A Spatial Microsimulation Model for Social Policy Evaluation

Ballas, Dimitris; Clarke, Graham; Turton, Ian

doi:10.1007/978-94-017-2296-4_8

Cited by 17 publications

(18 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T he m ain m ethod in our research is agent-based com putational econom y (ACE) [13,29], w hich includes heterogeneity, bounded rationality, non-equilibrium dynam ics and direct interactions am ong econom ic agents [9]. A C E m odels are often used to study effects o f policies on m acroeconom ic and spatial dynam ics [4 ], and have already been im plem ented in different areas o f m acroeconom ic policy such as fiscal [2], m onetary [11,21], m acroprudential [1,3,19] and labor m arket policy [18,23,25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…T he m odel consists of 715 thousand individuals w ithin households along w ith their associated attributes and provides a spatial decision support tool for the local council officers. In particular, the system can be used to describe current conditions in neighborhoods, predict future trends in the com position and health of population and conduct analysis to m easure the likely im pact o f policy interventions at the local level [4 ]. Sim ilar system in R ussia was designed for Saint-Petersburg [27].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Simulating Budget System in the Agent Model of the Russian Federation Spatial Development

Mashkova

Novikova

Savina

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

In this paper we present methods, data and algorithms for simulating budget system in the model of the Russian Federation spatial development. We show the place of this task in methodology of our research and give a brief overview of the background results. Key objects of the budget system in the model are the federal budget, regional budgets and extra budgetary funds: pension, medical and social insurance. We determine revenues and expenditures of the budgets and funds on the basis of federal laws, Budget and Tax codes of the Russian Federation. Since the exact reproduction of excise rates and customs duties is problematic due to model aggregation, we calculate model rates of excises and duties for selected sectors on the basis of retrospective data pre sented on the budget system portal and in the federal input-output table. Structure of the budget expenses in the model was simplified by aggregating expenditure items into major groups correlated with sectors of the economy. Presented algorithm of the budget system simulates interaction of the federal, regional budgets and extra-budgetary funds with agents and organizations in the model. For validation of the budget system algorithms in the model we conduct retrospective modeling for the federal budget and the budget of Belgorod region in 2014.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Simulating Budget System in the Agent Model of the Russian Federation Spatial Development

Mashkova

Novikova

Savina

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…Advances in spatial microsimulation have meant that the ability to see the effect of policy changes for small areas is now possible (Ballas et al , 2003; Harding et al , 2008; Tanton et al , 2008). Further, using the spatial microsimulation approach, estimates of poverty for subgroups of the population can easily be calculated (Anderson, 2007a; Ballas et al , 2007; Miranti et al , 2008b; Tanton et al , 2008; Brewer et al , 2009).…”

Section: Introductionmentioning

confidence: 99%

Spatial microsimulation as a method for estimating different poverty rates in Australia

Tanton

2011

Population Space and Place

View full text Add to dashboard Cite

This paper uses spatial microsimulation to calculate small area poverty severity and headcount poverty in Australia using the Foster‐Greer‐Thorbeck method. The estimates of poverty severity across Australia, and for certain subgroups of the population, are then compared with headcount poverty rates. Areas of difference are highlighted, and the reasons for some of these differences are investigated further. Because the spatial microsimulation gives unit record data on incomes in each small area, we can compare the microsimulated income distributions for the small areas to see what drives the difference between headcount poverty and poverty severity. We find that the headcount poverty rate is in the same quintile as poverty severity for 63% of people in Australia. The headcount poverty rate tends to be lower than poverty severity in areas where there is extreme poverty, and this is confirmed by looking at the microsimulated income distribution of areas where the differences were greater. This may be because poverty severity is giving greater weight to the extreme poor compared with headcount poverty. There were very few areas where the headcount poverty was higher than poverty severity. Looking at older single people, the picture was very different. Only 36% of aged single people were in the same quintile. In many areas, the headcount poverty was much higher than poverty severity. Areas where the headcount poverty rate was higher than the poverty severity tended to be areas where there were a number of people very close to the poverty line, which is common for aged single people on a pension. Copyright © 2009 John Wiley & Sons, Ltd.

show abstract

“…This provides a much more flexible base for subsequent simulations of the impacts of policy change or of socio-demographic or economic change. In recent years, both the UK and Australia have been spearheading efforts to develop synthetic small area household data using spatial microsimulation models (Voas and Williamson, 2000;Williamson, 2001;Ballas et al, 2003;Ballas et al, 2005a;Ballas et al, 2005b;Chin et al, 2005;Ballas et al, 2006a;Ballas et al, 2006b;Chin et al, 2006a;Chin et al, 2006b;Phillips et al, 2006;Chin and Harding, 2007).…”

Section: Introductionmentioning

confidence: 99%

Urban and Rural Estimates of Poverty: Recent Advances in Spatial Microsimulation in Australia

Tanton

Harding

McNamara

2010

Geographical Research

View full text Add to dashboard Cite

This paper reports on the calculation of poverty rates for small areas in Australia using a spatial microsimulation model. The spatial microsimulation methodology used involves reweighting data from confidentialised unit record files (CURFs) from surveys conducted by the Australian Bureau of Statistics (ABS) to small area census data, also from the ABS. The method is described in this paper, and then maps of poverty using poverty rates derived from this small area estimation method are shown for the eastern coast of Australia and its capital cities. Further analysis of poverty rates in capital cities is then conducted. We find that areas of higher poverty risk can be clearly identified within Sydney, Melbourne, Canberra and Brisbane. We also find that areas of high poverty are frequently 'buffered' by areas of moderate poverty. This is not always the case since, in some areas, we find a high poverty area neighbouring a low poverty area but, generally, there appears to be a moderate poverty 'buffer' in most capital cities.

show abstract

A Spatial Microsimulation Model for Social Policy Evaluation

Cited by 17 publications

References 23 publications

Simulating Budget System in the Agent Model of the Russian Federation Spatial Development

Simulating Budget System in the Agent Model of the Russian Federation Spatial Development

Spatial microsimulation as a method for estimating different poverty rates in Australia

Urban and Rural Estimates of Poverty: Recent Advances in Spatial Microsimulation in Australia

Contact Info

Product

Resources

About