# **Box 9: The IMMPA Model: Adjustment Policies and the Poor in Brazil**

Brazil has made significant progress in controlling inflation and achieving stabilization since the Real Plan of 1994. Despite the 1999 currency crisis, which put pressure on the exchange rate and led to the adoption of a flexible exchange rate regime, inflation remained low. This stabilization was achieved through a prudent fiscal policy and active management of short-term interest rates by the central bank.

Agénor and others (forthcoming) provide an interpretation of the IMMPA framework to analyze the impact of adjustment policies on poverty and income distribution in Brazil. The interpretation involves extensions to the prototype framework in order to capture important characteristics specific to the Brazilian economy. In particular, the Brazilian model allows for open unskilled urban unemployment, a distinction between product wage and consumption wage in the determination of skilled worker wages, the possibility of congestion effects linked to the use of public services in urban areas, and bond financing of public sector deficits, while excluding borrowing from the central bank.

The poverty and distributional implications of a 10 percent increase in the interest rate are presented in the table below. In the short run, the poverty headcount index increases only slightly in both rural and urban areas. However, in the long run, the increase in the official interest rate would cause the headcount index to rise by 2 percent in urban areas and 3 percent in rural areas. The households engaged in urban informal activities are the hardest hit group. Poverty among this group increases by about 5 percent. Income inequality increases as well. Overall, higher interest rates (a tight monetary policy) will lower inflation, but at the expense of greater poverty and income inequality.

**Brazil Simulation Results: Impact of a 10 Percentage Point Increase in the Interest Rate**

|**Poverty and Distributional**|**Period 1**|**Period 2**|**Period 3**|**Period 8**|**Period 9**|**Period 10**|
|---|---|---|---|---|---|---|
|**Indicators, Consumption Based**|||||||
|**Poverty headcount**|||||||
|Rural households|–0.1|0.1|0.2|1.5|2.6|3.0|
|Urban households|–0.6|0.0|–0.1|1.0|1.5|2.2|
|Informal|–0.1|0.0|0.1|2.5|3.3|4.9|
|Formal unskilled|–1.8|–0.1|0.0|0.5|0.8|0.9|
|Formal skilled|–0.1|–0.1|–0.4|0.0|0.2|0.3|
|Capitalists and rentiers|0.5|0.5|0.0|–1.0|–1.0|–0.7|
|Economy|–0.5|0.0|0.0|1.1|1.7|2.3|
|**Distributional indicators**|||||||
|Gini coefficient|–2.3|–0.3|0.0|3.8|4.6|5.3|
|Theil index|–1.2|–0.3|–0.1|3.7|4.9|6.2|

_Source_ : Agénor and others, forthcoming.

(6) capitalists. The macroeconomic model is linked to unit record data in a way that is analogous to impact analysis with PovStat. Following a shock to the model economy, the macro-model generates real growth rates in per capita consumption and disposable income for all six household categories and for each year in the simulation horizon (up to 10 years). These growth rates are then used to scale per capita consumption and disposable income appropriately in each group. New poverty and inequality indicators (the Foster–Greer–Thorbecke family, the Gini, and the Theil) are computed and compared to the baseline values. The difference is attributed to the shock. In this dynamic 53