Result Based Transfers
Marcos C. Holanda
Universidade Federal do Ceará
[email protected]
Result Based Transfer
• Fiscal transfers are the oxygen that keep political unions
alive.
• Fiscal Transfers:
– First generation  “The King” will
– Second generation  Output based
– Third generation  Result Based
• RBT is the big “carrot” ( stick, carrot and sermons) of RBM
- Result Based Management.
Result Based Transfer
RBT are a powerful tool to promote the key message
of RBM:
What matters is not what you did but what you
delivered.
Besides that it change the incentive:
FROM : If a get worse I get more
TO : If a get better I get more
Result Based Transfer
• An example of RBT is a New Law, signed by the Governor of the State of
Ceará on December 17, 2007, that regulate the way the State distribute
part of its VAT revenue among its municipalities.
• In Ceara, as in the rest of Brazil, we have:
75% to State
ICMS (VAT)
25% to Municipalities
75% → Defined by Federal
Law
(Econômic Activity)
25% → Defined by State Law
(Selected Variables)
*ICMS is the denomination for the State’s Value Added Tax .
Law
ICMS Law
• The old law:
5% → Municipality Population
25% → Defined by State Law
(Selected Variables)
12,5% → Expenditure on Education
7,5% → Equally distributed
• The new law
25% → Defined by State Law
18% → Education (performance of
students on standardized exams)
5% → Health (Infant Mortality Rate)
(Selected Variables)
2% → Enviroment (Appropriate Waste
Disposal System)
ICMS Law
• Education:
 Indicators considered in the coeficient:
• Student approval rate in first five grades of elementary school
• Average grades of Second Year Students in reading exams
• Average grades of Fifth Year Students in math and portuguese
 Formula:
Level
Advance



Approv i
Scorei
Scorei 
Coeficienti  0,2
 0,8 0,4 
 0,6 

Approv
Score
Scorei 
i
i
i
i


i


* Score is adjusted for Standard Deviation and the proportion of students
that were tested
 Health:
 Indicator considered in the coeficient: Infant Mortality Rate (IMR)
 Formula:
Level
Coeficienti  0,5
Advance
DMi
DMi
 0,5
 DMi
 DMi
i
i
DMi  100  IMR i
Controls for potencial
“gaming” behavior
Groups of students
Municipality A
Municipality B
Results
Only some groups receive
higher attention, which can
raise the average of the
municipalities in detriment
of the majority of students.
All groups receive attention,
reducing the dispersion
between the results of
students.
Average Grade = 200
Standard Deviation = 50
AG
0,5  SD
8
Average Grade = 150
Standard Deviation = 30
AG
0,5  SD
 10
Controls for potencial
“gaming” behavior
Students enrolled
(NE)
Students Assessed
(NA)
Results
Average Grade = 200
Municipality A
NA/NE = 0 ,50
AG 
NA
N E  100
Average Grade = 150
NA/NE = 1
Municipality B
AG 
NA
N E  150
Convergence
Municipality Share
0.4
0.35
0.3
%
0.25
COEF. 2008
0.2
COEF. 2009
0.15
COEF. 2010
0.1
0.05
0
0
20
40
60
80
100
Municipality
120
140
160
180
200
Robin Hood Effect
200.00%
% Var. mun. Coeficiente
150.00%
100.00%
50.00%
0.00%
0.00000
0.05000
0.10000
0.15000
0.20000
-50.00%
-100.00%
Mun. Coeficient
0.25000
0.30000
0.35000
0.40000
Robin Hood Effect
350
300
250
% Var. IQE
200
150
100
50
0
0
5000
10000
15000
-50
-100
Per-capita GDP
20000
25000
Gainers & Losers
2008 - 2009 - 2010
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
106
111
116
121
126
131
136
141
146
151
156
161
166
171
176
181
Gainers & Losers
200%
Net G-L in the share.
150%
100%
50%
-50%
-100%
Net G-L
0%
Race to the Top
Net Gain 2008-2010
Top 20
Abaiara
145,75%
Pedra Branca
97,91%
Milhã
87,85%
Ibaretama
86,08%
Brejo Santo
85,81%
Mauriti
85,55%
Campos Sales
74,58%
Deputado Irapuan Pinheiro
70,45%
Penaforte
69,44%
Poranga
68,17%
Salitre
67,12%
Senador Sá
66,77%
Ererê
63,18%
Aiuaba
63,09%
Piquet Carneiro
62,43%
Catunda
60,84%
Morrinhos
60,65%
Granjeiro
60,21%
Ipaumirim
53,15%
Pacujá
50,16%
Race to the Bottom
Net Loss 2008-2010
Botton 20
Guaramiranga
-64,69%
Acarape
-61,51%
Aratuba
-44,65%
Groaíras
-43,98%
Itaiçaba
-40,36%
Independência
-37,11%
Irauçuba
-36,03%
Aracati
-34,77%
Juazeiro do Norte
-31,33%
Icapuí
-31,06%
São Benedito
-30,85%
Cruz
-30,25%
Fortim
-29,97%
Umirim
-28,51%
Mucambo
-28,47%
Capistrano
-28,10%
Ubajara
-27,67%
Parambu
-27,61%
Ibicuitinga
-25,15%
Acopiara
-24,76%
Convergence in Scores
80
% Var Score
60
40
20
0
120
140
160
180
200
220
240
-20
-40
Score MAT
70
60
% Var. Score
50
40
30
20
10
0
-10 120
-20
130
140
150
160
170
Score PORT
180
190
200
210
Divergence in Standard
Deviation
80
% Var SD MAT
60
40
20
0
-20
20
25
30
35
40
45
50
55
60
65
-40
-60
SD MAT
50
40
% Var SD PORT
30
20
10
0
-10
20
25
30
35
40
-20
-30
SD PORT
45
50
55
Conclusions
• The distribution of resources became more
homogeneous and smaller municipalities gained
a chance to increase their revenue.
• In education, there have been improvements in
students performances, but at a cost of greater
inequality.
• Some municipalities behaved strategically and
took advantage of the new law for significant
gains in their budgets.
Conclusions
• To reduce the risk of “poverty trap” in some
municipalities the state has an important role in
“explain” the New Law to society and municipal
governments.
• It seems that the main goal of the law is being
achieved. As each municipality compete with
each other’s to obtain better performances they
all advance and get close in a higher level of
performance.
References
• Marcos Costa Holanda, Marcelo Ponte Barbosa, Leandro Oliveira
Costa, Metodologia de Cálculo da Nova Lei do ICMS Municipal,
NOTA
TÉCNICA
IPECE
n
º
33.
http://www.ipece.CE.gov.br/publicacoes/notas_tecnicas/NT_33.
PDF
• Marcos Costa Holanda, Marcelo Ponte Barbosa, Leandro de
Oliveira Costa, Memória de Cálculo dos Coeficientes de Distribuição
do ICMS Municipal 2009, NOTA TÉCNICA IPECE n. º 32.
http://www.ipece.CE.gov.br/publicacoes/notas_tecnicas/NT_32.
PDF
THANKS!!!
[email protected]