Abstract. The last three decades have witnessed a great deal of research effort devoted to measuring the private output elasticity of public capital. The wide range of available estimates have precluded any consensus so far, however. This paper reconciles the empirical findings of the literature by quantitatively analyzing a sample of 578 estimates collected from 68 studies for the 1983-2008 period. Using meta-regression analysis, we show how study design characteristics and publication bias can explain a large fraction of the variation across estimates. We find a short-run output elasticity of public capital supplied at the central government level of 0.083, which increases to 0.122 in the long run. If, in addition, only core infrastructure at a regional/local level of government is considered, these estimates are almost doubled. The average output elasticity of public capital amounts to 0.106. Our results suggest that public capital is undersupplied in OECD economies.

Publication bias distorts the available empirical evidence and misinforms policymaking. Evidence of publication bias is mounting in virtually all fields of empirical research. This paper proposes the endogenous kink (EK) meta‐regression model as a novel method of publication bias correction. The EK method fits a piecewise linear meta‐regression of the primary estimates on their standard errors, with a kink at the cutoff value of the standard error below which publication selection is unlikely. We provide a simple method of endogenously determining this cutoff value as a function of a first‐stage estimate of the true effect and an assumed threshold of statistical significance. Our Monte Carlo simulations show that EK is less biased and more efficient than other related regression‐based methods of publication bias correction in a variety of research conditions.

Meta‐analysis has become the conventional approach to synthesizing the results of empirical economics research. To further improve the transparency and replicability of the reported results and to raise the quality of meta‐analyses, the Meta‐Analysis of Economics Research Network has updated the reporting guidelines that were published by this Journal in 2013. Future meta‐analyses in economics will be expected to follow these updated guidelines or give valid reasons why a meta‐analysis should deviate from them.

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Some companies improve their production performance using manufacturing or operations models. In the last decade these models have come to be known as "X" production systems (XPS), or company-specific production systems. XPS systems have been oriented mainly to implement lean manufacturing and continuous improvement principles, but have shown little progress in terms of sustainability principles. The emergence of databases (DBs), big data, and business intelligence (BI) systems have enabled the creation of system panels to measure performance and manage business processes. These panels also allow assessment of the implementation of the principles, methods, and tools of the XPS. The effectiveness of these systems requires sufficient IT infrastructure and an acceptable quality of data, but the key performance indicator (KPI) panels of XPSs often lack consistent and acceptable data. In other cases, the IT infrastructure does not cover the needs of the personnel managing the operations, which limits the progress of XPSs. This paper documents the positive experience of one company in jointly auditing and improving data quality and IT infrastructure, which better aligned its XPS with sustainability objectives. It explores, using an empirical case study where the researchers were actively involved, the possibility of complementing the KPI panel system of XPS assessment systems with the principles of sustainability, data quality, and IT infrastructure. The paper describes the development and testing of an assessment tool based on those principles, and offers a number of results that may benefit similar small-and medium-sized companies (SMEs).In order to increase their competitiveness, these XPSs prioritize and deploy improvement projects and routines linked to a series of principles (e.g., total quality, shortest lead-time). These principles are concepts that companies use to differentiate their work from others. Remarkably, however, most XPS principles seem to be misaligned with the principles of sustainability. In fact, while some companies are currently implementing approaches for aligning their business strategy with sustainability (e.g., References [5,6]), most companies do so only occasionally or not at all [7]. The present paper is motivated by this alignment, or lack thereof, which has been suggested to be "the next phase of business sustainability" [8].Another question tackled in this research concerns the measurement and control of performance. Companies continuously measure the effectiveness of their actions not only with operations, but also with improvement projects that implement the principles, traditionally by means of queries to databases (DBs), pivot tables, etc. The last years have witnessed the development of big data and business intelligence (BI) systems to measure and support decision making, among other purposes. Given their success, many advanced countries are investing in big data and BI systems, taking advantage of the boom of the Industry 4.0 concept [9]. As in other areas, big data and BI sys...

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