Alternative balanced scorecards built from paradigm models in strategic HRM and employment/industrial relations and used to measure the state of employment relations and HR system performance across U.S. workplaces

This paper constructs alternative balanced scorecards based on high‐performance work system (HPWS) and employment relations system (ERS) models. The models are depicted and compared in diagrams and used as framework skeletons for building separate HPWS and ERS scorecards, intended to provide a detailed data picture of the operational health and performance of an organization's employment/HR system and its operations, processes, and inputs/outputs. The scorecards are filled in with nationally representative data from 2,000+ U.S. workplaces using more than 50 employment/HR indicators, as reported by separate panels of managers and employees. The indicators for each workplace are aggregated into an overall HR/employment system score, ranked from low‐to‐high, and graphed as frequency distributions. These distributions provide a unique snapshot picture of the mean and dispersion of the state of employment relations and HR system performance for companies across the United State. They also reveal that “models matter” since the HPWS and ERS scorecards provide distinctly different evaluation assessments.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Terror per Capita

Studies on the correlates of terrorism usually analyze total numbers of attacks or victims per country. However, what we may ultimately care about in terms of policy recommendations is the likelihood of any individual being subject to the respective phenomenon. Thus, we propose and explore a simple alternative measure of terrorism: terror per capita. Studying terror per capita across 162 countries from 1970–2015, the associated correlates differ substantially in terms of sign, levels of statistical significance, and magnitude from those when analyzing total terror. We illustrate two cases in point, serving as proof of concept. First, democracy, often associated with more total terror, emerges as a marginally negative predictor of terror per capita. Second, a larger share of Muslims in society is associated with a positive and statistically significant link to total terror, but emerges as a negative predictor of terror per capita. We find similar changes in sign and statistical relevance for GDP per capita and language fractionalization as correlates of terrorism. Depending on the policy question, studying terror per capita can greatly enhance our understanding of terrorism drivers, especially when analyzing data across countries with vastly differing population sizes.     

Real Exchange Rate Misalignments in the Euro Area

Open Economies Review - Building upon a Behavioural Equilibrium Exchange Rate model, estimated at a quarterly frequency since 1999 on a broad sample of 57 countries, this paper assesses both the...     

An analysis of the global position of U.S. manufacturing

This article provides an overview of the changing export and import flows of U.S. manufactured goods in the global economy, offers some thoughts on the repercussions of these changes, and outlines possible outcomes. © 2003 Wiley Periodicals, Inc.     

Industry Classifications: Aim, Scope and Techniques

Industry classifications select essential characteristics of technology and markets, condensating the vast heterogeneity of competitive environments into a smaller number of salient types. Although frequently applied in empirical studies on industrial economics, technological development, international trade, and competitiveness, we still find little or no methodological discussion and a striking lack of awareness for the different approaches pursued. This interpretative survey systematically collects information about the aim, scope and techniques relevant to the major classifications currently used in applied economic studies.     

Predicting Household Poverty: A Methodological Note with a Kenyan Example

Household surveys provide data that is used for identifying and measuring the poverty status of households and individuals. However, carrying out such surveys is expensive, especially in poor developing countries. Thus it is important to make maximum use of the available survey data in developing countries, especially in sub‐Saharan Africa, where such data are expensive to collect and analyse. This paper develops a simple method for using poverty indices derived from survey data for a given year, to predict poverty rates for subsequent periods without having to conduct a new household survey. We illustrate the workings of the method with data from Kenyan household surveys for 1994 and 1997.     

The Local Whittle Estimator of Long-Memory Stochastic Volatility

We propose a new semiparametric estimator of the degree of persistencein volatility for long memory stochastic volatility (LMSV) models.The estimator uses the periodogram of the log squared returnsin a local Whittle criterion which explicitly accounts for thenoise term in the LMSV model. Finite-sample and asymptotic standarderrors for the estimator are provided. An extensive simulationstudy reveals that the local Whittle estimator is much lessbiased and that the finite-sample standard errors yield moreaccurate confidence intervals than the widely-used GPH estimator.The estimator is also found to be robust against possible leverageeffects. In an empirical analysis of the daily Deutsche Mark/USDollar exchange rate, the new estimator indicates stronger persistencein volatility than the GPH estimator, provided that a largenumber of frequencies is used.