Stockholm Studies in Sociology New Series 67

ACTAUNIVERSITATISSTOCKHOLMIENSIS Stockholm Studies in Sociology New Series 67

Modeling Organizational Dynamics

Distributions, Networks, Sequences and Mechanisms

Hernan Mondani c Hernan Mondani, Stockholm University 2017 Cover image: c Hernan Mondani, 2017 Typeset in LATEX

ISBN 978-91-7649-673-2 (print) ISBN 978-91-7649-674-9 (digital) ISSN 0491-0885

Printed in Sweden by US-AB, Stockholm 2017 Distributor: Department of Sociology, Stockholm University To my indomitable tenacity and my generous creativity

Contents

List of Figures xi

List of Tables xiii

List of Papers xv

Acknowledgments xvii

Sammanfattning xxv

1 Organizational Dynamics and the Logics of Change1 1.1 Introduction and Research Challenges ...... 1 1.1.1 Social Organizing and Organizational Change . . . . .1 1.1.2 Theories on Organizational Change ...... 3 1.1.3 Methods for Organizational Change ...... 4 1.2 Organizational Dynamics ...... 6 1.2.1 The Logics of Change ...... 6 1.2.2 Aim of the Thesis ...... 6 1.3 Outline of the Thesis ...... 7

2 Modeling Organizational Dynamics9 2.1 A Phenomenology of Organizational Dynamics ...... 9 2.1.1 Counting and Sorting Objects ...... 10 2.1.2 Dependencies Between Objects ...... 12 2.1.3 Change Events ...... 13 2.2 The Modeling Process ...... 14 2.3 Modeling Stages ...... 16 2.3.1 Observation: the Realm of Data ...... 16 2.3.2 Abstraction: the Realm of Concepts ...... 16 2.3.3 Representation: the Realm of Measures and Methods . 17 2.3.4 Analysis: the Realm of Result Representations . . . . 20 2.4 Articulating the Logics of Change ...... 20

vii 3 Data 25 3.1 The Database ...... 25 3.1.1 Brief Data Description ...... 25 3.1.2 Data Complexity ...... 26 3.1.3 Limitations ...... 27 3.2 Related Data Sources ...... 29 3.3 A Note on Simulated Data ...... 29

4 The Fabric of Organizational Change 31 4.1 Structures: Crystallized Change ...... 31 4.1.1 Multifaceted Crystallization ...... 31 4.1.2 Structural Compositions and Imbalances ...... 32 4.2 Organizational Growth: Local Change ...... 33 4.2.1 Multifaceted Locality ...... 33 4.2.2 Growth Processes ...... 33 4.2.3 Extreme Growth Events ...... 33 4.2.4 Social Space ...... 34 4.3 Labor Mobility: Relational Change ...... 35 4.3.1 Multifaceted Relations ...... 35 4.3.2 Transfers Between Organizations ...... 35 4.3.3 Organizational Communities ...... 35 4.3.4 Social Distance and Relations ...... 36 4.4 Distributions: Counting and Sorting Entities ...... 36 4.4.1 A Landscape of Distributions ...... 36 4.4.2 Method: Heavy-tailed Distribution Fitting ...... 38 4.4.3 Method: Homogeneity Analysis ...... 38 4.5 Networks: Quantifying Relational Dependencies ...... 39 4.5.1 A Landscape of Networks ...... 39 4.5.2 Method: Complex Network Analysis ...... 39 4.5.3 Network Centrality and Assortativity ...... 40 4.5.4 Network Stability and Community Structure . . . . . 40

5 The Unfolding of Organizational Change 45 5.1 Employment Trajectories: Path-dependent Change ...... 45 5.1.1 Multifaceted Path Dependency ...... 45 5.1.2 Individual Employment Trajectories ...... 46 5.1.3 Trajectories and Organizational Dynamics ...... 46 5.2 Sequences: Quantifying Path Dependence ...... 46 5.2.1 A Landscape of Sequences ...... 46 5.2.2 Method: Sequence Analysis ...... 47 5.2.3 Sequence Similarity and Sequence Diversity . . . . . 47

viii 5.3 Mechanisms: Exploring the Inner Workings of Change . . . . 48 5.3.1 A Landscape of Mechanisms ...... 48 5.3.2 Positive Feedback and Social Inﬂuence ...... 48 5.3.3 Method: Agent-based Models and Simulation . . . . . 49 5.3.4 Method: Physical Analogies ...... 50

6 Overview of the Studies 53

7 Concluding Remarks 65 7.1 Conclusions ...... 65 7.1.1 The Fabric of Organizational Change ...... 65 7.1.2 The Unfolding of Organizational Change ...... 66 7.2 Further Research ...... 67 7.2.1 Data Challenges ...... 67 7.2.2 Theoretical Challenges ...... 67 7.2.3 Methodological Challenges ...... 69 7.3 Outlook ...... 70

Appendices 73 A Base Files for Data Representations ...... 73 B Reﬂections ...... 77

References 87

List of Figures

2.1 Phenomenological features ...... 11 2.2 The modeling process ...... 15 2.3 Data representations ...... 19

6.1 Study I and its articulating logic ...... 55 6.2 Study II and its articulating logic ...... 57 6.3 Study III and its articulating logic ...... 59 6.4 Study IV and its articulating logic ...... 61 6.5 Study V and its articulating logic ...... 63

List of Tables

6.1 Summary of the studies ...... 54

A.1 Base ﬁle layouts for data representations ...... 75

xiii

List of Papers

The following papers are included in this thesis.

Study I Informing organizational growth models with longitudinal individual- level data. Sector, industry and inter-organizational movement statistics in the Stockholm Region Hernan Mondani Under review.

Study II The evolving network of labor ﬂows in the Stockholm Region. Sector dynamics, connectivity and stability Hernan Mondani Under review.

Study III The underlying geometry of organizational dynamics: Similarity- based social space and labor ﬂow network communities Hernan Mondani Submitted manuscript.

Study IV The sequence approach to organizational dynamics: Quantify- ing positive feedback mechanisms in employment trajectories Hernan Mondani Submitted manuscript.

Study V Fat-Tailed Fluctuations in the Size of Organizations: the Role of Social Inﬂuence Hernan Mondani, Petter Holme and Fredrik Liljeros PLoS ONE 9(7), e100527 (2014). DOI: 10.1371/journal.pone.0100527

Study V is partly based on the doctoral candidate’s master’s thesis (Mondani, 2012).

Acknowledgments

This thesis is about models of organizational change and the logics articulating them. So it is only fair for the acknowledgments section to begin with a small exercise in logic. It goes like this. The narrative structure of acknowledgment sections in sociology theses at the Department of Sociology, Stockholm University, during the last 5 years, boils down to the following logic. Let D be a doctoral candidate on the verge of defending her thesis. The process of writing a thesis, just like any other social process, unfolds in a dynamic social space, a ﬁeld of interaction spanned by the candidate’s social properties and those of her contacts. Upon the time of acknowledgment writing ta, and provided that the narrative structure is known to D, the need arises to choose a proper narrative instance, which implies the selection of people, actions and events within a reasonable period of time that are worthy of recognition. D proceeds consequently to probe her social space for information, where B(D,r,tA) is a social space neighborhood centered around her with radius r. The social space neighborhood is further partitioned into classes, which induce a naming order typically in a priority scale N ={supervisor/s → colleague/s → friend/s → family (peripheral) → family (nuclear)}. The people in the neighborhood can be associated with a set of actions deemed worthy of being acknowledged A(p|D) on the basis of a certain criteria that are either universally accepted within the time period of writing, or derived from the latest theses using a suitable heuristic. The acknowledgment text can thus be written with the help of an algorithm of the form:

for every o in N: u := random number for every p in B(D,r,tA|o): write "To " + p for every a in A(p|D): write "thanks for doing " + a + phr[u] where phr is a set of standard phrases presumably suited for the occasion, for instance: phr = {"I will never forget you.", "You are simply the best.", "I couldn’t have done it without you.", ...}. The random number is intended to add variation to an otherwise totally deterministic text.

xvii So now we are ready to move on to the particular narrative instance of this thesis. When I— doctoral candidate H— look around in my social space neighborhood B(H,r,tA), I notice that throughout the years I have been privileged in various ways. Today, after a somehow long journey, I believe my greatest privilege has been to be able to create, bound only by my own limitations, to allow myself to be engulfed by my own ghosts, and reinvent myself as I see fit. To waste as much time as I wanted and to explore new opportunities as I deemed necessary. This freedom is not naturally given, though; someone has to pay for it. So my first acknowledgments go to everyone who has contributed to financing my current social space state B(H,r,tA). Since I believe important things should be named first, I introduce a reverse naming order N˜ = flip(N) here (which also translates into exploring the social space neighborhood from the center and outwards, in the direction of increasing r, a procedure somehow more appealing, mathematically speaking). I would first like to thank my family: my father Luis Pedro, my mother Adriana Angela and my brother Mariano. You have always been relentlessly supportive of my projects, and in particular of my decision to start anew in Sweden. My mom should be especially commended for her strength and pa- tience, and not the least for suggesting that I should look for some social science application to my modeling interests. To both my parents: thanks for always putting me and my brother above everything else. You have taught us many important things, and have been wise and good-willed enough to do it in the only way fundamental things can be taught: by subtle, consistent, exem- plifying insinuation. A good example has to do with my relationship to books and reading. There was never a formal moment, not that I can remember anyhow, where the importance of books was pointed out to me. But books have always been around at home: many topics in many rooms. And besides your bed, so that even after a hard day’s work a page or two should precede your decent into the dark unknown. So it is even to this day, that I always have more books around me than I can read. I draw comfort and peace of mind from what the poet Jorge Luis Borges adequately called ‘the tutelary presence of books’. Thanks for that and for so much more. And to Mariano: I hope this is a somehow acceptable thesis, even if you do not see much science in my work (you are probably right). In my trip towards these latitudes, some people have been instrumental for me to apply for my studies and during my firsts years of integration. Thanks to Guillermo Raúl Zemba, Jorge Clot and Juan Miguel Marcet for their permanent encouragement, to my Swedish teacher Ricardo Naidich for valuable counseling in many matters, to Teresita and Staffan Qvarnström for vital support in my integration years, and to my KTH coordinator and supervisor Kjell

xviii Carlsson and Patrik Henelius. I am above all most grateful to Sweden, my adoptive country since 2010, and to a number of Swedish institutions that have made this possible by paying for my education, from my beginnings at the Royal Institute of Technology (KTH) to Stockholm University (SU). In particular, my warm thanks to the Department of Sociology at SU, where I have spent the most part of the last years. Despite my coming from a completely different background, the people at the Department have always made me feel welcomed and at home, and by letting me be part your world you have enriched mine enormously. To my main supervisor Fredrik Liljeros. It is only fair to say that I would not have fitted into any other sociology group in the country. That alone is reason enough to be grateful. You employed my as a research assistant, and later on have always included me in your very interdisciplinary network of scientific colleagues. We are different in many regards, but overall our similarities have a fair chance of outweighing the differences. There are simply not many people around that would think your reading a book on symmetry in mathematical physics is a good idea. Thank you also for you persistent allmänbildning efforts —which I do appreciate despite your opinion on the matter. Maybe someday I will visit Upplands Väsby, that Nordic Shangri-La I have heard so much about. Sincere thanks to my secondary supervisor Peter Hedström, for giving me access to data and facilities at the institutes he has directed without ever asking for anything in return. And to Yvonne Åberg for some crucial initial advice on Swedish registers and data issues. Thanks also to my other secondary supervisor Petter Holme, for valuable substantial input and help on my initial works and for always including me in network workshops and the like. Tom Britton at SU Mathematical Statistics has contributed with financing for Study V. As we increase the social space radius r, we find the colleagues I have interacted with most often for the past years. Åke Sandberg, you were my first colleague when I was an assistant at the Department, and I am happy to say we have kept contact even after you moved far away. Thank you for those first moments and the years afterwards. The more permanent members of the room where I sit have been Johanna Palm and Amir Rostami, i.e. basically Johanna. Johanna, it has been really nice sharing the room with you, and engaging in so many interesting conver- sations on all topics imaginable. It has also been rewarding to reflect on our developments, as well as seeing you become a sharper researcher and improve your skills over the years. I think we are both better than we were some years ago, and that is already something. Thanks for faking enthusiasm when the first snow comes to town every year, and for satisfying my Italian need for knowing where people are and what they are doing by e-mailing me when you

xix work from home or are sick. Other previous inhabitants and occasional visitors in our room that should be mentioned and thanked are Sanja Magdalenic, Magnus Haglunds, Pär Bendz, and Elias le Grand. To my two main collaborators during this process (in order of appearance): Amir Rostami and Livia Sz. Oláh. Amir, our collaboration over the years has taken us to a whole lot unexpected places, from some small network analyses in a little article to a whole research project, a new course created from scratch, and infinitely many on-going plans for articles, textbooks, software. Much work, much unusual work indeed. And I have been angry at you at times; you always deserved it. And maybe I too. Now, let me think again: no, it was basically you. But we have anyhow moved forward, each one from its own perspective, personal history and background. A trip not without com- plications, but always forward. There is a reassuring feeling here, that exists beyond the recognition for our work, or the goals we reach. It is about the joy we find in creating our own worlds, as we want them to be, our way, while trying to be truthful to ourselves. Thank you, my loyal colleague, for the opportunity to grow professionally and to temper my character by working on many challenging topics simultaneously, for giving me the confidence to dare to start new projects, and for working all day long to secure a future for our research ambitions. Let us see what lies up ahead. Livia, our collaboration has opened up unexpected new perspectives and opportunities for me. Your attention to detail, your structured way of working, and your deep emotional involvement in your work are some of the things we have in common. Thank you for innumerable reading recommendations, for so many interesting talks on everything from teaching to life, as well as for pushing for me to hang out with demographers at seminars, conferences and at the Linnaeus Center on Social Policy and Family Dynamics in Europe (SPaDE). I promise to work (a bit) harder on my Hungarian after I defend; I know which prize awaits me. Among the doctoral students I have had the pleasure to meet during these years, Sara Roman deserves special thanks. Sara, our uncountable readings during those early years introduced me to a whole world of new ideas and to our common interests in language, social psychology and cognitive science. As if that was not enough, you were also the number one fan of the social organizing course I co-created with Amir. Even if we never co-authored a single word, my work is simply inconceivable without your insights. And now, as this thesis crystallizes into its incomplete yet definitive form, I rejoice in the sight of your silent, humble thoughts wandering joyfully through ages and pages. Thank you so terribly much.

xx I should also acknowledge the researchers at the Stockholm University De- mography Unit (SUDA). In particular, Martin Kolk (sorry our paper is still not finished) and Kieron Barclay— two of the most intelligent persons I have ever met—, Linda Kridahl, Johan Dahlberg, Elina Elveborg Lindskog, Ida Viklund, Andrea Monti, Kathrin Morosow, Maria Brandén, Sofi Ohlsson-Wijk, Ognjen Obucina and Gerda Neyer. Linda, my walking companion: I admire your in- exhaustible energy, your resolute joy for life and your generosity; thank you so much for all those glimpses of your world. And special thanks to Jani Turunen for being such a wonderful and knowledgeable person (Finnish is next on my list). And to my current and future collaborator Helen Eriksson: you still have not understood how fantastic you are; I hope I get to be around when you do. And look Helen: I told I would beat you to the finish line! Betty Thomson, Gunnar Andersson and everyone working at SUDA should be commended for having created a remarkable research environment. The engineer in me, although dormant and rapidly aging, can still recognize a fine-tuned machinery when he sees it. Many thanks to Christofer Edling (Lund University) and Fredrik Movitz (SU Sociology) for very useful and sociologically relevant comments for my half time and final seminars respectively; only the gods knows what you have been through, and yet you have proceeded gallantly. I have furthermore benefited enormously from exchanges and discussions in my other projects, so thanks to Amir Rostami, Jens Rydgren, Christofer Edling, Mikaela Sundberg, Joakim Sturup, Christoffer Carlsson, Livia Sz. Oláh and Helen Eriksson. In- numerable theoretical discussions were kindly sponsored by Petter Bengtsson and Lars Udéhn, and Göran Ahrne for my work together with Amir. Special thanks to Richard Swedberg for counseling and stimulating discussions. And special thanks to Karin Bergmark, for always being a supportive, encouraging and most kind colleague to me. I have also enjoyed fruitful talks and discussions with others members of Fredrik Liljeros’ group during the years: Veronika Fridlund, Xin Lu, Monica Nordvik, Jens Malmros, Martin Camitz and Susanne Strömdahl. Alden Klov- dahl, whom I meet in my early days as an assistant, was a wonderful company and a source of information and inspiration. Further acknowledgment is due to the people and the groups and institutes I have been active in or coupled to. At the Institute for Future Studies (IFFS) special thanks to directors Peter Hedström and Gustaf Arrhenius for providing a space for me and my colleagues to meet and work. And to Moa Bursell, Pontus Strimling and the group of David Sumpter (Uppsala University), and Erika Karlsson and Karita Sirén. From the people at the Institute for Analyt- ical Sociology (IAS, Linköping University) I would like to mention particularly Selcan Mutgan, Chanchal Balachandran, Tim Müller, Thomas Grund and

xxi Gunn Elisabeth Birkelund. And finally to the researches in various network groups —Petter Holme’s, Martin Rosvall’s, Jari Saramäki’s—, in particular Fariba Karimi, to Tom Britton at SU Mathematical Statistics, and Johan Lind, Pontus Strimling and Kimmo Eriksson at SU Cultural Evolution. The people in the conferences I have gone regularly should be mentioned too: Jens Christian Claussen in Bremen, Guillermo Raúl Zemba and Fernando Nicchi in Buenos Aires, and Eduardo Martínez for support and wise companionship throughout the years. My eclectic background combined with a shameless attitude towards many aspects of human interaction has allowed me to effortlessly navigate the du- bious frontiers separating the territories of qualitative and quantitative social research. Therefore, I have benefited from many enlightening discussions with Vanessa Barker— one of my early-days acquaintances, whose lowest point ever was to claim that I am a natural sociologist—, Lauren Dean, Rebecca Ye Rongling, Lina Eklund, Filip Roumeliotis, Mikaela Sundberg and Elena Bog- danova. Special thanks to Petter Bengtsson, a man of singular intelligence. And many thanks to Stina Bergman Blix for lots of inspirational talks on methods and narratives. My action set A(p|H) also includes quite a bit of teaching. To my teaching colleagues Martin Hällsten, Love Bohman, Gergei Farkas, Linda Kridahl and Amir Rostami: thanks for the opportunity and the support. And to my students: thank you for contributing to shape me into a less bad teacher and for making me understand things differently by trying to explain them to you. Supervising my essay students had taught me a lot about writing too. The members of the doctoral student council, past and present, deserve much recognition. Anton Bjuggren Andersson, Margarita Chudnovskaya, An- drea Dunlavy, Per Engzell, Tina Goldschmidt, Simon Hjalmarsson, Klára Cap-ˇ ková, Sara Kjellsson, Ari Klængur Jónsson, Martin Kolk, Elina Elveborg Lind- skog, Kathrin Morosow, Eva-Lisa Palmtag, Sara Roman, Filip Roumeliotis, Kristina Sonmark, Olof Östergren. Your work for all PhD students is most appreciated. I would also like to thank the organizers and participants of the doctoral student lunch workshop, and other doctoral students and assistants I have interacted with and not named before (sorry if I have omitted anyone): Ellinor Anderberg, Linus Andersson Rydell, Laure Doctrinal, Andreas Hoherz, Lou Huuva, Susanne Kelfvé, Amanda Netcher, Pierre Nikolov, Anna-Karin Nylin, Erik Rosenqvist, Victoria Rosenström, Carl Sandberg, Edvin Sandström, Rou- jman Shahbazian, Thomas Sommer-Houdeville, Max Thaning, Sofiya Voytiv, Rosa Weber, Linda Weidenstedt, Johan Westerman, Filip Wigselius. The people taking care of administration and student affairs at our De- partment are a great source of relief and help, and fun to be around. Saemu-

xxii ndur Grettisson, thanks for innumerable nerdy talks on computers and stuff, and Katja Forsberg for being a great colleague and company. Maria Bagger- Sjöbäck, Mia Lind, Anna Borén, Anna-Carin Haag, Thomas Nordgren, Peter Åkerbäck, Snorri Karlsson, Malin Abdi: you have all been wonderful to me. And thanks to older/newer members Lina Bäckstrand, Isabelle Andersson, Pia Raninen, Åsa Eklöf, Leila Zoubir. And special thanks to Saemundur Grettis- son, Katja Forsberg, Tina Goldschmidt and Andreas Hoherz for our work at the environmental group, to Leila Zoubir, Kathrin Morosow, Anna Borén and Maria Bagger-Sjöbäck for work and the web group, and Marie Evertsson for our collaboration for the compilation of the doctoral student booklet. Thanks to Magnus Nermo and Charlotta Stern for their continuous efforts in managing and improving the Department’s organizational practices. And to Magnus Bygren, Marie Evertsson and Sunnee Billingsley for their work as directors of doctoral studies. Finally, I would like to mention other post- docs, lecturers and professors (not named before) that I have been in contact with over the years: Amber Beckley, Eva Bernhardt, Chiara Comolli, Sven Drefahl, Ann-Zoﬁe Duvander, Marit Gisselmann, Nina-Katri Gustafsson, Bar- bara Hobson, Juho Härkönen, Trude Lappegård, Carl le Grand, Lisa Lindén, Li Ma, Alexander Miething, Eleonora Mussino, Mona Mårtensson, Daniel Ritter, Lisa Salmonsson, Tiziana Sardiello, Tove Sohlberg, Árni Sverrisson, Ryszard Szulkin. Last by not least, I would like to thank the commands, functions and software packages I use most often in my programming activities: in Stata collapse, reshape, merge, compress; everything in the R packages ggplot (Wickham, 2016), homals (de Leeuw and Mair, 2009) and traMineR (Gabad- inho et al., 2011), and in the python package networkx (Hagberg et al., 2008). To all of you I say, in all honesty: phr[2]="You are simply the best".

Hernan Mondani Frescati, Stockholm January 22, 2017

xxiii

Sammanfattning

Organisationsförändring över tid utgör en central fråga inom organisationsso- ciologisk forskning. De underliggande mekanismerna som ger upphov till organisationers stabilitet och dynamik är svåra att studera. Detta beror på att det är svårt att följa organisationer och dess medlemmar över tid och också på att de traditionella metoderna inte har förmågan att fånga vissa dynamiska aspek- ter så som till exempel tröskeleffekter och positivåterkopplingsmekanismer. Syftet med denna avhandling är att skapa ökad förståelse för social organisering genom att utveckla modeller för att utforska och beskriva underliggande strukturer och mekanismer. Särskilt fokus läggs på själva modelleringspro- cessen och tillämpningen av nya begrepp och verktyg för att få nya insikter gällande centrala organisationssociologiska frågeställningar. Avhandlingen består av fem studier (I-V) och en övergripande kappa. Med hjälp av longitudinella anställningsdata i Stockholms län mellan åren 1990 och 2003 studeras organisationsstruktur, arbetskraftsrörlighet och anställnings- banor, samt dess utveckling över tid. Avhandlingens kappa kontextualiserar studierna och introducerar en översikt av relevanta teorier, metoder, begrepp och modeller som har betydelse för studiet av organisationsdynamik. Studie I är en analys av organisationstillväxten inom olika sektorer och industrigrenar. Tidigare forskning har visat på att tillväxtprocesser ger upphov till så kallade ‘heavy-tailed’ fördelningar för storleks och tillväxttakt. Studien återﬁnner liknande statistiska fördelningar i Stockholms län och delar upp dem i lognormalfördelade interorganisatoriska anställdrörelser. Studie II är en analys av anställdas mellanorganisatoriska rörelser som rep- resenteras som länkar i ett arbetskraftnätverk där organisationer utgör noder. Organisationer inom samma sektor tenderar att vara länkade med varandra oftare än med organisationer verksamma i skilda sektorer. Offentliga organisationer uppvisar vidare en mer stabil nod- och länkstruktur. Studie III använder sig av homogenitetsanalys och lämpar sig för att kunna kartlägga det sociala rummet av organisationer i Stockholms län. Ett socialt avstånd deriveras därigenom och de interorganisatoriska rörelserna analyser- ade i studier I och II visar sig äga rum mellan organisationer som är nära varandra i socialt rum. Effekten av nätverksgrupperingar (så kallade ‘network communities’) undersöks, och de organisationer som hör till samma community visar sig även vara närmare i socialt rum.

xxv Studie IV erbjuder ett annat angreppssätt för studiet av organisationsdynamik baserad på analysen av anställningssekvenser. En sekvens består av anställningstillstånd så som exempelvis att vara anställd vid en stor privat or- ganisation. Analysen fokuserar på de olika sekvenserna och dess egenskaper. En positivåterkopplingsmekanism som beror är beroende av sekvensmångfald ﬁnns hos vissa grupper, speciﬁkt inom stora offentliga organisationer. Slutligen är studie V en simuleringsanalys där en modell för sambandet mellan social påverkan och organisationstillhörighet testas. Effekten av kontextuell social påverkan modelleras genom en parameter som fångar hur känslig den enskilda individen är för den relativa storleken av majoritetens medlem- skap. De tidigare observerade och svårförklarade organisationstillväxtfördel- ningarna uppstår som ett fall mellan extrema parametervärden som modellerar hög respektive låg kontextuell påverkan. Avhandlingen i sin helhet lyfter fram ett processorienterat angreppssätt för modelleringen av social organisering och dess underliggande dynamik samt föreslår en uppsättning metodologiska verktyg som kan komma till användning även i analysen av andra sorters sociala processer, och vilka kan vara relevanta för utveklingen av den kvantitativa sociologin.

xxvi 1. Organizational Dynamics and the Logics of Change

One of the many phenomena sociologists occupy themselves with is social change. Change can exist in many forms: social stratification, contested political institutions, the development of social movements, and more. Social organizing is the process that brings about organizational change. Although very relevant, social organizing is also very hard to study. In this first chapter, we outline our view on organizing and organizational change. We present a brief overview of the theoretical and methodological machinery previously used to tackle questions on organizational change. After an outline of the research challenges, we define organizational dynamics and the need to search for the logics of organizational change. The chapter ends with the aim of the thesis and an outline of its remainder.

1.1 Introduction and Research Challenges

1.1.1 Social Organizing and Organizational Change The social world is teeming with organizations. Organizations come into existence, are transformed and disappear. New organizational niches are created and destroyed, changes involving multiple levels of aggregation and actors with competing interests affect society at large (Ahrne, 1994). Metaphorically, we can imagine the organizational world as a landscape in constant change. These processes of organizational change permeate the social world in every possible aspect. Indeed, it is hard to imagine a realm of modern human activity that is neither taking place or closely affected by the developments and decisions taking place within organizations or in their interface (Ahrne and Brunsson, 2011b). Understanding organizational change is thus of central importance for so- cietal development and also of great academic interest for social scientists. But big questions come with big caveats. In order to see what the limitations of this case are, we have to reﬂect upon what we actually can observe when we look at organizations and their activities. Organizations, individuals, buildings, meetings, paperwork, e-mail communication, the consequences of decisions.

1 Those are the concrete objects that make up the evolving organizational landscape. They can be observed, probed, interacted with. This is the tangible aspect of organizational reality. We can furthermore take a look at two successive instances of the same phenomenon and notice whether those elements are still there, or if they have changed in some regard. For instance, we could be able to see if the employee composition of a certain workplace is the same as it was last time we checked, or if on the contrary some change has occurred. And now we come to the core of the empirical issue: the process of organizational change itself cannot be observed; it can only be inferred from comparison of two successive experiential instances of organizational reality. Organizations and their components are the concrete, visible aspects of the process of social organizing underneath. This underlying level of reality is as real as it can be, in the sense that it generates the organizational reality we observe, yet it cannot be directly observed. Organizations are the subject, while organizing is the verb— as Scott and Davis(2007) put it. If we want to understand organizations and how they change, we need to improve our understanding of the processes underlying that change.1 Our phenomenological reasoning so far has underlined the fact that organizing processes are central yet only indirectly inferable entities. The empirical difficulties associated with studying organizational change are not restricted to the non-observability of the underlying processes of change, though. Em- pirical research is also bounded by the availability of data on how the above mentioned elements of the organizational landscape actually change over time. That is to say, in order to study the effects of change in a given organizational population, the researcher needs to follow the entities in that population— be organizations, individual members or activities— over time, and take note of any change in their properties. This is a difficult task to achieve, sometimes even an impossible one. We should mention for the sake of completeness that organizing and organizational change are not at all the exclusive domain of quantitative research, even though we (for empirical and methodological reasons) focus exclusively on quantitative applications. Organizing appears in qualitative sociology, for example in the works collected in (Hernes and Maitlis, 2010). Also, Czar- niawska(2014) advances a processual constructionist approach to organizing where actors come together to construct, de-construct and reconstruct sets of action nets. For all these reasons, processes of organizational change are extremely difficult to investigate empirically. But additionally, they present the scientific observer with a combination of theoretical and methodological challenges.

2 1.1.2 Theories on Organizational Change

Van de Ven and Poole(1995) propose an ideal-typical classification of theories on social change, consisting on four types. Firstly, theories of the life- cycle type advance a developmental view of social change as an organic growth process ruled by deterministic principles; Auguste Comte would be a notable proponent of this view. The second type is evolutionary theories: they view social change as the outcome of (biologically inspired) evolution processes and competition for survival. Charles Darwin and Stephen J. Gould would be ex- ponents of this view. The third type is social change seen as the consequence of a dialectic process; conflict is the central element, and Friedrich Hegel and Karl Marx its main proponents. Lastly, the forth ideal type of social change theories is the teleological; social change here is generated by goal enactment and the process of reaching consensus. Max Weber, the symbolic interaction- ists like George H. Mead and Herbert A. Simon would be examples of thinkers in this view.2 As can be seen, our approach to organizing and organizations is process- oriented. We therefore define an organization as an open system of interacting individuals and activities with emergent properties that are different from the properties of its constituent elements. This definition corresponds quite closely to the open system perspective on organizations presented in (Scott and Davis, 2007). The theories of organizational change we will mainly touch upon or refer to in the thesis fall essentially under the second ideal type, i.e. evolutionary theories. These theoretical frameworks are not intended to provide hypotheses suitable to be tested under a statistical inference context. Our use for theories relies much more heavily on borrowing concepts, both for inspirational and critical purposes, and using them as building blocks in our own models of organizational change. Within organizational change theories of the evolutionary type, we will draw inspiration from population ecology and its academic descendants, and the evolutionary approach to organizational theory. For the first case, and stem- ming from the seminal idea in (Hannan and Freeman, 1977) two closely related yet distinct research areas have branched off: organizational ecology and organizational demography. The population ecology aspect of organizational ecology was originally concerned with macro-level models of growth, selection and adaptation, where a type of organization is conceptualized like a species in an ecological system (Hannan and Freeman, 1989).3 Organizational demography (Carroll and Hannan, 2000) conceptualizes an organizational population by analogy to a human population. Organizations are born, grow and die at different rates, and those rates are dependent among a number of factors.

3 For the second case of the evolutionary tradition in organization theory, our main sources of conceptual inspiration are (Aldrich, 1979; Aldrich and Ruef, 2006), also in evolutionary economics (Baum and Singh, 1994; Nelson and Winter, 1982). The main processes governing organizational change (at all levels of aggregation) are variation of a certain trait (say an organizational form), selection according to some fitness and a survival-of-the-fittest rule, and retention of evolutionary successful traits. A series of theoretical (or conceptual) challenges emerge at this point. Proper evolutionary models suffer from conceptual issues. The biological analogies they rest upon have their limitations; see for example the critique by Astley(1985). For once, it is difficult— even virtually impossible— to speak of universal typologies of organizational forms and combinations of forms, thereby confining much of the evolutionary analyses to specific cases with somehow limited generalization power. Evolutionary theories have also some contradictions. For example, some theories claim there is a tendency for organizational forms to adapt to the environment (Meyer and Rowan, 1977) while some claim a tendency towards inertia in organizational forms (Hannan and Freeman, 1984). When we talk about the time evolution of a certain organizational variable in the thesis, we are using the term in a very relaxed version. That does not prevent us though from using some concept that are evolutionary in origin, like the idea of inertia and retention that will be later associated with the notion of stability.4

1.1.3 Methods for Organizational Change

This short overview is concentrated on quantitative methods, and again it does not suffer from any pretensions at being exhaustive. We have established that organizational change is very difﬁcult to study. In a sense, we could sum up all our previous comments by claiming that organizing is a complex process to study. Complexity is an umbrella term for a number of features that natural and man-made systems have. We can use this concept to classify the methodological speciﬁcations by means of which organizational change theories and concepts have being implemented. Some methodological discussions fall under what we might call complexity- based methods for organizational change. This set of methods seek to capture one or more complexity features as a way to get insights into organizational dynamics (Anderson, 1999). Topics include organizations as adaptive systems (Carley, 1997), nonlinear change, behavior far from equilibrium or in punctuated equilibrium settings (Meyer et al., 2005), emergence and self- organization (Liljeros, 2001), differentiation and integration in evolutionary models (Carroll and Burton, 2000), hierarchical structures and feedback loops.

4 Although much progress has been made in the last decades, these methods are still in their infancy. First of all, their usage is marginal relative to mainstream organizational research. Plus, sometimes the accounts are purely descriptive or tautological; others they are based on the simulation of speculative scenarios, or on toy models not calibrated with data. An important limitation is the focus on one complexity feature at the time. This calls for more empirically grounded complexity analyses that look at the multi-dimensionality of organizational change. The other set of methods is related to mainstream research. Although it is apparent that complexity features are commonplace in organizational change phenomena, most of the time quantitative methods for organizational change (and for other sociological questions too) are studied through the lens of some form of statistical regression model. We might call these discussions regression- based methods for organizational change. The main issue with those models is their linearity, i.e. the property that the sum of individual contributions of independent variables produces an association equal to the sum of the variable’s associations considered individually (Sørensen, 1998). Linear (or lin- earizable) models are necessary for estimation assumptions to be met, and are even related to more fundamental assumptions like equilibrium in neoclassical economics. In the case of evolutionary models, it holds that proper evolutionary models require proper evolutionary data and methods as well, with detailed information on time-variant forms, speciﬁcation of competition forces, carrying capacities, niches, etc. The typical techniques applied for longitudinal panel- like organizational ecological data include survival analysis (Carroll and Han- nan, 2000; Hannan and Brandon Tuma, 1979) and partial likelihood regression (Hannan and Freeman, 1989). Consequently, the methodological challenges that emerge are related, on the one hand, to the limited use and scope of complexity-based descriptions of organizational change, and, on the other hand, to the difﬁculty that linear regression-based models have to capture those complexity features. There is a need for methods that aim at modeling these features with an empirically grounded basis.

5 1.2 Organizational Dynamics

We have seen that there are a number of research challenges facing scholars of organizational change. Firstly, some challenges are empirical: social organizing is not directly observable and their consequences extremely difﬁcult to study on a large scale and for long periods of time, as that requires the longitudinal tracing of all organizations, individuals and organizational memberships. Secondly, there are theoretical challenges: evolutionary theories have had difﬁ- culties to conceptualize an invariant unit of organizational change, at the same time that those theories suffer from important contradictions that coexist. Fi- nally, there are methodological challenges: traditional regression models fail to capture essential complexity features of organizational change.

1.2.1 The Logics of Change Organizational dynamics comprises the phenomenological description of organizational entities, their interactions, and the underlying mechanisms giving rise to the observed phenomena. Talking speciﬁcally about the concept of dynamics, one could make an analogy with mechanics, the ﬁeld of physics that studies the movement of objects. The mere description of how an object moves is called kinematics, while dynamics is the study of why the object moves the way it does, for example through the application of a physical force.5 Organizational dynamics is about trying to get at the logics of organizational change, which we conceptualize as made up of two layers: the underlying fabric of organizational change on the one hand, and the unfolding of organizational change on the other hand. The fabric of change is the substrate that sustains the observed instances of organizational change.6 The unfolding layer in its turn has to do with the mechanisms of change.7

1.2.2 Aim of the Thesis This thesis seeks to improve our understanding of social organizing by using models to explore and describe the logics of the structures and mechanisms underlying organizational change. Particular emphasis is given to the modeling process, the use of new concepts and analogies, and the application of interdisciplinary methods to get new insights into traditional organizational sociological questions. The studies in the thesis tackle different dimensions of organizational dynamics, from organizational structures and organizational growth to labor mobility and employment trajectories, using Swedish longitudinal data on employment in the Stockholm Region. Different data representations— distributions, networks, sequences and mechanisms— and an array of non- traditional quantitative methods are explored and applied.

6 The thesis attempts to address, at least partially, the research challenges outlined above. It addresses the empirical challenges by making use of longitudinal data that links individuals and organizations and allows researchers to follow them over time. The theoretical challenges are addressed by high- lighting the use concepts and advancing the idea of creating knowledge by successive approximation to the articulating logics of the underlying reality. And lastly, it addresses the methodological challenges by applying an array of non-traditional interdisciplinary quantitative methods, like complex network analysis and sequence analysis, that explicitly model combinations of these complex features of organizational dynamics on an empirically grounded basis. In this way, we are able to conceptualize and see things we could not have seen with more traditional approaches.

1.3 Outline of the Thesis

The remainder of this thesis is organized as follows. Our view of the modeling process and its application to organizational dynamics comes in Chapter2. The database and related data sources are outlined in Chapter3. Chapter4 describes the framework for the investigation of underlying structures of organizational change, by giving an account of the relevant analytical dimensions, data representations and methods. Chapter5 features a similar layout as the previous chapter, now for the description of analytical dimensions, data representations and methods for unveiling mechanisms of unfolding organizational change. We go through a summary of the studies and their articulating logics in Chapter6. Finally, conclusions and further research are discussed in Chapter7. A number of appendices complement the thesis. A more detailed description of the base ﬁles in the dataset is provided in AppendixA. To conclude, AppendixB presents some reﬂections with a more detailed treatment of some of the most relevant ideas taken up in the course of the thesis.

7 Notes to Chapter 1

1. In the process of going through different organizing frameworks, it became apparent to us that the term ‘organizing’ can be used to denote the process of sorting objects, for example in archival work. Technically speaking, this is an organizing process as well, because it deals with categorizing entities under an overarching logic in one single totality. But it is not of the kind of organizing we address here. 2. Herbert A. Simon is a very relevant figure who does not feature in this thesis as much as his contribution to the modeling of social systems would deserve. We touch tangentially upon his work through his collaboration with Yuri Ijiri on organizational size distributions (Ijiri and Simon, 1977), and even more tangentially in connection with James G. March in the classic (March and Simon, 1958). But Simon’s contribution largely exceeds this kind of questions. He has for instance also written about models in the social sciences, starting with the know piece on rationality (Simon, 1957) and later (Simon, 1977). 3. In a relatively recent article about organizational ecology’s take on organizational evolution, in particular for the emergence of diversity of organizational forms, Rey- don and Scholz(2009) criticize this approach. The critique comprises many aspects, but essentially claims that the approach, drawing mainly from evolutionary ecology in biology, confounds the aggregation level at which evolution takes place. The selection process would unfold at the level of the organization, and not at the level of the organizational form. 4. Organizational time at the level of daily microinteractions can be regarded as an additional perspective on organizational change, although it is not the one adopted or referred to in the thesis (Shipp and Cole, 2015). 5. The reader will notice that we are sometimes prone to physical analogies; more specifically, to some form of physicalism i.e. the application of physical concepts and tools to non-physical sciences. Such reflections will be mainly relegated to chapter endnotes and appendixes, but can nevertheless be of interest to broaden the reader’s perspective. 6. The English term ‘fabric’ denotes in colloquial language a piece of cloth, and also the underlying constituent of a thing. It got to English through French and originally from the Latin word ‘fabrica’ meaning building as well as something being made. It also denotes a structure that has a design behind. Source: The Online Etymology Dictionary at http://www.etymonline.com . 7. Distinguishing between an underlying fabric of change and the unfolding of change serves the purpose of providing a description in terms of a structural component, the fabric, and a process that unfolds on top of it. Similar ideas can be seen in the topological interpretation of physical and social processes given in (Atkin, 1972). There, the fabric is called a ‘backcloth’ and the process on top a ‘pattern of activities’.

8 2. Modeling Organizational Dynamics

Whatever fate awaits research on organizational change; whether a deeper understanding of the underlying principles behind these complex organizational dynamics is reached or, on the contrary, whether we utterly fail in our quest, it will not be because of lack of models. Speciﬁcations for all imaginable phenomena and settings have been proposed. Mathematical tools of all sorts have been applied. In this chapter, we ﬁrstly present the phenomenological features of organizational dynamics that constitute the focus of the studies in the thesis. Then we go through the stages of the modeling process, and propose the notion of an articulating logic binding all model elements together. Related logics and their prospects are mentioned as well.

2.1 A Phenomenology of Organizational Dynamics

Models are constructs that help us describe how organizational change comes about. We write down models to try to get at the underlying process of social organizing that describes the phenomenological features of observed reality. But this of course depends heavily on our views on the nature of both organizational reality and the character of its inquiry. Our empirical reasoning in the previous chapter is in line with the philosophical standpoint of critical realism (Bhaskar, 1978). In the critical realist perspective, the organizational world presented in Chapter1 can be elucidated in the following way. We experience the organizational world by observing events in reality; those are the changes in what organizations, individuals, buildings and their interrelations look like. But this experienced, phenomenological world is assumed to be the manifestation as well as the product of underlying and more fundamental constructs called mechanisms— in this case social organizing itself. Theories that look for underlying mechanisms satisfy the so called causal criteria for existence (Collier, 1994, ch.2). Another way to approach the discussion on processes underlying reality is provided by Watts(2014). The author claims that in order to go beyond our

9 sociological common sense, the tacit knowledge, we should aim at describing the generative mechanisms of the reality we observe. The study of organizational dynamics begins with the identification of relevant phenomenological features. But how are these phenomenological features to be identified? We can say that, at least at the level of individual cognition, there exist principles that condition how we approach the world. One of them is captured by the notion of differential accessibility (Kahneman, 2003): when we look at reality, some features “catch our eye” more than others; they are simply more salient. A similar thing happens with theories. For example, mentioning ‘organizational ecology’ (Hannan and Freeman, 1989) would most likely lead the listener to think of concepts like ‘selection’, ‘environment’ and ‘structural inertia’. This of course reflects a whole lot of social processes in the background, probably the most important of which is the semantic structure of existing research traditions. But nevertheless, this results in a tendency to focus on certain aspects of reality; not an extremely surprising fact but a relevant one nonetheless. In our case, the selection is implicit in the selection of theories and complexity questions presented in Chapter1. It does focus on some elements only, and to a certain extent it reflects the abductive process that will be presented in the next chapter. The phenomenological features that play a predominant role in our studies are shown in Figure 2.1. The arrangement corresponds to the underlying construct of objects and dependencies between objects, and the observed events of organizational change. This is, in a phenomenological sense, a very systemic view of organizational phenomena: entities, interactions among them and how they change over time.1 We should note at this point that the reasoning is partially recursive: the choice of features, the hierarchical ordering of their relevance, is in itself the output of some model, predicated on specific worldviews, categorization schemes, research traditions, etc. This is not as much an obstacle as an intrinsic property of the modeling process, and it should be embraced rather than dodged.

2.1.1 Counting and Sorting Objects

Inquiry on organizational dynamics demands the specification of the entities that are changing. So a natural first phenomenological dimension has to do with defining objects and characterizing them. Sometimes it is the only thing the modeler can hope for: to categorize the objects into groups and count how many objects there are in each group. We can conceive of two kinds of categorization systems: given and emergent categorization. A given categorization is built around existing categories.

10 CHANGE EVENTS

Levels of change Stability

Path dependence

COUNTING AND SORTING DEPENDENCIES BETWEEN OBJECTS OBJECTS

Similarity Relations Locality of Categories interactions

Size Superposition

Figure 2.1: Phenomenological features of organizational dynamics. The diagram shows the aspects of organizational reality that are relevant for the studies in the thesis. The different kinds of organizational objects (organizations, employee movements, etc.) are dependent on each other and through interaction generate change events that we observe.

11 They can be social categories out there, or they can be the result of state classi- ﬁcation schemes, e.g. to group organizations into industrial activity branches. On the contrary, emergent categorization has to do with categories of objects that are not readily available from the start. Emergent categories may be rooted in our cognitive structure, and some researchers talk about the formation and dissolution of cognitive islands in our minds (Zerubavel, 1996). They can as well derive from given categories that we recombine according to some criterion. For instance, we have the intuitive idea that some categories are “closer” to each other than to other categories. This corresponds to the notion of similarity that will be widely used in our studies.2 A ﬁnal element in this phenomenological dimension is size. In their book on organizational demography, Carroll and Hannan(2000, ch.14) state that organizational size is the main predictor variable for quite a few organizational outcomes. There are properties of the composition of quantities in a group of organizations that depend on size in a precise way, and this phenomenological size dependency is called scaling, a feature observed in city dynamics (Betten- court et al., 2007) and originally in the growth of biological entities (Thomp- son, 1992), but also in organizational variables (Amaral et al., 1997).

2.1.2 Dependencies Between Objects

Relations between objects are also part of their structure, and constitute the substrate on top of which social organizing plays out. Observing dependencies is a way to get a glimpse of the underlying structure. The challenge is twofold: on the one hand, because it is difficult to get data on dependencies between objects, and on the other hand, because dependencies are not very well modeled in traditional regression approaches. A phenomenological feature that is crucial for our understanding of organizational dynamics is the locality of interactions. The notion is quite easy to grasp: nothing is more than one step away. Nothing we can do —maybe even think of— can be reached in less, or more than one step. Everything we know about the world seems to point in that direction.3 However, the local character of reality is very complex and has many dimensions. For instance, interaction can be local but still far-reaching due to the small-world effect (Watts, 1999).4 More on the locality of interactions in Reflection B.1. A last point in this phenomenological group is the idea of superposition. We have already encountered this idea in our methodological outline in Chap- ter1 when we talked about linearity in regression models. Outcomes are usually modeled as a linear combination of effects, implicitly assuming that the total effect is an additive superposition of the individually considered effects. More on superposition comes in Reflection B.2.

12 2.1.3 Change Events This thesis is about organizational dynamics as a means to describe and improve our understanding of organizational change. Within that context, we can identify two domains of change: hierarchical and temporal. The hierarchical domain is made up of levels of change. The definition of the levels of change is an empirical question, but the levels mean something in themselves: they are emergent properties of the levels below in the hierarchy, and are conditioned by the levels above in the hierarchy. The topic of emergence is a fundamental principle in complexity research (Johnson, 2001).5 From the lowest level and upwards we first distinguish the macro-economic level, made up of systemic long-term historical processes involving a whole economic region, shocks, crises, policy-making. At the ecological/ community level we have competition and cooperation processes between organizations in the same population, the birth and demise of organizational populations (Aldrich and Ruef, 2006), selection and adaptation to the environment, niche dynamics (Hannan and Freeman, 1989). The industry/ sector level features the birth, death and change of organizations within a population (Car- roll and Hannan, 2000), and the grouping of organizations under larger meta- organizations (Ahrne and Brunsson, 2011a). At the organization level we have evolving organizational forms (Hannan and Freeman, 1986) and divisions, and processes of organizational identity formation (Hannan, 2005). At the lowest level, the workplace level we see interactions among employees in a workplace, questions on team performance and leadership (Kilduff and Krackhardt, 2008); and the individual level about performance, entrance and exit, and how employment careers unfold. The levels have to do with the hierarchical organization of change, but there is also the temporal organization. On the temporal domain, one crucial phenomenological feature is stability. This is associated with the notions of equilibrium and non-equilibrium in social processes, but also with the stability of change itself, something we will come back to in our discussion on structures in Chapter4. More on stability comes in Reflection B.3. Lastly, organizational change may unfold locally, but interactions are often times dependent on previous histories. Consequently, another aspect of temporal organization of change is time ordering and the remaining phenomenological feature in this dimension is path dependence.

13 2.2 The Modeling Process

Perhaps even more important than the definition of a model is the modeling process itself as a scientific practice suitable of being described, communicated to other researchers, improved upon and ultimately reproduced within the scientific community. We can draw parallels to our reasoning on organizations and social organizing from the previous chapter. Much like concrete organizations, interacting individuals and their activities can be a proxy for researchers to pay more attention to and to get a grasp of the underlying processes, models give us the chance to look at the modeling process itself.6 A sketch of the stages and elements of the modeling process is shown in Figure 2.2. The layout with the process stages of abstraction, representation and analysis is inspired by (Brandes et al., 2013) who presented a scheme for the formulation of network concepts and measures within network science. In order to keep the description simple, we follow the “typical” pathway forward and claim that the modeling process begins with some form of data, the result of an observation of some phenomenon. Then there is a stage of abstraction that consists on creating (or associating) concepts to the data, through the lens of a given analytical dimension. Concepts are then the starting point of the next process stage, representation, where the data are formatted into a data representation that suits the analytical dimension under study, and concepts are put to operational use by coupling them to a particular method and a set of measures. Finally, the process stage of analysis involves applying the method to the data and processing the results. Needless to say, the whole modeling process is in the long run cyclical, eventually getting back to the starting point, as conclusions from previous analyses feed back into procedures to observe reality and gather new data. Notice again how the sketch shows a “typical” line of progress in the modeling exercise, going forward from data to results. Any real project will be plagued with looping repetitions, dead-ends, going back and forward between stages. But that would result in a visually exhausting and not very informative picture full of arrows. A model is then a theoretical construct constituted by a set of model elements coupled with an articulating logic specifying how the elements of the model should work together. The model elements, in their turn, are a combination of concepts abstracting particular aspects of reality, and measures and methods to operationalize them into some data representation.7 We will structure the rest of this chapter using the logic of classes and instances of those classes.8 For example, the phenomena being observed, e.g. a change in organizational size in an organizational population, can be thought of as a class whose observational instances we call data. A register on the number of employees over time would be an example of an instance.

14 Observation Abstraction Representation Analysis

Analytical Data Phenomenon Method Results dimension representation

Result Data Concepts Measures representation

Articulating logic

Figure 2.2: The modeling process. Process stages are shown in the top row. The middle row shows the classes deﬁning the start and end points of each process stage. The bottom row shows the instances of those classes. The articulating logic binds all model elements together. 15 2.3 Modeling Stages

An important point to note here is that our approach to modeling is not purely deductive, or rather it is quite non-deductive. Indeed, we neither aim at nor claim to be logically deducing propositions and hypotheses about the dynamics of organizations from a few starting axioms.9 Our approach to organizational dynamics is not purely inductive either, as we do not only abstract regularities from empirical observations. We believe the approach to be closer to a form of abductive reasoning— Charles S. Peirce, from (Swedberg, 2014a, ch.5)— where the researcher tries to infer some theoretical logic on the basis of observation. The abductive process is then much more intuitive than logical, and subject to dynamical revision and update as new data, ideas, results become available.

2.3.1 Observation: the Realm of Data The effects of organizational change have to be observed in order to be studied. As we saw in the previous chapter, this statement is deceitfully trivial, for a number of reasons. In the ﬁrst place, it rests upon and depends on the philosophical standpoint and interpretative machinery of the observer. From a critical realist point of view, reality in general and in our case organizational reality exists objectively and uniquely, independently of the observer. In the second place, because as mentioned we cannot observe the process itself, only its effects. At the empirical level, the level of experiencing reality, we have various phenomena or phenomenological features. The observation stage of the modeling process produces phenomenological instances that we call data. These are the observables the modeler works with.10 A more speciﬁc discussion on the data for this thesis is presented in Chapter3.

2.3.2 Abstraction: the Realm of Concepts The next stage in the modeling process is abstraction, the process of turning some observed phenomenon into one or more analytical dimensions. An analytical dimension as an class can stand for many different things. It can be a particular aspect of organizational reality like an organizational structure, or a speciﬁc process like organizational growth. By abstraction, the transitive domain of concepts and theoretical models is used to understand the generative mechanisms of reality (Bhaskar, 1978). Exploring the underlying logics of organizational dynamics requires for the researcher to abstract a relevant dimension of analysis. That may even mean tackling two or more dimensions at the time and outlining the interplay

16 between them. This is why our conceptual framework is not built around a single organizational change theory. Rather, it focuses on concepts borrowed from various theories and perspectives. The four analytical dimensions, our concepts classes, are: structures, organizational growth, labor mobility and employment trajectories. The analytical dimensions presented here are not mutually exclusive. That is to say, they overlap partially. For instance, a trajectory is also a succession of mobility steps, and therefore the former is very much dependent on the latter. Furthermore, they are not collectively exhaustive either, meaning that there exist many— potentially inﬁnitely many— additional analytical dimensions left out of our studies. To name a few examples, some process-related dimensions could be competition, and differentiation and integration within organizations (Lawrence and Lorsch, 1967). Related to stages in the organizational life cycle: birth, merging and disbanding of organizations in an organizational population (Hannan and Freeman, 1989). Analytical dimensions are composed, or instantiated, in concrete concepts. The debate on the use of concepts in social research comprises different views. Some researchers like Swedberg(2014a) believe that coming up with emerging empirically-driven new concepts is (or should be) the landmark of the theorizing process. Others emphasize the need for a proper operationalization of existing concepts into logically consistent arrangements of measurable quantities, using all from classical formal logic (Goertz, 2006) to state-of-the-art developments in fuzzy logic (Hannan et al., 2007). Our take on this debate is closer to the latter view, in the sense that we do not seek to come up with new concepts but to reuse existing ideas in a new framework. The use of concepts is essential to any theory, but some researchers put extra emphasis on the study and development of concepts as a theorizing tool (Stinchcombe, 1968; Swedberg, 2014b).

2.3.3 Representation: the Realm of Measures and Methods

Concepts in general and organizational concepts in particular live in the realm of ideas. They can be manipulated, redeﬁned and combined with other concepts. But in order to put them to practical use, they have to be coupled to one or more concrete, measurable entities. The representation stage of the modeling process does precisely this: it applies one or more analytical dimensions to some data representation, turning concepts into measures and methods. The data is formatted into a data representation.11 Ultimately, data representations constitute attempts at dimensionality reduction: they seek to somehow simplify the original data and say something meaningful about the underlying dynamics, at the cost of losing some information.12 The data representa-

17 tion and the measures operationalize the concepts in the model. In a similar way as the abstraction stage of the modeling process is the province of concepts; the representation stage is the province of measures and methods. A measure is the operationalization of a concept. The method in its turn is a series of steps that takes the measures and generates a certain outcome. Measures and methods are tightly connected to each another, and to the data representation, to the point that they should be understood as part of a whole like shown in Figure 2.2. The different data representations used in our studies are shown schemat- ically in Figure 2.3. Even when the data are individual-based from the start, there are some data representations that tend to focus on the organization as the unit of analysis; these are the distribution and the network. On the other hand, there are two other data representations that are essentially individual-based: the sequence and the mechanism. One interesting aspect of thinking in terms of data representations is that they can be combined in a single study. For example, we can have a network of employee flows between organizations and at the same time study the distribution of network properties emerging from the aggregation of labor flows. The data representations presented here are not all-encompassing either. Other possible data representations within this framework but not considered here are: i) a workplace labor flow network like the one in (Collet and Hed- ström, 2013), and ii) a network of individuals sharing a workplace over time. Additionally, other data representations of a different kind could include multilevel representations (an organization-based data representation) and life- course (individual-based) (Blanchard et al., 2014). Hierarchical data representations (which are built into the both the community structure and sequence clustering algorithms) are not directly exploited. Specific methods to capture hierarchies are available, for instance in relation with network flows (Luo and Magee, 2011). To conclude, another possible data representation could be the so-called ‘hyperstructures’, which are structures embedded in structures of a higher order (Baas, 2006).

18 iainlcag vrtm.Mr hnoedt ersnaincnb combined be can study. representation orga- same data of the one workings than in inner More the time. into over path change look quantify nizational to to attempt way mechanisms a networks and are dependencies, variables, sequences organizational dependencies, relational sorting data: quantifying and about organizational counting of about feature are phenomenological distributions salient a captures resentation 2.3: Figure

Individual-based Organization-based aarpeettosfrognztoa yais ahdt rep- data Each dynamics. organizational for representations Data Distribution Sequence

Mechanism Network

19 2.3.4 Analysis: the Realm of Result Representations The ﬁnal stage is the analysis, that takes concepts measured from the data representation and turns them into results. In the same way as different analytical dimensions and data representations can be combined in a single study, different results cam be integrated into a single uniﬁed picture. This is ultimately one of the ideas behind this thesis: the possibility to look at the same dynamics from different complementary angles and, by doing so, to be able to see new aspects of the phenomenon. The class of entities that we call results is instantiated in various result representations that help researchers in the interpretation of results. Typically for the traditional methods we relate to, results come in the form of tables of estimations from a statistical model. They can also be summarized in statistical plots. A distinction can be made between information visualization and visualization of statistical information (Gelman and Unwin, 2013). The former is associated with the interactive visualizations of large datasets commonly seen websites and interdisciplinary research journals, while the later is rather the province of statisticians and statistical analyses. However, the use of relatively advanced visualization tools for statistical results is still limited. There is a risk than even statisticians are falling behind and not taking full advantage of the potentialities available, as Gordon and Finch(2015) point out. In recent times, there is a growing number of social scientists advocating for the use of more advanced visualization techniques. Both relatively new techniques like plots of large networks and innovative variations of traditional summaries of statistical information like correlation matrices are proposed (Healy and Moody, 2014).13

2.4 Articulating the Logics of Change

The challenge for the modeler is to put all the pieces outlined in this chapter together. In an age where data and methodological tools are increasingly abundant and accessible, it becomes even clearer that the burden is less on that side than on the part of concrete combinations of ideas with tools and methods. In this thesis, we advance the notion of an articulating logic. The basic idea is that different models may be built around different logics. In this context, we can think of a logic as a description of the workings of the model, the ‘glue’ that binds data, concepts, measures, method and results together. Theoretical developments leveraging on the notion of logic exist in social science. To the best of our knowledge, the most systematic treatment of logics applied to social phenomena is due to Kontopoulos(1993) and his discussion on the logics of social structures. Kontopoulos’ overarching and seemingly all-

20 encompassing review of sociological theories is an admirable piece of work, featuring logics from many social processes and even other related interdisciplinary ones. Another approach consists on emphasizing the use of diagrams as visual aids for theorizing (Swedberg, 2016). The difference between these views and our approach is that we conceive of an articulating logic as a binding element for all other model elements, not just concepts like Kontopoulos or visual aids like in the case of Swedberg. Additionally, applications of fuzzy logic to theory formation and category deﬁnition play an important role in, for example (Pólos and Hannan, 2002, 2004).14 An overview of the studies, their main results and the logics articulating organizational change in each case is presented in Chapter6.

21 Notes to Chapter 2

1. Systemic ideas in sociology can be inscribed in a larger stream of system-based traditions, dating at least back to general systems theory in the classical formulation of von Bertalanffy(1973) and mathematical set theory applied to systems (Lin, 2002). On a philosophical level, system-based ontologies are the cornerstone of philosophies like the one championed by Bunge(1979). A classical conceptualization for social systems comes in the structural-functionalist theory of Parsons (1968). 2. The idea of similarity has bearing for concept formation as well. Wittgenstein’s notion of ‘family resemblance’ has been associated with the creation of concepts that fulfill necessary but not always sufficient conditions in their definition (Goertz, 2006). 3. It should be noted, even at the risk of being trivial, that our statements about locality are circumscribed to the realm of macroscopic processes such as individuals interacting in society. Ideas about locality are radically different when considering really small size scales at the quantum level. 4. For a review on small-world research in social networks, see (Schnettler, 2009). 5. Descriptions in terms of levels should emphasize the co-evolution of organizational change at different aggregation levels. A more classical level-based logic can be formulated in terms of micro-meso-macro levels see (Turner, 2010a,b, 2012). Ideas of system complexity and co-evolving levels are of interest to different disciplines. Holling(2001) developed the concept of ‘panarchy’ as an integrated framework for the analysis of complex adaptive ecological and man-made systems. Huge attention there is given to the notion of the cyclic character of co-evolution, meaning that different levels reorganize themselves in a cascading process with the adjacent levels under and on top. 6. Within the philosophy of science, there is an abundant body of literature on what a model in the social sciences is, what it should be and how it should be used to test statements about the social world (Hegselmann et al., 1996). 7. A question of capital importance when it comes to modeling concerns to what extent models have any explanatory power at all, and if so what kind of explanatory claims are reasonable to expect from models. Jebeile and Kennedy(2015) talk about ‘representationalist’ attempts at scientific explanation through modeling, meaning those accounts that see models as representing reality to some degree. Representationalist accounts claim that a model explains the phenomena if it is a good representation of it. The authors further argue that this view is incomplete, inasmuch as it underplays the role of idealizations to abstract the fundamental aspects of reality and see new connections. Model-based explanations should be seen as an activity. This view is close to our interpretation of how the modeling process should go about finding and interpreting observations about reality. 8. This scheme is inspired by a rather straightforward parallel to object-oriented programming languages, which are based on objects that belong to certain classes.

22 Class belonging determines the properties that objects have and the methods that can be applied to them (Stefik and Bobrow, 1985). The only alteration here is that we prefer to talk about class instances not to confuse the term ‘object’ with the critical realist assertion of the term. 9. Deductive axiomatic theoretical frameworks— we could perhaps refer to them as “Euclidean” by family resemblance— are no strangers to sociological theories, although the strive for such derivations may have already passed. A couple of examples include Blau’s macro theory of social structures (Blau, 1977), and Festinger’s theory of social comparison processes (Festinger, 1954). 10. We will discuss mainly quantitative data in this thesis. It should be kept in mind that similar statements can be formulated for qualitative data, provided that we use the same philosophical frame of reference or one that is compatible with it with respect to its fundamental features, chiefly assumptions on objective versus constructed reality. 11. Data representations in this thesis are quantitative due to the nature of the register data we use. This fact should not hinder us from using the same apparatus for qualitative data representations, for example interview transcripts, photogra- phy collections, etc. 12. Formally, statisticians— and physicists for that matter— talk about the number of degrees of freedom of a dataset. A dataset of N data points can be thought of as spanning an N-dimensional space where each axis coordinate is given by the corresponding value. Specifying values, linking them or otherwise summarizing them is equivalents to fixing points in this space, and it results in a reduction of the degrees of freedom and consequently of the dimensionality of this fictitious space. 13. A consideration paralleling our comment on Herbert A. Simon is here due to another important figure: Edward R. Tufte. Originally a statistician, Tufte is a pioneer of information visualization (Tufte, 2001). His insights extend beyond the mere design of figures to encompass ideas for the layout of page margins, notes and reference lists, to form a page that works as a harmonic totality. This can be adequately called a Tufte style in its own right, as it is apparent by the existence of for instance LATEX templates that mimic it, see: http://www.latextemplates.com/template/tufte-style-book 14. Taking this argument one step further, one could (at least in principle) imagine a set of concepts and a logic going through a sort of highly constrained evolutionary process, involving competing forces in academia and other relevant stakeholders. A theory could be conceptualized as the eventual dynamical outcome of such process.

3. Data

In this chapter, we present the database on employment in the Stockholm Re- gion. We describe the main features of the dataset along with its limitations and the historical context of the period under analysis. Additionally, we mentioned related data sources and comment on the generation and use of another— yet not less important— type of data: the output of simulation models of social systems.

3.1 The Database

In Chapter1, we argued that data sources for organizational dynamics suffer from a series of limitations, and we then outlined some empirical challenges in connection with those limitations. The chief limitation is the lack of longitudinal, system-wide linked data. Our database falls into a class of data that can address this issue.

3.1.1 Brief Data Description

The original database consists of a compilation of governmental registers put together by Statistics Sweden (Statistiska centralbyrån, SCB). The database as a whole contains anonymized information on demographic, educational and occupational variables. This thesis makes use of a subset of these data consisting of a number of employment-related variables. This is its turn is a matching between individual-based variables and organization-based variables. In our analyses, the data is further aggregated at the workplace and organization levels. On the individual’s side, there is information on all individuals 16 years old or older that were employed by an organization registered in the Stockholm Region (Stockholms län) at some point during the years 1990 to 2003. In total, the database contains data on 1,473,033 individuals (723,468 females and 749,565 males) and 750,718 organizations. For each individual and each calender year, we have information on the individual’s gender, age group and organizational membership (workplace and organization) for her largest source of income.

25 On the organization’s side, the data comes from the company database (now företagsdatabasen, FDB) and the employment status in November. In- formation on the organizations ownership sector and the workplace’s main activity is available.1 Workplaces, organizations and individuals have unique anonymized longitudinal numbers (so called företagens och arbetställenas dynamik, FAD) that allow for full traceability during the whole time period. This is one of the key features of the dataset. Let us say a few words about the historical context the data is located in. The macroeconomic context configures the historical background against which organizational changes unfolds. It is a period effect of organizational change, as opposed to, for instance, cohort effects specifically associated with the year of creation of an organization (Aldrich and Ruef, 2006, ch.8). The period 1990-2003 for Sweden in general and for the Stockholm Re- gion in particular was characterized by important changes in the labor market structure. The observation window starts with the Swedish economic crisis and can be therefore partitioned for analytical purposes into three distinct periods. The first period goes from 1990 to 1993, and is characterized by the worst of the 1990s crisis, with unemployment rising, mainly in the private sector. The period 1994-2000 marks a recovery phase, now affecting mainly the ser- vice sector in privately-owned organizations, and witnessing the progressive shrinking of the public sector. The third and final period from 2001 to 2003 is a period of stagnation and very slow decline, marked by the dot-com bub- ble. Some of the major structural transformations taking place in this period include the economic crisis at the beginning of the 1990s and the shrinking of public sector, both as a consequence of the crisis and privatization. There are also transfers of activities from the regional to the communal level, and central activities from Stockholm to other regions (Bergmark and Palme, 2003; Palme et al., 2002).

3.1.2 Data Complexity

Data comes in all formats, sizes and ﬂavors, and social data is certainly not the exception. The last decades have seen a staggering increase in the quantity and resolution of data on humans and their social interactions. The image that comes to mind is probably one of large online social network databases, like Facebook or Twitter. However, the same applies to the data collected by states at the national, regional and municipal level. Connelly et al.(2016) deﬁne these as a source of found data, as opposed to made data which is the data collected either from an experiment or through a survey on a representative subset of the population. So all of a sudden it becomes crucial to get a better grasp of the potential

26 these governmental data have. From a computer science point of view, the term big data can be characterized as “an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications” (Mohanty et al., 2015, p.30-31). Three features commonly referred to as defining characteristics of big data are: i) the sheer volume of data, ii) the variety of data types, and iii) the ve- locity at which the data must be processed. Criterion (i) is essentially met by our database: it is total population data we are talking about, so it is massive at least relative to its scope. Criterion (ii) is somehow met for the original dataset, to the extent that the data is a collection of multiple registers and qualitatively different data types on education, income, employment, and so on. The missing element comes with criterion (iii): the data is not processed at high speed, neither is generated at high speed for that matter. Speed here we interpret as a speed that is comparable with the speed of the process being measured. That is to say, the database does not register all employment changes that are hap- pening under one year’s notice (see limitations below) thus missing to observe all relevant events in the same time scale they occur. So, technically speaking, our database is not big data. However, some deci- sive features make the data unique. These are, on the one hand, full traceability of people and organizations and some longitudinal covariates for all registered organizations with employees in a whole region over a relatively long period of time, and, on the other, the links between employees and organizations. It is in this sense that the database is complex: it is susceptible to be represented in many qualitatively different ways; it lends itself to various data representations. Schematic layouts of the base files for the various data representations are available in AppendixA. Additionally, some examples of human mobility through time and space are sketched in Reflection B.4.

3.1.3 Limitations

There are a number of data-related limitations that we better mention at this point. Measuring employment at the end of each year means that we will fail to observe job changes within a shorter time span. On the other hand, it also means that we avoid seasonal effects, a fact that can be relevant for studies that have more to do with time series analysis.2 The use of the ﬁrst source of income also introduces a bias in the kind of employment relationships we capture. Second and third sources of income are available, but not for the FAD longitudinal numbers. An argument here could be that the ﬁrst source of income is a proxy for effective time devoted to a given activity, and thus what we are measuring is a proxy for effective labor force size.

27 Probably the most important limitation has to do with the fact that these are data on employment within the Stockholm Region, so what we are capturing is essentially regional organizational change over the period of analysis. Fur- thermore, this specification is likely to impact different organizational groups differently: public sector organizations at the communal level are by definition locally bounded and thus their employees will be for the most part com- missioned to locally bounded workplaces, while private organizations or even public organizations with branches in different regions may move employees between regions without changing their organizational membership. Interna- tional labor mobility is another aspect we cannot consider with the available data. Geography is a dimension that is not present in our analyses. There is geographical area information in the database, but the codes (SAMS) indicate the centroid of the area. Although this is definitely a first approximation to the real spatial distribution, some analyses can become problematic, typically all forms of network analysis. The absence of geography is a limitation, but its impact is at the same time restricted, because we only look at internal regional dynamics along a single axis, i.e. employment. It would be a much more se- rious limitation if we tried to understand, for instance, the correlation between residential and employment patterns in space. Data on occupational classes (by standard för svensk yrkesklassificering, SSYK) is available but not in a totally longitudinal way, meaning the coding is not harmonizable over the whole time period. Given the relevance of this dimension, particularly for the individual-based representations— namely sequences and mechanisms, see Chapter5— it would be worth to further investigate the possibilities to use these codes, even for very specific settings and variable combinations. Finally, there are types of organizational relationships other than formal employment that are not covered by the scope of this dataset. One main category left out is voluntary associations, except of course for the individuals that are formally employed as employees of such organizations. Volunteering is an important and complementary element of the social relations we study (Wil- son, 2000). Other relations may include secondary jobs, combined flexible forms of employment, etc.

28 3.2 Related Data Sources

There are a few other studies have used the database, some for completely different questions, some for complementary ones. They tackle research questions such as unemployment among young people (Hedström, 2005, ch.6), school segregation (Spaiser et al., 2016), and individual-level social ties created by workplace mobility (Collet and Hedström, 2013). There are a few other longitudinal employment datasets that combine to some extent employer and employee data, e.g. the Finnish and Mexican data used in (Guerrero and Axtell, 2013) and the Japanese corporate database (Saito et al., 2007). Other sources of governmental data on organizations are: the Stockholm Municipality (Stockholms stad), Statistics Sweden’s Labor Force Survey (Ar- betskraftsundersökningarna, AKU).3

3.3 A Note on Simulated Data

A point is worth making regarding the use of simulation output data. In an era of huge sources of empirical data, for organizational studies in particular but also for social phenomena in general, the increasing volume of simulation- based data should not be overlooked. Simulated data must be treated as a data source in its own right. In fact, many simulation models only use simulated data.4 Furthermore, protocols for the rigorous statistical analysis of simulated data are still in its infancy compared to more traditional statistical inference methods. For instance, it is only recently that the issue of sensitivity analysis for agent-based simulation models has begun to be formalized and given the attention it deserves (Kleijnen, 2005; ten Broeke et al., 2016; Thiele et al., 2014). A related point concerns the so-called meta simulation models, where one writes down a model of a model for the systematic design of computer experiments (Santner et al., 2003).

29 Notes to Chapter 3

1. Ownership sector according to a harmonized version of the institutional sector code standards up to and after year 2000. Industrial activity coded according to industrial activity branch standards (standard för svensk näringsgrensindelning, SNI). This is a composition of four coding schemes according to the year: SNI69, SNI92, SNI2002 and SNI2007. Statistics Sweden harmonizes the ﬁrst two coding schemes, and there are equivalence tables for converting to the two following schemes. 2. For example, let there be a time series consisting of the number of employees in a given organization over time. If the time stamps are of higher resolution than a year, then some of the observed variation could be due to periodic seasonal effects. There are proper methods to handle this kind of situations, like detrended ﬂuctuation analysis (DFA), see e.g. (Ausloos et al., 1999). 3. Links to public statistics on organizations: • Labor Force Statistics (in particular Arbetskraftsundersökningarna, AKU) http://www.scb.se/hitta-statistik • Stockholm Municipality (Stockholms stad): http://statistik.stockholm.se 4. Study V in the thesis is an example of such a model.

30 4. The Fabric of Organizational Change

According to our phenomenological discussion and philosophical perspective, organizational change unfolds on top a substrate of real organizational objects and their relations. In this chapter, we go through this structural aspect of organizational dynamics, that we call the underlying fabric of organizational change. Concepts along the analytical dimensions of structures, organizational growth and labor mobility, as well as data representations and methods for distributions and networks are discussed.

4.1 Structures: Crystallized Change

4.1.1 Multifaceted Crystallization Organizational dynamics seek to study the processes and the logics behind organizational change. At the same time, certain objects in the organizational landscape change quite a lot and relatively fast, while others are somehow slower to change, or even more, resistant to change. The organizational ecological literature talks about organizational inertia in the adaptation to environmental change (Hannan and Freeman, 1984). In this light, we define a structure as a form of crystallized organizational change. The crystal being a metaphor for a structure, and not so much a metaphor for a total regularity. To begin with, one could say that the most elementary structure of crystallized change is the organization itself, thought of as a snapshot of a process of social organizing .1 But how to decide if a process is giving rise to a structure or not? Here is when the phenomenological feature of stability comes into play. How stable an entity is can determine whether it crystallizes into a structure or not. The formal organizations we look at tend to be proper social structures with a command hierarchy, defined membership, rules, a system of sanctions and rewards and collective resources (Ahrne, 1994). But our interest in structures is broader than that. There exists a multifaceted crystallization. Quantities like for instance the flow of employees moving from one organization to another are dynamical, they give rise to changes in the organizations that are linked by

31 the movement, and they may also change in time themselves. But if this flow is maintained over time, the quantity that started off as a temporary connection can become more permanent, and in that sense acquire a more structural character. It is therefore that the other analytical dimensions we will consider have an underlying structural flavor to it. We should mention that our definition of structure does not correspond to the typical assertion of the term ‘structure’ in organizational research. The term ‘organizational structure’ can be otherwise used in the sense of the composition of the hierarchical positions in an organization, or even to refer to specific organizational forms.

4.1.2 Structural Compositions and Imbalances

The easiest and perhaps more natural way to approach organizational structure has to do with categorizing the organizational system at a certain level of aggregation, and looking at the composition, i.e. the count of entities in each category. This is because structures exist at different levels of aggregation. At the level of the organization, we can characterize the social structure of an organization by aggregation of demographic properties of its employees. At the industry and sector level, it can be a matter of composition by activity and organizational ownership, emergent properties of that level of aggregation. Of particular relevance for composition analyses is the concept of assortativity, namely the property of organizations being similar in one property (like ownership sector) to be linked more often to other organizations sharing the same property. At the individual level, we can conceive of a given organization as a collection of employment trajectories, and characterize the population of organizations by the trajectory composition. Another individual-level characteristic that we explore in simulations is the network of personal contacts an employee has, and its effect on organizational membership. These imbalances may not even be the product of organizational change, but of intrinsic differences in the regimes and activity logics of different populations of organizations. For example, a regional bureaucracy or a central hospital require a minimum amount of people to operate. Another concept related to structural imbalances is the notion of centrality, which we can describe as the fact that organizations are not equally important along a certain dimension, maybe regarding their connectivity to other organizations, or their brokerage capacity, their growth patterns, etc.

32 4.2 Organizational Growth: Local Change

4.2.1 Multifaceted Locality Organizational dynamics unfold locally, but locally with respect to what? Lo- cality can be studied along different axes. Change can be local in time or local in space. We explore the temporal aspect of change locality by organizational growth processes, and the spatial aspect by a relational construction with spatial properties: the social space.2

4.2.2 Growth Processes Structural features such as the size composition of a population of organizations may change in various ways. If we look at change from the perspective of the individual organization we can observe how its size changes over time, and that constitutes an organizational growth process. We can say that our interpretation of growth processes is local in time; otherwise they should not be treated theoretically as part of the structural component of organizational dynamics, but rather as part of the unfolding component. Organizational growth processes exhibit statistical properties that defy out intuitive understanding of how such processes unfold. The existence of these regularities has important implications for our understanding of social processes and of the models social scientists use to study them. Some of the core concepts and ﬁndings of organizational growth statistics have been present for at least the last century, and scholars from different disciplines have taken notice of them.3

4.2.3 Extreme Growth Events The most remarkable characteristic of organizational growth processes is, para- doxically, something rarely observed in organizational growth processes. Ex- treme events of growth are relatively large changes in size occurring during a relatively short period of time. From an inferential standpoint, extreme events are what Taleb(2007) calls ‘black swans’; one has to wait a long time to observe them and in the meantime might erroneously infer they do not exist. The author is thinking primarily about huge and sudden ﬁnancial events, but the same can be said about a great many other systems, natural and man-made systems (Albeverio et al., 2006). In the dynamics of organizations, extreme events correspond to large expansions and great declines.4 The mechanisms underlying these extreme events are still unclear, although a distinction can be made between perspectives that see extreme events as endogenous and those seeing them as external shocks.

33 4.2.4 Social Space

Locality has a temporal aspect, and we have conceptualized it through growth processes. But there is another aspect of locality that we describe here. The notion of social space can be conceived as a region of social interaction that conditions and facilitates action has been advanced by various researchers and in different contexts. There are two fundamentally different perspectives on social space. In order to see the difference, it is useful to make the following thought experiment: let us assume that society consists only of individuals and the organizations they engage themselves in. What would happen if all organizations and its members suddenly disappeared? One line of speculation could argue that the underlying structure of society would still be there, so that if a new individual or organization where to pop up again, she would be placed in a social location corresponding to its socioeconomic characteristics, precisely as it would have been before the disappearance. Another line of speculation would suggest that if all individuals and organizations vanished, nothing would be left, because the fabric of society is made precisely of what we have taken out. If a new individual or organization popped up, it could not be placed anywhere; there would be, literally, nowhere to place her. These two views correspond to two distinct social space conceptualiza- tions. Social space can be defined à la Blau following (Blau, 1977) —this is why it is sometimes called Blau space— as the orthogonal space spanned by relevant socioeconomic variables (age, educational status, gender), in which every social entity occupies a position. This is an absolute view on categories as given and on spatial dimensions as immutable entities independent of the individuals or organizations occupying them.5 Social space can on the contrary be defined relationally, à la Bourdieu following (Bourdieu, 1984). Individuals, organizations and the categories they belong to generate the interaction space together, and positions in social space are defined in relation to the positions of every other entity and category. This approach, which is the one we investigate in the thesis, is closely associated with the emergence and reproduction of social fields in various formula- tions (Fligstein and McAdam, 2012; Martin, 2003), and with mechanisms for the formation and reproduction of classes and class stratification (Bourdieu, 1987; Breen and Rottman, 1995).6 Sorting entities by proximity in social space can thus be a proxy for underlying connections between organizational objects as a basis to explain the structural differences we observe, for instance in terms of labor movements between organizations.

34 4.3 Labor Mobility: Relational Change

4.3.1 Multifaceted Relations

Labor mobility as an analytical dimension looks at the relational aspect of organizational change. As usual with organizations, relations can be seen either from the point of view of the individual or from the point of view of the organization. Our modeling approach is quite based on the organizational-level consequences of labor mobility, and how relations connect organizations. For employment relations in particular, there is are a number of interorganizational relations that are relevant, like job searching.

4.3.2 Transfers Between Organizations

Dependencies between organizations can be realized in various ways. There could be hierarchical dependencies between organizations at different levels. Competition between organizations is another possible type of dependency. One way to conceptualize dependencies it through the idea of transfers, meaning that organizations through exchange become dependent on each other. In its turn, different kind of exchanges may take place between organizations, from information and knowledge, to contacts and material exchanges.

4.3.3 Organizational Communities

Another theoretical aspect of organizations and their dynamical landscape is the theorizing around organizational communities. An organizational community is an ecology of coexisting populations of organizations in an environment (Aldrich and Ruef, 2006, ch.11). The easiest way to picture an organizational community from a phenomenological point of view is to imagine walking into a district, say a commercial street, and observing the organizations around us. There would be many organizations and of various kinds— shops, restaurants, cinemas. They all go about their own business, but at the same time they share a space and a common commercial purpose. Organizational communities are merely a particular case of social communities (Tönnies, 1957) i.e. socially cohesive groups based on stable close relations that occur more often within the group that with the outside world. And precisely because communities are relational structures, they can become an aspect of labor mobility as well, and conceptualize how labor ﬂows between organizations look like.

35 4.3.4 Social Distance and Relations

If we assume the relational conceptualization on social space as an underlying structure for organizational change, then it becomes interesting to ask how that space would look like. In other words, one attempts to characterize the geometry of social space. At the same time, the structure of social space has to conform to the locality of interactions, one of our phenomenological principles of reality. This is where the concept of social distance comes into play. Social distance is an analytical construct that represents the notion of proximity in social space. The smaller the social distance, the higher the likelihood the actors have to interact or inﬂuence each other (McPherson, 2004). Com- pletely paralleling our previous discussion on social space, social distance can also be conceptualized in absolute or in relational terms, and we will use the latter view. Finally, the operationalization of social distance is a matter of representation measures that we leave for the next section.7

4.4 Distributions: Counting and Sorting Entities

4.4.1 A Landscape of Distributions

Organizing processes, the structures they crystallized into, the path-dependent trajectories they are composed of. They all leave quantitative footprints along their way. The study of distribution questions has to do with the search for statistical patterns in these quantitative footprints. Speciﬁcally, researchers look for statistical patterns that recur across different settings and time periods. They constitute a statistical regularity. Statistical regularities are arguably the most prominent type of quantitative social fact, in the spirit of Durkheim(1982). A well-studied example of a statistical regularity in social phenomena is homophily, i.e. the observed fact that individuals are more often linked to other individuals that are similar to themselves along one or more dimensions (McPherson et al., 2001). An example from organizational dynamics is the distribution of changes in organizational size over time, typically measured by the growth rate.8 The statistical pattern observed in this case— which we will elaborate on in the subsection below— is extremely robust and has been observed in all kinds of growth processes, see empirical review in (Mondani et al., 2014). Of particular interest to us are a class of statistical distributions called heavy-tailed distributions— or alternatively fat-tailed distributions.9 As many studies have shown, the organizational landscape turns out to be full of such patterns, from the probability distribution of organizational size, going through the distribution of labor ﬂow links between organizations, and the mentioned

36 rate of growth in size. These distributions are interesting because of two main reasons we explain below.

Outliers Matter, and Are Not Such On the one hand, heavy-tailed distributions stress the importance of outliers in organizing, and presumably social processes in general. It is useful to lay out the discussion along a comparison with a baseline case, here the normal (or Gaussian) distribution. In a Gaussian world, there is little room for outliers. A normally distributed variable, for example the length of humans of a given age range, varies (or fluctuates) around a typical value, with very small differences, above or below the typical value.10 This is not the case with organizations. To use the same example: they come in all sizes, small family business all the way up to huge international conglomerates. One can hardly imagine what the typical size of an organization might be, and this has been shown to hold even when analyzing particular industries (Bottazzi et al., 2002; Bottazzi and Secchi, 2003).11 Another non-Gaussian pattern holds for the distribution of some kinds of events, like the mentioned growth-rate distribution, analyzed originally in the context of price fluctuations (Mandelbrot, 1983, 1997). In this case, the non- Gaussian character of these patterns translated in the fact that large variations in size during relatively short periods of time are more likely to happen than in a Gaussian world. Crises, economic expansions are among the ‘external shock’ types of phenomena that can cause large size fluctuations, but also en- dogenously generated processes.12 For all these reasons, outliers in organizational dynamics are ubiquitous and strictly speaking should not be considered outliers at all (Stanley, 1995; Stanley et al., 1996; Stanley and Plerou, 2001).13

Slower Decay The second reason to take up heavy-tailed distributions in this context has to do with the statistics that arise when interacting objects move away from each other. Previously, we have discussed that interactions are local, so it follows that a separation in space should lead to an attenuation of interactions, either in their frequency, their strength or both. In natural science parlance, interactions are said to decay when objects move away from each other. This is indeed the case for the decay of the likelihood for social interaction with geographical distance (Butts et al., 2012), intercity transport (Zipf, 1946) and many other geography-dependent networks (Barthélemy, 2011). In- terestingly, the mathematical forms of the decay (and there are lots of them) correspond in many cases to heavy-tailed distributions. Within our social space

37 theoretical formulation, the premise is that points that are close in social space are more similar to each other that those further apart. This opens up the possibility to compute similarity distributions in a purely geometrical fashion, as is the case with the Euclidean distance distribution, and check for the decay in an analogous way as for geographical distances.14

4.4.2 Method: Heavy-tailed Distribution Fitting The first method we employ for characterization of distributions is heavy-tailed distribution fitting (Hagberg et al., 2008). It is essentially a parametric approach to statistical estimation by maximum likelihood (Clauset et al., 2009), based on a research tradition on this type of distributions advocating for the use of proper statistical fitting techniques as a reaction to the relative disre- gard of statistical methods originally underlying some work in the interface of economics and physics, see the critique in (Gallegati et al., 2006).15 An alternative framework for the study of heavy-tailed distributions is given by extreme value theory. This constitutes a whole field of statistical research, with specific methods and dedicated journals. However, as far as we understand, extreme value techniques rely on assumptions on the indepen- dence of observations, pretty much like a traditional linear regression model does. This assumption is not reasonable in our case; our observations are so dependent on each other that we can build a network out of them!16

4.4.3 Method: Homogeneity Analysis Our approach to social space is based on similarity between organizational entities. It is then reasonable to use a similarity-based method. This method is thus intended to generate a proxy for social space by sorting organizations based on the similarity of their categorical variable values, e.g. belonging to a certain industrial activity, having similar number of employees, etc. Both organizations and categorical variable values are given coordinates in the same space. A social distance between organizations can then be derived as a distance measure out of the coordinates of those organizations in social space. The method is called homogeneity analysis (de Leeuw and Mair, 2009). It belongs to the Giﬁ17 family of methods (Michailidis and de Leeuw, 1998) and is an extension of the Multiple Component Analysis (MCA) method most known among sociologists for its application by Bourdieu(1984).

38 4.5 Networks: Quantifying Relational Dependencies

4.5.1 A Landscape of Networks

It is hard to think of something more distinctively sociological than the idea of individuals and relations between then, of groups and relations between groups. Social organizing and network questions go hand in hand. Many phenomenological features of the organizational world can be represented as networks. Speciﬁcally, the network representation has been proposed as a theoretical paradigm for organizational research (Borgatti and Foster, 2003). Networks exist within, between and around organizations (Kilduff and Krackhardt, 2008). In particular, interorganizational networks are relevant because they capture nonlinearities, transmission and diffusion processes. All things that can have a large impact on the dynamics and do not appear so prominently when analyzing e.g. data from a survey.

4.5.2 Method: Complex Network Analysis

From a representation standpoint, a network can be thought of as the simplest meaningful data representation of a system of entities interacting with each other (von Bertalanffy, 1973). The entities are typically the nodes of the network, while interactions are represented as links between nodes. Furthermore, interaction is local with respect to the links between entities, which is in line with our phenomenological assumptions about locality. Networks are relevant descriptions because they explicitly model complex effects that arise when a group of entities interact over time. Therefore, a lot of the characteristics networks are known to give rise to— feedback effects, nonlinear diffusion processes, tipping points between two distinct regimes— are features shared by many systems, and defining characteristics of complex systems. Almost a hundred years after Moreno and Jennings(1938) published their seminal work on the statistical analysis of configurations in social relations (sociograms), it is then almost trivial to argue that network analysis is a useful quantitative tool for the study of social networks. Having said that, methods for network analysis have taken many forms depending on the scientific field, from the original mathematical developments in graph theory (Bondy and Murty, 2008) to social network analysis (Wasserman and Faust, 1994), to the network aspect of complexity science (Newman, 2010) and network science (Brandes et al., 2013).18

39 4.5.3 Network Centrality and Assortativity Many conceptual aspect of structures and local change can be measured— or at least a proxy for them be found— through the tools of network analysis. For instance, in our conceptual discussion before we presented centrality as an intrinsic structural imbalance of the organizational landscape. Researchers have come up with relational operationalizations of the centrality concept in terms of centrality measures (Freeman, 1978). An organization can be central with respect to the number of labor ﬂow links to other organizations as well as depending on the particular structure of those linkages. Additionally, many of these measures vary with organizational size, prompting us to perform a scaling analysis of network centrality with size, in the spirit of the analyses named previously (Bettencourt et al., 2007). The other point here has to do with the operationalization of assortativity. The assortativity coefﬁcient (Newman, 2003) is a measure for quantifying mixing patterns of nodes with respect to properties like ownership sector. Net- works in social systems typically exhibit positive assortativity along node and link properties, meaning that nodes with similar properties tend to be linked more often than with nodes having other properties (there are consistent ex- ceptions to these regularities, typically the case of the dominant heterosexual partnership pattern where nodes of opposite gender are linked more often).

4.5.4 Network Stability and Community Structure There are different ways to model the relationship between processes and networks. One can represent a certain process takes place with a network serv- ing as the underlying relational structure. Examples of this first case include spreading processes, social diffusion and contagion. We focus on the process of social influence through networks, developed late on in Chapter5. One can also represent how the network itself changes in time (Dorogovt- sev and Mendes, 2003; Holme, 2015). Examples of this second case are studies on the stability of networks. A typical concept that appears prominently is how robust (or conversely, how vulnerable) a network— like the Internet— is to different kinds of attacks (Albert et al., 2000; Holme and Kim, 2002). Some stability-related methods for network analysis are global, some are local. In line with our phenomenological features, we implement a local method, to find the network backbone (Bajardi et al., 2011) of a network. Lastly, social cohesion can be formulated in terms of density of relationships, and is therefore suitable of being represented as a network. Within context of the network representation, a network community is essentially a subset of the network such that the links among the nodes within the same community occur more often than the links between different communities. The field of

40 community detection is a rapidly expanding research area of network science; see the review in (Fortunato, 2010) and an updated version in (Fortunato and Hric, 2016). We explore the relation between within and between community ﬂows as a dimension of social space.

41 Notes to Chapter 4

1. An information-based perspective on crystallized change is conceivable as well. From a complexity science point of view, organizations and cultural products in general can be conceptualized as crystallized objects of our imagination process (Hi- dalgo, 2015, part II). 2. Changes in physical space are of course coupled to both temporal changes and changes in social space, for instance when it comes to social network patterns (Butts et al., 2012). We do not dwell into the geographical dimension but it is still worth noticing this. 3. A pioneer in theorizing about growth applied to social phenomena, now relegated for other reasons, is Spencer(1974). He belongs to the life-cycle tradition on social change theories outlined in (Van de Ven and Poole, 1995). In his view, growth is common to social and organic systems. Functionalist ideas such as structural and functional differentiation with increasing size, mutual dependence of the parts in the collective, and complexity increase by growth, are among the notions advanced by Spencer. 4. As outlined in the main text, the key point about extreme growth events is that low fluctuations occur most of the time, and large fluctuations occur occasionally. An example of another framework for processes with sudden peaks of activity in human dynamics is the so-called ‘bursts’ (Barabási, 2005, 2011). The baseline distribution for the growth process is the Gaussian distribution, while the reference here is the Poisson distribution. 5. In a structural-functionalist formulation, Parsons(1964, p.25) defined status (the positional aspect of social structure) as the place of the actor in the structure, and role (the more processual aspect of the structure) as what the actor does in his relations with others, in the context of the functional significance for the social system. When the actor is the object of orientation, the status component intervenes; when the actor is subject of orientation, the role component does. 6. In the early days of classical physics, these two positions where embodied by New- ton, whose view of physical space is analogous to Blau’s, and Leibniz, who proposed a relational perspective more similar to Bourdieu’s social space. But the formulation of physical laws on the basis of a relational framework is not confined to Leibniz’s view. Atkin(1972) proposed a reformulation of physics— and social relations, as a matter of fact— in purely relational terms, using mathematical tools from topology. The advantage of any topological formulation is by definition that it is independent of the metric, i.e. the distance measures we define on the space, and thus allows for the identification of invariants in the relational structure. Social network analysts have termed the associated network representation as hypergraphs (Wasserman and Faust, 1994, ch.4). 7. Ideas about social distance were present in early sociological works too. Sorokin (1927, ch.1) defines social space essentially in Blau terms, with dimensions of social positions corresponding to group belonging and hierarchical status, that he

42 calls horizontal and vertical dimensions respectively. The social distance between two individuals is simply the distance in their respective coordinates. Furthermore, social distance is embedded in geographical space, and physical distance is essential for the establishment and reproduction of social networks (Butts et al., 2012).

8. Mathematically, given an organization with size St at the end of one time period— say a year— and size St+1 at the end of the next period, the growth rate is defined as r(t,t + 1) = log10(St+1/St ). This is not the only measure, but it is the one most commonly used in the literature we refer to, see e.g. (Amaral et al., 1998). 9. In conventional practice, the term ‘distribution’ is commonly used to denote the probability density function (PDF) of a random variable. A distribution proper is the cumulative function of a probability density function (CDF). 10. There are a number of definition issues regarding fluctuations. Generally speaking, a fluctuation measures variation around a typical value. The term ‘fluctuation’ can thus be akin to a statistical variance. But economists can talk about fluctuations in, say, the GDP of a country, to refer to what we here denote as growth rate, e.g. (Gabaix, 2011). The root of the fluctuation, a standard deviation, is termed ‘volatility’ (Canning et al., 1998). Finally, and in a more colloquial use, a quantity is said to fluctuate when it changes with respect to a certain reference value. 11. For the size distribution, the inverse square distribution seems to be a recurring statistical pattern, a statistical regularity (Caldarelli et al., 2004), see for instance in the distribution of American companies (Axtell, 2001). Additionally, and just for the sake of completeness, let us say that it is unfortunate that the literature on organizational ecology and demography (Carroll and Hannan, 2000; Hannan and Freeman, 1989) uses the term density to refer to the size of a population. This convention probably stems from ecology where keeping the area constant implies that density and size are the same thing. 12. Many, if not perhaps almost all of the researchers doing complex systems models and complex networks cited in our discussions on growth and networks have a background in a related natural phenomenon: modeling the growth of surfaces and crystals. In that context, ideas of fractals and self-similarity motivate the emergence of the observed statistical growth patterns (Jensen et al., 1996; Kaandorp, 1994; Vicsek, 1989). 13. Talking about logics, and in very general terms, fluctuations in Gaussian processes fluctuations belong to logic of the Central Limit Theorem, while fluctuations in non-Gaussian processes belong to the logic of multiplicative processes (Redner, 1990; Sornette, 1998). 14. The concept of distance and how to operationalize it is central to defining the met- rics of a space. The question of defining a suitable representation of social space is then very much related to the type of metric the researcher chooses. This is a highly nontrivial attempt, as some network researchers have pointed out that it is not even clear whether social space must have a metric at all (Girvan and Newman, 2002). More information about all sorts of distance measures can be found in the encyclopedia of distances (Deza and Deza, 2014).

43 15. Another aspect of the methodological differences between regression models and the methods we advance in the thesis regards the observed population. Regression models are originally designed for random sampling, while we advocate for the need for and the practical application to total population data. These differences become crucial to bear in mind when discussing notions like p-values, model fit tests, significance. For more on the use (and misuses) of p-values see (Vidgen and Yasseri, 2016). 16. A Plea for Regression Analysis. We regard all models as complementary of each other. Even when advancing alternative and relatively unexplored methodological tools, we should note that more traditional regression models have been fruitful in many ways. For once, they might actually be the only systematic method at our disposal to accumulate knowledge and test hypothesis about social facts under a unified statistical framework, and this prospect should not be underestimated. Consequently, the thesis can also be seen as an attempt to shed light into aspects not typically captured by those models and outline the difficulties in doing so. 17. Gifi is actually a nom de plume that designates the research group led by Dutch statistician Jan de Leeuw. 18. Barabási’s most recently published textbook is actually called ‘Network science’, but we believe one should not be hasty in confusing this approach with ‘network science’ as outlined in (Brandes et al., 2013). Barabási’s approach is rooted in the tradition of complexity science and statistical physics, while Brandes et. al’s approach seems definitely closer to psychology and social network analysis and even computer science.

44 5. The Unfolding of Organizational Change

The more structural analytical dimensions of organizational dynamics discussed in Chapter4 can be thought of as the setting, the backdrop against which other processes of change unfold. In this chapter, we go through this complementary aspect of organizational dynamics: the unfolding of organizational change. This will take us one level deeper in our description of social organizing and its dynamics. Concepts along the analytical dimension of employment trajectories, as well as data representations and methods for sequences and social mechanisms are discussed.

5.1 Employment Trajectories: Path-dependent Change

5.1.1 Multifaceted Path Dependency

Processes are more fundamental than events, because processes are more likely to generate events than the other way around. Within the critical realist perspective, one would add that a process is more fundamental than an event because it belongs to a deeper level of reality, the “real” reality versus the “actual” reality (Collier, 1994). A trajectory as an analytical concept can be deﬁned as the path an object goes through in a given space. Space is used here in the most general sense: it can mean geographical space, for example a train moving along a railway describes a trajectory in geographical space. It can also mean social space, for instance a individual’s career upwards towards positions of higher and higher social status describes a trajectory in social space. Or, naturally, trajectories can unfold both in geographical and in social space simultaneously. One deﬁning characteristic of trajectories is their path dependency, namely, the fact that a position in a trajectory is dependent on the previous positions the object has been in.

45 5.1.2 Individual Employment Trajectories The relation between individuals and organizations is characterized by a number of tensions. Humans are, as Ahrne(1994, ch.2) puts it, ‘organizational cen- taurs’. In a life-course— and therefore individual-level— perspective, employment trajectories appear in various contexts (Pollock, 2007; Scherer, 2001). From the individual’s point of view, the employment trajectory consists of the sequence of employment states the individual has been at, and the time it has spent in each state. A person can for instance have worked the ﬁrst 10 years of her professional life in large public organizations, to then change to the private sector and work in small organizations; this description would specify her employment trajectory.

5.1.3 Trajectories and Organizational Dynamics Trajectories are not only tractable as individual-level analytical constructs. They can also be analyzed from the organization’s point of view. Therefore, the employment trajectory analytical dimension addresses organizational change from the longitudinal standpoint of the career trajectories the employees working for the organization, aggregated at the organization level. Changes in trajectory composition during different time periods can be a proxy for structural transformations at the macro-level.

5.2 Sequences: Quantifying Path Dependence

5.2.1 A Landscape of Sequences Social processes leave many kinds of footprints as they unfold. In particular, they leave a trace of events in their path, what we may call a path-dependent trail of change. Taking the sequence perspective on representations for organizational dynamics, we can see that there are many sequence-related phenomenological features to be found in organizational dynamics. It is thus not surprising that many process questions can be formulated or represented in terms of sequence questions (Abbott and Tsay, 2000). Originally coming from historical sociology (Abbott, 1983), the sequence data representation conceives of social processes as sequences of states and transitions between states.

46 5.2.2 Method: Sequence Analysis Within current life-course research, there exist two distinct methodological approaches. One approach is event-history analysis (Wu, 2003) characterized by the estimation of the risk of experiencing a transition, for example a change from education to first job. Organizational demography uses event-history models to estimate for instance the rates of founding and disbanding of organizations (Carroll and Hannan, 2000). The method employed in this thesis is a descriptive method called sequence analysis (Billari, 2001). Originally coming from biology and the analysis of genetic sequences, this method was first introduced to sociologists by Ab- bott(1983) in the context of historical sociology. For many years, the use of sequence analysis was limited to very specific domains of inquiry and partially hindered by the specificity of the software used to implement the analyses (Blanchard et al., 2014, pp.8-11). However, sequence-related methods have recently seen a revival period, with increasingly available longitudinal data on various phenomena and software implementations in some of the statistical analysis platforms most commonly used by social scientists (Brzinsky- Fay and Kohler, 2010; Gabadinho et al., 2011). Domains of application span many disciplines, from genetics to time-use behavior (Cornwell, 2015).

5.2.3 Sequence Similarity and Sequence Diversity Sequence similarity is given by the number of state permutations that are necessary to perform in order to turn one sequence into another, and deﬁnes a distance between each pair of sequences. The algorithm typically used for sequence comparison is called optimal matching (Abbott and Forrest, 1986). Agglomerative clustering methods such as the Ward method are used in order to obtain emergent categories of sequences called sequence clusters. The idea of similarity between objects is closely related to the notion of diversity. Similar objects are grouped together; diverse objects get apart. Di- versity questions on the organizational world appear regularly. They are also at the core of some research programs like the mentioned organizational ecology, that asks itself the question of why there are so many different organizational forms (Hannan and Freeman, 1989). These questions are part of a larger set of questions within the study of complex systems. Applying the classiﬁcation in Page(2011, ch.1) to our sequences, we distinguish between three kinds of diversity: i) variation of an attribute within a sequence— for example individual gender—, ii) diversity of sequence types— for example by combining employment trajectories with trajectories along other axes—, and iii) diversity of composition— for example on how to combine different states in a sequence. A measure of diversity we use is the Shannon entropy (Shannon, 1948).

47 5.3 Mechanisms: Exploring the Inner Workings of Change

5.3.1 A Landscape of Mechanisms Distributions summarize the quantitative footprints of organizational change, networks help us see the interactions underlying change, while sequences characterize the path-dependent trail left behind by organizational change. Mecha- nisms, in their turn, seek to go one step deeper and represent the inner workings of change itself. In that sense, mechanism questions aim straight at the core of the critical realist view of underlying, generative entities. Mechanism descriptions are, simply put, modeled mechanisms, simulations in the transitive domain that seek to reproduce those underlying entities in the intransitive domain (Bhaskar, 1978).1 It can now be seen how the argumentative pathway in this chapter has taken us to explore representations of constructs at a deeper and deeper level of reality. That is why the topics were laid out in this order. Organizational dynamics can thus be represented as a superposition of social mechanisms acting at different levels of aggregation and time geographies. There are many deﬁnitions of a social mechanism (Demeulenaere, 2011; El- ster, 1989, 2007; Hedström, 2005; Hedström and Swedberg, 1996; Stinch- combe, 1991). The basic idea is that there exist entities— be individuals, organizations or other actors— that interact, typically in combination with a network structure as relational substrate, and macro-patterns emerge from their local micro interactions (Coleman, 1986; Schelling, 1978). In their generative role, social mechanisms aim at reproducing some observed stylized facts— a term coined by Kaldor(1961)— in terms of those micro-level interactions. For example, one would like to write down a mechanism representation of a social process that brings about a statistical distribution characterizing an organizational population, like the size or growth-rate distributions.

5.3.2 Positive Feedback and Social Influence Over the decades, researchers have defined different social mechanisms; see the review of mechanistic models in (Holme and Liljeros, 2015). These con- ceptualizations can be regarded as a form of middle-range theory, originally conceived as theories that lie between working hypotheses of everyday research and unifying grand theories (Merton, 1968b, ch.2).2 Specifically, we focus on two mechanisms that we deem relevant for understanding the unfolding of organizational change as emerging from interactions between individuals within the organizations they are members of. The first one is a positive feedback mechanism. Positive feedback is a notion that captures a nonlinear effect in time: an increment in a quantity tends to produce a

48 further increment later on. There are many realizations of this positive feedback mechanism; one is the so-called Matthew effect, as described for example by Merton(1968a). In his example from the sociology of science, notorious researchers like Nobel Price laureates get, by virtue of their notoriety, access to more research grants, citations, etc., which increases their notoriety even more and gets them access to disproportionately many new resources. These resources do not go to other less known scientists, who in their turn get disproportionately low shares. Other names for positive feedback mechanisms include cumulative advantage. In the thesis, we model two types of positive feedback mechanisms. The first positive feedback mechanism has to do with seniority in employment sequences, and postulates that the longer an individual has been employed by an organization, the more likely it is for the person to stay. The interacting entities here are employees, or rather their employment trajectories. The second positive feedback mechanism is based on a network representation of positive feedback, additive replication mechanism (Schwarzkopf et al., 2010). The idea is that the more members in a given organization an individual is linked to, the more likely she is to change membership to that organization (or stay if the organization happens to be her own).3 Mechanisms can be combined. In particular, our additive replication mechanism is combined with a contextual social influence mechanism on organizational membership. Given individuals interacting in a contact network of acquaintances, social influence will be in place if the membership of an individual is changed based on the membership of her acquaintances in a social group.4 In our case, the mechanism is motivated by the social psychological concept of preference falsification (Kuran, 1995). But there are other influence-based models include the rational imitation mechanism (Hedström and Swedberg, 1998, ch.12) and the iterative equation models of (Friedkin, 2001).

5.3.3 Method: Agent-based Models and Simulation

The underlying mechanisms of organizational change cannot be observed. It is then quite natural to attempt to get at their inner workings by simulation. The method we use in relation with social mechanisms is agent-based models, ABM (Squazzoni, 2012). In the social sciences, this model class has found a wide range of applications, from the seminal Schelling model of residential segregation (Schelling, 1971), to generative economic models (Epstein, 2014; Epstein and Axtell, 1996); and the reference work by Macy and Willer(2002). In particular, agent-based models are one of the cornerstones of the analytical sociology approach (Hedström, 2005) to mechanisms-based theorizing and

49 modeling. An agent-based model consists of a set of entities, the agents, that have deﬁned properties and act according to their properties and other agent’s properties and actions. An overall macro-level pattern, like segregation in a neighborhood or the growth distributions of an organizational population. Many different dimensions can be incorporated into these models, such as memory effects, multiple levels of aggregation, access to information, and so forth. For a detail on the speciﬁcation standards and aspects of ABMs, see (Müller et al., 2013).5

5.3.4 Method: Physical Analogies Finally, our last organizational dynamical method is actually no method at all. Analogies, physical analogies in this case, have theoretical relevance inasmuch as they have the potential to give new insights into know phenomena (Swed- berg, 2014a). The term ‘method’ here aims merely at subsuming this technique under the same category as the previously presented methods. The role of physical analogies in this thesis, then, is inscribed in a relatively old, positivist tradition seeking law-like regularities for social phenomena that could emulate the experimental regularities observed in physics; see brief historical comment in (Mondani, 2012, ch.9).6 In more recent times, this approach has been picked up by natural scientists and come to be known as sociophysics (Castellano et al., 2009) and econophysics (Chakrabarti et al., 2006).7 The search for laws of social phenomena can very well be question- able enterprise, at least in the natural science sense of ﬁnding universal immutable relations between a limited set of variables from which other propositions might be logically derived. But that should not hinder us from getting inspiration from formal models in the natural sciences.

50 Notes to Chapter 5

1. Philosophically, mechanisms can be seen as processes within systems that bring about certain system outcomes (Bunge, 1997). Mechanisms also feature as the main explanatory concept in certain causality arguments for explanations in social phenomena (Hedström and Ylikoski, 2010). 2. A pioneer of the social mechanism approach whose contribution should be mentioned is Georg Karlsson. In his book on social mechanisms, the author gives a broad overview of models and empirical evidence for a number of mechanisms, ranging from diffusion models on networks, through marriage choice models, to social psychological communication theories (Karlsson, 1958). The mathematical formalism resembles— for the most part— the one we would normally associate with the work of Coleman(1964). 3. In a network context, a variation of positive feedback is given by preferential attachment: very “popular” people with lots of links tend to get disproportionately many new links, further increasing their popularity, as modeled by the preferential attachment model (Barabási and Albert, 1999). 4. In processes of individuals adapting to their peers and contexts, social influence mechanisms are just one aspect of the story. Another mechanism that we have mentioned before is homophily. In a homophily-dominated setting, individuals will establish relationships based on similarities in some of their characteristics. In a social-influence-dominated setting, individuals that are already related to each other will become more similar by virtue of their interaction. There is still an ongoing debate as to whether social influence or homophily dominate, for example in network research on the social transmission of obesity patterns (Christakis and Fowler, 2007). 5. The terms ‘agent’ and ‘actor’ appear in the names of various modeling tools and methods. The term agent seems to appear more frequently in the context of agent- based simulation (Epstein, 2006; Macy and Willer, 2002), while the term actor is commonly used in other frameworks like stochastic actor-oriented network models (Snijders et al., 2010) that focus on evolving network structures. The difference between the two terms and how it plays out in actual modeling practice is a point worth noticing. 6. On a more personal note, statistical mechanics was the start of the author’s own journey into the study and modeling of social phenomena. 7. No account of sociophysics would be complete without mentioning Serge Galam, who considers himself the creator of this approach (Galam, 2012). His work has mainly focused on opinion formation and voting models.

6. Overview of the Studies

We are now in a suitable position to provide an overview of the studies in this thesis. Each study takes a data source as input, uses a set of concepts from one or more analytical dimensions, operationalizes them with the help of a set of measures from one or more data representations, and applies one or more methods to the data in order to extract some results. The strength of this approach to organizational dynamics lies precisely in the combined application of all these elements. We present a summarized overview of the studies in Table 6.1. Following the table, more detailed summaries appear. They consist of an upper box com- prising the relevant phenomenological features taken up in the study, followed by an abstract. The articulating logic that binds the model elements together is shown last. Solid lines in the diagram show logical links either on the theoretical domain (leftmost part) or on the methodological domain (rightmost part). Dashed lines show the couplings between concepts and measures. Such logics, as mentioned when ﬁrst introduced, are no attempt at develop- ing an axiomatic grand theory of organizational dynamics, but rather at trying out simple and concrete schemes that can be used in applications to shed light on particular aspects of organizational change that had previously remained unseen or underdeveloped.

53 Table 6.1: Summary of the studies. Detail by data source (data ﬁle names according to AppendixA), analytical dimension (with concepts in the cells), data representation (with measures in the cells) and method.

Study I Study II Study III Study IV Study V Growth and Labor flow Social space and Employment Growth and movements network communities sequences social influence

Stockholm Region Stockholm Region Stockholm Region Stockholm Region Data (organization and Simulated data (organization base file) (network base file) (trajectory base file) network base files)

Organizational size, Centrality, size, Social space and distance, Employment trajectory, Social contacts, Structure sector/industry composition sector assortativity organizational community trajectory composition organizational membership

Extreme events, inter- Growth Social distance change Extreme events organizational movements

Labor flow, Mobility Community labor flows Membership flow

Analytical labor flow stability

dimension

Trajectories Employee seniority

No. of employees, growth Network measures, Euclidean distance, States, Growth rate, Distribution rate, employee movements size scaling distance growth time in state number of contacts

Centrality, assortativity, Network Network community Contact network network backbone

Data Entropy, Sequence sequence clusters

representation Additive replication, Mechanism Positive feedback contextual social influence

Heavy-tailed distribution Complex network Homogeneity analysis, Agent-based modeling, Method Sequence analysis fitting analysis community detection physical analogy 54 Study I. Informing organizational growth models with longitudinal individual-level data. Sector, industry and inter-organizational movement statistics in the Stockholm Region

Phenomenological features: • counting and sorting: sectors, industries (categories), organizational size • dependencies: growth (locality), interorganizational movements (relations), superposition of distributions • change in time: sectors, industries (levels of growth)

This study constitutes our first incursion into organizational dynamics: an analysis of organizational size (i.e. number of employees) and growth (rate of change of size) distributions. We apply heavy-tailed curve fitting to find possible candidate distributions. We further use given categories on ownership sector and industrial activity to study differentials in the distributions. Typ- ically observed aggregated statistical patterns of growth are shown to be the result of a superposition of distinct category-dependent distributions. For example, the familiar power-law size distribution for the total population is actually a superposition of lognormally distributed publicly owned organizations and truncated power-law distributed private organizations. An indirect and complementary relational aspect is brought by the distributions of movements between organizations, which are found to follow a lognormal distribution.

Organizational measured as size Number of employees differentials in + / - DISTRIBUTION Sector/ industry composition

Employee Heavy-tailed measured movements distribution fitting as

Movements =

give occasionally rise to Growth-rate

Extreme lead to events large

Figure 6.1: Study I (growth and movements) and its articulating logic.

Study II. The evolving network of labor ﬂows in the Stockholm Re- gion. Sector dynamics, connectivity and stability

Phenomenological features: • counting and sorting: sectors (categories), assortativity (similarity), scaling (size) • dependencies: labor ﬂows (relations), movements (locality) • change in time: labor ﬂow (stability), sector-level change

This is the first study in the series to explicitly formulate the question of relational stability in organizational dynamics in terms of the network spanned by the movement of employees between organizations and over the years. Re- lational similarity between public and private organization is first assessed through the assortativity of the labor flow network. The stability of the movement links is studied with a random assignment method resulting in the so- called network backbone. The scaling of number of links with organizational size is explored and yields a linear increase as in previous studies. On the ownership sector level, public organizations are shown to be more stable, and the movement links to be more stable as well as present for longer periods of time. Contextual effects of macroeconomic trends are also reflected in the network measures.

effect measured with Size Size scaling grows with Network Centrality dependent number on size measures of links aggregate distribution Sector number assortativity of paths Degree centrality Complex network analysis mixing Betweenness pattern centrality

Labor flow measured Assortativity with coefficient changes over time Labor flow Network stability backbone contributes to structural stability

Figure 6.2: Study II (labor ﬂow network) and its articulating logic.

Study III. The underlying geometry of organizational dynamics: Similarity-based social space and labor ﬂow network communities

Phenomenological features: • counting and sorting: similarity-based space (emergent categories) • dependencies: labor ﬂow community (relations), proximity in social space (locality), distance (superposition) • change in time: social distance (stability)

In this third study, a similarity-based à la Bourdieu social space is constructed by homogeneity analysis for the population of large organizations (1,000 employees or more) in the dataset. Interactions between organizations are represented by employee flows. The study explores whether the locality principle holds in social space, i.e. whether organizations closer in social space are more likely to be linked by movement flows, and whether organizations belonging to the same network community are closer in social space. Indication for both assertions is found. The growth dimension comes back in the form of the distribution of change in social distance, which is found to follow a pattern similar to organizational growth-rate distributions. Contextual effects of specific historical periods on the distance measures are further explored. Finally, so-called gravity laws from geographical networks are explored in social space and some differences and similarities found.

GROWTH

Social distance change Homogeneity Social space analysis growth proximity process groupings Distance growth based on changes in time Organizational measured as Euclidean Social distance community distance

smaller closer within connections within Community through detection

Community a proxy is Network labor flows community

Figure 6.3: Study III (social space and communities) and its articulating logic.

Study IV.The sequence approach to organizational dynamics: Quan- tifying positive feedback mechanisms in employment trajectories

Phenomenological features: • counting and sorting: states (given categories), similarity-based sequence clusters (emergent categories) • dependencies: relations (trajectories), locality of sequence interaction • change in time: positive feedback mechanisms (micro-level change), state (stability), state sequences (path dependence)

The fourth study focuses on employment trajectories and sequences, and seeks to isolate path-dependent employment transitions. Organizational dynamics is conceptualized as an evolving landscape of employment sequences interacting locally within each organization. The study aims at teasing out the existence of positive feedback mechanisms that depend on the sequence of employment states of the people employed by an organization. Employment state sequences are grouped by similarity into sequence clusters— a form of emergent categorization —using the tools from sequence analysis. We ﬁnd indication for a positive feedback mechanism only in a limited number of cases, mainly at the beginning of the time period.

is a sequence of employment States Employment trajectory

describe the spend Time in state organization by

Trajectory composition sequence lowers Sequence analysis diversity

sequence Entropy similarity Sequence clusters lowers Employee leads to seniority distinct Positive higher feedback

MECHANISM

Figure 6.4: Study IV (employment sequences) and its articulating logic.

Study V. Fat-tailed ﬂuctuations in the size of organizations: the role of social inﬂuence

Phenomenological features: • counting and sorting: organizational membership (categories), size • dependencies: contact network (relations), membership information and growth (locality) • change in time: phases of change, inﬂuence mechanisms, growth in organizations

The last study of the thesis tackles the question of underlying mechanisms of organizational change by virtue of a simulation study. Instead of sequence- based modeling like in the forth study, we implement an individual-based model. We formulated an agent-based model representing organizational membership change based on the membership of personal contacts. The stochastic rule to change organization is local, based on the individual’s network neighborhood. A contextual effect is incorporated through a physical analog to a temperature parameter, seeking to represent a group-level social pressure to stay in the organization. Varying the the temperature-like parameter leads to a new interpretation of organizational growth patterns in terms of a transition between two clearly distinct faces, rejoining the exploration started in Study I.

GROWTH

changes measured suddenly Extreme events by Organizational Growth membership rate influenced increase by Social Number of Agent-based contacts represented contacts modeling by proportional decrease to change in Physical analogy membership Additive replication Membership temperature flow mediates Contextual social influence

on top of Contact combined network with

NETWORK

Figure 6.5: Study V (growth and social inﬂuence) and its articulating logic.

7. Concluding Remarks

In this ﬁnal chapter, we present the main conclusions of the thesis in the light of the introductory chapters and the results of the studies. Some foreseeable research challenges at the empirical, theoretical and methodological levels are discussed, as well as an outlook on future developments.

7.1 Conclusions

At the beginning the thesis, we set out to explore and describe the logics of the underlying structures and mechanisms of organizational change as a means to improve our understanding of social organizing. We also presented an overview of the studies, and how they tackle particular phenomenological features in the light of a critical realist standpoint, their main results and articulating logics. We now summarize the conclusions, using the same thematic layout as in the previous chapters, namely the distinction between the fabric and the unfolding of organizational change.

7.1.1 The Fabric of Organizational Change

Some results concern the more structural component of organizational dynamics, what we call the underlying fabric of organizational change. We found qualitatively significant differentials between public and private sector organizations, and to some extent between industrial activities where such analyses performed. Dependencies between organizations were studied through network effects, with organizations more often linked to other organizations in the same sector. We also found that labor flow network properties scale with size in a specific functional form found in scaling analyses of other systems, for example in city dynamics. Heavy-tailed growth statistics for size and growth rate have been observed in the Stockholm Region, a finding that is consistent with many other previous empirical observations. At the same time, some of the variation in structural imbalances can be sorted out through the construction of an underlying similarity-based social space. In fact, homogeneity analysis provides a geometrical interpretation of social distance, allowing us to see relational labor

65 flow patterns as a problem of spatially embedded networks. Social space as both a theoretical and methodological formalism allows to see the locality of interactions in a new light, and opens up new possibilities for research. This type of analysis requires the use of multiple concepts, methods and representations simultaneously, a feature that we consider essential to improve our understanding of processes of organizational change, and that will be impossible to realize empirically without longitudinal data that lends itself to different representations. Finally, how all these organizational objects (organizations, labor flows, employment sequences) change over time has been an important topic in the studies. In this point, we find consistent sector dependencies regarding the stability of employee flows and employment trajectories over time. All these quantities exhibit clear period dependencies as well. For instance, the worst of the crisis at the beginning of the 1990s leaves its traces in the distributions, labor networks and employment sequences we observe.

7.1.2 The Unfolding of Organizational Change

Other results are related to the processes unfolding on top of the underlying structure. Mainly in the last two studies, we explored the applications of two types of mechanisms. On the one hand, we used sequence-based positive feedback mechanisms in employment trajectories, and found that sequence diversity can be used to categorize groups of individuals and observe differentials in their employment trajectories. This is another illustration of the value of emergent categories as a complement to given categories, and of how the idea of similarity can be extended to many different sorts of objects. On the other hand, we used agent-based modeling to simulate mechanisms of social influence, and found that that the interplay between additive replication through social network contacts and contextual social influence can act in a single logic to generate emergent statistical regularities like the heavy-tailed growth-rate distributions for organizational growth. Growth itself has thus an unfolding character that is complementary to the structural one, as seen in our simulations. Moreover, part of the observed statistical pattern of heavy-tailed growth-rate distributions could be attributed to the similar pattern in social distance fluctuations in social space. A priori, there is no reason to believe this should be the case, nor an intuitive explanation as to why the pattern looks like that and not otherwise. This is a good example of a pattern that we would not have been able to see without using a combination of more than one data representation and non-traditional quantitative methods.

66 7.2 Further Research

More challenges always remain, and this thesis constitutes just a starting point for a myriad of interesting avenues of research. We will structure the rest of the discussion by classes in the modeling process, to emphasize the fact that model elements can (and perhaps should) always be recombined to get new insights.

7.2.1 Data Challenges

A data-related challenge has to do with the actual possibility to generalize any of our conclusions to other processes of social organizing. For example, our model of network-mediated social influence in Study V could be relevant for voluntary organizations. Also, working in profit versus non-profit organization is a dimension that we could explore with the data and that has been shown to play a role for example in movement dynamics (Becchetti et al., 2013). The applicability to other contexts and time periods is also interesting to discuss. One factor that makes the Stockholm case special is the large fraction of people and activities concentrated in the public sector, with roughly half of the employed population working for publicly-owned organizations. This is not the case of all countries— not even of all Swedish regions. Talking in broader terms, the studies carried out in the thesis have had a very privileged starting point: longitudinal population data of a relatively high level of complexity (as described in Chapter3). This is the exception rather than the rule, though. Taken as a social whole, the organizational world remains largely unexplored. Some group of natural scientists working on social problems see the future of social science in the “big data revolution” and the application of data mining and non-traditional methods like network science (Havlin et al., 2012). But some other members of that community, although pushing in that direction, are not as optimistic (Watts, 2011). This paradigm choice is in itself a data challenge, one that, significantly, is actively engaging mostly people outside social science departments.

7.2.2 Theoretical Challenges

Throughout the thesis, we have spent a considerable amount of time discussing concepts as a basis for model building. Ultimately, models should play an important role in theory development. We should mention once again the approach by Hannan et al.(2007) where concepts such as group categories are deﬁned with the help of fuzzy logic, i.e. allowing for a continuum of possibilities instead of the usual dichotomous belonging versus not belonging variables.

67 It remains to be seen to what extent fuzziness has a chance to occupy a place among the tools for concept construction.1 Another theoretical challenge is posed by interdisciplinary research itself. As a counterpart of the enormous research opportunities it opens, interdisci- plinarity runs the risk of getting lost in a superﬁcial midst of overlapping yet potentially incompatible research traditions. These difﬁculties, maybe common to all complex system research, should be taken into account if any cumulative progress comparable to the one of traditional approaches is to be made.

Structures, Organizational Growth and Labor Mobility Regarding structures, let us say that our treatment puts quite a lot of weight on the structural component of organizational dynamics. Much remains to be done on this regard, though. For example, measures of structures and structural change should in a further step be combined to build more complex and richer dynamical indicators, and the correlations between these should be studied to gain a more comprehensive understanding of the phenomenon. Social space as a proxy for underlying relational structures was discussed and applied to some length. Towards the end of his tutorial article on Blau space, McPherson(2004) enumerates some conjectures based on the social space perspective. Conjecture 7 is about the association between organizational composition and the “shape” of organizations in Blau space, as a way to get at the relationship between organizational demography and the population ecology of organizations, thus unifying the two strains of ecological research we mentioned in Section 1.1. Even when using a relational approach à la Bourdieu, the idea of combining social space geometries with organizational compositional properties is still a valid and interesting point to explore. Organizational growth as a process has been tackled so far mostly in relation to the heavy-tailed size and growth-rate distributions and the possible generative mechanisms underlying them. Much research remains to be done that will allow us to combine growth even to a larger extent with the other data representations, for instance to see how mobility and trajectories are correlated to organizational growth for different groups and across time. The labor mobility dimension concentrated on issues of centrality, assortativity and stability. Yet, the literature on labor market dynamics offers a wide variety of more speciﬁc processes to explore (Berg and Kalleberg, 2001).

68 Employment Trajectories

Concerning employment trajectories, the main conceptual challenge may be associated with the very deﬁnition of a trajectory. For instance Abbott(1983) already advanced the idea that such paths may exist at different levels and co- evolve with each other. The coupling of more than one trajectory dimension— e.g. geography with employment— is another challenge for future research.

7.2.3 Methodological Challenges Distributions and Networks

We have discussed two organization-based data representations: distributions and networks. On the distribution side, it is worth reminding ourselves that this thesis uses parametric statistics exclusively. A whole other range of tools in Bayesian analysis is available, both for distribution analysis— e.g. in political science (Jackman, 2004)— and within a network context, where it can help in the identiﬁcation of missing links and nodes (Hric et al., 2016). Another family of statistical models that should be explored at some point is spatial statistics. The stochastic version of it, point process models (Ripley, 2004), attempts to write down spatial versions of know processes, e.g. by extending the Poisson process to a given spatial geometry. On the network side of the story, there are many further developments to be discussed. But let us just to signal out an important one that is related to recent developments in network science: the extension of community algorithms to detect overlapping communities (Lancichinetti et al., 2009). This is a promis- ing ﬁled of research that could help to explore the interfaces between different organizational groupings. Naturally, the community measures used here, like modularity, have to be adjusted for this new framework (Lázár et al., 2010). Finally, formal statistical models for inference models on social networks have become increasingly available and used, including the so-called stochastic actor-oriented models— Siena model class (Snijders, 2011)— and exponential random graph models (Frank and Strauss, 1986; Lusher et al., 2013).

Sequences and Mechanisms

For the two individual-based data representations, sequences and mechanisms, we can identify potentially interesting research avenues as well. Sequences are very rich objects are there is much to be done on that front. In particular, the formalization of a genetic-algorithm treatment of sequence-based simulations is worth exploring (Sivanandam and Deepa, 2008). This is indeed not too much of a stretch, because many of the concepts and terminology used in sequence

69 comparison and matching come originally or are equivalent to the terminology used in genetic algorithms. A combination of individual-level trajectories and social space homogeneity analysis is also interesting. On the other hand, a deeper treatment of social mechanisms beyond the “traditional” ones like positive feedback and social inﬂuence would require a deeper discussion on superposition of mechanisms, or mechanisms acting simultaneously at different levels of aggregation. It is not clear to us whether our current logic of using mechanisms-based simulation as a simulation of some simple aspect of a phenomenon can be applied when dealing with more complex setups. A more fruitful approach may lie in the combination of sequences and mechanisms with multilevel models. This model class has been present in traditional regression analysis (Hox, 2002), and has also its relational analogue in multilevel networks (Boccaletti et al., 2014).

7.3 Outlook

Throughout the thesis, we have highlighted the need for a process-oriented view on social organizing as a means to get to the underlying dynamics of organizational change. The use of path-dependent constructs, contextual factors and relational dependency structures has been illustrated in the studies. Our approach has stressed the use of concepts and interdisciplinary tools to obtain richer descriptions of the organizational world. In that sense, it is somehow closer in spirit to the kind of processual sociology advocated by Abbott (2016). The points about articulating logics, though still presented in an em- bryonic stage, can shed light into alternative ways of thinking about theories and theorizing, new frameworks for hypothesis formulation, complementing views on qualitative and quantitative research, and other types of descriptions. Concepts, ideas, recurring patterns, life-long obsessions and questions. Everything will be transmuted eventually. When and by which means is a completely different matter.

70 Notes to Chapter 7

1. The use of fuzzy logic has found its way into applications on concept formation (Belohlávek and Klir, 2011). This research is mathematically close in spirit to (Hannan et al., 2007), though more grounded on cognitive science and principles of general systems theory.

Appendices

A Base Files for Data Representations

A schematic view of the different base file structures for the data representations used in the thesis is shown in Tables A.1. The layout in Table A.1a illustrates the structure of the base file for time-dependent studies of organizations. It is used in Studies I and III. The layout is essentially a long-formatted list with yearly snapshots of organizational properties. It consists of the year, the anonymized unique organization ID number, organizational-level properties like ownership sector, the anonymized unique workplace ID number, workplace-level properties like the industrial activity of the workplace, and aggregated employee properties like the amount of employees of a given gender and age group employed in that workplace that year. In the example in the table, we see that in the year 1990 there are two organizations, numbers 1 and 23. Organization 1 is a public organization with two workplaces: one in administration with 145 employees and one in education with 110 employees. Organization 23 is a private organization with 24 employees working in a single manufacturing workplace. One year later in 1991, there is only the public organization, and its size has diminished. And so forth. The second layout, shown in Table A.1b, is the network base file. It is used in Studies II and III. The base file consists of a long-formatted list, so- called edge list. It is simply a list of the interorganizational movements at the end of a given year. The movements go from a source organization to a target organization. The amount of employees changing job is called weight of the edge. Following the example in the table, the second row in the table would mean that 23 employees are registered as working in organization 1 until 1990, moved to organization 94 and are registered as employed there at the end of 1991. The third and final layout comes in Table A.1c. It is the trajectory base file used in Study IV. The base file consists of a wide-formatted list where the rows are year-based sequences, i.e. combinations of predefined states. The count shows how many employees of each gender feature in each sequence. This is not a yearly list, but a holistic list that takes into account the whole

73 time period in each unit. There are as many rows in the base ﬁle as different combinations of employment states in the population. In the example in the table, there are 201 female employees that are in state 1 for the whole time period. On the contrary, 125 males change to employment state 2 at the end of the period, and so forth. These three base ﬁles are then combined and reshaped to meet the input requirements of the different methods used in the studies.

74 Table A.1: Base ﬁle layouts for data representations.

(a) Organization base ﬁle (long format).

Year Org. ID Sector WP ID Industry Size 1990 1 Public 28 Administration 145 1990 1 Public 35 Education 110 1990 23 Private 58 Manufacture 24 1991 1 Public 28 Administration 122 1991 1 Public 35 Education 95 ...... 1993 23 Private 58 Manufacture 15 1994 23 Private 58 Manufacture 15 ......

(b) Network base ﬁle (long format).

Year Source org. Target org. Weight 1991 1 2 10 1991 1 94 23 . . . . 1 . . 1991 3 1 59 1991 9 1 13 ...... 1992 1 3 50 ......

State State ··· State Gender Count 1990 1991 ··· 2003 state1 state1 ··· state1 Female 201 state1 state1 ··· state2 Male 125 state2 state2 ··· state1 Male 706 ...... state5 state5 ··· state5 Female 36

B Reﬂections

The idea with these short reflexions is to elaborate on some of the topics touched upon in the previous chapters, and that we deem particularly interesting and relevant to discuss further. The first and second sections— on locality and superposition— have a more interdisciplinary character, in order to show how the same concepts and ideas emerge in different fields and settings. The third and fourth sections— on stability and human mobility— have more of a historical character, and are intended to offer some perspective on the development of old questions that are relevant nowadays.

B.1 Locality in Physics and in the Social Sciences In his book ‘Everything is obvious: once you know the answer’ physicist and sociology professor Duncan J. Watts makes a balance of the impact that thou- sands of physicists, mathematicians and computer scientists working on social phenomena have had during the last 20 years. His account is not without skep- ticism: he claims that progress in substantial questions is not remarkable. So perhaps, he continues, social questions are hard to grasp in themselves, not just hard for social scientists (Watts, 2011, pp.ix-xi). This is in line with physicist Dirk Helbing’s statement when he says “My own judgement is that it is less hopeless to develop mathematical models for social systems than most social scientists usually think, but more difficult than most natural scientists imagine” (Helbing, 2012, p.1). A combination of many different aspects makes a social problem “hard”. Some of them are outlined in the phenomenological features analyzed in this thesis. Among them, the notion of locality has played a fundamental role. We have seen different aspects through which the locality of change can manifest itself, from fluctuations of social distances in social space to stepwise time and network changes in organizational growth phenomena. But there are many more aspects to contemplate, and that is what makes the local aspect of social phenomena “hard”, or complex we could say. We have for instance not gone too much into the details of the local structure of social networks, at least not for higher-order configurations like triangles (Faust, 2007). We could also elaborate on the locality of decision making that a network model implies, like in the case of stochastic actor-oriented models (Sni- jders et al., 2010).

77 As a complement, these reflections elaborate on two additional aspects of locality: gravitation laws and cascading in multilayer processes. A good starting point is this quote from Robert K. Merton’s ‘Social theory and social structure’: “The fact that the discipline of physics and the discipline of sociology are both identifiable in the mid-twentieth century does not mean that the achievements of the one should be the measure of the other. [. . . ] Perhaps sociology is not yet ready for its Ein- stein because it has not yet found its Kepler— to say nothing of its Newton, Laplace, Gibbs, Maxwell or Planck.” (Merton, 1968b, p.47). Merton, following in the footsteps of others before him— like the founding father of sociology, Auguste Comte— believed in the possibility of a formalized sociological discipline that, even if not like physics, could still conform itself to the logical practices and scientific standards of knowledge accumulation through observation of recurrent social patterns and systematic hypothesis testing (Merton, 1945). In the quote, though, Merton warns us about the developmental abyss separating modern physics from sociology, and advocates for a more modest inspiration based on earlier physical theories. This takes us to the first aspect of locality we will discuss here: the gravity law in geographical space. The grav- itational model for population settlements was proposed by Zipf(1946) as an analogue to Isaac Newton’s law of gravitation. Given two cities of populations P1,P2 respectively, separated by a commuting distance D1,2, the exchange flow F1,2 of people and goods between the cities follows the expression

P1 · P2 F1,2 ∝ , D1,2 where the populations play the role of masses in the Newtonian universe, and the geographical dependence is with the inverse of the distance and not with the inverse of the distance squared. This “law”— or rather this statistical regularity— is a case of the search for statistical laws in the social sciences (Gentile, 1942) and an example of decay of interaction with geographical distance. A similar model for communication patterns was proposed around the same period (Boalt and Janson, 1957), and the approach eventually ex- panded into a whole model family (Erlander and Stewart, 1990). A further exploration along these lines comes in Study III. The other aspect of locality is related to levels of aggregation. As mentioned in the phenomenological discussion in Chapter2, the various emergent levels of aggregation of social reality interact and co-evolve with each other.

78 Interaction within each level is local, but the links between levels introduce new dependencies that can lead to far-reaching global consequences. One such effect is cascading effects in interdependent networks. For example, let us imagine a power supply network and an Internet-based communication network. They depend on each other: the communication network is used by the power stations to balance the power grid, while the grid provides the electrical supply to run the communication network. A failure in a power station would take some communication nodes ofﬂine, further shut- ting down other power stations and communication nodes in a cascading effect (Buldyrev et al., 2010). All of this happens locally, but the interaction between different levels results in global outcomes. The study and modeling of multilayer networks is emerging during the last few years as a vibrant new research topic in network science (Boccaletti et al., 2014).

79 B.2 The Superposition Principle and General Linear Reality

The idea of superposition was presented in the main text in close association with the idea of linearity in regression models. Speciﬁcally, the additivity of effects means that the total effect on the outcome variable (say, the growth rate of an organization) is equal to the superposition—i.e. the sum— of the individual effects that the independent variables (say, the size of an organization during the previous period and its industrial activity) would have had if operating individually upon the dependent variable. From a strictly methodological standpoint, the linear assumption makes statistical estimations much easier, at the same time that it facilitates result interpretation in terms of individual contributions ceteris paribus (Sørensen, 1998). From a broader modeling standpoint, however, the assumption of additivity of effects— where the superposition principle applies— can be seen as a statement about the underlying reality. The ubiquity of generalized linear models prompted Abbott(1988) to coin the term general linear reality, that points more to a kind of worldview, a linear lens through which researchers look into the social world. This worldview has consequences for how researchers look at social questions, and thus it is important to know the domain of validity and the limitations of the superposition principle. In relation to that, how does the usage of the superposition principle look like in other disciplines? Superposition plays a key role in many areas of classical physics. For example, in Newtonian mechanics, the sum of a number of forces applied to an object produces an effect— acceleration— that is equal to the sum of the accelerations that the forces would exert upon the object if acting individually. In other words: the superposition principle applies. It does so as well in the case of electric charges and the classical electromagnetic forces between charged objects. There are other phenomena, though, where models consist of effects that are not additive, typically everything that has to do with waves (light, sound, vibrations) violates the superposition principle (Feynman et al., 2011). The non-applicability of the superposition principle is the landmark of nonlinear phenomena. And there are many such phenomena, both in physical, biological and social systems. Perhaps the phenomenon we have discussed more extensively is network-related representations and processes on networks. A related topic is linearity as a ﬁrst-order approximation to nonlinearity. Within a regression framework, this issue is usually handled (typically) by adding square terms to the linear combination of the basic model. For example, in an ecological model to estimate the founding rate rd of American national labor unions during the 19th century, a Poisson regression model is written down that includes the logarithm of the population size a given year

80 2 log(Nt ) and the square of the population size that year Nt , plus additional terms (Hannan and Freeman, 1989, Table 9.1):

2 rd = β1 log(Nt ) + β2Nt + ...

A different way to think about this problem would be to conceive of the additive effects as a ﬁrst-order approximation to the phenomenon. Rather than in- corporating nonlinearities through higher-order terms, these higher orders are conceptualized as progressively more reﬁned corrections to the main model. But then one would need to expand all variables to higher orders, as follows:

2 2 rd = β0 + β1 log(Nt ) + β2 log(Nt ) + ··· + γ1Nt + γ2Nt + ...

This is most likely one of the reasons why many models in complex systems science do not work with many variables simultaneously, if not by using a system of linear equations.

81 B.3 Stability of Coupled Linear Systems: Rashevsky, Coleman and Beyond The notion of stability has been repeatedly called upon in the thesis, as it has provided the key concept to characterize the structural component of organizational dynamics. Stability is all about whether a quantity varies in time or rather settles in the proximity of the state it is in. In a linear model, e.g. for the number of organizations in a population N regressed on some organizational covariates, stability is hardly a relevant question. The model assumes equilibrium and therefore the estimation cannot change over time. Nonlinearities can be incorporated through an explicit modeling of time, for instance in the context of growth models. The simplest Malthusian growth model represents the rate of change of size in time as proportional to the current size, that is to say dN/dt = a · N. This leads to an exponential growth that, depending on the sign of the parameter a, can stabi- lize itself and die out or continue increasing indeﬁnitely. However, real-world processes can co-evolve with each other. So what happens when we couple two or more linear equations, i.e. when we require one process to depend on time and on other processes? A situation like that could be modeled by a system of linear equations of the form ( dN1/dt = a · N1 + b · N2

dN2/dt = c · N1 + d · N2, where dN/dt is the rate of change in time of the number of organizations and a,b,c,d are four parameters to be speciﬁed or estimated. The roots of this modeling approach to social problems may be found in the work of Nicolas Rashevsky, who is, by the way, one of the departure points in Karlsson’s social mechanism approach (Karlsson, 1958) mentioned in Chap- ter5. It is likewise a contributor in (Lazarsfeld, 1955, ch.2), a volume on mathematical thinking in the social sciences. His research is closely associated with that of Anatol Rapoport in the mathematical physics of biological systems. Additional traces may be found in the already cited classical work on organizations by March and Simon(1958). Nowadays, a sociologist interested in formal models would most likely not associate these differential equations with Rashevsky, Lazarsfeld or March, but rather with James S. Coleman and his ‘Introduction to Mathematical So- ciology’(Coleman, 1964). In this remarkable volume, Coleman goes through an in-depth review and application of the foundations of different mathematical tools— from networks to differential equations— and their relevance for various social problems. Harrison C. White is another prominent ﬁgure contributing to the discussion on formal models in sociology (White, 2000).

82 A complementary framework for the analysis of coupled linear systems of the form shown in the equation above, and the stability of their solutions is available from complexity science (Strogatz, 1994, ch.5). We can imagine that, as time elapses, the solutions of the system of equations describe a curve, a trajectory in the xy-plane. The nature of the trajectory depends on the starting point and on the particular combination of parameter values. By fixing a parameter combination and plotting the trajectories for various starting points, we can define a portrait. The curves may eventually converge to a single point, meaning that the values of x and y become stable; this constitutes a stable fixed point. Similar limit objects can exist, for example trajectories may eventually end up in a stable cycle, which is a closed orbit-like trajectory that never changes. Even more interestingly, these limit solutions— points, cycles, surfaces— can be attracting or repelling, so certain combinations of x,y-values may never be reached. The example serves to illustrate the myriad of new situations that arise when processes are coupled to each other. This is a very current discussion, for example, when it comes to networks of interdependent networks (Gao et al., 2012), or mechanisms of mechanisms.

83 B.4 Human Mobility Landscapes: a Trail of Breadcrumbs Through Time and Space

In the main text, we discussed the complexity of our database in relation to the definition of big data and the idea of multiple distinct data representations. One of the representations we have had most use for is the network of employee movements between organizations. These reflections present some historical and modern examples of records of human mobility in the Western world, in order to broaden the perspective on big data and large public records. Some examples come from population data collected in public statistics. Mobility in administrative data has been linked to the current situation of big governmental datasets and their usage (Connelly et al., 2016). However, it is worth pointing out that nation-states have collected and registered information since early times. In the biblical account, Joseph and Mary travel from Nazareth to Bethlehem for tax registration purposes: Emperor Augustus had ordered the first complete census of the Roman world; this is a historical fact. Sometimes historical records can get a new life. Recently, the mobility notable historical figures has been used as a proxy for understanding cultural transformations and interrelations of cultural centers over time, by looking at a database on birth and death locations of more than 150,000 individuals over the last 2,000 years (Schich et al., 2014). The authors find, for instance, that France and Germany have very different development patterns: winner-takes- all is the dynamics characterizing Paris and the notable people that end up there, while the situation in Germany is more de-centralized with no single city standing out from the rest. Mobility takes place in many domains simultaneously; one of them is social space. In Sweden, there is a long tradition of using register data for longitudinal studies, as illustrated by the book edited by Carl-Gunnar Janson (Jan- son, 2000), containing longitudinal examples from the Level of Living Survey (Levnadsnivåundersökningen, LNU) and Project Metropolitan. Geographical mobility can be traced in Swedish studies as well. Two cases are SIMSAM— the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (http://www.simsam.nu/)— and the studies on microsimulation models at Umeå University led by Einar Holm. Finally, mobility can also take place online on the Internet: people leave “digital breadcrumbs” as they surf the web, to use the expression by Watts (2011). Social media data like Facebook or Twitter are a new type of big data, of an unprecedented resolution and scale. In academia, the increasingly growing field of computational social science (Cioffi-Revilla, 2014; Conte et al., 2012; Lazer et al., 2009) deals with methodological developments to explore this new landscape, as new phenomena are out there waiting to be discovered.

84 At the same time, this new type of big data may pose new ethical and information management challenges on several levels. On the one hand, because of the nature and encompassing character of the information stored on people’s lives, preferences, habits and so on. But also on the mobility level, since studies have shown that for example mobile phone data can be used to accurately track and predict the displacement of large numbers of people (Lu et al., 2012, 2013).

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