Can a new working mother tax credit in Spain modify women’s labour supply?

L. Ayala (URJC) and M. Paniagua (IEF - URJC)

2nd Microsimulation Research Workshop

11-12 Oct 2012, Bucharest, Romania

1. Introduction2. In-work benefits

1. Definition2. Previous experiences3. The WMTC in Spain4. Definition of a new IWB3. Methodology4. Data5. Simulation6. Conclusions

1. Introduction: family support public expenditure in Spain

Fuente: Infancia en Cifras, 2009. Cuentas integradas de protección social en términos SEEPROS. Eurostat.

Spain is one of the countries in Europe with the lowest family benefits.

1. Introduction: female activity rates – a comparison

1. Introduction: low-income workers

Spain is one of the European countries with the smallest salary mean

1. Introduction: female poverty rates – a comparison

1. Introduction: motivation

Within this frame -> need to find policies gathering both the increase in female activity rates and enlarging salaries for low-income workers.

Alternatives – first approach: IN-WORK BENEFITS (positive experiences in general terms)

2. In-work benefits1. Definition

Employment conditional benefits

Dependent children also considered

Aim at low income families – low wage earners

Target: increase labour supply and redistribute income towards low income families (poverty reduction)

Different ways of designing IWBs – depending on the target group

2. In-work benefits1. Definition

According to Saez (2002) the optimal IWB has the following form:

2. In-work benefits2. Previous experiences

Long tradition in Anglo-saxon countries

Other OECD countries start to implement IWB

Mediterranean countries have little history implementing IWBs

In Spain the closest program to an IWB is the working mother tax credit

2. In-work benefits3. The working mother tax credit in Spain

Introduced in 2003, is a refundable tax credit for working mothers with children under 3.

The condition to be elegible is paying social security contributions (thus being working)

If the contributions are defined by the variable “sic” (monthly) the tax credit is min(sic,100 euros) monthly.

2. In-work benefits3. The working mother tax credit in Spain

2. In-work benefits3. The working mother tax credit in Spain

2. In-work benefits4. A new IWB (remove WMTC)

3. Methodology

EUROMOD as the static microsimulation tool to calculate disposable income, before and after the new in-work benefit.

The model takes into account individuals’ behaviour within the labour market:

The individuals’ preferences are represented by a direct utility function.

The individual has also a budget constraint defined in terms of hours worked, the gross wage rate, total hh income and the tax system.

);,( XhyUU

);,,( XVwhTVwhy

3. Methodology Maximazing the utility taking into account the budget constraint (not an easy issue)

Use discrete approach (Van Soest, 1995; Aaberge et al., 1995) where individuals maximize their utility by selecting the number of hours they wish to work subject to the constraint that only a discrete number of hours levels are available:

The utility has a random term, since the determinants of any individual’s behaviour can never be known with certainty. Therefore:

where is the measured utility and the error term

ni hhh ,...,1

iiiii UXhUU )/(

)/( XhU i i

3. Methodology

Quadratic form for the deterministic part of the utility function:

Observed heterogeneity enters through the parameters of income and hours within the utility function:

hyyhyhU hyyhyh 22

X

X

hhh

yyy

10

10

3. Methodology Within this framework there is a probability distribution over available hours level influenced by the properties of the i

iiij

jii

ijijij

nniiii

jjii

jjii

ji

ii

dfUUF

UUP

UUUUP

jUUP

jUUP

jUUP

hhPp

)()(

)(

),...,(

) (

) (

) (

)(

11

3. Methodology If follows the EVD:i

The specification is the multinomial logit

j

ii Uj

UihhPp

)exp(

)exp()(

)exp()( ef

Elasticities are measured on expected hours work

Need data for the estimations

4. Data

Spanish module of the EU_SILC 2006 (2005 income)

Target group: o women (utility maximization takes place at hh level but males are considered inelastic)o aged in [18,50]o no self-employedo with children in [0,3]

Sample of 1.128

5. Simulation Three scenarios:

o Baseline (2005 policies - WMTC)o IWB reform without behavioural changeso IWB + LS

Idhh Hours worked (sample)

Hours worked (all budget)

Income Response

1 40 0 100 0

101 40 20 130 0

201 40 40 200 1

Idhh Hours worked (sample)

Hours worked (all budget)

Income Response

3 0 0 90 1

103 0 20 150 0

203 0 40 210 0

For those not working need to estimate wages -> Heckman’s method to correct selection bias

5. Simulation Calibration of error term: given the estimated preferences I keep 80 “good” draws from the error term.

o Good means that the maximization of new utilities (considering deterministic part and random error) takes place at the observed hours point.

o If the draw is good, it’s used to derive preferred choice after the reform.

o individual transition probabilities can be approximated by taking the mean of the predicted transitions between hours in the budget over the 80 repetitions.

5. Simulationlhgw Coef. Std. Err. z P>z [95% Conf. Interval] lhgw deh 0.4354224 0.0868505 5.01 0 0.2651985 0.6056463age2 -0.0027951 0.0004686 -5.96 0 -0.0037136 -0.0018767pwe 0.2154167 0.0301094 7.15 0 0.1564034 0.2744301_cons 1.248414 0.2691161 4.64 0 0.7209556 1.775871select deh 0.4007836 0.107996 3.71 0 0.1891153 0.6124519age2 -0.0018991 0.000611 -3.11 0.002 -0.0030967 -0.0007016pwe 0.1108538 0.0390525 2.84 0.005 0.0343123 0.1873953couple -0.6305237 0.1608245 -3.92 0 -0.9457339 -0.3153136cin3_6 -0.4160186 0.0968228 -4.3 0 -0.6057879 -0.2262493ohi 0.0009951 0.000085 11.7 0 0.0008285 0.0011618_cons -0.8561875 0.2935475 -2.92 0.004 -1.43153 -0.280845mills lambda -0.8654883 0.1131421 -7.65 0 -1.087243 -0.6437339rho -1 sigma 0.86548829 lambda -0.86548829 0.1131421

Heckman method to estimate wages for those not in employment

5. Simulation

Conditional logit

Conditional (fixed-effects) logistic regression Number of obs = 3375 LR chi2(7) = 512.88 Prob > chi2 = 0 Log likelihood = -979.50047 Pseudo R2 = 0.2075 response Coef. Std. Err. z P>z [95% Conf. Interval] yd 0.0616942 0.0026835 -1.13E+01 0 -0.0354491 -0.0249301h -0.0301896 0.0168397 3.66 0 0.0286891 0.0946994yd2 4.50E-06 9.05E-07 4.97 0 2.73E-06 6.28E-06h2 0.0010909 0.0002086 5.23 0 0.000682 0.0014998yd_h -0.0000506 0.0000112 -4.51 0 -0.0000727 -0.0000286yd_deh 0.0043033 0.0006153 6.99 0 0.0030973 0.0055094h_deh -0.0074037 0.0032807 -2.26 0.024 -0.0138337 -0.0009737

5. Simulation

0 20 40

0 0.1239251 0.1389593 0.1279861 0.3908705

20 0.0653856 0.0742977 0.077375 0.2170583

40 0.1408131 0.1173343 0.1339239 0.3920713

0.3301238 0.3305913 0.339285

Transition probabilities

5. Simulation

Original yem and WMTC

Original yem and IWB

Modified yem - LS - and IWB

Gini (positive values) 0.2970415 0.2954506 0.2909903 -0.54% -2.04%Theil (positive values) 0.1486666 0.1469531 0.1436652 -1.15% -3.36% p90/p10 (all values) 4.233 4.194 4.103 -0.92% -3.07%p90/p50 (all values) 1.9 1.87 1.865 -1.58% -1.84%p10/p50 (all values) 0.449 0.446 0.455 -0.67% 1.34%p50/p10 (all values) 2.227171492 2.242152466 2.197802198 0.67% -1.32% A(0,5) (positive values) 0.07458 0.07384 0.0723 -0.99% -3.06%A(1) (positive values) 0.15512 0.15385 0.15149 -0.82% -2.34%A(2) (positive values) 0.54988 0.55017 0.55198 0.05% 0.38% Poverty line 558.13 567.03 558.4 At-risk-of poverty rate (FGT 0) 0.1947 0.191975 0.186643 -1.40% -4.14%At-risk-of poverty rate (FGT 1) 0.060573 0.059333 0.059152 -2.05% -2.35%

Results on income distribution

5. Simulation

Age Scenario 1 Scenario 2 Scenario 3 dif 2-1 dif 3-1

(0,20] 0.155185 0.139629 0.141559 -10.02% -8.78%

(20,25] 0.23333 0.228625 0.212053 -2.02% -9.12%

(25,30] 0.151266 0.14904 0.149729 -1.47% -1.02%

(30,35] 0.110828 0.107075 0.101103 -3.39% -8.77%

(35,40] 0.119837 0.117615 0.102627 -1.85% -14.36%

(40,45] 0.168766 0.162366 0.165825 -3.79% -1.74%

(45,50] 0.20343 0.203706 0.198572 0.14% -2.39%

(50,55] 0.154715 0.153081 0.153792 -1.06% -0.60%

(55,) 0.237879 0.237435 0.236957 -0.19% -0.39%

0.1947 0.192205 0.186872 -1.28% -4.02%

Decile Scenario 1 Scenario 2 Scenario 3

1 1 0.998033 0.952381

2 0.947398 0.922389 0.839194

3 0 0.00202 0.011429

4 0 0 0.019144

5 0 0 0.011293

6 0 0 0.012059

7 0 0 0.012055

8 0 0 0.006848

9 0 0 0.004684

10 0 0 0

Population 0.1947 0.192205 0.186872

At-risk-of-poverty rates by age group At-risk-of-poverty rates by decile

5. Simulation

Differences within centiles (scenarios 2-1 and 3-1)

5. Simulation

Monthly Annually

New IWB 261,000,000.00 € 3,132,000,000.00

WMTC 84,400,000.00 € 1,012,800,000.00

2,119,200,000.00 €

Cost of the reform: comparison with other programs

5. Simulation

Decile DecilaGainers by income decile

417.63 € 1.00 145,566.80

565.92 € 2.00 157,429.20

691.28 € 3.00 141,007.80

815.13 € 4.00 274,950.70

930.22 € 5.00 352,164.90

1,061.69 € 6.00 378,119.60

1,226.81 € 7.00 311,858.30

1,422.32 € 8.00 440,144.90

1,767.77 € 9.00 335,577.10

10,338.42 € 10.00 155,348.10

Gainers by income decile

5. Simulation

HH typeGainers by hh type

1 adult -

2 adults without dependent children -

other hh type without dependent children

-

1 adult with dependent children 60,782.10

2 adults with dependent children 2,232,578.00

other hh type with dependent children 398,807.00

5. Simulation

Mean income by deciles within the three scenarios

6. Conclusions Aims:

Simulate an IWB in Spain for working mothers and see if their income increases as well as their hours offered (behavioural model)

Analyse the redistributive effects on income distribution

Results:

Number of hours offered increases (extensive margin 0-20, 0-40)

Inequality reduces

Reduction of poverty rates

Youngest women and couples with children are particularly better off

6. Conclusions Implications for the policy maker:

IWBs represent a feasible option for those countries with less tradition of in-work support

The cost is reasonable compared to other programs aimed at low-income households

Warnings:

Relatively small sample

Need to find more robust specification for the utility function

Transition 40-20, 40-0 higher than expected – check unobserved heterogeneity within the utility function

Thank you for attending this session.Comments are welcome !

milagros.paniagua@ief.minhap.es