Replication package include:

1) "Data description.txt" - outlines how to obtain original micro-data from INE.es used to generate monthly age and parity specific fertility rates for Spain 2016-2021. 
2) "Prepare_data.do" - Stata script used to create time series data from micro data provided by INE.es. Stata 16/SE used.
3) "for_R.txt" - Monthly data on age-specific fertility rates in Spain used to generate time series.
4) "Figures_and_analysis.r" - R script used to generate all figures and results for the paper. Run in R 4.3.0

*****************************************
Data description for Fallesen and Cozzani (2023)  "Partial Fertility Recuperation in Spain Two Years After the Onset of the COVID-19 Pandemic." Demographic Research.

The paperanalysis time series data based of Spanish birthr ecords and vital statistics. The data needed for generating monthly time series are supplied as "for_R.txt" and the analysis can be carried out using "figures_and_analysis.R", both supplied in the replaication bundle. We use R 4.3.0.

The data generating the monthly birth overviews that the time series are build on are directly donwloadable from INE.ES, but not supplied with the paper due to size of the data sets. Below we describe the data and how to access it. For generating the analysis file "for_R.txt", use the Stata 16 script "prepare_data.do".

***********************
**** POPULATION DATA

* Download Spanish Statistical Office Resident population by date, sex and age (31304) from INE.ES at https://ine.es/jaxiT3/Tabla.htm?t=31304
* All periods 2016-2021, women aged 15-49, National Total.

N = 45,640 obs
Vars = 5 variables

For description of variables, see prepare_data.do

************************************************
** BIRTH RECORDS
************************************************

* Download Spanish Birth Registers for 2016-2021 from
* Spanish Statistical Office INEbase / Demography and population /Demographic phenomena /Birth Statistics. Vital statistics / Results/ Microdata 
* https://www.ine.es/uc/RzJPyg9C

N = 2,180,329 cases
Vars = 185 variables

For description of variables, see prepare_data.do