Organizational growth processes exhibit interesting statistical regularities, chiefly the heavy-tailed pattern of the size and growth-rate (i.e. yearly change in size) distributions. In spite of its ubiquity, empirical studies of growth are often limited to private activities and specific sectors, and generative models on the other hand are built on simplified assumptions and only aim at reproducing stylized facts. In this study, we use a unique Swedish longitudinal database on employment in the Stockholm Region, to analyze the interplay between organizational growth statistics by ownership sector, industrial activity and inter-organizational employee movements during a period of 14 years. We fit distributions for organizational size and growth rates. We find that the body of the aggregate growth-rate distribution is dominated by public sector growth, while the private sector dominates the tails. Industries with mostly public organizations tend to have a lognormal size distribution, while privately-owned industries are better fitted by a truncated power law. Growth-rate distributions are fitted to an exponential power (Subbotin) distribution. We decompose the change in size into incoming and outgoing employee movements, and find that the distribution of aggregated movements is well approximated by a lognormal distribution. Most organizations that do not grow have however in- and outgoing movements, but these mostly cancel each other out.

Evolutionary theories of organizational change aim at finding processes that introduce structural variations in organizational variables and the conditions under which they can survive and be reproduced. However, the theory is limited by the lack of knowledge on interactions between organizations and the stability of interaction patterns over time. In this study, we use the network of interorganizational labor flows and tools and concepts from network science to inform the study of organizational evolution at the level of sector dynamics, in particular along the dimensions of connectivity and stability of labor flow patterns. We use a unique Swedish longitudinal register on employment in the Stockholm Region from 1990 to 2003. We find that the network is characterized by positive sector assortativity, and the public sector is relatively more tightly connected than the private one. A stability analysis shows that public organizations survive longer time in the dataset, and movements within publicly-owned organizations are the most stable while movements within the private sector are least stable. A network backbone overlap analysis shows that movements within the public sector are structurally stable over larger periods, while the ones within the private sector change quickly after a few years. We also find that the distributions for degree, interorganizational flows and betweenness centrality are highly skewed and “fat”-tailed; the public sector consistently has fatter tails than the private sector in all distributions. Implications for our understanding of how publicly and privately owned organizations are connected and react to external shocks are discussed.

In this article, we use Swedish longitudinal register data to study the effect that similarity in organizational properties has on the interaction between organizations. We map out the social space of large organizations in the Stockholm Region and the interplay between social distance and the network communities of employee movements between organizations. We firstly use homogeneity analysis to describe the dynamics of organizations in terms of the time evolution of their similarity. Our results show that most categorical variables are quite stable over time. Organizations linked through employee movement edges have a lower average distance in social space than non-linked organizations. Secondly, we look at network community dynamics in social space. Employee flows between organizations in different communities exhibit a so-called gravity law from spatial statistics, decaying more slowly than observed geographical networks, meaning that employees reach out regions of social space further than of physical space. Finally, the rate of change of distance in homogeneity space exhibits a statistical distribution similar to the ones found in various other growth processes in natural and man-made systems.

In this study, we use Swedish longitudinal data on employment in the Stockholm Region to study the extent of positive feedback mechanisms in the structure and change of employment trajectories. We represent employment trajectories as sequences of yearly employment states, and describe them with the tools of sequence analysis. Firstly, our descriptive results show that employment patterns are generally not stable in time. At least a quarter of the employed population spends the whole time period in one state. Sequence diversity measured by an entropy index is shown to be high and quite stable. There is more female participation in large public sector organizations, and women have larger mean time in those organizations too. Men's participation and mean time is higher in private sector organizations and more equally distributed. A sequence clustering reveals four distinct groupings. One cluster is always dominated by full sequences in large organizations of the public sector. Secondly, we use the sequence clusters as input to quantify positive feedback effects on the likelihood for an employee to stay in an organization. We find indication for a positive feedback mechanism only in a limited number of cases, mainly at the beginning of the 1990s for large highly diverse public organizations. Gender differences exist but are not very pronounced.

Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter delta that weights the relative fraction of an individual's neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.