Incidence of corporate tax credit on profits,
wages and employment: the case of France1
First draft: January 2017
PRELIMINARY
Clément Carbonnier
Université de CergyPontoise, THEMA
Clément Malgouyres
Loriane Py
Banque de France
Banque de France
Camille Urvoy
Sciences Po
Abstract
This paper exploits a far-reaching French reform as well as a very rich set of administrative data
to evaluate the impact of a corporate tax credit aimed at reducing labor costs on several outcomes:
employment, profit and wages. The effects of the Competitiveness and Employment Tax Credit
(CETC), a refundable tax credit based on the wagebill, introduced in France in 2013, are estimated
thanks to double (and triple) difference methodologies, instrumented by the intensity of the intention
to treat, thanks to data at the firm and individual levels on the period 2010-2014. Our results
show that this relatively large tax break - about 17 billion euros per year - does not succeed in
boosting employment in the first two years after being set. We do not see neither clear-cut positive
impact on corporate profits. However, wages seem to have increased significantly in more intensively
treated firms, particularly those of white-collar employees. If these results cast doubts about the
effectiveness of such tax credits, they give new quasi-experimental evidence about the importance
of rent sharing in the labor market.
Keywords: tax credit, evaluation, employment and wage incidence;
JEL codes: D22, H25, H32 .
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We would like to thank institutions that built the databases (Insee, DGFiP, ACOSS, Douanes) and made them
available for research on the CASD, and particularly their agents who diligently answered our questions about their
content. We also thank Marc Ferracci, Sara Guillou, Yannick L’Horty, Etienne Lehmann, Benoit Ourliac, Bruno
Palier, Etienne Wasmer for their comments in ad hoc meeting organized by France Strategy or in academic seminars.
This work is supported by public grants overseen by the French National Research Agency (ANR) as part of the
“Investissements d’avenir” program within the frameworks of the Centre d’accès sécurisé aux données - CASD (ANR10-EQPX-17) and the LIEPP center of excellence (ANR-11-LABX-0091, ANR-11-IDEX-0005-02).
Researchers from the Banque de France participated to this work, the views expressed in this paper are their own
and do not necessarily reflect those of the Banque de France or the Eurosystem.
1
1
Introduction
In the context of a globalized economy in which many developed countries face low economic growth,
some governments have sought to increase firm competitiveness and boost economic activity by
implementing policies aiming at reducing labor costs. These policies can take two forms: they
can consist in direct labor tax cuts, as it was the case during many years in France. They can
also be indirect and take the form of corporate income tax credit (CIT) based on the workforce,
as recently implemented by the French government with the competitiveness and employment tax
credit (CETC). However, in response to these tax incentives, firms can respond in different ways: by
hiring more employees, by increasing the net profit or by sharing the benefit with their employees
through wage increases.
In this paper, we evaluate a far-reaching French reform, taking advantage of a very rich set
of administrative data, in order to understand how firms react in response to tax cuts and labor
cost shocks. The competitiveness and employment tax credit (CETC) was introduced in France
in January 1st 2013. This scheme consists of a CIT credit equal to 4% of the eligible wagebill
in 2013 and 6% of that eligible wagebill in 2014. Crucial for identification strategy, the eligible
wagebill corresponds to the sum of gross wages for employees paid less than two and half-time the
hourly minimum wage. In other words, the wages of salaried just above this threshold (defined
exogenously) are not eligible to the CETC and this discontinuity generates important variation in
the intensity of treatment between firms (even for firms with very similar wage structure) that we
are going to exploit.
Our empirical analysis relies on a very rich dataset at the firm and individual levels. Very
precise data on firms wage structure are found in annual social declarations database (DADS) at
the level of each job (one observation per position for each company and one observation per job for
each employee), built by Insee (French statistic agency). This database, exhaustive at the level of
salaried jobs, also informs about the type of position held both in contractual terms and in terms of
tasks. General information on the corporate structure of production and profits come from FARE
database and consist in corporate tax return which are collected by the General Directorate of
Public Finance - DGFiP - matched with survey data build by Insee. This database is exhaustive
at the level of the companies. DGFiP also specifically builds MVC file informing the company’s
entitlements to CETC and its imputations, deferrals or reimbursements.
Our empirical strategy consists in estimating the effects of the CETC by comparing the evolution
of employment, wage and profits (in levels or in growth rates) for companies more or less beneficiaries
of the CETC due to the existence of this threshold in wagebill eligibility (double and triple differences
estimations). To ensure the exogeneity of the treatment - the magnitude of CETC perceived - we
also instrument the CETC actually received by the CETC that firms could get, according to the
characteristics of their production structure the years preceding the introduction of the CETC. To
check for the robustness of this identification strategy, fixed effects and controls are introduced to
2
ensure the common trend assumption between the treated and control firms. Finally, the potential
existence of remaining diverging trends is also directly tested through placebo regressions.
Our results suggest that CETC did not succeed in boosting employment in the two first years
(2013 and 2014). We do not find neither a significantly positive impact on profits. However,
the estimates show that the corporate income tax credit has been partially shifting on to wages.
Differencing the effect on wages per type of worker, it appears that workers in permanent contracts
and white collar are the main indirect beneficiaries of the scheme. These results bring new evidence
on the existence of rent sharing in favour of insiders and in particular for white-collar workers.
An important literature has been dedicated to the evaluation of policies aiming at reducing
labor cost. Many studies focus on social contribution cuts. Bohm and Lind (1993) and Bennmarker
et al. (2009) for Sweden, and Korkeaäki and Uusitalo (2009) for Finland have developed estimates
based on geographical differences in rates (taking advantage of regional reforms in social security
contributions), and conclude to the absence of effect on employment. Social contribution cuts in
France appear to have had more favorable effects on employment (Crépon & Desplatz 2001, Kramarz
& Philipon 2001, Chéron et al., 2008). A potential reason could be that they are more targeted
on low wages. However, Huttunen et al. (2013) use double difference estimation method (by age
group) to assess the impact of social contribution cuts targeting low-wage workers in Finland: they
found no impact at the extensive margin and only a very limited impact at the intensive margin.
One reason of the importance of low-wage targeting for explaining the impact of social contribution cuts comes from wage incidence. Gruber (1994), Anderson and Meyer (1997, 2000), and
Murphy (2007) demonstrate, through natural experiments in the United States, that the share
of social contributions actually paid by employers is inversely proportional to the level of wages.
Moreover, taxation incidence on wages is not limited to social contributions: three recent empirical
analyses (Arulampalam et al., Dwenger et al., 2011, Liu & Altshuler 2013) found that about half
of the CIT rate cuts were passed on to employees through wage increases.
Our contribution to the literature is threefold. First, we evaluate the impact of the CETC
jointly on three different outcomes (employment, wage and profits) and thus provide new quasiexperimental evidence on the incidence of corporate income tax credit. Moreover using detailed
information on individual workers, we are able to document the existence and magnitude of rent
sharing in the labor market and more importantly to which categories of employees it is most
relevant (job stayers versus new hires, white-collars or blue-collars). Finally, our estimates, based
on a large and still ongoing CIT credit program, point to weak employment effects, which casts new
doubts on the relative effectiveness of such incentives. Given the popularity of cuts in corporate
income tax as a way to boost economic activity, our results are informative to the current policy
debate.
The rest of the paper is organized as follows. The databases are presented in section 2 and
the identification strategy is detailed in section 3. The results of the estimates are presented and
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discussed in section 4. Section 5 concludes and put into perspective the impacts of CETC onto the
different output in order to draw the global picture of the CETC aftermaths.
2
Data
This empirical analysis is based on three administrative databases, built from firms returns to
the tax agency (DGFiP, the French General Directorate for Public Finance) and to the institution responsible to collect social contribution (ACOSS). DGFiP has computed, since the reform,
a database specifically informing about the amount and use of the CETC at the firm-level (MVC
database). They also provide in association with Insee (the French statistical agency) a database
on firms accounting (FARE database). ACOSS provides in association with Insee a database on
workers and wages at the contract level (DADS database). We got access to these databases for the
years 2010 to 2014.
2.1
MVC database
DGFiP specifically built the MVC files informing the firms’ initializations of CETC rights. This
database, which began to be created for the 2013 vintage, contains five variables for all firms
likely to benefit from the CICE - i.e. more than 800,000 observations. These five variables are:
initialization, the amount of tax credit to which the company is entitled, initialized on its tax returns;
increase, upward adjustments given the evolution of the company’s wage structure; decrease, similar
downward adjustments; imputation, the amount of CETC that companies were able to deduct from
their CIT.
These variables allow us to understand the CETC distribution. After pairing with the other
databases (and the loss of some companies absent from certain bases), the total amount initialized
in 2013 is 9.8 billion euros. A large number of companies benefit from a relatively small amount
of CICE, with about EUR 2 756 for micro-enterprises and EUR 24 492 for SMEs, whereas the
amounts received by large companies are ten to one hundred times larger: the 288 large companies
present in the base have initiated in 2013 a tax credit approximately equal to that of the 496,750
micro-enterprises.
Initializations, besides being highly variable, represent relatively small amounts for companies:
it exceeded one percent of turnover for only one quarter of the companies in 2013 and less than half
in 2014 (which also illustrates the increase in CETC amounts between 2013 and 2014). Moreover,
the collection of these amounts remains very spread out over time because of the nature of the
tax credit nature of the CETC. The econometric results depend on this since any CETC impacts
which would occur through the relaxation of budgetary constraints could only be observed after
some years and cannot be estimated in our framework. Note that these mechanisms should not be
at stake given that French firms are not budget constraints (Kremp and Sevestre 2013). On the
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contrary, incentive effects - especially in terms of employment linked to labor costs - are quicker to
occur and our econometric framework should therefore be adapted to estimate them.
2.2
FARE database
General information on the production structure of companies and their benefits is presented in the
FARE database of the ESANE system (annual business statistics). It is built by Insee on the basis
of the tax data, social declarations and a survey. The purpose of the survey questionnaire is to
produce structural business statistics; it should be noted that the questionnaire sent to companies
was amended in 2011. This database covers all firms (including firms without employees) with the
exception of the financial sector and farms.
Our dependent variables regarding the average workforce or the profitability of the firm come
from this dataset. On this last point, the accounting entry for the CETC is unclear and it is likely
that the different companies have accounted for it differently (deduction of labor costs, operating
subsidies, other operating income or CIT deduction). Thus, the various measures of profit may
or may not take into account the CETC according to how it is accounted for. We have tried to
address this problem by considering three profit indicators: EBIT and EBITDA as a proportion of
turnover, as well as operating income as a proportion of operating costs.
We also use variables from FARE as controls: productivity (value added divided by average
workforce) and capital stock (tangible and intangible assets). As a robustness test, we also consider
the set of control variables used by Gilles et al. (2016): margin rate (EBITDA / VA), economic
profitability (EBITDA / fixed assets), productivity (VA / workforce), capital intensity (tangible
assets / workforce), share of exports in turnover, investment rate (tangible investments / VA),
debt ratio (borrowings and debts on the share capital, emission premiums, income from operations,
investment subsidies on liabilities and other equity), financial drawdown rate (interest on loans /
EBITDA).
While all other databases are defined at the firm level (with the SIREN number identifying
them), the FARE files compute combinations for some of them, which is called profiling. Indeed,
some major groups have transformed parts of their production chain into independent legal units,
while decisions remain at the central level. In order to provide a better overview of the productive
structure, Insee gathers different legal units (with different SIREN numbers) into a single entity.
For the six historical profiled companies, which only appear in the database in their profiled form,
we consider the profiled company and similarly profile the other databases. The hundreds of other
profiled companies are present in the database both under their individual SIREN and under their
profiled SIREN. We consider for them only the individual SIREN.
2.3
DADS database
The annual social data declaration (DADS) files contain information on each salaried contract in
each company: net and gross wages, working time, socio-professional categories, types of contracts,
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sex of the employee... There is one observation per contract for each company and employee. Thus,
the same employee can be found several times in the dataset if she has contracts with several
companies. It is therefore a database to be used from the point of view of the companies and not
of the employees. In addition, it is important to know that DADS are presented in the form of
regional files and that observations concerning employees of an enterprise located in one region but
residing in another region are present in the regional files of the two regions. A first work before
starting the analysis therefore consisted in purifying these databases from double accounts.
Moreover, for each item, the values of the variables are also given for the previous year. This
makes it possible to construct changes in the variables from one year to the next for each item.
Indeed, the identifiers of the contracts are not recognizable from one vintage to the other and it is
therefore not possible to build a panel of contracts. On the other hand, the company identifiers are
the SIRENs, stable over time, and we therefore constitute panels of companies. Thus, as far as wage
increases are concerned, we have operated in two ways. On the one hand we calculated the average
wages per firm each year, and compared them from one year to the next. Since the changes can
be due both to changes in the wages themselves or to the structure of employment in the company,
we have also calculated the growth hourly wage for each position present two following year in the
same firm. Then, we aggregated by calculating for each year the average of individual wage growth.
Pay data is accurate in that it is at the job level, but relatively imprecise as to what it covers.
Gross remuneration includes “all remuneration received by the employee under her contract of
employment, before deducting compulsory contributions”. For instance, it includes bonuses for end
of fixed-term contracts (corresponding to 10% of the amounts received during the contract). This
can lead to biases in the observation of hourly wage growth since these bonuses inflate the total
gross earnings for the contract year but not the number of hours worked. In order to measure the
growth of hourly wages, it is therefore necessary to purge the bases of these observations at the end
of the contract.
In order to carry out our identification strategy, it is necessary to be able to measure the potential
CETC to which an enterprise would have been entitled before the actual reform implementation,
according to its productive structure. However, this is not possible on the basis of actual tax data,
which did not collect such information prior to 2013. However, the DADS database allows us to
approach these values thanks to the precision on wage structures companies. It is indeed possible to
calculate the share of wagebill below 2.5 minimum wage, and to compute a potential CETC. This
calculation for the years 2013 and 2014 is very close to the amounts of CETC actually initialized
with the tax departments and presented in the MVC database. Similar wage structure indicators
are also built at other thresholds: 1.5, 2, 3 and 3.5 minimum wage.
2.4
Building the merged database
We are working on the matching of the three previously presented databases. After the DADS-MVC
matching, we calculate the ratio of the CETC imputed on the basis of the DADS database over the
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actual CETC initiated in the MVC database. We exclude from the sample the firms whose ratio
belongs to the upper percentile in 2013 or in 2014. We then perform the matching with the FARE
database. Only the companies present in the 3 bases (DADS-FARE-MVC) are kept.
We then make two selections. First, we keep only companies that have at least one full-time
equivalent job over the year, so as not to be biased by the empty shells. Then, we constructed
the balanced panel database over the period 2010-2014. There are then slightly fewer than 500,000
firms in the final database used for the estimates.
3
Identification Strategy
The aim of the present evaluation is to use the French CETC reform to estimate the impact of
wage costs’ decreases on firms’ behavior. Calling Y the dependent variable and C the cost variable
(depending on the types of behavior studied, we can look at the total production costs T C or wagebill
only W B only). We intend to measure the elasticity Y,C = ∂ ln(Y )/∂ ln(C). This elasticity may
result from various economic mechanisms. This may be a form of rent sharing in the case of wage
increases resulting from CIT rates decreases (Arulampalam et al. 2012, Liu and Altshuler 2013,
Dwenger et al. 2011), substitution between production factors whose relative prices have been
modified (Chirinko et al., 2011, Karabarbounis and Neiman 2013) or changes in the volume of
output due to lower prices associated with lower costs.
Obviously, firms’ production costs are strongly related to firms’ behavior, and it is not possible
to directly estimate the link between costs and the various dependent variables. The reform of
the CETC not only needs to be evaluated per se, it also provides an opportunity to assess how
firms react to an (indirect) decrease in wage cost. Indeed, it exogenously alters the production
costs through a tax credit based on wages lower than 2.5 minimum wage. We use this exogenous
variation in production costs to implement a double difference estimation of the impact of wage
costs on firms’ behavior.
However, such an estimate generally requires dividing firms in two groups: the treatment group
containing firms impacted by the reform and the control groups containing those which are not.
It is not possible here to set up a control group because virtually all companies benefit from the
reform. However, the extent to which CETC reduces production costs varies widely among firms,
including between like-minded firms in the same economic sector. Figure 1 present the distribution
of treatment intensity within two category of firms (categorization with respect to four sizes and
six industries), which correspond to the two extreme in terms of distribution of treatment intensity.
Both show a large variation in treatment intensity.
We therefore implement double difference estimations on the treatment intensity, considered as
a continuous variable. The logarithm of the dependent variable is regressed on the logarithm of
the production cost (less the CETC from 2013). This intensity of treatment can be considered on
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the basis of wage costs only (regressions on employment and wages) or on total production costs
(profits).
However, two main reasons may cause estimation bias:
1/ Reverse causality: any company increasing its payroll one year - for reasons independent
of the CETC - de facto increases the intensity of its treatment. Thus, treatment intensity is
fundamentally endogenous to payroll growth (as long as it remains below 2.5 minimum wage).
2/ Common trend assumption: any double difference estimate requires that the common
trend assumption between more intensively and less intensively treated groups be verified. Here,
this means that firms that are more or less intensely treated by the CETC (i.e. companies whose
eligible wagebill is more or less important compared to production costs) would have behave the
same way in the absence of the CETC. This is not the case and it is therefore necessary to ensure
that regressions are effectively controlling for potential heterogeneity of trends.
The statistical treatment of these two potential biases will be different and presented in the next
two subsections.
3.1
Reverse causality
In order to tackle the reverse causality issue, a common solution used in the literature is to assign
treatment on the lagged values of the variables which constitute the tax base or the subsidies. This
strategy was used by Auten and Carroll (1999) in their estimation of the impact of the taxation
of earned income, by applying the variation on the rate of earned income the year preceding the
reform. In our case, it comes to use the relative stability in the production structure and to consider
the ratio of eligibility the years preceding the introduction of the CETC as a proxy of the ratio of
Figure 1: Distribution of treament intensity among manufacturing and personal services SMEs
Source: DADS-FARE-MVC 2013-2014
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eligibility ex ante. In other words, we use the ratio of eligibility that firms would have had given
their production structure before the implementation of the CETC so as not to take into account
their endogenous response behavior in the computation of the ratio of eligibility and avoid the
reverse causality issue. The same type of methodology was used in the case of France by Crépon et
Desplatz (2001) to evaluate the impact of social contribution rebates in France.
In order to ensure that the reverse causality is not anymore an issue, one has to check for the
validity of the instrument. A good instrument must fulfill two conditions: it has to be exogenous
(contrary to the regressor that it is intended to instrument) and is has to be highly correlated
with this regressor. Regarding the first criterion, temporal lags ensure exogeneity. The intensity of
treatment is calculated with previous year wage structure, which has been chosen by firms in order
to adapt the 2012 economic situation (with past dependency), without knowing about the existence
and future introduction of the CETC. Indeed, the tax credit has been voted at the very end of
2012, and was presented and discussed in very short time at the end of this year. Consequently,
the intensity of the intention to treat (potential treatment prior to firm behavior in response to
treatment) can be considered as exogenous.
Regarding the second criterion, one can analyze the power of prediction of the effective treatment
(i.e. of the CETC initialized in 2013 according to the tax records in the MVC database). In our
case, it comes from the relative stability of firm production structure over time. Indeed, regressions
of effective treatment (tax credit effectively initialized by companies in 2013 according to the MVC)
on the instrument (i.e. tax credits predicted according to the wagebill of wages inferior to 2,5 the
minimum wage the year preceding the reform), reveal the predictive power of this instrument. The
coefficient is always highly significant and very near from unity. Besides, these first stage regressions
contribute to explain most of the variance of the instrumented variable : more than 90% of the
CETC initialized when measured as as a share of wage cost and between 66% and 80% of CETC
initialized when measured as a share of total costs.
The main principle of the double difference estimation is to compare the evolution of treated
and control groups before and after a reform. In order to do so, we estimate a panel regression
with individual fixed effects, a time dummy and a time dummy interacted with the treated group.
Given that we our control group is not composed of firms which do not benefit from the tax credit
(extensive margin) but rather by firms less intensively treated (intensive margin), we interact the
time dummy with the “intention to treat” (i.e. the tax credit that firms could get given their
production structure the years preceding the reform). This is summarized in the following equation
1.
ln(Yi,t ) = α + β13 .Ii,t .1[t=2013] + β14 .Ii,t .1[t=2014] +
X
j
where Ii,t = − ln(1 −
i
CICEi,t−1
Ci,t−1 )
γj .Xj,i,t +
X
γf .1[f ] + i,t
(1)
f
is the intention to treat computed as a share of production costs.
Note that depending on the specification, production costs correspond to the total wagebill (from
the DADS database) or to the total production costs (from the FARE database). Xj,i,t stand for
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the values of different controls j, 1[f ] for the different fixed effects and i refers to firms and t to
year. For ensuring exogeneity, as for treatment variables, control variable are also lagged one year.
The coefficients β13 et β14 can be interpreted as elasticities of the variable Yi,t relative to production
costs for 2013 and 2014. Note that a “negative sign” was added in front of the intensity variable
for the ease of interpretation given that the CETC represents a diminution in costs. This choice
was made so that regressions coefficients can be easily interpreted as the impact of the CETC: a
positive sign in result tables means that the CETC has a positive impact on the outcome variable.
3.2
The common trend assumption
To be relevant, the double difference estimation relies on a strong assumption: the one of “common
trend assumption”. In other words, this method is valid if and only if firms with different intensity
to treat follow the same trend before the introduction of CETC. This assumption, when verified,
means that differences between the treated and less treated firms would have remained constant
over time in the absence of the policy under evaluation, and therefore ensure that relatively less
intensively treated firms can serve as valid counter-factual for more intensively treated firms. In
the opposite case, if those firms would not follow the same trend before the reform, it would be
impossible for the econometrician to disentangle, in the post-reform evolution between the treated
and less intensively treated firms (double difference estimation), what can be attributable to the
policy from what can be attributable to any other confounding factor.
As this is not always the case, one can take into account any potential pre-reform different
trends by including several controls for capturing these trends. This becomes a common trend
ceteris paribus. In this purpose, we add may controls in the regressions. First, we introduce “sector
× year” and “firm-size × year” fixed effects in order to capture all specific sectoral and size class
trends. We also control for firm fixed effects to control for all the unobservable which are specific
to a firm and constant over time. Moreover we add different time-varying controls at the firm-level
which can also influence our outcome variables: productivity (measured as a ratio of valued added
on average employment), capital stock (tangible and intangible assets) as well as firm average wage.
Moreover, we add many controls of a firm production structure. In order to capture most of the
intrinsic differences pre-reform between the firms which (will) become more intensively and less
intensively treated after the introduction of the CETC, we control for the share of wagebill inferior
to 2.5 the minimum wage, Ii,t , without interacting it with the year dummies.
Moreover, as in France, there are some yearly variations in the minimum wage and some exemptions which are proportional to the minimum wage, these variations can impact the total wagebill
and therefore also impact our outcome variables. As a consequence, we also add controls of the
share of wagebill exposed to the minimum wage variations to avoid estimation bias. In particular,
[1,5]
M Si,t
M Si,t
[1,5]
M Si,t its
a =
we introduce IM ISCi,t
∗ 1[t=a] for the different years a, where M Si,t is the gross wagebill
of firm i in year t and
wagebill when workers payed less than 1.5 the minimum wage are
considered.
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Moreover, we also include specifications in which we replace our set of controls by the one used
by Gilles et al. (2016): profit margin (Gross operating surplus/value added), economic rentability
(Gross operating surplus/tangible and intangible assets), productivity (Value added/workers), capital intensity (tangible assets / workers), share of export in the turnover, investment rate (tangible
investments/value added), rate of debt (borrowing and debts on the sum of share capital, issue
premium, investment subsidy in the liabilities and other equities), rate of financial burden (borrowing interest/gross operating surplus), as well as different elements of the composition of workers by
gender (share of women) by socio-professional category (share of blue-collars, white-collars etc.) or
by type of contract (full-time, part-time, short-term or long-term contracts).
In order to check that these controls properly capture the intrinsic pre-reform differences between firms relatively more or less intensively treated, a usual test consists in operating ”placebo”
regressions. The idea is to proceed to the same regression as described in equation 1, but only on
the years preceding the effective introduction of the CETC, in order to measure the ” fictive effect”
of the introduction of the CETC in 2012. This comes to estimate the following equation 2.
ln(Yi,t ) = α + βplacebo .Ii,t .1[t=2012] +
X
γj .Xj,i,t +
j
X
γf .1[f ] + i,t
(2)
f
The placebo test is valid only if the coefficient βplacebo is not significantly different from zero;
meaning that the dependance of the outcome variable in the structure of production which induces
the intention to treat is stable before the introduction of the CETC. In other words, this is a test
of the common trend assumption. If the placebo test validates the common trend assumption, the
coefficients β13 and β14 can be seen as unbiased estimates of the elasticity of the outcome variable
Y on wage cost (or production costs depending on the outcome of interest). If the placebo test
rejects the common trend assumption, one has to better control for this trend heterogeneity.
One way to do it is to estimate a triple difference rather than a double one, which reduced from
is given by equation 3.
∆ ln(Yi,t ) = α + β13 .∆Ii,t .1[t=2013] + β14 .∆Ii,t .1[t=2014] +
X
j
γj .∆Xj,i,t +
X
γf .1[f ] + i,t
(3)
f
where ∆ stands for first difference estimator. In this specification ”in trend”, the firm fixed effects
measures the trend (out of treatment) in the growth rate of the outcome variable Y specific to each
firm.
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4.1
Results
Employment
The objectives of the CETC was primarily to boost employment; this is usually the main goal of
policies aiming at decreasing labor cost. There are several ways of counting employment: the two
most usual being the number of workers employed (whatever their working time) and the number
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of full-time equivalent jobs (or equivalently the number of hours worked). We analyze these two
measures as dependent variables. Furthermore, if the number of hours are only given by the DADS
database, the workforce in each firm is given by two different sources: the DADS and the FARE
databases. The two variables are regressed for checking the robustness of our results.
Moreover, let’s recall that we are doubling the estimates: a series of regressions is weighted by
workforce in 2012 and the other is not weighted. Unweighted regressions give more importance
to small firms because they are more numerous; they reveal the behavior of firms, considered as
decision-making units. Conversely, weighted regressions give more weight to firms with a greater
share of jobs; their coefficients are closer to an interpretation of macroeconomic effects. We choose
to present both types of estimates because they are both relevant and complementary.
Considering all kind of workers, the results of estimations are presented in tables 4 to 9 in
appendix A.1. The unweighted regressions validate the common trend assumption, with placebo
tests very close to zero (not significant despite very small standard errors). Hence, the double
difference regressions may be trusted. The main results are summarized in table 1.
Table 1: CETC impact on employment
Dependent variable
Workforce DADS Workforce FARE
Without SMIC control
Placebo test
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
With SMIC control
Placebo test
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Hours DADS
-0.05778
(0.0523)
-0.524***
(0.0542)
-0.517***
(0.0454)
1789248
0.973
-0.0138
(0.0403)
-0.206***
(0.0435)
-0.187***
(0.0376)
1789102
0.979
-0.0133
(0.0386)
-0.259***
(0.0398)
-0.196***
(0.0360)
1789247
0.982
-0.0585
(0.0537)
-0.499***
(0.0547)
-0.490***
(0.0470)
1788824
0.973
0.0391
(0.0438)
-0.180***
(0.0444)
-0.144***
(0.0400)
1788684
0.979
0.0705
(0.0467)
-0.222***
(0.0416)
-0.141***
(0.0386)
1788823
0.982
Notes: Regression of the dependent variable (logarithm of workforce from DADS or FARE databases and yearly
hours) on the intensity of the intention to treat, with controls for firm productivity, capital stock, mean wage and
wage structure and fixed effects: year×industry, year×size and firm.
Robust standard errors in parentheses (firm level cluster), * p < 0.05, ** p < 0.01, *** p < 0.001
Sources: DADS, FARE, MVC 2010-2014.
They give the same results whatever the specification and whatever the employment indicator:
the coefficient are small and no positive impact of CETC on employment appears. Conversely, the
12
coefficients are significantly negative, yet very small. This result is counter-intuitive. The weighted
regressions give more noisy results, with larger standard errors. The placebo tests are validated only
for the DADS workforce indicator: in that case, double regression coefficients are close to zero and
not significant. For the other two indicators, the placebo tests reject the common trend assumption
for double difference estimations. Triple difference estimates are not significant for the workforce
FARE, neither for the hours in 2013 but they are negative for the hours in 2014.
Given that average effects can hide heterogeneity between workers, we sought to reproduce these
estimates for different categories of employees apart. For these categorized dependent variables, we
use only workforce extracted from DADS (as such details are not available in the FARE database).
The first categorization concerns the socio-professional categories, the results of the regressions
being presented in tables 10 to 19 in appendix A.2. The placebo tests validate the common trend
assumption for trademen and entrepreneurs and for intermediate occupations: the estimates for
CETC impact are small and not significant. For executives and higher intellectual occupations, only
the weighted specifications validate the placebo test and their estimates are also not significant. For
blue collar workers, we find the same results as for global estimations, with validated placebo tests
and negative coefficients of impact estimation. Lastly, double difference placebo tests fail for other
employees, and the triple difference coefficients are small but significantly negative.
As presented in tables 20 to 23 in appendix A.3, no difference appears between male and female workers: the results for both are the same, and very close to those obtained for all workers
estimations. Concerning contract types (tables 24 to 27 in appendix A.4, the results are mainly
inconclusive but the placebo tests fail. Triple difference estimates are not significant because of
large standard errors.
4.2
Profits
If the CETC has not been used to increase the workforce, keeping the tax credit as net profit is
another possible use. However, it is not trivial to properly measure it because there is no automatic
way of including it in firm accounts. Therefore, we consider three profit indicators: gross margins
(EBIT as a proportion of turnover), net margins (EBITDA as a proportion of turnover), and
operating margins (operating income as a proportion of operating costs). Companies were advised
to account for the CETC as “deduction of personnel costs”. If they did it in this way, then the
CETC should appear in all three profit indicators. However, many other accounting entries were
possible, some involving not taking into account this writing in one or more indicators.
The results of the estimation of CETC on each of the three profit indicators are presented in
table 28 to 33 in appendix B and main rsults are summarized in table 2. The results differ between
the profit indicators. For net and gross margins, the weighted regressions are not very precise
with very large standard errors, but the unweighted regressions present very small standard errors.
Their placebo tests are validated and the estimations of the CETC impact on these indicators give
small and not significant results. Conversely, the regressions of operating costs are more valid when
13
weighted with the 2012 workforce. In that case, the coefficient of the placebo test is very close
to zero. The CETC impact estimates are the same in the two central specifications (regressions 3
and 4): there is no impact in 2013 but a positive impact appear in 2014. However, this result is
weak because it is not significant at the 5% threshold (the p-value is equal to 5.01% for the two
regressions).
Table 2: CETC impact on profits
Dependent variable
Net margins Gross margins Operating Margins
Without SMIC control
Placebo test
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
With SMIC control
Placebo test
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
-6.617
(5.516)
0.483
(1.118)
-2.103
(2.249)
1787711
0.566
2.004
(1.514)
1.460
(1.113)
-0.103
(0.599)
1787737
0.442
-0.0968
(0.0995)
0.0845
(0.146)
0.239
(0.122)
1788260
0.717
-5.222
(4.202)
0.483
(1.118)
-2.103
(2.249)
1787711
0.566
2.103
(1.211)
1.460
(1.113)
-0.103
(0.599)
1787737
0.442
-0.0457
(0.104)
0.0804
(0.146)
0.239
(0.122)
1788260
0.718
Notes: Regression of the dependent variable on the intensity of the intention to treat, with controls for firm productivity, capital stock, mean wage and wage structure and fixed effects: year×industry, year×size and firm, and weighted
with 2012 workforce.
Robust standard errors in parentheses (firm level cluster), * p < 0.05, ** p < 0.01, *** p < 0.001
Sources: DADS, FARE, MVC 2010-2014.
It is therefore difficult to interpret these results rigorously. It seems that there is no impact on
gross or net margins, and we cannot say for sure that they exist on operating margins even if this is
very plausible on reading the results. This possible divergence, and the great difficulty in obtaining
precise results, can arise from the fact that the companies have very varied accounting records of
the CICE, and that this therefore appears at various places on their balance sheet, more or less well
taken into account by our different margin indicators. However, if we trust the weighted regressions
on operating margins, they mean that about a quarter of the CETC has been kept by firm as profit.
14
4.3
Wages
If only a quarter of the CETC has translated into increases in firm profits and if they did not
use this credit to hire, firm might have shared the benefits of the tax credit with their employees.
First of all, the tax credit scheme is very particular since it is based on the payroll by generating
a significant threshold effect: the CETC rate is constant (4% in 2013 and 6% starting in 2014)
and suddenly died down to 2.5 SMICs. Thus, a full-time employee paid two and a half times the
minimum wage opened in 2014 an annual tax credit of 2,600 euros for his employer. If she had been
paid even a few euros more, her employer could not have received any tax credit. Hence, employers
may be particularly reluctant to grant increases to their employees close to the threshold or they
will seek to set the wage of their new recruits as far as possible below this threshold.
In their extreme configurations, strong employer reactions to these two issues (increases and
hires) could lead to bunching at the threshold (Saez 2010). However, Carbonnier et al. (2014)
showed that a gap discontinuity in a framework where the assignment variable was only imperfectly
controlled could lead not to a point of accumulation but to a discontinuity in the values of the
Table 3: CETC impact on wages
Dependent variable: mean hourly wage of stable employees
Executives
Intermediate
Blue collar
& intellectual
occupations
workers
Without SMIC control
Placebo test
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
With SMIC control
Placebo test
Intention to treat, intensity, 2013
Intention to treat, intensity, 2014
Observations
R2
0.300
(0.283)
0.929***
(0.231)
0.772***
(0.142)
549992
0.884
0.190
(0.304)
0.372
(0.215)
0.422**
(0.149)
613060
0.881
0.515
(0.269)
-0.465
(0.372)
-0.160
(0.186)
932378
0.897
0.274
(0.280)
0.865***
(0.229)
0.754***
(0.137)
549869
0.884
0.175
(0.300)
0.233
(0.235)
0.372*
(0.147)
612930
0.881
0.490
(0.267)
-0.482
(0.376)
-0.141
(0.184)
932181
0.897
Notes: Regression of the dependent variable on the intensity of the intention to treat, with controls for firm productivity, capital stock, mean wage and wage structure and fixed effects: year×industry, year×size and firm.
Robust standard errors in parentheses (firm level cluster), * p < 0.05, ** p < 0.01, *** p < 0.001
Sources: DADS, FARE, MVC 2010-2014.
15
variables involved. Carbonnier et al. (2016) study the two hypotheses and did not found the tiniest
sign of wage setting behavior around the threshold.
However, the wage behavior may be more spreadly distributed. The benefit of the CETC may
be partially redistributed to employees independently on their wage position vis à vis the threshold.
In order to test this, we apply our identification strategy to wage indicators. The results for all
types of employees are presented in table 34 to 39 in appendix C.1. Three different indicators are
considered: mean yearly wage per employee (based on FARE workforce indicator), mean hourly
wage of staying employees and the mean of individual hourly wage growth. For yearly and hourly
wages, double difference placebo tests fail but triple difference estimates are significantly positive.
For the mean of hourly wage growth, placebo tests validate the common trend assumption for the
weighted specifications. In that case, the estimates are also significantly positive; moreover, their
value around 50% is very close to previous estimates of the CIT incidence on wages (Arulampalam
et al. 2012, Dwenger et al. 2011, Liu & Altshuler 2013).
For separating by worker type, we cannot keep the FARE indicator and regress only the two
DADS indicators (mean hourly wage and hourly wage growth). The results by socio-professional
category are presented in table 40 to 59 in appendix C.2, and the main results are summarized in
table 3.
For all socio-professional categories, the unweighted regressions violate the common trend assumption. Conversely, the placebo tests of the weighted specifications succeed. From those regressions, it appears that wages increased strongly for executives and higher intellectual occupations,
and in a lower extent for intermediate occupations (the results are the same with the two wage
indicators). However, the two indicators give opposite results in the case of blue collar workers and
other employees: the coefficient are not significant (but negative) for mean hourly wage, although
they are small but significantly positive for mean growth of hourly wages in 2014. Last, no clear
difference appears between wages of workers in permanent or fixed term contracts, whose results
are presented in table 60 to 67 in appendix C.3.
5
Conclusion
In this paper, we exploit a large French CIT reform, introduced in 2013, (competitiveness and
employment tax credit, CETC) to assess the impact of corporate tax aiming at reducing labor costs
on firm behavior. Our empirical analysis relies on three exhaustive databases which contain precise
information at the firm and individual levels on the period 2010-2014. We set an identification
strategy in double (and triple) difference based on the intensity of the intention to treat to quantify
the impact of the introduction of this tax credit on three different outcomes: employment, profit
and wages.
Due to important heterogeneity in the way firms write the CETC in their account, estimations of
the impact on profit are difficult to interpret. The results are mainly inconclusive even if a positive
16
impact is weakly found. On the contrary, the results concerning employment and wages are much
more clear-cut and robust. The CETC has no positive impact on employment. Even more, some
counter-intuitive results are found for lower socio-professional categories, whereas the coefficients
are close to zero for upper socio-professional categories. Conversely, the impact on wage appears
strong and significant. The overall incidence is found about 50%: half of the CETC benefits has
been distributed to employees through wage increases. This results is close to previous estimates in
the literature. Moreover, the results on wages differ depending on the socio-professional category
of the employees. The stronger impact is found for executive and higher intellectual occupations;
intermediate occupations benefit from mean wage increases. For blue collar workers and other
employees, the results are not always significant and appear less robust. Nevertheless, it seems
that they also benefit from wage increases, but smaller. These results give new evidence about the
importance of taking into account rent sharing in favor of white-collar employees when it comes to
assess the effectiveness of such tax incentives.
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18
A
A.1
Results of CETC assessment: impact on employment
Impact on employment, all employess
Table 4: CETC impact on employment, workforce DADS
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.689∗∗∗
(0.0907)
(2)
-0.0758
(0.0505)
(3)
-0.0578
(0.0523)
(4)
-0.0585
(0.0537)
(5)
(6)
-0.0713
(0.0858)
-0.132∗
(0.0633)
1918585
0.685
-0.517∗∗∗
(0.0536)
-0.430∗∗∗
(0.0450)
1918584
0.971
-0.524∗∗∗
(0.0542)
-0.517∗∗∗
(0.0454)
1789248
0.973
-0.499∗∗∗
(0.0547)
-0.490∗∗∗
(0.0470)
1788824
0.973
-9.380∗∗∗
(0.123)
-6.301∗∗∗
(0.0835)
1347311
0.718
-0.273∗∗∗
(0.0482)
-0.364∗∗∗
(0.0434)
1345161
0.983
-0.236∗∗
(0.0721)
0.164
(0.145)
1438938
0.006
√
√
-0.303∗∗∗
(0.0727)
-0.0841
(0.148)
1438938
0.300
√
√
√
-0.348∗∗∗
(0.0750)
-0.680∗∗∗
(0.154)
1348159
0.305
√
√
√
√
-0.322∗∗∗
(0.0779)
-0.595∗∗∗
(0.161)
1347902
0.305
√
√
√
√
√
0.735∗∗∗
(0.0505)
1.483∗∗∗
(0.0978)
1347311
0.166
√
√
-0.368∗∗∗
(0.0633)
-1.161∗∗∗
(0.129)
1345161
0.496
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 5: CETC impact on employment, workforce DADS (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-5.144∗∗∗
(1.378)
(2)
-1.314∗∗
(0.422)
(3)
-0.784
(0.438)
(4)
-0.740
(0.435)
(5)
(6)
-4.408∗∗∗
(0.915)
-2.969∗∗∗
(0.786)
1918585
0.905
-0.121
(0.280)
-0.208
(0.412)
1918584
0.997
-0.177
(0.277)
-0.135
(0.351)
1789248
0.998
-0.185
(0.277)
-0.178
(0.347)
1788824
0.998
-3.804∗
(1.902)
-2.631∗
(1.340)
1347311
0.918
0.140
(0.220)
0.131
(0.289)
1345161
0.999
2.296∗∗
(0.751)
2.776
(1.769)
1438938
0.059
√
√
1.099∗
(0.557)
0.132
(1.552)
1438938
0.396
√
√
√
0.938
(0.559)
0.0342
(1.400)
1348159
0.398
√
√
√
√
0.963
(0.549)
0.0584
(1.363)
1347902
0.398
√
√
√
√
√
3.069∗∗∗
(0.507)
4.421∗∗∗
(1.173)
1347311
0.220
√
√
0.598
(0.319)
-0.488
(0.837)
1345161
0.584
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
19
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 6: CETC impact on employment, workforce FARE
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.153∗
(0.0762)
(2)
-0.0565
(0.0397)
(3)
-0.0138
(0.0403)
(4)
0.0391
(0.0438)
(5)
(6)
0.273∗∗∗
(0.0729)
0.115∗
(0.0536)
1918368
0.758
-0.180∗∗∗
(0.0434)
-0.168∗∗∗
(0.0377)
1918360
0.978
-0.206∗∗∗
(0.0435)
-0.187∗∗∗
(0.0376)
1789102
0.979
-0.180∗∗∗
(0.0444)
-0.144∗∗∗
(0.0400)
1788684
0.979
-9.118∗∗∗
(0.114)
-6.151∗∗∗
(0.0759)
1347204
0.762
-0.515∗∗∗
(0.0438)
-0.418∗∗∗
(0.0385)
1345085
0.983
-0.425∗∗∗
(0.0604)
0.359∗∗
(0.119)
1438658
0.012
√
√
-1.076∗∗∗
(0.0598)
-1.172∗∗∗
(0.116)
1438578
0.332
√
√
√
-1.135∗∗∗
(0.0575)
-2.893∗∗∗
(0.119)
1347980
0.411
√
√
√
√
-1.075∗∗∗
(0.0612)
-2.657∗∗∗
(0.135)
1347723
0.411
√
√
√
√
√
-0.529∗∗∗
(0.0550)
0.199
(0.105)
1347204
0.016
√
√
-1.176∗∗∗
(0.0613)
-1.314∗∗∗
(0.125)
1345085
0.350
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 7: CETC impact on employment, workforce FARE (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-2.072
(1.267)
(2)
-2.138∗∗∗
(0.615)
(3)
-1.603∗∗
(0.575)
(4)
-1.415∗
(0.576)
(5)
(6)
-2.937∗∗∗
(0.758)
-1.844∗∗
(0.603)
1918368
0.882
-0.390
(0.276)
-0.0924
(0.242)
1918360
0.995
-0.404
(0.288)
-0.0813
(0.237)
1789102
0.996
-0.354
(0.289)
-0.0824
(0.230)
1788684
0.996
-7.549∗∗∗
(1.738)
-4.830∗∗∗
(1.103)
1347204
0.897
-0.172
(0.308)
-0.0597
(0.211)
1345085
0.997
1.824∗
(0.834)
4.982∗∗∗
(1.499)
1438658
0.032
√
√
-0.0159
(0.628)
0.540
(0.942)
1438578
0.366
√
√
√
-0.0299
(0.557)
-1.228
(0.855)
1347980
0.479
√
√
√
√
0.0203
(0.563)
-1.242
(0.852)
1347723
0.479
√
√
√
√
√
0.424
(0.419)
2.222∗∗
(0.687)
1347204
0.053
√
√
-0.288
(0.611)
-0.395
(0.905)
1345085
0.419
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
20
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 8: CETC impact on hours worked
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.103
(0.0732)
(2)
-0.0370
(0.0372)
(3)
-0.0133
(0.0386)
(4)
0.0705
(0.0467)
(5)
(6)
0.195∗∗
(0.0699)
0.0913
(0.0519)
1918584
0.773
-0.242∗∗∗
(0.0397)
-0.176∗∗∗
(0.0360)
1918583
0.981
-0.259∗∗∗
(0.0398)
-0.196∗∗∗
(0.0360)
1789247
0.982
-0.222∗∗∗
(0.0416)
-0.141∗∗∗
(0.0386)
1788823
0.982
-9.619∗∗∗
(0.105)
-6.461∗∗∗
(0.0711)
1347310
0.777
-0.601∗∗∗
(0.0390)
-0.442∗∗∗
(0.0361)
1345160
0.986
-0.506∗∗∗
(0.0552)
0.466∗∗∗
(0.113)
1438936
0.014
√
√
-1.194∗∗∗
(0.0534)
-1.128∗∗∗
(0.108)
1438935
0.363
√
√
√
-1.264∗∗∗
(0.0525)
-3.238∗∗∗
(0.113)
1348157
0.407
√
√
√
√
-1.215∗∗∗
(0.0684)
-2.811∗∗∗
(0.156)
1347900
0.408
√
√
√
√
√
-0.586∗∗∗
(0.0475)
0.348∗∗∗
(0.0946)
1347309
0.017
√
√
-1.310∗∗∗
(0.0550)
-1.297∗∗∗
(0.114)
1345158
0.381
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 9: CETC impact on hours worked (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-2.982∗∗
(1.156)
(2)
-2.416∗∗∗
(0.587)
(3)
-1.856∗∗∗
(0.554)
(4)
-1.601∗∗
(0.558)
(5)
(6)
-4.183∗∗∗
(0.765)
-2.724∗∗∗
1918584
0.908
-1.147∗∗∗
(0.322)
-0.793∗∗
1918583
0.997
-1.181∗∗∗
(0.333)
-0.793∗∗
1789247
0.997
-1.147∗∗∗
(0.331)
-0.796∗∗
1788823
0.997
-6.188∗∗∗
(1.846)
-4.080∗∗∗
1347310
0.919
-1.003∗∗
(0.317)
-0.700∗∗
1345160
0.998
1.249
(0.782)
4.761∗∗∗
(1.430)
1438936
0.033
√
√
-0.817
(0.547)
0.162
(0.809)
1438935
0.374
√
√
√
-0.878
(0.527)
-2.783∗∗∗
(0.824)
1348157
0.425
√
√
√
√
-0.914
(0.554)
-2.679∗∗
(0.840)
1347900
0.425
√
√
√
√
√
-0.431
(0.445)
1.536∗
(0.689)
1347309
0.049
√
√
-1.092
(0.565)
-0.535
(0.839)
1345158
0.413
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
21
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
A.2
Impact on employment, per socioprofessional category
Table 10: CETC impact on employment, tradesmen and entrepreneurs
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.00247
(0.110)
(2)
0.367∗∗∗
(0.0935)
(3)
0.365∗∗∗
(0.0961)
(4)
0.361∗∗∗
(0.0963)
(5)
(6)
0.0623
(0.101)
0.0544
(0.0745)
353074
0.203
0.0635
(0.0882)
0.146∗
(0.0685)
330467
0.864
0.0330
(0.0893)
0.0789
(0.0701)
321581
0.866
0.0590
(0.0909)
0.0658
(0.0836)
321521
0.866
-1.662∗∗∗
(0.124)
-1.108∗∗∗
(0.0826)
257103
0.220
-0.0690
(0.0971)
0.108
(0.0753)
233056
0.893
-0.314∗
(0.143)
-0.182
(0.249)
227062
0.006
√
√
-0.192
(0.160)
0.0297
(0.291)
207842
0.211
√
√
√
-0.279
(0.162)
0.0710
(0.300)
203744
0.212
√
√
√
√
-0.229
(0.166)
-0.00224
(0.339)
203716
0.213
√
√
√
√
√
0.0965
(0.0965)
0.732∗∗∗
(0.179)
222269
0.015
√
√
-0.211
(0.159)
0.0367
(0.303)
203009
0.259
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 11: CETC impact on employment, tradesmen and entrepreneurs (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
4.479
(3.992)
(2)
-0.304
(3.753)
(3)
-0.494
(3.901)
(4)
-0.599
(3.667)
(5)
(6)
-0.784
(2.910)
1.307
(2.700)
353074
0.701
-2.576
(3.729)
-2.284
(2.902)
330467
0.910
-2.738
(3.855)
-2.280
(2.895)
321581
0.910
-2.427
(3.463)
-2.134
(2.671)
321521
0.910
2.963
(3.001)
3.697
(2.440)
257103
0.797
0.389
(2.218)
0.731
(1.817)
233056
0.987
-0.990
(4.249)
-2.780
(8.329)
227062
0.211
√
√
4.646
(6.090)
7.862
(13.15)
207842
0.460
√
√
√
4.965
(6.410)
4.053
(9.724)
203744
0.482
√
√
√
√
5.884
(6.527)
8.262
(9.157)
203716
0.482
√
√
√
√
√
1.018
(2.714)
2.766
(5.198)
222269
0.228
√
√
2.628
(4.762)
7.948
(10.85)
203009
0.514
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
22
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 12: CETC impact on employment, executives and higher intelectual professions
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.923∗∗∗
(0.142)
(2)
0.668∗∗∗
(0.104)
(3)
0.673∗∗∗
(0.110)
(4)
0.709∗∗∗
(0.111)
(5)
(6)
-0.764∗∗∗
(0.132)
-0.102
(0.0962)
789800
0.630
0.777∗∗∗
(0.0981)
1.008∗∗∗
(0.0773)
756315
0.948
0.840∗∗∗
(0.101)
0.871∗∗∗
(0.0801)
707047
0.951
0.869∗∗∗
(0.102)
0.932∗∗∗
(0.0871)
706869
0.951
-12.06∗∗∗
(0.205)
-7.813∗∗∗
(0.139)
560279
0.702
1.604∗∗∗
(0.106)
1.598∗∗∗
(0.0838)
526144
0.961
0.605∗∗∗
(0.158)
-0.219
(0.288)
533708
0.005
√
√
1.446∗∗∗
(0.169)
0.929∗∗
(0.314)
505723
0.218
√
√
√
1.616∗∗∗
(0.175)
1.910∗∗∗
(0.331)
475912
0.224
√
√
√
√
1.632∗∗∗
(0.177)
1.707∗∗∗
(0.348)
475824
0.224
√
√
√
√
√
-0.0936
(0.109)
-1.014∗∗∗
(0.214)
503452
0.041
√
√
1.663∗∗∗
(0.157)
2.857∗∗∗
(0.331)
476147
0.368
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 13: CETC impact on employment, executives and higher intelectual professions (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-4.013∗
(1.824)
(2)
0.112
(0.841)
(3)
0.338
(0.814)
(4)
0.468
(0.789)
(5)
(6)
-6.125∗∗∗
(1.487)
-3.396∗∗
(1.067)
789800
0.815
0.360
(0.521)
0.912∗
(0.429)
756315
0.990
0.548
(0.495)
0.762
(0.424)
707047
0.991
0.519
(0.490)
0.793
(0.417)
706869
0.991
-21.67∗∗∗
(2.371)
-14.28∗∗∗
(1.560)
560279
0.845
1.302∗∗
(0.442)
1.534∗∗∗
(0.394)
526144
0.993
0.326
(1.012)
0.256
(1.905)
533708
0.021
√
√
0.826
(0.962)
1.144
(1.777)
505723
0.219
√
√
√
1.506
(0.956)
1.217
(1.717)
475912
0.229
√
√
√
√
1.423
(0.941)
0.855
(1.686)
475824
0.229
√
√
√
√
√
-0.720
(0.545)
-2.734∗
(1.111)
503452
0.050
√
√
1.804∗
(0.857)
2.520
(1.740)
476147
0.317
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
23
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 14: CETC impact on employment, intermediate professions
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.488∗∗
(0.165)
(2)
0.199
(0.124)
(3)
0.259∗
(0.132)
(4)
0.247
(0.132)
(5)
(6)
0.322∗
(0.153)
0.193
(0.110)
902289
0.541
0.204
(0.115)
-0.0165
(0.0896)
858572
0.931
0.159
(0.119)
-0.0108
(0.0933)
804799
0.932
0.144
(0.120)
-0.0118
(0.0953)
804631
0.932
-7.270∗∗∗
(0.183)
-4.978∗∗∗
(0.122)
637875
0.662
0.246
(0.126)
0.0504
(0.0979)
593723
0.946
0.0302
(0.190)
-0.909∗∗
(0.344)
600607
0.003
√
√
-0.0920
(0.203)
-1.137∗∗
(0.373)
568091
0.216
√
√
√
-0.192
(0.211)
-0.730
(0.392)
534994
0.218
√
√
√
√
-0.161
(0.213)
-0.577
(0.400)
534902
0.218
√
√
√
√
√
1.394∗∗∗
(0.132)
2.009∗∗∗
(0.257)
567462
0.044
√
√
-0.0481
(0.183)
-0.436
(0.369)
535690
0.380
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 15: CETC impact on employment, intermediate professions (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-2.051
(1.979)
(2)
0.138
(1.327)
(3)
0.628
(1.269)
(4)
0.629
(1.243)
(5)
(6)
-1.438
(1.451)
0.0426
(1.124)
902289
0.821
1.710
(0.876)
2.228∗∗
(0.740)
858572
0.988
1.301
(0.873)
1.910∗∗
(0.728)
804799
0.989
1.103
(0.866)
1.834∗
(0.730)
804631
0.989
-6.553∗∗
(2.516)
-3.623∗
(1.582)
637875
0.867
1.481
(0.777)
2.292∗∗∗
(0.686)
593723
0.991
0.651
(1.628)
2.806
(3.318)
600607
0.025
√
√
-0.675
(1.480)
-0.115
(3.108)
568091
0.233
√
√
√
-0.904
(1.510)
0.293
(3.134)
534994
0.237
√
√
√
√
-0.947
(1.495)
0.885
(3.107)
534902
0.238
√
√
√
√
√
1.257
(0.990)
3.765
(2.139)
567462
0.068
√
√
0.834
(1.295)
3.363
(2.733)
535690
0.393
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
24
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 16: CETC impact on employment, blue collar workers
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.458∗∗
(0.159)
(2)
0.0206
(0.113)
(3)
0.0545
(0.117)
(4)
0.0772
(0.119)
(5)
(6)
0.435∗∗
(0.147)
0.132
(0.106)
1303700
0.652
-0.430∗∗∗
(0.107)
-0.645∗∗∗
(0.0808)
1275183
0.949
-0.520∗∗∗
(0.109)
-0.523∗∗∗
(0.0826)
1219684
0.949
-0.488∗∗∗
(0.110)
-0.515∗∗∗
(0.0839)
1219398
0.949
-5.488∗∗∗
(0.181)
-3.818∗∗∗
(0.120)
936661
0.705
-0.906∗∗∗
(0.116)
-0.967∗∗∗
(0.0877)
907248
0.959
-0.637∗∗∗
(0.174)
-1.300∗∗∗
(0.311)
924834
0.004
√
√
-1.102∗∗∗
(0.186)
-2.199∗∗∗
(0.330)
900648
0.208
√
√
√
-1.146∗∗∗
(0.190)
-2.635∗∗∗
(0.340)
865529
0.212
√
√
√
√
-1.093∗∗∗
(0.193)
-2.484∗∗∗
(0.350)
865389
0.212
√
√
√
√
√
1.760∗∗∗
(0.125)
3.238∗∗∗
(0.235)
889023
0.045
√
√
-1.483∗∗∗
(0.167)
-3.356∗∗∗
(0.313)
865537
0.360
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 17: CETC impact on employment, blue collar workers (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.881
(2.415)
(2)
-1.179
(1.495)
(3)
-0.807
(1.442)
(4)
-0.639
(1.418)
(5)
(6)
-3.025
(1.873)
-3.264∗
(1.316)
1303700
0.851
-0.882
(1.048)
-2.355∗
(1.025)
1275183
0.992
-1.299
(1.061)
-2.657∗∗
(1.013)
1219684
0.992
-1.269
(1.060)
-2.637∗∗
(0.994)
1219398
0.992
-5.938
(3.179)
-4.416∗
(1.970)
936661
0.889
-2.035
(1.145)
-3.253∗∗
(1.008)
907248
0.994
-1.084
(1.910)
-2.766
(3.673)
924834
0.045
√
√
-2.599
(1.864)
-8.186∗
(3.292)
900648
0.261
√
√
√
-2.629
(1.926)
-9.042∗∗
(3.305)
865529
0.272
√
√
√
√
-2.694
(1.921)
-8.873∗∗
(3.330)
865389
0.272
√
√
√
√
√
0.748
(1.345)
1.935
(2.721)
889023
0.065
√
√
-4.764∗∗
(1.813)
-13.60∗∗∗
(3.260)
865537
0.347
√
√
√
√
√
∗
∗∗
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
25
p < 0.05,
p < 0.01,
∗∗∗
p < 0.001
Table 18: CETC impact on employment, other employees
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0552
(0.126)
(2)
-0.451∗∗∗
(0.0947)
(3)
-0.427∗∗∗
(0.101)
(4)
-0.386∗∗∗
(0.103)
(5)
(6)
0.0897
(0.116)
-0.135
(0.0841)
1575088
0.601
-0.675∗∗∗
(0.0881)
-0.683∗∗∗
(0.0680)
1555338
0.944
-0.703∗∗∗
(0.0917)
-0.587∗∗∗
(0.0711)
1443379
0.946
-0.685∗∗∗
(0.0935)
-0.526∗∗∗
(0.0743)
1443032
0.946
-6.156∗∗∗
(0.140)
-4.087∗∗∗
(0.0949)
1100658
0.701
-0.608∗∗∗
(0.0971)
-0.662∗∗∗
(0.0752)
1077019
0.957
-0.193
(0.144)
0.145
(0.260)
1137222
0.003
√
√
-0.455∗∗
(0.153)
-0.276
(0.275)
1114875
0.203
√
√
√
-0.452∗∗
(0.159)
-0.613∗
(0.291)
1038707
0.206
√
√
√
√
-0.449∗∗
(0.161)
-0.437
(0.301)
1038494
0.206
√
√
√
√
√
1.080∗∗∗
(0.102)
2.512∗∗∗
(0.194)
1059497
0.037
√
√
-0.512∗∗∗
(0.151)
-1.111∗∗∗
(0.274)
1036475
0.294
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 19: CETC impact on employment, other employees (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-1.467
(1.778)
(2)
-2.610∗
(1.198)
(3)
-2.332∗
(1.188)
(4)
-2.169
(1.162)
(5)
(6)
-4.835∗∗∗
(1.176)
-3.009∗∗
(0.998)
1575088
0.859
-1.627∗
(0.728)
-1.558∗
(0.680)
1555338
0.992
-1.710∗
(0.759)
-1.427∗
(0.662)
1443379
0.992
-1.644∗
(0.749)
-1.452∗
(0.654)
1443032
0.992
-3.053
(2.398)
-2.296
(1.745)
1100658
0.898
-1.471∗
(0.631)
-1.316∗
(0.556)
1077019
0.994
1.585
(1.518)
4.680
(2.904)
1137222
0.026
√
√
-0.239
(1.266)
0.725
(2.374)
1114875
0.249
√
√
√
-0.399
(1.283)
-1.088
(2.318)
1038707
0.257
√
√
√
√
-0.249
(1.265)
-0.904
(2.279)
1038494
0.257
√
√
√
√
√
1.081
(0.833)
4.350∗∗
(1.555)
1059497
0.046
√
√
-1.302
(1.284)
-1.225
(2.303)
1036475
0.297
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
26
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
A.3
Impact on employment, per sex
Table 20: CETC impact on employment, male workers
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.641∗∗∗
(0.104)
(2)
0.0221
(0.0601)
(3)
-0.00194
(0.0622)
(4)
0.00206
(0.0635)
(5)
(6)
-0.00640
(0.0994)
0.0717
(0.0738)
1759050
0.663
-0.406∗∗∗
(0.0640)
-0.195∗∗∗
(0.0536)
1753754
0.966
-0.443∗∗∗
(0.0646)
-0.336∗∗∗
(0.0541)
1671000
0.966
-0.434∗∗∗
(0.0652)
-0.316∗∗∗
(0.0556)
1670619
0.966
-9.495∗∗∗
(0.128)
-6.246∗∗∗
(0.0874)
1261108
0.737
-0.207∗∗∗
(0.0582)
-0.175∗∗∗
(0.0512)
1253816
0.978
-0.258∗∗
(0.0857)
0.395∗
(0.174)
1306841
0.005
√
√
-0.439∗∗∗
(0.0872)
-0.317
(0.179)
1297615
0.296
√
√
√
-0.456∗∗∗
(0.0892)
-0.841∗∗∗
(0.185)
1244137
0.299
√
√
√
√
-0.442∗∗∗
(0.0914)
-0.781∗∗∗
(0.191)
1243920
0.299
√
√
√
√
√
0.631∗∗∗
(0.0603)
1.523∗∗∗
(0.118)
1251588
0.145
√
√
-0.363∗∗∗
(0.0735)
-0.988∗∗∗
(0.154)
1242047
0.521
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 21: CETC impact on employment, male workers (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-4.776∗∗∗
(1.363)
(2)
-1.186∗
(0.486)
(3)
-0.710
(0.509)
(4)
-0.658
(0.506)
(5)
(6)
-3.999∗∗∗
(0.974)
-2.415∗∗
(0.791)
1759050
0.899
0.104
(0.301)
0.117
(0.414)
1753754
0.997
0.0375
(0.298)
0.153
(0.352)
1671000
0.997
0.0228
(0.297)
0.112
(0.346)
1670619
0.997
-4.691∗
(1.961)
-3.062∗
(1.374)
1261108
0.916
0.335
(0.245)
0.411
(0.293)
1253816
0.998
2.337∗∗
(0.742)
2.992
(1.768)
1306841
0.049
√
√
1.103∗
(0.553)
0.121
(1.576)
1297615
0.384
√
√
√
0.930
(0.553)
-0.000684
(1.419)
1244137
0.385
√
√
√
√
0.942
(0.545)
0.0149
(1.379)
1243920
0.385
√
√
√
√
√
3.173∗∗∗
(0.547)
4.744∗∗∗
(1.206)
1251588
0.197
√
√
0.780∗
(0.327)
0.162
(0.850)
1242047
0.580
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
27
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 22: CETC impact on employment, female workers
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.404∗∗∗
(0.108)
(2)
-0.101
(0.0637)
(3)
-0.0388
(0.0673)
(4)
-0.0357
(0.0682)
(5)
(6)
-0.0713
(0.103)
-0.0938
(0.0760)
1710076
0.625
-0.428∗∗∗
(0.0673)
-0.400∗∗∗
(0.0562)
1704058
0.964
-0.421∗∗∗
(0.0697)
-0.432∗∗∗
(0.0582)
1579937
0.965
-0.390∗∗∗
(0.0702)
-0.381∗∗∗
(0.0608)
1579566
0.965
-8.508∗∗∗
(0.137)
-5.714∗∗∗
(0.0929)
1194984
0.733
-0.224∗∗∗
(0.0652)
-0.337∗∗∗
(0.0561)
1186175
0.976
-0.119
(0.0897)
0.00769
(0.185)
1269062
0.004
√
√
-0.259∗∗
(0.0915)
-0.391∗
(0.190)
1259353
0.289
√
√
√
-0.316∗∗∗
(0.0956)
-0.910∗∗∗
(0.201)
1172635
0.292
√
√
√
√
-0.292∗∗
(0.0981)
-0.793∗∗∗
(0.211)
1172407
0.292
√
√
√
√
√
1.307∗∗∗
(0.0661)
2.286∗∗∗
(0.131)
1181403
0.071
√
√
-0.281∗∗∗
(0.0831)
-1.147∗∗∗
(0.175)
1170076
0.453
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 23: CETC impact on employment, female workers (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-4.723∗∗
(1.511)
(2)
-1.462∗∗∗
(0.396)
(3)
-0.917∗
(0.407)
(4)
-0.875∗
(0.404)
(5)
(6)
-5.108∗∗∗
(0.968)
-3.255∗∗∗
(0.857)
1710076
0.883
-0.213
(0.304)
-0.319
(0.446)
1704058
0.997
-0.261
(0.301)
-0.255
(0.385)
1579937
0.997
-0.279
(0.301)
-0.315
(0.383)
1579566
0.997
-4.132∗
(2.028)
-2.606
(1.415)
1194984
0.913
0.00343
(0.233)
0.00409
(0.316)
1186175
0.998
2.408∗∗
(0.799)
3.070
(1.842)
1269062
0.044
√
√
1.211∗
(0.607)
0.502
(1.588)
1259353
0.380
√
√
√
1.055
(0.608)
0.324
(1.445)
1172635
0.382
√
√
√
√
1.062
(0.596)
0.408
(1.412)
1172407
0.382
√
√
√
√
√
3.039∗∗∗
(0.459)
4.457∗∗∗
(1.176)
1181403
0.143
√
√
0.469
(0.330)
-0.890
(0.868)
1170076
0.546
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
28
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
A.4
Impact on employment, per contract
Table 24: CETC impact on employment, permanent contracts
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.578∗∗∗
(0.0858)
(2)
0.199∗∗∗
(0.0500)
(3)
0.278∗∗∗
(0.0525)
(4)
0.304∗∗∗
(0.0534)
(5)
(6)
0.319∗∗∗
(0.0814)
0.233∗∗∗
(0.0594)
1896865
0.707
0.265∗∗∗
(0.0524)
0.222∗∗∗
(0.0426)
1896082
0.967
0.295∗∗∗
(0.0535)
0.320∗∗∗
(0.0431)
1767851
0.968
0.301∗∗∗
(0.0541)
0.323∗∗∗
(0.0473)
1767439
0.968
-8.833∗∗∗
(0.118)
-5.865∗∗∗
(0.0793)
1331572
0.723
0.426∗∗∗
(0.0545)
0.314∗∗∗
(0.0440)
1328235
0.976
0.141
(0.0760)
-0.527∗∗∗
(0.139)
1415726
0.005
√
√
0.246∗∗
(0.0771)
-0.499∗∗∗
(0.140)
1411397
0.255
√
√
√
0.246∗∗
(0.0796)
-0.0350
(0.146)
1321919
0.263
√
√
√
√
0.276∗∗∗
(0.0822)
0.246
(0.161)
1321669
0.263
√
√
√
√
√
0.684∗∗∗
(0.0605)
0.561∗∗∗
(0.110)
1325259
0.027
√
√
0.273∗∗∗
(0.0778)
-0.314∗
(0.146)
1319033
0.299
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 25: CETC impact on employment, permanent contracts (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-2.965∗
(1.319)
(2)
-1.780∗
(0.726)
(3)
-1.474∗
(0.695)
(4)
-1.398∗
(0.684)
(5)
(6)
-2.723∗∗
(0.906)
-2.256∗∗∗
(0.642)
1896865
0.859
-0.219
(0.401)
-0.377
(0.284)
1896082
0.994
-0.289
(0.419)
-0.419
(0.291)
1767851
0.994
-0.191
(0.412)
-0.332
(0.286)
1767439
0.994
-4.010∗
(1.880)
-2.937∗
(1.181)
1331572
0.879
0.459
(0.388)
0.0475
(0.274)
1328235
0.996
1.978∗
(0.868)
2.213
(1.717)
1415726
0.031
√
√
0.739
(0.613)
-0.539
(1.151)
1411397
0.310
√
√
√
0.840
(0.619)
-0.496
(1.025)
1321919
0.329
√
√
√
√
1.003
(0.633)
-0.304
(1.048)
1321669
0.329
√
√
√
√
√
1.388∗∗
(0.480)
0.892
(0.802)
1325259
0.047
√
√
0.693
(0.604)
-1.028
(1.042)
1319033
0.343
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
29
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 26: CETC impact on employment, fixed term contracts
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.197
(0.194)
(2)
-0.601∗∗∗
(0.174)
(3)
-0.536∗∗
(0.184)
(4)
-0.481∗∗
(0.185)
(5)
(6)
1.797∗∗∗
(0.199)
1.284∗∗∗
(0.141)
1123096
0.433
0.852∗∗∗
(0.197)
0.482∗∗∗
(0.145)
1059876
0.852
1.008∗∗∗
(0.205)
0.657∗∗∗
(0.151)
1003032
0.853
1.009∗∗∗
(0.205)
0.694∗∗∗
(0.158)
1002776
0.853
-1.055∗∗∗
(0.198)
-0.702∗∗∗
(0.130)
788470
0.576
1.241∗∗∗
(0.220)
0.592∗∗∗
(0.161)
710624
0.873
1.458∗∗∗
(0.295)
0.0237
(0.509)
688308
0.033
√
√
1.922∗∗∗
(0.344)
0.619
(0.634)
605976
0.244
√
√
√
2.081∗∗∗
(0.357)
0.200
(0.664)
577498
0.246
√
√
√
√
2.196∗∗∗
(0.374)
0.794
(0.760)
577390
0.247
√
√
√
√
√
3.938∗∗∗
(0.220)
5.286∗∗∗
(0.386)
656096
0.131
√
√
0.924∗∗
(0.298)
-0.461
(0.608)
577715
0.494
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 27: CETC impact on employment, fixed term contracts (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.479
(1.888)
(2)
-1.188
(0.836)
(3)
-0.746
(0.808)
(4)
-0.434
(0.805)
(5)
(6)
1.195
(1.687)
0.111
(1.303)
1123096
0.751
1.261
(1.485)
0.574
(1.183)
1059876
0.965
1.295
(1.531)
0.802
(1.210)
1003032
0.966
1.140
(1.515)
0.609
(1.181)
1002776
0.966
-0.778
(2.543)
-1.336
(1.617)
788470
0.820
1.506
(1.503)
0.482
(1.095)
710624
0.973
1.998
(1.765)
0.578
(2.678)
688308
0.239
√
√
1.667
(1.736)
0.0498
(2.413)
605976
0.450
√
√
√
1.923
(1.799)
0.570
(2.340)
577498
0.454
√
√
√
√
1.344
(1.803)
-0.000286
(2.314)
577390
0.455
√
√
√
√
√
3.666∗
(1.531)
4.646∗
(2.093)
656096
0.289
√
√
1.064
(1.714)
-0.282
(2.839)
577715
0.568
√
√
√
√
√
∗∗
∗∗∗
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
30
∗
p < 0.05,
p < 0.01,
p < 0.001
B
Results of CETC assessment: impact on profits
Table 28: CETC impact on net margins
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.194
(0.522)
(2)
-0.142
(0.502)
(3)
-0.0615
(0.308)
(4)
-0.720
(0.799)
(5)
(6)
-0.615
(0.418)
-0.0915
(0.314)
1914304
0.013
-0.390
(0.384)
-0.101
(0.293)
1914163
0.483
-0.225
(0.264)
-0.123
(0.216)
1787711
0.477
-0.225
(0.264)
-0.123
(0.216)
1787711
0.477
-0.545
(0.357)
-0.147
(0.279)
1346316
0.008
-0.0895
(0.378)
0.116
(0.296)
1344144
0.563
-0.233
(0.699)
1.116
(0.859)
1434556
0.001
√
√
-0.0657
(0.703)
1.345
(1.103)
1433814
0.173
√
√
√
-0.302
(0.402)
-0.0633
(0.721)
1345299
0.254
√
√
√
√
-0.0328
(0.516)
0.309
(0.812)
1345299
0.254
√
√
√
√
√
-0.138
(0.416)
1.466∗
(0.703)
1346193
0.001
√
√
0.281
(0.652)
1.567
(1.085)
1343979
0.194
√
√
√
√
√
∗
∗∗
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
p < 0.05,
p < 0.01,
∗∗∗
p < 0.001
Table 29: CETC impact on net margins (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-3.967
(3.201)
(2)
-5.386
(4.838)
(3)
-6.617
(5.516)
(4)
-5.222
(4.202)
(5)
(6)
-3.275
(3.644)
-2.693
(2.951)
1914304
0.028
0.648
(1.017)
-1.694
(2.119)
1914163
0.528
0.483
(1.118)
-2.103
(2.249)
1787711
0.566
0.483
(1.118)
-2.103
(2.249)
1787711
0.566
-3.370
(3.152)
-2.905
(3.416)
1346316
0.021
1.154
(1.332)
-1.205
(2.868)
1344144
0.547
2.701
(2.267)
-0.728
(6.745)
1434556
0.013
√
√
5.388
(4.697)
12.91
(20.65)
1433814
0.157
√
√
√
5.265
(4.564)
13.84
(18.53)
1345299
0.225
√
√
√
√
4.010
(3.811)
11.80
(17.30)
1345299
0.225
√
√
√
√
√
-1.587
(2.160)
-6.098
(5.957)
1346193
0.015
√
√
6.464
(5.739)
15.25
(23.12)
1343979
0.161
√
√
√
√
√
∗
∗∗
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
31
p < 0.05,
p < 0.01,
∗∗∗
p < 0.001
Table 30: CETC impact on gross margins
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.328
(0.368)
(2)
-0.236
(0.321)
(3)
0.181
(0.198)
(4)
0.417∗
(0.206)
(5)
(6)
-0.721∗
(0.287)
-0.503∗
(0.249)
1914361
0.019
-0.565∗
(0.249)
-0.434
(0.225)
1914222
0.582
-0.0198
(0.155)
-0.0915
(0.137)
1787737
0.496
-0.0205
(0.155)
-0.0915
(0.137)
1787737
0.496
-0.109
(0.292)
-0.155
(0.221)
1346334
0.015
-0.252
(0.246)
-0.194
(0.209)
1344170
0.654
-0.263
(0.433)
-0.665
(0.736)
1434633
0.002
√
√
-0.0921
(0.429)
-0.673
(0.897)
1433915
0.206
√
√
√
0.552∗∗
(0.204)
0.111
(0.522)
1345333
0.302
√
√
√
√
0.0287
(0.228)
-0.607
(0.554)
1345333
0.302
√
√
√
√
√
-0.0592
(0.278)
-0.419
(0.608)
1346237
0.002
√
√
0.0794
(0.411)
-0.381
(0.895)
1344037
0.212
√
√
√
√
√
∗
∗∗
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
p < 0.05,
p < 0.01,
∗∗∗
p < 0.001
Table 31: CETC impact on gross margins (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.638
(1.236)
(2)
1.057
(1.340)
(3)
2.004
(1.514)
(4)
2.103
(1.211)
(5)
(6)
1.651
(2.012)
-2.656
(1.649)
1914361
0.016
1.487
(1.063)
-2.541
(1.576)
1914222
0.459
1.460
(1.113)
-0.103
(0.599)
1787737
0.442
1.460
(1.113)
-0.103
(0.599)
1787737
0.442
0.864
(2.172)
-3.095
(1.770)
1346334
0.015
1.465
(1.097)
-2.515
(1.709)
1344170
0.499
0.510
(1.597)
-17.50
(11.97)
1434633
0.007
√
√
0.787
(1.587)
-20.23
(11.40)
1433915
0.174
√
√
√
0.173
(1.395)
-5.864
(4.596)
1345333
0.342
√
√
√
√
-0.696
(1.301)
-7.279
(4.549)
1345333
0.342
√
√
√
√
√
2.623
(1.413)
-14.99
(11.13)
1346237
0.008
√
√
0.279
(1.686)
-21.39
(12.22)
1344037
0.170
√
√
√
√
√
∗
∗∗
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
32
p < 0.05,
p < 0.01,
∗∗∗
p < 0.001
Table 32: CETC impact on operating margins
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.288∗∗∗
(0.0772)
(2)
-0.299∗∗∗
(0.0563)
(3)
-0.203∗∗∗
(0.0491)
(4)
-0.170∗∗
(0.0577)
(5)
(6)
-0.166∗
(0.0702)
-0.167∗∗∗
(0.0496)
1915842
0.164
-0.0385
(0.0572)
-0.0716
(0.0427)
1915793
0.780
-0.0779
(0.0511)
-0.0962∗∗
(0.0363)
1788260
0.657
-0.0895
(0.0505)
-0.0976∗∗
(0.0363)
1788260
0.659
-0.529∗∗∗
(0.0754)
-0.392∗∗∗
(0.0519)
1346628
0.035
0.0862
(0.0591)
0.0561
(0.0418)
1344452
0.733
0.374∗∗∗
(0.103)
0.121
(0.140)
1436021
0.004
√
√
0.397∗∗∗
(0.100)
0.281
(0.193)
1435461
0.173
√
√
√
0.391∗∗∗
(0.0757)
0.299
(0.158)
1345866
0.313
√
√
√
√
0.0532
(0.0929)
-0.129
(0.180)
1345866
0.370
√
√
√
√
√
0.154∗
(0.0688)
0.00376
(0.103)
1346582
0.005
√
√
0.353∗∗∗
(0.0899)
0.241
(0.177)
1344379
0.202
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 33: CETC impact on operating margins (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.144
(0.114)
(2)
-0.214∗
(0.0960)
(3)
-0.0968
(0.0995)
(4)
-0.0457
(0.104)
(5)
(6)
0.0280
(0.189)
0.132
(0.108)
1915842
0.098
0.0845
(0.145)
0.229
(0.123)
1915793
0.764
0.0845
(0.146)
0.239
(0.122)
1788260
0.717
0.0804
(0.146)
0.239
(0.122)
1788260
0.718
-0.581∗∗
(0.208)
-0.230
(0.134)
1346628
0.061
0.106
(0.155)
0.299
(0.154)
1344452
0.766
0.607
(0.495)
2.367
(2.171)
1436021
0.014
√
√
0.647
(0.459)
2.692
(2.108)
1435461
0.202
√
√
√
0.602
(0.479)
3.028
(2.141)
1345866
0.273
√
√
√
√
0.441
(0.654)
2.770
(2.447)
1345866
0.316
√
√
√
√
√
0.204
(0.183)
2.177
(1.819)
1346582
0.017
√
√
0.613
(0.485)
2.842
(2.223)
1344379
0.214
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
33
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
C
C.1
Results of CETC assessment: impact on wages
Impact on wages, all employess
Table 34: CETC impact on hourly wages
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0952∗
(0.0410)
(2)
1.411∗∗∗
(0.0295)
(3)
1.459∗∗∗
(0.0314)
(4)
1.435∗∗∗
(0.0332)
(5)
(6)
0.530∗∗∗
(0.0361)
-1.314∗∗∗
(0.0837)
1781338
0.672
1.570∗∗∗
(0.0282)
0.785∗∗∗
(0.0852)
1776161
0.938
1.519∗∗∗
(0.0288)
3.475∗∗∗
(0.0888)
1658604
0.940
1.512∗∗∗
(0.0292)
3.381∗∗∗
(0.0932)
1658208
0.940
-16.78∗∗∗
(0.0820)
1.818∗∗∗
(0.0633)
1256276
0.606
1.555∗∗∗
(0.0300)
0.773∗∗∗
(0.0895)
1243156
0.952
0.0629
(0.0433)
-1.342∗∗∗
(0.0852)
1257528
0.014
√
√
1.107∗∗∗
(0.0441)
0.722∗∗∗
(0.0873)
1217018
0.259
√
√
√
1.163∗∗∗
(0.0433)
3.310∗∗∗
(0.0912)
1142419
0.339
√
√
√
√
1.142∗∗∗
(0.0445)
3.182∗∗∗
(0.0986)
1142195
0.339
√
√
√
√
√
1.646∗∗∗
(0.0317)
1.805∗∗∗
(0.0644)
1179747
0.011
√
√
1.107∗∗∗
(0.0464)
0.715∗∗∗
(0.0918)
1140166
0.263
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 35: CETC impact on hourly wages (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0576
(0.269)
(2)
0.946∗∗∗
(0.177)
(3)
0.830∗∗∗
(0.163)
(4)
0.790∗∗∗
(0.165)
(5)
(6)
0.478
(0.293)
0.736∗∗∗
(0.204)
1781338
0.773
1.419∗∗∗
(0.202)
1.403∗∗∗
(0.152)
1776161
0.940
1.327∗∗∗
(0.203)
1.192∗∗∗
(0.141)
1658604
0.946
1.245∗∗∗
(0.204)
1.182∗∗∗
(0.139)
1658208
0.946
-19.00∗∗∗
(0.289)
-12.29∗∗∗
(0.196)
1256276
0.724
1.474∗∗∗
(0.186)
1.448∗∗∗
(0.155)
1243156
0.959
0.589∗
(0.270)
-0.343
(0.695)
1273843
0.059
√
√
1.521∗∗∗
(0.273)
1.320
(0.740)
1236017
0.291
√
√
√
1.659∗∗∗
(0.286)
3.008∗∗∗
(0.643)
1160251
0.324
√
√
√
√
1.586∗∗∗
(0.290)
2.863∗∗∗
(0.644)
1160022
0.324
√
√
√
√
√
1.680∗∗∗
(0.186)
1.817∗∗
(0.661)
1195084
0.066
√
√
1.500∗∗∗
(0.253)
1.360∗
(0.632)
1158019
0.301
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
34
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 36: CETC impact on growth rate of hourly wages
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.197∗∗∗
(0.0310)
(2)
0.538∗∗∗
(0.0307)
(3)
0.800∗∗∗
(0.0294)
(4)
0.788∗∗∗
(0.0303)
(5)
(6)
0.0499∗
(0.0250)
-0.284∗∗∗
(0.0159)
1781333
0.020
0.604∗∗∗
(0.0249)
0.0873∗∗∗
(0.0157)
1776155
0.266
0.723∗∗∗
(0.0247)
0.778∗∗∗
(0.0168)
1658600
0.354
0.715∗∗∗
(0.0258)
0.776∗∗∗
(0.0175)
1658204
0.354
0.916∗∗∗
(0.0224)
0.287∗∗∗
(0.0142)
1256271
0.017
0.841∗∗∗
(0.0304)
0.239∗∗∗
(0.0189)
1243146
0.335
-0.0516
(0.0508)
-4.141∗∗∗
(0.0885)
1273838
0.011
√
√
0.560∗∗∗
(0.0522)
-2.882∗∗∗
(0.0884)
1236017
0.138
√
√
√
0.632∗∗∗
(0.0486)
1.353∗∗∗
(0.0880)
1160251
0.298
√
√
√
√
0.625∗∗∗
(0.0496)
1.392∗∗∗
(0.0917)
1160022
0.298
√
√
√
√
√
1.629∗∗∗
(0.0351)
-0.813∗∗∗
(0.0645)
1195079
0.009
√
√
0.580∗∗∗
(0.0547)
-2.855∗∗∗
(0.0933)
1158019
0.140
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 37: CETC impact on growth rate of hourly wages (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.375∗∗
(0.139)
(2)
0.254
(0.146)
(3)
0.277∗
(0.138)
(4)
0.334
(0.173)
(5)
(6)
0.0681
(0.121)
-0.245∗∗∗
(0.0715)
1781333
0.045
0.529∗∗∗
(0.114)
0.0653
(0.0668)
1776155
0.286
0.620∗∗∗
(0.118)
0.498∗∗∗
(0.0786)
1658600
0.326
0.529∗∗∗
(0.134)
0.456∗∗∗
(0.0833)
1658204
0.327
0.877∗∗∗
(0.0973)
0.278∗∗∗
(0.0619)
1256271
0.040
0.856∗∗∗
(0.135)
0.256∗∗∗
(0.0754)
1243146
0.351
0.153
(0.237)
-3.488∗∗∗
(0.376)
1273838
0.032
√
√
0.787∗∗
(0.240)
-2.169∗∗∗
(0.370)
1236017
0.177
√
√
√
0.898∗∗∗
(0.245)
0.767
(0.407)
1160251
0.259
√
√
√
√
0.726∗
(0.303)
0.471
(0.466)
1160022
0.259
√
√
√
√
√
1.704∗∗∗
(0.156)
-0.468
(0.258)
1195079
0.033
√
√
0.730∗∗
(0.240)
-2.369∗∗∗
(0.362)
1158019
0.180
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
35
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 38: CETC impact on mean yearly wages
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.308∗∗∗
(0.0739)
(2)
1.695∗∗∗
(0.0555)
(3)
1.786∗∗∗
(0.0583)
(4)
1.838∗∗∗
(0.0629)
(5)
(6)
0.413∗∗∗
(0.0691)
0.942∗∗∗
(0.0517)
1918585
0.478
1.821∗∗∗
(0.0553)
1.924∗∗∗
(0.0444)
1918584
0.904
1.775∗∗∗
(0.0568)
1.797∗∗∗
(0.0457)
1789248
0.907
1.775∗∗∗
(0.0570)
1.842∗∗∗
(0.0480)
1788824
0.907
-15.61∗∗∗
(0.108)
-9.807∗∗∗
(0.0739)
1347311
0.566
1.520∗∗∗
(0.0547)
1.786∗∗∗
(0.0464)
1345161
0.933
-0.117
(0.0840)
-1.811∗∗∗
(0.165)
1438938
0.005
√
√
0.885∗∗∗
(0.0861)
0.0958
(0.167)
1438938
0.220
√
√
√
0.943∗∗∗
(0.0872)
3.034∗∗∗
(0.174)
1348159
0.250
√
√
√
√
0.951∗∗∗
(0.0959)
3.386∗∗∗
(0.206)
1347902
0.251
√
√
√
√
√
0.779∗∗∗
(0.0587)
0.620∗∗∗
(0.116)
1347311
0.135
√
√
0.865∗∗∗
(0.0764)
1.091∗∗∗
(0.151)
1345161
0.417
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 39: CETC impact on mean yearly wages (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
1.697∗
(0.774)
(2)
0.733∗
(0.356)
(3)
0.644
(0.347)
(4)
0.823∗
(0.349)
(5)
(6)
-0.0936
(0.545)
0.360
(0.414)
1918584
0.908
0.375
(0.247)
0.625∗
(0.275)
1918583
0.997
0.400
(0.249)
0.508∗
(0.258)
1789247
0.997
0.405
(0.253)
0.572∗
(0.258)
1788823
0.997
-20.15∗∗∗
(0.709)
-13.15∗∗∗
(0.534)
1347310
0.919
0.575∗
(0.285)
0.576∗
(0.243)
1345160
0.998
-1.396∗∗
(0.519)
-2.053
(1.348)
1438938
0.036
√
√
-0.386
(0.542)
-0.224
(1.348)
1438938
0.215
√
√
√
-0.0781
(0.554)
1.358
(1.197)
1348159
0.240
√
√
√
√
-0.176
(0.567)
1.436
(1.175)
1347902
0.240
√
√
√
√
√
-1.465∗∗
(0.460)
-2.277∗
(0.976)
1347311
0.178
√
√
-0.0852
(0.464)
-0.211
(0.730)
1345161
0.447
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
36
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
C.2
Impact on wages, per socioprofessional category
Table 40: CETC impact on hourly wages, tradesmen and entrepreneurs
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.219
(0.169)
(2)
1.519∗∗∗
(0.104)
(3)
1.557∗∗∗
(0.107)
(4)
1.589∗∗∗
(0.110)
(5)
(6)
0.202
(0.152)
1.538∗∗∗
(0.115)
253383
0.593
1.461∗∗∗
(0.0982)
1.668∗∗∗
(0.0785)
231772
0.949
1.436∗∗∗
(0.0989)
1.520∗∗∗
(0.0785)
225938
0.950
1.414∗∗∗
(0.100)
1.560∗∗∗
(0.0859)
225887
0.950
-27.17∗∗∗
(0.235)
-16.75∗∗∗
(0.148)
184285
0.549
1.917∗∗∗
(0.104)
2.004∗∗∗
(0.0830)
161652
0.960
0.387∗
(0.152)
-1.620∗∗∗
(0.286)
152659
0.018
√
√
2.231∗∗∗
(0.162)
2.410∗∗∗
(0.317)
135165
0.294
√
√
√
2.245∗∗∗
(0.155)
5.943∗∗∗
(0.315)
132861
0.362
√
√
√
√
3.123∗∗∗
(0.181)
7.233∗∗∗
(0.391)
132841
0.365
√
√
√
√
√
2.610∗∗∗
(0.106)
2.841∗∗∗
(0.203)
149545
0.017
√
√
2.301∗∗∗
(0.166)
2.546∗∗∗
(0.324)
132110
0.297
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 41: CETC impact on hourly wages, tradesmen and entrepreneurs (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.246
(1.680)
(2)
1.509
(1.185)
(3)
1.359
(1.230)
(4)
1.464
(1.158)
(5)
(6)
-4.972∗
(1.985)
-0.955
(1.207)
253383
0.625
-1.320
(1.229)
0.636
(0.964)
231772
0.949
-1.668
(1.256)
0.311
(0.993)
225938
0.950
-1.720
(1.102)
-0.224
(0.833)
225887
0.950
-29.22∗∗∗
(2.157)
-16.78∗∗∗
(1.493)
184285
0.633
-2.128
(1.227)
0.0808
(1.038)
161652
0.964
-1.944
(1.920)
3.243
(2.833)
152659
0.280
√
√
-0.181
(2.148)
6.852∗
(3.154)
135165
0.374
√
√
√
0.0672
(2.215)
10.00∗∗
(3.064)
132861
0.388
√
√
√
√
0.331
(1.764)
8.485∗∗
(3.015)
132841
0.389
√
√
√
√
√
-0.771
(1.209)
6.190∗∗∗
(1.823)
149545
0.290
√
√
-0.901
(1.678)
5.370
(2.758)
132110
0.405
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
37
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 42: CETC impact on growth rate of hourly wages, tradesmen and entrepreneurs
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-1.178∗∗∗
(0.138)
(2)
0.490∗∗∗
(0.148)
(3)
0.778∗∗∗
(0.143)
(4)
0.847∗∗∗
(0.153)
(5)
(6)
-0.250∗
(0.111)
-0.986∗∗∗
(0.0726)
253382
0.027
1.272∗∗∗
(0.117)
-0.119
(0.0780)
231771
0.307
1.346∗∗∗
(0.113)
0.985∗∗∗
(0.0782)
225937
0.368
1.464∗∗∗
(0.140)
1.212∗∗∗
(0.0921)
225886
0.368
2.732∗∗∗
(0.0951)
1.001∗∗∗
(0.0619)
184284
0.022
2.234∗∗∗
(0.139)
0.601∗∗∗
(0.0927)
161650
0.352
1.211∗∗∗
(0.237)
-8.862∗∗∗
(0.426)
152658
0.021
√
√
2.675∗∗∗
(0.259)
-5.219∗∗∗
(0.473)
135165
0.191
√
√
√
2.677∗∗∗
(0.240)
1.739∗∗∗
(0.451)
132861
0.311
√
√
√
√
4.403∗∗∗
(0.306)
3.305∗∗∗
(0.560)
132841
0.317
√
√
√
√
√
4.650∗∗∗
(0.157)
-1.947∗∗∗
(0.298)
149544
0.019
√
√
2.780∗∗∗
(0.266)
-5.015∗∗∗
(0.486)
132110
0.193
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 43: CETC impact on growth rate of hourly wages, tradesmen and entrepreneurs (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-2.061
(1.624)
(2)
-0.891
(1.558)
(3)
-1.216
(1.614)
(4)
-1.072
(1.470)
(5)
(6)
-2.086∗
(0.861)
-0.256
(0.627)
253382
0.130
-0.623
(0.854)
0.749
(0.716)
231771
0.378
-0.730
(0.862)
0.861
(0.677)
225937
0.381
-0.779
(0.829)
0.889
(0.653)
225886
0.381
0.386
(0.657)
1.328∗∗
(0.513)
184284
0.098
0.0467
(0.723)
0.770
(0.678)
161650
0.444
2.265
(2.689)
2.652
(3.526)
152658
0.107
√
√
2.876
(2.860)
3.509
(4.126)
135165
0.295
√
√
√
3.203
(2.910)
6.869∗
(3.488)
132861
0.318
√
√
√
√
3.676
(2.415)
5.976
(3.276)
132841
0.319
√
√
√
√
√
2.294∗
(1.015)
3.751
(1.935)
149544
0.194
√
√
1.261
(1.574)
0.375
(3.394)
132110
0.421
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
38
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 44: CETC impact on hourly wages, executives and higher intellectual occupations
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0262
(0.0700)
(2)
1.296∗∗∗
(0.0498)
(3)
1.355∗∗∗
(0.0525)
(4)
1.356∗∗∗
(0.0529)
(5)
(6)
0.606∗∗∗
(0.0633)
1.108∗∗∗
(0.0475)
616347
0.431
1.444∗∗∗
(0.0481)
1.462∗∗∗
(0.0379)
585994
0.896
1.424∗∗∗
(0.0500)
1.327∗∗∗
(0.0391)
549992
0.897
1.428∗∗∗
(0.0502)
1.359∗∗∗
(0.0455)
549869
0.897
-20.28∗∗∗
(0.0981)
-12.77∗∗∗
(0.0665)
440770
0.344
1.636∗∗∗
(0.0525)
1.606∗∗∗
(0.0411)
407486
0.916
0.307∗∗∗
(0.0744)
-1.289∗∗∗
(0.141)
396526
0.012
√
√
1.358∗∗∗
(0.0791)
0.700∗∗∗
(0.153)
362464
0.255
√
√
√
1.467∗∗∗
(0.0797)
3.179∗∗∗
(0.161)
342782
0.294
√
√
√
√
1.471∗∗∗
(0.0801)
3.315∗∗∗
(0.180)
342724
0.295
√
√
√
√
√
1.978∗∗∗
(0.0534)
2.040∗∗∗
(0.107)
375526
0.013
√
√
1.367∗∗∗
(0.0825)
0.625∗∗∗
(0.161)
342580
0.265
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 45: CETC impact on hourly wages, executives and higher intellectual occupations (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.355
(0.389)
(2)
0.330
(0.280)
(3)
0.300
(0.283)
(4)
0.274
(0.280)
(5)
(6)
0.416
(0.296)
0.605∗∗
(0.202)
616347
0.397
1.025∗∗∗
(0.226)
0.874∗∗∗
(0.151)
585994
0.880
0.929∗∗∗
(0.231)
0.772∗∗∗
(0.142)
549992
0.884
0.865∗∗∗
(0.229)
0.754∗∗∗
(0.137)
549869
0.884
-17.35∗∗∗
(0.360)
-11.13∗∗∗
(0.279)
440770
0.357
1.226∗∗∗
(0.261)
1.032∗∗∗
(0.166)
407486
0.909
0.326
(1.012)
0.256
(1.905)
533708
0.021
√
√
0.826
(0.962)
1.144
(1.777)
505723
0.219
√
√
√
1.506
(0.956)
1.217
(1.717)
475912
0.229
√
√
√
√
1.423
(0.941)
0.855
(1.686)
475824
0.229
√
√
√
√
√
-0.720
(0.545)
-2.734∗
(1.111)
503452
0.050
√
√
1.804∗
(0.857)
2.520
(1.740)
476147
0.317
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
39
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 46: CETC impact on growth rate of hourly wages, executives and higher intellectual occupations
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.426∗∗∗
(0.0581)
(2)
0.594∗∗∗
(0.0586)
(3)
0.784∗∗∗
(0.0580)
(4)
0.795∗∗∗
(0.0585)
(5)
(6)
0.0521
(0.0462)
-0.588∗∗∗
(0.0309)
616343
0.024
0.863∗∗∗
(0.0468)
-0.144∗∗∗
(0.0315)
585989
0.316
0.990∗∗∗
(0.0475)
0.522∗∗∗
(0.0342)
549989
0.361
0.987∗∗∗
(0.0475)
0.545∗∗∗
(0.0373)
549866
0.361
1.759∗∗∗
(0.0405)
0.556∗∗∗
(0.0272)
440766
0.016
1.281∗∗∗
(0.0569)
0.143∗∗∗
(0.0375)
407478
0.371
0.108
(0.0945)
-6.516∗∗∗
(0.169)
396522
0.015
√
√
0.970∗∗∗
(0.100)
-4.211∗∗∗
(0.180)
362464
0.187
√
√
√
1.138∗∗∗
(0.0965)
0.132
(0.182)
342782
0.286
√
√
√
√
1.130∗∗∗
(0.0970)
0.0928
(0.189)
342724
0.286
√
√
√
√
√
2.572∗∗∗
(0.0632)
-1.617∗∗∗
(0.123)
375522
0.013
√
√
0.943∗∗∗
(0.105)
-4.348∗∗∗
(0.190)
342580
0.188
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 47: CETC impact on growth rate of hourly wages, executives and higher intellectual occupations
(wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-1.002∗∗∗
(0.236)
(2)
-0.299
(0.253)
(3)
-0.275
(0.257)
(4)
-0.292
(0.253)
(5)
(6)
-0.0710
(0.177)
-0.508∗∗∗
(0.119)
616343
0.056
0.556∗∗
(0.183)
-0.166
(0.122)
585989
0.314
0.613∗∗
(0.194)
0.174
(0.129)
549989
0.330
0.594∗∗
(0.194)
0.164
(0.126)
549866
0.330
1.408∗∗∗
(0.168)
0.471∗∗∗
(0.103)
440766
0.052
1.129∗∗∗
(0.229)
0.186
(0.128)
407478
0.369
1.018∗
(0.432)
-3.639∗∗∗
(0.656)
396522
0.053
√
√
1.696∗∗∗
(0.447)
-1.964∗∗
(0.672)
362464
0.203
√
√
√
1.778∗∗∗
(0.477)
0.808
(0.639)
342782
0.242
√
√
√
√
1.743∗∗∗
(0.470)
0.651
(0.648)
342724
0.243
√
√
√
√
√
2.467∗∗∗
(0.316)
-0.746
(0.466)
375522
0.055
√
√
1.479∗∗∗
(0.423)
-2.572∗∗∗
(0.621)
342580
0.212
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
40
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 48: CETC impact on hourly wages, intermediate occupations
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.0885
(0.0735)
(2)
0.855∗∗∗
(0.0529)
(3)
0.839∗∗∗
(0.0554)
(4)
0.827∗∗∗
(0.0560)
(5)
(6)
0.313∗∗∗
(0.0672)
0.462∗∗∗
(0.0494)
690174
0.339
0.694∗∗∗
(0.0509)
0.695∗∗∗
(0.0394)
653113
0.877
0.672∗∗∗
(0.0525)
0.573∗∗∗
(0.0407)
613060
0.876
0.663∗∗∗
(0.0538)
0.568∗∗∗
(0.0427)
612930
0.876
-13.38∗∗∗
(0.0968)
-8.637∗∗∗
(0.0644)
490975
0.283
0.750∗∗∗
(0.0556)
0.737∗∗∗
(0.0430)
451297
0.900
-0.192∗
(0.0806)
-1.278∗∗∗
(0.146)
440465
0.007
√
√
0.520∗∗∗
(0.0857)
0.113
(0.157)
403630
0.234
√
√
√
0.587∗∗∗
(0.0879)
1.869∗∗∗
(0.167)
380194
0.261
√
√
√
√
0.494∗∗∗
(0.101)
1.597∗∗∗
(0.228)
380126
0.261
√
√
√
√
√
1.185∗∗∗
(0.0589)
1.408∗∗∗
(0.113)
416180
0.009
√
√
0.598∗∗∗
(0.0888)
0.185
(0.164)
380517
0.243
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 49: CETC impact on hourly wages, intermediate occupations (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.381
(0.357)
(2)
0.201
(0.319)
(3)
0.190
(0.304)
(4)
0.175
(0.300)
(5)
(6)
-0.0227
(0.293)
0.133
(0.190)
690174
0.440
0.425∗
(0.209)
0.518∗∗∗
(0.151)
653113
0.879
0.372
(0.215)
0.422∗∗
(0.149)
613060
0.881
0.233
(0.235)
0.372∗
(0.147)
612930
0.881
-14.54∗∗∗
(0.312)
-9.537∗∗∗
(0.206)
490975
0.402
0.476∗
(0.227)
0.504∗∗
(0.158)
451297
0.909
0.223
(0.404)
-0.0383
(0.783)
440465
0.038
√
√
0.671
(0.420)
0.863
(0.762)
403630
0.247
√
√
√
0.734
(0.435)
1.852∗
(0.778)
380194
0.257
√
√
√
√
0.477
(0.482)
1.520
(0.805)
380126
0.258
√
√
√
√
√
0.988∗∗∗
(0.263)
1.561∗∗
(0.512)
416180
0.044
√
√
0.627
(0.423)
0.737
(0.739)
380517
0.257
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
41
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 50: CETC impact on growth rate of hourly wages, intermediate occupations
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.335∗∗∗
(0.0551)
(2)
0.369∗∗∗
(0.0539)
(3)
0.553∗∗∗
(0.0554)
(4)
0.563∗∗∗
(0.0565)
(5)
(6)
-0.191∗∗∗
(0.0451)
-0.408∗∗∗
(0.0280)
690170
0.013
0.376∗∗∗
(0.0441)
-0.0154
(0.0286)
653108
0.306
0.473∗∗∗
(0.0452)
0.528∗∗∗
(0.0308)
613057
0.350
0.410∗∗∗
(0.0520)
0.491∗∗∗
(0.0366)
612927
0.350
0.746∗∗∗
(0.0399)
0.211∗∗∗
(0.0249)
490971
0.010
0.649∗∗∗
(0.0547)
0.169∗∗∗
(0.0352)
451289
0.365
-0.147
(0.0883)
-3.565∗∗∗
(0.151)
440461
0.009
√
√
0.486∗∗∗
(0.0926)
-2.239∗∗∗
(0.159)
403630
0.157
√
√
√
0.577∗∗∗
(0.0924)
1.076∗∗∗
(0.166)
380194
0.247
√
√
√
√
0.466∗∗∗
(0.110)
0.860∗∗∗
(0.220)
380126
0.247
√
√
√
√
√
1.355∗∗∗
(0.0611)
-0.600∗∗∗
(0.111)
416176
0.008
√
√
0.494∗∗∗
(0.0972)
-2.274∗∗∗
(0.168)
380517
0.159
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 51: CETC impact on growth rate of hourly wages, intermediate occupations (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.552∗∗
(0.194)
(2)
-0.109
(0.221)
(3)
0.0143
(0.206)
(4)
0.146
(0.255)
(5)
(6)
-0.291
(0.156)
-0.281∗∗
(0.0944)
690170
0.049
0.255
(0.138)
0.0784
(0.0964)
653108
0.318
0.348∗
(0.140)
0.498∗∗∗
(0.0951)
613057
0.344
0.181
(0.194)
0.448∗∗∗
(0.105)
612927
0.346
0.651∗∗∗
(0.138)
0.303∗∗∗
(0.0858)
490971
0.048
0.684∗∗∗
(0.160)
0.341∗∗∗
(0.100)
451289
0.389
0.305
(0.334)
-2.187∗∗∗
(0.533)
440461
0.042
√
√
0.890∗∗
(0.330)
-1.046∗
(0.527)
403630
0.200
√
√
√
0.892∗∗
(0.345)
1.918∗∗∗
(0.580)
380194
0.262
√
√
√
√
0.553
(0.491)
1.498∗
(0.714)
380126
0.263
√
√
√
√
√
1.613∗∗∗
(0.214)
0.374
(0.318)
416176
0.045
√
√
0.825∗
(0.324)
-1.148∗
(0.503)
380517
0.206
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
42
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 52: CETC impact on hourly wages, blue collar workers
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.474∗∗∗
(0.0807)
(2)
0.533∗∗∗
(0.0503)
(3)
0.590∗∗∗
(0.0510)
(4)
0.510∗∗∗
(0.0606)
(5)
(6)
-0.261∗∗∗
(0.0729)
-0.0468
(0.0536)
974183
0.317
0.188∗∗∗
(0.0467)
0.270∗∗∗
(0.0363)
946840
0.888
0.186∗∗∗
(0.0468)
0.142∗∗∗
(0.0360)
932378
0.888
0.124∗
(0.0507)
0.116∗∗
(0.0374)
932181
0.888
-9.120∗∗∗
(0.128)
-5.889∗∗∗
(0.0834)
723597
0.296
0.217∗∗∗
(0.0508)
0.289∗∗∗
(0.0393)
692884
0.908
-0.338∗∗∗
(0.0777)
-0.848∗∗∗
(0.140)
661534
0.005
√
√
0.0806
(0.0825)
0.0483
(0.146)
626244
0.211
√
√
√
-0.0155
(0.0819)
2.240∗∗∗
(0.150)
618627
0.273
√
√
√
√
-0.153
(0.0950)
2.123∗∗∗
(0.175)
618534
0.274
√
√
√
√
√
0.589∗∗∗
(0.0529)
0.877∗∗∗
(0.101)
654058
0.008
√
√
0.115
(0.0817)
-0.0341
(0.146)
619032
0.217
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 53: CETC impact on hourly wages, blue collar workers (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.251
(0.357)
(2)
0.490
(0.285)
(3)
0.515
(0.269)
(4)
0.490
(0.267)
(5)
(6)
-2.201∗∗
(0.676)
-1.190∗∗
(0.415)
974183
0.528
-0.524
(0.362)
-0.133
(0.177)
946840
0.896
-0.465
(0.372)
-0.160
(0.186)
932378
0.897
-0.482
(0.376)
-0.141
(0.184)
932181
0.897
-13.25∗∗∗
(0.549)
-8.765∗∗∗
(0.383)
723597
0.507
-0.420
(0.391)
-0.0520
(0.186)
692884
0.910
-0.751
(0.504)
-0.807
(0.923)
661534
0.038
√
√
-0.0241
(0.541)
0.474
(0.981)
626244
0.193
√
√
√
-0.0599
(0.551)
1.386
(0.969)
618627
0.207
√
√
√
√
-0.0679
(0.559)
1.184
(1.014)
618534
0.207
√
√
√
√
√
0.440
(0.456)
1.429
(0.825)
654058
0.042
√
√
-0.111
(0.547)
0.258
(0.908)
619032
0.203
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
43
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 54: CETC impact on growth rate of hourly wages, blue collar workers
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.277∗∗∗
(0.0545)
(2)
0.319∗∗∗
(0.0509)
(3)
0.662∗∗∗
(0.0499)
(4)
0.609∗∗∗
(0.0532)
(5)
(6)
-0.0684
(0.0451)
-0.352∗∗∗
(0.0285)
974180
0.018
0.327∗∗∗
(0.0438)
-0.0944∗∗∗
(0.0273)
946838
0.285
0.366∗∗∗
(0.0432)
0.629∗∗∗
(0.0280)
932376
0.353
0.304∗∗∗
(0.0493)
0.620∗∗∗
(0.0292)
932179
0.353
0.562∗∗∗
(0.0390)
0.0740∗∗
(0.0242)
723594
0.014
0.497∗∗∗
(0.0511)
0.0332
(0.0319)
692880
0.343
-0.110
(0.0843)
-3.018∗∗∗
(0.145)
661532
0.008
√
√
0.270∗∗
(0.0884)
-2.093∗∗∗
(0.146)
626244
0.125
√
√
√
0.102
(0.0851)
1.867∗∗∗
(0.147)
618627
0.248
√
√
√
√
0.0144
(0.0970)
2.050∗∗∗
(0.167)
618534
0.248
√
√
√
√
√
0.991∗∗∗
(0.0571)
-0.739∗∗∗
(0.103)
654056
0.008
√
√
0.247∗∗
(0.0890)
-2.072∗∗∗
(0.148)
619032
0.126
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 55: CETC impact on growth rate of hourly wages, blue collar workers (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.679∗
(0.278)
(2)
-0.171
(0.275)
(3)
-0.125
(0.264)
(4)
-0.189
(0.264)
(5)
(6)
-0.164
(0.181)
-0.393∗∗
(0.150)
974180
0.053
0.244
(0.175)
-0.0893
(0.133)
946838
0.327
0.298
(0.183)
0.410∗∗
(0.127)
932376
0.358
0.270
(0.184)
0.336∗∗
(0.128)
932179
0.358
0.574∗∗∗
(0.158)
0.116
(0.129)
723594
0.049
0.690∗∗∗
(0.186)
0.190
(0.152)
692880
0.382
0.353
(0.402)
-2.690∗∗∗
(0.759)
661532
0.039
√
√
1.048∗
(0.418)
-1.506
(0.828)
626244
0.164
√
√
√
0.978∗
(0.414)
1.248
(0.787)
618627
0.228
√
√
√
√
1.001∗
(0.415)
0.904
(0.802)
618534
0.228
√
√
√
√
√
1.599∗∗∗
(0.274)
-0.240
(0.487)
654056
0.040
√
√
0.985∗
(0.404)
-1.638∗
(0.761)
619032
0.169
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
44
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 56: CETC impact on hourly wages, other employees
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.257∗∗∗
(0.0579)
(2)
0.701∗∗∗
(0.0418)
(3)
0.790∗∗∗
(0.0444)
(4)
0.764∗∗∗
(0.0457)
(5)
(6)
-0.211∗∗∗
(0.0524)
-0.0675
(0.0384)
1206408
0.389
0.111∗∗
(0.0387)
0.0934∗∗
(0.0301)
1173815
0.894
0.107∗∗
(0.0403)
-0.0417
(0.0313)
1083049
0.895
0.102∗
(0.0406)
-0.0398
(0.0321)
1082779
0.895
-9.451∗∗∗
(0.0870)
-6.189∗∗∗
(0.0574)
843634
0.371
0.0288
(0.0437)
0.0368
(0.0335)
803146
0.916
-0.680∗∗∗
(0.0646)
-1.547∗∗∗
(0.114)
815071
0.005
√
√
-0.269∗∗∗
(0.0688)
-0.718∗∗∗
(0.123)
767166
0.221
√
√
√
-0.241∗∗∗
(0.0712)
1.038∗∗∗
(0.131)
710571
0.253
√
√
√
√
-0.245∗∗∗
(0.0722)
0.933∗∗∗
(0.139)
710414
0.253
√
√
√
√
√
0.411∗∗∗
(0.0462)
0.570∗∗∗
(0.0869)
755063
0.006
√
√
-0.284∗∗∗
(0.0717)
-0.819∗∗∗
(0.131)
709014
0.229
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 57: CETC impact on hourly wages, other employees (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.159
(0.216)
(2)
0.651∗∗∗
(0.171)
(3)
0.686∗∗∗
(0.174)
(4)
0.657∗∗∗
(0.174)
(5)
(6)
-0.731∗∗
(0.283)
-0.443∗
(0.173)
1206408
0.566
-0.209
(0.185)
-0.0289
(0.136)
1173815
0.909
-0.214
(0.190)
-0.117
(0.137)
1083049
0.910
-0.221
(0.186)
-0.125
(0.137)
1082779
0.910
-12.07∗∗∗
(0.301)
-8.004∗∗∗
(0.197)
843634
0.545
-0.327
(0.183)
-0.139
(0.142)
803146
0.927
-0.977∗∗∗
(0.271)
-1.200∗
(0.506)
815071
0.018
√
√
-0.583∗
(0.284)
-0.394
(0.519)
767166
0.213
√
√
√
-0.557
(0.290)
0.570
(0.543)
710571
0.226
√
√
√
√
-0.590∗
(0.287)
0.391
(0.571)
710414
0.227
√
√
√
√
√
0.106
(0.197)
0.917∗
(0.433)
755063
0.022
√
√
-0.725∗∗
(0.273)
-0.714
(0.519)
709014
0.223
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
45
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 58: CETC impact on growth rate of hourly wages, other employees
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0959∗
(0.0445)
(2)
0.390∗∗∗
(0.0434)
(3)
0.558∗∗∗
(0.0451)
(4)
0.539∗∗∗
(0.0463)
(5)
(6)
-0.0794∗
(0.0386)
-0.272∗∗∗
(0.0221)
1206404
0.010
0.272∗∗∗
(0.0358)
-0.0298
(0.0222)
1173810
0.278
0.349∗∗∗
(0.0373)
0.503∗∗∗
(0.0241)
1083046
0.322
0.344∗∗∗
(0.0381)
0.490∗∗∗
(0.0252)
1082776
0.322
0.365∗∗∗
(0.0351)
0.0161
(0.0196)
843630
0.010
0.350∗∗∗
(0.0441)
0.0280
(0.0280)
803138
0.344
-0.225∗∗
(0.0703)
-2.593∗∗∗
(0.122)
815067
0.006
√
√
0.0940
(0.0738)
-1.919∗∗∗
(0.122)
767166
0.134
√
√
√
0.189∗
(0.0750)
1.450∗∗∗
(0.130)
710571
0.219
√
√
√
√
0.198∗∗
(0.0760)
1.529∗∗∗
(0.137)
710414
0.220
√
√
√
√
√
0.849∗∗∗
(0.0487)
-0.458∗∗∗
(0.0915)
755059
0.005
√
√
0.116
(0.0786)
-1.896∗∗∗
(0.131)
709014
0.136
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 59: CETC impact on growth rate of hourly wages, other employees (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.437∗
(0.208)
(2)
0.0427
(0.192)
(3)
0.0577
(0.183)
(4)
0.0139
(0.182)
(5)
(6)
-0.0255
(0.172)
-0.235
(0.169)
1206404
0.037
0.217
(0.148)
-0.0229
(0.154)
1173810
0.313
0.310∗
(0.158)
0.371∗
(0.154)
1083046
0.337
0.286
(0.159)
0.335∗
(0.160)
1082776
0.337
0.623∗∗∗
(0.151)
0.165
(0.139)
843630
0.040
0.544∗∗
(0.185)
0.162
(0.169)
803138
0.386
0.145
(0.292)
-2.283∗∗∗
(0.602)
815067
0.029
√
√
0.563∗
(0.286)
-1.566∗
(0.641)
767166
0.189
√
√
√
0.647∗
(0.302)
0.962
(0.639)
710571
0.242
√
√
√
√
0.631∗
(0.303)
0.807
(0.664)
710414
0.243
√
√
√
√
√
1.399∗∗∗
(0.212)
0.125
(0.464)
755059
0.032
√
√
0.564∗
(0.282)
-1.675∗∗
(0.641)
709014
0.196
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
46
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
C.3
Impact on wages, per contracts
Table 60: CETC impact on hourly wages, permanent contracts
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0865∗
(0.0417)
(2)
1.417∗∗∗
(0.0301)
(3)
1.465∗∗∗
(0.0321)
(4)
1.444∗∗∗
(0.0337)
(5)
(6)
0.606∗∗∗
(0.0367)
0.974∗∗∗
(0.0280)
1765105
0.665
1.612∗∗∗
(0.0287)
1.637∗∗∗
(0.0235)
1758902
0.937
1.572∗∗∗
(0.0295)
1.345∗∗∗
(0.0232)
1642565
0.939
1.567∗∗∗
(0.0297)
1.368∗∗∗
(0.0247)
1642179
0.939
-16.50∗∗∗
(0.0824)
-10.43∗∗∗
(0.0537)
1244904
0.600
1.600∗∗∗
(0.0306)
1.632∗∗∗
(0.0251)
1230002
0.952
0.0910∗
(0.0442)
-1.342∗∗∗
(0.0852)
1257528
0.014
√
√
1.114∗∗∗
(0.0453)
0.722∗∗∗
(0.0873)
1217018
0.259
√
√
√
1.169∗∗∗
(0.0448)
3.310∗∗∗
(0.0912)
1142419
0.339
√
√
√
√
1.148∗∗∗
(0.0460)
3.182∗∗∗
(0.0986)
1142195
0.339
√
√
√
√
√
1.687∗∗∗
(0.0323)
1.805∗∗∗
(0.0644)
1179747
0.011
√
√
1.123∗∗∗
(0.0476)
0.715∗∗∗
(0.0918)
1140166
0.263
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 61: CETC impact on hourly wages, permanent contracts (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.0267
(0.309)
(2)
0.951∗∗∗
(0.194)
(3)
0.854∗∗∗
(0.182)
(4)
0.816∗∗∗
(0.184)
(5)
(6)
0.270
(0.268)
0.485∗
(0.191)
1765105
0.764
1.340∗∗∗
(0.169)
1.250∗∗∗
(0.127)
1758902
0.941
1.260∗∗∗
(0.168)
1.058∗∗∗
(0.119)
1642565
0.946
1.191∗∗∗
(0.177)
1.037∗∗∗
(0.115)
1642179
0.946
-18.49∗∗∗
(0.258)
-12.07∗∗∗
(0.194)
1244904
0.723
1.454∗∗∗
(0.171)
1.328∗∗∗
(0.128)
1230002
0.961
0.572∗
(0.270)
-0.546
(0.564)
1257528
0.048
√
√
1.485∗∗∗
(0.272)
1.153∗
(0.585)
1217018
0.290
√
√
√
1.637∗∗∗
(0.278)
2.777∗∗∗
(0.563)
1142419
0.315
√
√
√
√
1.561∗∗∗
(0.291)
2.523∗∗∗
(0.568)
1142195
0.315
√
√
√
√
√
1.759∗∗∗
(0.171)
1.767∗∗∗
(0.421)
1179747
0.055
√
√
1.474∗∗∗
(0.257)
1.035
(0.555)
1140166
0.298
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
47
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 62: CETC impact growth rate of hourly wages, permanent contracts
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.197∗∗∗
(0.0308)
(2)
0.532∗∗∗
(0.0304)
(3)
0.792∗∗∗
(0.0292)
(4)
0.777∗∗∗
(0.0303)
(5)
(6)
0.0514∗
(0.0246)
-0.288∗∗∗
(0.0159)
1765100
0.020
0.605∗∗∗
(0.0245)
0.0861∗∗∗
(0.0156)
1758896
0.263
0.721∗∗∗
(0.0244)
0.779∗∗∗
(0.0168)
1642561
0.358
0.711∗∗∗
(0.0261)
0.774∗∗∗
(0.0179)
1642175
0.358
0.900∗∗∗
(0.0220)
0.272∗∗∗
(0.0141)
1244899
0.016
0.839∗∗∗
(0.0300)
0.237∗∗∗
(0.0188)
1229992
0.329
-0.0458
(0.0503)
-4.118∗∗∗
(0.0877)
1257523
0.012
√
√
0.555∗∗∗
(0.0517)
-2.872∗∗∗
(0.0877)
1217018
0.144
√
√
√
0.621∗∗∗
(0.0482)
1.385∗∗∗
(0.0873)
1142419
0.317
√
√
√
√
0.615∗∗∗
(0.0497)
1.447∗∗∗
(0.0945)
1142195
0.317
√
√
√
√
√
1.611∗∗∗
(0.0349)
-0.831∗∗∗
(0.0639)
1179742
0.010
√
√
0.575∗∗∗
(0.0542)
-2.838∗∗∗
(0.0925)
1140166
0.146
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 63: CETC impact on growth rate of hourly wages, permanent contracts (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.446∗
(0.181)
(2)
0.188
(0.161)
(3)
0.254
(0.166)
(4)
0.326
(0.195)
(5)
(6)
0.119
(0.146)
-0.266∗∗
(0.0820)
1765100
0.055
0.593∗∗∗
(0.141)
0.0838
(0.0779)
1758896
0.289
0.688∗∗∗
(0.146)
0.508∗∗∗
(0.0925)
1642561
0.324
0.567∗∗∗
(0.171)
0.451∗∗∗
(0.0994)
1642175
0.325
0.958∗∗∗
(0.109)
0.279∗∗∗
(0.0676)
1244899
0.052
0.960∗∗∗
(0.172)
0.298∗∗
(0.0933)
1229992
0.357
0.372
(0.304)
-3.387∗∗∗
(0.508)
1257523
0.042
√
√
1.041∗∗∗
(0.307)
-2.077∗∗∗
(0.517)
1217018
0.186
√
√
√
1.151∗∗∗
(0.324)
0.752
(0.520)
1142419
0.260
√
√
√
√
0.912∗
(0.388)
0.410
(0.575)
1142195
0.260
√
√
√
√
√
1.895∗∗∗
(0.190)
-0.423
(0.317)
1179742
0.044
√
√
0.920∗∗
(0.285)
-2.465∗∗∗
(0.444)
1140166
0.195
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
48
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 64: CETC impact on hourly wages, fixed term contracts
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
0.350
(0.231)
(2)
0.434∗
(0.184)
(3)
0.514∗∗
(0.191)
(4)
0.467∗
(0.193)
(5)
(6)
0.544∗
(0.251)
0.722∗∗∗
(0.184)
164184
0.327
0.951∗∗∗
(0.225)
0.753∗∗∗
(0.183)
116928
0.853
0.914∗∗∗
(0.237)
0.706∗∗∗
(0.191)
111676
0.852
0.877∗∗∗
(0.239)
0.691∗∗∗
(0.193)
111627
0.852
-8.028∗∗∗
(0.273)
-5.121∗∗∗
(0.187)
113442
0.314
0.912∗∗∗
(0.251)
0.724∗∗∗
(0.203)
69315
0.864
0.452
(0.316)
-0.596
(0.601)
65167
0.021
√
√
1.069∗
(0.422)
1.323
(0.920)
41856
0.269
√
√
√
1.189∗∗
(0.436)
3.218∗∗∗
(0.963)
40108
0.288
√
√
√
√
1.197∗∗
(0.437)
3.304∗∗∗
(0.968)
40097
0.288
√
√
√
√
√
0.934∗∗∗
(0.265)
0.245
(0.519)
62727
0.021
√
√
1.012∗
(0.436)
0.989
(0.959)
40275
0.271
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 65: CETC impact on hourly wages, fixed term contracts (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.874
(0.890)
(2)
-0.565
(1.004)
(3)
-0.625
(1.025)
(4)
-0.553
(0.957)
(5)
(6)
0.930
(0.962)
1.521∗∗
(0.533)
164184
0.502
0.935
(1.005)
1.245∗
(0.548)
116928
0.819
0.907
(1.018)
1.410∗∗
(0.499)
111676
0.820
0.997
(0.969)
1.480∗∗
(0.493)
111627
0.820
-10.46∗∗∗
(0.807)
-5.549∗∗∗
(0.581)
113442
0.508
1.399
(0.807)
1.716∗∗
(0.566)
69315
0.870
2.568
(1.398)
1.718
(3.071)
65167
0.239
√
√
2.745
(1.672)
1.408
(4.048)
41856
0.435
√
√
√
2.809
(1.706)
4.341
(3.353)
40108
0.450
√
√
√
√
2.791
(1.633)
4.407
(3.323)
40097
0.450
√
√
√
√
√
2.309∗
(0.931)
0.344
(2.314)
62727
0.256
√
√
2.813∗
(1.394)
4.792
(3.270)
40275
0.485
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
49
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 66: CETC impact on growth rate of hourly wages, fixed term contracts
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.175
(0.235)
(2)
0.411
(0.248)
(3)
0.688∗∗
(0.257)
(4)
0.672∗
(0.261)
(5)
(6)
-0.666∗∗
(0.233)
-0.750∗∗∗
(0.158)
164183
0.044
0.294
(0.268)
-0.0149
(0.193)
116926
0.487
0.290
(0.280)
0.422∗
(0.203)
111674
0.493
0.304
(0.282)
0.454∗
(0.204)
111625
0.493
-0.247
(0.228)
-0.453∗∗
(0.152)
113441
0.054
0.438
(0.332)
0.159
(0.231)
69315
0.534
-0.264
(0.456)
-2.674∗∗∗
(0.811)
65167
0.019
√
√
-0.274
(0.602)
-3.020∗
(1.206)
41856
0.196
√
√
√
-0.295
(0.620)
0.335
(1.265)
40108
0.219
√
√
√
√
-0.272
(0.624)
0.445
(1.269)
40097
0.219
√
√
√
√
√
0.738∗
(0.371)
-0.596
(0.680)
62727
0.021
√
√
-0.510
(0.631)
-3.519∗∗
(1.259)
40275
0.198
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001
Table 67: CETC impact on growth rate of hourly wages, fixed term contracts (wheighted regressions)
Placebo test
Double difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
Observations
R2
Triple difference
Intention to treat intensity, 2013
Intention to treat intensity, 2014
(1)
-0.274
(0.801)
(2)
0.675
(0.871)
(3)
0.715
(0.897)
(4)
0.649
(0.897)
(5)
(6)
0.430
(0.885)
-0.00402
(0.516)
164183
0.184
1.630
(0.994)
0.703
(0.612)
116926
0.554
1.723
(1.025)
0.727
(0.600)
111674
0.558
1.767
(1.004)
0.733
(0.599)
111625
0.558
-1.194
(0.897)
-1.071∗
(0.497)
113441
0.191
1.568
(1.027)
0.464
(0.673)
69315
0.617
1.498
(1.683)
-4.656∗
(2.362)
65167
0.128
√
√
1.783
(2.017)
-4.397
(3.263)
41856
0.296
√
√
√
1.932
(2.099)
-2.439
(3.386)
40108
0.309
√
√
√
√
2.024
(2.053)
-2.389
(3.360)
40097
0.309
√
√
√
√
√
2.240
(1.308)
-4.358∗
(1.976)
62727
0.133
√
√
1.766
(1.944)
-4.557
(3.240)
40275
0.311
√
√
√
√
√
Observations
R2
Industry × year fixed effects
Size × year fixed effects
Firm fixed effects
Firm characteristics controls
Minimum wage exposure controls
Alternative control set
Robust standard errors in parentheses (firm level cluster),
Sources: DADS, FARE, MVC 2010-2014.
50
∗
p < 0.05,
∗∗
p < 0.01,
∗∗∗
p < 0.001