Session VII: Simulating the Distributional Impacts of the 1999 devaluation of the Brazilian Real

DEC Course on Poverty and Inequality Analysis

Module 7: Evaluating the Distributional and Poverty Impacts of Economy-wide Policies

Session VII: Simulating the Distributional Impacts of the 1999 devaluation of the Brazilian Real.

Francisco H. G. Ferreira

Can the distributional impacts of macroeconomic shocks be predicted?:

A comparison of top-down macro-micro models with historical data for Brazil

Francisco H. G. Ferreira, Philippe G. Leite, Luiz A. Pereira da Silva (World Bank) and Paulo Picchetti (Universidade de São Paulo)

Chapter 5 in Bourguignon, Bussolo and Pereira da Silva (eds.) 2008. The Impact of Macroeconomic Policies on Poverty and

Income Distribution (Washington, DC: Palgrave Macmillan and the World Bank)

The Brazilian Financial Crisis of 1999.

The “float” of the currency on January 15, 1999 average annual parity with the USD goes up from R$1.161 (annual 1998 average) to one USD to R$1.816 (annual 1999 average corresponding to a 56.4% devaluation)

A temporary rise the central bank policy rate (BACEN’s Selic) during the period corresponding to October 1998 till May 1999. Monthly rate raised from 1.47% in August 1998 up to 3.33% in March 1999 (corresponding to annualized rates of almost 50%).

SBA arrangement with the IMF credibility of the policy framework tightening in the fiscal stance corresponding to a reduction of the Consolidated Public Sector Borrowing requirements (PSBR) from 7.5% of GDP down to 5.8% of GDP, i.e. a cut of 1.7% of GDP).

Objective: impact evaluation of a program or policy

);(/);(

)0()1(:

pCPAELwRELwy

PyPyy

iiiiiiiii

iiiii

yi : real incomewi : wage rateLi : labor supplyEi : self-employment, non-wage incomeRi : net transfer incomeAi : socio-economic characteristicsCi : consumption characts. household-specific P price indexp : general price index

Define impact for individual i or household as the difference in income with and without the program

(policy):

Household Survey (HHS), i individual households

Compare the distribution of y|P=1 with the distribution of y|P=0.Calculate changes in inequality or poverty across the two distributions.

p

prices

w wage

C

filter

L employt.

R transfers

A households character.

)0()1(:

);(/);(

iiiii

iiiiiiiii

PyPyy

pCPAELwRELwy

Partial equilibrium independent shocks

Household Survey (HHS), i individual households

iii

iiiiiiiii

yyy

pCPAELwRELwy

1,

);(/);(

p

prices

w wage

L employt.

R transfers

A households character.

Macro framework, general/partial equilibrium

Evaluation of macro economic policies.Macro to micro linkages

Instead of « exogenous and independent » shocks use « endogenous and dependent» shocks to 'microsimulate' the effect of policies on all individuals in the micro data sets, and the poor

some consistency constraints will be « binding » (e.g., budget envelope for social programs, real GDP growth, etc.)

LAVs

Top-down macro-micro-simulation approach

Household occupational choice model

model

Household income determination model

model

With Sectoral Disaggregation to modelFactor Markets

General Equilibrium Macroeconomic Model

“Top” Level : Macro

“Bottom” Level : Micro

Linkage Aggregate Variables ( LAVs)

Individual /Household occupational choice modelIndividual/Household income determination model

Novel Aspects of the Linkage Exercise

Test top-down linkage with macro-econometric model on top (not CGE, confidence intervals for parameters) and micro-simulation at bottom

Changes in LAVs respond to “known” periodicity (e.g., annual) at the top (not “convergence process” of CGE)

Endogeneize in macro model key features of emerging markets:Structural features : e.g., increase of “informality” in labor market; usage of substitutable semi-skilled laborShocks: change in ERR portfolio choices by banks and holders of domestic debt financial crisis

If historical simulation (H) is robust , counterfactual (C) is possible as alternative macro-responses with different outcomes for poverty and distribution (compare program/policy (P) with counterf. (C)).

);(/);(

)()();()(:

pCPAELwRELwy

PPyCPyHPyPPyy

iiiiiiiii

iiiiiiiii

• Conventional IS-LM macroeconometric model with a disaggregated labor market and financial sector, estimated with 1981-2001 annual data or more (NA and historical HHS)

• Real economy: 6 sectors: Urban/Rural, Tradable/Non-Tradable, formal/Informal

• Labor market: 3 skills, skilled, semi-skilled and unskilled, mobile across skills; supply and demand modeled by sector and skill endogenous unemployment, supply side – sector specific production functions with three-level nested CES. Fernandes and Menezes-filho (2001) substitution between capital and labor and all kinds of labor, except between high-skill and low-skill labor.

• financial sector, see Bourguignon, Branson and de Melo [1989]: 8 assets: currency, deposits, bonds, dom. loans (debt) and equity shares; forex currency, forex loans to residents and forex bonds. 6 agents: households, private firms, commercial banks, Government, central bank and foreigners.

Bottom Layer - Micro-simulation model (Ferreira and Leite)

Brazil – Top Layer = Macroeconometric Model (Castro, Pereira da Silva and Picchetti [2003])

An overview of the macro model

UTF UNF UNI RTF RNF RNI

Labor Market

Financial Sector

Aggregation Matrix

RNI_Y

RNF_YRTF_Y

UNI_Y

UTF_Y

UNF_Y

Labor Income

DisposableIncome

- Taxes

PrivateConsumption

Government

Central Bank

Foreign Sector

PrivateConsumption

Imports

Exports

GovernmentConsumption

RNI_KRNF_K

RTF_KUNI_K

UNF_KUTF_K

Taxes

TransfersLoans

Payments

Loans

Payments

Reserves

Historical Simulation 96-2001 Macro Variables

.0052

.0056

.0060

.0064

.0068

.0072

90 91 92 93 94 95 96 97 98 99 00 01

Real GDP Real GDP (Baseline)

Real GDP

.0038

.0040

.0042

.0044

.0046

.0048

.0050

.0052

.0054

90 91 92 93 94 95 96 97 98 99 00 01

Actual AGG_YDISP_REAL (Baseline)

AGG_YDISP_REAL

.0030

.0032

.0034

.0036

.0038

.0040

.0042

.0044

90 91 92 93 94 95 96 97 98 99 00 01

Real Private Consumption ExpendituresAGG_HHS_CONS_REAL (Baseline)

Real Private Consumption Expenditures

.0008

.0009

.0010

.0011

.0012

.0013

.0014

90 91 92 93 94 95 96 97 98 99 00 01

Actual GOV_CONS_REAL (Baseline)

GOV_CONS_REAL

.00072

.00076

.00080

.00084

.00088

.00092

.00096

.00100

.00104

.00108

90 91 92 93 94 95 96 97 98 99 00 01

Real Gross Fixed Capital FormationReal Gross Fixed Capital Formation (Baseline)

Real Gross Fixed Capital Formation

-4

-2

0

2

4

6

8

90 91 92 93 94 95 96 97 98 99 00 01

Real GDP growth Real GDP growth (Baseline)

Real GDP growth

-15

-10

-5

0

5

10

15

90 91 92 93 94 95 96 97 98 99 00 01

ActualFBK_TOTAL_REAL_GROWTH (Baseline)

FBK_TOTAL_REAL_GROWTH

-4

-2

0

2

4

6

90 91 92 93 94 95 96 97 98 99 00 01

ActualHHS_CONS_REAL_GROWTH (Baseline)

HHS_CONS_REAL_GROWTH

0.0E+00

4.0E+07

8.0E+07

1.2E+08

1.6E+08

2.0E+08

90 91 92 93 94 95 96 97 98 99 00 01

Actual XBSZN (Baseline)

XBSZN

0.0E+00

4.0E+07

8.0E+07

1.2E+08

1.6E+08

2.0E+08

90 91 92 93 94 95 96 97 98 99 00 01

Actual MBSZN (Baseline)

MBSZN

-40000

-30000

-20000

-10000

0

10000

90 91 92 93 94 95 96 97 98 99 00 01

Actual BOP_CA (Baseline)

BOP_CA

-8000

-4000

0

4000

8000

12000

16000

90 91 92 93 94 95 96 97 98 99 00 01

Actual BOP_TB (Baseline)

BOP_TB

3000000

3500000

4000000

4500000

5000000

5500000

6000000

90 91 92 93 94 95 96 97 98 99 00 01

Actual AGG_NH_L (Baseline)

AGG_NH_L

1.6E+07

2.0E+07

2.4E+07

2.8E+07

3.2E+07

3.6E+07

90 91 92 93 94 95 96 97 98 99 00 01

Actual AGG_NI_L (Baseline)

AGG_NI_L

2.1E+07

2.2E+07

2.3E+07

2.4E+07

2.5E+07

2.6E+07

2.7E+07

2.8E+07

90 91 92 93 94 95 96 97 98 99 00 01

Actual AGG_NL_L (Baseline)

AGG_NL_L

24

26

28

30

32

34

90 91 92 93 94 95 96 97 98 99 00 01

Actual CARGA (Baseline)

CARGA

-4

-3

-2

-1

0

1

90 91 92 93 94 95 96 97 98 99 00 01

Actual FIN_TR_PRIM_Y (Baseline)

FIN_TR_PRIM_Y

0

5

10

15

20

25

30

90 91 92 93 94 95 96 97 98 99 00 01

Actual FIN_CG_INTPAY_Y (Baseline)

FIN_CG_INTPAY_Y

-6

-5

-4

-3

-2

-1

0

1

2

90 91 92 93 94 95 96 97 98 99 00 01

Actual FIN_PS_PRIM_Y (Baseline)

FIN_PS_PRIM_Y

0

100000

200000

300000

400000

500000

600000

700000

800000

90 91 92 93 94 95 96 97 98 99 00 01

Actual FIN_PS_INTPAY_REAL (Baseline)

FIN_PS_INTPAY_REAL

0

100000

200000

300000

400000

500000

600000

90 91 92 93 94 95 96 97 98 99 00 01

Actual FIN_GG_DBT_DOM (Baseline)

FIN_GG_DBT_DOM

-5

0

5

10

15

20

25

90 91 92 93 94 95 96 97 98 99 00 01

Actual FIN_CG_DBT_DOM_Y (Baseline)

FIN_CG_DBT_DOM_Y

-20

-10

0

10

20

30

40

90 91 92 93 94 95 96 97 98 99 00 01

Actual RER_DEV (Baseline)

RER_DEV

4

8

12

16

20

24

28

32

36

40

90 91 92 93 94 95 96 97 98 99 00 01

Real Interest Rate, Certificates of DepositReal Interest Rate, Certificates of Deposit (Baseline)

Real Interest Rate, Certificates of Deposit

0

10

20

30

40

50

60

70

80

90 91 92 93 94 95 96 97 98 99 00 01

Actual WC_REAL (Baseline)

WC_REAL

-30

-20

-10

0

10

20

30

40

90 91 92 93 94 95 96 97 98 99 00 01

Actual SELIC_REAL (Baseline)

SELIC_REAL

0

400

800

1200

1600

2000

2400

2800

90 91 92 93 94 95 96 97 98 99 00 01

Actual Baseline

INFL_GPIF

0

500

1000

1500

2000

2500

3000

90 91 92 93 94 95 96 97 98 99 00 01

Actual INFL_WPI (Baseline)

INFL_WPI

0

400

800

1200

1600

2000

2400

2800

90 91 92 93 94 95 96 97 98 99 00 01

Actual INFL_DEF_AGG_Y (Baseline)

INFL_DEF_AGG_Y

Sectoral Production Functions

1

(1 ) ay K L

1

(1 )a Q UL L L

1

(1 )Q iQ hL L L

1

(1 )U l iUL L L

Composite Labor: Qualified and Unqualified Jobs

Qualified Jobs: High and Intermediate Skill Workers

Unqualified Jobs: Intermediate and Low Skill Workers

Brazil, Financial Crisis Scenario – What we do:

Simulate 1999 financial Crisis with Macroeconometric Model 48 LAVs Run the micro-simulation modelER shock and policy rate change (1999) Run historical simulation with macroeconometric model generate 48 LAVs to feed microsimulation model

Depart from 1998 HHS (PNAD), use LAVs generated by macro model to simulate 1999, converge micro simulations to match macro generated LAVs

Compare results of combined micro-macro simulation with actual 1999 data from HHS (PNAD)

Types of simulation experiments undertaken

Top Level Macro ModelLinkage Aggregate Variables (LAVs)

Botom Level Micro Model

Experiment 1:Representative

Household Group (RHG)

No macro-simulation

LAVs : actual observed changes of average

income and employment for each RHG

No micro-simulation: Each individual receives the average income and

employment change of the RHG he/she belongs to

Experiment 2:

Pure Micro Simulation using

the Household Income Micro-

Simulation model

No macro-simulation No LAVs

Pure micro-simulation: micro model runs so that its

average results for each RHG converge to the actual

observed average income and employment change of

the economy's RHGs

Experiment 3:Full Macro-Micro Linkage model

Macro simulation: macro model runs to replicate the 1999 financial crisis

LAVs : simulated changes of average

income and employment for each RHG

Micro-simulation: micro model runs so that its average results for each RHG

converge to the simulated average income and

employment change of the model's RHGs

The Household-Level Data and the Micro-econometric model

Data Set: Pesquisa Nacional por Amostra de Domicílios (PNAD) 1998 & 1999

Main variablesearnings occupation total household income per capita

Insufficient detail on capital incomes, production for own consumption and incomes in kind


The model consists of three equations:Occupational Choice

sjzzII ihjjihihssihsj

sk

ZZ

Z

his

khishi

shi

ee

eZPsj

),(Pr


Earnings equation

Household income aggregation

ihgihgsih xw log

hn

i sh

sihs

hh ywI

ny

1

3

10

1

Recall: Structure of the micro-macro model

Household occupational choice model

model

Household income determination model

model

With Sectoral Disaggregation to modelFactor Markets

General Equilibrium Macroeconomic Model

“Top” Level : Macro

“Bottom” Level : Micro

Linkage Aggregate Variables ( LAVs)

Individual /Household occupational choice modelIndividual/Household income determination model

Recall: The LAV structure(One for Urban; one for Rural)

SectorsFormalTradable

FormalNon-Tradable

Informal

Unemployed

HH Low Skill

f w f w f w f

Groups

Int.Skill

f w f w f w f

HighSkill

f w f w f w f

Employment: Actual and Simulated

Units of workers

Percentage of workers by

category

Units of workers


category

Percent Change in

each category

(LAVs as in Table 3)

Units of workers


category

Percent Change in

each category

predicted by Macro-Micro

model

Units of workers


category

Actuals Oberved Changes

(True LAVs)

50,553,471 51,620,283 51,636,813 51,936,699

Urban sector 48,890,805 51,752,096 5.85% 51,749,274 5.85% 51,936,699 6.23%

Low skill 17,979,587 100.0% 18,043,135 100.0% 18,047,040 100.0% 0.38% 17,796,772 100.0% -1.02%

unemployed 1,510,124 8.4% 1,623,210 9.0% 7.49% 1,623,511 9.0% 7.51% 1,623,210 9.1% 7.49%

formal tradable sector 2,215,668 12.3% 2,112,696 11.7% -4.65% 2,112,601 11.7% -4.65% 2,097,070 11.8% -5.35%

formal non tradable sector 3,492,153 19.4% 3,098,839 17.2% -11.26% 3,106,724 17.2% -11.04% 3,316,862 18.6% -5.02%

Informal sector 10,761,642 59.9% 11,208,390 62.1% 4.15% 11,204,204 62.1% 4.11% 10,759,630 60.5% -0.02%

Intermediate skill 27,447,162 100.0% 28,290,953 100.0% 28,306,510 100.0% 3.13% 28,930,933 100.0% 5.41%

unemployed 3,723,117 13.6% 4,265,261 15.1% 14.56% 4,260,740 15.1% 14.44% 4,265,261 14.7% 14.56%

formal tradable sector 4,405,361 16.1% 4,556,787 16.1% 3.44% 4,550,183 16.1% 3.29% 4,524,002 15.6% 2.69%

formal non tradable sector 8,059,227 29.4% 7,872,205 27.8% -2.32% 7,890,490 27.9% -2.09% 8,145,375 28.2% 1.07%

Informal sector 11,259,457 41.0% 11,596,700 41.0% 3.00% 11,605,097 41.0% 3.07% 11,996,295 41.5% 6.54%

High skill 5,126,722 100.0% 5,286,195 100.0% 5,283,263 100.0% 3.05% 5,208,994 100.0% 1.60%

unemployed 322,980 6.3% 381,562 7.2% 18.14% 378,920 7.2% 17.32% 381,562 7.3% 18.14%

formal tradable sector 754,070 14.7% 782,972 14.8% 3.83% 784,868 14.9% 4.08% 750,564 14.4% -0.46%

formal non tradable sector 2,421,679 47.2% 2,323,764 44.0% -4.04% 2,322,273 44.0% -4.10% 2,362,070 45.3% -2.46%

Informal sector 1,627,993 31.8% 1,797,897 34.0% 10.44% 1,797,202 34.0% 10.39% 1,714,798 32.9% 5.33%

1998 Actual from PNAD

1999 simulated by the Macro Model

only

1999 simulated by the Micro-Macro

Model1999 Actual from PNAD

(A) (B) (C) (D)

Overall Occupational / Employment with the

unemployed

Wages: Actual and Simulated


1999 simulated by

the Macro Model only

LAVs as in Table 5

Actuals Oberved Changes

(True LAVs)


1999 simulated by

the Micro-Macro Model

(A) (B) (C) (D) (E) (F)

Urban SectorLow skill

formal tradable 453.16 449.94 -0.71% -0.55% 450.67 450.63 formal non tradable 385.45 439.01 13.90% 4.96% 404.56 439.26 Informal 264.81 258.76 -2.29% -1.86% 259.90 258.36 average for the category 316.37 317.38 0.32% -1.12% 312.84 317.60

Intermediate skillformal tradable 627.56 541.31 -13.74% 14.56% 605.48 542.23

formal non tradable 545.90 548.47 0.47% 0.26% 547.30 548.57 Informal 398.90 385.44 -3.37% -2.62% 388.45 384.54 average for the category 492.39 468.42 -4.87% -2.84% 478.40 468.28

High skillformal tradable 2,011.47 1,869.99 -7.03% -0.72% 1,984.81 1,876.71 formal non tradable 1,761.17 1,678.20 -4.71% -4.46% 1,682.62 1,682.82 Informal 1,391.40 1,315.27 -5.47% -4.68% 1,326.29 1,319.82 average for the category 1,677.40 1,575.78 -6.06% -4.31% 1,605.11 1,581.12

Wage (non-zero earnings) in nominal BRL per

month

Wage (non-zero earnings) in nominal BRL per

month

Linkage Aggregate Variables (LAVs) in

percent change for each category for 1999/1999

Micro-simulations

Solution of system of 42 equations

gsfee

esj g

s

sk

ZZ

Z

gkhishi

shi

,......Pr

gxExp gg

gi gsihgihgs

Wg..ˆ

Simulation

Solve the system of 42 equations changing all constant (0 and ) terms.

Calibrated so that micro-simulation reproduces changes in aggregate structure of employment obtained in macro-economic framework.Newton-Rapshon algorithm.Minimize the sum of squared differences between the left- and the right-hand side of equations.

Results: Earnings (I)

Figure 5 - Comparison between

Actual Observed Changes & Experiment 1 - using Representative Households Groups (RHG)

-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

0 10 20 30 40 50 60 70 80 90 100

Percentiles

Lo

g d

iffe

ren

ce

Actual Experiment 1 - RHG

Percent changes between 1999 and 1998 in Nominal Income (in Reais, R$) / Month for each percentile of the distribution in Brazil

Results: Earnings (II)

Figure 6 - Comparison between


Experiment 2- using Pure Micro Simulation model

-12.00%

-10.00%

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

0 10 20 30 40 50 60 70 80 90 100

Percentiles

Lo

g d

iffe

ren

ce

Actual Experiment 1 - RHG Experiment 2 Pure Micro-Simulation


Results: Earnings (III)Figure 7 - Comparison between


Experiment 2- using Pure Micro Simulation modelExperiment 3 - using Full Macro-Micro Linkage model

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0 10 20 30 40 50 60 70 80 90 100

Percentiles

Lo

g d

iffe

ren

ce

Actual Experiment 1 - RHG

Experiment 2 - Pure Micro Simulation Experiment 3 - Full Macro-Micro Linkage


Results: Aggregate Poverty and Inequality Indices

IndicatorsActual for 1998

from PNAD

Experiment 1: Representative

Household Group (RHG)

Experiment 2: Pure Micro

Simulation using the Household Income Micro-

Simulation model

Experiment 3: Full Macro-Micro Linkage model

Actual for 1999 from PNAD

p0 (Headcount)

28.1% 29.9% 29.8% 30.0% 29.2%

p1 11.6% 12.4% 12.5% 12.5% 12.1%p2 6.5% 6.9% 7.0% 7.0% 6.7%e0 0.662 0.639 0.657 0.655 0.645e1 0.715 0.694 0.708 0.709 0.693e2 1.731 1.661 1.704 1.710 1.567

Gini 0.593 0.585 0.590 0.591 0.587

Mean (Monthly average Income in nominal BRL)

257.31 250.65 256.79 255.56 258.18

Population 151 150 153 156 154

Winners and Losers

Figure 8: Comparison betweenActual Observed Changes &

Winners and Losers

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

0 10 20 30 40 50 60 70 80 90 100

Percentiles

Lo

g d

iffe

ren

ce

RHG-Observed Micro-Macro Model-Observed Micro-Macro Model-Simulated

Conclusions: Occupations

The macro-micro model captures a great deal of the occupational effect of the 1999 crisis on the occupational structure in Brazil.

a significant increase (+12.8% / +12.7%) in unemployment in both rural and urban areasa rise in unemployment particularly large for workers with intermediate and high skill levels in urban areas (+14.6% / +14.4 and +18.1% / +17.3% respectively)a decline in the employment of urban workers with low skills (-1.8% / -0.3%)an increase in the level of informality in both rural and urban areas (+1.1% / +0.1% and +3.5% / 4.0% respectively)a growth of informality in particular in urban areas for workers with intermediate and high levels of skills (+6.5% / +3.1% and +5.3% / +10.4% respectively)

Conclusions: Earnings

The model underestimates slightly changes in earnings for all but one category of workers (i.e. the workers with intermediate level of skills in the formal tradable sector)

Overall, the macro-micro model captures also a great deal of the actually observed changes in (nominal) earnings in Brazil from 1998 to 1999. Mean earnings fell for all three urban categories of workers; by –1.12% (+0.32%) for workers with low skill level; by –2.84% (-4.87%) for workers with intermediate skill level; by –4.31% (-6.06%) for workers with high skill level;The picture is more mixed in rural areas. There, the only winners among low-skilled workers were those employed in the formal non-tradable and the informal sectors (and this is well predicted by the model). The main losers (-4.04%) among intermediate and high skilled workers were those in the formal tradable sector (and this is predicted by the model, -7.78%). And the main winners (+12.07%) among intermediate and high skilled workers were those in the formal tradable sector (and this is over-predicted by the model, 29.33%).

Conclusions

Occupations: predictive performance of the macro-micro model is relatively goodEarnings: less satisfactory. Under-prediction of declines in wages.

May be due to an insufficient disaggregation of the wage LAVs across occupations, or to the functional form of factor remuneration –negatively affected by lower economic activity and rise in unemployment-- in the macroeconomic modeling.

The end result in terms of the counterfactual income distribution for Brazil in 1999: compensating errors, leading to a relatively good prediction of the poverty and inequality levelsRHGs worse than macro-micro approach

Documents

Session VII: Simulating the Distributional Impacts of the 1999 devaluation of the Brazilian Real