of taxation on economic development: New insights from a panel cointegration approach

In this paper, we study the role of taxation on long-run income performance. In the theoretical part of the study, we develop a stylized model based on Barro (1990), in which income taxation has two contradictory roles in the standard Solow (1956) setup: on the one hand, taxation appropriates resources that would otherwise be used for physical capital accumulation, and on the other, it is the source of government spending, which is used to support private production. In the empirical part of the study, the impact of consumption tax, personal income tax, corporate profit tax and property tax on income is estimated using the common correlated effects (CCE) panel cointegration approach, which allows for cross-sectional dependencies and provides both panel- and country-specific results. The panel findings for 30 OECD countries for the period of 1995–2016 indicate that only consumption tax has a statistically significant negative effect on long-run income. However, because the type and sign of the tax coefficients are heterogeneous for the country-specific results, we conclude that taxation has heterogeneous effects on income.     

The risk spillover between China’s economic policy uncertainty and commodity markets: Evidence from frequency spillover and quantile connectedness approaches

Based on the frequency spillover method extended by Baruník and Křehlík (2018), we explore the risk spillover relationship between China’s economic policy uncertainty (CNEPU) and commodity futures in different frequency domains with daily settlement price data of 14 commodity futures in China. The results show that the risk spillover relationship between CNEPU and the commodity market mainly occurs in the short term. Quantile connectedness results show that economic policy uncertainty, which mainly plays the role of risk transmitter, is more closely related to the commodity market during the market boom and recession. Soybeans, soybean meal, and corn have shown high investment value in the process of market recovery, which is exposed to less risk spillover from policy uncertainty. Finally, the economic crisis with different characteristics will have specific impacts on asymmetric risk spillovers based on certain impact mechanisms.     

Conceptualizing and measuring “industry resilience”: Composite indicators for postshock industrial policy decision-making

Can resilience be a relevant concept for industrial policy? Resilience is usually described as the ability of a socioeconomic system to recover from unexpected shocks. While this concept has caught the attention of regional economics researchers seeking to understand the different patterns behind regional recovery after a disruption, it is increasingly recognized that resilience can have policy-relevant conceptual applications in many other regards. In this paper, we apply it to industries and define the “industry resilience” concept and measurements. Our contribution is twofold. Theoretically, we frame industry resilience as a useful conceptual framework for policy-making to support the selection of industrial policy targets that are more capable of recovering after unexpected shocks. In addition, industry resilience can mitigate government failures by supporting decision-makers in promoting both economically and socially sustainable structural change. Methodologically, building on post-2008 U.S. data, we develop two composite indicators (CIs) to separately analyze quantitative and qualitative postshock variations in sectoral employment. Such CIs support policy-makers in visualizing sectoral performances dynamically and multidimensionally and can be used to compare each sector both to other sectors and to its counterfactual. Our results highlight that sectors react heterogeneously to shocks. This points to the relevance of tailoring vertical industrial policies according to sector features and the aims of industrial policy initiatives.     

How do natural resource endowment and institutional quality influence the nexus between external indebtedness and welfare in Africa?

Resource-rich African countries are often saddled with high external indebtedness. Yet, their management of resource endowment, a logical source of debt repayment, also remains a challenge, alongside their characteristic weak institutions. We investigate the relationship between external indebtedness and welfare whilst considering the pervasive influence of both natural resource rents and the quality of institutions. Using a two-stage analysis, we find that the quality of institutions, mineral- and oil-resource rents negatively affect indebtedness, while rents of aggregated natural resources, which include agricultural commodities, increase indebtedness. In the second stage, we find that welfare is enhanced by the quality of institutions, mineral- and oil-resource rents. These sets of results are interestingly conditional on the degree of resource endowment and the income level of countries, alongside the interesting effects of external indebtedness on welfare, both of which, importantly, nuance past results on the “resource curse”. Furthermore, the proxy for welfare matters: the human development index proxy reflects more the theoretical expectations of unsustainable indebtedness on welfare, than does the GDP per capita alternative. These and other results of our paper, which hold useful policy guides for African countries, are robust to alternative estimation techniques and other checks.