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    Do labor tax rebates facilitate firm growth? An empirical study on French establishments in the manufacturing industry, 2004-2011

    Aziza Garsaa EconomiX, University of Paris Ouest,

    Nanterre La Dfense and University of Paris 1 Panthon

    Sorbonne.

    Nadine Levratto1 EconomiX, University of Paris Ouest,

    Nanterre La Dfense Centre d'Etudes de l'Emploi (CEE)

    and Kedge Business School. [email protected]

    Abstract In order to reduce labor costs and stimulate job creation, many governments implement a large set of devices mainly consisting in reduced rates of social contributions. The evaluation of their effect is still controversial. Unlike previous research, our purpose is to appraise to what extent firm growth reacts to a decrease in the cost of labor per employee. We tackle this question using an unbalanced panel of French establishments operating in the manufacturing industry between 2004 and 2011. We run estimations using a 2-STEP estimator making it possible to estimate the impact of explanatory variables on job creation at any point of the distribution of establishments employment growth rate while also controlling for individual fixed effects component. Our results show that the effect of the decrease in the labor cost generated by tax rebates mainly benefits fast growing and large establishments. Indeed, the change in the number of employees in other establishments is significantly less affected, even though the effect remains positive, by the reduction in social contributions. This is particularly the case for smaller establishments as well as for those whose growth is stagnant or negative. These results lead us to reconsider the relevance of large-scale policies aiming at reducing labor costs in the same way for all establishments, regardless of size or financial health.

    Keywords Firm growth, Job creation, Reduced social security contributions, Labor cost, Quantile estimations on panel data.

    JEL Classification C14, J3, J38, L25.

    1 Corresponding author.

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    1 Introduction Excessive labor cost is blamed for unemployment. This claim has motivated many governments to

    implement a large set of devices aiming at reducing labor cost. Among the different schemes available, the

    reduction in social security contributions (RSSCs) targeting low wages has become one of the most popular.

    This approach is supported at the international level. A report published by the Organisation for Economic Co-

    operation and Development (OECD) clearly suggests that such a measure yields a double dividend for

    employers and employees. The report encourages governmental policy which would "reduce direct taxes (social

    security contributions and income taxes) on those with low earnings where this would shift the structure of labor

    demand towards low-wage workers, while protecting their incomes." (OECD 2005)

    From the classical theory of the labor market, the fall in the relative cost of labor resulting from the

    RSSCs is known to have a positive effect on employment (Nickell and Bell 1997). This relationship rests upon

    an equilibrium framework synthesized by the Layard-Nickell diagram (1986). As pointed out by Calmfors

    (1994), job creation resulting from the cuts in business and payroll taxes rests upon a double effect. Firstly, the

    firm reacts to the fall in the relative cost of unskilled labor by substituting it for skilled labor (Malinvaud 1998)

    and/or equipment (Mihoubi 1997) within its production process. Secondly, a volume or competitiveness effect

    intervenes since companies generally lower their prices thanks to a decrease in production costs, thus improving

    their competitiveness. The higher demand companies face leads them to increase their volume of production and,

    consequently, to create jobs (Turquet 2002). On the whole, whatever the transmission channel, the decrease in

    labor taxes makes it possible to improve the situation on the labor market.

    Numerous applied studies discuss the incidence of measures impacting labor cost on job creation.2 They

    can be regrouped into two categories. A first generation of studies, initiated by Brittain (1971), extended by

    Beach and Balfour (1983) and updated by Kugler and Kugler (2009), pays attention to time series and focuses on

    international comparisons. A second generation of works, launched by Hamermesh (1979), is based upon

    microdata able to reflect the broad range of payroll taxes applicable to individuals participating in the labor

    market. The results they provide are not conclusive (Holmlund 1983; Anderson and Meyer 2000; Lang 2003)

    and are still debated.

    The monitoring, follow-up and evaluation of public policies initiated by the French government from the

    end of the nineties also motivated an abundant literature tackling the question of the effects of labor cost

    reduction policies. Whereas the initial latter research preferred to adopt macroeconomic models and aggregated

    data (Laffargue 2000), a radical changed happened after the publication of a paper by Crpon and Desplatz

    (2001). Following Sneessens (1993), who introduced micro-data in evaluation studies, Crpon and Desplatz

    (2001) innovated by introducing the ex-ante labor cost into a propensity score method.3 Most of the papers

    2 Remy (2005) and Ourliac and Nouveau (2012a, 2012b) propose comprehensive surveys on the literature. 3The method of propensity score (Rosenbaum and Rubin 1983), or propensity score matching, is the most developed and popular strategy for causal analysis in observational studies. Introduced by Rosenbaum and Rubi(1983), it involves calculating the conditional probability (propensity) of being in the treated group (of the exposure) given a set of covariates, weighting (or sampling) the data based on these propensity scores, and then analyzing the outcome using the weighted data.

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    published since then conclude that the rebates on social security contributions paid by employers have had a

    positive effect4 and generate jobs.

    The underlying reasons for policy intervention are thus not beyond dispute. The arguments opposing

    labor tax rebates and social security exemptions are traditionally based on windfall gains. First, they arise when

    companies would have enrolled new employees irrespective of public subsidies. Muehlemann and Pfeifer (2013)

    point out that, in Germany, policies enabling a rm to employ workers at reduced working hours may also

    generate substantial windfall gains for rms that had no intention to lay off workers in the rst place. Second,

    market distortions occur because the most supported companies that employ low-wages workers may have a

    clear crowding-out effect on companies hiring more skilled, thus more highly paid workers (Cheron et al. 2008).

    Labor tax rebates are thus considered as an artificial seedbed for less efficient firms, which would have been ejected the market if they had not been subsidized (Santarelli and Vivarelli 2007; Stam et al. 2009).

    Notwithstanding the theoretical arguments against public intervention, the increasing cost of these

    measures in times of economic austerity renews the interest raised by this question. Reports by the OECD (2005)

    and the French Employment Orientation Board (Conseil dOrientation pour lEmploi) (2013) attest to the

    necessity to ensure that taxpayers money is spent effectively and efficiently. To complete the numerous and

    important results already obtained, this paper aims at proposing a new way to address the problem.

    Unlike previous research focusing on the causal effect of different types of social security contributions

    and their reforms on labor market outcomes, our purpose is to assess to what extent employers react to a

    decrease in the cost of labor per employee by hiring additional workers. We tackle this question at the finest

    level, that of the establishment, using a unique database built from data provided by the French National Institute

    of Statistics and Economic Studies (Institut National de la Statistique et des Etudes Economiques - INSEE) and

    the Central Agency for Social Security Bodies (Agence Centrale des Organismes de Securit Sociale - ACOSS).

    It consists in quarterly data related to employment, wages, exemptions and social security contributions.

    Our study provides some insight into the question of why establishments may differ in their propensity

    for creating jobs not only because of the sensitivity of the demand for labor (Hamermesh 1993) but because

    other individual characteristics are taken into account thanks to the adoption of a multivariate model of firm

    growth (Coad 2009). We show first that the rate of exemption is far from being either the sole, or even the main

    determinant of job creation. Instead, and this is a second point, size, industry, qualifications and market

    dynamics dominate in determining establishment decisions. Third, an additional clarification results from the use

    of quarterly data which helps to determine how the policy measure and the period interact to produce the final

    result.

    The remainder of the paper is structured as follows. Section 2 presents the policy implemented in order to

    push down the labor cost thanks to reduced rates of social security contributions. Section 3 specifies the model

    used. Section 4 presents the data and provides descriptive analysis. Section 5 exhibits the results of estimations

    run on the total sample, whereas Section 6 presents estimations on appropriate subsets to check the robustness of

    our analysis. Section 7 concludes and provides some recommendations.

    4Bunel and L'Horty (2012) show that intensity of the effect at the individual level depends on the firm's characteristics.

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    2 The policy To tackle a structurally high level of unemployment, France has implemented a large set of measures

    aiming at reducing the labor costs of low skilled workers, through reduced social security contributions targeting

    low wages. This kind of public policy was initially introduced in 1993 as a strictly targeted instrument,

    specifically devoted to employees whose wages were below 1.2 times the minimum wage.

    As the unemployment rate has continued to rise, the scope of the measures has been progressively

    extended through major changes introduced in the second half of the nineties. The last 2003-2005 reform was

    implemented in order to simplify the complex situation inherited from the accumulation of several schemes, and

    to unify the targeted exemptions from social security contributions for all companies. All these changes

    concerned simultaneously the nature of the social security contribution which can be paid either by the

    employees or by the employers and the maximum threshold of eligibility. As a consequence, the cost of these

    policies for the public budget has increased dramatically. It rose to 29.9 billion Euros in 2012.

    Despite the numerous changes brought by the different governments, the basic principle has remained the

    same since the introduction of the first system. It consists in granting exemptions from employers' social security

    payments in order to reduce the total cost per employee. All the schemes mainly concern low-skilled labor which

    is the category most affected by unemployment. The rebates corresponding to the different measures

    implemented from 2004 to 2012 are presented in figure 1. This figure highlights the general principle underlying

    RSSCs. The amount of the rebates decreases as the wage increases until becoming null beyond a certain

    threshold. Being stimulated to bring people on the job, companies increase their workforce and are thus more

    likely to contribute to a decrease in unemployement.

    Fig. 1 Evolution of the RSSCs according to the number of minimum wage

    Source: Authors' calculations

    0,00%

    5,00%

    10,00%

    15,00%

    20,00%

    25,00%

    30,00%

    11,

    031,

    061,

    091,

    121,

    151,

    181,

    211,

    241,

    27 1,3

    1,33

    1,36

    1,39

    1,42

    1,45

    1,48

    1,51

    1,54

    1,57 1,6

    1,63

    1,66

    1,69

    1,72

    1,75

    1,78

    1,81

    1,84

    1,87 1,9

    1,93

    1,96

    1,99

    Exem

    ption

    rate

    Minimum wage

    Fillon 1 (January, 2003) Fillon 2 (December, 2004) Fillon Majore (July, 2007)

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    3 Model specification In order to address the impact of RSSCs on job creation and bring additional elements to the discussion,

    we estimate a growth model, augmented for variables describing the rate of rebate on social security contribution

    paid by any establishment. Our basic econometric model directly derives from the multivariate model of firm

    growth (see Coad 2009 for a survey).

    The growth rate of establishment i at time t is computed as:

    = + + + +

    = denotes the growth rate of the number of employees of establishment

    i at time t and the logarithm of the average number of employees.

    The change in the number of employees is more appropriate than any other explained variable

    approximating firm growth as it fits with the goal of the policy under review. In addition to this situational

    argument, three other more technical ones can be put at the forefront. First, as pointed out by Coad and Holzl

    (2010) employment is a robust proxy for firm growth. Unlike sales, which often compete with job growth as the

    right indicator to observe, employment is not influenced by the price level and its changes so that no deflation

    index is required. Second, in order to measure the change in sales, accounting is required. However, small firms

    often benefit from a lump sum taxation scheme and, for this reason, are not obliged to declare their sales to the

    tax administration. Third, when available, their annual turnover is not always reliable so that for measuring

    the growth of small firms employment may be more robust to the manipulation of reported sales and profits.

    (Coad and Holzl 2010, p.2). These arguments legitimate the use of the yearly variation in the number of

    employees as a dependent variable in the model.

    The explanatory variables are defined in the following manner. stands for the apparent exemption

    rate. It is computed as a ratio of the total amount of social security exemption to the payroll of the establishment

    i at time t. is a lagged variable measuring the number of employees working in the establishment i at time t-1. The third variable, is the logarithm of the age of the establishment i at time t. The model also contains a composed errors term = + whereis the individual fixed effects and the idiosyncratic

    error term. Their comprehensive definition is presented in Appendix 2.

    4 Data and descriptive analysis 2.1 Data

    To assess the effects of the RSSCs on firm growth, we use a unique data set of French manufacturing

    establishments over the period 2004-2011.

    The characteristics of these establishments are gleaned from three sources. The first one is the Register of

    Businesses and Establishments (REE or Rpertoire des Entreprises et des Etablissements) made available by the INSEE, which manages the single identification number that is allocated to individuals and corporate bodies.

    The second one is the so-called Local Knowledge of the Productive System (CLAP or Connaissance Locale de l'Appareil Productif), provided by INSEE, which contains information about the characteristics of enterprises and establishments (number, size, sector of activity) and wages paid. The third source is a data set provided by

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    the ACOSS, which gathers the information about the amount of RSSCs offered to any establishment, the number

    of employees and the payroll. The data are available over the period 2004-2011.

    Table 1 Structure of the panel

    Number of establishments Number of employees 2004 2011 2004 2011 2004-2011

    Sector (NAF, Rv.2, 2008)5 % Number % Number % Number % Number Variation (%) Manufacture of food products, beverages, and tobacco products (from 10.1 to 12.00Z)

    20,7 5 491 22,7 8 057 11,6 86 099 12,2 119 842 39,2

    Manufacture of textiles, wearing apparel, and leather (from 13.1 to 15.20Z) 5,2 1 391 4,9 1 746 4,9 36 467 3,9 38 745 6,2

    Manufacture of wood; articles of straw and plaiting materials; paper and paper products; and Printing and reproduction of recorded media (from 16.01 to 18.20Z)

    10,9 2 885 10,2 3 637 8 59 558 7,2 70 772 18,8

    Manufacture of coke and refined petroleum products; chemical products; pharmaceutical products; rubber and plastic products; and other non-metallic mineral products (from 19.1 to 23.99Z)

    14 3 722 13,6 4 815 21,2 158 173 23,5 231 299 46,2

    Manufacture of chemicals and chemical products (from 20.1 to 20.60Z)

    19.2 717 18,4 889 31 49 068 28 64 920 32,3

    Manufacture of basic pharmaceutical products and pharmaceutical preparations (from 21.1 to 21.20Z)

    5 189 5,1 247 22,5 35 709 20,3 47 127 32,0

    Manufacture of basic metals; and fabricated metal products (from 24.1 to 25.99B)

    17,8 4 735 16,9 6 006 16 119 428 15,4 152 108 27,4

    Manufacture of computer, electronic and optical products; electrical equipment; and machinery and equipment n.e.c. (from 26.1 to 28.99B)

    9,9 2 635 9,9 3 503 18 134 046 18,1 178 469 33,1

    Manufacture of motor vehicles, trailers and semi-trailers; and other transport equipment (from 29.1 to 30.99Z)

    2,5 658 2,4 857 10,1 75 052 9,2 90 505 20,6

    Manufacture of furniture; Other manufacturing; and Repair and installation of machinery and equipment (from 31.0 to 33.20D)

    19 5 032 19,4 6 887 10,2 76 166 10,5 103 077 35,3

    Total 100 26 549 100 35 508 100 744 993 100 984 821 32,2

    After having merged the three files, we obtain a dataset which has to be cleansed of all the establishments

    presenting missing or faulty data and of all the establishments with no employees at a given moment. The rule of

    elimination adopted is dictated by the estimation technique. To run a growth model the data of at least three

    successive years are needed. After performing these operations, we obtain an unbalanced panel containing

    41,400 French manufacturing establishments over the period 2004-2011.

    Table 1 presents the structure of the panel in 2004 and 2011 according to the number of employees and

    the number of establishments. Establishments operating in the food and tobacco industry represent the largest

    share in our sample (about 20%). By contrast, they employ only 11.6% of the total number of employees. On the

    5 The French classification of activities, revision 2 (NAF rev. 2, 2008) is the national statistical classification of economic activities in force since the 1st of January 2008 and defined by INSEE.

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    opposite, establishments operating in the manufacture of petroleum, chemical, pharmaceutical and non-metallic

    mineral products employ more than 20% of the total number of employees in 2004 and 2011, but they only

    represent about 14% of the total number of establishments over the same periods.

    These disparities can be explained by a firm size effect. French economy as a whole and, also, the panel

    we use are characterized by a large number of small and medium-sized enterprises so that there is a reverse

    relationship between the share of establishments and the share of employees. It may, however, differ from one

    industry to another. On average, more than 60% of the establishments forming the panel employed fewer than 10

    employees in 2004. On the opposite, less than 10% of plants employed 50 employees and more during the same

    period.

    According to Table 1, the total number of employees in the Manufacturing industry (Section C) increased

    by more than 32% from 2004 to 2011. It is higher in the establishments operating in the petroleum, chemical,

    pharmaceutical, rubber and non-metallic mineral products industries (46%) but significantly lower for the

    establishments in the Manufacture of textiles, wearing apparel, and leather industry (6.2%) in the same period.

    2.2 Growth rate distribution This global increase in the number of employees in the French manufacturing industry between 2004 and

    2011 does not, however, prevent most businesses from showing no growth at all (see Table 3 in Appendix 2).

    Just a small minority of them experienced high growth or a decrease in the number of employees. Such a

    distribution of the growth rate confirms a phenomenon already observed and extensively studied (Buldyrev et al.

    1997; Bottazzi and Secchi 2006). Most of the companies neither grow nor decrease. Instead, job creation and

    destruction is caused by firms situated on the left and right tails of the firm growth rate distribution.

    Figure 2 represents the kernel density estimation of French establishments employment growth rate

    during the period 2004-2011.The distribution of employment growth rate displays a characteristic tent-shape

    probability density and looks like the Laplace distribution with fat tails.

    Fig. 2 Kernel density estimation of the employment growth rate (2004-2011)

    The Kernel density is computed using Epanenchnikov kernel. Y axis is in log scale. The graph has been done by the SAS 9.3 software using the proc kde package.

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    Several studies on firm growth have demonstrated that the Laplace distribution has a good fit with the

    empirical firm growth rate distribution (Stanley et al. 1996; Bottazzi and Secchi 2003b; Fagiolo and Luzzi 2006;

    Coad and Rao 2008; Coad and Holzl 2010; etc.). This result is valid at almost every level of aggregation as

    shown by Bottazzi et al. (2001) for the worlds top 150 pharmaceutical firms between 1987 and 1997, Bottazzi et

    al. (2002) for Italian manufacturing industry over the period 1989-1996, or Coad (2007) for French

    manufacturing firms between 1996 and 2002.

    According to Bottazzi and Secchi (2003a), the convergence of the growth rate distribution to the Laplace

    law can be explained by the self-reinforcing effect roughly similar to a kind of success-breeds-success mechanism. Indeed, the probability for a firm to catch a growth opportunity depends on the number of

    opportunities it took before. Such a cumulative advantage process refers to a Polya urn scheme (Dosi et al. 1994)

    since the set of decisions the company faces for any given circumstance is conditioned by the decisions it has

    made in the past. The number of employees employed in the other companies does not change because the

    number of market opportunities they can take is limited. The thickness of the tail depends then on the fact that a

    minority of firms capture the majority of market opportunities.

    Descriptive statistics and correlation matrix are reported in Appendix 2.

    5 Regression results This Section presents the results of the estimations of the employment growth model using an unbalanced

    panel of 41,400 French manufacturing establishments over the period 2004-2011. The model has been estimated

    by Ordinary Least Squares (OLS)6, Fixed Effects (FE) and quantile regression on panel data (2-STEP), a recent

    estimation technique developed by Canay (2011).

    Coad and Holzl (2009) highlight three advantages associated with the use of simple QR to estimate a firm

    growth model.

    Firstly, this technique permits to keep high growth firms in the sample instead of considering them as

    outliers and dropping them. Since the correlation coefficients are estimated along the conditional distribution of

    the dependent variable, this avoids misleading results caused by the estimation of average effects of the

    explanatory variables, as ordinary OLS do, when high growth firms are kept in the dataset Secondly, QR

    estimator guarantees the robustness of the estimations results when errors are not normally distributed. This is

    the case in our case because growth rate distribution follows a Laplace distribution with fat tails (Figure 2).

    Thirdly, QR does not require error terms to be identically distributed at all points of the conditional distribution

    of the dependent variable. Eliminating this constraint allows us to estimate the effects of the regressors at any

    point of the distribution of growth rate.

    The 2-STEP estimator we use has additional advantages. As the simple QR regression, it makes it

    possible to estimate the impact of explanatory variables on job creation at any point of the distribution of

    establishments employment growth rate while also controlling for individuals fixed effects component (see

    Appendix 1 for more details on this estimation technique). This estimator also enables us to control endogeneity

    6 In our case, the OLS regression is a non-robust estimator. This leads us to report the estimations obtained for a reference only. The Hausman test, which tests the hypothesis of a null correlation between individual fixed effects and one of the explanatory variables of the model, rejects the null hypothesis and provides arguments in favor of the adoption of a fixed effects model.

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    bias due to the correlation between individual fixed effects ( ) and independent variables. Because of the

    technical superiority and the robustness of the 2-STEP estimation, we only interpret the results provided by this

    estimation technique.

    Nevertheless, it is worth noticing that whatever the estimation technique used, the results are convergent.

    There is a consistent finding that a decrease in social security contributions positively influences the employment

    growth rate of establishments operating in the French manufacturing industry. This result confirms the findings

    from the previous empirical literature (Crpon and Desplatz 2001; Laffargue 1996; Cahuc 2003; Bunel and

    LHorty 2011; Bunel and LHorty 2012) but, in addition, it makes it possible to differentiate the effect of such a

    measure considering the value of the growth rate. This higher degree of precision is of high interest since the

    labor cost reduction policies aim at creating new jobs and at maintaining existing ones.

    Figure 3 presents the estimated coefficients of the exemption rate as a function of employment growth

    rate distribution. We only provide graphs for this variable of interest. Table 5 in Appendix 3 reports the detailed

    results.

    Our estimations show that RSSCs positively contribute to employment growth over the period 2004-

    2010. Indeed, the estimated coefficients of exemption rate (RSSC) are always positive and significant in all the

    points of the growth rate of employment conditional distribution. However, their influence depends on the value

    of the growth rate as shown by the positive slope of the curve between the 10th and the 90th quantile.

    Fig. 3 Estimated coefficients for the variable RSSC (2-STEP)

    The graph shows the values of the estimated coefficient of the variable RSSC as a function of the conditional distribution of the employment growth rate. The graph has been made using the using the grqreg package in STATA 12 software. The estimation has been run on 231,241 observations over the period 2004-2011.

    The establishments experiencing a high growth rate benefit more from RSSCs than those with a stable or

    decreasing number of employees. The estimated coefficient of the variable RSSC doubles between the two

    extreme points of the conditional distribution: it equals 0.385 on the 10th quantile and 0.711 on 90th.

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    These differences can be explained by an overestimation of the impact of RSSCs on the employment

    growth rate of high growth establishments (situated at the extreme right of the growth rate distribution) due to a

    demand effect (Bottazzi and Secchi 2003a). For these plants, the reduction in social security contributions

    contributes less than demand to job creation. This is consistent with the result of a recent survey of enterprises

    according to which a shrinking market is a major impediment to further growth (Glady and Malshe 2014)

    The results of our estimations also confirm the role played by a firms characteristics in determining

    employment growth.

    As expected, small establishments grow faster than their large counterparts7 (Evans 1987; Hall 1987;

    Wagner 1992; Das 1995; Liu et al. 1999; Oliveira and Fortunato 2006; Fagiolo and Luzzi 2006). This relation is

    well documented and comes from the fact that small firms are assumed to have a lower probability of survival.

    Provided that investment costs are proportional to capacity and are sunk in case of exit, small companies adopt

    more gradual investment plans than those implemented in large companies. This strategy yields a lower expected

    growth rate for large firms since small plants are far from the optimal level and must make sustained efforts to

    reach it. This virtuous process is facilitated by the decreasing average costs of production experienced by small

    firms below the minimum efficient scale. Once the turning point is reached the average cost curve flattens

    (Simon and Bonini 1958; Lotti and Santarelli 2001)

    The result we obtain is, however, different from the previous literature, according to which young firms

    generally tend to grow faster than old ones8. This result holds partly. Indeed, the establishments at the extreme

    right part of the distribution are the only ones for which the growth rate negatively depends on age. As in the

    seminal paper by Evans (1987), according to which firm growth decreases with age when size is controlled for,

    we find that young firms grow faster than more experienced ones. Following Jovanovic (1982), one may

    consider that new entrants benefit from better information about the characteristics of the market than insiders so

    that they perform better. This interpretation is also supported by Navaretti et al. (2012) who conclude that age

    has a negative effect on the growth rate of firms with an increasing number of employees. On the contrary, it

    does not have any effect on the employment growth rate of downsizing ones.

    We obtain a similar opposition concerning the influence of age on changes in the number of employees.

    Whereas the sign of the variable lnAge is negative on the right part of the distribution of employment growth rate, it is positive on the left side. Indeed, among the establishments with a decreasing number of employees

    (from the 10th to the 25th percentile of the growth rate distribution), old ones are likely to grow faster than

    young ones. Such a result is also obtained by Das (1995). It gives support to the existence of a learning-by-doing process which reflects the ability of the firm to learn from its experience, to build a reputation and a credible

    brand image (Arrow 1962). These factors make possible a better and easier access to external finance and market

    opportunities, and therefore enable firms to grow faster.

    These empirical results lead us to draw several conclusions. Firstly, the employment growth of

    establishments operating in the French manufacturing industry has been positively affected by RSSCs over the

    7 See Audretsch et al. (2004) for a survey of several empirical studies on firm growth rate. 8 Audretsch (1995a, 1995b) provides further evidence on the negative effects of firm size and age on firm growth in the US, whereas Dunne and Hughes (1994) consider this question using a sample of 2,000 listed and unlisted UK companies, which all survived the period 1975-1980. Both find that the variable Age is found to be negatively related to growth.

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    period 2004-2011. Secondly, the positive effect of RSSCs on establishments growth rate depends essentially on

    their performance. Rebates on social security contributions significantly strengthen the growth rate of growing

    establishments, whereas their effect is significantly lower, but still positive, for the plants that had decreasing

    employment. Finally, the firm growth process is not exclusively determined by the reduction in labor cost.

    Establishments internal characteristics such as size and age play a role at least as important as the one played by

    RSSCs.

    6 Robustness checks The analysis performed in the previous Section considered aggregated data. In this section we strictly run

    the same investigations, but at a disaggregated level. Preceding this way enables us to check for the robustness of

    the previous findings and to test to what extent their reliability resists a more disaggregated analysis. We split the

    initial panel according to firm size, following the typology established by Coad (2007). Four size classes are

    identified:

    - Establishments employing fewer than 10 employees;

    - Establishments employing between 10 and 19 employees;

    - Establishments employing between 20 and 49 employees;

    - Establishments employing 50 employees and more.

    Each establishment is assigned to one class according to the number of employees declared at the

    beginning of the period covered in this research9: As in the previous Section, we use the transformed variable of

    the difference in logarithm of the number of employees from one year to another as an explained variable. Still,

    we only present the graphs associated to the estimated coefficients of the variable RSSC for each size class. The

    full results of the estimations are presented in Tables 6, 7, 8, and 9 in Appendix 3.

    The four graphs presented in Figure 4 present the estimated value of the coefficient associated with

    RSSCs according to the size class. It is firstly worth noticing that the overall shape of the different curves is

    quite similar and that they are all characterized by a steady increase along the distribution of the growth rate.

    Secondly, this pattern is strictly identical to the one resulting from the estimations on the total population

    presented in the previous Section. As a consequence, one may consider that the decreases in payroll taxes have a

    clear, positive effect on the growth rate of the establishments operating in the French manufacturing industry.

    For any size class, the magnitude of the estimated coefficient of the variable RSSC is positively

    correlated with the employment growth rate. The effect of RSSCs on growth is lower for establishments which

    experience a rapid decline than for those that did not grow or have increased the number of employees between

    2004 and 2011. The higher effect of RSSC is observed for high growth plants. This result suggests that the

    reduction in labor cost caused by tax rebates is an accelerator and not a trigger of job creation.

    The increase in the influence of RSSCs along the growth rate distribution is most acute for the smallest

    establishments employing fewer than 10 employees. Its value almost triples between the 10th and the 90th

    quantile ranging from 0.20 to 0.60. A smoother but still positive orientation still holds for medium-sized

    establishments employing 10 to 19 or 20 to 49 employees. It increases by 20% only from the extreme left to the

    extreme right of the distribution of the growth rate. By contrast, for establishments employing 50 employees or

    9 It corresponds to the first entry of an establishment in the database between 2004 and 2009.

  • 12

    more, the estimated coefficient of RSSC does not continuously grow throughout the conditional employment

    growth rate distribution. This pattern only holds from the 25th to the 75th quantile, a portion of the distribution

    where the estimated coefficient rises from 2.057 to 2.185.

    a. Establishments employing fewer than 10 employees

    b. Establishments employing between 10 and 19 employees

    c. Establishments employing between 20 and 49 employees

    d. Establishments employing 50 employees or more

    Fig. 4 Estimated coefficients for the variable RSSC by size class (2-STEP)

    The graphs show the values of the estimated coefficient associated with the variable RSSC as a function of the conditional distribution of the employment growth rate by size class. These graphs have been made using the grqreg package in STATA 12 software. From top left to bottom right, the graphs respectively show the estimated coefficient of RSSC for establishments employing fewer than 10 employees (139,937 observations over the period 2004-2011); for establishments employing between 10 and 19 employees (36,912 observations); for establishments employing between 20 and 49 employees (30,074 observations); and for establishments employing 50 employees or more (24,318 observations).

    Another substantial result lies in the differentiated effect of reduced rates of social security contributions

    according to the size class as shown by the values of the estimated coefficients. Indeed, the estimated

    coefficients associated with the variable RSSC increase with the size of the establishments, regardless of the

    growth rate of employment. Overall, large establishments profit more from the policy than small ones. This

    result echoes the difficulties faced by small firms in accessing the market, in developing their activities, and,

    finally, in growing. It also confirms that labor cost is far from being neither the sole, nor even the main obstacle

    to growth but that its decrease can make it easier.

    7 Summary and conclusions In this paper we have sought to empirically assess the role which the reduction in social security

    contributions on payroll may play in decreasing labor cost and engendering establishments' growth. We handled

  • 13

    this question using a large dataset containing information about more than 40,000 plants operating in the

    manufacturing industry between 2004 and 2010.

    We have obtained the following empirical results. First, the relationship between the growth rate in the

    number of employees and the reduction in social security contributions paid by employers is positive and robust

    to alternative estimation techniques. Moreover, it also depends on the growth rate in the number of employees.

    Indeed, our results demonstrate that the sensitivity of growth to this policy is unevenly distributed along the

    growth rate distribution. Second, reductions in social security contributions have had different effects depending

    on the size class to which the establishments belong. Globally, breaking down the total sample by size class

    confirms the overall trend outlined in the global analysis. It, however, also reveals some peculiarities. Indeed, the

    growth rate of establishments employing 50 employees or more is much more sensitive to RSSCs than the one

    estimated for the three smaller size classes.

    The econometric strategy adopted enables us to provide differentiated results about the efficiency of labor

    cost reduction policies going beyond the general trends usually emphasized by the literature aiming at generating

    evidence. The novelty of the results achieved comes from the use of a quite innovative econometric technique,

    i.e. quantile regression for panel data with fixed effects introduced by Canay (2011) and still barely used. This

    method highlights the difference of sensitivity to a decrease in payroll tax rebates according to the

    establishments growth rate. For the whole sample, the effects of RSSCs tend to be the highest on the right part

    of the distribution. This result confirms the idea that this policy tends to promote job creation in successful

    businesses, whereas it only tempers job destruction in declining ones.

    Two striking findings for the practitioners emerge from our estimation of the effects of targeted

    employment reductions in payroll taxes. First, the measured net employment effects tend to be considerably

    higher for large establishments which are also less exposed to barriers to growth since they have already

    surpassed the minimum efficient scale. The second striking conclusion emerging from our estimations is that

    growing establishments are the prime beneficiaries of the measure. This support to the winners means that policymakers are confronted with an intricate dilemma inasmuch as budgetary constraints also increase with this

    policy. They can either take the risk of losing money by subsidizing businesses that do not need any help in

    hiring new workers, thus creating a windfall effect for the former. Or, they can decide to curb these policies,

    whose effects remain controversial. This dilemma could be resolved by defining the target groups even more

    sharply, so that RSSCs are restricted to the establishments below a certain size and/or to those hiring real

    outsiders on the labor market. However, there is a danger that extremely selective measures will be a deterrent to

    job creation due to the additional complexity of the system.

    Acknowledgments We are grateful to Cyrille Hagner Director of Economic Studies at the ACOSS for having provided us

    the datasets used in this paper and for his helpful comments and suggestions during this research. We also thank

    the participants to the SASE conference in Chicago, July 2014, where a first version of this paper has been

    presented. All errors remain ours.

  • 14

    Appendix 1 The quantile estimation on panel data method (Canay 2011) In our application we use the Canay (2011) method briefly described hereafter10.

    = + +

    ( , ) = 0

    t = 1,,T and i = 1,, n respectively represents the indexes of time periods and individuals. The vector

    Xincludes explanatory variables. The constant stands for the unobserved individual-specific heterogeneity.

    u is an error term changing over time. Canay (2011) proposes then the following two-step procedure, noted 2-

    STEP:

    Step 1 estimates the individual heterogeneity parameters such as EY X with E(. ) = T (. ) and the Within or fixed effects estimator of .

    Step 2 determines the transformed variable from the method made available by Koenker and Bassett (1978). It proceeds according to the following maximization program:

    () argmin

    [(( )]

    According to Canay (2011), this method provides a consistent and asymptotically normal estimator of

    () if and only if11:

    1. (, , )~. . et () = 0, where: where ( )

    2. For all , , where is convex and compact space and is a closed subinterval of [0,1].

    3. has bounded conditional on X density and (, , ) [(, , )] has a Jacobian matrix such as:

    - (, , ) =

    (, , ) is continuous and fully-ranked,

    - (, , ) =

    (, , ) is uniformly continuous

    Where = (, ) and (, , ) = ( + ) where () = ( < 0)

    Canay (2011) proposes two possible methods to estimate the asymptotic variance of the coefficients: the

    covariance Kernel and the bootstraps. Bootstraps present serious advantages (D'Haultfoeuille and Givor 2012)

    and Monte-Carlo simulations provided by Canay (2011) for T = 10 and N = 100 show better performance than

    previous estimators (Koenker 2004; Koenker and Bassett 1978; Abrevaya and Dahl; 2008) and a bias which

    looks very decent (Campos and Centeno 2012). Like some authors, such as Bargain and Kwenda (2009), who

    compare the wage gap in the informal sector, Matano and Naticchioni (2012), who aim at disentangling the role

    played by different theoretical explanations in accounting for the urban wage premium along the wage

    distribution, or Campos and Centeno (2012), also interested in the evolution of public wages and the public-

    private wage gaps, we also adopt the method proposed by Canay (2011) to estimate how the effects of

    employers' social security payment rebates on job creation differ across the growth distribution. As pointed out

    10 This estimation method has also been used by Levratto et al. (2013). 11 This presentation is directly inspired by Campos and Centeno (2012).

  • 15

    by Galvao (2011), "the quantile regression model has a significant advantage over models based on the

    conditional mean, since it will be less sensitive to the tail behavior of the underlying random variables

    representing the forecasting variable of interest, and consequently will be less sensitive to observed outliers." (p.

    3).

    Appendix 2 Data description Table 2 Definitions and sources of the variables

    Variable Definition Source Explained variable

    Difference between the logarithm of the establishment average number of

    employees between t and t-1 ACOSS

    Explanatory variables

    Lagged value of the logarithm of the establishments average number of employees ACOSS

    The logarithm of the age of the establishment= date of creation of the

    establishment-t CLAP-REE

    The apparent exemption rate = total amount of social security exemption

    /payroll of the establishment i at time t ACOSS

    Table 3 Descriptive statistics of the main explanatory variables

    Mean Standard deviation p10 p25 p50 p75 p90

    Growth -0.010 0.242 -0.223 -0.077 0.000 0.065 0.195 Growth* -0.010 0.649 -0.864 -0.385 0.042 0.426 0.793 lnEmp_1 2.138 1.367 0.560 1.179 1.981 2.918 3.980 lnAge 2.099 0.911 0.693 1.609 2.197 2.773 3.135 RSSC 0.090 0.073 0.009 0.033 0.072 0.132 0.198

    Number of observations = 231,241. Pi is the ith percentile of the distribution of a given variable.

    Table 4 Correlation matrix

    Growth Growth* lnEmp_1 lnAge RSSC Growth 1.000 Growth* 0.314*** 1.000 lnEmp_1 -0.091*** -0.955*** 1.000 lnAge -0.081*** -0.197*** 0.191*** 1.000 RSSC 0.061*** 0.377*** -0.342*** -0.162*** 1.000

    Number of observations = 231,241. The stars indicate the degree of significance (*for 10%, **for 5% and ***for 1%). Growth* is the dependent transformed variable where:Growth = (Growth ).

  • 16

    Appendix 3 Estimation results Table 5 Total sample

    OLS Fixed Effects 2-STEP

    10% 25% 50% 75% 90% VARIABLES Growth Growth Growth* Growth* Growth* Growth* Growth* lnEmp_1 -0.0121*** -0.443*** -0.413*** -0.434*** -0.444*** -0.454*** -0.470*** (0.000393) (0.00203) (0.000388) (0.000284) (0.000120) (0.000220) (0.000406) lnAge -0.0189*** -0.00451 0.0153*** 0.00548*** -0.000786*** -0.0117*** -0.0251*** (0.000653) (0.00302) (0.000781) (0.000415) (0.000281) (0.000453) (0.000799) RSSC 0.109*** 0.546*** 0.385*** 0.421*** 0.523*** 0.661*** 0.711*** (0.00745) (0.0185) (0.0127) (0.00761) (0.00337) (0.00665) (0.0106) 2006 -0.00576*** -0.0128*** 0.00169 0.00169 -0.00519*** -0.0167*** -0.0324*** (0.00204) (0.00179) (0.00237) (0.00144) (0.00101) (0.00167) (0.00293) 2007 -0.00297 -0.0175*** 0.00608*** 0.00696*** -0.00614*** -0.0251*** -0.0487*** (0.00199) (0.00186) (0.00231) (0.00132) (0.000898) (0.00159) (0.00285) 2008 -0.00824*** -0.0318*** 0.00120 0.000725 -0.0173*** -0.0414*** -0.0675*** (0.00197) (0.00202) (0.00235) (0.00134) (0.000837) (0.00164) (0.00268) 2009 -0.0460*** -0.0739*** -0.0539*** -0.0369*** -0.0436*** -0.0791*** -0.113*** (0.00195) (0.00221) (0.00257) (0.00131) (0.000832) (0.00155) (0.00265) 2010 -0.0272*** -0.0733*** -0.0487*** -0.0363*** -0.0457*** -0.0811*** -0.114*** (0.00195) (0.00247) (0.00245) (0.00133) (0.000838) (0.00158) (0.00256) 2011 -0.0102*** -0.0623*** -0.0449*** -0.0313*** -0.0374*** -0.0625*** -0.0858*** (0.00196) (0.00274) (0.00256) (0.00135) (0.000857) (0.00155) (0.00273) Constant 0.0634*** 0.940*** 0.656*** 0.813*** 0.921*** 1.050*** 1.228*** (0.00231) (0.00694) (0.00306) (0.00187) (0.00106) (0.00199) (0.00348) Number of observations 231,241 231,241 231,241 231,241 231,241 231,241 231,241

    Number of establishments

    41,400 41,400 41,400 41,400 41,400 41,400 41,400

    Fisher 447.0*** 5887*** R 0.0171 Adj R 0.0171 R within 0.218 R between 0.00673 R overall 0.00902 Pseudo R 0.7416 0.7704 0.7760 0.7535 0.7091

    Standard errors estimated by Bootstrap are in parentheses (number of Bootstrap samples = 500). The stars indicate the degree of significance (*for 10%, **for 5% and ***for 1%). Growth* is the dependent transformed variable where:Growth = (Growth ).

  • 17

    Table 6 Establishments employing less than 10 employees

    OLS Fixed Effects 2-STEP 10% 25% 50% 75% 90% VARIABLES Growth Growth Growth* Growth* Growth* Growth* Growth* lnEmp_1 -0.0408*** -0.527*** -0.473*** -0.517*** -0.523*** -0.531*** -0.577*** (0.000956) (0.00257) (0.00154) (0.00102) (0.000373) (0.00115) (0.00158) lnAge -0.0277*** 0.0179*** 0.0352*** 0.0286*** 0.0219*** 0.00947*** 0.000349 (0.000917) (0.00407) (0.00129) (0.000769) (0.000398) (0.000805) (0.00126) RSSC 0.0819*** 0.384*** 0.232*** 0.260*** 0.370*** 0.531*** 0.567*** (0.00895) (0.0213) (0.0146) (0.00920) (0.00352) (0.00914) (0.0130) 2006 -0.00585** -0.0132*** 0.00118 -0.00141 -0.00758*** -0.0226*** -0.0418*** (0.00286) (0.00250) (0.00370) (0.00225) (0.00133) (0.00271) (0.00440) 2007 -0.00449 -0.0200*** 0.00655* 0.00470** -0.0138*** -0.0373*** -0.0642*** (0.00280) (0.00260) (0.00374) (0.00212) (0.00125) (0.00267) (0.00427) 2008 -0.00808*** -0.0323*** -0.000255 -0.00369* -0.0264*** -0.0544*** -0.0798*** (0.00276) (0.00285) (0.00347) (0.00216) (0.00124) (0.00268) (0.00432) 2009 -0.0393*** -0.0676*** -0.0446*** -0.0328*** -0.0443*** -0.0912*** -0.123*** (0.00273) (0.00314) (0.00378) (0.00217) (0.00116) (0.00252) (0.00408) 2010 -0.0263*** -0.0713*** -0.0430*** -0.0370*** -0.0496*** -0.0974*** -0.126*** (0.00273) (0.00354) (0.00409) (0.00212) (0.00112) (0.00248) (0.00396) 2011 -0.0142*** -0.0665*** -0.0554*** -0.0449*** -0.0502*** -0.0835*** -0.0985*** (0.00275) (0.00395) (0.00384) (0.00215) (0.00122) (0.00259) (0.00416) Constant 0.118*** 0.638*** 0.320*** 0.511*** 0.619*** 0.755*** 0.969*** (0.00321) (0.00775) (0.00492) (0.00304) (0.00126) (0.00367) (0.00530) Number of observations 139,937 139,937 139,937 139,937 139,937 139,937 139,937

    Number of establishments 26,076 26,076 26,076 26,076 26,076 26,076 26,076

    Fisher 410.7*** 5037*** R 0.0257 Adj R 0.0257 R within 0.285 R between 0.000697 R overall 0.0161 Pseudo R 0.4380 0.5478 0.6264 0.6286 0.5856

    Standard errors estimated by Bootstrap are in parentheses (number of Bootstrap samples = 500). The stars indicate the degree of significance (*for 10%, **for 5% and ***for 1%). Growth* is the dependent transformed variable where:Growth = (Growth ).

  • 18

    Table 7 Establishments employing between 10 and 19 employees

    OLS Fixed Effects 2-STEP

    10% 25% 50% 75% 90% VARIABLES Growth Growth Growth* Growth* Growth* Growth* Growth* lnEmp_1 0.0269*** -0.345*** -0.311*** -0.337*** -0.353*** -0.369*** -0.379*** (0.00280) (0.00489) (0.00455) (0.00265) (0.00192) (0.00199) (0.00311) lnAge -0.00764*** -0.0348*** -0.0167*** -0.0258*** -0.0329*** -0.0419*** -0.0536*** (0.00124) (0.00611) (0.00177) (0.000929) (0.000731) (0.000860) (0.00161) RSSC 0.128*** 0.950*** 0.838*** 0.857*** 0.920*** 0.974*** 1.024*** (0.0186) (0.0525) (0.0284) (0.0138) (0.0116) (0.0146) (0.0245) 2006 -0.00167 -0.00554 0.00455 0.00366 -0.000564 -0.00757** -0.0203*** (0.00382) (0.00341) (0.00398) (0.00301) (0.00239) (0.00328) (0.00560) 2007 0.000524 -0.00858** 0.00427 0.00794*** -0.000629 -0.0101*** -0.0294*** (0.00374) (0.00355) (0.00439) (0.00259) (0.00242) (0.00311) (0.00512) 2008 -0.00799** -0.0280*** -0.00441 -0.00124 -0.0142*** -0.0312*** -0.0582*** (0.00372) (0.00397) (0.00421) (0.00293) (0.00224) (0.00308) (0.00485) 2009 -0.0553*** -0.0754*** -0.0612*** -0.0441*** -0.0475*** -0.0681*** -0.104*** (0.00369) (0.00426) (0.00515) (0.00316) (0.00227) (0.00283) (0.00468) 2010 -0.0244*** -0.0624*** -0.0431*** -0.0306*** -0.0412*** -0.0592*** -0.0892*** (0.00369) (0.00470) (0.00449) (0.00293) (0.00232) (0.00306) (0.00488) 2011 -0.000115 -0.0393*** -0.0252*** -0.0109*** -0.0203*** -0.0344*** -0.0589*** (0.00370) (0.00514) (0.00416) (0.00298) (0.00217) (0.00298) (0.00455) Constant -0.0683*** 0.921*** 0.658*** 0.808*** 0.928*** 1.058*** 1.200*** (0.00861) (0.0177) (0.0143) (0.00790) (0.00617) (0.00649) (0.0103) Number of observations 36,912 36,912 36,912 36,912 36,912 36,912 36,912

    Number of establishments 6,211 6,211 6,211 6,211 6,211 6,211 6,211

    Fisher 64.29*** 687.4*** R 0.0154 Adj R 0.0152 R within 0.168 R between 0.134 R overall 7.62e-05 Pseudo R 0.2285 0.3063 0.3625 0.3753 0.3610

    Standard errors estimated by Bootstrap are in parentheses (number of Bootstrap samples = 500). The stars indicate the degree of significance (*for 10%, **for 5% and ***for 1%). Growth* is the dependent transformed variable where:Growth = (Growth ).

  • 19

    Table 8 Establishments employing between 20 and 49 employees

    OLS Fixed Effects 2-STEP

    10% 25% 50% 75% 90% VARIABLES Growth Growth Growth* Growth* Growth* Growth* Growth* lnEmp_1 0.0542*** -0.242*** -0.209*** -0.235*** -0.251*** -0.268*** -0.290*** (0.00288) (0.00554) (0.00381) (0.00214) (0.00147) (0.00190) (0.00309) lnAge -0.00315** -0.0360*** -0.0188*** -0.0277*** -0.0331*** -0.0411*** -0.0536*** (0.00144) (0.00736) (0.00168) (0.000777) (0.000624) (0.000898) (0.00164) RSSC 0.192*** 1.577*** 1.409*** 1.418*** 1.508*** 1.586*** 1.613*** (0.0261) (0.0783) (0.0275) (0.0154) (0.0142) (0.0165) (0.0237) 2006 -0.00185 -0.00621 0.00187 0.00162 0.000166 -0.00542** -0.0170** (0.00439) (0.00391) (0.00398) (0.00243) (0.00218) (0.00266) (0.00699) 2007 0.00679 -0.00437 0.0134*** 0.00910*** 0.00455** -0.00297 -0.0179*** (0.00428) (0.00403) (0.00381) (0.00202) (0.00207) (0.00271) (0.00590) 2008 -0.00349 -0.0232*** 0.00374 -0.00121 -0.0100*** -0.0220*** -0.0414*** (0.00425) (0.00440) (0.00375) (0.00239) (0.00206) (0.00273) (0.00601) 2009 -0.0572*** -0.0772*** -0.0766*** -0.0486*** -0.0437*** -0.0600*** -0.0887*** (0.00423) (0.00476) (0.00510) (0.00256) (0.00218) (0.00235) (0.00554) 2010 -0.0216*** -0.0585*** -0.0354*** -0.0270*** -0.0332*** -0.0512*** -0.0793*** (0.00424) (0.00525) (0.00433) (0.00229) (0.00209) (0.00247) (0.00563) 2011 0.00849** -0.0274*** -0.000658 0.00250 -0.00204 -0.0162*** -0.0432*** (0.00427) (0.00574) (0.00434) (0.00239) (0.00208) (0.00235) (0.00566) Constant -0.204*** 0.814*** 0.552*** 0.718*** 0.830*** 0.959*** 1.144*** (0.0112) (0.0247) (0.0146) (0.00837) (0.00574) (0.00769) (0.0135) Number of observations 30,074 30,074 30,074 30,074 30,074 30,074 30,074

    Number of establishments 4,984 4,984 4,984 4,984 4,984 4,984 4,984

    Fisher 85.58*** 326.3*** R 0.0250 Adj R 0.0247 R within 0.105 R between 0.0691 R overall 0.00137 Pseudo R 0.2165 0.3253 0.3894 0.3999 0.3685

    Standard errors estimated by Bootstrap are in parentheses (number of Bootstrap samples = 500). The stars indicate the degree of significance (*for 10%, **for 5% and ***for 1%). Growth* is the dependent transformed variable where:Growth = (Growth ).

  • 20

    Table 9 Establishments employing 50 employees and more

    OLS Fixed Effects 2-STEP

    10% 25% 50% 75% 90% VARIABLES Growth Growth Growth* Growth* Growth* Growth* Growth* lnEmp_1 0.0210*** -0.112*** -0.101*** -0.110*** -0.117*** -0.122*** -0.133*** (0.00200) (0.00690) (0.00136) (0.000629) (0.000562) (0.000798) (0.00135) lnAge 0.00110 -0.0357*** -0.0242*** -0.0294*** -0.0316*** -0.0367*** -0.0492*** (0.00207) (0.0103) (0.00149) (0.000636) (0.000524) (0.000952) (0.00186) RSSC 0.491*** 2.210*** 2.069*** 2.057*** 2.117*** 2.185*** 2.137*** (0.0442) (0.152) (0.0328) (0.0164) (0.0137) (0.0210) (0.0400) 2006 -0.0177*** -0.0209*** -0.00400 -0.00139 -0.00397** -0.00903*** -0.0326*** (0.00634) (0.00550) (0.00429) (0.00205) (0.00189) (0.00331) (0.00875) 2007 -0.0123** -0.0218*** -0.000448 0.00325* 0.00101 -0.00267 -0.0328*** (0.00605) (0.00559) (0.00368) (0.00167) (0.00176) (0.00298) (0.00767) 2008 -0.0153** -0.0334*** -0.00128 -0.00211 -0.00665*** -0.0160*** -0.0509*** (0.00601) (0.00604) (0.00315) (0.00162) (0.00176) (0.00292) (0.00685) 2009 -0.0566*** -0.0777*** -0.0620*** -0.0346*** -0.0327*** -0.0427*** -0.0889*** (0.00600) (0.00661) (0.00505) (0.00216) (0.00177) (0.00288) (0.00674) 2010 -0.0358*** -0.0621*** -0.0358*** -0.0226*** -0.0241*** -0.0341*** -0.0762*** (0.00600) (0.00730) (0.00414) (0.00175) (0.00171) (0.00295) (0.00657) 2011 -0.00718 -0.0280*** 0.00437 0.0151*** 0.0115*** 0.00559* -0.0369*** (0.00606) (0.00811) (0.00452) (0.00174) (0.00188) (0.00298) (0.00673) Constant -0.144*** 0.530*** 0.347*** 0.447*** 0.519*** 0.602*** 0.771*** (0.0123) (0.0403) (0.00898) (0.00418) (0.00370) (0.00575) (0.0115) Number of observations 24,318 24,318 24,318 24,318 24,318 24,318 24,318

    Number of establishments 4,129 4,129 4,129 4,129 4,129 4,129 4,129

    Fisher 35.73*** 87.55*** R 0.0131 Adj R 0.0127 R within 0.0376 R between 0.00203 R overall 0.000108 Pseudo R 0.3053 0.4240 0.4652 0.4322 0.3450

    Standard errors estimated by Bootstrap are in parentheses (number of Bootstrap samples = 500). The stars indicate the degree of significance (*for 10%, **for 5% and ***for 1%). Growth* is the dependent transformed variable where:Growth = (Growth ).

  • 21

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