Social Evaluation of Alternative Basic Income Schemes in Italy R. Aaberge (Statistics Norway, Oslo)...
Transcript of Social Evaluation of Alternative Basic Income Schemes in Italy R. Aaberge (Statistics Norway, Oslo)...
![Page 1: Social Evaluation of Alternative Basic Income Schemes in Italy R. Aaberge (Statistics Norway, Oslo) U. Colombino (Department of Economics, Torino) S. Strøm.](https://reader036.fdocuments.us/reader036/viewer/2022062500/5697bfe81a28abf838cb61a3/html5/thumbnails/1.jpg)
Social Evaluation of Alternative Basic Income
Schemes in Italy
• R. Aaberge (Statistics Norway, Oslo)
• U. Colombino (Department of Economics, Torino)
• S. Strøm (Department of Economics, Oslo)
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We develop a model of labor supply which
features:
• simultaneous treatment of spouses’ decisions
• exact representation of complex tax rules
• quantity constraints on the choice of hours of work
• choice among jobs that differ with respect to hours, wage rate and other characteristics
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Traditional approach
max U(C, h)
s.t.
C=f(wh, I)
h0,T]
where:
C = net income
h = hours of work
w = wage rate
I = other income
T = total available time
f( ) = tax rule
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Our approach
max U(C, h, j)
s.t.
C=f(wh, I)
(h, w, j) B
where:
j = other job characteristics
B = opportunity set
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The approach we use is different from the
traditional approach
• Traditional model:
max U(C, h)
s.t.
C = f(wh, I)
h0,T]
• Our
model:
max U(C, h, j)
s.t.
C = f(wh,I)
(h, w, j) B
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The opportunity set in the traditional approach
h
w
T0
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The Flat Tax
0
Gross Income
Net Income
45o
FT
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The Negative Income Tax
0
Gross Income
Net Income
45o
NIT
G
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The Workfare Scheme
0
Gross Income
Net Income
45o
WF
G
Hmin
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The opportunity set in our model (the numbers represent hypothetical
densities or relative frequencies of alternatives in the corresponding
“spot”)
0.1
0.3
0.025
h
w
0
0.20.01
0.1
0.15
0.015
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Jobs differ not only w.r.t. wage (w) and
hours (h) but also other characteristics ()
h
w
w0
h0
0
(w0 , h0 , 0 )
-
+
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The opportunity set contains different
number of jobs with different characteristics
• This is taken into account by specifying a frequency or density function:
• p(h,w) = density of jobs with hours and wage (h,w).
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Basic assumptions
• U(C, h, j) = V(C, h) (h,w,j) =V(f(wh,I), h)
(h,w,j)
• V(f(wh,I), h) is the systematic component
(h,w,j) is the stochastic component
• Prob( < u) = exp(-1/u)
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Choice probability
Given the assumptions, the probability (density) that the household chooses a job (h,w) is given by:
B y xdxdy y x p h I yx f V
h)p(h,w) V(f(wh,I),w h
) , () , ( ) ), , ( (
) , (
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Imputation of the choice set
• The choice set B is in principle infinite
• In the estimation and simulation, B is replaced by a subset A B
• Subset A contains 200 elements (jobs) sampled according to a procedure (“importance” sampling) suggested by McFadden (1978)
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Importance Sampling of the Sub-Set A
• Estimate an empirical density q(h,w)
• If A must contain M elements, sample M-1 points from q(h,w)
• Add the chosen job to make a set A containing M elements
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The choice density, given choice-set A, then
becomes:
]),(/[)],(/[
)\,(
)(
Ax,yyxqp(x,y)x)V(f(yx,I),
whqp(h,w)h)V(f(wh,I),Awh
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The key differences with respect to other discrete choice models of labour
supply are:
• Our discrete model is an estimation device for an underlying continuous model
• In other discrete choice model of labour supply the choice set is typically fixed a-priori an equal for every one
• In our model we estimate the composition of the choice set (i.e. p(h,w)), which can differ from household to household
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Model Estimation
By specifying parametric forms for V( ) and p( ) we can estimate the parameters of the utility function V( ) and of the opportunity density p( ) by Maximul Likelihood
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Model Specification
• V(C, h) is a Box-Cox form
• p(h,w) = g1(h)g2(w)g0
• p(0,0) = 1-g0
• g1(h) is uniform with a “peak”for full time
• g2(w) is log-normal
• g0 is a logistic function [0,1] of personal characteristics
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The data
• We use the Bank of Italy’s Survey of Household Income and Wealth 1993
• We exclude single person households
• Both partners must belong to the age group 18-54
• Retired and self-employed are excluded
• The selected sample contains 2160 households
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Policy Simulation
• A policy is a change in the opportunity set B and/or in the tax rule f( )
• Let B be the new opportunity set and f the new tax rule
• In order to simulate household behavior we solve the new problem:
max U(C, h, j)
s.t.
C = f(wh, I)
(h,w,j) B
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We simulate the effects of three tax reforms:
• A flat tax (FT)
• A negative income tax, with a guaranteed income equal to 3/4 the poverty level (NIT)
• A workfare system with a guaranteed income equal to 3/4 the poverty level, provided that the household works at least 1000 hours (WF)
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It turns out that you can generate the same tax revenue either with:
• The 1993 tax rule
• A 18.4% FT
• A NIT that supports income up to 3/4 the poverty level and then applies a 28,4% tax rate
• A WF that requires 1000 hours worked, supports income up to 3/4 the poverty level and then applies a 27,3% tax rate
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Wife’s participation rate under alternative tax rules
40
40,5
41
41,5
42
42,5
43
43,5
44
44,5
45
part. 43,7 45 41,9 42,5
Basis(93) FT NIT WF
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Wife’s hours of work (if employed) under alternative tax
rules
1570
1580
1590
1600
1610
1620
1630
hours 1590 1623 1589 1597
Basis(93) FT NIT WF
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Husband’s hours of work (if employed) under alternative tax
rules
1940
1950
1960
1970
1980
1990
2000
2010
2020
2030
2040
hours 1972 2036 1976 2001
Basis(93) FT NIT WF
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Household gross (Y) and net (C) income under alternative tax rules
(000000 ITL)
0
10
20
30
40
50
60
70
Basis(93)FTNITWF
Basis(93) 54,5 43,2
FT 60,2 49,1
NIT 55,9 44,8
WF 56,7 45,7
Y C
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Gini coefficients for the distribution of disposable
household income
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
Basis(93)FTNITWF
Basis(93) 0,323 0,282
FT 0,332 0,332
NIT 0,343 0,298
WF 0,336 0,301
Y C
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Disposable income variations under alternative tax reforms, by 1993 disposable income decile
0
2
4
6
8
10
12
14
16
I 4 3,8 3,2
II 3,6 1,4 2,1
III-VIII 5 1 2
IX 8 1,7 2,9
X 14,3 3,9 5,5
FT NIT WF
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Measurement of Utility
Vi(mi, wi, fk) =
utility reached by household i when endowed with exogenous income mi
and wages wi, under tax regime fk
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Measurement of Utility
Equivalent Income yik:
(King 1983)
Vi(mi, wi, fk) = VR(yik, wR, f*)
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The Efficiency Effect is the percentage variation
of average utility (as measured by equivalent
income)
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The equality effect is based on Atkinson’s Index in the case of
SWak and on Aaberge (1992) in the case of
SWbk
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The percentage variations of both Sak
and Sbk can be decomposed into an
Efficiency Effect and an Equality Effect
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Social Welfare
King (1983):
SWak= i(yik)1-a /(1-a)a = inequality aversion parameter
Aaberge (1992):
SWbk = iyik(1-F(yik)b-1 )b/(b-1)b = inverse inequality aversion parameter
where F is the distribution function of y under tax regime k.
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Welfare Gain of Household i from the reform (f0 to f1)
WGi(0,1) = yi1 - yi0
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Percentage of “welfare-winners” under alternative tax reforms
49
50
51
52
53
54
55
56
% 51,8 55 55,6
FT NIT WF
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Percentage of “welfare-winners” under alternative tax reforms, by 1993 welfare level (equivalent
income) decile
0
10
20
30
40
50
60
70
I 41,5 65,3 64,8
II 43,5 59,2 59,3
III-VIII 52 54,6 55,4
IX 60,1 51,4 52,6
X 60,9 46,2 47,6
FT NIT WF
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Percentage of “welfare-winners” under alternative tax reforms, by 1993 household income decile
0
20
40
60
80
100
I 14,1 74,1 66,7
II 19 43,7 45,8
III-VIII 51,3 44,8 50,9
IX 86,5 51,1 60,4
X 90,6 64,9 71,5
FT NIT WF
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Percentage of “welfare-winners” under alternative tax reforms, by 1993 household income decile
0
10
20
30
40
50
60
70
80
90
100
I II III-VIII IX X
FTNITWF
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Percentage of “welfare-winners” under alternative tax reforms, by 1993 welfare (equivalent income)
decile
0
10
20
30
40
50
60
70
I II III-VIII IX X
FTNITWF
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Wefare Gains (King, 1983) of a WF Reform by welfare decile.Mean CWG = 1724 (000 ITL)
-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
Losers -2656 -2773 -3958 -5551 -9668
All 2750 2165 1835 1793 -478
Winners 5732 5540 6531 8459 9776
I IIIII-VIII
IX X
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Wefare Gains (King, 1983) of a NIT Reform by welfare decile.Mean CWG = 1643 (000 ITL)
-10000
-8000
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
Losers -2620 -2762 -3998 -5595 -9719
All 3039 2208 1736 1573 -808
Winners 6082 5634 6526 8408 9726
I IIIII-VIII
IX X
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Wefare Gains (King, 1983) of a FT Reform by welfare decile.Mean CWG = 3105 (000 ITL)
-10000
-5000
0
5000
10000
15000
20000
Losers -5528 -5641 -6029 -6607 -8299
All -122 457 2848 6307 7325
Winners 7051 8310 1105 1492 1746
I IIIII-VIII
IX X
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Percentage variations of Social Welfare and its components
(Efficiency and Equality)a = 0
0
0,5
1
1,5
2
2,5
Efficiency 2,1 0,8 1,1
Soc. Wel 2,1 0,8 1,1
Equality 0 0 0
FT NIT WF
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Percentage variations of Social Welfare and its components
(Efficiency and Equality)a = 1
-1
-0,5
0
0,5
1
1,5
2
2,5
Efficiency 2,1 0,8 1,1
Soc. Wel 1,4 1,3 1,4
Equality -0,69 0,49 0,3
FT NIT WF
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Percentage variations of Social Welfare and its components
(Efficiency and Equality)a = 2
-2
-1,5
-1
-0,5
0
0,5
1
1,5
2
2,5
Efficiency 2,1 0,8 1,1
Soc. Wel 0,5 1,9 1,8
Equality -1,57 1,09 0,69
FT NIT WF