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Studies of the impact of trade restrictiveness on growth, poverty or unemployment are frequent in the academic literature. Few authors, however, provide a precise definition of what they mean by trade restrictiveness. When they do, the definition is unlikely to have tight links with trade theory. The objective of this article is to fill this gap by providing for 78 developing and developed countries clearly defined indicators of trade restrictiveness that are well grounded in trade theory. Results suggest that poor countries tend to have more restrictive trade policies but they also face higher trade barriers on their exports.

The objective of this work is to use multiple Data Envelopment Analysis (DEA)/Benefit of the Doubt (BoD) approaches for the readjustment and exploitation of the Human Development Index (HDI). The HDI is the leading indicator for the vision of \"development as freedom\"; it is a Composite Index, wherein three dimensions (income, health, and education), represented by four indicators, are aggregated. The DEA-BoD approaches used in this work were: the traditional BoD; the Multiplicative BoD; the Slacks Based Measure (SBM) BoD; the Range Adjusted Model (RAM) BoD; weight restrictions; common weights; and tiebreaker methods. These approaches were applied to raw and normalized HDI data from 2018, to generate 40 different rankings for 189 countries. The resulting indexes were analyzed and compared using Social Network Analysis (SNA) and information derived from DEA itself (slacks, relative contributions, targets, relative targets and benchmarks). This paper presents useful DEA derived indexes that could be replicated in other contexts. In addition, it contributes by presenting a clearer picture of the differences between BoD models and offering a new way to appreciate the world's human development panorama.

This paper presents a coincident and a leading composite monthly indicator for the world business cycle—the Global Economic Barometers. Both target the world’s output growth rate and consist of economic tendency surveys results from many countries around the world. The calculation of these indicators comprises two main stages. The first consists of a variable selection procedure, in which a pre-set correlation threshold and the targeted leads to the reference series are used as selection criteria. In the second stage, the selected variables are combined and transformed into the respective composite indicators, computed as the first partial least squares factor with the reference series as response variable. We analyse the characteristics of the two new indicators in a pseudo real-time setting and demonstrate that both are useful additions to the small number of indicators for the global business cycle published so far. Finally, yet importantly, the Barometers were quick to plunge in the beginning of March 2020 and have since then given a reliable real-time reflection of the economic consequences of the Covid-19 pandemic.

The last recession in Europe has shown us that econometric models that factor in the qualitative perceptions and expectations of businesses and consumers—along with commonly used quantitative macroeconomic variables—can produce better results in explaining and forecasting economic activity. The European Commission's Business and Consumer Surveys (BCS) conducted by the European Commission (EC) are high-quality source for this kind of \"soft\" variables. One of the composite indicators based on BCS is the economic sentiment indicator (ESI), which is the main leading indicator for overall economic activity. We propose two new models for constructing the ESI. The first model is based on minimizing the sum of absolute values of estimation errors. The second model is based on maximizing the number of correctly predicted directions of change for GDP growth rates. Rather than using the EC's official standardization procedure for data, our models use \"raw\" data, thus simplifying the process of preparing the data. The models were tested for various prognostic horizons (up to four quarters in advance), using aggregated quarterly data for the European Union from 1996Q4 to 2019Q2. The results show that our new models significantly improve the ESI's predictive power, especially in predicting the direction of change of GDP growth rates, which is the main purpose of the BCS indicators. The best results are obtained for predictions made up to one quarter in advance, for which the second model correctly predicts the direction of change of GDP growth rates in 78.89% of cases versus the official ESI's 65.56%.

Regime shifts are massive, often irreversible, rearrangements of nonlinear ecological processes that occur when systems pass critical transition points. Ecological regime shifts sometimes have severe consequences for human well-being, including eutrophication in lakes, desertification, and species extinctions. Theoretical and laboratory evidence suggests that statistical anomalies may be detectable leading indicators of regime shifts in ecological time series, making it possible to foresee and potentially avert incipient regime shifts. Conditional heteroscedasticity is persistent variance characteristic of time series with clustered volatility. Here, we analyze conditional heteroscedasticity as a potential leading indicator of regime shifts in ecological time series. We evaluate conditional heteroscedasticity by using ecological models with and without four types of critical transition. On approaching transition points, all time series contain significant conditional heteroscedasticity. This signal is detected hundreds of time steps in advance of the regime shift. Time series without regime shifts do not have significant conditional heteroscedasticity. Because probability values are easily associated with tests for conditional heteroscedasticity, detection of false positives in time series without regime shifts is minimized. This property reduces the need for a reference system to compare with the perturbed system.

The contribution of recent “multidimensional indices of poverty” may not be as obvious as one thinks. There are two issues in assessing that contribution: whether one believes that a single index can ever be a sufficient statistic of poverty, and whether one aggregates in the space of “attainments,” using prices when appropriate, or “deprivations,” using weights set by the analyst. The paper argues that we should aim for a credible set of multiple indices rather than a single multidimensional index. Partial aggregation will still be necessary, but ideally the weights should be consistent with well-informed choices by poor people.

Active leading indicators (ALIs) have the potential to identify safety hazards and prompt immediate actions to prevent incidents. Currently, there is a major gap in research that incorporates a fully automated ALI system because implementation has been hindered by a lack of established industry thresholds of measurable performance that would trigger an actionable response. Therefore, this paper addresses this gap by presenting a new method that utilizes the Internet of Things (IoT) to collect quantifiable data which can trigger an actionable response in real time based on established thresholds. This novel method integrates the Construction Industry Institute (CII) active leading indicator framework with a prototype IoT-based system. Significantly, the ALI provides the physical–virtual feedback loop, which is an essential aspect of the IoT system because it provides real-time feedback to both the users and systems. This paper also identifies potential inputs to the ALI framework from emerging IoT-enabled systems. A case study was presented to initially validate the IoT-based ALI framework. Bluetooth-enabled heart rate monitors were issued to workers in a hazardous and critical mining construction site. The ALIs that were recorded included heart rate and body temperature. Thresholds were established that alerted the monitoring safety staff when a worker exhibited potentially unsafe conditions. The results of the study demonstrated the feasibility of the system. Additionally, other results included worker resistance; non-disclosing of medical conditions, and limitations for IoT connectivity.

Thomas (2009) dismisses the Worldwide Governance Indicators (WGI) as an ‘elaborate and unsupported hypothesis’ because of the failure to demonstrate the ‘construct validity’ of these indicators. We argue that ‘construct validity’ is not a useful tool to assess the merits of the WGI, and even if it were, Thomas provides no evidence of any practical consequences of failure to meet the criteria of construct validity.

We examine the information content of a unique set of macroeconomic, bank-specific, market and credit registry variables as regards their ability to forecast non-performing loans using a panel data set of nine Greek banks. We distinguish between business, consumer and mortgage loans and investigate their differences with respect to their optimal predictors. The quasi-AIM approach (Carson et al. in Int J Forecast 27:923–941, 2010) is utilized in order to take into account heterogeneity across banks and minimize estimation uncertainty. In addition, we calculate a number of forecasting measures in order to take into account the policy makers’ preferences. We find that market variables, specifically the supermarket sales, confidence indices for the services and construction sector and the business sentiment index represent good forecasting variables for most categories of NPLs. In addition, industrial production is the optimal predictor for consumer NPLs and imports for business NPLs. Finally, bank-specific variables represent top-performing leading indicators for business NPLs. Our results have significant implications for stress-testing credit risk in a top-down manner and for supervisory and macro-prudential policy design.