The Shock Impact Simulation is done through a macroeconomic modelling system called SISMod that brings new possibilities to allow timely quantitative assessments on the ex-ante and ex-post impact of various types of shocks (market, economic, sociopolitical, climatic, etc.) on household vulnerability. It identifies and profiles vulnerable groups, estimating to what extent they are in need. The simulations can help governments in anticipating shocks derived from specific policies or externalities, making sure the most vulnerable are protected by the most adequate safety nets. In shock-affected countries, the model provides early estimates of the impacts of shocks before any field assessment, informing the initial response. The model is designed ad hoc for each country, based on context-specific data and informing on specific output variables of interest.
SNAP Methodological Note on How to Simulate the Impact of a Shock.
The model used for the Shock Impact Simulation was firstly developed together by WFP and FAO in 2009 and was recognized by other governmental and non-governmental institutions in the countries it has been applied. Shock Impact Simulation is converted from a statistical analysis system (SAS) model to a user friendly platform for ease of use at country level. In the region covered by WFP Regional Bureau in Cairo (RBC), the model has been currently developed in Yemen, Lebanon, Kyrgyz Republic and Egypt. The Shock Impact Simulation module will soon be expanded to other countries in the region.
The foundation of the Shock Impact Simulation analysis is a set of modules, taking into account household incomes and expenditure and estimating demand/supply/price transmission elasticities based on data from household surveys, national food price collection systems, and other assessments. The process determines the interaction between production and income-generation decisions (income effects) and consumption decisions (price effects), which quantify the impacts of price changes and income changes on household food consumption.
In SNAP, to overcome data availability limitations, the analysis is carried out using the light version. The model SISMod-Light adopts the Agricultural Household Model (AHM) approach developed by Singh et al. (1986). In this model, household consumption decisions are based on household income, which comprises agricultural profits as well as wages, social provisions and any other type of income. Income generation and the allocation of income to expenditure are based on separable decisions, which maximize income and utility in a two-step process. The Agricultural Household Model incorporates both household production and consumption. It integrates price effects – which are presumed to be exogenous – and takes interactions between them into account.
Unlike in pure consumer models, in the Agricultural Household Model the household budget is endogenous and depends on production decisions that contribute to income through profits. This implies that the additional effects of profits, which can be simultaneously positive and negative, are added to the standard Slutsky equation (Taylor, 2003). For example, an increase in the price of staples unleashes to two opposing forces: the traditional price effects (where household food demand decreases when the price rises), and the opposing effect of agricultural profits (which, by contributing to household total income, lift budget constraints thereby increasing household food demand). Therefore, a change in price of a given commodity affects both supply and demand decisions.
SISMod’s AHM is then extended by a two-stage-demand system. The first stage of the demand system models consumption decisions by allocating total expenditure to broad commodity groups. For example, the broad commodity groups include food, clothing, utilities, housing, transport, communication, and personal and health care. A Seemingly Unrelated Regression is used to estimate a Linear Expenditure System (LES) for the first stage of the demand system. LES is a widely used traditional approach functional form derived from maximization of the utility function subject to the expenditure restriction.
In the second stage, a Linear Almost Ideal Demand System (LAIDS) (Deaton, 1986) is estimated for the food groups (staple, vegetable and fruits, oil and fats, pulses, etc.) and modelling consumption decisions by allocating food expenditure to food groups. Shock Impact Simulation was firstly developed together by WFP and FAO in 2009 and was recognized by other governmental and non-governmental institutions in the countries it has been applied. Shock Impact Simulation is converted from a statistical analysis system (SAS) model to a user friendly platform for ease of use at country level. In the region covered by WFP Regional Bureau in Cairo (RBC), the model has been currently developed in Yemen, Lebanon, Kyrgyz Republic and Egypt. Shock Impact Simulation will soon be expanded to other countries in the region.
The foundation of Shock Impact Simulation analysis is a set of modules, taking into account household incomes and expenditure and estimating demand/supply/price transmission elasticities based on data from household surveys, national food price collection systems, and other assessments. The process determines the interaction between production and income-generation decisions (income effects) and consumption decisions (price effects), which quantify the impacts of price changes and income changes on household food consumption.
In SNAP, to overcome data availability limitations, the analysis is carried out using the light version. Shock Impact Simulation-Light adopts the Agricultural Household Model (AHM) approach developed by Singh et al. (1986). In this model, household consumption decisions are based on household income, which comprises agricultural profits as well as wages, social provisions and any other type of income. Income generation and the allocation of income to expenditure are based on separable decisions, which maximize income and utility in a two-step process. The Agricultural Household Model incorporates both household production and consumption. It integrates price effects – which are presumed to be exogenous – and takes interactions between them into account.
Unlike in pure consumer models, in the Agricultural Household Model the household budget is endogenous and depends on production decisions that contribute to income through profits. This implies that the additional effects of profits, which can be simultaneously positive and negative, are added to the standard Slutsky equation (Taylor, 2003). For example, an increase in the price of staples unleashes to two opposing forces: the traditional price effects (where household food demand decreases when the price rises), and the opposing effect of agricultural profits (which, by contributing to household total income, lift budget constraints thereby increasing household food demand). Therefore, a change in price of a given commodity affects both supply and demand decisions.
Shock Impact Simulation’s AHM is then extended by a two-stage-demand system. The first stage of the demand system models consumption decisions by allocating total expenditure to broad commodity groups. For example, the broad commodity groups include food, clothing, utilities, housing, transport, communication, and personal and health care. A Seemingly Unrelated Regression is used to estimate a Linear Expenditure System (LES) for the first stage of the demand system. LES is a widely used traditional approach functional form derived from maximization of the utility function subject to the expenditure restriction.
In the second stage, a Linear Almost Ideal Demand System (LAIDS) (Deaton, 1986) is estimated for the food groups (staple, vegetable and fruits, oil and fats, pulses, etc.) and modelling consumption decisions by allocating food expenditure to food groups.
