Post a Comment Print Share on Facebook
Featured CGPJ Ucrania Rusia Carles Puigdemont Bruselas

Social Security gains 140,504 affiliates in the last month, to exceed 20.9 million

MADRID, 16 Jun.

- 3 reads.

Social Security gains 140,504 affiliates in the last month, to exceed 20.9 million

MADRID, 16 Jun. (EUROPA PRESS) -

Social Security gained 140,504 affiliates in the period from mid-May to mid-June, to exceed 20.9 million contributors for the first time, according to data published this Friday by the Ministry of Inclusion, Social Security and Migrations. In contrast, in seasonally adjusted terms, Social Security lost 3,754 affiliates in the same period.

Thus, with these latest data available, the total number of affiliates stands at 20,929,745 affiliates, which is 941,000 more than at the end of 2021 and 1.2 million more since the start of the pandemic.

The department headed by José Luis Escrivá specifies that the daily affiliation data has been above 20.92 affiliates on June 12, 13, 14 and 15.

In the last six months (from December 15 to June 15) affiliation to Social Security has grown by 598,263 people, the highest figure for this period in the historical series.

In seasonally adjusted terms, the increase in affiliates stood at 458,362 people in the same period, also the highest in the series, which practically equals the job creation registered in all of 2022.

The fortnightly affiliation statistics offered by the Ministry of Inclusion, Social Security and Migrations since January 2023 is based on two advances that aim to provide a "clearer" vision of the evolution of affiliation and allow monitoring of the labor market with data more frequently than monthly.

The first novelty is the new monthly seasonal adjustment factors, which include in their preparation both the years with the greatest impact of the pandemic (2020 and 2021) and the first full year of recovery (2022).

The second advance is the seasonal adjustment of daily data, with a methodology that allows debugging the daily affiliation figures from the effects of seasonality and calendar. With them, you can monitor the labor market with more frequent data (weekly, fortnightly) than monthly.