Distribution of marginal effective tax rate in Croatia: do taxes and benefits prevent people from getting employed?

Slavko Bezeredi & Ivica Urban Institute of Public Finance, Zagreb

2013 EUROMOD research workshop University of Lisbon, October 2013

Goals Do taxes and benefits prevent people from

getting employed in Croatia? How high is the marginal effective tax rate

(METR) for long-term unemployed and inactive people?

...speculating (in our model) whether to remain out of work or to get employment

...people from a micro-data sample

2

Problems (A) Calculate net household income, taxes

and benefits, paid/received miCROmod – microsimulation model ...uses new 2010 Croatian income survey

(harmonised with EU-SILC) (B) Obtain gross wages for unemployed and

inactive, because they are not available in the sample

Wage regression – „selection problem” – tobit II model

3

Model A person Q is planning what to do in the next

one-year period ...calculates what would be her household’s

income in two different hypothetical states: “0” remains unemployed or inactive “1” gets employed at full-time job

M = marginal effective tax rate (METR) X, Y, T, B = household’s pre-fiscal income, post-fiscal income, taxes and benefits

4

Model

5

s s s sY X T B

X X Y Y T T B B

X Y T B

1 0 1 0 1 0 1 0( ) ( ) ( ) ( )

X Y T BM

X X

s s sQw othX X X pre-fiscal income = Q’s gross wage +

+ other gross incomes in Q’s household

oth othX X1 0

QwX 0 0

Qw

QQw Qw

X Y T BM

X X

1

1 1

Model

6

QwX 1

Not available in the dataset...

1,...,i k

i k n1,...,

working

not-working

i i iw i k1 1 1ln( ) , 1,...,x b e

„selection problem” – because the „not-working” are out of sample

i i ih i n*2 2 2 , 1,...,x b e ih* 1

ih* 0

i works

i does not work

i iiiw i k x b h1*

21 1ln( ) , 1,...,

Data

EU-SILC Croatia for 2010 6,403 households with 16,948 members investigated: long-term unemployed and

inactive people aged 16 to 65 pensioners, students and unable to work are

excluded from the analysis

7

Data

Workers: 4.460 persons who worked more than 1000 hours during the year and reported a positive gross wage

Unemployed: 1.616 persons who declared themselves as unemployed during the whole year (0 working hours)

Inactive: 684 persons who declared themselves as “housewifes” or “other inactive” during the greater part of the year (0 working hours)

8

Population structure

9

    Gender Age Education level Children

Share M F 16-25 26-55 56-65 0-8 9-12 >12 0 1-2 >2

Share in pop. 100 49.7 50.3 20.2 58.1 21.8 21.9 64.2 13.9 71.0 24.4 3.6

Employed 43.9 46.2 41.5 24.0 59.1 21.5 18.8 47.8 65.1 36.3 64.0 55.2

Self-employed 4.7 6.5 2.9 0.7 6.1 4.6 4.2 4.6 5.8 3.8 6.9 6.4

Unemployed 15.8 16.0 15.7 18.3 17.1 10.0 17.9 16.4 9.8 15.8 14.5 21.5

Housewifes 5.0 0.1 9.8 0.5 5.8 6.8 15.2 2.5 0.5 4.5 5.5 9.7

Other inactive 0.6 0.7 0.5 0.8 0.5 0.8 1.1 0.5 0.2 0.7 0.5 0.2

Students 11.7 11.9 11.6 55.1 1.1 0.0 16.1 11.2 7.2 16.4 0.3 0.4

Unable to work 0.9 1.2 0.6 0.6 1.0 0.8 2.5 0.5 0.0 1.1 0.3 0.0

Pensioners 17.5 17.5 17.4 0.0 9.2 55.6 24.2 16.4 11.4 21.4 8.0 6.6

Total 100 100 100 100 100 100 100 100 100 100 100 100

Working 48.5 52.7 44.4 24.7 65.3 26.0 23.0 52.4 70.9 40.1 70.9 61.6

16 to 65 years

Variables

10

Variable DescriptionGender Female = 1; Male = 0

Age Age in years

Married 1= married

Divorced 1= divorced

Children 1 Number of children aged 0-6

Children 2 Number of children aged 7-14

Children 3 Number of children aged 15-18

Zagreb 1 = lives in Zagreb

PPDS 1 = lives in PPDS

Primary educ. 1 = primary education

Secondary educ. 1 = secondary education

Tertiary educ. 1 = tertiary education

Health problems 1 = yes; 0 = no

Other incomeNatural logarithm of household market income (excluding a person’s wage) per member divided by 100

Hourly wage Natural logarithm of hourly wage in HRK

Activity 1 1 = Sections A, B and C

Activity 2 1 = Sections D, E and F

Activity 3 1 = Sections G, H and I

Activity 4 1 = Sections J, K, L and M

Activity 5 1 = Sections N through U

Manager 1 = Managerial position

Probit regression (marginal effects)

11

Working vs. unemployed &

inactive

Working vs. unemployed

Working vs. inactive

1 2 3 4

Gender -0.149*** -0.066*** -0.110***

Age 0.056*** 0.048*** 0.010***

Age^2/100 -0.071*** -0.059*** -0.014***

Married 0.094*** 0.106*** -0.013*

Divorced 0.176*** 0.144*** 0.017*

Children 1 -0.047*** -0.043*** -0.015***

Children 2 -0.037*** -0.031*** -0.011***

Children 3 -0.007 0.000 -0.009*

Zagreb 0.081*** 0.052*** 0.028***

PPDS -0.063*** -0.049*** -0.021***

Secondary education 0.318*** 0.219*** 0.131***

Tertiary education 0.351*** 0.258*** 0.070***

Health problems -0.225*** -0.222*** -0.056***

Other income 0.000 0.003* -0.002**

Number of Obs 6740 6056 5124Wald chi2(13) 1000.91 580.05 676.3

Prob > chi2 0 0 0Pseudo R-Squared 0.1594 0.1067 0.4364

Log Pseudolikelihood -932144.14 -820416.68 -270966.86

Three models:(1) Not working are unemployed and inactive together(2) unemployed only(3) inactive only

Wage regression

12

Wage equations

Coefficients A Coefficients B

Gender -0.147*** -0.156***

Age 0.028*** 0.032***

Age^2/100 -0.020* -0.025***

Zagreb 0.184*** 0.189***

PPDS -0.037* -0.041**

Secondary education 0.218*** 0.245***

Tertiary education 0.602*** 0.641***

Health problems -0.074 -0.091**

Activity 1 -0.088*** -0.088***

Activity 2 -0.063** -0.062**

Activity 3 -0.114*** -0.114***

Activity 4 0.108*** 0.108***

Manager 0.311*** 0.311***

Heckman's lambda -0.052  Constant 2.657*** 2.533***

Number of Obs 4440 4440Wald chi2(13) 127.86 137.66

Prob > chi2 0 0Pseudo R-Squared 0.3471 0.3471

Log Pseudolikelihood 0.45027 0.45023

METR - results

13

54.5

35.0

7.33.2

<30

30-50

50-70

>70

METR (%)

71.4

22.7

2.8 3.2

<30

30-50

50-70

>70

METR (%)

Unemployed Inactive

METR by groups

14

  Unemployed Inactive

  000 people Mean >70(%) 000 people Mean >70(%)

All 417 33.2 3.2 155 29.7 3.2

Gender            

Men 207 33.7 4.0 10 31.1 1.1

Women 210 32.7 2.4 145 29.6 3.3

Age            

16-25 90 31.8 6.2 7 40.0 15.2

26-55 266 33.6 2.7 102 30.6 3.8

56-65 61 33.8 1.0 46 26.3 0.0Education             Primary 108 37.9 10.5 101 29.9 4.9 Secondary 276 31.4 0.7 52 29.3 0.0 Tertiary 34 32.6 0.0 2 31.8 0.0

Children            

0 217 30.7 2.1 47 27.0 0.0 1 or 2 152 34.2 1.7 82 27.8 0.7 3 or more 48 41.2 12.7 26 40.8 16.8Marital status             No spouse 175 30.8 3.0 17 29.3 0.8 Employed or pensioner 168 31.5 0.1 117 27.2 0.2 Inactive or unemployed 74 42.1 10.2 21 42.5 19.3

Decomposition of METRfor people with METR>50%

15

0 10 20 30 40 50 60 70 80

All____________

Unemployed

Inactive

Gender_______

Men

Women

Age___________

16-25

26-55

56-65

Education____

Primary

Secondary

Tertiary

Children_____

0

1 or 2

3 or more

Spouse_______

no spouse

E or P a

I or U b

Marginal effective tax rate (%)

SSC PIT CHB BSA UNB

Conclusion

distribution of METR for long-term unemployed and inactive; for various subgroups

for majority of unemployed and inactive people METR is relatively low, and should not be the factor detrimental to entering employment

55% of unemployed and 71% of inactive have low METR (<30%)

very high METR (>70%) for 3.2% particularly vunerable persons: (a) with three

and more children, (b) whose spouses are also inactive or unemployed, (c) with primary education

the results suggest that policies to make work pay should target these most vulnerable groups

16