Resources

Private primary care clinics in Finland in 2016–2018: https://doi.org/10.17632/dwgrkmf7rg.1

Public primary care clinics (health stations) in Finland in 2016–2018: http://doi.org/10.17632/kpwzs2p4w9.1

Use the following table to fix the Finnish municipality classification to any year between 1980–2024. The table includes all municipalities which have been subject to a merger during 1980–2024.

Tutorial: Fix the municipality classification to year 2018 by replacing the existing municipality ID conditionally, e.g. in Stata:

//Create a copy of your municipality ID variable

gen  municipality_original_num=[name of your municipality ID variable]

//Make sure it is numeric
destring municipality_original_num, replace

//Add the 2018 municipality ID from the mergers data

merge m:1 municipality_original_num using [add file path here]mergers.dta, keep(master match) keepusing(mun_2018) nogenerate

//Replace the municipality ID for those IDs that are in the data (if mun_2018 is missing, then the ID did not change in 1980–2024)
replace municipality_original_num=mun_2018 if  mun_2018!=.

//Rename
rename municipality_original_num municipality_2018_num

//Drop the temporary variable

drop mun_2018

In R:

#Required libraries

library(readr)

library(dplyr)

#Import the mergers data

mergers <- read_csv2("[add file path here]mergers.csv",

col_select = mun_2018)

#Add the 2018 municipality ID to your original data

df <- left_join([add name of your original table here],

mergers,

by=("[name of your municipality ID variable]" = "municipality_original_num"))

#Replace the municipality ID for those IDs that are in the data (if mun_2018 is missing, then the ID did not change in 1980–2024) and drop the temporary column

df <- df %>%

mutate([name of your municipality ID variable] = ifelse(!is.na(mun_2018), 

mun_2018,

[name of your municipality ID variable])) %>%

select(-mun_2018)

Data:   [xlsx]   [txt]   [dta]   [csv]

Source code: [do]