This research is aimed at developing a methodology for assessing the e cacy of a
macroeconomic model of the Kazakhstani pension provision system. A hierarchy of indicators of the
pension system e cacy is built using the graph method. The representativeness of these indicators
is confirmed by using expert assessment and factor analysis. A multi-factor assessment model is
built using an additive convolution of the normalized values of the 2014–2019 resulting indicators
with a breakdown by regions, regarding the coe cients of their significance. A regression model is
developed to show the dependence of the pension system e cacy on the share of the accumulative
system in the structure of retirement scheme financing. The optimal part of the accumulative system
amounting to 79.5% of the total system is determined to be the level at which the pension system
e ciency is maximized. A neural model for predicting the pension system e cacy under the influence
of labor market indicators is built. The size of the minimum required annual payroll deductions from
the wages of persons working according to the accumulative system is calculated depending on the
length of service; this minimum size ensures a replacement rate of 40% with regard to the optimal
ratio of the accumulative and solidarity pension systems. These findings will be useful to state bodies
when developing and clarifying directions for reforming the pension system in Kazakhstan.
