Figures and tables in Increasing the impact of dynamic microsimulation modelling

Abstract

This paper considers the degree of progress made by the field of dynamic microsimulation over the past five decades. It highlights the expanding breadth of the field, both in terms of the number of countries and in terms of the broadening policy area. It also outlines concerns in relation to lack of emphasis historically in relation to the transmission of codified and in the sometimes proprietary ownership model. Moving forward, an improved focus on the codification and peer review of methodologies used in the field is suggested. In terms of tacit knowledge transmission, the organisation of more regional meetings of the International Microsimulation Association is encouraged. In terms of future areas for model development, the opportunities are to incorporate behaviour to a greater extent, utilise more data in the big data revolution, to increase the field of microsimulation into new policy areas, and to share code and work in networks. Finally, the role of peer review is highlighted.

This article contains 1 figure and 5 tables.

Figures

**Figure 1.** Complexity in dynamic microsimulation models – inter-temporal microsimulation models.

Tables

Table 1. Proportion of articles published in different formats 2013+.

Journal	Mimeo	Book	Conference Proceedings
0.67	0.16	0.09	0.07

Source: Google Scholar.

Table 2. Share of articles by application area 2013+.

Application Area	Share of Papers
Labour Market	0.23
Education	0.03
Wealth	0.04
Income Distribution & Social Protection	0.03
Pensions	0.13
Health	0.18
Elderly Care	0.07
Demography	0.14
Energy, Environment and Land Use	0.10
Spatial	0.04

Source of bibliographic analysis: Google Scholar.

Table 3. Progress achieved.

Classification in Hoschka (1986)⁴	Achievement
Behaviour	Better Micro-Econometrics, albeit often limited to comparative statics.
Access to Data	Vastly improved access, especially in administrative data; although some reversals in survey panel data.
Model Development	Development of open-source microsimulation platforms with shared models (MODGEN, LIAM2, JAS-mine, FEM).
Computer Hardware	Huge improvements.
Policy Areas	New policy areas, e.g. “health microsimulation”.
Validation	About 10% of the investment time in developing a dynamic microsimulation model is taken up by the actual construction, the remaining 90% is validation (Caldwell & Morrison, 2000).
Complexity	Constant struggle.

Table 4. Who are the participants in world congresses of microsimulation?

World Congress	Research	University	Government	Private Sector
Turin	21%	54%	21%	4%
Canberra	20%	42%	34%	4%

Source: Google Scholar.

Table 5. Where to next?

Classification in Hoschka (1986)	Requirement
Behaviour	Still too little focus on causality. However much of existing literature is not possible to extrapolate.
Access to Data	Era of big data: how to utilise?
Model Development	What are the best methods to use? The need for more methodological research.
Computer Hardware	Cloud computing.
Policy Areas	Big global questions like impact of climate change and greater market risk; other policy areas.
Validation	Confidence intervals and Monte Carlo.
Complexity	Constant trade-off.