The heterogeneity of carbon Kuznets curves for advanced countries: comparing homogeneous,heterogeneous and shrinkage/Bayesian estimators

We investigate Carbon Kuznets Curves (CKC) relationships for advanced countries grouped in policy relevant groups – North America and Oceania, South Europe, North Europe – by means of various homogeneous, heterogeneous and shrinkage/Bayesian panel estimators. We try to provide an answer to the question ‘how sensitive are the CKC estimates to changes in the level of parameters' heterogeneity?’. We do find that in coherence with their ‘policy and economic’ commitment to carbon reductions and environmental market-based instruments implementation, bell shapes are present only for northern EU, which leads the group of advanced countries. The other two lag behind. We show for the first time that CKC shapes are present if we net out Europe of the southern and less developed countries. This is coherent with the Kuznets paradigm. The negative side of the tale is that they characterize a bunch of few countries. Other advanced countries lag behind and are far from reaching a CKC dynamics. Heterogeneous and Bayesian estimators clearly show this, with the EU presenting turning points closely around $13?000 per capita Gross Domestic Product (GDP). Heterogeneous panel estimates also show that for lagging countries presumed bell shapes turn into linear relationships. The stability of outcomes across models is stronger when we compare heterogeneous rather than homogeneous models. If it is compared with other studies, our analysis highlights a relative lower variability across specifications.     

Has Brexit affected employment in Japanese affiliates in the UK?

On 23 June 2016, a referendum was held in the United Kingdom (UK) to decide whether the country should leave or remain a member of the European Union (EU). The ‘Leave’ vote won by 52 per cent to 48 per cent of voters, leading to the EU–UK Withdrawal Agreement that came into force in 2020 and the Trade and Cooperation Agreement that came into force in 2021. Brexit is not exclusively a European matter; it affects third parties as well. In this article we examine the presence of Japanese affiliates in Europe during 2000–21 by analysing data obtained from the Toyo Keizai's Overseas Japanese Companies database. While the UK has been a major beneficiary of Japanese investments in Europe, its share of Japanese affiliates in Europe has decreased, especially during 2000–10. Brexit has had no negative impact on direct employment created by Japanese affiliates in the UK in the short term, but its impact in the long term could be exacerbated by any further policy divergence between the UK and the EU.     

Economics of externalities and public policy

In this paper we study a particular case of “multiple” externalities associated to the production of a good/activity, whose external effects can change from positive to negative depending on the level of output (intersecting externalities). To analyze their impact on the public policy we propose a very simple two-agent partial equilibrium model in the technological context of externalities. In a static framework, the centralized solution always implies an optimal policy, which may consist of taxation or subsidization depending on the individual optimum and on the technology parameters. In a dynamic model with local knowledge of the efficiency function and instantaneous output adjustments, such an optimal policy can be structurally stable or unstable. In the latter case, under small changes of the parameters the policy may switch from low taxation/subsidization to high taxation/subsidization or vice versa, or even jump discontinuously from taxation to subsidization or vice versa. Furthermore, the decentralized solution based upon “tradable rights” can be economically equivalent to the centralized solution in the form of taxation policy but the two solutions may be not politically equivalent.

Global Imbalances: The Role of Emerging Asia

This paper investigates the role played by emerging Asia in the emergence and evolution of the global trade imbalances. Based on simulations in a general-equilibrium model of the world economy, we find that a productivity slowdown in the nontradable sector of these economies in the second half of the 1990s fits regional macroeconomic developments relatively well, but has limited spillover effect to the United States trade balance. In contrast, an increase in the desired level of emerging Asia net foreign assets starting in 2001 not only fits regional developments relatively well, but also has a significant spillover effect to the United States.     

Corporate governance and sustainability in water utilities. The effects of decorporatisation in the city of Naples,Italy

The recent economic literature has largely investigated sustainability in the provision of public utilities, highlighting the role of governance models in the determination of economic results. Little attention however has been devoted to the social and environmental dimensions of sustainability. In Italy, a long-lasting debate on governance structures in the water sector has been fuelled by scholars and policymakers for more than 30 years, whereas the first (and unique) experiment involving the return of full public management in water provision is taking place in the city of Naples. The present work analyses this peculiar case study, aiming to assess the effects of a major governance shift that occurred in the early 2010s—that is, decorporatisation—in terms of economic, social and environmental sustainability. We resort to a bundle of qualitative and quantitative techniques to address the research question and our exploratory results suggest that decorporatisation was overall beneficial: Although little changed in terms of economic sustainability, the social and environmental dimensions benefitted from the shift in governance.     

Multicollinearity in regression: an efficiency comparison between L<Subscript>p</Subscript>-norm and least squares estimators

Multicollinearity is one of the most important issues in regression analysis, as it produces unstable coefficients’ estimates and makes the standard errors severely inflated. The regression theory is based on specific assumptions concerning the set of error random variables. In particular, when errors are uncorrelated and have a constant variance, the ordinary least squares estimator produces the best estimates among all linear estimators. If, as often happens in reality, these assumptions are not met, other methods might give more efficient estimates and their use is therefore recommendable. In this paper, after reviewing and briefly describing the salient features of the methods, proposed in the literature, to determine and address the multicollinearity problem, we introduce the L_pmin method, based on L_p-norm estimation, an adaptive robust procedure that is used when the residual distribution has deviated from normality. The major advantage of this approach is that it produces more efficient estimates of the model parameters, for different degrees of multicollinearity, than those generated by the ordinary least squares method. A simulation study and a real-data application are also presented, in order to show the better results provided by the L_pmin method in the presence of multicollinearity.     

Mixed‐frequency models with moving‐average components

Temporal aggregation in general introduces a moving‐average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed‐frequency (MF) model. The MA component is generally neglected, likely to preserve the possibility of ordinary least squares estimation, but the consequences have never been properly studied in the MF context. In this paper we show, analytically, in Monte Carlo simulations and in a forecasting application on US macroeconomic variables, the relevance of considering the MA component in MF mixed‐data sampling (MIDAS) and unrestricted MIDAS models (MIDAS–autoregressive moving average (ARMA) and UMIDAS‐ARMA). Specifically, the simulation results indicate that the short‐term forecasting performance of MIDAS‐ARMA and UMIDAS‐ARMA are better than that of, respectively, MIDAS and UMIDAS. The empirical applications on nowcasting US gross domestic product (GDP) growth, investment growth, and GDP deflator inflation confirm this ranking. Moreover, in both simulation and empirical results, MIDAS‐ARMA is better than UMIDAS‐ARMA.     

Ethnographic Case Study (ECS): Abductive modeling of ethnography and improving the relevance in business marketing research

By extending interpretative methods to business settings, this paper formalizes a model of Ethnographic Case Study (ECS) built upon extensive literature review and abductive elaboration of two-year fieldwork on 12 Italian companies. Objectives and related contributions are twofold. First, key compulsory and complementary stages of ECS marketing research are presented for business contexts. Second, the paper envisions the quality of the knowledge generated through the ECS inquiry, and argues that the methodological peculiarities of this approach may help reduce the relevance gap affecting business research. The systematic cooperation between researchers and practitioners along the ECS phases may benefit relevance through (i) the fine tuning of reciprocal expectations, (ii) the sharing of the research experience, (iii) the multiplication of the beneficiaries of the findings granted by ECS, and (iv) the participation in the process of knowledge dissemination. In this light, the ECS model supports the convincement that methods are not only a way to theory validation but also to theory discovery.     

Factor‐Based Identification‐Robust Interference in IV Regressions

Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity asymptotically and in finite samples. Results are derived using asymptotic settings that are commonly used in both the factor and weak‐instrument literature. For the Anderson–Rubin statistic, we also provide analytical finite‐sample results that do not require any underlying factor structure. An illustrative Monte Carlo study reveals the following. Factor‐based tests control size regardless of instruments and factor quality. All factor‐based tests are systematically more powerful than standard counterparts. With informative instruments and in contrast to standard tests: (i) power of factor‐based tests is not affected by k even when large; and (ii) weak factor structure does not cost power. An empirical study on a New Keynesian macroeconomic model suggests that our factor‐based methods can bridge a number of gaps between structural and statistical modeling. Copyright © 2015 John Wiley & Sons, Ltd.