**Survey (Sakernas).** More specifically, the analysis utilizes data from the August wave of Sakernas for each year between the years 2016 and 2023. The analysis explores the relationship between the labor market outcomes – that is, employment rates, unemployment rates, hours worked, and income – of younger persons and older persons at the province level using ordinary least squares (OLS) regression. The employment rate for a given age group is calculated as a share of the population within the age group, while the unemployment rate for a given age group is calculated as a share of the labor force within the age group. The sample is divided into three age groups: 15 to 24 (“youth”), 25 to 54 (“prime-aged”) and 55 to 64 (“old”). This relationship will be analyzed by gender, education level, and sector of employment, as shown in Figure 13. Formally, the basic model that will be analyzed is as follows:<sup>10</sup>

where Ypt is the labor market outcome of interest for province p in year t, that is, employment rate (as a share of the population), formal employment rate (as a share of employment), unemployment rate (as a share of the core labor force, i.e. those working and those looking for a job), hours worked in the previous week, and income of the young and prime-aged. The independent variable of interest is captured by oldemppt, which captures the employment rate of older persons in a given province and year. The vector Xpt includes a set of province-specific, time-varying explanatory variables, namely GRP per capita, GRP growth, the provincial unemployment rate, the provincial poverty rate, the structure of the economy (i.e. the relative shares of the industrial and service industries), the share of younger persons in the relevant age group (i.e. youth aged 15 to 24 or prime-aged persons aged 25 to 54) and the share of persons with a primary education or less. The variable γt is a set of year dummies to control for economic conditions in a given year. Given the small sample size and the inclusion of numerous province-specific, time-varying explanatory variables, province dummies are excluded to preserve degrees of freedom.

**Variations to the basic model include analyses by gender, education level, and sector of employment.** The relationship between the formal employment rate of older and the labor market outcomes of younger persons (including the formal employment rate of younger persons) is also analyzed. The motivation for this is because the planned increase in the retirement age is likely to affect workers who are formally employed rather than those who are informally employed. It would therefore be important to understand the relationship between the formal employment rates of older and younger persons.

**The model used in this paper is similar to that used by Munnell and Wu (2013) for the United States and China.** The reason is because like Munnell and Wu (2013), this paper uses household survey data to compute the variables of interest at the province level within a country and to study the relationships between the employment rates of older persons and the labor market outcomes of younger persons. A similar approach was taken by Jasmin and Abdur Rahman (2021). Gruber et al. (2010) and Apella (2024) utilized the same variables of interest, but both papers looked at the relationship between the employment rates of older persons and labor market outcomes of younger persons across several countries. Other papers such as Martins et al. (2009) and Kondo (2016) analyzed the relationship at the firm level.

> 10 This model is similar to that analyzed by Munnell and Wu (2013) for the United States and China, and Bertoni and Brunello (2017) for Italy.

10