Transcript Simulating the Distributional Impacts of the 1999

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
Define impact for individual i or household as the difference in
income with and without the program (policy):
yi : ( yi Pi  1)  ( yi Pi  0)
yi  wi Li  Ei  R( wi Li  Ei ; Ai ) / P (Ci ; p )
yi
wi
Li
Ei
Ri
Ai
Ci
p
:
:
:
:
:
:
:
:
real income
wage rate
labor supply
self-employment, non-wage income
net transfer income
socio-economic characteristics
consumption characts.  household-specific P price index
general price index
C
p
prices
w
R
L
transfers
employt.
wage
A
households
character.
  filter
Household Survey (HHS), i individual households
yi  wi Li  Ei  R( wi Li  Ei ; Ai ) / P (Ci ; p )
yi : ( yi Pi  1)  ( yi Pi  0)
Compare the distribution of y|P=1 with the distribution of y|P=0.
Calculate changes in inequality or poverty across the two distributions.
Partial equilibrium independent shocks
Evaluation of macro economic policies.
Macro to micro linkages
Macro framework, general/partial equilibrium
p
prices
w
L
R
wage
employt.
transfers
A
households
character.
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
Household Survey (HHS), i individual households
yi  wi Li  Ei  R( wi Li  Ei ; Ai ) / P(Ci ; p)
yi  yi , 1  yi
Top-down macro-micro-simulation approach
“Top” Level : Macro
General Equilibrium Macroeconomic Model
With Sectoral Disaggregation to model
Factor Markets
Linkage Aggregate Variables ( LAVs )
“Bottom” Level : Micro
Individual /Household occupational choice model
Household income determination model
Household occupational choice
model
Individual/Household
income
determination
model
model
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 labor
Shocks: 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)).
yi : ( yi Pi  P)  ( yi Pi  H ); ( yi Pi  C )  ( yi Pi  P)
yi  wi Li  Ei  R ( wi Li  Ei ; Ai ) / P(Ci ; p )
Brazil – Top Layer = Macroeconometric Model
(Castro, Pereira da Silva and Picchetti [2003])
•
•
•
•
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 threelevel 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)
An overview of the macro model
Labor Market
UTF
UNF
UNI
RNF
RTF
Labor Income
- Taxes
RNI
Taxes
Loans
Payment
s
RTF_Y
UNF_Y
UTF_Y
UNI_Y
UNI_K
RNF_Y
RTF_K
UNF_K
UTF_K
Aggregation Matrix
Transfer
s
Disposable
Income
Government
RNI_K
RNF_K
Financial Sector
RNI_Y
Loans
Government
Consumption
Exports
Payment
s
Imports
Central Bank
Private
Consumption
Foreign Sector
Private
Consumption
Reserve
s
Historical Simulation 96-2001 Macro Variables
Real GDP
AGG_YDISP_REAL
.0072
Real Private Consumption Expenditures
.0054
.0044
.0052
.0068
Real Gross Fixed Capital Formation
.00108
.0013
6
.00100
.0040
.0012
.0048
.00096
.0038
.0011
2
.00088
.0036
.0044
91
92
93
94
95
Real GDP
96
97
98
99
00
01
91
92
93
94
Actual
15
10
95
96
97
98
99
00
01
-2
.00076
.0030
90
Real GDP (Baseline)
FBK_TOTAL_REAL_GROWTH
0
.00080
.0038
90
.00084
.0009
.0032
.0040
.0052
.0010
.0034
.0042
.0056
4
.00092
.0046
.0060
Real GDP growth
8
.00104
.0042
.0050
.0064
GOV_CONS_REAL
.0014
.0008
90
91
92
AGG_YDISP_REAL (Baseline)
93
94
95
96
97
98
99
00
01
.00072
90
91
Real Private Consumption Expenditures
AGG_HHS_CONS_REAL (Baseline)
HHS_CONS_REAL_GROWTH
92
93
94
Actual
95
96
97
98
99
00
01
-4
90
91
GOV_CONS_REAL (Baseline)
XBSZN
92
93
94
95
96
97
98
99
00
01
90
Real Gross Fixed Capital Formation
Real Gross Fixed Capital Formation (Baseline)
MBSZN
91
92
93
BOP_CA
6
2.0E +08
2.0E +08
4
1.6E +08
1.6E +08
0
2
1.2E +08
1.2E +08
-10000
0
8.0E +07
8.0E +07
-20000
-2
4.0E +07
4.0E +07
-30000
94
95
Real GDP growth
96
97
98
99
00
01
Real GDP growth (Baseline)
BOP_TB
10000
16000
12000
5
8000
0
4000
-5
0
-10
-15
-4
90
91
92
93
94
95
96
97
98
99
00
01
0.0E +00
90
91
Actual
FBK_TOTAL_REAL_GROWTH (Baseline)
92
93
94
95
96
97
98
99
00
01
0.0E +00
90
91
92
Actual
HHS_CONS_REAL_GROWTH (Baseline)
AGG_NH_L
93
AGG_NI_L
6000000
94
95
Actual
96
97
98
99
00
01
-40000
90
91
92
XBSZN (Baseline)
93
94
95
Actual
AGG_NL_L
3.6E +07
-4000
96
97
98
99
00
01
-8000
90
91
93
94
95
Actual
CARGA
2.8E +07
92
MBSZN (Baseline)
96
97
98
99
00
01
3.2E +07
FIN_TR_PRIM_Y
34
1
32
0
2.8E +07
30
-1
28
-2
26
-3
92
93
94
95
96
97
98
99
00
01
00
01
BOP_TB (Baseline)
FIN_CG_INTPAY_Y
30
25
2.6E +07
5000000
91
Actual
2.7E +07
5500000
90
BOP_CA (Baseline)
20
2.5E +07
4500000
15
2.4E +07
2.4E +07
4000000
10
2.3E +07
2.0E +07
3500000
5
2.2E +07
3000000
1.6E +07
90
91
92
93
94
Actual
95
96
97
98
99
00
01
2.1E +07
90
91
92
93
AGG_NH_L (Baseline)
94
Actual
FIN_PS_PRIM_Y
95
96
97
98
99
00
01
24
90
91
AGG_NI_L (Baseline)
800000
1
700000
0
600000
-1
500000
-2
400000
-3
300000
-4
200000
-5
100000
-6
0
93
94
95
Actual
FIN_PS_INTPAY_REAL
2
92
96
97
98
99
00
01
-4
90
91
92
AGG_NL_L (Baseline)
93
94
95
Actual
FIN_GG_DBT_DOM
96
97
98
99
00
01
0
90
91
CARGA (Baseline)
92
93
94
Actual
FIN_CG_DBT_DOM_Y
95
96
97
98
99
00
01
25
40
500000
20
30
400000
15
20
300000
10
10
200000
5
0
100000
0
-10
0
-5
-20
91
92
93
Actual
RER_DEV
600000
90
FIN_TR_PRIM_Y (Baseline)
94
95
96
97
98
99
FIN_CG_INTPAY_Y (Baseline)
Real Interest Rate, Certificates of Deposit
40
36
32
28
24
20
90
91
92
93
94
Actual
95
96
97
98
99
00
01
12
8
90
91
FIN_PS_PRIM_Y (Baseline)
92
93
Actual
WC_REAL
94
95
96
97
98
99
00
01
90
91
FIN_PS_INTPAY_REAL (Baseline)
92
SELIC_REAL
94
95
96
97
98
99
00
01
90
91
FIN_GG_DBT_DOM (Baseline)
92
93
2800
30
2400
20
2000
10
1600
94
Actual
95
96
97
98
99
00
01
4
90
91
92
FIN_CG_DBT_DOM_Y (Baseline)
INFL_GPIF
40
70
50
93
Actual
80
60
16
93
94
Actual
INFL_WPI
95
96
97
98
99
00
01
RER_DEV (Baseline)
2800
2500
2400
2000
2000
1600
40
1500
0
1200
1200
30
-10
20
800
-20
10
0
91
92
93
94
Actual
95
96
97
98
99
WC_REAL (Baseline)
00
01
0
90
91
92
93
Actual
94
95
96
97
98
99
SELIC_REAL (Baseline)
00
01
800
500
400
-30
90
1000
400
0
90
91
92
93
94
95
Actual
96
97
98
Baseline
99
00
01
0
90
91
92
93
94
Actual
95
96
97
98
99
INFL_WPI (Baseline)
00
01
90
91
92
93
Actual
94
95
96
97
98
99
91
92
93
94
95
96
97
98
99
00
01
Real Interest Rate, Certificates of Deposit
Real Interest Rate, Certificates of Deposit (Baseline)
INFL_DEF_AGG_Y
3000
90
00
INFL_DEF_AGG_Y (Baseline)
01
Sectoral Production Functions
y    K

La   L

Q
 (1   )L 

a
 (1   )L 

U

1

1


Composite Labor: Qualified and Unqualified Jobs
LQ   L

iQ
 (1   )L 

h

1

Qualified Jobs: High and Intermediate Skill Workers
LU   L

l
 (1   )L 

iU

1

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 model
ER 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
Representative
Experiment 1: Household Group
(RHG)
Experiment 2:
Experiment 3:
Pure Micro
Simulation using
the Household
Income MicroSimulation model
Full Macro-Micro
Linkage model
Top Level Macro Model
Linkage Aggregate
Variables (LAVs)
Botom Level Micro Model
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
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
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
No macro-simulation
Macro simulation: macro
model runs to replicate
the 1999 financial crisis
The Household-Level Data and the Micro-econometric
model
Data Set: Pesquisa Nacional por Amostra de
Domicílios (PNAD) 1998 & 1999
Main variables
earnings
occupation
total household income per capita
Insufficient detail on capital incomes, production for
own consumption and incomes in kind
The Household-Level Data and the Micro-econometric
model
The model consists of three equations:
Occupational Choice

I j  s  I z ih  s  ihs  z ih  j  ihj j  s
Pr j  s  P ( Z hi ,  ) 
e
s
e
Z hi  s
Z hi  s
 e
k s
Z hi  k

The Household-Level Data and the Micro-econometric
model
Earnings equation
log wih   gs  xih  g   ih
Household income aggregation
yh

1  nh 3
s

   I s w ih  y 0 h 
n h  i 1 s 1

Recall: Structure of the micro-macro model
“Top” Level : Macro
General Equilibrium Macroeconomic Model
With Sectoral Disaggregation to model
Factor Markets
Linkage Aggregate Variables ( LAVs )
“Bottom” Level : Micro
Individual /Household occupational choice model
Household income determination model
Household occupational choice
model
Individual/Household
income
determination
model
model
model
Recall: The LAV structure
(One for Urban; one for Rural)
Sectors
Formal
Tradable
Formal
Non-Tradable
Informal
Unemployed
HH
Low
Skill
f
w
f
w
f
w
f
Groups
Int.
Skill
f
w
f
w
f
w
f
High
Skill
f
w
f
w
f
w
f
Employment: Actual and Simulated
Overall Occupational /
Employment with the
unemployed
1998 Actual from
PNAD
1999
simulated by the Macro Model
only
1999
simulated by the Micro-Macro
Model
1999 Actual from PNAD
(A)
(B)
(C)
(D)
Units of
workers
Percentage
of workers by
category
50,553,471
Urban sector
Low skill
unemployed
formal tradable sector
formal non tradable sector
Informal sector
Intermediate skill
unemployed
formal tradable sector
formal non tradable sector
Informal sector
High skill
unemployed
formal tradable sector
formal non tradable sector
Informal sector
Units of
workers
Percent
Change in
Percentage
each
of workers by
category
category
(LAVs as in
Table 3)
51,620,283
48,890,805
Units of
workers
Percent
Change in
Percentage
each
of workers by category
category
predicted by
Macro-Micro
model
51,636,813
51,752,096
17,979,587
1,510,124
2,215,668
3,492,153
10,761,642
100.0%
8.4%
12.3%
19.4%
59.9%
18,043,135
1,623,210
2,112,696
3,098,839
11,208,390
100.0%
9.0%
11.7%
17.2%
62.1%
27,447,162
100.0%
28,290,953
100.0%
3,723,117
4,405,361
8,059,227
11,259,457
13.6%
16.1%
29.4%
41.0%
4,265,261
4,556,787
7,872,205
11,596,700
15.1%
16.1%
27.8%
41.0%
5,126,722
100.0%
5,286,195
100.0%
322,980
754,070
2,421,679
1,627,993
6.3%
14.7%
47.2%
31.8%
381,562
782,972
2,323,764
1,797,897
7.2%
14.8%
44.0%
34.0%
Units of
workers
Percentage
of workers by
category
Actuals
Oberved
Changes
(True LAVs)
51,936,699
5.85%
51,749,274
7.49%
-4.65%
-11.26%
4.15%
18,047,040
1,623,511
2,112,601
3,106,724
11,204,204
100.0%
9.0%
11.7%
17.2%
62.1%
0.38%
7.51%
-4.65%
-11.04%
4.11%
17,796,772
1,623,210
2,097,070
3,316,862
10,759,630
100.0%
9.1%
11.8%
18.6%
60.5%
-1.02%
7.49%
-5.35%
-5.02%
-0.02%
28,306,510
100.0%
3.13%
28,930,933
100.0%
5.41%
4,260,740
4,550,183
7,890,490
11,605,097
15.1%
16.1%
27.9%
41.0%
14.44%
3.29%
-2.09%
3.07%
14.7%
15.6%
28.2%
41.5%
14.56%
2.69%
1.07%
6.54%
5,283,263
100.0%
3.05%
100.0%
1.60%
378,920
784,868
2,322,273
1,797,202
7.2%
14.9%
44.0%
34.0%
17.32%
4.08%
-4.10%
10.39%
7.3%
14.4%
45.3%
32.9%
18.14%
-0.46%
-2.46%
5.33%
14.56%
3.44%
-2.32%
3.00%
18.14%
3.83%
-4.04%
10.44%
5.85%
51,936,699
4,265,261
4,524,002
8,145,375
11,996,295
5,208,994
381,562
750,564
2,362,070
1,714,798
6.23%
Wages: Actual and Simulated
Wage (non-zero earnings)
in nominal BRL per
month
1999
1998 Actual simulated by
from PNAD
the Macro
Model only
(A)
(B)
Linkage Aggregate
Variables (LAVs) in
percent change for each
category for 1999/1999
Wage (non-zero earnings)
in nominal BRL per
month
LAVs as in
Table 5
Actuals
Oberved
Changes
(True LAVs)
1999
1999 Actual simulated by
from PNAD
the MicroMacro Model
(C)
(D)
(E)
(F)
Urban Sector
Low skill
formal tradable
formal non tradable
Informal
average for the category
453.16
385.45
264.81
449.94
439.01
258.76
-0.71%
13.90%
-2.29%
-0.55%
4.96%
-1.86%
450.67
404.56
259.90
450.63
439.26
258.36
316.37
317.38
0.32%
-1.12%
312.84
317.60
627.56
545.90
398.90
541.31
548.47
385.44
-13.74%
0.47%
-3.37%
14.56%
0.26%
-2.62%
605.48
547.30
388.45
542.23
548.57
384.54
492.39
468.42
-4.87%
-2.84%
478.40
468.28
2,011.47
1,761.17
1,391.40
1,869.99
1,678.20
1,315.27
-7.03%
-4.71%
-5.47%
-0.72%
-4.46%
-4.68%
1,984.81
1,682.62
1,326.29
1,876.71
1,682.82
1,319.82
1,677.40
1,575.78
-6.06%
-4.31%
1,605.11
1,581.12
Intermediate skill
formal tradable
formal non tradable
Informal
average for the category
High skill
formal tradable
formal non tradable
Informal
average for the category
Micro-simulations
Solution of system of 42 equations
Prg  j  s 

e
e
Z h is
Z h is
 f s ......s, g
g
 e
k s
Z h ik

  Exp ˆ gs  x ih  g   ih  p g w g Wg.. g
ig sg
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)
8.0%
6.0%
4.0%
Log difference
2.0%
0.0%
0
10
20
30
40
50
60
70
-2.0%
-4.0%
-6.0%
Percent changes between 1999 and 1998 in Nominal Income (in Reais, R$) / Month
for each percentile of the distribution in Brazil
-8.0%
Actual
Experiment 1 - RHG
-10.0%
-12.0%
Percentiles
80
90
100
Results: Earnings (II)
Figure 6 - Comparison between
Actual Observed Changes &
Experiment 1 - using Representative Households Groups (RHG)
Experiment 2- using Pure Micro Simulation model
8.00%
6.00%
4.00%
Log difference
2.00%
0.00%
0
10
20
30
40
50
60
70
80
-2.00%
-4.00%
-6.00%
Percent changes between 1999 and 1998 in Nominal Income (in Reais, R$) / Month
for each percentile of the distribution in Brazil
-8.00%
-10.00%
Actual
Experiment 1 - RHG
-12.00%
Percentiles
Experiment 2 Pure Micro-Simulation
90
100
Results: Earnings (III)
Figure 7 - Comparison between
Actual Observed Changes &
Experiment 1 - using Representative Households Groups (RHG)
Experiment 2- using Pure Micro Simulation model
Experiment 3 - using Full Macro-Micro Linkage model
0.08
0.06
0.04
Log difference
0.02
0
0
10
20
30
40
50
60
70
80
-0.02
-0.04
-0.06
-0.08
-0.1
Percent changes between 1999 and 1998 in Nominal Income (in Reais, R$) / Month
for each percentile of the distribution in Brazil
-0.12
Percentiles
Actual
Experiment 1 - RHG
Experiment 2 - Pure Micro Simulation
Experiment 3 - Full Macro-Micro Linkage
90
100
Results: Aggregate Poverty and Inequality Indices
Indicators
p0
(Headcount)
p1
p2
e0
e1
e2
Gini
Actual for 1998
from PNAD
Experiment 2:
Pure Micro
Experiment 1:
Simulation using Experiment 3:
Representative
Actual for 1999
the Household Full Macro-Micro
Household
from PNAD
Income MicroLinkage model
Group (RHG)
Simulation
model
28.1%
29.9%
29.8%
30.0%
29.2%
11.6%
6.5%
0.662
0.715
1.731
0.593
12.4%
6.9%
0.639
0.694
1.661
0.585
12.5%
7.0%
0.657
0.708
1.704
0.590
12.5%
7.0%
0.655
0.709
1.710
0.591
12.1%
6.7%
0.645
0.693
1.567
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 between
Actual Observed Changes &
Winners and Losers
6.00%
4.00%
Log difference
2.00%
0.00%
0
10
20
30
40
50
60
70
80
-2.00%
-4.00%
-6.00%
Percentiles
RHG-Observed
Micro-Macro Model-Observed
Micro-Macro Model-Simulated
90
100
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 areas
a 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 overpredicted by the model, 29.33%).
Conclusions
Occupations: predictive performance of the macromicro model is relatively good
Earnings: 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 levels
RHGs worse than macro-micro approach