1. 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 MIAESR & NIESR
Martin Weale & Paolo Lucchino, NIESR
November, 2013

2. 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

3. 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

4. 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
Attanasio, Banks, Meghir, & Weber (1999), “Humps and bumps in lifetime consumption”, Journal of Business & Economic Statistics, 17, pp
Rust and Phelan, 1997, Econometrica
French, 2005, American Economic Review

5. 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.

6. 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

7. 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

8. LINDA: model methodologyhT wT
hT-1
wT-1
time
hT-2
wT-2 h1 w1

9. Using LINDA

10. Using LINDA

11. LINDA: Output

12. LINDA: Output

13. 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.

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

15. 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”

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

17. 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)

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

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

20. 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