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This study asks whether working in a R&D intensive industry affects a worker's wage profile. If R&D investment translates into transferable human capital or knowledge, workers' mobility constitutes a negative externality from the point of view of the firm/industry that bears the cost of R&D activities. A steepening of the wage profile would address such externality.

Using PSID data combined with US BEA data on US manufacturing industries' R&D intensities between 1981 and 1992, regression analysis is used to explore the hypothesis that, similarly to general training, industry R&D steepens a worker's wage‐experience profile.

In general the evidence is mixed. The results obtained from biennial wage growth regressions support to some extent the hypothesis that exposure to R&D activities allows a specific group of workers to accumulate general human capital for which they pay a positive price in early stages of their career.

An important caveat applies to the results. Unlike previous research by Møen who uses firm level R&D, the results found in this study are generated by using industry level R&D, which, being possibly affected by severe measurement errors, may bias the estimated coefficients towards zero.

This study complements Møen's evidence based on Norwegian wages with the effects of industry‐specific R&D intensities on the earnings profile in US manufacturing industries. By investigating whether industry R&D affects the return to experience and/or to tenure this study addresses an overlooked issue of which type of skills R&D allows workers to accumulate.

Logistics real estate has been experiencing a recent rebirth led by the growth of retailing and e-commerce. Although these sectors are looking for facilities matching their logistics needs, the identification of the most suitable building becomes a challenging task. To date, from both the practitioner’s and academic perspectives there is a lack of models for assessing the quality of logistics facilities together with functionality (i.e. whether a warehouse is suitable for hosting a given logistics activity). The purpose of this paper is to fill this gap by developing a rating model for assessing the quality and functionality of logistics facilities.

A three-pronged methodology was adopted. First, a Systematic Literature Network Analysis (SLNA) was carried out to identify the relevant features that must be taken into consideration when assessing logistics real estate. Second, a Delphi method involving experts in the field was used to fine-tune the list of features that emerged from the SLNA process and to evaluate the importance of each feature from a company perspective. The rating model was developed and validated through pilot tests on 27 logistics facilities.

The rating model is divided into four sections: location, technical specifications, external spaces and internal areas. As an output, the model determines the building quality and main functionality, together with a gap analysis to detect the weakest emerging elements.

This research fills an identified research gap in the logistics real estate literature. Specifically, it offers a quantitative and shared evaluation method, which can be used to estimate building quality and functionality, thus extending the scope of the previous assessment methods available.