The influence of decision making costs on the effectiveness of tax incentives to save Results from the HMRC/HMT/ESRC Joint Research Programme on Taxation Analysis Justin van de Ven (justin.van@unimelb.edu.au), MIAESR & NIESR Martin Weale & Paolo Lucchino, NIESR November, 2013

Aims of the research Facilitate use by Whitehall of current best practice methods to analyse savings and employment responses to policy Advance our understanding of household sector savings decisions Explore the role of decision costs as influences on responses to tax incentivised savings schemes Start by noting that the work reported here was supported under the HMRC/HMT/ESRC Join Research Programme on Taxation Analysis

Outline Analysing saving and employment decisions The Lifetime Income Distributional Analysis (LINDA) Model Empirical analysis The influence of decisions costs on the effectiveness of ISAs

Analysing saving & employment decisions A spectrum of behavioural assumptions detailed statistical analysis detailed numerical analysis Back-of-an- envelope analysis detailed statistical analysis very broad behavioural assumptions no formal model of behaviour formal model of behaviour – poor approx. of uncertainty formal model of behaviour – uncertainty explicitly considered See, for example, Will personal accounts increase pension saving? Pensions Policy Institute (2007) Disney, R., Emmerson, C. & Wakefield, M. (2007), “Tax reform and retirement saving incentives: Evidence from the introduction of Stakeholder pensions in the UK”, Institute for Fiscal Studies Working Paper, WP19/07. Attanasio, O. & Rohwedder, S. (2003), “Pension wealth and household saving: evidence from pension reforms in the UK”, American Economic Review, 93, pp. 1499-1521. Attanasio, Banks, Meghir, & Weber (1999), “Humps and bumps in lifetime consumption”, Journal of Business & Economic Statistics, 17, pp. 22-35. Rust and Phelan, 1997, Econometrica French, 2005, American Economic Review

LINDA: A model for Whitehall Structural model of household consumption, labour supply, and investment decisions (van de Ven and Lucchino, 2013) Life-cycle framework Motivating observations (Attansio & Webber, 2010) Resolution of puzzles Model specifics Microsimulation of individual households that vary over a range of characteristics Decisions modelled at annual intervals from age 20 to 120 Demographics explicit Hard and soft liquidity constraints Uncertainty over wages, employment, marital status, investment returns, and time of death Modelling effort represents current best practice in the micro-economic analysis of dynamic decision making at the household level “motivating observations” refers to the basic motivation underlying the objective to create a model that is suitable for use in Whitehall. The “motivation” here is the observation that the life-cycle framework has had minimal impact on policy making in practice – noted by A&W “resolution of puzzles” refers to the empirical regularities that underlies the basic structure of the life-cycle framework. Note: the box is an animation that will appear when you press enter.

LINDA: A model for Whitehall Projects panel data forward and back through time from a population cross-section (WAS) Policy relevant basis for analysis Effort has been expended to make the model accessible to non-specialists: Excel front-end Summary statistics reported through Excel Simulated panel data reported in standard format

LINDA: model methodology Preferences: Budget constraint: Evolution of wages: U = utility of individual i at age a Gamma = relative risk aversion E = expectations operator Delta = discount factor A = maximum life-span (120 in analysis) Phi = probability of death at age j, given survival to age a Psi = salience (decision) cost, consequent on first contribution to ISA (indicated by d^(ISA)) Z = warm glow model of bequests

LINDA: model methodology hT wT hT-1 wT-1 time hT-2 wT-2 h1 w1

Using LINDA

Using LINDA

LINDA: Output

LINDA: Output

Empirical analysis The innovative nature of the LINDA model has important advantages for conducting empirical analysis see Lucchino & van de Ven (2013) Key findings: Intertemporal elasticity of substitution at population averages in region of 0.5. Allowing for decision costs helps to match the model to observed rates of participation in ISAs Do not dwell on this aspect of the analysis in the presentation, as the behavioural responses are likely to be of greater interest – but we do need to make clear the model parameters were adjusted to reflect observed survey data. Parameterisation of intertemporal elasticity higher than commonly found, and has important policy implications Decision costs are represented as a discrete reduction in utility Micro-data concerning investments made through the ISA scheme suggest that net annual rates of take-up averaged across a representative sample of the British population aged between 20 and 60 do not exceed 5%. In the absence of salience costs, the structural model assumed here suggests that 48% of the population aged 20 to 60 would choose to make their first ISA investment in 2006/07, equivalent to three out of every four individuals who are reported to hold no ISA investments at that time.

Utility price of leisure (A), experience effects (B) and labour supply Empirical analysis Utility price of leisure (A), experience effects (B) and labour supply

Empirical analysis Discount factor(A), relative risk aversion (A), preference for bequests (B) and consumption Note that I have replaced “discount factor” here with “discount rate”

Empirical analysis Discount factor(-A), relative risk aversion (A), preference for bequests (A) and pension participation

Simulated effectiveness of ISAs ISAs do not motivate appreciable increases in household saving, with or without decision costs in the absence of decision costs, households are projected to invest heavily through ISAs, but off-set almost all of this saving against other wealth decision costs reduce the scale of projected ISA investments, but leave most other effects on population averages qualitatively unchanged the form that decision costs are assumed to take has an important influence on determining distributional responses to ISAs van de Ven (2013)

Behavioural Results Projected savings behaviour of individuals born between 1977 and 1986, by existence of ISAs and decision costs over ISA participation (£000, 2006)

Welfare effects of ISAs (£2006) Behavioural Results Welfare effects of ISAs (£2006)

Directions for further research Adaptation of model framework An on-going process Testing alternative empirical specifications and behavioural hypotheses Stylised forms to explore intertemporal elasticity (evidence of time variation?) Appropriate moments for empirical identification (novelty of approach) Form of decision costs? Applied policy analyses