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Training Subsidies and the Wage Returns to Continuing Vocational Training: Evidence from Italian Regions. Giorgio Brunello (Padova) Simona Comi (Milano Bicocca) Daniela Sonedda (Piemonte Orientale). Training in this paper is. Formal - PowerPoint PPT Presentation
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Training Subsidies and the Wage Returns to Continuing
Vocational Training: Evidence from Italian Regions
Giorgio Brunello (Padova)Simona Comi (Milano Bicocca)Daniela Sonedda (Piemonte
Orientale)
Training in this paper is Formal Continuing vocational rather than
initial vocational (after full time education has ended)
Mainly workplace training initiated by firms (but not exclusively)
Training matters Broad consensus among policy
makers that training matters for employment, productivity and individual well being
Yet applied economists do not have a consensus view on the wage returns to training
Two extreme cases Lynch, 1992, finds that one week of
training raises hourly wages by 0.2% Bartel, 1995, finds that one day of
training raises wages by 2 percent The literature often finds returns of at
least 3 percent for a week of private sector training – large relative to returns to 1 year of schooling (10 percent)
Estimating these returns is difficult
itititit xw 'ln
Participation in training is not random
Training correlated with individual un-observables (ability)
Methods used in the literature Fixed effects estimates:
If un-observables are time invariant the within estimator is appropriate
required assumptions are: 1. earnings growth is the same for
participants and non-participants 2. temporary shocks that affect
wages do not affect training
IV estimates We need an exclusion restriction
A variable which affects training but does not affect directly wages or the probability of receiving a positive wage
Difficult
The Acemoglu and Pischke model Imperfect product and labour
markets General skills Firms are willing to train even when
the imparted skills are general Frictions and imperfections reduce
the transferability of skills
Sketch of the model Two periods First period: training takes place and the
employer pays the training costs At the start of second period the match
may end because of an exogenous shock If the match survives, bargaining over
wages Production occurs
Notation
)()(1)()()(
vfGqwvf
output
Worker outside option
wages
Probability of employment
Wage setting Nash bargaining
The firm has outside option equal to zero
Outcome of the bargain )()()()( vfvw
Training costs are bygones
The training decision
scvfq )()()()1)(1()(
s= training subsidy
0)()()()1)(1( ''' scvfgq
Training increases with the subsidy
The subsidy affect training directly and wages and employment indirectly via training
Implication Training policies such as training subsidies
are a good candidate to instrument training in an earnings regression
However: national training policies that affect all individuals equally cannot work
We need that training policies affect only some groups (ex:
training policies only in some areas) the intensity of policies differs among groups
In this paper
We use regional training policies (training grants) as instrument
They differ across regions AND over time
Italian institutional setup Training policies are regional
policies Regional governments have
substantial autonomy in Allocation of training expenditures to
their budget Timing of their invitations to tender Ability to pay quickly
CVT policies in Italy Levy / grant type (funded by social security
contributions with a grant mechanism to award funds) European Social Fund (largest; Objective 4
during 1994-99; Directives 1 and 2 during 2000-06– lifelong learning)
Laws 236/94 and 53/00 Industry based training funds (from late 2004)
Tax deductions (Tremonti 2001 and 2002) – time dummies
ESF, laws 236/93 and 53/00
Funded at least in part by a compulsory levy of 0.30% on payroll
Regional implementation, especially from 2001
Regional and time variation in expenditure plans and invitations to tender (impegni)
Our data We collect from regional
publications the regional invitations to tender associated to Laws 236 and 53
Data on ESF expenditure plans and invitations to tender partly from ISFOL and partly from the National Audit Court (Corte dei Conti)
ABR
BAS
CAL
CAM
EMI FVG
LAZ
LIGLOMMAR
PIE
PUG
SAR
SIC
TAA
TOS
UMB
VEN
010
2030
4023
6/53
real
trai
ning
ince
ntiv
es p
er h
ead;
sum
199
7-20
04
0 20 40 60 80 100FSE real training incentives per head; sum 2000-2004
Cumulated Real Training Incentives, by Region; real euros per head
Resources allocated to training subsidies from the 0.30% compulsory levy
Source: ISFOL, 2006
Table 1. Regional Planned Training Expenditures. Cumulated stock 1994-2005. Real Euros per head. ESF 236 and 53 Piemonte
100.74
23.10
Lombardia 64.98 15.58 Trentino Alto Adige 251.36 35.43 Veneto 94.77 21.06 Friuli Venezia Giulia 144.33 32.88 Emilia Romagna 153.32 31.47 Liguria 89.51 19.96 Toscana 71.60 15.75 Marche 62.66 16.82 Umbria 104.14 23.52 Lazio 61.48 16.63 Abruzzi 68.96 19.86 Campania 26.00 12.97 Puglia Basilicata
10.69 42.27
11.91 30.68
Calabria 18.12 7.36 Sicilia 34.86 5.10 Sardegna 69.27 15.74
11.
52
2.5
3st
ock
of tr
aini
ng in
cent
ives
1998 2000 2002 2004 2006year
PIE LOMTAA
12
34
1998 2000 2002 2004 2006year
FVG EROLIG VEN
12
34
1998 2000 2002 2004 2006year
TOS UMBMAR LAZ
05
1015
stoc
k of
trai
ning
ince
ntiv
es
1998 2000 2002 2004 2006year
ABR CAMPUG
010
2030
40
1998 2000 2002 2004 2006year
BAS CALSIC SAR
Empirical model
irtirtWrtWirtrtWirt TQXw 31''ln
irtrtTrtTirtrtTirt TSQXT ''
T=training stock
TS: stock of training incentives per head at constant prices
The specification Contextual effects
Regional and time dummies (wage bargaining is national in Italy)
Changes in regional labour markets Regional unemployment rate
Changes in R&D expenditure Regional share of high tech industries
Reverse causality – negative shocks reduce wages and induce regions to spend more on incentives
First lag of training incentives
The data
Match regional data on training incentives with micro data (ILFI)
ILFI: indagine longitudinale sulle famiglie italiane
Collects current and retrospective information
Why ILFI? Has info on wages and covers relevant
period (1999, 2001, 2003, 2005) Pluses:
Has good info on training – not only training incidence but also training episodes – plus retrospective info: can be used to compute training stock as discounted number of episodes
Minuses: info on training duration has many missing
values – we decide not to use it Tends to omit shorter episodes (usually the
case in household surveys)
Table 2. Sum of Training Episodes and Percentage of Workers receiving any Training. By region. Year: 2005 Sum of
training episodes
% of trained
workers
Sum of training episodes for
trained workers Piemonte
1.071
0.442
2.419
Lombardia 0.725 0.355 2.042 Trentino Alto Adige and Veneto 1.362 0.514 2.647 Friuli Venezia Giulia 2.111 0.722 2.923 Emilia Romagna 0.704 0.422 1.666 Liguria 0.653 0.307 2.125 Toscana 1.209 0.493 2.450 Marche and Umbria 0.958 0.375 2.555 Lazio and Abruzzi 0.982 0.377 2.604 Campania 0.271 0.171 1.583 Puglia Basilicata and Calabria
0.788 1.057
0.384 0.228
2.050 4.625
Sicilia and Sardegna 0.557 0.285 1.950 Source: see the Appendix
Training stock
1)1( ititit TIT
Presentation of results
First stage estimates 2SLS (LATE) Variations on the main theme
Table 4. First stage estimates – full sample and subsample with positive earnings. Private sector employees only. Dependent variable: cubic root of the training stock and training stock T. 13 regions. (1) (2) (3) (4) Full sample
Cube root of T 1998-2005
Full sample Linear T
1998-2005
Subsample with wage>0
Cube root of T 1999,2001,2003,
2005
Subsample with wage>0 Linear T
1999,2001,2003, 2005
Lagged incentives stock 0.002*** 0.003*** 0.002*** 0.003** [0.000] [0.001] [0.001] [0.001] F-test Elasticity
29.93 0.408
14.40 0.310
10.10 0.350
6.16 0.300
Observations 11495 11495 4850 4850 R-squared 0.140 0.118 0.153 0.123 Note: Clustered robust standard errors in brackets; *** p<0.01, ** p<0.05, * p<0.1; each regression includes a constant, regional, year and industry dummies. For additional controls see paper.
Implications (ceteris paribus)
One additional real euro per head spent in training subsidies from time t-x to time t-1 increases the discounted training stock at time t by 0.6 percent, a small amount.
To increase the average individual training stock by 10%, regions would have to spend an additional 13.47 euro per head (40 million euro in Lombardia)
Table 5. Ordinary least squares and IV estimates. Dependent variable: log monthly real earnings. 13 regions. Years 1999, 2001, 2003 and 2005 (1) (2) (3) (4) RE RE RE-IV RE-IV Training Stock (Cube Root) 0.047*** 0.273** [0.012] [0.123] Training Stock 0.021*** 0.149** [0.006] [0.060] Marginal effect of current episode Marginal effect of a week of training Elasticity
0.032 0.007 0.010
0.021 0.005 0.007
0.186 0.044 0.063
0.149 0.035 0.050
Observations 4850 4850 4850 4850 Note: Robust standard errors in brackets: *** p<0.01, ** p<0.05, * p<0.1. Each regression includes a constant, industry, regional and time dummies.
Comments No evidence of weak instruments with
cube root specification 2SLS estimates: one additional training
episode - that raises T by one unit -raises monthly earnings by 18.6 percent
A week of training raises earnings by 4.4 percent (average duration: 21 days)
LATE, not ATE or ATT
0.0
1.0
2.0
3.0
4m
argi
nal r
etur
n
0 10 20 30time since investment
5 percent depr rate 15 percent depr rate
Figure 3. Marginal returns to a week of training
Marginal returns decline over time, especially in our baseline
Average marginal effect
Over a 20 years period, the average marginal effect of a training episode is 1.35% in the preferred specification
Exploring heterogeneous effects Interact both the training stock
and the instrument with gender, age and firm size
In the case of firm size there are significant differences
Table 6. Effect of incentives on training and IV estimates of the effect of training on log wages. With interactions with firm size. 13 regions. (1) (2) Lagged incentives stock x 100 0.168*** [0.017] Lagged incentive stock x Firm size>100 dummy x 100
0.033*** [0.012]
Lagged Training Stock Lagged Training Stock x Firm Size>100 Dummy Elasticity firms with less than 100 employees Elasticity firms with more than 100 employees
0.401
0.410
0287*** [0.125]
-0.114* [0.066]
Marginal effect firms with less than 100 employees Marginal effect firms with 100 or more employees
0.219
0.109
Observations 11495 4474
0.0
2.0
4.0
6m
argi
nal r
etur
n
0 10 20 30time since investment
firms with < 100 employees firms with at least 100 employees
Figure 4. Marginal returns to a week of training; depr. rate:0.15
Interpretation of results: I Small firms with less than 100
employees often don’t have the resources and the facilities to train
Small firms train much less than large firms
Marginal benefits of training are decreasing in the quantity of training
When policies induce smaller firms to train, the benefits are much larger
MB
MCS
MCL
Interpretation II Small firms have lower bargaining
power In order to retain their trained
employees, they need to pay higher wage premia
Potential biases I Informal training
Additional subsidies raise formal training and reduce informal training: we over-estimate effects
Nothing we can do as informal training is not measured
Potential biases II Additional incentives induce firms to
choose longer training course and reduce shorter courses: we over-estimate effects
More incentives affect training quality as less productive courses are added in: we over-estimate effects
We regress average duration on training incentives and find no significant effect. If quality is related to duration this suggests that these biases may be small
Back of the envelope If T increases by 1 today annual earnings of
compliers increase by 2645 euro (from 14222) – this is not the average treatment effect
1 euro spent in subsidies increases the training stock today by 0.002; hence earnings increase by 5.29 (2645*0.002) for compliers
After 10 years these increases are only 20 percent of current increases
Yet
Since the average effect on the treated is different from LATE, we cannot go further than this – we would need to know the wage return of a broader group in the population of interest
Conclusions We find evidence that
The wage returns to training for those affected by training policies are relatively high
These large effects are mostly limited to small firms; trained workers in large firms who comply with the training policies have much lower returns
Conclusions Training incentives work but
moderately so: one euro per head spent in an average region (3 million euro) increases the stock of training by 0.6 percent