This book, which is the first to be published in the emerging field of farm-level microsimulation, highlights the different methodological components of microsimulation modelling: hypothetical, static, dynamic, behavioural, spatial and macroâmicro. The author applies various microsimulation-based methodological tools to farms in a consistent manner and, supported by a set of Stata codes, undertakes analysis of a wide range of farming systems from OECD countries. To these case studies, O'Donoghue incorporates farming policies such as CAP income support payments, agri-environmental schemes, forestry planting incentives and biomass incentives â in doing so, he illuminates the merits of microsimulation in this environment.
Agriculture in OECD countries is one of the most regulated industries, most heavily dependent for income upon political expenditures, has special rules in the tax code. The sector also has one of the most complex mixes of outcomes from market goods such as food to non-market goods such as environmental services. It is a land-based business of heavy goods, and so the spatial dimension is important, and depends upon long-term investment decisions such as land purchase, land improvement or long-term land use changes such as forestry. There is very significant heterogeneity amongst farmers from small-scale hobby producers with off-farm income sources, to very poor low-income farmers, to highly mechanised, large-scale operations with multi-million euro investments.
Given the importance of food production in the provision of goods that are essential for survival, that agriculture is a biologically based sector that is prone to risk and volatility, that faces pressures in meeting the food requirements of the growing world population particularly in the face of climate and environmental constraints and that it is a sector that impacts upon the wider environment as one of the most significant land uses; it is unsurprising that a modelling field has developed to look at these issues at farm level.
Microsimulation Modelling
Thus, there is stakeholder interest in both the private and the public sectors, for information in relation to the ex-ante impact of market and policy changes across the distribution of farms and across the dimensions discussed above, including spatial and temporal. Ex-ante assessments of all European Commission proposals are now required within the policy development process. These include agricultural sector behavioural models like FARMIS (Offermann et al. 2005) or FSSIM, see Ciaian et al. (2013) for a discussion of these models. This book focuses on the development of farm-level models and discusses their evolution and application over recent years.
Microsimulation modelling is a simulation-based tool with a micro unit of analysis that can be used for ex-ante analysis. It is a micro-based methodology, utilising micro units of analysis such as individuals, households, firms and farms, using surveys or administrative data sets. It is a simulation-based methodology that utilises computer programmes to simulate public policy, economic or social changes on the micro population of interest (OâDonoghue 2014). It is essentially a computer-based laboratory for running policy and market experiments, whose development has been facilitated by the advent of the personal computer in the 1980s and the availability of micro-data that has allowed the field to grow very rapidly.
For most of its history as a field, since Orcutt (1957, 1960) the focus has been on the household unit of analysis and focus on related policy such as tax, social policy, pensions etc. There is now a growing literature based on firms (Buslei et al. 2014) or farms (Richardson et al. 2014). Whether formally called microsimulation modelling or not, micro-based ex-ante simulation-based analysis is now used extensively around the world for policy analysis and design (Shrestha et al. 2016).
The field is multidisciplinary, reflecting the different policy focuses, but is bound together by researchers who utilise computer-based simulation models to simulate the impact of public policy and/or economic and social change on micro units such as households, firms and farms. Depending upon the policy area, the discipline has different names. For some, particularly those working in public finance, social policy and rural development, the field is called microsimulation; for others in the agricultural policy, it is farm-level modelling, while for others in labour economics, it is a branch of applied micro econometrics. However, methodologically, there is much in common and much that can be learned from the different fields. It is particularly appropriate in this time of economic crisis to focus on methodologies that can facilitate better policy design.
Modelling Complexity
As a modelling framework, microsimulation modelling is a mechanism of abstracting from reality to help us understand complexity better. Figure 1.1 outlines potential sources of complexity in a static, single time period microsimulation model.
Fig. 1.1
Sources of complexity in policy design and evaluation. Source OâDonoghue (2014)
In the context of policy design and evaluation, complexity can take the form of
Population structure of the population,
Behavioural response to the policy
Policy structure
These levels of complexity themselves interact with each other, resulting in a degree of complexity that is difficult to disentangle without recourse to a model.
Consider first the dimension of population complexity. The first dimension of complexity in relation to population is whether an analysis takes place on a population with limited or extensive heterogeneity. The former equates to a typical farm model (Hemme et al. 2014), while the latter includes distributional farm models such as Louhichi et al. (2010). The next dimension of complexity is policy complexity. This relates to the range of different policy or socio-economic impacts and the degree of policy complexity as many microsimulation models try to replicate the fine detail of legislation in modelling policy to the different types of policy modelled as in implemented CAP policy or agri-environmental policy. The third dimension of complexity is behaviour. Models that abstract from behavioural response are known as static microsimulation models. However, many policies are explicitly aimed at influencing policy as in the case of attempts to improve environmental outcomes on farms (Hynes et al. 2008; Ramilan et al. 2011).
In the case of models that incorporate either spatial dimensions or inter-temporal dimensions, the level of complexity is increased further (Fig. 1.2). Land use and spatially targeted policy (Miller and Salvini 2001; Lau and Kam 2005) or spatially targeted socio-economic effects (van Leeuwen and Dekkers 2013) require spatial models. Policies which depend upon long-term horizons such as afforestation models (Ryan et al. 2015) utilise inter-temporal or dynamic models.
Fig. 1.2
Enhanced complexity in inter-temporal and spatial microsimulation models. Source OâDonoghue (2014)
1.2 Farm-Level Microsimulation
Farm-level simulation modelling has historically developed as a parallel field to microsimulation modelling in that relatively few farm-level papers appear in microsimulation conferences or journals or vice versa (Shrestha et al. 2016). However, fundamentally, the objectives are similar, micro-level simulations of policy and economic change. Farm-level micro-simulation combines biological, business and policy modelling. Farm-based micro-level simulation modelling differs from other microsimulation-based models in that incomes partially derive from biological processes. Farms also have specific business structures, but in concept, in terms of profit, output and costs, are not much different from firm-level models. The farmin...
