Nonlinearities in the real exchange rates: new evidence from developed and developing countries

This paper investigates nonlinearities in the dynamics of real exchange rates. We use Monte Carlo simulations to establish the size properties of the Teräsvirta-Anderson test, when the dynamics of the real exchange rate is influenced by an exogenous process. In addition, we show that a modified nonlinearity test, which includes additional right-hand-side variables, performs much better than the original in both Monte Carlo exercises and in the actual data on 1431 bilateral real exchange rate series. Finally, we investigate the dynamics of the real exchange rate for both developed and developing countries using the modified test for the recent floating period. In general, the results find a greater incidence of nonlinear dynamics for developing country real exchange rates.     

Evaluation of the green technology innovation efficiency of China's manufacturing industries: DEA window analysis with ideal window width

Data Envelopment Analysis (DEA) window analysis with ideal window width is applied to evaluate the green technology innovation efficiency of 28 manufacturing industries in China during 2006–2014. The obtained results are compared with those calculated using the traditional DEA model, and convergence analysis of the efficiency is conducted. Five years is the obtained ideal window width and DEA window analysis with ideal window produces results closer to reality for China's manufacturing industry. The overall efficiency of the green technology innovation in the manufacturing sector is low following a wave-shaped curve – first decreasing, then increasing and decreasing again, with large inter-industrial differences. There are 8 high-, 14 medium- and 6 low-efficiency industries. A convergence trend in the green technology innovation efficiency within the 28 manufacturing industries exists, implying a catch-up effect between them.     

Are China’s regional agricultural productivities converging: How and why?

This article tests the hypotheses of convergence to a single level of total factor productivity (TFP), and a steady state of TFP growth rate in China’s agricultural sector. Based on multilateral TFP estimates we found that China’s agricultural sector has rebounded in recent years from a slower TFP growth in the 2005–2007 period. While convergence test results confirm a “catch-up” effect that provinces with lower TFP levels tend to grow faster than others, estimated rates of β convergence are conditional on how we capture the heterogeneity effect across regions. The rates of β convergence range from 0.016 to 0.039 under different model specifications. Estimates show that higher growth rates of educational attainment, R&D, and intermediate goods density (per unit of labor) can enhance TFP growth. Unfortunately, there is no evidence of an overall σ convergence, indicating that TFP levels are not converging except in the South region. It implies that to catch up with leading provinces, it would require extra efforts for those lagging behind by increasing their region-specific research investment, promoting rural educational attainment, and enhancing embodied technical change.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

Unleashing the Power of Networks: Shareholder Activism,Sustainable Development and Corporate Environmental Policy

In recent years, a new trend has emerged in which shareholder activists have formed networks to empower shareholders and magnify shareholder voices. This study explores the structural patterns and effectiveness of shareholder activism networks and shows how those networks affect corporate sustainability policies. We draw upon stakeholder influence theory, stakeholder network management theory and recent studies on activism networks to examine a shareholder activism network formed around environmental issues. The study found that (1) the structure of shareholder activist networks is largely driven by organizational attributes such as organization type, organizations’ human resources, media visibility and history; and (2) activist organizations with high centralities and eigenvector centralities enjoy more efficient results. This study contributes to our understanding of the business responses to shareholder demands on improving environmental performance and paves the way for future research on sustainable development through partnerships with shareholder networks. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

Krazy Kimmy's: A Case in Fraud Examination

This two‐part case allows for maximum student engagement through preparation, in‐class discussion, and through a Part B case that is part of the Teaching Note. Through this case, students work to identify possible fraud schemes, plan an investigation, and perform horizontal analysis calculations. Students will learn about the concept of ethical hacking, red flag identification, internal control weaknesses, and investigation considerations for fraud schemes. Further, in‐class planning for next steps and investigation can occur, providing an opportunity for group work and in‐depth learning. This case offers flexibility for instructors in that it can be covered over one class or multiple classes, or can be assigned as an independent or group assignment. This teaching note provides an overview of the case along with guidance for teaching this case.     

Structural transformation in emerging economies: leading sectors and the balanced growth hypothesis

The paper uses the World Input-Output Database to address patterns of structural transformation in BRIC countries, Indonesia, South Korea, Mexico and Turkey. Sectoral drivers of aggregate labour productivity growth, and the relative importance of within-sector versus employment reallocation effects on aggregate labour productivity growth, are evaluated using growth accounting decomposition methods. Decomposition results are used to assess how patterns of structural transformation relate to macroeconomic performance in terms of aggregate labour productivity, output and employment growth. Together with the construction of ‘Hirschman compliance indices’, decomposition results are also used to shed light on the balanced versus unbalanced growth debates. The paper goes on to assess the extent of complementarities between manufacturing and information and communications technology-intensive advanced services through intermediate inputs, comparing the eight emerging countries with G7 countries over time.     

On the power of indicators: how the choice of fuel poverty indicator affects the identification of the target population

In light of the creation of the EU Energy Poverty Observatory (EPOV) in January 2018 and the increase in debates on how fuel poverty is measured, we propose a critical analysis of fuel poverty indicators and demonstrate that choosing a given indicator is central to the identification of the fuel-poorpopulation.

First, we conducted an inter-indicator analysis to show how profiles of fuel-poor households vary depending on the indicator selected. We designed a multidimensional approach based on a multiple correspondence analysis and a hierarchical and partitioning clustering analysis to study characteristics of fuel-poor households. We highlight the difficulty of identifying a fuel-poor ‘typical profile’ and show that the composition of the population depends on the choice of the indicator.

Second, we applied an intra-indicator analysis using two objective expenditure-based indicators with thresholds. In particular, we conducted a sensitivity analysis based on a logit model including variables describing household and dwelling characteristics. We show that the profiles of fuel-poor households as well as the drivers of fuel poverty vary considerably with the chosen threshold level.Given these findings, we stress the need to review how we currently rely on conventional fuel poverty indicators to identify target groups and give some recommendations.     

Network analysis of the Caucasus’ image

Destination image is influenced by many factors, including destination promotion materials, the mass media as a general familiarity agent, the perceivers’ own characteristics and even researchers’ methodological choices. To isolate and minimize the impact of research on destination image, different qualitative and quantitative designs and analysis techniques have been utilized. However, no previous study utilized the network analysis technique, which may be useful to reveal a picture of destination image with the interconnections and hidden dynamisms of dimensions as well as its correlates. The present study applies this technique on qualitative data from an online sample of Americans on their perceptions of a relatively remote and unfamiliar destination, the Caucasus region, in order to hone in on the dramatic impact of mass media on destination image. Results revealed networks of meanings with residue of mass media messages about Boston bombing, with some differences among different genders and education levels.