Abstract
This paper presents a novel nonparametric methodology to evaluate convergence in an industry, considering a multi-input multi-output setting for the assessment of total factor productivity. In particular, we develop two new indexes to evaluate sigma-convergence and beta-convergence that can be computed using nonparametric techniques such as Data Envelopment Analysis. The methodology developed is particularly useful to enhance productivity assessments based on the Malmquist index. The methodology is applied to a real world context, consisting of a sample of Portuguese construction companies that operated in the sector between 2008 and 2010. The empirical results show that Portuguese companies tended to converge, both in the sense of a and beta, in all construction activity segments in the aftermath of the financial crisis.

Abstract
Decision making in companies requires an assessment of the efficiency and productivity of individual inputs to provide insights into the scope for improvement of inputs' use. This paper estimates an input-specific Luenberger productivity growth indicator that can be decomposed to identify the contributions of input-specific technological change, technical efficiency change and scale efficiency change. These components for a specific input sum up to the aggregated indicators which are then compared with the traditional Luenberger indicator. The application focuses on panel data of Spanish and Portuguese construction firms over the period 2002-2011, accounting for three inputs: materials, labor and capital. The results show that aggregated productivity change and its components computed from the input-specific productivity indicator are different from those obtained using a traditional approach. The results also indicate that productivity change is negative for labor and capital for construction firms in both Spain and Portugal, while productivity change of materials is positive for Portugal and negative for Spain. Productivity decline is worse for capital in the Spanish construction firms, and for labor in Portugal.

Abstract
This paper develops an innovative methodology to assess municipal performance concerning the environmental efficiency of new buildings construction, focusing on the consumption of different types of materials. This study aims to support local governments in the definition of policies for improvements in service provision based on the results of a benchmarking study. The methodology developed includes two stages. The first step concerns the evaluation of municipal environmental efficiency using Data Envelopment Analysis and the identification of factors that may explain different levels of performance. The second step enables the classification of municipalities in terms of the efforts required to achieve environmental efficiency. For this purpose, we used clustering analysis, namely the k-means algorithm. To illustrate the methodology developed, we analyzed the data of the major materials used in the construction of new buildings (metals, non-metallic minerals, fossil fuels, and biomass) in the municipalities of Lisbon metropolitan area between 2003 and 2009. The study revealed that the environmental efficiency of new buildings construction varies considerably among municipalities, suggesting a high potential for performance improvement.

Abstract
This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.

Abstract
The purpose of this paper is to characterize the competitive positioning of the construction industry companies and evaluate their financial performance. The methodology proposed involves three major stages., The first stage concerns the identification of the competitive positioning of companies within the construction sector. This is achieved using a hierarchical clustering algorithm suitable for large datasets and mixed type variables. The second stage is the analysis of performance of the different clusters. This is done using the Data Envelopment Analysis technique. To characterize in detail the main performance features of each cluster, a decision tree is used to extract the main rules concerning the performance spread within each cluster and the gap between the cluster best practices and the national benchmarks. The third stage concerns the analysis of the strengths, weaknesses and areas of potential improvement for contractors in each competitive positioning. This required the analysis of benchmark companies of each cluster. The methodology proposed was applied for the analysis of performance of all contractors that operate in the Portuguese construction industry.

Abstract
This paper presents an exploratory study to assess the efficiency level of construction companies worldwide, exploring in particular the effect of location and activity in the efficiency levels. This paper also provides insights concerning the convergence in efficiency across regions. The companies are divided in three regions (Europe, Asia and North America), and in the three main construction activities (Buildings, Heavy Civil and Specialty Trade). We analyze a sample of 118 companies worldwide between 1995 and 2003. Data envelopment analysis is used to estimate efficiency, and the Malmquist index is applied for the evaluation of productivity change. Both methods were complemented by bootstrapping to refine the estimates obtained. A panel data truncated regression with categorical regressors is used to explore the impact of location and activity in the efficiency levels. The results reveal that the efficiency of North American companies is higher than the European and Asian counterparts. Other important conclusion points to a convergence in efficiency levels across regions as in North America productivity remains stable, whereas in Asia and Europe productivity improves.