Sample size and statistical significance of hazard regression parameters. An exploration by means of Monte Carlo simulation of four transition models based on Hungarian GGS data

In this paper, we explore the relation between sample sizes of female respondents aged 18-44 and the statistical significance of parameter estimates in four piecewise constant proportional hazard regression models by means of microsimulation. The underlying models for first marriage, first birth, second birth, and first divorce are estimated from Hungarian GGS data and interpreted and used as typical event-history models for the analysis of GGS data in general. The models are estimated from the full biographies as well as from three- and six-year inter-panel biographies of the simulated samples. The simulation results indicate that there is great sensibility of the parameters that reach statistical significance to the sample size precisely in the sample range of the GGS. This means that any reduction or increase in the sample size will notably affect the statistical analysis of the data. Marginal gains in terms of the number of significant parameters are especially high up to 3.000 respondents when applying rather modest thresholds of significance. For higher thresholds, marginal gains remain steep for sample sizes up to 5.000 respondents. When analyzing inter-panel histories, especially for a single three-year interval, the likelihood that parameter estimates are significant is very moderate. For 6-year inter-panel histories, we get better results, at least for a sample size of at least 3.000. When reducing the sample size to below 3.000, the number of significant results for inter-panel histories deteriorates rapidly.event history surveys, microsimulation, samples, simulation

Cooperative R&D and Firm Performance

We analyse the impact of R&D cooperation on firm performance differentiating between four types of R&D partners (competitors, suppliers, customers, and universities & research institutes), and considering two performance measures: labour productivity and productivity in innovative (new to the market) sales. Using data on a large sample of Dutch innovating firms in two waves of the Community Innovation Survey (1996, 1998), we examine the impact of R&D (collaboration) in 1996 on subsequent productivity growth in 1996-1998. We find that supplier and competitor cooperation have a significant impact on labour productivity growth, while competitor cooperation and collaboration with universities & research institutes positively affects growth in innovative sales per employee. Innovative sales are furthermore stimulated by incoming spillovers (not due to collaboration) from customers and universities. The results confirm a major heterogeneity in the rationales and goals of R&D cooperation, with competitor and supplier cooperation focused on incremental innovations improving the productivity performance of firms, while university cooperation and again competitor cooperation are instrumental in creating and bringing to market radical innovations generating sales or products that are novel to the market, improving the growth performance of firms.research and development ;

Testing for complementarity and substitutability in case of multiple practices

A number of recent empirical studies of firm-level productivity (growth) have been concerned with establishing potential complementarity between multiple organizational design practices. These papers have drawn conclusions on basis of the effect of the interaction term between each possible pair of practices. In this paper we show that this approach may lead to misleading results in case more than two practices are considered. We develop a proper testing procedure for complementarity and substitutability in case there are multiple organizational practices that affect output. The testing methodology is illustrated by empirical examples of three and four innovation practices affecting productivity. The testing framework can easily be applied to test for supermodularity.industrial organization ;

The Productivity Effects of Internal and External R&D: Evidence From a Dynamic Panel Data Model

We examine the impact of internal and external R&D on labor productivity in a 6-year panel of Dutch manufacturing firms. We apply a dynamic linear panel data model that allows for decreasing or increasing returns to scale in internal and external R&D and for economies of scope. We find complementarity between internal and external R&D, with a positive impact of external R&D only evident in case of sufficient internal R&D. These findings confirm the role of internal R&D in enhancing absorptive capacity and hence the effective utilization of external knowledge. The scope economies due the combination of internal and external R&D are accentuated by decreasing results to scale at high levels of internal and external R&D. The analysis indicates that on average productivity grows by increasing the share of external R&D in total R&D.R&D, Innovation, Complementarity, Panel Data

Complementarity in R&D cooperation strategies

This paper assesses the performance effects of simultaneous engagement in R&D cooperation with different partners (competitors, clients, suppliers, and universities and research institutes). We test whether these different types of R&D cooperation are complements in improving productivity. The results suggest that the joint adoption of cooperation strategies could be either beneficial or detrimental to firm performance, depending on firm size and specific strategy combinations. Customer cooperation helps to increase market acceptance and diffusion of product innovations and enhances the impact ofcompetitor and university cooperation. On the other hand, smaller firms also face diseconomies in pursuing multiple R&D cooperation strategies, which may stem from higher costs and complexity of simultaneously managing multiple partnerships with different innovation objectives.management and organization theory ;

Mean-field optimal control and optimality conditions in the space of probability measures

We derive a framework to compute optimal controls for problems with states in the space of probability measures. Since many optimal control problems constrained by a system of ordinary differential equations (ODE) modelling interacting particles converge to optimal control problems constrained by a partial differential equation (PDE) in the mean-field limit, it is interesting to have a calculus directly on the mesoscopic level of probability measures which allows us to derive the corresponding first-order optimality system. In addition to this new calculus, we provide relations for the resulting system to the first-order optimality system derived on the particle level, and the first-order optimality system based on $L^2$-calculus under additional regularity assumptions. We further justify the use of the $L^2$-adjoint in numerical simulations by establishing a link between the adjoint in the space of probability measures and the adjoint corresponding to $L^2$-calculus. Moreover, we prove a convergence rate for the convergence of the optimal controls corresponding to the particle formulation to the optimal controls of the mean-field problem as the number of particles tends to infinity