- Original article
- Open Access
An ex ante evaluation of the Revenu de Solidarité Active by micro–macro simulation techniques
© Canova et al. 2015
- Received: 14 November 2014
- Accepted: 16 April 2015
- Published: 27 August 2015
This paper aims to investigate the effects of the introduction of an active welfare state measure in France, the Revenu de Solidarité Active, which replaced the old system of social minima. By using a micro–macro simulation model, we characterize the effects on households’ disposable income, labor supply, wages, GDP, public deficit, and other micro and macroeconomic aspects. Our findings suggest that although increasing public expenditure, the reform largely repays its cost by reducing involuntary unemployment, increasing labor supply and private consumption and thus improves GDP and the deficit/GDP ratio. If the great recession did not occur, poverty and inequality would have been significantly reduced by the RSA reform.
JEL codes: I38, C63, C68, J22, H31
- Revenue de Solidarité active
- Active welfare state
- Labour supply
- Policy evaluation
In this paper we aim to study the micro and macroeconomic implications of the implementation of an active welfare state reform in France, the Revenu de Solidarité Active (RSA). RSA is a modification of the pre-existent minimum income scheme, which had been adapted in 2009 after experimentation and had substituted the Revenu Minimum d’Insertion (RMI) and the Allocation Parent Isolé (API). To this aim we use SYSIFF 2006, a micro–macro simulation model for the French fiscal system based on the 2006 Budget de Familles survey data, a nationally representative household budget data survey collected in France every five years (see Canova et al. 2009 and Magnani et al. 2013 for a technical description of the model).
The international crisis, together with the Euro-zone recession, has put substantial pressure on the nature and structure of welfare states, particularly with regards to minimum income schemes. Governments are therefore trying to react in order to design policy instruments more adapt to answering the needs of a modern and inclusive active welfare state, which, at the same time, does not produce negative effects on labor supply with the beneficiaries trapped into a sort of never-ending job instability.
‘Universalistic measures’ as simple and comprehensive schemes (AT, BE, CY, CZ, DE, DK, FI, NL, PT, RO, SI, SE) open to all potential applicants with insufficient means to support themselves. In some countries (e.g. AT, DE) unemployment benefits and social assistance schemes are separated, whereas in others (e.g. PT, SE) just one tool covers both needs.
A ‘basic and discretionary measure’: Some countries (EE, HU, LT, LV, PL, SK) have quite simple and non-categorical schemes, with rather restricted eligibility and coverage of people in need, often due to the low income level at which the means test is set.
An ‘integrated network of different categorical measures’: other Member States (ES, FR, IE, MT, UK) have developed a complex network of different, often categorical, non-contributory schemes supporting specific target groups such as lone parents, the ill or disabled, the unemployed, carers, survivors and pensioners and low-paid workers. In some cases these categorical measures are accompanied by a general scheme of last resort.
Finally, there are Member States (BG, EL, IT) who have very ‘limited, partial or piecemeal arrangements’, which are in effect restricted to quite narrow categories of people and do not cover many of those in urgent need of income support.
With regards to Italy, no widespread minimum income mechanisms have been created at a national level, even though innovative examples of local welfare systems exist through experimentations; however, they are often weakened by a fragmented and inefficient legal framework for social protection together with the targetization to specific characteristics.
Greece too has been implementing a pilot scheme established by the Law 4093/2012 in order to ensure a decent minimum standard of living. First, implementation regards 13 municipalities for the cost of €20 million (period 2013–2014). The amount of the minimum income starts from 200 euros for a single person and increases depending on the recipient’s marital status. For married couples it adds 100 euros for each adult member of the family other than the spouse and 50 euros for each child. To make it clear, a couple with two children without any source of income will receive the entire amount, i.e., 400 euro. Availability criteria include residence, income and assets.
With an unemployment rate at 11.4% at the European level2 in December 2014, the background of this article is the investigation of how restructuring the welfare state can be necessary in order to guarantee both social protection to workers and an incentive for people to work and therefore contribute to boost the economy. Through the reform of RSA, France has gone towards a minimum income scheme combining flexibility of the measures, therefore, thinking towards efficiency of public expenditure, and security of workers. In a word, RSA constitutes a typical example of the flexicurity3 framework.
The research questions we try to answer are: does the introduction of RSA produce significant positive effects in terms of employment, poverty and inequality? Is the cost of the reform sustainable for the French public finances?
The rest of the paper is organized as follows. Section 2 sums up what literature has produced in terms of empirical evaluation of costs of RSA. Section 3 describes the mechanisms to compute the fiscal instruments under investigation. Section 4 presents SYSIFF 2006, the micro–macro simulation model used to perform the reform simulations. Section 5 reports the results and Section 6 concludes.
The crisis has determined a huge impact on the number of potential beneficiaries of RSA: in September 2010, 1.8 million households (which corresponds to 3.8 million individuals, or about 6% of total population). 1.1 million households benefited from basic RSA — the full benefit given to the unemployed — which is in line with the coverage of previous measure, RMI. The crisis led to a sharp increase in people eligible for this minimum income scheme, in the order of 20% in the first year.
Numbers in June 2011 showed a stable path for RSA beneficiaries (1.87 million households and 3.9 million people) with a slowing down in the increase (3.7%) but a significant boost of families which get the basic RSA, (1.4 millions).
But statistics worsened again at the end of 2013, with RSA beneficiaries increased to 2.3 million households (4.9 million people), 7% of the whole population (Cazain 2014).
Most of the beneficiaries are in the cohort 25–34 years old (36%), while recipients in the 50–65 population constitute 21% of the entire sample. About 75% of RSA recipients have benefited from the scheme for more than 1 year, while 33% is still getting this transfer after 3 years (OECD 2011).
In terms of the impact of RSA on labor supply and the labor market, any evaluation is extremely complex and therefore only qualitative investigations are available so far from the government side. To our knowledge, a complete ex-post evaluation of RSA does not exist in literature. Therefore, we could not find any official report with final estimations of costs and number of beneficiaries.
An experiment involving 15,800 households of potential beneficiaries had been run in 34 Departments from 2007 to 2009 (Comitè National de Evaluation RSA 2012), but Thibault (2014) criticized its results because of confounding effects in the payment schedule and non-comparable duration of the transfers. Timing of experimentation was not sufficient to provide empirical evidence able to modify or even cancel the generalization of the measure to the entire population, and, for this reason, the experiment has been consider more a simulation of the potential effects of RSA than a statistical test of its efficacy.
Besides that, RSA implementation took place during the crisis, changing completely the setting and the context of the experiment.
Data from the report 2012 show that RSA beneficiaries find more often temporary or part-time jobs. On average, 3% of the beneficiaries find a job each month, but, overall, recipients seem to be trapped in a long term unemployment path.
A few empirical works tried to evaluate instead the effects of RSA focusing on specific aspects. Danzin et al. (2012), for example, found that RSA increase the opportunity to exit unemployment only for single women with children.
Bargain and Vicard (2012), using yearly data from INSEE (the French statistical institute) and including a representative sample of the French population over the period 2004–2011, assessed the effect on employment of the over 25-year-olds, finding that RMI had a slight positive impact on employment, while RSA did not produce any significant effect in 2010–2011.
Bargain and Doorlay (2013), using a regression discontinuity method, study the effect of the pre-2009 French social assistance program, the RMI, on labor supply, and they find a drop between 5 and 9% in the employment rate of young high-school dropouts. The introduction of RSA restored financial incentives to work and alleviated the inactivity trap.
Final, a study by Domingo and Pucci (2012) has been devoted to people who, even if eligible for the RSA in 2011, did not get the transfer or the total amount available. The article addresses the problem of non-take up, estimating that more than 400,000 people would have exited poverty if RSA had been received by all potential beneficiaries.
SYSIFF2006 is a combination of a behavioral microsimulation model for the French tax-benefit system tightly integrated into a multi-sectorial static Computational General Equilibrium (CGE) model. The integration of the micro and macro model relies on an iterative approach similar to Aaberge et al. (2007) and Peichl (2009) (See Bourguignon and Bussolo 2013 and Figari et al. 2015 for a review of micro–macro integration techniques). While a detailed description of the micro–macro model, including all estimation results, full CGE specification and calibration, and the iteration procedure can be found in Magnani et al. (2013), a short summary of the main characteristics is provided below.
4.1 The SYSIFF2006 behavioral microsimulation model
SYSIFF2006 (Système d’Imposition Fiscale Français du 2006) is an arithmetical microsimulation model for the French fiscal system integrated with two behavioral models concerning consumption and labor supply decisions. It is a microsimulation model since it is based on micro data on a sample of families representative of the French population (the Budget des Familles 2006 – the household budget survey provided by INSEE). The arithmetical part of the model simulates, for each of these families, social contributions, income taxes, VAT, local taxes and social benefits due or to be received by the state. The behavioral part includes two different microeconometric estimations: a quadratic, almost ideal demand system for consumption decisions and a discrete-choice labor supply model with involuntary unemployment.
The estimation of consumption demand is based on the Almost Ideal Demand System proposed by Deaton and Muellbauer (1980) and extended by Banks et al. (1997) with the introduction of a quadratic income term in the demand functions that fulfils the necessity of having a higher rank demand system (useful when Engel curves are non-linear). Along with the quadratic extension, we also introduce demographic heterogeneity through an income translating function, firstly introduced by Gorman (1976). The system of demand equations is estimated simultaneously by Full Information Maximum Likelihood, and a generalized Heckman correction for zero expenditures is applied (Shonkwiler and Yen 1999).
A standard way to estimate labor supply is to consider that individuals choose the optimal number of hours worked in order to maximize their well-being under a budget constraint. The non-linearity and non-convexity of the budget constraint, due to the characteristics of the tax system, implies the impossibility to derive an explicit solution to this standard utility maximization problem. In the last decades, this problem has often been addressed by using discrete choice labor supply, first introduced by Van Soest (1995). This approach allows for directly estimating the utility function parameters without the need of a Marshallian labor supply function. In particular, discrete choice models have the advantage of capturing behavioral changes in corner solutions, accounting for market rigidities and avoiding the computational and analytical difficulties arising from non-linear and non-convex budget constraints, since the budget constraint is computed by the microsimulation model and introduced directly into the utility function (See Aaberge and Colombino 2014 for a review of labor supply models integrated within microsimulation models).
The analysis of the distribution of the work alternatives has led to the choice of four work alternatives: not to work (0 hours), 50% part-time (18 hours), 80% part-time (28 hours), and full-time (36 hours). The estimates of labor supply are performed on a sub-sample of potential wage earners separately for single men, single women and couples. In particular, for each single (man or woman) we define a utility level for each of the four alternatives depending on individual characteristics and the yearly disposable income associated to each alternative. In contrast, for each couple, we estimate the work decision jointly by considering eight alternatives, four for the woman and two (full-time work or not to work) for the man. Then, we define a utility level for each of the eight alternatives depending on family characteristics and the yearly disposable income of the family associated to each alternative. Of course, in order to compute the disposable income for the non-observed alternatives it is necessary to generate a potential salary for the unemployed. Potential salaries are estimated using a Heckman correction model (Heckman 1979) to account for potential self-selection bias issues.
As for the discrete choice labor supply model, we extend the standard approach, which implicitly assumes that all non-working people choose not to work, by considering that unemployment may be either voluntary or involuntary, as in Magnac (1991), Bingley and Walker (1997), and Haan and Uhlendorff (2013). Our micro dataset allow us to identify involuntarily unemployed: 19.7% of working age individuals do not work but only 6.3% of them is involuntarily unemployed, implying that the unemployment rate is 7.3%. Involuntary unemployment is introduced into the model by randomly assigning (respecting the actual distribution of observed choices) a choice among the work alternatives to involuntarily unemployed. Thus, the involuntarily unemployed would choose to work but cannot find a job. Then, we estimate the probability of being involuntarily unemployed and rank individuals according to this probability. If at macro level the unemployment rate increases, more individuals from this ranking will be assigned to not work even if their actual choice would be to work.
4.2 The CGE model
The CGE model that represents the macro component of our Micro–macro simulation model is a multisectoral and static model with two foreign zones: the Eurozone and the rest of the world. The model is built by using the 2006 French input–output data-set provided by INSEE. The construction of the SAM (Social Accounting Matrix), necessary to calibrate our CGE model, is completed by using national accounts concerning the government accounts and the balance of payments.
An important feature of the CGE model is its macro closure. The macroeconomic equilibrium condition states that aggregate investments must be equal to aggregate savings. The neoclassical closure, which is the most frequently used in general equilibrium models, implies that investments are then savings-driven, i.e., the macroeconomic equilibrium condition determines the aggregate investment. The use of the neoclassical closure implies that a shock which increases the value of a component of the aggregate demand produces a strong and unreasonable effect on investments, while the effect on the GDP is negligible since GDP is determined by the supply of production inputs that are assumed to be fully employed in the economy.
With respect to the neoclassical closure, the Keynesian closure consists of fixing the level of investments at a predetermined level (see Álvarez-Martínez and Polo 2012) and to endogenize the unemployment rate. The unemployment rate is then determined in order to satisfy the macroeconomic equilibrium condition between investments and aggregate savings, implying that aggregate production is demand-driven. In particular, and in contrast to neoclassical models, the macroeconomic equilibrium may be an under-unemployment equilibrium, implying that unemployment appears in the case in which the level of the aggregate demand is insufficient.
However, even the Keynesian closure presents a major shortcoming since the reaction of the unemployment rate may often be excessive. This is why we chose to use in our CGE model a closure rule which is between the neoclassical and the Keynesian ones. The idea is the following: with a neoclassical closure in which investments are savings-driven, an increase in the current account, or in any other component of the aggregate demand, produces a crowding-out effect on investments; in contrast, with a Keynesian closure, the same shock produces no effects on investments (if investments are fixed at a given value) or just an indirect effect via the interest rate. Our idea is to introduce an investment function which takes into account the (partial) crowding-out effect on investments produced by a change in the components of the aggregate demand. Thus, the introduction of this investment function allows us to build a CGE model with a macro closure that is between the neoclassical and the Keynesian ones.
4.3 Integration of the two models
The Micro and Macro models are integrated by an iterative approach. This means that the models are run sequentially, and the output of each model is an input to the other. Iterations continue up to a point in which from one iteration to the other variations of all relevant variables are sufficiently small.
The workflow of the model can be summarized as follows (again, for more details, see Magnani et al. 2013). First (optional), the CGE model simulates a shock and determines the macroeconomic effects, in particular, the percentage variations of (i) the equilibrium domestic wage, (ii) the equilibrium consumer prices of the goods and services, (iii) the consumer price index, and (iv) the equilibrium unemployment rate.
Second, these variations are passed to the Microsimulation model in order to compute, for each individual, the effects on (i) the labor supply, (ii) the demand of goods and services, (iii) the employees and employers social contributions, (iv) the taxes on incomes, (v) the transfers from the government, and vi) the disposable income. Fiscal reforms, such as the RSA, can be (optionally) introduced in this step.
Third, the individual effects are then aggregated, and the percentage variations computed in the Microsimulation model are passed to the CGE model as exogenous variables, in particular: (i) the total quantity of labor supply, (ii) the total demand of goods and services, (iii) the total contributions paid by employees and employers, (iv) the total income taxes collected, and (v) the total transfers paid by the government.
Fourth, the CGE model is then solved by considering the new values of the exogenous variables determined in the Microsimulation model. The solution obtained by the CGE model (i.e., the percentage variations of the equilibrium prices) is then introduced in the Microsimulation model again, and so on.
Iterations are stopped when variations in all relevant variables remain (sufficiently) unchanged between consecutive iterations.
To assess the impact of the RSA reform, we simulate three different scenarios. The baseline scenario is named “baseline” and corresponds to the last year of full implementation of RMI, 2008. As our model is based on the 2006 data and fiscal system, we update both components to reflect the 2008 system and aggregate data (for example we update incomes to obtain 2008 aggregate values, but we do not change the distribution). This scenario is used as reference for comparisons with the RSA reform, which is then evaluated as if it substituted RMI and API in 2008.
The first reform scenario refers to the very short run. In this case we only present the results of an arithmetic simulation of the introduction of RSA that we call “short run”. Here behavioral responses and macro adjustments are omitted, and it is supposed to show what would happen the day after the reform. In the long run both behavioral reactions and macroeconomic adjustments are allowed. Since there is no indication for the moment about eliminating PPE, for simplicity of exposition, we present only the results of a long run scenario in which PPE survives and only mention the main differences in case of PPE elimination. Full simulation results are available upon request. We name this scenario “long run.”
It is important to stress that in these simulations, we assume that other macroeconomic conditions are unchanged, so we are able to avoid the effects of the big recession that occurred just after the introduction of the RSA. At least we can provide evidence that the introduction of the RSA contributed to alleviate the effects of the crisis, since in our simulations, it produced an increase in real GDP and a reduction in the unemployment rate.
5.1 The costs of the reform
One of the most important points of the reform is its impact on the public budget. Evaluating the cost of the introduction of RSA is decisive to assess the effectiveness of the new instrument and its efficiency in terms of resource allocation.
First of all, a distinction must be made between the short and long run. In the long run, the new social minimum is intended to rationalize the whole system of households’ benefits by increasing incentives to start working. In this scenario, RSA and PPE are going to coexist, and costs must be estimated taking that properly into account.
Costs of minimum income benefits and rsa (In Billions of €)
Cost of the reform
5.2 Labor supply reactions
As mentioned earlier, much of the debate around the reform was about its potential positive effect on labor supply. Some critics suggested that the introduction of RSA would have changed a temporary job trap into a part-time trap, because working part-time entitles a larger amount of benefit.
Long run labor supply reaction of singles (Transition Matrix)
Long run labor supply reaction of couples (Transition matrices)
These relatively modest results seem to partially confirm the studies which tried to assess the overall effects in term of unemployment and found no significant impact (Bargain and Vicard 2012). However, even though figures are not large in percentage terms, they correspond to relevant absolute numbers.
5.3 Poverty and inequality
Poverty and inequality before and after the reform
Poverty gap ratio
As to the baseline scenario, we find a poverty rate of almost 14%, a poverty gap ratio of about 3.7, a Gini coefficient of 29.5 and an interdecile ratio of 3.7. In the short run, the reform brings a quite substantial reduction in poverty, about 1.4 percentage points, which is slightly strengthened by the behavioral reaction in the long run. Overall, this corresponds to an 11% reduction in poverty. On a similar pattern is the intensity of poverty, with the PGR falling from 3.7 to just above 3.3, a reduction of more than 10%.
Inequality is also reduced, even though by a smaller extent. In the long run, the Gini is reduced by almost 0.8 percentage points, a 2.6% reduction. The interdecile ratio decreases by 3.7%.
The behavioral response caused by the introduction of the RSA seems to go in the right direction, taking more people out of poverty, but the overall effect is quite limited, possibly due to a macroeconomic compensating effect of wage reduction, as detailed in the next section. The main effect, however, is the immediate one, that is the provision of an income complement to low wage workers quite likely to bring them above the poverty line.
Percentage of winners, average percentage gain and percentage of losers and average loss
Singles with children
Couples without children
Couples with 1 child
Couples with 2 children
Couples with 3 or more children
5.4 Macroeconomic adjustments
Macroeconomic adjustments of the RSA reform
% Variation 1
Involuntary unemployment rate
Real rate of remuneration of capital
Consumer Price Index
Private saving rate
Perhaps the most relevant effect at the macro level is the 15.2% reduction in the involuntary unemployment rate, roughly 1.3 percentage points, which goes in the right direction according to the aims of the RSA reform. As a consequence, the total labor supply increases by 0.3%, which implies an increase by 0.2% of the real GPD. The increase in labor supply also implies a wage adjustment, with a reduction of 0.2%. Total capital in the economy are almost unchanged, with a slight increase in the remuneration of capital. Prices of goods are unchanged overall, although private consumption increases by 0.8%. Total investments see a relevant reduction (1.1%) due to a decrease in the saving rate by consumers (−4.6%). Finally, government expenditure increases by 0.2%, but thanks to the positive effect on the GDP, the deficit/GDP ratio decreases by 6.6%, going from 2.4% to 2.2%. Interestingly enough, on an ex-ante perspective, the positive macroeconomic effects that RSA has on the economy surpasses its cost for the government, allowing for an improved deficit/GDP ratio.
This paper is the first systematic evaluation of the impact of the RSA reform on French economic system. After the experimentation in 2008 and the generalization of the measure to the entire population, the crisis has produced a significant and unexpected impact on the number of beneficiaries, which are now close to 5 million people. The possibility of evaluating both at the micro and macro level the effects of the reform can shed light on some issues: we use a micro–macro simulation model (SYSIFF2006) in order to simulate the monetary cost of the reform and its effects on labor supply, poverty and inequality, together with the corresponding macroeconomic adjustments, net of the great recession effects.
According to our simulations, the RSA reform produces a long run reduction of poverty of almost 1.6 percentage points (from 14% to 12.4%), corresponding to about 400,000 families out of poverty.
By integrating the microsimulation model into a CGE model, we then simulate the short run and long run effect of the RSA reform on job opportunities of beneficiaries, finding only slight variations for couples (again, confirming Bargain and Vicard 2012) but a significant effect for singles, with 1.1% of total singles (about 7% of the non-working) who exit unemployment/inactivity and find a stable full-time job.
A quite interesting result is obtained in terms of macroeconomic implications in a scenario without economic crisis. If the RSA was working as a minimum income scheme in a pro-cycle phase, the overall effect in terms of GDP (+0.2%) would have produced a positive impact on the deficit/GDP ratio, therefore counterbalancing the effect of the increased public expenditure with a multiplier effect able to boost the aggregate demand.
The macroeconomic context in which RSA has been implemented was extremely unfavorable, especially for the governmental goal of increasing efficiency of job-search activities. In this respect, any tentative ex-post evaluation is necessarily questionable because the scheme has been operating under extraordinary conditions which were not foreseeable. As our evaluation was performed ex-ante, we are able to evaluate the possible impact that RSA could have had if the great recession did not happen, and the results indicate an overall positive evaluation, which effectively combines a back to work strategy with social protection.
4Projet de Loi généralisant le revenu de solidarité active et réformant les politiques d’insertion, n°7 Sénat Session Ordinaire de 2008–2009.
5This is the position, for example, of Thomas Piketty: http://www.liberation.fr/economie/2009/11/13/le-rsa-contribue-a-favoriser-l-emiettement-du-travail_593459.
6We use the OECD-modified equivalence scale: a weight of 1 is assigned to the household head, 0.5 to each further adult, and 0.3 to each child.
Acronym (Full name (in French)): Short description
RMI (Revenu minimum d’insertion): Minimum income before the reform
RSA (Revenu de solidarité active: Minimum income after the reform
(Prime de Retour à l’Emploi): Forfeit given to people that find a job and exit RMI
API (Allocation Parent Isolé): Benefit for single women with children in charge
PPE (Prime pour l’Emploi): Tax credit for low wage workers
SMIC (Salaire minimum interprofessionnel de croissance): Minimum wage for full-time workers
(Pensions alimentaires): Transfers for families after a divorce or for people who live outside the fiscal unit
(Prestations Familiales): Households’ benefits
AL (Allocation Logement): Housing benefits
PAJE (Prestation Accueil Jeunes Enfants): Set of benefits for families with more than 2 children
APJE (Allocation Pour Jeunes Enfants): Set of family benefits substituted by PAJE in 2005
ARS (Allocation Rentrée Scolaire): Family benefit for families with children going to school
(Prestations en nature): Transfers in kind
(Indemnités journalières, maladie): Reimbursements provided by work contracts in case of accident
(Assurance maladie): Health insurance
(Assurance accidents du travail): Insurance scheme provided by employer for work accidents
AES (Allocation Education Spéciale): Family benefit for children with disability
RAP (Revenus Activités Professionnelles): Income from work
(Revenus Nets Catégoriels): Net income from work
(Allocation Chômage): Unemployment benefit
AAH (Allocation aux adultes handicapés): Household’s benefit for invalid people
AEEH (Allocation Education Enfants Handicapés): Household’s benefit for families with invalid children.
The authors thank François Bourguignon, Riccardo Magnani, Lucia Mangiavacchi, Xisco Oliver and one anonymous referee for their useful comments, discussions and help. We thank for their useful comments the participants to the Lunch Seminars at the Paris School of Economics, to the First Microsimula conference “Microsimulation as a tool for the analysis of public policies: methods and applications” (Paris School of Economics), and to the 2nd General Conference of the International Microsimulation Association “Microsimulation: bridging data and policy” (Ottawa). The authors acknowledge financing to the French Government (CNRS, project n. BLAN06-2_139446). Luca Piccoli and Amedeo Spadaro acknowledge financing to the Spanish Government (Ministerio de Ciencia e Innovación, project n. ECO2011-28999).
Responsible editor: Alan Barrett
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