R&D? A Small Contribution to Productivity Growth

In this paper I calibrate the contribution of R&D investments to productivity growth. The basis for the analysis is the free entry condition. This yields a relationship between the resources devoted to R&D and the growth rate of technology. Since innovators are small, this relationship is not directly a¥ected by the size of the R&D externalities, the presence of scale effects or diminishing returns in R&D after controlling for the growth rate of output and the interest rate. The resulting contribution of R&D to productivity growth in the US is smaller than three to five tenths of one percentage point. Interestingly, this constitutes an upper bound for the case where innovators internalize the consequences of their R&D investments on the cost of conducting future innovations. From a normative perepective, this analysis implies that, if the innovation technology takes the form assumed in the literature, the actual US R&D intensity may be the socially optimal.RESEARCH AND DEVELOPMENT; PRODUCTIVITY GROWTH; TOTAL FACTOR PRODUCTIVITY

Using Investment Data to Assess the Importance of Price Mismeasurement

This paper presents a new approach to assess the role of price mismeasurement in the productivity slowdown. I invert the firm's investment decision to identify the embodied and disembodied components of productivity growth. With a Cobb-Douglas production function, output price mismeasurement only should affect the latter. Contrary to the mismeasurement hypothesis, I find that in the Post-War period, disembodied productivity grew faster in the hard-to-measure than in the non-manufacturing easy-to-measure sectors, and that disembodied productivity slowed down less in the hard-to-measure than in the easy-to-measure sectors since the 70's. These results hold a fortiori when capital and labor are complements.INVESTMENT; PRICE MISMEASUREMENT; PRODUCTIVITY SLOWDOWN; TOTAL FACTOR PRODUCTIVITY ;EMBODIED AND DISEMBODIED PRODUCTIVITY

Two Ways to Rule Out the Overconsumption Paths in the Ramsey Model with Irreversible Investment

In this note I develop two approaches to rule out the overconsumption paths in the Ramsey model with irreversible capital. The ørst focuses on the multiplier of the irreversible constraint and is applied to the situation where preferences are CES and the production function is Cobb-Douglas. The second, relies on a revealed preference argument and is used to rule out overconsumption paths when the preferences are strictly concave and the initial level of per effective capital is below its steady state level.RAMSEY GROWTH MODEL; IRREVERSIBLE CAPITAL; OVERCONSUMPTION PATHS

R&D: A Small Contribution to Productivity Growth

In this paper I evaluate the contribution of R&D investments to productivity growth. The basis for the analysis are the free entry condition and the fact that most R&D innovations are embodied. Free entry yields a relationship between the resources devoted to R&D and the growth rate of technology. Since innovators are small, this relationship is not directly affected by the size of R&D externalities, or the presence of aggregate diminishing returns in R&D after controlling for the growth rate of output and the interest rate. The embodiment of R&D-driven innovations bounds the size of the production externalities. The resulting contribution of R&D to productivity growth in the US is smaller than three to five tenths of one percentage point. This constitutes an upper bound for the case where innovators internalize the consequences of their R&D investments on the cost of conducting future innovations. From a normative perspective, this analysis implies that, if the innovation technology takes the form assumed in the literature, the actual US R&D intensity may be the socially optimal.

R&D? A Small Contribution to Productivity Growth

In this paper I evaluate the contribution of R&D investments to productivity growth. The basis for the analysis are the free entry condition and the fact that most R&D innovations are embodied. Free entry yields a relationship between the resources devoted to R&D and the growth rate of technology. Since innovators are small, this relationship is not directly affected by the size of the R&D externalities, or the presence of aggregate diminishing returns in R&D after controlling for the growth rate of output and the interest rate. The embodiment of R&D- driven innovations bounds the size of the production externalities. The resulting contribution of R&D to productivity growth in the US is smaller than three to five tenths of one percentage point. This constitutes an upper bound for the case where innovators internalize the consequences of their R&D investments on the cost of conducting future innovations. From a normative perspective, this analysis implies that, if the innovation technology takes the form assumed in the literature, the actual US R&D intensity may be the socially optimal.Research and Development, productivity growth, total factor productivity

Dynamic Consistency of Conditional Simple Temporal Networks via Mean Payoff Games: a Singly-Exponential Time DC-Checking

Conditional Simple Temporal Network (CSTN) is a constraint-based graph-formalism for conditional temporal planning. It offers a more flexible formalism than the equivalent CSTP model of Tsamardinos, Vidal and Pollack, from which it was derived mainly as a sound formalization. Three notions of consistency arise for CSTNs and CSTPs: weak, strong, and dynamic. Dynamic consistency is the most interesting notion, but it is also the most challenging and it was conjectured to be hard to assess. Tsamardinos, Vidal and Pollack gave a doubly-exponential time algorithm for deciding whether a CSTN is dynamically-consistent and to produce, in the positive case, a dynamic execution strategy of exponential size. In the present work we offer a proof that deciding whether a CSTN is dynamically-consistent is coNP-hard and provide the first singly-exponential time algorithm for this problem, also producing a dynamic execution strategy whenever the input CSTN is dynamically-consistent. The algorithm is based on a novel connection with Mean Payoff Games, a family of two-player combinatorial games on graphs well known for having applications in model-checking and formal verification. The presentation of such connection is mediated by the Hyper Temporal Network model, a tractable generalization of Simple Temporal Networks whose consistency checking is equivalent to determining Mean Payoff Games. In order to analyze the algorithm we introduce a refined notion of dynamic-consistency, named \epsilon-dynamic-consistency, and present a sharp lower bounding analysis on the critical value of the reaction time \hat{\varepsilon} where the CSTN transits from being, to not being, dynamically-consistent. The proof technique introduced in this analysis of \hat{\varepsilon} is applicable more in general when dealing with linear difference constraints which include strict inequalities

Checking Dynamic Consistency of Conditional Hyper Temporal Networks via Mean Payoff Games (Hardness and (pseudo) Singly-Exponential Time Algorithm)

In this work we introduce the \emph{Conditional Hyper Temporal Network (CHyTN)} model, which is a natural extension and generalization of both the \CSTN and the \HTN model. Our contribution goes as follows. We show that deciding whether a given \CSTN or CHyTN is dynamically consistent is \coNP-hard. Then, we offer a proof that deciding whether a given CHyTN is dynamically consistent is \PSPACE-hard, provided that the input instances are allowed to include both multi-head and multi-tail hyperarcs. In light of this, we continue our study by focusing on CHyTNs that allow only multi-head or only multi-tail hyperarcs, and we offer the first deterministic (pseudo) singly-exponential time algorithm for the problem of checking the dynamic-consistency of such CHyTNs, also producing a dynamic execution strategy whenever the input CHyTN is dynamically consistent. Since \CSTN{s} are a special case of CHyTNs, this provides as a byproduct the first sound-and-complete (pseudo) singly-exponential time algorithm for checking dynamic-consistency in CSTNs. The proposed algorithm is based on a novel connection between CSTN{s}/CHyTN{s} and Mean Payoff Games. The presentation of the connection between \CSTN{s}/CHyTNs and \MPG{s} is mediated by the \HTN model. In order to analyze the algorithm, we introduce a refined notion of dynamic-consistency, named $\epsilon$-dynamic-consistency, and present a sharp lower bounding analysis on the critical value of the reaction time $\hat{\varepsilon}$ where a \CSTN/CHyTN transits from being, to not being, dynamically consistent. The proof technique introduced in this analysis of $\hat{\varepsilon}$ is applicable more generally when dealing with linear difference constraints which include strict inequalities.Comment: arXiv admin note: text overlap with arXiv:1505.0082