Institutions of modern societies have often been argued to evolve, but their evolution is rarely examined in Darwinian and empirical terms. Here we use the categorical data set MaxRange on nations’ regime-types and their constituting institutions historically for analysis of their phylogeny. The MaxRange data set covers all nations in the world system of nations since 1600 with details on accountability structures, executive strength and degree of centralization, constitutional strength, democracy and other aspects of the political systems. We describe and analyze the character of the “true phylogeny” of regime-type evolution in the nations of the world 1600--2020. Our results indicate that evolutionary network rather evolutionary tree models have superior fit to data. Using the extracted phylogeny, we also investigate the dynamic networks of nations in terms of regime-types, accountability structures and their related background factors such as religions and language. Network centrality measures of nations and regime-types provide new important explanatory variables for network-adjusted survival analyses of various institutions, from types and levels of democracy to details of regime-types, including institutions like parliamentarism, presidentialism, and their hybrid forms.

The overarching aim in this paper is to contextualize civic and citizenship education practices, something which promises to settle an old social scientific dispute about the sources of trust in institutions: Is trust in institutions rooted in social psychology, culture and socioeconomic factors, or is it a response to institutional performance? Here, we analyze ICCS 2009 (International Civic and Citizenship Education Study) data at student level (approximately 140,000 students) in 37 countries and territories merged with the 2009 Quality of Government dataset that includes the World Values Surveys (WVS) national aggregates on trust and confidence. We also added MaxRange institutional data for 2009. First, a regression tree analysis is made, second a series of orindary least squares (OLS) regressions, and third, a structural equation model. Results strongly support the institutional performance theory and further explains the interaction between the political institutions and the political culture. In fact the former make possible the latter, so that attitudes and participation may be critical to established institutions while taking part in them. Influences on trust in institutions thus exist on all three levels investigated: institutional, cultural and individual-psychological. However, the interacting political-institutional and political-cultural influences dominate.

A new data set provides vital information about the world’s political institutions, from 1789 on a monthly and yearly basis and from 1600 on a yearly basis. The yearly data set from 1600 has more than 90,000 country–year observations, and the monthly data set from 1789 more than 600,000 observations—by far the most comprehensive to date, offering several advantages over other available ones. The data set aggregates specific attributes to create nominal and ordinal rankings of political regimes on a scale of 1 to 1,000. In addition to supporting a rigorous classification of democratic and nondemocratic regimes, it allows researchers to trace institutional variations and to explore alternative ways of aggregating political institutions. As a research instrument, the MaxRange data set permits historically minded scholars to address a number of issues related to the dynamics of political institutions in an unprecedented manner. © 2017 by the Massachusetts Institute of Technology and The Journal of Interdisciplinary History, Inc.

This paper therefore looks closer into climate gas emission and the shift to non-fossil energy in Sweden. What types of organizations are behind the shift to non-fossil energy use, what are the relative effects on emissions, to what extent can these interactive dynamics be considered eco-innovations? Do these effects vary between public and private organizations, and if so, can they be related to specific institutions and policies? Methods include statistical survival analyses, in particular Cox regression. These analyses inform us why energy sources shift. Results indicate that wood fuel and solid waste increase as sources of energy while fossil oil has decreased between 2003 and 2010. This result is in line with industrial and environmental policies of the Swedish governments that present these facts as institutionally and policy-related ‘green innovation’. However, our analysis contests such a conclusion and it is noticed that the shift to non-fossil sources of energy has not led to verifiable decreases in green-house gas emissions. Results instead suggest that ‘green’ innovation of non-fossil energy was mostly the effect of low-tech innovation in public organizations with no fundamental effect on CO₂ emissions. © Springer International Publishing AG, Part of Springer Science+Business Media 2015

The MaxRange dataset provides information on political institutions for all countries of the world going back to 1789 on a monthly and yearly basis, and to 1600 on a yearly one.  The yearly dataset spanning 1600 to 2014 has over 90,000 country-year observations, and in its monthly format from 1789 has over 660,000 observations.  The time-series data are at least 200 years longer than any other comparable time-series dataset on political institutions.  Created by Max Rånge, the datasets aggregate specific attributes to create nominal and ordinal rankings of political regimes on a 1-100 scale (the MaxRange1 dataset) and on a 1-1,000 scale (the MaxRange2 dataset).  At the same time, however, the codes for each attribute underlying the categorization of political regimes are also included.  It is more detailed than any other dataset on political institutions, yielding up to 1,000 different unique combinations of institutional features.  In addition to supporting a rigorous classification of democratic and nondemocratic regimes, the dataset allows researchers to exploit institutional variation and to explore alternative ways of aggregating political institutions.  The MaxRange dataset on political institutions is by far the biggest and most comprehensive political regime dataset to date, and it offers several advantages compared to other available data.  In particular, the availability of monthly time-series data provides greater detail and reliability to support more accurate research on political transitions.  

In what sequences have nations changed institutionally in history and does that order matter for later democratization? If so, are there historical-institutional pathways of “civilizations”? These previously neglected research problems are addressed in this paper on the basis of a new, unique, and enormous data set tracking all political institutions and systems in the world monthly since 1789. The aim is both empirical and theoretical: to take steps toward an understanding of the sequential aspects of political-institutional evolution. Results visualize sequences at regime level that show few signs of path dependency. They also show that democracy may emerge in all types of regimes, though at varying paces. Separating religious-majority nations, Muslim systems are less affected by democracy diffusion than other religious-majority nations. Muslim political systems also exhibit larger regime type unpredictability. Taken together with estimates of GDP per capita, majority religions explain a minor share of discrepancies between regime types: wealth of nations is more important than majority religion on a general, regime type diversity level. However, specifications of institutional details will have to be made in future research in this new area of historical political-institutional study.    

In theory, innovation processes lie behind the evolution of national systems as they create interacting dynamics among organisations. Institutions and policies are considered means for influencing these interactive dynamics, such as shifting innovative focus from traditional to environmentally oriented production, more environmentally friendly types of energy use, or environmental protection measures, products or services. Institutions and policies are thus considered drivers of change in technologies, processes, markets, raw materials or organisational forms--innovation in a Schumpeterian sense. Shifts in energy sources, from fossil to non-fossil sources, among organisations in the Swedish innovation system therefore call for explanations in terms of changed institutions and policies and their resulting eco-innovations. This paper looks more closely into climate gas emission and the shift to non-fossil energy in Sweden; what types of organisations are behind the shift to non-fossil energy use, what are the relative effects on emissions, to what extent can these interactive dynamics be considered eco-innovations, and if so, can they be related to specific institutions and policies? Quantitative analysis of evolving innovation processes in national systems is not always possible due to a lack of reliable and multi-level time-series data sets. This is also true for eco-innovations ('green' innovations). In the Swedish case, there are detailed data sets at national, regional, organisational and employee levels, making possible the estimation of evolutionary models. Register data can be merged with time series on environmental energy consumption and emissions. Data allow for a detailed analysis of environmentally oriented innovation from at least 2003. Analyses in this paper are based on time-series of data on the recent shift from fossil to non-fossil energy sources in the Swedish innovation system, as well as data on emissions, and potentially innovation promoting parameters at organisational and employee levels. Methods are quantitative, and Cox regression is used. Previous investigations of the energy use of Swedish organisations reveal a clear shift from fossil to non-fossil energy use. This is described both in terms of cumulative energy use and effects on emissions of carbon dioxide. Data provide us with information for conclusions on why energy sources change and in interaction with what organisational parameters. For example, wood fuel and solid waste increase as sources of energy while fossil oil has decreased during the years 2003 to 2010. This result is in line with national industrial and environmental policies and presented as institutionally and policy related 'green innovation'. But a quantitative analysis contests such a conclusion and it is noticed that the shift to non-fossil sources of energy has not led to verifiable decreases in green-house gas emissions. Public ownership is the single most important contributor to green innovation in non-fossil energy use. Still, CO₂emissions are not fundamentally reduced by this low-tech shift, since they do not affect end-of-pipe reductions. What we observe is in fact wind-fall gains rather than eco-innovations behind the Swedish shift from fossil to non-fossil energy use.