A production-theory approach to migration is adopted in this paper to address the role of migrant workers from extra-EU countries in Italian manufacturing firms. The adoption of flexible functional forms to model firm-level technology lets us directly derive different measures of elasticity from the coefficients of the estimated production and cost functions. The use of foreign labour is shown to affect the industry composition in favour of low skill intensive sectors and the estimated cross demand elasticities confirm the complementarity between migrant and native workers found in previous studies. However, the two labour inputs prove to be substitutes in terms of the Morishima elasticity of substitution: in general, firms tend to increase the foreign labour intensity of production in response to a decline in migrants’ wage, while the migrant to domestic labour ratio responds to changes in the domestic workers’ wage only for firms in low skill intensive sectors.

The truncated (below zero) normal distribution is considered. Some existing results are surveyed, and a recursive moment formula is used to derive the first four central moments in terms of the mean and variance of the underlying normal and in terms of lower moments of the truncated distribution. Bounding and monotonicity of the moments of the truncated distribution are considered and some previously unknown features of the distribution are presented. Moment results are used to derive a test of the distributional form. The distribution is commonly used in economics, particularly in the stochastic frontier literature. Application to the stochastic frontier model is briefly considered.

The standard data envelopment analysis measures of the Charnes–Cooper–Rhodes (CCR) and slacks-based measure (SBM) are ratio-form efficiency measures, which do not yield the closest projections. However, because of difficulties implementing projections based on standard measures, the closest projections identified by means of least-distance measures may be preferable. Taking into account the practical significance of closest projection points, Aparicio et al. (J Prod Anal 28:209–218, 2007) proposed a least-distance approach based not only on the 1-norm (Manhattan), 2-norm (Euclidean), and ∞-norm (Chebyshev), but also on the procedure presented by Cherchye and Van Puyenbroeck (Eur J Oper Res 132(2):287–295, 2001). Recently, Tone (Eur J Oper Res 200(2):901–907, 2010) presented a least-distance version of SBM (or equivalently, the enhanced Russell graph measure). However, these authors examined neither the occurrence of multiple optimal projections nor the strong/weak monotonicity of the ratio-form least-distance efficiency measures over the efficient frontier of the production technology. Furthermore, it is not well known that the standard measures of CCR and SBM suffer from the occurrence of multiple optimal solutions or efficient targets. Therefore, the present paper also investigates the possibility of multiple optimal targets and axiomatic properties of ratio-form efficiency measures within a unified p-norm efficiency measurement framework.

Alternative models of productivity predict a range of its determinants besides that of research and development (R&D). We investigate the robustness of R&D vis-a-vis a dozen productivity determinants in a panel of 16 Organisation for Economic Co-operation and Development countries through panel cointegration, bootstrap simulations and extensive sensitivity tests. Domestic knowledge stocks, international knowledge diffusion and human capital remain robust across all measures. The cross-country differences in accumulated knowledge stocks and human capital appear to explain productivity differences across countries.

This paper is about the relation between output and labour productivity measures for individual production units and for aggregates such as industries, sectors, or economies. In the framework of discrete time periods several useful, symmetric expressions are derived and confronted with results from the literature.

Much of the literature on static efficiency measurement models assumes that the inputs are fully used for producing outputs in the same period, with the result that no time interdependence exists between the input utilization and output realizations for a production unit in consecutive periods. A review of the literature on non-parametric dynamic efficiency models identifies five key factors of the inter-temporal dependence between input and output levels over different periods: (i) production delays; (ii) inventories (inventories of exogenous inputs or inventories of intermediate and final products); (iii) capital or generally quasi-fixed factors (and associated embodied technological change, vintage specific capital); (iv) adjustment costs; and (v) incremental improvement and learning models (disembodied technological change). This paper reviews the literature and finds that the dynamic issues associated with adjustment costs and capital have received considerable attention in the literature, whereas the dynamic issues associated with inventories have received less attention. The paper concludes with suggestions for future research such as relaxing the perfect anticipation/knowledge assumption for future variables, prices, and states. Moreover, Dynamic Network Data Envelopment Analysis has provided a unifying framework for some dynamic factors, but further development of these models is necessary including meaningful applications.

This paper makes two important contributions to the literature on prediction intervals for firm specific inefficiency estimates in cross sectional SFA models. Firstly, the existing intervals in the literature do not correspond to the minimum width intervals and in this paper we discuss how to compute such intervals and how they either include or exclude zero as a lower bound depending on where the probability mass of the distribution of \( u_{i} |\varepsilon_{i} \) resides. This has useful implications for practitioners and policy makers, with greatest reductions in interval width for the most efficient firms. Secondly, we propose an ‘asymptotic’ approach to incorporating parameter uncertainty into prediction intervals for firm specific inefficiency (given that in practice model parameters have to be estimated) as an alternative to the ‘bagging’ procedure suggested in Simar and Wilson (Econom Rev 29(1):62–98, 2010). The approach is computationally much simpler than the bagging approach.

This paper presents a firm-level analysis of multifactor productivity (MFP) in Italy between 1998 and 2004. Using data envelopment analysis (DEA) technique, MFP change are measured for 31 industries and decomposed into efficiency change and technical change (which in turn is interpreted as the combined effect of technical progress and scale economies). The results highlight the stagnation in many Italian production activities and even a decrease in MFP in some industries. A non-parametric statistical test on the results obtained from DEA reveals that the analysed larger firms have been more likely to perform better in efficiency than the smaller ones. This outcome seems to complement the Schumpeterian view of a relatively high attitude of larger firms towards technological innovation and productivity growth.

This paper analyses the productive efficiency of 141 public hospitals from 1998 to 2004 in two Canadian provinces; one a small province with a few small cities and a generally more rural population and the other a large province that is more urban in nature, with a population who mainly live in large cities. The relative efficiencies of the hospitals, the changes in productivity during this time period, and the relationship between efficiency and the size or scale of the hospitals are investigated using data envelopment analysis. The models for the production of health care use case mix adjusted hospital discharges as the output, and nursing hours as inputs. We find clear differences between the two provinces. Making use of ‘own’ and ‘meta’ technical efficiency frontiers, we demonstrate that efficient units in the larger and more urban province are larger than non-efficient units in that province. However, efficient hospitals in the smaller and more rural province are smaller than non-efficient hospitals in that province. Overall, efficient hospitals in the larger more urban province are larger than efficient hospitals in the smaller more rural province. This has interesting policy implications—different hospitals may have different optimal sizes, or different efficient modes of operation, depending on location, the population they serve, and the policies their respective provincial governments wish to implement. In addition, there are lessons to be learned by comparing the hospitals across the two provinces, since the inefficient hospitals in the small rural province predominantly use hospitals from the large urban province as benchmarks, such that substantially larger improvement potential can be identified by inter-provincial rather than intra-provincial benchmarking analysis.

We employ data envelopment analysis (DEA) methods to construct the world production frontier, which is in turn used to decompose (labor) productivity growth into components attributable to technological change (shift of the production frontier), efficiency change (movements toward or away from the frontier), physical capital deepening, and human capital accumulation over the 1965–2007 period. Using this decomposition, we provide new findings on the causes of polarization (the emergence of bimodality) and divergence (increased variance) of the world productivity distribution. First, unlike earlier studies, we find that efficiency change is the unique driver of the emergence of a second (higher) mode. Second, while earlier studies attributed the overall change in the distribution exclusively to physical capital accumulation, we find that technological change and human capital accumulation are also significant factors explaining this change in the distribution (most notably the emergence of a long right-hand tail). Robustness exercises indicate that these revisions of earlier findings are attributable to the addition of (more recent) years and a much greater number of countries included in our sample. We also check to see whether our results are changed by a correction for the downward bias in the DEA construction of the frontier, concluding that these corrections affect none of our major findings (essentially because the level correction roughly washes out in changes.)