Is it worth raising the normal retirement age? A new model to estimate the employment effects
https://doi.org/10.3326/pse.49.3.1 | Published online: September 3, 2025 Table 1
The effect of raising the normal retirement age in previous OECD and IMF studies
Figure 1
Model innovations give larger employment effects from raising the retirement age Table 2
Summary table of the variables
Note: ER 55-74 is the employment rate of people aged 55-74 expressed in percentage; UBGR is the gross unemployment benefit levels expressed in percentage of the previous gross earning; ALMP is the detrended active labour market policy spending on employment as a share of GDP per capita; Average tax wedge is the average tax wedge for a couple with 2 children and prime earner at 100% and second earner at 67% of the average wage; Excess coverage is the difference between the coverage of wage bargaining agreement, expressed in percentage, and the share of workers who are represented by unions covered; EPL is the OECD Strictness of employment protection for regular contract from individual and collective dismissals, the indicator, which measures the strictness of employment protection on a scale from 0 to 6, where higher values indicate more stringent regulations; ETCR is the component on regulation of network industries of the OECD PMR indicator, which measures the pro-competitive regulatory settings on a scale from 0 to 6, where higher values indicate more restrictive regulations; ER 25-54 is the employment rate of people aged 25-54 expressed in percentage; LE 65 is the life expectancy at 65. This last variable is computed by multiplying the life expectancy in years for women and for men by the share of men and women aged 65+ on the population aged 55-74 expressed as percentage. These statistics are calculated on the estimation sample, which starts from 1992 and ends in 2019, with different coverage among countries. Source: For ER 55-74, OECD Employment database; for UBGR, OECD Social Protection and Well-being database; for ALMP, OECD Labour database; for Average tax wedge, OECD Tax statistics database; for Excess coverage, ICTWSS database; for EPL, OECD Labour database; for ETCR, OECD PMR indicator database; for ER 25-54, OECD Employment database; for LE 65, OECD Health database; and OECD Employment database. Table 3
Long term equations explaining the older age employment rate
Note: The table shows the estimated coefficients of the long-term equation of the model. Model (1) corresponds to the base model. Model (2) is the same as model (1) but instead of using the normal retirement age for pensions as pension policy variable, the share of population above the normal retirement age is used. Model (3) uses the database created by Geppert et al. (2019) integrated with the data from OECD (2021, 2023), which distinguish between minimum retirement age and normal retirement age. Model (4) introduces the distinction between countries where the private pension funds are important. Model (5) is equivalent to model (4), but the pipeline effect for early exit countries is added. The RMSE and the adjusted R2 shown in the table refer to the long-term equation only and hence are computed excluding the lagged and leading variables used in the Dynamic OLS methodology to estimate it. *** p < 0.01, ** p < 0.05, * p < 0.10. Source: Author’s calculation. Table 4
Correlation among retirement ages
Note: The table shows the correlation among the retirement age variables. Source: Author’s calculation. Table 5
The predictions of the model are consistent with those of single-country studies
Source: Author’s calculations. Figure 2
Policy simulations changing the gap between the minimum and normal retirement ages (percentage points) Figure 3
The countries with the lowest old age employment rate also have big retirement age gaps Figure A1
Model visualisation Figure A2
Comparing the effects on ER-55-74 and on AALME under the proposed model
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