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

Christian Adam, Christoph Knill, Xavier Fernández-I-Marín

April 2017

Code Video Source Document DOI

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.

Type

Journal article

Publication

Policy Sciences

"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.

The paper has been awarded with the the _Wissenschaftspreis Bürokratie_ of the Institut der deutschen Wirtschaft Köln (Cologne Institut for Economic Research), 2017.

Figures and videos of the evolution of government portfolios

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)
  }
}

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

Abstract

Figures and videos of the evolution of government portfolios

Australia

Austria

Belgium

Canada

Denmark

Finland

France

Germany

Greece

Ireland

Italy

Japan

Korea, Republic of

Netherlands

New Zealand

Norway

Portugal

Spain

Sweden

Switzerland

Turkey

United Kingdom

United States

Government effectiveness against rule growth

Code