1 Methodology and Composition of the Institutions Climate Index Methodology of the Institutions Climate Index

Methodology of the Institutions Climate Index A key problem in the institutions literature is that the large number of possible candidate regressors quickly leads the number of regressors to exceed the number of observations. In addition, multicollinearity is often rampant within a specific category. Finally, several indicators might proxy for the same institution. In contrast to capital and labour, institutions are not directly measurable. Thus, research into the institutional determinants of economic performance has to make use of indicators that proxy for specific variables of interest. This may cause identification problems due to the fact that distinct variables may proxy for similar institutions. One can begin to address these issues by aggregating variables with similar interpretations into separate categories. The standard procedure in the literature (see survey by Aron, 2000) is to average or sum indices – implicitly subjectively assigning equal weight to all variables chosen to be included in the analysis. In contrast to studies that weigh variables ex ante by assigning identical or arbitrary weights, we employ Factor Analysis (FA) to generate weights. We therefore present a simple, but important extension of Hall and Jones (1999) to construct institutional indices. Their work gave rise to an extensive literature on growth and development accounting based on institutional data. Our addition is that the regressors are weighted by their relevance, taking into account similarities between regressors. Factor Analysis addresses the variable identification issue by reducing the dimensionality of the dataset. Factor Analysis returns the smallest number of linear combinations of regressors that spans a similar data space as all candidate regressors. The linear combinations are commonly called factors. We employ Factor Analysis in a twostep procedure. In the first step, several candidate regressors (components) that are all suspected to proxy for a similar institutional proxy (such as political stability) are combined under one sub-index heading. All variables in one sub-index are then subjected to Factor Analysis, which establishes the linear dependencies among candidate regressors. In the second step, we perform Factor Analysis on all sub-indices that belong to one category heading. As is common practice we restrict ourselves in both steps to those factors with an eigenvalue greater than unity to span the matrix of independent variables as efficiently as possible. The informational content of each component in a sub-index and each sub-index in a category is reflected by the weight that is assigned in the linear combination. Hence, the weights are specified by the data and not by the researcher. After reducing the dimensionality of the independent variables, we analyze how many factors are economically relevant in determining economic performance in OECD countries; hence the factors are regressed on GDP per capita growth rates. Instead of focusing on an index that relates to a single cross section of countries, we decided to base the Index on information not only across countries, but also across time. Thus, we regress the factors on the average GDP per capita growth rate on three cross sections 1994, 1998, 2002. To smooth cyclical movements we employ 4-year average GDP p.c. growth rates as our dependent variable. Hence the first cross section relates to the average growth rate of 1990 to 1994, the second to 1994 to 1998 and the third to 1998 to 2002. To account for the fact that institutions are seen to lead growth, we use two-year lags of each institutional factor as independent variables. To establish economic relevant sub-indices, only those factors were retained that proved to exhibit significant explanatory as well as predictive power. Specifically, we retained only those factors whose coefficient estimate’s tvalue exceeds unity and therefore improves the regression. This procedure resulted in a set of factors that are able