Is the basic life-cycle theory of consumption becoming more relevant? Evidence from Portugal


In this paper we report and discuss estimates of life-cycle consumption profiles obtained using microdata for Portuguese households. The estimated profiles are much flatter than the profiles usually reported in the literature for other countries, namely the Netherlands, the UK and the USA. In addition, we also report estimates of cohort and business cycle effects on consumption. The estimated cohort effects are consistent with the post-war progress in median standards of living. However, there is a deceleration in the trend of consumption growth for more recent cohorts. The business cycle estimates suggest that the recent debt crisis has had a strong negative impact on household consumption.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

  2. 2.

    Alternative perspectives on this issue can be found in Chiuri and Del Boca (2010), Angelini and Laferrère (2013) and Ahn and Sánchez-Marcos (2017).


  1. Aguiar, M., & Hurst, E. (2013). Deconstructing life cycle expenditure. Journal of Political Economy, 121(3), 437–492.

    Article  Google Scholar 

  2. Ahn, N., & Sánchez-Marcos, V. (2017). Emancipation under the great recession in Spain. Review of Economics of the Household, 15(2), 477–495.

    Article  Google Scholar 

  3. Alessie, R., & Ree, J. (2009). Explaining the hump in life cycle consumption profiles. De Economist, 157(1), 107–120.

    Article  Google Scholar 

  4. Alexandre, F., Aguiar-Conraria, L., Bação, P., & Portela, M. (2017). Poupança e Financiamento da Economia Portuguesa. Lisboa: Imprensa Nacional Casa da Moeda.

    Google Scholar 

  5. Altonji, J. G., Kahn, L. B., & Speer, J. D. (2016). Cashier or consultant? Entry labor market conditions, field of study, and career success. Journal of Labor Economics, 34(S1), S361–S401.

    Article  Google Scholar 

  6. Alves, N., & Cardoso, F. (2010). Household saving in Portugal: micro and macroeconomic evidence. Economic Bulletin. Banco de Portugal (Winter), 47–67.

  7. Angelini, V., & Laferrère, A. (2013). Parental altruism and nest leaving in Europe: evidence from a retrospective survey. Review of Economics of the Household, 11(3), 393–420.

    Article  Google Scholar 

  8. Antão, P., Boucinha, M., Farinha, L., Lacerda, A., Leal, A. C., & Ribeiro, N. (2009). Financial integration, financial structures and the decisions of households and firms. In Banco de Portugal (Ed.), The Portuguese Economy in the Contexts of Economic, Financial and Monetary Integration, Chapter 7, (pp. 415–545). Lisbon: Banco de Portugal.

  9. Attanasio, O. P. (1998). Cohort analysis of saving behavior by U.S. households. Journal of Human Resources, 33(3), 575–609.

    Article  Google Scholar 

  10. Attanasio, O. P., & Weber, G. (1995). Is consumption growth consistent with intertemporal optimization? Evidence from the Consumer Expenditure Survey. Journal of Political Economy, 103(6), 1121–1157.

    Article  Google Scholar 

  11. Attanasio, O. P., & Weber, G. (2010). Consumption and saving: models of intertemporal allocation and their implications for public policy. Journal of Economic Literature, 48(3), 693–751.

    Article  Google Scholar 

  12. Baldwin, R., Beck, T., Bénassy-Quéré, A., Blanchard, O. J., Corsetti, G., Grauwe, P. D., Haan, W. D., Giavazzi, F., Gros, D., Kalemli-Ozcan, S., Micossi, S., Papaioannou, E., Pesenti, P., Pissarides, C., Tabellini, G., & diMauro, B. W. (2015). Rebooting the Eurozone: Step 1—agreeing a crisis narrative. CEPR Policy Insight 85. London: Centre for Economic Policy Research.

  13. Balli, F., & Tiezzi, S. (2010). Equivalence scales, the cost of children and household consumption patterns in Italy. Review of Economics of the Household, 8(4), 527–549.

    Article  Google Scholar 

  14. Banco de Portugal (2009). The Portuguese Economy in the Context of Economic. Financial and Monetary Integration. Lisboa: Banco de Portugal.

  15. Banco de Portugal (2018). Household consumption inequality in Portugal. Economic Bulletin. Banco de Portugal, 53–72.

  16. Banks, J., Blundell, R., Levell, P., & Smith, J. P. (2016). Life-cycle consumption patterns at older ages in the US and the UK: Can medical expenditures explain the difference? NBER Working Papers 22513, Cambridge, MA: National Bureau of Economic Research, Inc.

  17. Banks, J., Blundell, R., & Tanner, S. (1998). Is there a retirement-savings puzzle? American Economic Review, 88(4), 769–788.

    Google Scholar 

  18. Browning, M., & Crossley, T. F. (2001). The life-cycle model of consumption and saving. Journal of Economic Perspectives, 15(3), 3–22. Summer.

    Article  Google Scholar 

  19. Bütikofer, A., & Gerfin, M. (2017). The economies of scale of living together and how they are shared: estimates based on a collective household model. Review of Economics of the Household, 15(2), 433–453.

    Article  Google Scholar 

  20. Castro, G. L. (2006). Consumption, disposable income and liquidity constraints. Economic Bulletin. Banco de Portugal, 2006, 75–84.

  21. Castro, G. L. (2007). The wealth effect on consumption in the Portuguese economy. Economic Bulletin. Banco de Portugal, 2007, 37–55.

  22. Chiuri, M., & Del Boca, D. (2010). Home-leaving decisions of daughters and sons. Review of Economics of the Household, 8(3), 393–408.

  23. Cribb, J., Hood, A., & Joyce, R. (2017). Entering the labour market in a weak economy: scarring and insurance. IFS Working Papers W17/27, London: Institute for Fiscal Studies.

  24. De Nardi, M., French, E., & Jones, J. B. (2010). Why do the elderly save? The role of medical expenses. Journal of Political Economy, 118(1), 39–75.

    Article  Google Scholar 

  25. Deaton, A. (1997). The analysis of household surveys: a microeconometric approach to development policy. Washington, DC: The World Bank.

    Google Scholar 

  26. Deaton, A. S. and Paxson, C. (1994). Saving, growth, and aging in Taiwan. In Studies in the Economics of Aging, NBER Chapters, (pp. 331–362). Cambridge, MA: National Bureau of Economic Research, Inc.

  27. Di Stefano, E. (2019). Leaving your mamma: why so late in Italy? Review of Economics of the Household, 17(1), 323–347.

  28. Eurostat (2003). Household budget surveys in the EU—methodology and recommendations for harmonisation (pp. 2003). Luxembourg: Office for Official Publications of the European Communities.

    Google Scholar 

  29. Farinha, L. (2009). Wealth effects on consumption in Portugal: a microeconometric approach. In Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies. Lisbon: Banco de Portugal.

  30. Fernández-Villaverde, J., & Krueger, D. (2007). Consumption over the life cycle: facts from consumer expenditure survey data. The Review of Economics and Statistics, 89(3), 552–565.

    Article  Google Scholar 

  31. Fukuda, K. (2006). A cohort analysis of female labor participation rates in the U.S. and Japan. Review of Economics of the Household, 4(4), 379–393.

    Article  Google Scholar 

  32. Gordon, R. (2016). The Rise and Fall of American Growth: the U.S. Standard of Living since the Civil War. The Princeton Economic History of the Western World. Princeton, New Jersey: Princeton University Press.

    Google Scholar 

  33. Gourinchas, P.-O., & Parker, J. A. (2002). Consumption over the life cycle. Econometrica, 70(1), 47–89.

    Article  Google Scholar 

  34. Grossbard, S., & Amuedo-Dorantes, C. (2007). Cohort-level sex ratio effects on womenas labor force participation. Review of Economics of the Household, 5(3), 249–278.

    Google Scholar 

  35. INE (2017). Inquérito ás Despesas das Famílias 2015/2016. Lisboa: Instituto Nacional de Estatística.

  36. Jappelli, T. (1999). The age-wealth profile and the life-cycle hypothesis: a cohort analysis with time series of cross-sections of Italian households. Review of Income and Wealth, 45, 57–75.

    Article  Google Scholar 

  37. Lim, G. C., & Zeng, Q. (2016). Consumption, income, and wealth: evidence from age, cohort, and period elasticities. Review of Income and Wealth, 62(3), 489–508.

    Article  Google Scholar 

  38. Martins, N. C., & Villanueva, E. (2006). The impact of mortgage interest-rate subsidies on household borrowing. Journal of Public Economics, 90(8–9), 1601–1623.

    Article  Google Scholar 

  39. Nakamura, T. (1986). Bayesian cohort models for general cohort table analyses. Annals of the Institute of Statistical Mathematics, 38(2), 353–370.

    Article  Google Scholar 

  40. OECD (2007). Pensions at a glance 2007: public policies across OECD countries. Paris: OECD Publishing.

    Google Scholar 

  41. Palumbo, M. G. (1999). Uncertain medical expenses and precautionary saving near the end of the life cycle. The Review of Economic Studies, 66(2), 395–421.

    Article  Google Scholar 

  42. Passero, W., Garner, T. I., & McCully, C. (2014). Understanding the relationship: CE survey and PCE. In Carroll, C.D., Crossley, T.F., & Sabelhaus, J. (Eds), Improving the measurement of consumer expenditures, (pp. 181–203). Chicago, IL: University of Chicago Press.

  43. Reis, R. (2013). The Portuguese slump and crash and the euro crisis. Brookings Papers on Economic Activity, 44(1), 143–210.

    Article  Google Scholar 

  44. Speckman, P. (1988). Kernel smoothing in partial linear models. Journal of the Royal Statistical Society. Series B (Methodological), 50(3), 413–436.

    Article  Google Scholar 

  45. Statistical Office of the European Communities (2015). Being young in Europe today. Luxembourg: Statistical Office of the European Communities.

  46. Zhou, S. (2012). Explaining the saving puzzles in urban China. Review of Income and Wealth, 58(3), 504–530.

    Article  Google Scholar 

Download references


This research is financed by National Funds of the FCT—Portuguese Foundation for Science and Technology within the project UID/ECO/03182/2019. This paper benefited greatly from the comments of the Editor, the Co-Editor and two anonymous referees.

Author information



Corresponding author

Correspondence to Pedro Bação.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Appendix 1: Classification of household expenditures

In this paper we follow the approach of Fernández-Villaverde and Krueger (2007) in dividing total expenditure among five categories: nondurables, durables, health, clothing and footwear, and education. For the last three categories we used the corresponding categories in the Portuguese Household Budget Survey (IDEF). We then classified the remaining categories in IDEF as either durable or nondurable expenditure. Our classification is presented in Table 4.

Table 4 IDEF—expenditure category codes

However, as a result of differences in the classification of household expenditures, comparison of our results with results presented in other papers in the literature is not straightforward. While the IDEF uses the United Nations’ Classification of Individual Consumption According to Purpose (COICOP), the Consumer Expenditure Survey (CEX) data used by Fernández-Villaverde and Krueger (2007) does not. Passero et al. (2014) provide a thorough discussion of the relationship between these data and the ‘Personal consumption expenditures’ (PCE) data, released by the US Bureau of Economic Analysis, which is based on the COICOP. Their discussion is helpful for understanding the problems arising when comparing studies that use different classifications of expenditures.

The major difference between our categories and those in Fernández-Villaverde and Krueger (2007) concerns Education. In IDEF this category includes only fees (tuition and administrative). Other education related expenditures are considered to be part of other categories. Most notably, books are part of recreation and culture expenditures, while school transportation is part of transport services. In CEX, these expenditures (and others such as “Food or board while attending school”) are included in the Education category.

In Fernández-Villaverde and Krueger (2007) expenditures on durables include owned dwelling, rented dwelling, house equipment, vehicles, books and electronic equipment. Our measure expands on this by including insurances connected with dwelling. Note that according to Passero et al. (2014, p. 192), the PCE (COICOP) measures of insurance do not match those in CEX. Likewise, our classification of expenditure on nondurables includes, for example, social protection, travel insurance, other insurance or financial services, but the COICOP definition of these items also does not match that in CEX.

IDEF includes the major components of health expenditure, as the CEX does. However, a significant part of the Portuguese population has access to the National Health Service for free. Over time, free access has been restricted, which may help explain the decreasing frequency of zero values in the sample—see Table 1.

Appendix 2: Imputation of age

Our IDEF dataset only contains the age of the household’s reference person for the 2010 survey. For the other surveys, we know the age class of the reference person. In the cases of the 2000 and 2015 surveys, the age classes are five years long, from [25;29] until [70;74]. In the case of the 2005 survey, the age classes are [25;29], [30;44], [45;64] and [65;74]. Therefore, we used two versions of the multinomial logit model for imputing the age, one for the 2000 and 2015 surveys, the other for the 2005 survey.

The multinomial logit model assumes that, given \(J\) possible values (\({V}_{j}\)) for the outcome variable \({Y}_{i}\) (the value taken for individual \(i\)), the probability of observing alternative \(j\) is given by:

$$P({Y}_{i}\,=\,{V}_{j}\vert {\bf{x}}_{i})\,=\,\frac{\exp ({\bf{x}}_{i}^{\prime}{\boldsymbol{\beta}}_{j})}{{\sum \limits_{k\,=\,1}^{J}}\exp ({\bf{x}}_{i}^{\prime}{\boldsymbol{\beta}}_{k})}$$

where \({{\bf{x}}}_{i}\) is a vector of characteristics of individual \(i\) and \({{\boldsymbol{\beta }}}_{j}\) (\(j\,=\,1,\ldots ,J\)) is the matching vector of coefficients corresponding to alternative \(j\). To identify the coefficients, a normalization restriction, such as \({{\boldsymbol{\beta }}}_{J}\,=\,{\bf{0}}\), is required.

To impute the age, we estimated the model represented by Eq. (4) using the observations of the 2010 survey for each five-year age class. In other words, we estimated the model 10 times, one for each subset (corresponding to one of the age classes) of the 2010 survey. The \({Y}_{i}\) variable is the age of the reference person. The possible values are the ages in the corresponding age class (e.g. in the case of the [25;29] age class these are the values 25, 26, 27, 28 and 29). We used the resulting estimates of the \({{\boldsymbol{\beta }}}_{j}\)’s to impute the ages of the reference persons in the 2000 and 2015 surveys. For the 2005 survey we had to estimate the model for each subset of the 2010 data corresponding to one of the age classes reported in the 2005 survey: [25;29], [30;44], [45;64] and [65;74]. The explanatory variables (\({{\bf{x}}}_{i}\)) were the same in all cases and their choice was limited by the data available in IDEF: the gender of the reference person, whether the reference person has a partner, the number of ancestors in the household, the number of descendents in the household, the level of education of the reference person, the NUTS 2 region where the household resides, the work status of the reference person and the share of each category of expenditure in total expenditure.

The age imputed to each household was the age to which the model assigned the highest probability. Does our result on the flatness of the life-cycle consumption profile depend on this choice? To answer this question we constructed 500 alternative age samples. Each age sample was obtained by randomly drawing an age for each individual from the distribution implied for that individual by the estimated model. We re-estimated the consumption profile using each of these 500 age samples instead of the maximum-probability age. Then, for each of the newly estimated 500 consumption profiles, we computed the percent difference between maximum total expenditure across the life cycle and total expenditure at age 25. Table 5 presents the results. The maximum relative variation was always under 20%. It was below 14% for 81% of the simulated age samples. Using the maximum-probability age, in Section 4.1 we reported this change to be 13%. Therefore, our conclusion regarding the flatness of the consumption profile appears to be robust to the imputed age.

Table 5 Distribution of the maximum increase in total expenditure

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alexandre, F., Bação, P. & Portela, M. Is the basic life-cycle theory of consumption becoming more relevant? Evidence from Portugal. Rev Econ Household 18, 93–116 (2020).

Download citation

JEL classification

  • D15
  • E21


  • Cohorts
  • Consumption
  • Life-cyle
  • Microdata