the poverty simulator on the basis of group means and Lorenz functions estimated from the available household data.

# **Box 8: PAMS II A Reduced-Form Application of PAMS to Indonesia**

Essama-Nssah (2004) explains the implementation of a reduced-form version of PAMS using Indonesian data. The poverty outcomes of policies are analyzed by linking recursively the CGE model to a poverty simulator built upon a parameterization of the Lorenz curve. The aggregate formulation employs private consumption as the key linking variable, while the multisector interpretation relies on real disposable income. The poverty simulator of PAMS II is used to study the poverty implications of (1) terms-of-trade shocks, (2) changes in the balance of trade, and (3) changes in income tax or indirect taxes.

The reduced-form version developed for Indonesia (known as INDOPAMS) represents a significant improvement both computationally and in the modeling of the links between poverty and distributional outcomes and the macroeconomy. Furthermore, the version collapses the macro- and meso-modules of the original framework into a simple CGE model. This approach allows one to endogenize to some extent the growth process via factor accumulation.

In the application, the key linkage variable is the mean of the distribution of income or expenditure, the changes of which are determined by the economy-wide model. These changes are then fed into the Lorenz framework to predict the corresponding changes in poverty. In accordance with the ERH approach that underlies it, PAMS II assumes constant intra-group inequality. Thus, it can only predict changes in between-group inequality. Various scenarios were simulated to examine the impact on poverty and inequality indicators. The table below shows, for instance, the poverty implications of an export boom in the form of a 20 percent increase in the world price of exports. It is clear that the extent of poverty reduction depends crucially on the structure of the economy as represented by the elasticities of output transformation and import substitution. The higher these elasticities, the greater the extent of poverty reduction.

**Poverty Implications of an Improvement in the Terms of Trade**

|**Elasticity of Output**<br>**Transformation**|**Import Substitution**<br>**Elasticity**|**Headcount (Base)**|**Headcount (20%)**|**Poverty Gap (Base)**|**Poverty Gap**<br>**(20%)**|
|---|---|---|---|---|---|
|–0.20|0.20|0.161|0.145|0.023|0.019|
|–0.50|0.50|0.161|0.133|0.023|0.017|
|–0.75|1.26|0.161|0.128|0.023|0.016|
|–2.00|2.00|0.161|0.126|0.023|0.015|
|–5.00|5.00|0.161|0.121|0.023|0.014|
|_Source_:Essa|ma-Nssah 2004.|||||

This approach increases computational efficiency in a simulation environment. Thus, within the reduced-form version of PAMS (PAMS II) that runs in EViews (version 4.1 or later), poverty and distributional outcomes of shocks and policies are derived by linking recursively an appropriately disaggregated CGE model with a poverty and inequality simulator built upon a parameterization of the Lorenz curve. The approach underlying PAMS II is analogous to the ERH approach described earlier. The main difference with other implementations of this approach stems from the fact that, in the 48