Household Living Arrangements of Elderly People

Proportions of over 60s by household type and intergenerational structure in selected countries

This is the repository for the data gathering, cleaning and plotting as well as the graphic design for the Population Horizon Factsheet IV (2016, vol 13, issue 2).

This README includes an overview of the:

1. Brief
2. Data import
3. Outputs:

• Summary tables
• Factsheet pdf
• Interactive application (TODO)

Brief

The prevalance of different household living arrangements varies across countries and across different population groups. This factsheet focuses on people over 60 years of age and compares men and women in each country.

Using census microdata collected in the IPUMS-International database we summarise the types of households in which the elderly live using two typologies:

• http://www.ntvspor.net/canli-yayinthe household composition based on family nuclei, and
• the intergenerational structure of the household.

Data Import

The data import and tidying process is not fully automated since the raw data needed to be manually extracted from IPUMS

Raw Data

Data selection: I picked (manually) all country samples from IPUMS that (according to this table):

• have RELATED (RELATE detailed) codes for Grandchildren and Grandparents
• have a sample in the 2000s.

This leaves us with 22 countries and samples, I've added the UN regional and subregional classifications:

id	code	country	year	subregions	regions
1	51	Armenia	2011	WAS	AS
2	112	Belarus	2009	EEU	EU
3	72	Botswana	2011	SAF	AF
4	76	Brazil	2010	SAM	AM
5	156	China	2000	EAS	AS
6	214	DominicanRepublic	2010	CRB	AM
7	300	Greece	2001	SEU	EU
8	372	Ireland	2011	NEU	EU
9	404	Kenya	2009	EAF	AF
10	417	KyrgyzRepublic	2009	CAS	AS
11	484	Mexico	2015	CAM	AM
12	496	Mongolia	2000	EAS	AS
13	558	Nicaragua	2005	CAM	AM
14	275	Palestine	2007	WAS	AS
15	591	Panama	2010	CAM	AM
16	616	Poland	2002	EEU	EU
17	620	Portugal	2011	SEU	EU
18	630	PuertoRico	2000	CRB	AM
19	642	Romania	2002	EEU	EU
20	686	Senegal	2002	WAF	AF
21	710	SouthAfrica	2011	SAF	AF
22	840	UnitedStates	2000	NAM	AM

Data Extract

The data extract is downloaded as a single file - This dataset is over 3GB and therefore too large to import into R, so an sql query is used to extract individual country samples. Neither of these two sets of files are available here for licencing reasons, but the relevant summaries are.

Data Summaries

New variables

Definitions

Outputs

Summary Tables

Factsheet

Shiny application TODO