The Impact of the Great Recession on Fertility in Europe

Anna Matysiak1 – Tomáš Sobotka1 – Daniele Vignoli2

1 Wittgenstein Centre (IIASA, VID/ÖAW, WU), Vienna Institute of Demography/Austrian Academy of Sciences

2 University of Florence, DiSIA – Department of Statistics, Informatics, Applications

Source: Own computations based on Eurostat 2013 & national statistical offices

Changes in TFR in 2000-12(13),main European regions

Source: Own computations based on Eurostat 2013 & national statistical offices

Birth timing: accelerated postponement?

-30

-20

-10

0

10

20

30

15-19 20-24 25-29 30-34 35-39 40-44

Cha

nge

in f

ertil

ity r

ate

(%)

2005-8

2008-11

European Union

-30

-20

-10

0

10

20

30

15-19 20-24 25-29 30-34 35-39 40-44

Cha

nge

in f

ertil

ity r

ate

(%)

2005-8

2008-11

SPAIN

Changes in age-specific fertility three years before (2005-8) and three years into the recession (2008-11)

Why and how is the recent recession likely to have affected fertility?

Massive unemployment in some countries

Strongly affects young adults, further exacerbates the previous trend of their rising economic and employment uncertainty

Delayed home leaving, econ. independence (Aassve et al. 2012)

Rise in the share of NEETS & workless households

Falling incomes, rise in negative equity on housing (mortgages “under water”), foreclosures (US)

Massive cuts in government budgets, also for family support (double-dip effect on fertility?)

Prolonged duration of the recession; loss of hope in the future (Southern Europe)

Source: OECD 2014: Society at a Glance 2014. The crisis and its aftermath

Past research

• The effect of unemployment is unclear and depends on whether unemployment is measured at individual level or aggregate level

• Aggregate level unemployment usually depresses fertility (Simó Noguera et al. 2005, Berkowitz King 2005, Aaberge et al. 2005: 150, Adsera 2005, 2011, Neels et al. 2012, Currie and Schwandt 2014), the effects of individual unemployment are less clear

• The effects are sex- and age-specific and differentiated by social status / education (Kreyenfeld 2009, Pailhe and Solaz 2012, Neels et al. 2012, Currie and Schwandt 2014)

• Other aggregate-level factors found important in some studies: GDP change, consumer confidence, housing foreclosure rate, self-employment rate, fixed-term contracts

Limits of previous research

• Only few studies on the effects of the recent recession on fertility in Europe (Goldstein et al. 2011, overview by Eurostat / Lanzieri 2013)

• Lack of suitable (panel) data for sound multi-country studies

• Little or no use of regional data

• US: wider range of suitable surveys & research underway to study wide-ranging effect of the Great Recession on families (e.g., Guzzo 2012, Cherlin et al. 2013)

Aims, data, methods

Goals

Initial aim: Studying the impact of age, parity, education and aggregate-level conditions on first and second births NUTS-2 regions; EU-SILC

•Data problems, especially in the recession period (2011)

Goals

Initial aim: Studying the impact of age, parity, education and aggregate-level conditions on first and second births NUTS-2 regions; EU-SILC

•Data problems, especially in the recession period (2011)

Revised aim: Using “macro” data in 2000-12 for NUTS-2 regions to study the impact of aggregate-level employment conditions on fertility change

•Main contribution: using recent data covering extended period of the recession, using regions as a main unit

•Main drawback: losing individual-level dimension.

Data

• Coverage: 2000-12: EU, Switzerland, Norway; 276 NUTS-2 units

• Fertility: Age-specific fertility rates, cumulated into age groups (15-19, 20-24, 25-29, 30-34, 35-49) and Total Fertility Rates

• Employment conditions:

– Unemployment rates (ages 15-24, 25-64, 20-64),

– long-term unemployment (% of unemployed),

– % self-employed,

– young adults NEETs (not in employment, education, training, age 18-24)

• Other variables considered: Regional GDP change (not available > 2010), % with higher education (non-stationary), indicators on poverty, social exclusion (based on EU-SILC, high % missing, unstable);

Method

• Time series tested for stationarity:– Unemployment rates (UNMP), ASFR(20-24), ASFR(30-34) – first difference

stationary

– Remaining fertility indicators, Long-term unemployment (LTUNMP), NEETs,

Self-employment (SELFEMPL) – level stationary

• Random-effects linear regression (regions nested within countries) with a time trend (3 periods)

• Dependent variable: ΔTFRt or ΔASFRt

• Explanatory variables: – ΔUNMPt-1 for ages 20-64, 15-24 or 25+

– ΔLTUNMPt-1

– Δ NEETt-1

– Δ SELFEMPLt-1

Main results

All countries & regions combined

How a 10 pp. annual increase in

•unemployment rate

•the share of long-term unemployed

•in the % of young adult NEETs

•in the % self-employed

predicted to change fertility rates?

All countries & regions combined

Effects on TFR Effects on age-specific fertility

Insignificant results (p>0.1) shown by patterned fill

-0.040

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

Total-0.014

-0.012

-0.010

-0.008

-0.006

-0.004

-0.002

0.000

0.002

Age 15-19 20-24 25-29 30-34 35+

+10% unempl

+10% LT unempl

-0.040

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

Total

-0.014

-0.012-0.010

-0.008

-0.006-0.004

-0.002

0.0000.002

0.004

Age 15-19 20-24 25-29 30-34 35+

+10% NEETS

+10% selfempl

Country groups: effects of unemployment and long-term unemployment

Effects of 10pp increase on fertility (TFR & by age)

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% unempl+10% LT unempl

Western Europe

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% unempl+10% LT unempl

German-speaking

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% unempl+10% LT unempl

Southern Europe

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.03

-0.02

-0.02

-0.01

-0.01

0.00

0.01

0.01

0.02

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% unempl+10% LT unempl

Central & Eastern Europe

Country groups: effects of NEETs and self-employment

Effects of 10pp increase on fertility (TFR & by age)

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% NEETS+10% self-employed

Western Europe

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% NEETS+10% self-employed

German-speaking

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% NEETS+10% self-employed

Southern Europe

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

Total-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

Age 15-19 20-24 25-29 30-34 35+

+10% NEETS+10% self-employed

Central & Eastern Europe

Effect of the period: 2009-12

Additional (unexplained) period effect on the observed TFR: 2009-12 compared with 2005-8

“Predicting” TFR change since 2008

How much of the observed TFR change since 2008 “predicted” by our recession indicators? Unemployment + self-employment + NEETS

Explaining the difference: Other important factors omitted? Need for an improved model fit?

TFR change in Latvia 2007-13

The model predicts well fertility reversals, but not the magnitude of changes

How much of the observed annual TFR change “predicted” by our recession indicators? Unemloyment + self-employment + NEETS

Conclusions

• Clear effect of the recession on fertility

• Reflected both in unemployment and less standard proxies of economic uncertainty, also additional negative effect of the period 2009-12

• The role of uncertainty indicators varies by age, country groups / institutional settings (e.g. the importance of NEETs and self-employment in Southern Europe)

Future plans

• Extend the analysis by introducing country-level covariates and regional deviations from the country levels

• Considering other indicators of uncertainty at regional level (e.g. temporary employment)

• Elaborating the model, trying different specifications & interactions, conducting sensitivity tests

A. Matysiak and T. Sobotka’s research was funded by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC Grant agreement n° 284238 (EURREP).

EURREP website: www.eurrep.org