Rule growth and government effectiveness: why it takes the capacity to learn and coordinate to constrain rule growth

Abstract

This paper asks whether strong bureaucracies can effectively constrain the continuously growing stock of rules in modern democracies through organizational coordination and learning. To answer this question, the paper analyzes the growth of rule stocks in the areas of environmental policy and social policy in 23 OECD countries over the period between 1976 and 2005. To do so, it develops a new measure of rule growth based on the content of laws and regulations rather than their length. The analysis highlights that effective bureaucracies are indeed better able to contain rule growth in these areas than weak bureaucracies. Since rules have to be implemented, countries suffering from bureaucratic capacity and quality constraints thus appear to be stuck in an implementation deficit trap. Appropriate implementation is not only inherently more challenging for countries with weak public administrations, but the body of rules to be implemented also tends to grow quicker in these countries.

Publication
Policy Sciences
Date

"Rule growth and government effectiveness: why it takes the capacity to learn and coordinate to constrain rule growth" is an article by Christian Adam, Christoph Knill and Xavier Fernández i Marín published at Policy Sciences in 2016. This webpage contains supplemental online material that complements the published paper with DOI: 10.1007/s11077-016-9265-x.

Figures and videos of the evolution of government portfolios

Australia

Australia from Xavier Fernández i Marín on Vimeo.

Austria

Austria from Xavier Fernández i Marín on Vimeo.

Belgium

Belgium from Xavier Fernández i Marín on Vimeo.

Canada

Canada from Xavier Fernández i Marín on Vimeo.

Denmark

Denmark from Xavier Fernández i Marín on Vimeo.

Finland

Finland from Xavier Fernández i Marín on Vimeo.

France

France from Xavier Fernández i Marín on Vimeo.

Germany

Germany from Xavier Fernández i Marín on Vimeo.

Greece

Greece from Xavier Fernández i Marín on Vimeo.

Ireland

Ireland from Xavier Fernández i Marín on Vimeo.

Italy

Italy from Xavier Fernández i Marín on Vimeo.

Japan

Japan from Xavier Fernández i Marín on Vimeo.

Korea, Republic of

Korea, Republic of from Xavier Fernández i Marín on Vimeo.

Netherlands

Netherlands from Xavier Fernández i Marín on Vimeo.

New Zealand

New Zealand from Xavier Fernández i Marín on Vimeo.

Norway

Norway from Xavier Fernández i Marín on Vimeo.

Portugal

Portugal from Xavier Fernández i Marín on Vimeo.

Spain

Spain from Xavier Fernández i Marín on Vimeo.

Sweden

Sweden from Xavier Fernández i Marín on Vimeo.

Switzerland

Switzerland from Xavier Fernández i Marín on Vimeo.

Turkey

Turkey from Xavier Fernández i Marín on Vimeo.

United Kingdom

United Kingdom from Xavier Fernández i Marín on Vimeo.

United States

United States from Xavier Fernández i Marín on Vimeo.

Government effectiveness against rule growth

Government effectiveness against rule growth

Code

The code for the empirical models in the BUGS/JAGS declarative language follows:


model {
  for (s in 1:nS) {
    for (c in 1:nC) {
      YS[c, s, 1] ~ dt(mu[c, s], tau[s], nu[s])  # YS[,,1] refers to Means
      mu[c, s] <-
                     theta[s, 1] * veto.players[c]
                   + theta[s, 2] * gdp.capita[c]
                   + theta[s, 3] * gdpc.ratio[c]
                   + theta[s, 4] * gov.eff[c]
                   + theta[s, 5] * trade[c]
                   + theta[s, 6] * green[c]
                   + theta[s, 7] * socialist[c]
                   + theta[s, 8] * leaders.environmental[c]
                   + theta[s, 9] * liberal.social[c]
    }
    tau[s] <- pow(sigma[s],-2)
    sigma[s] ~ dunif(0, 4)
    nu[s] <- 1 + (-1*log(nu.trans[s]))
    nu.trans[s] ~ dunif(0, 1)
  }
  # Priors for main effects
  for (v in 1:5) {
    for (s in 1:nS) {
      theta[s, v] ~ dnorm(Theta[v], tau.theta[v])
    }
    Theta[v] ~ dnorm(0, 0.001)
    tau.theta[v] <- pow(sigma.theta[v], -2)
    sigma.theta[v] ~ dgamma(1, 5)
  }
  theta[1, 6] ~ dnorm(0, 0.001)  # environmental, green
  theta[2, 6] <- 0               # social, green
  theta[1, 7] <- 0               # environmental, socialist
  theta[2, 7] ~ dnorm(0, 0.001)  # social, socialist
  theta[1, 8] ~ dnorm(0, 0.001)  # environmental, leaders.environmental
  theta[2, 8] <- 0               # social, leaders.environmental
  theta[1, 9] <- 0               # environmental, liberal.social
  theta[2, 9] ~ dnorm(0, 0.001)  # social, liberal.social
  # Missing data for Consensus
  for (c in 1:nC) {
    veto.players[c] ~ dnorm(0, 0.5)
  }
}