  A class of random recursive sequences (Y_n) with slowly varying variances as
arising for parameters of random trees or recursive algorithms leads after
normalizations to degenerate limit equations of the form X\stackrel{L}{=}X.
  For nondegenerate limit equations the contraction method is a main tool to
establish convergence of the scaled sequence to the ``unique'' solution of the
limit equation. In this paper we develop an extension of the contraction method
which allows us to derive limit theorems for parameters of algorithms and data
structures with degenerate limit equation. In particular, we establish some new
tools and a general convergence scheme, which transfers information on mean and
variance into a central limit law (with normal limit). We also obtain a
convergence rate result. For the proof we use selfdecomposability properties of
the limit normal distribution which allow us to mimic the recursive sequence by
an accompanying sequence in normal variables.
