Abstract
The evidence base on the causal relation between informal care and susbjective well-being is scarce and often methodologically limited. Most research to date, using simple cross-sectional estimations or fixed-effect models, fails to consider reverse causality and unobserved heterogeneity and, thus provides biased estimates. Using panel data from the Longitudinal Internet Studies for the Social Sciences for the Netherlands over the period 2009–2018, this paper investigates the causal relationship between the informal care provision and caregivers’ life satisfaction and compares Ordinary Least Square (OLS) with Arellano-Bond system Generalized-Method-of-Moments estimates. When controlling for endogeneity biases, namely unobserved heterogeneity, reverse causality and dynamic endogeneity, the caregiving effect increases by almost 300%, highlighting that OLS with fixed-effects produces a downward biased estimation. Overall, providing care has a negative effect on life satisfaction with female caregivers being the most impacted, especially when providing housekeeping and personal support to their partners.
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Notes
Kristoffersen (2017, p. 846) referred to Ferrer-i Carbonell & Frijters (2004) and mentioned that « the assumption of cardinal comparability is often justified largely on the basis of statistical requirements, (…), rather than on reason». Thus, he followed the principle of simultaneous conjoint measurement to observe the response function's shape for subjective well-being indirectly. Using 11 waves from the HILDA survey, the author evaluated the association between life satisfaction and mental health (MH5). The results provide reasonable arguments in favour of ordinal and cardinal comparability.
Due to data limitations, we do not observe the health of the care recipient.
There are two GMM estimators. Firstly, the difference GMM, implemented by Arellano & Bond (1991), estimated one single difference equation where variables were first differentiated and instrumented by lagged variables. Secondly Arellano & Bover (1995) and Blundell & Bond (1998) developed a system GMM model in order to improve the efficiency of Arellano & Bond (1991)’s model, as lagged values were sometimes poor instruments for first differences. In this model the level equation was added to the difference equation, thus leading to additional instruments. Blundell et al. (2001) suggested a second rule-of-thumb to compare difference GMM with system GMM model efficiency. Following these authors, the autoregressive model should be initially estimated using pooled OLS and OLS with fixed-effect estimators. The pooled OLS estimate of the lagged outcome parameter should be considered as an upper-bound estimate while the one of the OLS with fixed-effects is the lower-bound estimate. A difference GMM estimate close or below the fixed-effect estimate suggests a downward bias and points to the use of the system GMM estimator (see Table B1, in Appendix for detailed results). According to Arellano & Bover (1995), the two-step GMM model provides more efficient and consistent estimates in the case of panel data than the one-step GMM model.
Standardized differences between raw and matched data are presented in Annexe D.
Full estimates are given in Appendix, see Table 23.
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Acknowledgements
We would like to thank Kelsey O'Connor and Anthony Lepinteur for their advised comments. The authors are also grateful to Agnès Gramain, Thomas Barnay, Eric Bonsang, Martjin Burger, Conchita d’Ambrosio, Florence Jusot, Bertrand Koebel, Mathieu Lefebvre and Marie-Louise Leroux for their very helpful suggestions. Additionally, we are grateful to Miranda Aldham-Breary for her proofreading. The LISS panel data were collected by CentERdata (Tilburg University, The Netherlands) through its MESS project funded by the Netherlands Organization for Scientific Research.
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Appendices
Appendix 1: Data
Different modules from the LISS panel data were combined. The personality questionnaire from the LISS Core study contains information on subjective well-being, the questionnaire background incorporates socio-demographic information, the questionnaire on social integration and leisure includes information on informal care provision, the questionnaire on work and schooling contains information on working time, and the questionnaire on health contains questions on objective health. In order to ensure consistency in our merge we made sure that for each year the selected questionnaire was the closest of the month on which the questionnaire about personality was administered as life satisfaction is the outcome. Table 13 sums up this merge procedure (see Table 14).
Appendix 2: Rule-of-Thumb (Blundell et al., 2001)
OLS | Difference GMM | |||
|---|---|---|---|---|
Pooled | Fixed effects | One step | Two step | |
L.life satisfaction | 0.62*** | 0.08*** | 0.01 | 0.03 |
(0.02) | (0.02) | (0.03) | (0.03) | |
Informal | −0.03 | −0.06** | −0.35** | −0.32** |
(0.02) | (0.03) | (0.18) | (0.14) | |
Constant | 2.47*** | 7.14*** | ||
(0.21) | (0.49) | |||
Observations | 9180 | 9180 | 7888 | 7888 |
Time-fixed effects | Yes | Yes | Yes | Yes |
Controls | Yes | Yes | Yes | Yes |
No. of instruments | 197 | 197 | ||
AR1 (p-value) | 0.00 | 0.00 | ||
AR2 (p-value) | 0.24 | 0.26 | ||
Hansen-J (p-value) | 0.35 | 0.35 | ||
Appendix 3: Detailed results
See Tables 15, 16, 17, 18, 19, 20, 21
, 22
and 23
Appendix 4 Matching postestimation test
Standardized Differences | ||
|---|---|---|
Raw | Matched | |
Life satisfaction | −0.074 | −0.011 |
Men | −0.317 | −0.062 |
Occupation | ||
Unemployed | 0.047 | 0 |
Out of the labor force | 0.281 | −0.007 |
Age | ||
15–24 | −0.273 | −0.002 |
25–34 | −0.355 | −0.004 |
35–44 | 0.041 | 0 |
45–54 | 0.178 | 0.004 |
65 and over | 0.160 | 0.001 |
Children | −0.116 | −0.019 |
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Blaise, M., Dillenseger, L. Informal Caregivers and Life Satisfaction: Empirical Evidence from the Netherlands. J Happiness Stud 24, 1883–1930 (2023). https://doi.org/10.1007/s10902-023-00663-1
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DOI: https://doi.org/10.1007/s10902-023-00663-1