Hector Fernandez
email: hector.fernandez@vu.nl

Corina Stratan
email: corina.stratan@vu.nl

Guillaume Pierre
email: guillaume.pierre@irisa.fr

Robust Performance Control for Web Applications in the Cloud

Keywords: Cloud computing, PaaS, Resource provisioning, Web applications

With the success of Cloud computing, more and more websites have been moved to cloud platforms. The elasticity and high availability of cloud solutions are attractive features for hosting web applications. In particular, the elasticity is supported through trigger-based provisioning systems that dynamically add/release resources when certain conditions are met. However, when dealing with websites, this operation becomes more problematic, as the workload demand fluctuates following an irregular pattern. An excessive reactiveness turns these systems into imprecise and wasteful in terms of SLA fulfillment and resource consumption. In this paper, we propose three different provisioning techniques that expose the limitations of traditional systems, and overcomes their drawbacks without overly increasing complexity. Our experiments conducted on both public and private infrastructures show significant reductions in SLA violations while offering performance stability.

INTRODUCTION

One of the major innovations provided by Cloud computing platforms is their pay-per-use model where clients pay only for the resources they actually use. This business model is particularly favorable for application domains where workloads vary widely over time, such as the domain of Web application hosting. Web applications in the cloud can request and release resources at any time according to their needs.

However, provisioning the right volume of resources for a Web application is not a simple task. Web applications are usually composed of multiple types of components such as Web servers, application servers, database servers and load balancers that distribute the incoming traffic across them. Complex performance behavior of these components makes it difficult to find the optimal resource allocation, even when the application workload is perfectly stable. The magnitude of the problem is further increased by the fact that Web application workloads are often very unstable and hard to predict. In the case of a sudden load increase there is a necessary tradeoff between reacting as early as possible to minimize the duration when the application underperforms because of insufficient processing capacity, and a slower approach to avoid situations where the load has already decreased when the new resources become available.

Faced with this difficult scientific challenge, the academic community has proposed a wide range of sophisticated resource provisioning algorithms [START_REF] Dejun | Resource provisioning of Web applications in heterogeneous clouds[END_REF][START_REF] Muppala | Regression-based resource provisioning for session slowdown guarantee in multi-tier internet servers[END_REF][START_REF] Urgaonkar | Agile dynamic provisioning of multi-tier internet applications[END_REF][START_REF] Vasić | DejaVu: Accelerating resource allocation in virtualized environments[END_REF]. However, we observe a wide discrepancy between these academic propositions and the very simple mechanisms that are currently available to cloud customers. These mechanisms are usually based on lower or upper thresholds on resource utilization. Crossing one of the thresholds triggers a pre-defined resource provisioning action such as adding or removing one machine. We postulate three possible reasons why sophisticated techniques are not more widely deployed: (i) the gains of using sophisticated provisioning strategies are too low to be worth the effort; (ii) implementing and evaluating these techniques is a difficult exercise, which is why real cloud systems rely on simpler techniques; and (iii) academic approaches mostly focus on unrealistic evaluations using simple applications and artificial workloads [START_REF] Do | Profiling applications for virtual machine placement in clouds[END_REF][START_REF] Islam | Empirical prediction models for adaptive resource provisioning in the cloud[END_REF].

This paper investigate which of these possible causes are the real problems, and aims to propose automatic scaling algorithms which provide better results than the simple ones without overly increasing complexity. We implemented and installed several resource provisioning mechanisms in ConPaaS, an open source platform-as-a-service environment for hosting cloud applications [START_REF] Pierre | ConPaaS: a platform for hosting elastic cloud applications[END_REF]. Our techniques use several levels of thresholds to predict future performance degradations, workload trend detection to better handle traffic spikes and dynamic load balancing weights to handle resources heterogeneity. We exercised these algorithms in realistic unstable workload situations by deploying a copy of Wikipedia and replaying a fraction of the real access traces to its official web site. Finally, we report on (i) implementation complexity; and (ii) potential gains compared to the threshold-based solution.

CONPAAS OVERVIEW

ConPaaS is an open-source runtime environment for hosting applications in Cloud infrastructures [START_REF] Pierre | ConPaaS: a platform for hosting elastic cloud applications[END_REF]. In ConPaaS, an application is designed as a composition of one or more elastic and distributed services. Each service is dedicated to host a particular type of functionality of an application. ConPaaS currently supports seven different types of services: two web application hosting services (PHP and JSP); a MySQL database service; a NoSQL database service; a MapReduce service; a TaskFarming service; and a shared file system service.

Each ConPaaS service is made up of one manager virtual machine (VM) and a number of agent VMs. Agent: The agent VMs hosts the necessary components to provide the service-specific functionality. Based on the performance requirements or the application workload, agent VMs can be started or stopped on demand. Manager: Each service is controlled by one manager VM. The manager is in charge of centralizing monitoring data, controlling the allocation of resources assigned to the service, and coordinating reconfigurations so no loss of service is experienced when adding or removing resources.

Figure 1 shows the architecture of the ConPaaS deployment for a typical PHP web application backed by a MySQL database. The application is deployed using two ConPaaS services: the PHP service and the MySQL service. As illustrated in Figure 1, the manager VM of each ConPaaS service includes a performance monitoring and a resource provisioning system. The monitoring component is based on Ganglia (Ganglia monitoring system, ), a scalable distributed monitoring system. The monitoring data is collected by the manager VM. For the PhP service, we implemented modules that extend Ganglia's standard set of monitoring metrics for measuring the request rate and response times of requests.

Each ConPaaS service can plug-in its own automatic resource provisioning policies, taking into account specificities of the service such as the structure of the service deployment, the complexity of the requests and service-specific monitoring data. So far our focus has been on the web hosting service, for which we implemented a number of automatic resource provisioning policies.

WIKIPEDIA APPLICATION

Most academic resource provisioning systems are evaluated using synthetic applications and workloads such as TPC-W, RuBiS and RuBBoS. However, these traditional benchmarks do not exhibit features of real Web applications such as traffic unstability as well as heterogeneous and constantly-changing request mixes [START_REF] Cecchet | BenchLab: An open testbed for realistic benchmarking of web applications[END_REF]. Instead of basing our evaluations on unrealistic workloads, we used the WikiBench benchmark [START_REF] Van Baaren | Wikibench: A distributed, wikipedia based web application benchmark[END_REF]. This benchmark uses a full copy of Wikipedia as the web application, and replays a fraction of the actual Wikipedia's access traces.

Hosting a copy of Wikipedia in ConPaaS requires two different services: a PHP web hosting and a MySQL service. The MySQL service is loaded with a full copy of the English Wikipedia articles as of 2008, which has a size of approximately 30GB. In the PHP service, the configuration was composed of one load balancer, one static web server and one or more PHP servers. We then use WikiBench to replay access traces from 2008 [START_REF] Urdaneta | Wikipedia workload analysis for decentralized hosting[END_REF]. The trace contains requests for static and dynamic pages. When replaying it, the most important performance bottleneck is the application logic of the application: PHP requests are processed an order of magnitude slower than simpler static web pages. In this article we therefore focus on the automatic scaling of the PHP tier.

As an example, in Figure 2(a), we show the PHP workload sampled from one trace, as the number of PHP requests per minute during approximately one day. Besides the obvious variations in number of re- For this experiment we used a fixed number of 5 PHP servers, which are sufficient for handling the workload from the access trace even at its peaks; this is why in this case the response time is not influenced by the intensity of the workload. However, the results show a relatively wide dispersion of the response time. The main reason for this dispersion is that the Wikipedia articles vary in complexity, requiring different amounts of information that needs to be retrieved from the database and assembled in a page.

We also conclude from this experiment that, since the response time of the application varies and cannot be properly predicted even when the request rate is known, a provisioning algorithm aiming to keep the application's response time under a certain limit should take into account several monitoring parameters beyond the request rate and the response time.

PROVISIONING ALGORITHMS

We evaluate three resource provisioning algorithms: a trigger-based algorithm representing a base-

Trigger-based provisioning

Existing cloud infrastructures adjust the amount of allocated resources based on a number of standard monitoring parameters. These are simple triggerbased systems which define threshold rules to increase or decrease the amount of computational resources when certain conditions are met. As an example, the Auto Scaling system offered by Amazon EC2 (Amazon Elastic Compute Cloud (EC2), ) can be used to define rules for scaling out or back an application based on monitoring parameters like CPU utilization, network traffic volume, etc. Similar triggerbased techniques are also used in platforms such as RightScale (RightScale, ) or OpenShift (OpenShift, ).

For the sake of comparison, we designed and implemented a trigger-based provisioning mechanism in ConPaaS. This technique monitors the percentage of CPU usage and application response time metrics, and assigns a lower and upper threshold for each metric. It then creates or deletes VMs whenever one of the threshold is crossed, as described in Algorithm 1. Analogous to traditional trigger-based systems, the thresholds are statically defined by the user before execution.

Even though this algorithm is simple and widely used in cloud platforms, we will show that it is too reactive. This is due to two main factors:

1) Workload heterogeneity: For some web sites, the workload fluctuates following an irregular pattern, with occasional short traffic spikes. An excessively reactive algorithm cannot handle these situations well, and will cause frequent fluctuations in the number of allocated resources; this has negative ef-fects on the stability and performance of the system. Considering the pricing model of cloud providers that charges users on a per-hour basis, trigger-based systems increase the infrastructure cost because they frequently terminate resources before this hour price boundary.

2) Resources heterogeneity: The performance of virtual instances provided by current clouds is largely heterogeneous, even among instances of the same type [START_REF] Dejun | EC2 performance analysis for resource provisioning of serviceoriented applications[END_REF]. Simple trigger-based provisioning systems do not take this heterogeneity into account, thus providing less efficient resource allocation.

We believe that trigger-based provisioning mechanisms can be improved without drastically increasing their complexity. We now present two techniques which aim at solving the aforementioned drawbacks by relying on predictive and more accurate methods.

Feedback provisioning

To handle the workload heterogeneity and mitigate the reactiveness of trigger-based techniques, we designed and implemented an algorithm that relies on three simple mechanisms: integration of several metrics and automatic weighting of each metrics according to its predictiveness; estimating the overall workload trend; and definition of two levels of threshold ranges instead of one.

Weighted metrics: As pointed out in [START_REF] Singh | Autonomic mix-aware provisioning for nonstationary data center workloads[END_REF], provisioning decisions solely made on the basis of request rate or percentage of CPU usage can incur errors by under-or over-provisioning an application. We propose instead to use a collection of metrics (e.g. response time, request rate and CPU usage) likely to indicate the need to scale the system. Our algorithm continuously measures the correlation between each metric's values and the actual scaling decisions. This method enables to better adapt the scaling decisions to the current workload requirements by identifying how metrics affect to the processing time. We thus assign a weight to each metric (correlation coefficient) according to its predictiveness. For example, when hosting the MediaWiki application, the hosting system needs to deal with requests having a wide diversity of computing complexity, and with significant variations in the ratios of simple vs. complex requests. Our algorithm therefore associates a higher weight to the CPU usage metric than to the request rate, as it affects to the response times.

Workload's trend estimation: a difficult problem in any autoscaling system is the fact that any decision Algorithm 2: Feedback provisioning Data: Pre-defined metric threshold ranges, SLO threshold Result: Scaling decisions 1 Create a queue to store historical workload, q; 2 Establish two-level thresholds: pred thr and reac thr ; has a delayed effect. Provisioning extra resources can take a couple of minutes before they are ready to process traffic. Besides, cloud computing resources are usually charged at a 1-hour granularity so the scaling decision has consequences at least during the first hours after provisioning. It is therefore important to estimate the medium-term trend of response times in addition to any short-term bursts. We therefore estimate the response time trend using past values over a period of the same order of magnitude (30 min in our experiments). We classify workloads in one out of three categories: stable, increasing or decreasing.

Two-level thresholds: Initially, the users define a fixed threshold range based on their performance requirements (max and min required response time), denoted by SLO thresholds (Service Level Objective) in In Figure 3, these two extra thresholds called predictive and reactive, both with upper and lower bounds, create two "head-rooms" between the SLO threshold and them. The predictive head-room H 1 is intended to alert of future workload alterations when a metric exceeds its predictive bounds, thus increasing proportionally to its weight the chances to trigger scaling actions (denoted by s bck and s out in Algorithm 2 lines 12-17). Otherwise, the scaling chances will drop in the same proportion (Line 20). The reactive head-room H 2 is used to trigger scaling actions if the workload presents a increasing or decreasing variation (Lines 24-26), which may cause SLO violations if it keeps such a trend. This mechanism in conjunction with the workload trend estimation allow to better analyze the evolution of performance fluctuations, and as a consequence improves the accuracy of our decisions by reacting in advance. In the future, these two-levels of thresholds could be adjusted depending on the hardware configuration of each provisioned VM, as introduced in [START_REF] Beloglazov | Adaptive thresholdbased approach for energy-efficient consolidation of virtual machines in cloud data centers[END_REF].

To sum up, the feedback algorithm triggers a scaling action when a series of conditions are satisfied: (i) no previous scaling actions have been taken over the last 15min; (ii) the recent monitoring data have to exceed the predictive and reactive threshold ranges; (iii) the workload trend has to follow a constant pattern (increasing/decreasing). Although the combination of these techniques improves the accuracy of our measurements, the heterogeneous nature of the VM instances requires more flexible provisioning algorithms, as pointed out in [START_REF] Jiang | Performance Guarantees For Web Applications[END_REF].

Dynamic load balancing weights

The problem we consider here is the heterogeneity of cloud platforms. Different VMs have different performance characteristics, even when their specifications from the cloud vendor are the same. This issue can be addressed through various load balancing techniques, like assigning weights to the backend servers or taking into account the current number of connections that each server handles. Furthermore, the performance behavior of the virtual servers may also fluctuate, either due to changes in the application's usage patterns, or due to changes related to the hosting of the virtual servers (e.g., VM migration).

In order to address these issues in ConPaaS we implemented a weighted load balancing system in which the weights of the servers are periodically readjusted automatically, based on the monitoring data (e.g. response time, request rate and CPU usage). This method assigns the same weight to each backend server at the beginning of the process. The weights are then periodically adjusted (in our experiments, every 15min) proportionally with the difference among the average response times of the servers during this time interval. By adding this technique to the feedback-based algorithm, we noticed a performance improvement when running the benchmarks.

EVALUATION

To compare the provisioning algorithms described above, we ran experiments on two infrastructures: a homogeneous one (the DAS-4, a multi-cluster system hosted by universities in The Netherlands (Advanced School for Computing and Imaging (ASCI), )) and a heterogeneous one (Amazon Elastic Compute Cloud (EC2), ). The goal of our experiments was to compare the algorithms by how well they fulfill the SLOs and by the amount of resources they allocate.

Testbed configuration: As a representative scenario, we deployed the MediaWiki application using ConPaaS on both infrastructures, and we ran the Wikibench tools with a 10% sample of a real Wikipedia access trace for 24hours. We configured the experiments as follows: a monitoring window of 5min, a SLO of 700ms at the service's side (denoted by a red Line in Figures) and the same statistically-chosen performance threshold ranges for response time and CPU utilization. Note that, the weighted load-balancing provisioning technique was only evaluated on the heterogeneous platform Amazon EC2, as it only brings improvements in environments where VMs may have different hardware configurations.

Homogeneous Infrastructure

Our experiments on DAS-4 rely on OpenNebula as IaaS [START_REF] Sotomayor | Virtual infrastructure management in private and hybrid clouds[END_REF]. To deploy the Wikipedia services, we used small instances for the PHP service (manager and agents) and a medium instance for the MySQL service (agent). In DAS-4, OpenNebula's small instances are VMs equipped with 1 CPU of 2Ghz, and 1GiB of memory, while medium instances are equipped with 4 CPU's of 2Ghz, and 4GiB of memory. Resource consumption. To better understand the behavior of both algorithms, we shall also focus on the resource consumption illustrated on Figure 6. The excessively reactive behavior of the trigger-based algorithm can be noticed in the time intervals around t=350min and t=820min, where two scaling operations under-provision the system during a short period of time. These provisioning decisions provoked the SLO violations that are visible in Figure 4 in the same intervals of time. Besides affecting the sys- tem's stability, such short fluctuations in the number of provisioned resource also raise the cost of hosting the application since more VM instantiations will be triggered. When using the feedback algorithm, the system makes provisioning decisions by analyzing the workload's trend. Scaling actions are only triggered when having constant alterations in the workload, thereby providing a more efficient resource usage. We can see that the provisioning decisions on Figure 6 match well with the workload variations depicted on Figure 2(a).

SLO enforcement.

Discussion. Both algorithms are best-effort regarding the SLO fulfillment, and thus they do not handle well short alterations of the workload (with duration in the range of minutes). The heterogeneity of the PHP requests, as well as the VM scheduling time (2-5min), are in part responsible of these SLO violations.

Heterogeneous Infrastructure

Our experiments on Amazon EC2 used small instances for the PHP service (manager and agents) and a medium instance for the MySQL service (agent). EC2 small instance are equipped with 1 EC2 CPU, and 1.7GiB of memory, while medium instances are equipped with 2 EC2 CPU's, and 3.75GiB. SLO enforcement. Figure 8, Figure 9 and Figure 10 show the system performance of the trigger-based, feedback and dynamic load-balancing weights algorithms, respectively. As depicted on Figure 8, the performance of the trigger-based algorithm is even more unstable than in the case of the homogeneous infrastructure. Two of the three peaks in response time, at t=300min and t=820min, can be explained by the variations in the Wikipedia workload shown in Figure 2(a). However, there is a third peak between t=400min and t=500min that corresponds to an interval of time in which the workload trace shows a significant drop in the request volumes. During this period of time, the algorithm attempts to scale back the system but the new lower number of resources cannot handle the load, and the system is scaled out again. As this algorithm does not keep any history information, after a short time it attempts again to scale back the system, with the same result; thus, some oscillations occur in the number of allocated resurces, causing also SLO violations.

Figure 9 and Figure 10 show that the feedback and dynamic load-balancing algorithm reduce the number of SLO violations and provide a more stable performance pattern. Specifically, with the feedback algorithm we obtained 41.3% less SLO violations than with the trigger-based algorithm, while the dynamic load balancing algorithm had 47.6% less violations than the trigger-based one. This improvement of 6.3% responds to an efficient distribution of the incoming traffic across the allocated resources.

Resource consumption. As explained above, the trigger-based algorithm sometimes initiates series of scaling back operations quickly followed by scaling out operations, due to the fact that it does not use any history information that could prevent these oscillations. This can be seen on Figure 7, in the time interval between t=400min and t=500min. The feedback and dynamic load balancing algorithms have similar and more stable patterns of resource consumption, which show the benefits of using trend estimations and two-level threshold ranges.

Discussion. These runs show significant differences between the trigger-based algorithm on one hand, and the feedback and dynamic load balancing on the other hand. The results show that using a few heuristics as in the last two algorithms can help in improving the performance and stability of the system. We did not obtain however significant differences between the feedback and the dynamic load balancing algorithm. The last algorithm uses the additional technique of dynamically adjusting the load balancing weights, but the VMs participating in the experiments had relatively similar performance and adjusting their weights only made a small difference. On other infrastructures that are more heterogeneous than Amazon EC2, the dynamic load balancing technique may bring a greater performance improvement.

We also note that the frequent scaling actions launched by the trigger-based algorithm generally increase the infrastructure cost. An explanation comes from the frequent creation/removal of VM instances occurring within a time interval of 1hour. Even though this trace shows a clear daily variation which represents the traditional traffic pattern of web applications, more traces have been utilized to validate our autoscaling system in [START_REF] Fernandez | Autoscaling in heterogenous in cloud infrastructures[END_REF]

RELATED WORKS

There is a wide literature on issues related to dynamic resource provisioning for cloud web applications. However, most of these models require a deep understanding in mathematics or machine learning techniques which are not easily interpreted by non specialists. Besides the traffic in web applications is shaped by a combination of different factors such as diurnal/seasonal cycles that follows an irregular pattern, thus making extremely challenging the design and development of realistic and accurate provisioning mechanisms.

These well-known drawbacks force to IaaS like Amazon EC2, or PaaS like RightScale and OpenShift, to design simple trigger-based auto-scaling systems, instead of relying on approaches from academic research. Unfortunately, these scaling systems are imprecise and wasteful in terms of resource consumption and cost savings [START_REF] Ghanbari | Exploring alternative approaches to implement an elasticity policy[END_REF].

As a consequence, more relevant and realistic academic approaches have been proposed over the last years. [START_REF] Urgaonkar | Agile dynamic provisioning of multi-tier internet applications[END_REF] designed and imple- mented a predictive and reactive mechanism using a queuing model to decide the number of resources to be provisioned, and an admission control mechanism to face extreme workload variations. Even though the use of admission mechanisms enforce the performance requirements, it reduces the QoS of the service, and therefore affects user experience. Differently, [START_REF] Muppala | Regression-based resource provisioning for session slowdown guarantee in multi-tier internet servers[END_REF] proposed offline training techniques to gather information about the resource requirements of the current workload, and thereby to improve the accuracy of the scaling decisions. In the same vein, DejaVu [START_REF] Vasić | DejaVu: Accelerating resource allocation in virtualized environments[END_REF] and CBMG [START_REF] Roy | Efficient autoscaling in the cloud using predictive models for workload forecasting[END_REF] built similar mechanisms to classify the workload need by analyzing recent traffic spikes or the customer behavior. However, these approaches require additional resources to identify the workload requirements, and an exhaustive knowledge of the deployed applications; thus preventing its integration in existing autoscaling systems.

As a proposal closely related to ours, [START_REF] Ghanbari | Exploring alternative approaches to implement an elasticity policy[END_REF]) designed an autoscaling system using control theoretic and rules-based models. The authors claimed for simpler provisioning mechanisms in comparison with the sophisticated academic approaches. However, factors such as resource heterogeneity were not addressed in this system.

CONCLUSION

Our study demonstrated that traditional triggerbased provisioning mechanisms do not handle workload variation well and in some situations cause instability, by changing the number of provisioned VMs often in short time intervals. This behavior has a negative effect on the application's response time, leading to violations of the SLO.

By using a few techniques that are relatively easy to implement we showed that we can significantly reduce the number of SLO violations and improve the performance stability. Although there is still room for improvement in our techniques, from the experimental results we can draw the conclusion that implementing provisioning mechanisms that go beyond simple triggers is effective and should be considered when hosting a cloud application.

Figure 1 :

 1 Figure 1: ConPaaS system architecture.

  PHP request complexity Figure 2: One day of Wikipedia trace quests per minute, requests are also heterogeneous in complexity. To illustrate this heterogeneity, in Figure 2(b) we present the distribution of the response time values for the PHP requests during the execution of the trace shown in Figure 2(a).

Algorithm 1 :

 1 Trigger-based provisioning Data: Pre-defined metric threshold ranges, SLO threshold Result: Scaling decisions 1 while auto-scaling is ON do 2 Collect monitoring data of each metric, data i ; 3 if no recent scaling operation then 4 if avg(data i )>=SLO threshold max i for at least one metric then 5 ADD resources; 6 else if avg(data i )<SLO threshold min i for all metrics then 7 systems currently in production, and two new algorithms based on feedback techniques that alleviate the drawbacks of trigger-based systems by reacting in advance to traffic changes.

  3 while auto-scaling is ON do 4 Initialize variables s bck ands out to 0; 5 Collect monitoring data (last ∼5min), data; 6 Add the most recent response time value to q; 7 Estimate workload trend (last ∼30min) of q, td; 8 -Increasing, td = 1; Decreasing, td = 0; Stable, td = -1; 9 for each metric in data do 10 // Compute the weight w i of metric i 11 w i = correlation coefficient(metric i , response time); 12 if avg(metric i ) >= pred thr max i then 13 // Increment chances of scaling out 14 s out = s out + w i ; 15 else if avg(metric i ) < pred thr min i then 16 // Increment chances of scaling back 17 s bck = s bck + w i ; 18 else 19 // Decrease the chances 20 s bck =s bckw i ; s out =s outw i ; metric i ) >= reac thr max i and td = 1 and s out > s bck then 25 ADD resources; 26 else if avg(metric i ) < reac thr min i and td = 0 and s out < s

Figure 3 :

 3 Figure 3: Two-level thresholds

Figure 4 :

 4 Figure 4: Response time on DAS4 -Trigger-based.

  Figure 4 and Figure 5 represent the degree of SLO fulfillment of the trigger-based and feedback algorithms, indicating the average of response times obtained during the execution of the Wikipedia workload trace. In Figure 4 and Figure 5, each cross represents the average of response time obtained at monitoring window. The results from Figure 4 show that the trigger-based provisioning algorithm provokes an important amount of SLO violations at certain moments in time, due to its excessively reactive behavior. As we mentioned, this algorithm fails easily during traffic spikes, as it adds or removes VMs without evaluating the workload trend. The feedback algorithm, as shown on Figure 5, handles the traffic spikes better and can minimize the amount of SLO violations; specifically, there were 31.72% less SLO violations in comparison with the trigger-based algorithm.

Figure 5 :

 5 Figure 5: Response time on DAS4 -Feedback.

Figure 6 :

 6 Figure 6: Resource consumption on DAS4.

Figure 7 :

 7 Figure 7: Resource consumption on EC2.

Figure 8 :

 8 Figure 8: Response time on EC2 -Trigger-based.

Figure 9 :

 9 Figure 9: Response time on EC2-Feedback.

Figure 10 :

 10 Figure 10: Response time on EC2-Loadbalancing Weights.

ACKNOWLEDGMENTS

This work is partially funded by the FP7 Programme of the European Commission in the context of the Contrail project under Grant Agreement FP7-ICT-257438 and the Harness project under Grant Agreement 318521.


Richard Duhautois

Fabrice Gilles

Payroll tax reductions and job flows in France

In France, policies that aim at reducing labour cost have extended to more and more workers since the beginning of the 90s. Evaluations of the effect of payroll tax reduction often use estimations of labour demand equations. In this paper, we consider the impact of labour tax cuts on job creations and destructions through the Fillon reform (2003), by using a fixed effect instrumental variable approach and a sectoral pseudo panel dataset. Over 2002-2005, our estimates show that PTR let job flows unchanged.

Introduction

To reduce unemployment, payroll tax reductions on low wages have been implemented in many European continental countries since the beginning of the 90s. In France, economic policies have extended to more and more workers from the mandatory minimum wage within a fast-growing budget (2.9 billion € in 1992 to 29.9 billion € in 2009). Behind such policies is the view that lower labour costs increase employment. Nevertheless, on the one hand, the empirical effects on employment of payroll tax reduction are not clear. As reported in Blau and Kahn (1999) studies find small impacts on employment. Even for the literature which deals with estimating elasticities the results are rather mixed [START_REF] Hamermesh | Labor Demand[END_REF]Layard et al., 1991). On the other hand, when focusing on low skilled workers, results are clearer. As Neumark and Washer (2007) notice, the evidence for "disemployment" effects is strong for these workers. The employment effect of a reform that reduces the labour cost depends on several factors. If we focus on payroll tax reductions (hereafter PTR) paid by the employers, we can isolate three factors that strongly influence the efficiency of such a policy: the structure of the PTR, the elasticity of labour demand and labour supply to labour cost and the effect of PTR on wages. First, with regard to the question of the structure of PTR, the type of employees who benefit from PTR is essential; that is why we need to differentiate low skilled workers from high skilled workers. Second, the amount of PTR is important according to the elasticity of labour demand to labour costs. Third, since wages and employment are jointly determined, the duration of PTR is crucial.

Most papers focus on the net employment effect of labour costs. In our paper, we analyse the effect of Payroll Tax Reductions on job flows (hereafter JF), i.e. on job creation (hereafter JC) and job destruction (hereafter JD) and more generally on job reallocation (hereafter JR). Our idea is to test whether PTR increases JC or decreases JD through the implementation of the Fillon law (2003) in France. Indeed, some papers about job flows stipulate that job adjustment would be done through job creation in European countries whereas job adjustment would be done through job destruction in the United-States, where the labour market is supposed to be less regulated. We use concepts from the literature on gross job flows (Davis and Haltiwanger's definitions, 1990[START_REF] Card | Do Minimum Wages Reduce Employment? A Case Study of California, 1987-89[END_REF], 1999a and b) to estimate the employment effect of PTR. For this study, we merge three French administrative sources over 2002-2005 available at Insee (the French national statistical agency) and Acoss-Urssaf (the French Central Agency of Social Security Organisations). These Insee data enable us to run the analysis by distinguishing unskilled blue and white collar workers (hereafter the low skilled workers), skilled blue and white collar workers (hereafter the medium skilled workers) and managers, engineers, etc. (hereafter high skilled workers). The Acoss-Urssaf data allow us to get the amounts of PTR received by French establishments.

To evaluate the effect of PTR, we estimate job flows equations along with the empirical literature that deals with JC and JD determinants (Salvanes, 1997;[START_REF] Stiglbauer | Job Creation and Job destruction in a Regulated Labor Market: The Case of Austria[END_REF][START_REF] Gomez-Salvador | Gross job flows and institutions in Europe[END_REF]OECD, 2009). Since we have establishment data, our first idea was to test the relationship between PTR and job flows at the individual level. However, attempting to estimate the effect of PTR on job flows implies dealing with several kinds of endogeneity or selection problems. First, for an establishment, benefiting from an amount of PTR is not exogenous because it depends on wage and employment structures, as well as the fact that wages and employment are jointly determined. Second, net creation and destruction are not observed at the same time for a given establishment. Third, not all establishments employ all types of skills of workers: for instance, an establishment with no low skilled workers has a zero probability to destroy low skilled jobs. Fourth, over 2002Fourth, over -2005, many , many firms were created and other died; to cope with these problems, we decide to use a pseudo panel data approach [START_REF] Deaton | Panel data from time series of cross-sections[END_REF][START_REF] Verbeek | Pseudo panel data and repeated cross-sections[END_REF]. We group establishment data at the 4-Digit sectoral level to be able to perform linear regressions by keeping all establishments. We build three different aggregated files: a first panel contains a balanced panel of establishments with 10 employees or more; a second panel contains an unbalanced panel of establishments with the same sectors as in the first panel; a third panel contains an unbalanced panel of establishments with all sectors. To solve the underlined problems while evaluating the impact of PTR, we then apply fixed effect (hereafter FE) regression techniques on pseudo panel data that are identical to instrumental variable (hereafter IV) estimations on individual data, where the level of aggregation is used as an instrument [START_REF] Moffitt | Identification and estimation of dynamic models with a time series of repeated cross-sections[END_REF]. To take account for the fact that sectors of activity are different in size, we consider FE regressions weighting each cell with the square root of the cohort size (i.e. the number of workers) in each cell [START_REF] Deaton | Panel data from time series of cross-sections[END_REF]Dargay, 2007). As a robustness test, and to avoid the weak instrument caveat, we also apply an IV-within estimator. We perform these regressions for overall employment, as well as for the three kinds of workers. We find no impact of PTR on job flows, whatever the skill we consider.

The paper proceeds as follows. Section 2 briefly describes the payroll tax reductions in France. Section 3 surveys literature on micro empirical evidence and motivates for a new analysis. Section 4 presents the data. In Section 5, we displays Job Flows indicators and descriptive statistics dealing with those indicators, as well as with PTR over 2002-2005. Section 6 discusses the estimation strategy. The results and discussion stand in Section 7. Section 8 concludes.

Payroll taxes in France: the Fillon law

After several reforms since 1992, payroll tax reduction programs were harmonised in 2003 (The "Fillon reform"). In particular this last reform aimed at standardizing the different measures that had existed since the decrease in the standard working time duration from 39 down to 35 hours, in 2000. The "Fillon reform" simultaneously affected several components of labour costs. The minimum wage was raised in an exceptional way, the amount and structure of the payroll tax underwent large-scale change, and the laws governing overtime quota were profoundly modified.

First, the period 2003-2005 saw the harmonisation of six coexisting minimum wages: the five monthly wage guarantees (GMR -garanties mensuelles de rémunération) with the level of the 39 hour-minimum wage. The French minimum wage (called Smic) was introduced in 1970; it includes the basic wage, fringe benefits, and all other payments having the de facto character of a premium. Until 2009, the level of the hourly minimum wage was revised every year on July 1 st according to inflation, half of any increase in hourly blue collar wage levels and possible government extra boosts (from 2010, it is revised the first of January). When the 35-hour work week was introduced in January 2000, one of the principles enshrined in the legislation was a guarantee of the purchasing power of employees earning the minimum wage and benefiting from the working time reduction (WTR). The payment of these employees was determined on the basis of their monthly wage before WTR. So the GMRs correspond to the hourly minimum wage at the time of adoption of the 35-hour work week multiplied by 169 hours. Employees working a 35-hour work week therefore automatically earned a higher hourly wage than the hourly minimum wage for the 39-hour work week. The five "generations" of GMR applied to employees moving towards the 35-hour work week before July 1 st 1999, 2000, 2001, 2002 and 2003. Table 1 displays the levels and evolutions of the five GMR, as well as of the French minimum wage, over 2001-2005.

Second, the Fillon law aims at merging 2 PTR devices. Indeed, at the beginning of 2003, two programs of payroll tax reductions existed. Indeed, since June 1996, but before June 1998 and the implementation of the French 35 hours work week, there was a unique device that aimed at reducing employer payroll tax for low wage workers (the low wage payroll tax cut device; hereafter LWPTR). For each worker, every French establishment could benefit from this payroll tax that amounts to 18.6% of the wage at the Smic level and then decreases linearly towards 0 Euros for a wage that was larger than 1.3 times the minimum wage. In 1998 and 2000, Aubry 1 and 2 laws were adopted to reduce the standard working week from 39 to 35 hours -starting on 1 January 2000 for companies employing more than 20 people, and on 1 January 2002 for all other firms. The aim was to promote job creation and reduce unemployment by introducing work sharing. The Aubry laws did not oblige firms to adopt a 35-hour working week: firms can choose to reduce effective working time or pay overtime. These laws were an incentive for firms to implement a working time reduction: they diminished the payroll taxes of employers, who reduced the working hours of their employees. To benefit from these payroll tax cuts, firms had to sign agreements with unions to determine the size of the effective hours of work reductions, the increase in hourly wage rates (level of wage compensation) and the number of new jobs that would be created or preserved. For employees whose hours of work were cut, the working time reduction payroll tax reduction (WTRPTR) amounts to 26% times the wage at the GMR level (see supra), then decreasing linearly until 1.7 times the GMR. Hence, firms that decreased the effective working time of their workers benefited from a more generous system of payroll tax reductions to compensate additional costs of working time reduction. This last device replaces the previous of firms that decrease the working time of their workers. Overall, in France, PTR represents 29.9 billion Euros (Table 3) and 10.3 % of total payroll tax in 2009. 91 % of PTR are paid with state budget. 70% of PTR correspond to PTR on low wages. Since the beginning of the 90's, PTR has grown from 1.9 million in 1992 to 29.9 millions in 2009. In particular, they sharply rose during the implementation of the French 35 hours work week, between 1998 and 2001 (+7.2 percentage points). Although PTR decrease for WTRPTR establishments and increase for LWPTR establishments while implementing the Fillon reform, the whole amount PTR grew over 2001-2007. As well, the share of PTR in total PT, i.e. the ratio of PTR to PT, decreases over 2001-2007. [START_REF] Prévot | Les exonerations diminuent sous l'effet de la crise économique[END_REF].

Taking into account the fact that the "Fillon reform" simultaneously affected several components of labour costs, we want to evaluate to what extent PTR impact job flows, ie. job creation, job destruction and more generally job reallocation.

In this paper, we evaluate empirically the impact of the Fillon reform on job flows, hence considering a different approach to those of [START_REF] Bunel | Les effets des allègements de cotisations sociales sur l'emploi et les salaires : une évaluation de la réforme Fillon de 2003[END_REF] or [START_REF] Simmonet | Coût du travail et flux d'emploi : l'impact de la réforme de 2003[END_REF].

Impact of PTR on employment: micro empirical evidence and motivations

Effect of PTR on employment

The first study using micro data that analyses the effect of PTR on employment is [START_REF] Hamermesh | New Estimates of the Incidence of the Payroll Tax[END_REF]. Using the Panel Study of Income Dynamics over the 1968-1974 period of time, the author shows that an increase of payroll tax affects both employment and wages. He finds that an increase of 1% in payroll tax decreases wages by 0.3%. With the same methodology, Gruber (1997) shows that the 1981 reform of the social security system in Chile which reduced payroll tax had no effect on manufacturing employment, but on wages. The PTR only affected wages. [START_REF] Johansen | Wage and Employment Effects of Payroll Taxes and Investment Subsidies[END_REF] analyse the effect on wages of a payroll tax cut for the Norwegian manufacturing industry over 1983-1993. They find that, on average, a reduction of 1% in the labour costs increases the hourly wages by 0.4%. [START_REF] Benmarker | Do regional payroll tax reductions boost employment?[END_REF] use a panel of Swedish firms over the 2001-2004 period of time to evaluate a modification of the payroll tax legislation that differentiate regions in 2002. They analyse separately continuing firms and firms that enter or exit of the sample. First, they find no employment effect and a positive effect on wages for continuing firms. Second, when they add entries and exits, they find a positive effect on firm entry (an elasticity of around 0.1) and no effect on firm exit. Overall, and very interestingly, if there is a positive effect of payroll tax cut it is through firm entry. [START_REF] Korkeamaki | Employment and wage effects of a payroll-tax cut: evidence from a regional experiment[END_REF] use a panel of Finnish firms between 2001 and 2003 as well to evaluate a modification of the payroll tax legislation. They evaluate the employment and wage effects of PTR for firms which benefit from the payroll tax cut. As in [START_REF] Benmarker | Do regional payroll tax reductions boost employment?[END_REF], the employment effect is only due to firm entry. [START_REF] Cruces | Payroll taxes, wages and employment: Identification through policy changes[END_REF] use firm administrative data for Argentina to evaluate the relation between payroll tax, wages and employment. They find that changes in payroll affects partially the wages but have no significant effect on employment. As reported in Blau and Kahn (1999) studies find substantial impacts on wages but small impacts on employment.

For France, some previous studies investigate the relationship between PTR and employment. [START_REF] Crépon | Une nouvelle évaluation des effets des allégements de charges sociales sur les bas salaires[END_REF] analyse the effect of the reduction in labour cost of low wage workers during the 90s. They use an employer-employee dataset and show that employment increases by almost 500,000 employees between 1994 and 1997 because of payroll tax reduction. [START_REF] Kramarz | The Impact of Differential Payroll Tax Subsidies on Minimum Wage Employment[END_REF] use the French labour Force surveys over the 1990-1998 period of time and show that an increase in labour cost leads to an increase in the probability of losing jobs (the estimated elasticity is 1.5). [START_REF] Bunel | Les effets des allègements de cotisations sociales sur l'emploi et les salaires : une évaluation de la réforme Fillon de 2003[END_REF] analyse the effect on employment and wages of the Fillon law ( 2003) by merging two administrative data sources between 2002 and 2005. The Fillon reform enabled to standardize the different measures that had existed since the decrease of the legal working time duration from 39 to 35 hours (1998)(1999)(2000). They show that the impact of the reform is slightly negative for the 35 hours-firms and slightly positive for the 39-hours firms. At the end, the effect is ambiguous. Moreover, they show a positive effect on wages for both types of firms. [START_REF] Simmonet | Coût du travail et flux d'emploi : l'impact de la réforme de 2003[END_REF] estimate the effect of the same reform on transitions from unemployment to employment by distinguishing the two types of firms. Their idea is to measure the labour demand as the increase or the decrease in transitions. They show that the Fillon reform decreases transitions for the 35-hours firms and has no effect for the 39-hours firms.

With sectoral data, [START_REF] Jamet | Allègements généraux de cotisations sociales et emploi peu qualifié : de l'impact sectoriel à l'effet macro-économique[END_REF] analyses the consequences of PTR on low skilled employment between 1993 and 1997. She finds a positive employment effect on low skilled workers: about 150,000 jobs created or saved. Gafsi, L'Horty and Mihoubi ( 2005) also find that 150,000 low skilled jobs were created or saved during the 90's and find a negative effect on high skilled jobs. On the whole, the effect on whole employment is small.

Effect of PTR on job flows

Since the mid-80 and the beginning of the 1990s, a lot of papers distinguish job creation and job destruction among net employment variation. In particular, instead of simply considering the net variation in employment, those papers aim at studying job creations and job destructions along with the business cycle. Early papers include those of [START_REF] Leonard | In the Wrong Place at the Wrong Time: The Extent of Frictional and Structural Unemployment[END_REF], [START_REF] Davis | Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications[END_REF][START_REF] Card | Do Minimum Wages Reduce Employment? A Case Study of California, 1987-89[END_REF] or Blanchard and Diamond (1991) for the US, [START_REF] Boeri | Why are establishments so heterogeneous? An analysis of gross job reallocation in Germany[END_REF] for Germany, or Conti and Revelli (1998) for Italy. From a theoretical point of view and along with this empirical literature, [START_REF] Mortensen | Job Creation and Job Destruction in the Theory of Unemployment[END_REF] develop job search and matching models to propose a new way to model labor market, including JC and JD to model unemployment changes. Within this framework, a lot of papers study the consequences of labour market policy aiming at reducing labour cost through PTR (in particular) on job flows. [START_REF] Mortensen | New developments in models of search in the labour markets[END_REF] analyze the effect of taxation. The authors consider a job search economy, as well as Nash bargaining on wages and endogenous destruction rates. Studying the impact of changing alternative labor market institutions (unemployment benefit, firing cost, hiring subsidy or PTR), they show that a decrease in PT leads to a decrease in unemployment mainly through a reduction in JD rate. Using the same framework, [START_REF] Sinko | Labor Taxation, Job Creation and Job Destruction -Focusing on the Role of Wage Setting[END_REF] study the impact of PT and tax progression considering different types of wage determination (monopoly union, Nash bargaining or efficiency wages). Under MU, her analytical results are ambiguous. Numerical simulations show that PTR induce an increase in JC (through an increase of the surplus of a match), and a decrease in JD (through a fall in the reservation probability). Combining tax credit and proportional tax in a revenue neutral manner, she shows that tax progression may improve employment if wages are set in a bargaining framework; moreover, tax progression promotes the emergence of less productive jobs and thus lowers average job productivity. This result is confirmed by [START_REF] Pierrard | Impacts of selective reductions in labor taxation[END_REF] who considers a similar framework, considering an intertemporal general equilibrium model and two types of workers. The author shows that diminishing employer social contribution impacts positively employment, but this goes more through reducing JD than increasing JC; moreover, PTR targeted at minimum wage increase much more net employment that if it was targeted at other wages. Within a general equilibrium model with three skill levels, but considering exogenous job destruction, [START_REF] Batyra | Selective Reductions in Labor Taxation: Labor Market Adjustments and Macroeconomic Performance[END_REF] get the same result through a direct link between JC and minimum wage. Including job competition does not reverse their results but sharply reduces the welfare gains of high skilled. The authors thus recommend combining large PTR for low skilled workers, smaller PTR for medium skilled jobs and no rebate at all for high skilled jobs. Hence, these papers show that PTR should (i) increase JC and decrease JD (ii) be more efficient if they are more targeted on low skilled (or on low wage) workers.

From an empirical point of view, a recent strand of literature focuses on workers or on job flows magnitude (Job Reallocation, hereafter JR; JC or JD). In fact, Contini and Rivelli (1997), Davis and Haltiwanger (1999), [START_REF] Stiglbauer | Job Creation and Job destruction in a Regulated Labor Market: The Case of Austria[END_REF], Bassanini and Marianna (2009), [START_REF] Fuchs | The determinants of job creation and destruction: plant-level evidence for Eastern and Western Germany[END_REF] or the recent OECD survey (OECD, 2009) aims at studying job flows determinants. Within the same framework, some recent papers (Salvanes, 1997;[START_REF] Gomez-Salvador | Gross job flows and institutions in Europe[END_REF]OECD, 2010) tried to evaluate the impact of labor market institutions on job flows. Indeed, studying the effect of labour market rigidities on job turnover for seven countries (Norway, Denmark, Netherlands, Germany, Italy, Canada and the US), Salvanes (1997) shows that job flows tend to decrease through employment protection, whereas it tends to grow through an employment subsidy that increases job creation. As well, using panel data over 1995-2000 for 13 European countries, [START_REF] Gomez-Salvador | Gross job flows and institutions in Europe[END_REF] look at the role of labor market institutional features in the dynamics of job creation and destruction. Their results confirm (negative correlation between employment protection legislation and JF) or complete (negative impact of an employment subsidy on JD, consistent with Leonard and Van Audenrode (1993)) those of Salvanes (1997). They moreover show that the tax wedge (the difference between the labor cost paid by the firm and the consumption wage received by workers, i.e. the sum of worker wage and employer payroll taxes) lowers JR through JC.

In this paper, we evaluate empirically the impact of the Fillon reform on job flows, hence considering a different approach to those of [START_REF] Bunel | Les effets des allègements de cotisations sociales sur l'emploi et les salaires : une évaluation de la réforme Fillon de 2003[END_REF] or [START_REF] Simmonet | Coût du travail et flux d'emploi : l'impact de la réforme de 2003[END_REF].

The data

The data sources

We use data from two different administrative sources available at Insee (the French national statistical agency) and three at Acoss-Urssaf (the French Central Agency of Social Security Organisations). From Insee, the first data source is the DADS (Déclarations Annuelles de Données sociales), which is a matched employer-employee longitudinal data source, constructed from firm reports to the tax authority. The second source is another administrative source called FICUS (FiChiers Unifiés de Suse), which gives us measures of employment, value-added and other economic outcomes for most French firms. From Acoss-Urssaf, we use three databases called AROME (Application du Recouvrement pour l'Observation et la Mesure des Encaissements), ORME (Observation du Recouvrement sur les Mesures d'Emploi) and SEQUOIA (Système pour l 'Etude QUantitative et l'ObservatIon des Assiettes). They report information about establishments that benefited from payroll tax reductions.

The DADS data source includes data on all workers employed in private and semi public establishments. Insee has been receiving information from the tax authority since 1950 in order to elaborate statistics about employment and wages in France. Two files now exist based on this data source: (i) a panel (available from 1967) in which all workers born in October of an even3 year can be followed across time and firms; (ii) the "exhaustive data" available from 1993 in which all workers and establishments are followed by couple of years. In both files, individual wages, employment periods, age, sex, and the skill level of the workers are extremely precisely measured. In particular, these enable us to run the analysis by distinguishing unskilled blue and white collar workers (hereafter the low skilled workers), skilled blue and white collar workers (hereafter the medium skilled workers) and managers, engineers, etc. (hereafter high skilled workers), following the classification of [START_REF] Burnod | Employés qualifiés et non qualifiés : une proposition d'aménagement de la nomenclature des catégories socioprofessionnelles[END_REF]. The firm or establishment identifiers are also known for each observation, where an observation in both files corresponds to a person-establishment-year triplet. There is one main difference between the two files. In the panel, workers are followed across time. On the contrary, in the exhaustive file, legal restrictions prevent us from connecting information on individual workers between couples of years. In this article, we use the exhaustive dataaggregated by establishments -for the years 2002 to 2005. For each year, we have a sample of approximately 1,500,000 establishments.

The FICUS dataset give information about the firms to which establishments belong to. This information is available for all firms that are subject to the two major tax regimes. These regimes cover virtually the entire productive system, representing roughly 95 percent of taxable firms in terms of sales. The data were kept for the period 2000-2005. For each year, we have a sample of approximately 2,500,000 firms. They mostly contain various economic situation indicators: value-added, capital investment, firm's profits, etc.

We also need information about the nature of the PTR in every firm. For this, we use the ORME database provided by Acoss (Agence Centrale des Organismes de Sécurité Sociale -Central Agency of Social Security Organisations). This database allows us to identify different categories of establishments that benefited from PTR over 1999-2005 and to get the precise amount of money the establishment receive as PTR. This chiefly concerns the low wage rebate, the aids associated with Aubry 1 and 2 laws on the reduction of working time, as well as the two sections of the Fillon reform of 2003 -that affecting establishments that have adopted the 35-hours work week and that affecting other firms.

To get a precise idea of the magnitude of PTR relatively to the wage bill, we have to compute the usual indicator that is the share of PTR out of the total labour cost (including the PTR). For that purpose, we also need information on the whole wage bill, as well as on employers' taxes that are effectively reported by firms. These are provided by two other Acoss-Urssaf datasets, AROME (for employers' social contributions) and SEQUOIA (for wage bill, workforce numbers).

Hence, these three Acoss-Urssaf datasets contain aggregate data at the level of each establishment, including the wage bill, workforce numbers, PTR, the payroll taxes due to establishments affiliated to the general social security regime.

The final datasets

Public establishments have been excluded from the final sample, as have the establishments of firms with no right to PTR under the Fillon reform4 . Firms benefiting simultaneously from two types of aid or discontinuously, holding firms, domestic service firms, temporary employment agencies and public firms have all been excluded. Firms in which the growth rate of employment, production and labour costs are characterised by extreme values, and those in which the average gross hourly wage is lower than the minimum wage have been excluded. establishments (8 million employees) respectively5 . It may seem important to distinguish between the 3 types of panels. Indeed, the literature that deals with job flows show that a large part in job creation and destruction is due to firm creation or destruction (Davis and Haltiwanger, 1999 and Section 2). Hence, considering Panels 2 and 3 and not only Panel 1 will help us in taking account for establishment's demography.

Table 4 reports the establishment and employment distribution in each of the 3 Panels. Looking at average establishment size classes, we see that Panels 2 and 3 are composed by a larger part of small establishments than Panel 1. Moreover, the main part of workers is employed in smaller establishments. It is not surprising because, contrary to Panels 2 and 3, Panel 1 contains only establishments that can be followed over the whole 2002-2005 time period, and that are the biggest ones. Indeed, smaller establishments go easier to bankruptcy and a lot of establishments were created over2002-2005. Otherwise, there are large differences between the three panels across broad business groups: for instance, in Panel 1, there are much more establishments that come from equipment or intermediate goods business sectors and far fewer that come from the trade or personal services sectors. The same hold for the number of workers. Table 5 gives further information over all three panels. In particular, it confirms findings of Table 4 (see "Establishment features"). Moreover, it shows that there are smaller variations in employment, and larger labour productivity or capital intensity levels on average in Panels 2 and 3 than in Panel 1. Otherwise, concerning the worker composition, panels 2 and 3 exhibits larger part of workers aged fewer than 30 and a larger part of women. As to skills on the contrary, all three panels show one quarter of low skilled workers and about one third of high skilled workers.

Hence, even if it may seem to be more convenient to follow the same establishments across time to study the impact of the PTR on job flows, we may suffer from a lack of information considering only Panel 1 instead of Panels 2 or 3. That's why we will work with all three panels.

Job Flows and PTR over 2002-2005

In the first part of this section, we present concepts from the literature on gross job flows to estimate the employment effect of PTR. We use the Davis and Haltiwanger's definitions (1999). These definitions are well-known but it is always useful and practical to remember them. In the second part of this Section, we display descriptive statistics dealing with job flows and PTR.

Job flows measures

Gross job creation in t is measured by the difference in the jobs created between t -1 and t for an establishment which increases employment. For the sector s in t :

C EMP st est e S = ∈ + ∑ ∆
where S + is the sub-set of establishments e that increase employment between t -1 and t and

1 est est est EMP EMP EMP - ∆ = -
is the employment variation between t-1 and t within a

given establishment e.

Conversely, gross job destruction in t is measured by the difference in the jobs destroyed between t -1 and t for an establishment that reduces employment. For the sector s in t :

∑ - ∈ ∆ = S e est st EMP D
where S -is the sub-set of establishments e that reduce employment between t -1 and t . Gross job reallocation in t is measured by the sum of job creation and job destruction between t -1 and t : In order to express the previous measures as rates, we need the sector size. We take its average size between t -1 and t :

( )

1 5 . 0 - + = st st st EMP EMP Z
Hence, creation, destruction and reallocation rates the sector s are written:

st st st C JCR Z = , st st st D JDR Z = , st st st Z R JRR = (1)
As well, we can define job flows measures for each skill. Indeed, we can show that we have:

q q q est est est e Q e S Q e S Q EMP EMP EMP + + + - + ∈ ∈ ∩ ∈ ∩ ∆ = ∆ + ∆ ∑ ∑ ∑
for each skill q=L (low), M (medium) or H (high) worker with q Q ∈ and for each establishment that create employment within the category of q-skilled workers ( e Q + ∈

).

As above for all workers, we consider the average number of the q-skilled workers employed in t-1 and t to get the sector s gross job creation rate for the q-skilled workers:

q est e q q st q st EMP JCR Z + ∈ ∆ = ∑
In a similar way, we calculate q st JDR and q st JRR .

Linking the magnitude of job flows with PTR

Table 6 displays usual descriptive statistics on job flows for all workers6 . As usual, they show that job reallocation rates decrease with the average size of the firm; nevertheless, this relation is mainly due to that of job creation rates with average firm size. Moreover, job reallocation rates are larger among services than among manufacturing industries. These conclusions hold for all three panels. -0.0 -0.0 0.0 -0.0 -0.0 -0.0 4.5 0.6 3.9

Sources: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee).

Field: Firms employing 10 workers or more and coming from the private non-farm business and semi-public sectors over 2002-2005. Note: a Percentage of the average employment rate computed over t and t-1.

Looking at the evolution of job flows over 2002-2005 (Table 7), we have to remember that this time period corresponds to a recession or at least to a period characterized by low output growth rate, which may explain why job creations decrease or job destruction rate increase. However, if we look at what happens at every skill level, we see that job destruction decreases for low and medium skilled workers, whereas job creation decreases for high skilled workers. Hence, job reallocation rates fall for all workers. These facts hold for all types of panels, except that the figures are mechanically higher in Panels 2 and 3 than in Panel 1 because the formers take into account the smallest establishments as well as establishments' demography. These results hold for each skill groups. JCR and JDR are higher for low skilled workers as the result of more mobility for these workers. Section 3 showed that the overall amounts of PTR given to firms rose over 2002-2005 considering the whole economy. According to our three panels, the same conclusions hold. Indeed (Table 8), whatever the panel we consider, both the amount of PTR and the PTR ratio to labour cost rise over 2002-2005. The question we ask next is whether the decrease in job destruction for low and medium skill workers, as well as the decrease in the job creation rate of high skill workers reflects an impact of PTR.

The econometric strategy

To evaluate the impact of varying PTR on job creation and destruction using establishments panel data, we want to estimate separately 3 job flows equations of the type (Gomez-Salvador ( 2004)):

it it it it JFR X PTR β γ ε = + ∆ +
(1) for JFR it =JCR it , JDR it or JRR it that are our outcome variables. Subscripts i and t denote establishment and time respectively. As in [START_REF] Bunel | The Effects of Reduced Social Security Contributions on Employment: An Evaluation of the 2003 French Reform[END_REF],

it PTR ∆
represents our variable of treatment and is the variation between t-1 and t in the ratio of the payroll tax reduction to the wage bill; it X refers to a multidimensional vector of control variables; and

it i it δ µ ε + =
is a composite error term, where i µ is an unobserved establishment effect.

Estimating (1) directly using establishments i data over 2002-2005 is very difficult for many reasons.

There are selection and endogeneity problems. First, it X is often supposed to be correlated with i µ . If genuine panel data are available, using a within estimator solve the problem. This is the case of Panel 1, where several observations are observed for the same establishment, but not that of Panels 2 and 3. However, Panel 1 suffers from large attrition because of firm demography (Section 4); considering only this panel may be misleading. Moreover, even for Panel 1, a given establishment cannot create and destroy jobs at the same time. Thus, there will be many zeros for each dependent variable while estimating (1) using establishments observations directly. A similar problem appears if we perform regressions on different skill groups: to be able to compare the effect of varying PTR according to different skills of workers, we have to work on the same establishments and consequently to impose that the establishment employs all types of skills; hence we may introduce a selection bias. Second, the variable of treatment (

it PTR ∆
) is endogenous. Indeed, as mentioned in Section 3, PTR for a given worker depends on her gross wage level; thus PTR should be correlated to the average wage level at the establishment. Besides, a given skill (either low, medium or high) group of workers is a function of certain professional categories and thus of wages (see supra, Section 4). In particular, as shown in Table 5, the establishment average wage of high skilled workers is larger than that of medium skilled workers, and that of medium skilled workers is larger than that of low skilled workers. Since any PTR amount decreases with wage level whatever the device we consider, PTR (and thus it PTR ∆

) should be correlated with the given skill wage [, even if it is difficult to prove it through any computation because the Acoss-Urssaf dataset only gives us the whole amount of PTR]. Otherwise, wages were proved to determine job flows (Davis and Haltiwanger, 1999). Wages are thus part of the it X

vector. Since wages and employment are jointly determined, wages are endogenous. Moreover, wages may also depend on minimum wages. In fact, the French minimum wage, as well as the five monthly wage guarantees were revised every year over 2002-2005 on July 1 st so that hourly minimum wages go to a unique value on July 2005 (see Table 1, Section 3). As often demonstrated in the literature [START_REF] Cserc | Le Smic, salaire minimum de croissance[END_REF]Koubi and Lhommeau, 2006;[START_REF] Cette | Les effets des hausses du SMIC sur le salaire moyen[END_REF], increases in minimum wages should spread to the wages distribution.

For all these reasons, we decide to use a pseudo panel data approach [START_REF] Deaton | Panel data from time series of cross-sections[END_REF][START_REF] Verbeek | Pseudo panel data and repeated cross-sections[END_REF]. We aggregate the individual data at the 4-Digit sectoral level (see Section 4 for the aggregation of establishment data) and consider the following equation:

st st st st JFR X PTR β γ ε = + ∆ + (2)
δ µ ε + = .
Here, sector aggregations are based on a large number of establishments, the number S of sectors is fixed, whereas the number of establishment n s per sector tends to infinity. We can treat st µ as fixed unknown parameters ( s st µ µ = ) so that we use the within estimator on the pseudo panel. In this case, indeed, [START_REF] Moffitt | Identification and estimation of dynamic models with a time series of repeated cross-sections[END_REF] shows that grouping can be viewed as an instrumental variable (IV) procedure. Each i µ of equations ( 1) is decomposed into a sector effect s µ and establishment i's deviation from this effect. If we note si z a dummy variable that is equal to 1 if establishment i is in sector s, we can write:

∑ + = s i si s i z υ µ µ (3)
Substituting ( 3) into (1) and defining ( )

1 ,......, i i Si Z z z = and ( )' ,......., 1 S µ µ µ =
we obtain:

it it it i i it JFR X PTR Z β γ µ υ δ = + ∆ + + + (4) If it PTR ∆
or it X are correlated with i µ , we can expect that they are correlated with i υ . In equations (4), only an instrumental variables estimator will be consistent for , and β γ µ .

Cohort dummies i Z interacted with time dummies provide valid instruments for all explanatory variables in the model (including the full set of cohort dummies - [START_REF] Deaton | Panel data from time series of cross-sections[END_REF]) ). In other words, to be in a sector is an appropriate instrument because it is correlated with it PTR ∆ or it X but not with it i δ υ + . [START_REF] Moffitt | Identification and estimation of dynamic models with a time series of repeated cross-sections[END_REF] shows that the within estimator on the pseudo panel (equation ( 2)) is identical to IV estimators on the individual panel dataset (equation ( 4)).

Results and discussion

Since we estimate JF equations, we first focus on JF determinants. Then, we display results.

7.1 Usual determinants for JF Within the strand of literature that analyzes the determinants of JF, several factors have been put in evidence (Salvanes (1997), Contini and Revelli (1998), Davis and Haltiwanger (1999), [START_REF] Stiglbauer | Job Creation and Job destruction in a Regulated Labor Market: The Case of Austria[END_REF], [START_REF] Gomez-Salvador | Gross job flows and institutions in Europe[END_REF], [START_REF] Fuchs | The determinants of job creation and destruction: plant-level evidence for Eastern and Western Germany[END_REF] or OECD ( 2009)). Tables 9a to c in appendix contain the corresponding correlations for JF when measured for all workers independently of their skill level for all three panels. In particular, Job Flows: -are smaller in bigger establishments. Average firm size is negatively correlated with the magnitude of JF. On the contrary, being a small French firm (ie. employing fewer than 20 workers) is positively correlated with it. Besides, for an establishment, belonging to a firm with more than one establishment should be negatively correlated with large JF, which is only the case for Panels 2 and 3; -are related with economic situation; positively with JC and negatively with JD for Panel 1. Different results are found for Panels 2 and 3; -should be negatively correlated with capital intensity; -are correlated with net employment variation: positively correlated with JC, but negatively correlated with JD; -are correlated with workers features within a firm: JF are bigger in firms where there are more workers that are younger than 30 years old; as well, a firm employing more women is characterized by larger JF; -are significantly related to wages: JF for a given population of workers is a priori negatively correlated to the average wage of the corresponding category of workers.

Results

We estimate the links between PTR and job creation or PTR and job destruction for total employees and for the three different workers' skill groups (low skilled, medium skilled and high skilled workers). For each skill group, we estimate the effect of the share of payroll tax reductions in the sector wage bill on job creation, job destruction and job reallocation controlling for a set of control variables presented in the previous sub-section. First, we use sector characteristics: the size of the given sector with total employment; the employment variation between t-1 and t to control for the "structural" growth of the sector; the share of multi-establishment firms and the share of small establishments (with fewer than 20 employees). Second, we use workers characteristics: the share of women and the share of young workers (fewer than 30 years-old); wages for each skill. Third, we use an economic and financial performance indicator: the growth rate of sectoral value-added between t-1 and t. Fourth, we consider the capital intensity ratio. All these variables vary with time enough to be introduced in the FE regressions applied to (pseudo) Panels 1, 2 and 3. These control variables are not always significant for each regression. We estimate the relationship between PTR and gross flows by skill groups considering equations of type (2) as such. However, sizes of the sectoral cohorts are rather different; which may induce heteroscedasticity. Hence, it is recommended to run regressions reweigthing by the square root of the size of each cohort, ie. of the employment level for the considered category of workers [START_REF] Deaton | Panel data from time series of cross-sections[END_REF] ; [START_REF] Devereux | Small sample bias in synthetic cohort models of labor supply[END_REF] ; [START_REF] Stiglbauer | Job Creation and Job destruction in a Regulated Labor Market: The Case of Austria[END_REF]). Moreover, standard errors are also corrected for serial correlation, heteroscedasticity and for sectoral clustering. Consequently, the sectoral dummies variables should satisfy the appropriate conditions for an instrumental variables estimator to be consistent. In particular, this requires that the instruments are relevant, i.e. appropriately correlated to the explanatory variables in the model. If this not the case, we may face weak instruments problem [START_REF] Bound | Problems with Instrumental Variables Estimation when the Correlation between Instruments and the Endogenous Explanatory Variable is Weak[END_REF] and estimates of the impact of PTR may be highly biased. To avoid such a caveat, we implement the within estimation of ( 2), instrumenting it PTR ∆ by its (first) lagged value

1 it PTR - ∆
. Results are reported the three last columns of Table 10. The p-value (equal to 0) associated to the F-test for weak instruments show the computed F-statistics is (largely) greater than the critical value (16.38) tabulated by [START_REF] Stock | ch. 5 in Identification and Inference for Econometric Models: Essays[END_REF] ; hence our instrument is not weak. Our findings show that PTR definitely did not affect at all job reallocation rates 7 (Table 11).

Concluding remarks

To reduce unemployment, payroll tax reductions on low wages have been implemented in many European continental countries since the beginning of the 90s. In France, economic policies have extended to more and more workers from the mandatory minimum wage within a fast-growing budget.

Most papers that analyse the impact of PTR on employment focus on the net employment effect of labour costs. In this paper, we examine to what extent Payroll Tax Reductions increases job creation or decreases job destruction separately.

To proceed, we first use concepts from the literature on gross job flows (Davis and Haltiwanger's definitions, 1990[START_REF] Card | Do Minimum Wages Reduce Employment? A Case Study of California, 1987-89[END_REF], 1999aand 1999b) to estimate the employment effect of PTR. For this study, we merge three different administrative sources over 2002-2005 that are available at Insee and Acoss-Urssaf. These data enable us to run the analysis by distinguishing unskilled blue and white collar workers (hereafter the low skilled workers), skilled blue and white collar workers (hereafter the medium skilled workers) and managers, engineers (hereafter high skilled workers).

To analyze the impact of PTR on job creation and destruction, we have to cope with four main problems. In fact, a firm that benefits from PTR is not exogenous for many reasons and in particular the fact that wages and employment are jointly determined. Moreover, considering job creation and destruction at the establishment level, we have to face the fact that there are many zeros for each dependant variable because an establishment cannot create and destroy jobs at the same time. As well, when we work with individual data, we have to impose that the establishment employs all types of skills -because, for instance, an establishment with no low skilled workers has a zero probability to destroy low skilled jobsso we may introduce a selection bias in our estimation. Finally, a lot of establishments were created or die over 2002-2005; hence, considering a genuine panel over our period of study may be misleading. For these reasons, we use a pseudo panel data approach [START_REF] Deaton | Panel data from time series of cross-sections[END_REF][START_REF] Verbeek | Pseudo panel data and repeated cross-sections[END_REF] at the 4-Digit sectoral level to be able to perform linear regressions by keeping most of the establishments over the 2002-2005 period of time. Indeed, estimation techniques based on pseudo panel data are identical to IV estimations where the level of aggregation is used as an instrument [START_REF] Moffitt | Identification and estimation of dynamic models with a time series of repeated cross-sections[END_REF]. To avoid the weak instrument's caveat that may also occur through applying the within estimator to pseudo panel data, we also use a within-IV estimator. Our results are the following. Whatever the dataset we consider, the model we estimate, there is no impact of PTR on job flows, even with regards to any of the skill groups.

Appendices

  With these establishment data, we build three different aggregated files at the 4-Digit sectoral level over 2002-2005. A first panel, called Panel 1, is a balanced panel of 97,424 establishments with 10 employees or more (4.8 million employees) over 2002-2005. A second panel, called Panel 2, is an unbalanced panel of establishments over 2002-2005 with the same sectors as in Panel 1. A third panel, called Panel 3, is an unbalanced panel of establishments over the 2002-2005 period of time with all sectors including those that are found in Panel 1. Panel 2 and Panel 3 contain 735,102 establishments (7.7 million employees) and 796,168

Field:

  Firms employing 10 workers or more and coming from the private non-farm business and semi-public sectors over2002-2005.

  , ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee).

Field:

  Firms employing 10 workers and more and coming from the private non-farm business and semi-public sectors over 2002-2005. Notes: P values within parentheses. *** (respectively ** and *) stands for significance at a 1% (respectively 5 or 10%) level.

Table 1 .

 1 Levels and evolutions of the GMRs and hourly minimum wage rates through the implementation of lawFillon (2003Fillon ( -2005)).

		Juil-01	Juil-02	Juil-03	Juil-04	Juil-05
	CPI growth rate		1.63%	1.89%	2.32%	1.72%
	Hourly minimum wage (Smic)	6.67	6.83	7.19	7.61	8.03
	Smic growth rate :		2.40%	5.27%	5.84%	5.52%
	GMR1	1 081.21	1 100.67	1 136.15	1 178.54	1 217.88
	(WTR before July 1999)		1.80%	3.22%	3.73%	3.34%
	GMR2	1 094.65	1 114.35	1 145.54	1 183.40	1 217.88
	(WTR after June 1999 and before
	January 2000)		1.80%	2.80%	3.30%	2.91%
	GMR3	1 113.45	1 133.49	1 158.62	1 190.14	1 217.88
	(WTR after December 1999 and
	before July 2001)		1.80%	2.22%	2.72%	2.33%
	GMR4	1 127.23	1 147.52	1 168.16	1 195.03	1 217.88
	(WTR after June 2001 and before
	July 2002)		1.80%	1.80%	2.30%	1.91%
	GMR5		1 154.27	1 172.74	1 197.37	1 217.88
	(WTR after June 2002)			1.60%	2.10%	1.71%
	Sources: Légifrance and Insee.
	Notes: Amounts are expressed in Euros. CPI: consumption price index.
	Reading: Hourly minimum wage was increased from 6.67 to 6.83 Euros between July 2001 and June 2002; hence, over that
	time period, the Smic rose by 2,4%.

Table 2

 2

	displays the

Table 2 .

 2 Changes in PTR devices through the adoption of the Fillon law(2003)(2004)(2005).

	Working

Time Reduction Payroll Tax Reduction Low Wage Payroll Tax Reduction

		1. Maximum reduction: 26% of the	2. Maximum reduction: 18.6% of the gross
	Before July 2003	gross wage (at the GMR1 level). Linearly decreasing with gross	wage. Linearly decreasing with it towards 0 Euros at a wage that is greater than 1.3 times
		wage until 1.7 times the GMR1,	the gross Smic.
		Then stable at 600 Euros.
	Between July 2003 and	3. Maximum reduction: 26% of the	4. Maximum reduction: 20.8% of the gross
	June 2004	gross wage (at the GMR level).	wage. Decreasing with it towards 0 Euros at
		Linearly decreasing with it towards	1.5 times the Smic.
	Between July 2004 and	0 Euros at 1.7 times the GMR2 (1
	December 2004	January 2000).	5. Maximum reduction: 23.4% of the gross
	Between January 2005 and June 2005	6. Maximum reduction: 26% of the gross wage. Decreasing towards 0	minimum wage. Decreasing towards 0 Euros at 1.6 times the Smic.
		Euros at 1.6 times the GMR2.
	Starting on 1 July 2005	7. Maximum reduction: 26% of the gross wage.
		Decreasing towards 0 at 1.6 times the Smic
	Sources: Légifrance and Insee.

Table 3 .

 3 Evolution of payroll tax reduction in France (1992-2009).

		1992 1995 1998 2001 2004	2007	2009
	Billion €	1.9	6.2	11	18.2	20.1	27.2	29.9
	Share in total Payroll Tax 1.4% 4.1% 6.3% 8.8% 8.9% 10.2% 10.3%
	Source: Social Security Organism

Table 4 .

 4 Distribution of firms and employment: average size classes and business sectors.

	Establoshment size classes

Source: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee).

Table 5 .

 5 Sample characteristics. Means or sums over 2002-2005.

	Variables / Samples	Panel 1	Panel 2	Panel 3
	Number of establishments:	97,424	735,102	796,468
	Total number of workers: a	4,777	7,656	8,049
	Establishment features:
	Average number of workers in an establishment	49.0	10.4	10.1
	Small estab. (< 20 workers) b	39.9%	90.4%	90.9%
	Firms with more than one establishment	40.8%	24.7%	24.1%
	Variation in employment:	144.1	104.1	96.1
	Performance indicators:
	Lagged establishment profit ratio	16.9%	17.3%	16.9%
	Value added growth rate	16.1%	26.3%	26.5%
	Labor productivity	14167	148236	141066
	Capital intensity:	1990	4241	4228
	Workers:
	Part of low skilled workers	24.4%	25.1%	24.9%
	Part of medium skilled workers	39.8%	39.9%	39.9%
	Part of high skilled workers	35.8%	35.0%	35.2%
	Part of women	41.2%	44.6%	44.7%
	Part of workers aged less than 30 years old	28.8%	31.4%	31.0%
	Wages: c
	All workers	189265	286608	220834
	Low skilled workers	102788	148917	115089
	Medium skilled workers	140370	207494	159564
	High skilled workers	286592	400577	306602

Sources: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee). Field: Firms employing 10 workers or more and coming from the private non-farm business and semi-public sectors over

2002-2005.

Notes: a Thousands of workers; b Percentage of establishments that employ fewer than 20 workers. c Euros a year.

Table 6 .

 6 Job flows, average firm size and business sectors.

	Sample		Panel 1			Panel 2		Panel 3
	Type of reallocation	JRR JCR JDR JRR JCR	JDR JRR JCR	JDR
	By sector of
	operation:
	Manufacturing:
	Agricultural and food industries	7.2 a 3.6	3.6 9.5 a	4.3	5.3 9.4 a	4.2	5.2
	Consumption goods	8.1	3.0	5.1	9.7	3.5	6.2 9.8	3.6	6.3
	Car industries	6.5	2.5	4.0	8.1	2.7	5.4 8.1	2.8	5.3
	Equipment goods	7.4	3.2	4.2	8.9	3.7	5.2 9.0	3.6	5.4
	Intermediate goods	7.4	2.7	4.6	8.7	3.1	5.6 8.6	3.0	5.6
	Energy	6.0	3.5	2.5	6.3	3.5	2.8 5.8	2.9	2.9
	Services:
	Construction	8.3	4.6	3.7 11.5 5.8	5.7 11.5	5.8	5.7
	Trade	7.1	4.0	3.1 10.0 4.8	5.2 10.1	4.8	5.3
	Transport	8.7	4.4	4.3 11.1 5.0	6.1 10.4	4.7	5.8
	Finance	12.3 4.3	7.9 12.8 4.6	8.2 12.8	4.6	8.2
	Housing	7.1	3.9	3.1 10.9 5.5	5.4 11.0	5.6	5.4
	Business services	11.2 6.0	5.1 13.0 6.3	6.8 13.0	6.3	6.8
	Personal services	7.8	3.7	4.1 12.1 5.4	6.7 12.3	5.5	6.8
	Education and social services	6.4	4.1	2.2	8.4	4.9	3.4 8.6	5.0	3.6
	By sectoral average
	firm size:
	Employment < 10	-0.0 -0.0 -0.0 12.0 5.6	6.4 12.1	5.7	6.4
	20 <= Employment < 50	8.2	4.3	3.8 11.2 4.9	6.3 11.2	4.9	6.3
	50 <= Employment < 100	8.4	4.4	4.1 10.3 4.6	5.7 10.3	4.7	5.7
	100 <= Employment < 250	9.0	4.3	4.7	8.6	3.3	5.3 8.7	3.2	5.5
	250 <= Employment < 500	6.7	2.8	3.9	6.1	2.7	3.4 6.1	2.7	3.4
	500 <= Employment < 1000	5.7	2.6	3.2	6.2	1.8	4.4 5.3	1.5	3.8
	1000 <= Employment < 2500	5.2	2.1	3.1 -0.0 -0.0	-0.0 6.6	1.2	5.4
	Employment >= 2500

Table 7 .

 7 Job flows for the different population of workers under consideration.

	Population	All workers	Low skilled workers Medium skilled workers High skilled workers
	Type of reallocation	JRR JCR JDR JRR	JCR	JDR	JRR	JCR	JDR JRR JCR JDR
				Panel 1. Balanced panel of establishments
	2003	8.3 a 4.3 3.9	20.4	9.9	10.4	16.2	8.0	8.2	15.0	8.6	6.9
	2004	7.5	3.7 3.8	17.1	8.0	9.0	14.6	6.9	7.7	13.8	7.3	6.0
	2005	8.8	3.9 4.9	17.6	8.6	8.9	15.2	6.8	8.4	16.2	7.0	7.8
		Panel 2. Unbalanced panel of establishments with the same sectors as in Panel 1
	2003	10.6 4.8 5.8	24.4	11.4	13.0	19.9	9.2	10.7 19.1	9.5	9.6
	2004	10.3 4.6 5.7	22.5	10.2	12.3	19.1	8.5	10.6 17.2	8.9	8.2
	2005	10.8 4.9 5.8	22.4	10.9	11.5	19.2	9.2	10.1 18.2	8.5	7.7
			Panel 3. Unbalanced panel of establishments with all sectors
	2003	10.5 4.7 5.8	24.6	11.6	13.1	19.9	9.1	10.8 18.8	9.3	9.5
	2004	10.3 4.5 5.7	22.6	10.2	12.4	19.1	8.5	10.6 17.0	8.8	8.2
	2005	10.8 4.9 5.8	22.8	11.2	11.6	19.2	9.1	10.1 18.1	8.4	9.7

Sources: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee).

Field: Firms employing 10 workers or more and coming from the private non-farm business and semi-public sectors over 2002-2005. Note: a Percentage.

Table 8 .

 8 Payroll tax cuts: overall amounts and tax cuts rates2003-2005.

	Year / PTR	Overall amounts a	Tax cuts rates b
	Panel 1. Balanced panel of establishments
	2003	5,616	4.13
	2004	5,769	4.18
	2005	5,770	4.14
	Panel 2. Unbalanced panel of establishments with the
		same sectors as in Panel 1
	2003	9,065	4.39
	2004	9,708	4.59
	2005	10,376	4.71
	Panel 3. Unbalanced panel of establishments with all
		sectors
	2003	9,467	4.33
	2004	10,109	4.52
	2005	10,784	4.62
	Sources: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS
	and FICUS (Insee).
	Field: Firms employing 10 workers and more and coming from the private
	non-farm business and semi-public sectors over 2002-2005.
	Notes: a Millions of Euros; b Percentage.

Table 10 .

 10 Effect of payroll tax reduction on job flows. Estimating job flows equations on pseudo panel data using a within estimator.

	Population / Sample	Panel 1	Panel 2	Panel 3
	Job reallocation rate
	All workers	-0.237 (0.357)	0.131 (0.295)	0.093 (0.257)
	By skill level:
	Low skilled workers	-1.610 (1.126)	-0.736 (0.824)	-0.896 (0.754)
	Medium skilled workers	0.429 (0.750)	0.187 (0.629)	-0.205 (0.521)
	High skilled workers	-0.151 (0.616)	-0.284 (0.540)	0.130 (0.450)
	Job creation rate
	All workers	0.143 (0.186)	0.198 (0.202)	0.139 (0.200)
	By skill level:
	Low skilled workers	-0.449 (1.015)	-0.172 (0.749)	-0.492 (0.657)
	Medium skilled workers	1.142* (0.606)	0.606 (0.442)	0.494 (0.381)
	High skilled workers	0.066 (0.541)	-0.073 (0.491)	0.041 (0.413)
	Job destruction rate
	All workers	-0.380 (0.331)	-0.067 (0.371)	-0.046 (0.318)
	By skill level:
	Low skilled workers	-1.161 (0.811)	-0.564 (0.520)	-0.404 (0.458)
	Medium skilled workers	-0.712 (0.644)	-0.419 (0.703)	-0.700 (0.569)
	High skilled workers	-0.217 (0.781)	-0.211 (0.656)	0.089 (0.553)
	Sources: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS
	(Insee).
	Field: Firms employing 10 workers and more and coming from the private non-farm
	business and semi-public sectors over 2002-2005.
	Notes: Standard errors are robust to heteroscedasticity, serial correlation and sectoral
	clustering. Standard error within parentheses. *** (respectively ** and *) stands for
	significance at a 1% (respectively 5 or 10%) level.

  Table 10 contains corresponding results. It shows that PTR let job reallocation rates unchanged, either considering job creation or job destruction rates. These results hold for any of the skill groups and all three panels.

Table 11 .

 11 Effect of payroll tax reduction on job flows. Estimating job flows equations on pseudo panel data using an IV-within estimator. , estimation techniques based on grouping individual data into cohorts are identical to instrumental variables approaches where the group indicators are used as instruments.

	Population / Sample	Panel 1	Panel 2	Panel 3
	All workers	-0.937 (0.897)	-0.632 (0.823)	0.181 (0.806)
	By skill level:
	Low skilled workers	-1.283 (2.853)	-0.585 (1.827)	-0.879 (1.841)
	Medium skilled workers	-1.424 (1.851)	-1.422 (1.545)	-1.139 (1.411)
	High skilled workers	-1.289 (1.554)	-1.928 (1.297)	-0.690 (1.371)
	Job creation rate
	All workers	0.322 (0.351)	-0.429 (0.446)	-0.156 (0.440)
	By skill level:
	Low skilled workers	3.832 (3.044)	1.140 (1.785)	0.310 (1.713)
	Medium skilled workers	0.725 (1.445)	-0.823 (1.082)	-0.669 (0.993)
	High skilled workers	0.107 (1.675)	-0.140 (1.113)	0.408 (1.001)
	All workers	-1.259 (0.817)	-0.203 (0.999)	0.338 (0.898)
	By skill level:
	Low skilled workers	-2.549 (2.009)	-1.725 (1.352)	-1.189 (1.245)
	Medium skilled workers	-2.149 (1.962)	-0.599 (1.716)	-0.470 (1.539)
	High skilled workers	-1.397 (1.403)	-1.798 (1.546)	-1.097 (1.464)
	Sources: AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and
	FICUS (Insee).
	Field: Firms employing 10 workers and more and coming from the private non-farm
	business and semi-public sectors over 2002-2005.
	Notes: Standard errors are robust to heteroscedasticity, serial correlation and sectoral
	clustering. Standard error within parentheses. *** (respectively ** and *) stands for
	significance at a 1% (respectively 5 or 10%) level.

However

Table 9a .

 9a Job flows and its determinants. Pearson's correlation coefficients. Panel 1 for 'All workers'. : AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee).Field: Firms employing 10 workers and more and coming from the private non-farm business and semi-public sectors over 2002-2005. Notes: P values within parentheses. *** (respectively ** and *) stands for significance at a 1% (respectively 5 or 10%) level.

	Variables / Type of reallocation	JRR	JCR	JDR
	Establishment features:
	Average number of workers	-0.144***	-0.225***	-0.001
	in an establishment	(<.001)	(<.001)	(0.979)
	Small estab. (< 20 workers)	0.108*** (<.001)	0.200*** (<.001)	-0.022 (0.427)
	Firms with more than one establishment	0.059** (0.035)	0.041 (0.147)	0.078*** (0.006)
	Performance indicators:
	Lagged establishment profit ratio	0.063** (0.026)	0.022 (0.439)	0.055** (0.049)
	Value added growth rate	-0.051* (0.071)	0.092*** (0.001)	-0.129*** (<.001)
	Labor productivity	0.013 (0.632)	0.014 (0.627)	0.005 (0.855)
	Capital intensity:	0.024 (0.396)	-0.049* (0.082)	0.065** (0.021)
	Variation in employment	-0.171*** (<.001)	0.331*** (<.001)	-0.435*** (<.001)
	Employment:
	Part of women	0.085*** (0.002)	0.113*** (<.001)	0.015 (0.602)
	Part of workers aged less than 30	0.110***	0.274***	-0.073***
		(<.001)	(<.001)	(0.009)
	Wages:
	All workers	-0.027 (0.330)	0.017 (0.550)	-0.043 (0.123)
	Low skilled workers	-0.131*** (<.001)	-0.053* (0.058)	-0.110*** (<.001)
	Medium skilled workers	-0.073*** (0.009)	-0.016 (0.558)	-0.072** (0.011)
	High skilled workers	-0.027 (0.343)	0.051* (0.073)	-0.067** (0.017)

Sources

Table 9b .

 9b Job flows and its determinants. Pearson's correlation coefficients. : AROME, ORME and SEQUOIA (Acoss-Urssaf) databases, DADS and FICUS (Insee).Field: Firms employing 10 workers and more and coming from the private non-farm business and semi-public sectors over2002-2005. Notes: P values within parentheses. *** (respectively ** and *) stands for significance at a 1% (respectively 5 or 10%) level.

	Panel 2 for 'All workers'.
	Variables / Type of reallocation	JRR	JCR	JDR
	Establishment features:
	Average number of workers	-0.382***	-0.375***	-0.204***
	in an establishment	(<.001)	(<.001)	(<.001)
	Small estab. (< 20 workers)	0.481*** (<.001)	0.449*** (<.001)	0.273 (<.001)
	Firms with more than one establishment	-0.081*** (0.004)	-0.070** (0.012)	-0.050* (0.076)
	Performance indicators:
	Lagged establishment profit ratio	0.102*** (<.001)	0.083*** (0.003)	0.067** (0.017)
	Value added growth rate	0.075*** (0.008)	0.042 (0.137)	0.063** (0.026)
	Labor productivity	0.009 (0.742)	0.007 (0.802)	0.006 (0.821)
	Capital intensity:	0.101*** (<.001)	-0.007 (0.082)	0.128*** (0.021)
	Variation in employment	-0.042 (0.138)	0.259*** (<.001)	-0.234*** (<.001)
	Employment:
	Part of women	0.165*** (<.001)	0.162*** (<.001)	0.088** (0.017)
	Part of workers aged less than 30	0.337***	0.423***	0.115***
		(<.001)	(<.001)	(<.001)
	Wages:
	All workers	0.011 (0.698)	0.078*** (0.006)	-0.042 (0.139)
	Low skilled workers	-0.088*** (0.002)	0.004 (0.873)	-0.111*** (<.001)
	Medium skilled workers	-0.039 (0.163)	0.046 (0.106)	-0.080*** (0.004)
	High skilled workers	-0.028 (0.328)	0.066** (0.019)	-0.080*** (0.004)

Sources

Table 9c .

 9c Job flows and its determinants. Pearson's correlation coefficients. Panel 3 for 'All workers'.

	Variables / Type of reallocation	JRR	JCR	JDR
	Establishment features:
	Average number of workers	-0.327***	-0.309***	-0.167***
	in an establishment	(<.001)	(<.001)	(<.001)
	Small estab. (< 20 workers)	0.454*** (<.001)	0.398*** (<.001)	0.255*** (<.001)
	Firms with more than one establishment	-0.181*** (<.001)	-0.115*** (<.001)	-0.135*** (<.001)
	Performance indicators:
	Lagged establishment profit ratio	0.074*** (0.002)	0.060** (0.012)	0.046* (0.058)
	Value added growth rate	0.048** (0.046)	0.026 (0.278)	0.039* (0.100)
	Labor productivity	0.004 (0.857)	0.004 (0.854)	0.002 (0.934)
	Capital intensity:	0.056** (0.020)	-0.001 (0.956)	0.070*** (0.004)
	Variation in employment	-0.038 (0.118)	0.214*** (<.001)	-0.209*** (<.001)

Centre d'Etudes de l'Emploi, Université Paris-Est Marne-la-Vallée, TEPP and CREST.

Universités de Lille, EQUIPPE and TEPP.

Since 2006 it has been containing all workers born in October.

France Telecom, Orange, La Poste, RFF, EDF, GDF, ADP, SNCF, Banque de France, RATP, SEITA.

The three panels must contain more than 30 observations each sector*year.

The same statistics are also available for any type of workers (according to skill groups).