Resumen
The aim of the study is to develop a pool of policy variables (potential indicators) that can be used by policy makers to eliminate the gender gaps in labor force participation rates (LFPR) for the 15-64 age group (formal age group). Granger-causality was used to investigate the predictive power of selected macroeconomic policy variables on one hand and gender disaggregated LFPRs on the other using data from USA, Finland and Sweden. The variables investigated include: employment by sector and by gender, age/sex disaggregated total employment variables, total employment by group and overall total employment, total unemployment by age, unemployment for total and by gender with advanced, intermediate and basic education, monetary, foreign direct investment, savings, international trade, compensation by sector, health expenditure, government expenditure/revenue, gender wage gap for self- employment/total employment. The results showed that all the variables investigated have potential predictive power for gender disaggregated LFPR, therefore they all present potential entry points for addressing gender gaps in LFPRs for the 15-64 age groups. Policy interventions influencing these variables can be used to target desired changes in gender disaggregated LFPR. However, the causal relationships differ by country, specific variable considered, and whether causality is investigated for formal male or female LFPRs. These results imply that: i) policy measures for increasing the male LFPR may differ from those required for increasing the female LFPR; ii) the effect of gender dis-aggregated LFPRs on the policy variables may differ by gender and by country; and iii) that although all the variables investigated have the potential to predict gender disaggregated LFPR, no general theory can be developed regarding causal relationship between these variables and gender disaggregated LFPRs. It signals the need for practitioners/researchers investigating issues involving LFPR to: always establish the underlying causal relationships between LFPR and other variables to determine whether to use dynamic or non-dynamic approaches for their investigations; and to come up with appropriate policy intervention.