Safae Elmisaoui
email: safae.elmisaoui@um6p.maeng

Lhachmi Khamar

Saad Benjelloun

Mohamed Khamar

Jean Michel Ghidaglia

).

Intensification of phosphate fertilizers granulator using CFD: transition phenomenon investigation

Fig. 1: Meshes for mesh convergence study

FuturE work & perspectives

Our research work is open to different directions. Many studies will be carried to evaluate the effect of the key operating parameters on the hydrodynamic flow within the rotary drum granulator and the evolution of fertilizer particles distribution as a function of time, for different granulator rotation speeds. Also the geometry characteristics impact (inclination angle) will be investigated. The chemical reaction will be added to correlate the physical to chemical aspects of the process. Hence, the CFD simulation can be the efficient tool for an intensified granulation process, respecting the physicochemical properties of the product.

General context

The granulation, using rotary drums technology, is an essential operation unit to form granules with good quality in many industries such as food processing, pharmaceutical, and mineral processing. The main objective of this work is to evaluate the effects of the key operation parameters of this process, especially the rotational speed on the behaviour of multiphase flow within an inclined rotary drum granulator by the use of computational fluid dynamics (CFD).

Mathematical model

The The gas volume 𝑉 𝑓 , f is defined by : 𝑉 𝑔 =  𝑉 𝛼 𝑔 𝑑𝑉.

The conservation of the gas and solid momentum is given by the following equations:

Results and discussion

The rotary granulator was set to different rotational speeds (8, 16, 24 and 32 rpm) in order to analyze the different regimes of solid motion. The results in Fig. 2 show the transition between the known typical transverse bed motions (rolling, cascading, and cataracting) during rotation. When the speed increase the arch of the kidney-shaped bed increase.

The model is discretised using finite volume method, and The First-Order-Upwind-method was used for interpolating the cell centre values to the cell boundaries

  Eulerian model that we used allows to simulate fluid-fluid and fluid-solid multiphase flows. Navier Stokes equations are presented by Euler-Euler approach and coupled to the Kinetic Theory of Granular Flow model. The continuity equations for gas-solid mass conservation are: ( 𝛼 𝑠 𝑣 𝑠 ) = 0 𝑣 𝑔 and 𝑣 𝑠 are the velocities of air and fertilizer particles.

Fig. 2 :

 2 Fig. 2: The distribution of fertilizer particles in different rotational speeds, and instants

  𝜕 𝜕𝑡𝛼 𝑔 𝜌 𝑔 𝑣 𝑔 + ∇. 𝛼 𝑔 𝜌 𝑔 𝑣 𝑔 𝑣 𝑔 = -𝛼 𝑔 ∇𝑝 + ∇. 𝜏 𝑔 + 𝛼 𝑔 𝜌

𝑔 Ԧ 𝑔 + 𝛼 𝑔 𝜌 𝑔 Ԧ 𝐹 𝑞,𝑔 + Ԧ 𝐹 𝑙𝑖𝑓𝑡,𝑔 + Ԧ 𝐹 𝑣𝑚,𝑔 + (𝐾 𝑔𝑠 𝑣 𝑔 -𝑣 𝑠 ) 𝜕 𝜕𝑡 𝛼 𝑠 𝜌 𝑠 𝑣 𝑠 + ∇. 𝛼 𝑠 𝜌 𝑠 𝑣 𝑠 𝑣 𝑠 = -𝛼 𝑠 ∇𝑝 -∇𝑝 𝑠 + ∇. ന 𝜏 𝑠 + 𝛼 𝑠 𝜌 𝑠 Ԧ 𝑔 + 𝛼 𝑠 𝜌 𝑠 Ԧ 𝐹 𝑞 + Ԧ 𝐹 𝑙𝑖𝑓𝑡,𝑠 + Ԧ 𝐹 𝑣𝑚,𝑠 + (𝐾 𝑔𝑠 𝑣 𝑔 -𝑣 𝑠 ) Where Ԧ 𝐹 𝑞 , Ԧ 𝐹 𝑙𝑖𝑓𝑡,𝑠 , and Ԧ 𝐹 𝑣𝑚 are external body force, a lift force and a virtual mass force, respectively and 𝐾 𝑠𝑔 is the momentum exchange coefficient


Kevin Bellinguer

Robin Girard

Guillaume Bontron

Georges Kariniotakis

SHORT-TERM PHOTOVOLTAIC GENERATION FORECASTING USING MULTIPLE HETEROGENOUS SOURCES OF DATA

Keywords: PV System, Forecast, NWP, Satellite Images

come

INTRODUCTION

Over the past years, environmental concerns and sustainable development have played a key role in the development of renewable energies sources (RES) in many countries. To promote carbon-free technologies, such as photovoltaic (PV) generation, policies based on subsidy schemes like feed-in tariffs were put in place for a period up to 20 years. These policies are coming to an end in several European countries and RES power plants have to participate directly in electricity markets. To do so, reliable power production forecasts for the next hours to the next days are needed. In addition to allow the participation to the intraday market, Intra-hourly forecasts can also contribute to optimize operation of storage units coupled to RES plants. From the perspective of grid operator, RES forecasts are also important to ensure a secure and economic power system operation especially under high RES penetration conditions.

Research in solar forecasting has been very active in the last years [START_REF]A Systematic Literature Review on big data for solar photovoltaic electricity generation forecasting -Viscondi[END_REF][START_REF] Diagne | Review of solar irradiance forecasting methods and a proposition for small-scale insular grids[END_REF]. Currently, there are several ways of forecasting power production of a PV plant. On one hand, based on incidental irradiance level and ambient temperature, extracted from Numerical Weather Predictions (NWP), physical models of PV plants production can be developed. The contribution of NWP data is meaningful for horizons above some hours ahead. On the other hand, by using the time series of the PV plant production combined with statistical models, shortterm production can be forecasted. Thus, these two approaches are efficient in two distinct time ranges as presented in Figure 1. A third family of so-called hybrid models consists in combining heterogeneous input data.

OBJECTIVES AND APPROACH

The aim of this paper is to present a novel deterministic hybrid approach able to use multiple heterogeneous sources of data as inputs. First we consider measured production data from nearby power plants as input to forecast the output of a specific PV plant. This data allows to exploit the correlation between the production data of spatially distributed PV sites. The classical spatio-temporal (ST) approach in the literature, based only on this source of data [START_REF]Probabilistic Model for Spatio-Temporal Photovoltaic Power Forecasting -Agoua[END_REF][START_REF]Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production -Agoua[END_REF], improves predictability for the next few minutes up to 6 hours ahead.

Then we extend the model by considering satellite images (i.e. global horizontal irradiance (GHI)) and Numerical Weather predictions (NWPs). The satellite images provide spatio-temporal information. The NWP data allows to extend the applicability of the model to day-ahead lead times so that, overall, the resulting model covers efficiently horizons ranging from a few minutes to day ahead. The modeling approach considered is an extension of the state-of-the-art ST models [START_REF]Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production -Agoua[END_REF].

The proposed model is designed according to the following criteria: low computational intensity for on-line application and robustness for situations of missing or corrupt data. The remaining parts of this article are structured as follows: section 2 describes the data used as inputs. Then section 3 presents the methodology and the extended models and section 4 presents the outcomes of the models. Section 5 draws the conclusions of the paper.

In this approach, multiple heterogeneous inputs are considered, namely production measurements, satellite images and NWP. These inputs cover a period of 2 years starting from 2015-01-01 to 2016-12-31 with a temporal resolution of 15 minutes.

PV power production

PV production measurements are provided by the Compagnie Nationale du Rhône (CNR), the France's leading producer of energy exclusively generated from renewable sources. The dataset is composed of nine PV farms located in the Rhône valley (Figure 2). The distance between each unit ranges from 7.3 to 133 km, while the installed power ranges from 1.2 to 12 MWp. The initial 10 min temporal resolution is interpolated to a 15 min time step to match the time resolution of other inputs. A quality check procedure is applied to remove days with production shutdown. In order to allow intercomparisons between the different PV plants, the initial power unit in MW is converted into W/m² by dividing the production by the site total solar panel area.

NWP

Two global models are considered:  The HRES-IFS model, which is the deterministic high resolution of the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). Forecasts are provided twice a day at 00:00 and 12:00 UTC with an hourly period and a interpolated spatial resolution of 0.1°x0.1° (native resolution: Gaussian grid O1280).



The ARPEGE model (Action de Recherche Petite Echelle Grande Echelle), provided by Météo-France 4 times a day, namely at 00:00, 06:00, 12:00 and 18:00 UTC. This model has an interpolated spatial resolution of 0.1°x0.1° and an hourly temporal resolution.

The weather forecasts models provide several parameters. Here, we focus on the Surface Solar Radiation Downwards (SSRD), which is the cumulated downward solar radiation at the surface. Then, the SSRD is interpolated from an hourly resolution to a 15 min resolution thanks to an interpolation method based on a clear sky model. An hourly-cumulated clear sky profile is used to normalize the hourly SSRD time series. Then an interpolation to a 15 min time step of the normalized time series is performed before being de-normalized by the cumulated clear sky profile with a 15 min resolution. Lastly, by subtracting consecutives values, the SSRD is transformed into GHI.

For each site of interest, the corresponding time series is constructed considering only the most recent forecast while putting aside the dissemination schedule (which can delay the real availability of the NWP outputs). Each time series obtained this way is horizon independent, that is to say, for all horizons the same forecasting time series is used.

Satellite images

Satellite images are satellite-made observations of the GHI. These observations cover a large spatial range and provide valuable spatial information regarding the weather conditions of the site's surroundings. As a result, this source is highly linked with the ST approach.

In the present study, the satellite images are obtained from the HelioClim-3 database with the HelioSat-2 method [START_REF] Gschwind | The HelioClim Project: Surface Solar Irradiance Data for Climate Applications -Blanc[END_REF]. The spatial and time resolutions are respectively 0.0625°x0.0625° and 15 min. Each pixel of the images is converted into a time series.

METHODOLOGY

This section presents the model used to forecast PV production and how heterogeneous inputs are considered. All developments are made with the R language [START_REF]R: A language and environment for statistical computing[END_REF].

A Spatio-Temporal approach

Initially developed for wind power forecasting, ST models are now drawing the attention of PV forecasting researchers [START_REF]Probabilistic Model for Spatio-Temporal Photovoltaic Power Forecasting -Agoua[END_REF], [START_REF]R: A language and environment for statistical computing[END_REF], [START_REF] Trindade | Spatial-Temporal Solar Power Forecasting for Smart Grids -Bessa[END_REF]. This method assumes that close PV sites undergo similar weather conditions but shifted in time. As a result, by considering production observations from nearby sites, forecasting models can benefit from additional sources of information. The ST approach is well suited for energy producers inasmuch as this new source is easily available for industrials with several narrow spatially distributed PV sites.

To comply with our low computational intensity requirement, a deterministic autoregressive (AR) model coupled with a ST approach is defined [START_REF]Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production -Agoua[END_REF]:

𝑃 𝑡+ℎ 𝑥 = 𝛽 ℎ 0 + ∑ ∑ 𝛽 ℎ 𝑙,𝑦 𝑃 𝑡-𝑙 𝑦 𝑦∈𝑋 𝐿 𝑠 𝑙=0
Where 𝑃 𝑡+ℎ 𝑥 is the production forecast of plant 𝑥 at time 𝑡 + ℎ. 𝑋 stands for the set of neighboring plants around 𝑥 (including x) and 𝐿 𝑠 is the maximum lag. The β parameters are fitted using the least squares method. For each horizon ℎ, a specific set of parameters is fitted. From now on, this model will be denoted as "ARST".

As a reference, we will consider the non spatiotemporal version of this approach, designated by "AR" and defined as follow:

𝑃 𝑡+ℎ 𝑥 = 𝛽 ℎ 0 ∑ 𝛽 ℎ 𝑙 𝑃 𝑡-𝑙 𝑥 𝐿 𝑙=0

Integration of exogenous data and LASSO regularization

In the literature, many solutions to integrate exogenous data are proposed. Regarding NWP, we can consider analog approaches [START_REF] Alessandrini | A novel application of an analog ensemble for shortterm wind power forecasting[END_REF] (i.e. clustering method), ensemble approaches [START_REF]Ensemble methods for wind and solar power forecasting -A state-of-the-art review -Ren[END_REF], … As for satellite images, advanced methods such as Cloud Motion Vector (CMV) [START_REF]Extraction cloud motion vectors from satellite images for solar power forecasting -Cros, Liandrat[END_REF] With 𝑁, the number of pixels from the satellite images.

Thereafter, this model is denoted as "ARXST" if the spatio-temporal approach is taken into account, "ARX" otherwise.

By taking into account the satellite images, a high number of new variables are introduced. To tackle the issue induced by the increased dimensionality, a feature selection procedure is implemented. The Least Absolute Shrinkage and Selection Operator (LASSO) is defined as:

𝛽 ̂𝐿𝐴𝑆𝑆𝑂 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝛽 { 1 2 𝑅𝑆𝑆(𝛽) + 𝜆|𝛽|}
With RSS the Residual Sum of Squares function.

Data stationarity

Time series forecasting methods such as Auto-Regressive Integrated Moving Average (ARIMA) models need a stationary time series. In the literature, different approaches are proposed to stationarize inputs: the Seasonal-Trend decomposition procedure based on Loess (STL) [START_REF]Operational photovoltaics power forecasting using seasonal time series ensemble -Yang[END_REF], normalization by a clear sky profile [START_REF] Bacher | Online short-term solar power forecasting[END_REF] … A clear-sky model is a model which estimates the irradiance considering a sky without clouds. In this study, we consider the MacClear clear-sky model [START_REF] Mcclear | a new model estimating downwelling solar radiation at ground level in clear-sky conditions -Lefèvre, Oumbe[END_REF]. To take into account the tilt angle of PV panels of the plants, the GHI projection formula from [START_REF]Modeling daylight availability and irradiance components from direct and global irradiance -Perez, Ineichen[END_REF] is implemented.

Moreover, the normalization procedure enables to remove the deterministic component of the irradiation due to the sun path. Consequently, only the stochastic part linked to clouds motion remains. This process enables to highlight the correlation between neighboring sites, which are impacted by the same clouds.

Performance measurement

To assess the performance of the proposed models, we use the Root Mean Square Error (RMSE):

𝑅𝑀𝑆𝐸 = √ 1 𝑁 ∑(𝑃 𝑖 𝑝𝑟𝑒𝑑 -𝑃 𝑖 𝑜𝑏𝑠 ) 2 𝑁

𝑖=1

With 𝑃 𝑖 𝑝𝑟𝑒𝑑 , the re-normalized production forecast and the 𝑃 𝑖 𝑜𝑏𝑠 , the corresponding observation. In the following section, all RMSE scores are averaged considering the performance of the nine available PV plants.

To compare the performances of the extended models (ARST, ARX, and ARXST) with the reference model (AR), the following comparison skill score is used:

𝑆𝑆(ℎ) = 1 - 𝑅𝑀𝑆𝐸(𝑚𝑜𝑑 𝑒𝑥𝑡𝑒𝑛𝑑𝑒𝑑 (ℎ)) 𝑅𝑀𝑆𝐸(𝑚𝑜𝑑 𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 (ℎ))

EVALUATION RESULTS

This section presents the outcomes obtained with the proposed models. The forecasts are computed using the year 2015 as a learning set, and the year 2016 as a testing set.

Integration of satellite images

For each of the nine available PV plants, the pixels inside a radius of 150 km around the position of the site of interest are considered, which represents around 2 000 pixels. Even if LASSO selection procedure can determine the relevant features, to reduce computational time, the number of features to compute must be reduced.

The first step is to determine the optimal radius around each site. As it can be seen on Figure 3, the main information from satellite images are contained in a 50 km radius area. This radius will be considered for the next part of the study. It is worth mentioning that the shape of the best correlated area is highly linked with the Rhône valley's topography. The more pixels are included, the better are the performances. With only the 10 best correlated pixels, a 6.00% improvement of the RMSE is reached at 1 hour horizon. By considering 100, 150 or 200 pixels, the mean performances improvement is slight. To find a trade-off between forecasting performances and computational time, for the next sections of this study, the number of pixels held is 100.

The influence of satellite images over performance forecast is prevailing for short-term horizon (i.e. from 30 min to 3-hour). Beyond 3-hour, the influence of satellite images is still visible. A further study regarding the coefficients weight of the pixels (attributed by the LASSO procedure) and their distance from the site of interest should be carried out to better understand this phenomenon. In this section, we compare the performances of the ARX model fed with the ARPEGE or the ECWMF NWP (Figure 5). Both models performance are very similar. Nevertheless, the ARX model using ARPEGE NWP input is slightly better for very short horizon (i.e. 15 and 30 min) and for horizons higher than 3h. Consequently, in the rest of this study we will focus on the ARPEGE NWP data.

Integration of NWP

The integration of NWP to the AR model, significantly improves performances for high horizons, because NWP are forecasted inputs whereas power measurements and satellite images are observations.

Integration of satellite images and NWP

This section presents the performances of the ARX model using production measurements, satellites images and ARPEGE NWP. For horizons higher than 1-hour, the main source of improvements is due to NWP data. For shorter horizon, the ARX model with satellite images is slightly better (Figure 6). At this stage, it is worth mentioning that outcomes obtained with NWP data are the best we can get. Indeed, our approach did not take into consideration the dissemination schedule of forecasts. Nevertheless, our results are consistent with those found in the literature (Figure 1).

The best performances for horizons higher than 45 min are obtained with the ARX model considering measurements, satellite images and NWP data. Nevertheless, the improvements are minor in contrast with performances reached by the ARX model with measurement and NWP inputs. This phenomenon could be explained by the fact that the information contained in the NWP data and the satellite images are redundant.

We observe the limitations of the LASSO procedure on Figure 6. For horizon shorter than 45 min, the ARX model with satellite data outperform the ARX model with satellite and NWP data. This could be explained by some statistical differences between the learning and the testing set. Even if a quality procedure has been applied to the measurements, some defaults are still present in the data (e.g. converters shutdown …). One way to improve the LASSO performances could be to reduce the temporal width of the learning and testing windows.

Spatio-Temporal influence

In the previous section, it has been shown that considering heterogeneous inputs improve the performance of the forecasting models from 3.7% at a 15 min horizon up to 40.2% for a 6h horizon. We will focus now on the influence of the ST approach.

The ST method enables to take into account the dependencies existing within a close set of PV plants (i.e. nearby plants are affected by the same clouds). Thereafter, for each site, its four closest neighbors are taken into account. The performances of the ARST model are very close to the performance of the ARX model with satellite images inputs (Figure 7). Nevertheless, the information contained in each data set is complementary inasmuch as the ARXST (with satellite images) model improves steadily, by around 1.25%, the forecasting performances in comparison with the ARST and the ARX with satellite images. Regarding the ARXST model with NWP inputs, a performances improvement is also present in comparison with the ARX model with NWP inputs (Figure 8). However, the influence of the ST approach shades off gradually until the 4-hour horizon. Beyond, the performances of the ARXST are comparable with those of the ARX model.

The same conclusions are drawn regarding the ARXST model with measurement, satellite images and NWP inputs (Figure 9). To summarize, ARXST model is more efficient than the ARX model: up to 45 min horizon, the ARXST model with measurement and satellite images has the best performances, beyond; it is the ARXST model with satellite images and ARPEGE NWP inputs (Figure 10).

CONCLUSION

This article proposes a deterministic approach considering heterogeneous sources to forecast the production of PV plants. Compared to the reference AR model, the ARX model extended with a ST approach improves performances, respectively from 5.7% 40.2% for a 15 min and a 6h horizon. The forecasts are obtained considering 2 years of training and testing. To fulfill operational requirements, it would be interesting to improve the proposed model to work with shorter learning periods. Moreover, in the way, the NWP time series is constructed, the performances of the ARX model extended with NWP are over-optimistic. In a future study, the performances of this model should be assess considering NWP time series, which respect the dissemination schedule.

Figure 1

 1 Figure 1 Spatial and temporal resolution of PV forecasting methods [3].

Figure 2 :

 2 Figure 2: PV farms located in southeast France.

Figure 3

 3 Figure 3 Pearson correlation between the normalized production time series and the normalized satellite data for various lags. Site considered: Saulce. Nevertheless, with a 50 km radius, the number of pixels is still high (i.e. around 230) and quite computationally expensive. The second selection step consists of determining the optimal number of pixels to use with the ARX model. To do so, for each horizon, each pixel is ranked accordingly to its correlation score with production data. Then different forecasts considering the N best correlated pixels (𝑁 ∈ [10,50,100,150,200]) are performed. Performance scores are displayed in Figure 4.

Figure 4

 4 Figure 4 Skill score obtained for ARX models using different number of pixels in regard to the AR model considering only measurement time series.

Figure 5

 5 Figure 5 Performance comparison between the ARX model using ARPEGE or ECMWF NWP inputs.

Figure 6

 6 Figure 6 Performance comparison between ARX model considering measurements, satellite images and NWP inputs.

Figure 7

 7 Figure 7 Performance of the ARXST model considering measurements and satellite images.

Figure 8

 8 Figure 8 Performance of the ARXST model with measurements and NWP.

Figure 9

 9 Figure 9 Performance of the ARXST model considering measurements satellite images and NWP.

Figure 10

 10 Figure 10 Perfromance comparison of the ARXST models with measurements, satellite images, and NWP.

  can be used. In this study, we use an extended version of the AR model called ARX, which consists in adding exogenous inputs as follows:

	𝐿 𝑠
	𝑃 𝑡+ℎ 𝑥 = 𝛽 ℎ 0 + ∑ ∑ 𝛽 ℎ 𝑙,𝑦 𝑃 𝑡-𝑙 𝑦	+ 𝛽 ℎ 𝑁𝑊𝑃 𝑁𝑊𝑃 𝑡+ℎ
	𝑙=0	𝑦∈𝑋
		𝑁
		+ ∑ 𝛽 𝑖,ℎ 𝑆𝑎𝑡 𝑆𝑎𝑡 𝑡 𝑖
		𝑖=1


Matteo Richiardi
email: matteo.richiardi@maths.ox.ac.uk

John Cockburn

Hélène Maisonnave Edehn

Luca Tiberti
email: luca.tiberti@ecn.ulaval.ca

INTERNATIONAL MICROSIMULATION ASSOCIATION Editorial

The Spring 2016 issue of the International Journal of Microsimulation is a special issue on CGE Microsimulation in Developing Countries, edited by John Cockburn, Hélène Maisonnave and Luca Tiberti. Concern for the distributive impacts of macroeconomic policies in developing countries has been increasing in recent years. Policy debates have focused progressively more on inclusive, or pro-poor, growth and, more broadly, economic and human development through the Millenium Development Goals (2000Goals ( -2015) ) and, more recently, the Sustainable Development Goals (2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030). In this context, the marriage of computable general equilibrium (CGE) and microsimulation models has become a dynamic area of policy research throughout the developing world.

In developing countries, local researchers, trained and mentored by international experts, possess RICHIARDI Editorial a clear comparative advantage in understanding how far-reaching macroeconomic reforms can influence the myriad facets of their economies and, ultimately, determine the micro-economic impacts on households and individuals. This special issue features six CGE-microsimulation studies of the microecononomic impacts of a broad range of macro policies applied to the agricultural sector, public education, trade, fiscality and social protection. Five of the six papers are written by teams of local researchers who have benefited from funding, training and mentoring offered by the Partnership for Economic Policy (PEP), which seeks to promote local perspectives in informing national and international debates related to economic policy, poverty, gender and sustainable development.

The issue continues a tradition of interest of the journal towards the combination of micro and macro models, with a first special issue on the topic, edited by Maurizio Bussolo and John Cockburn, published by the journal back in spring 2010. A review article, also focused on developing countries, was published a year earlier [START_REF] Davies | Combining Microsimulation with CGE and Macro Modelling for Distributional Analysis in Developing and Transition Countries[END_REF], while [START_REF] Peichl | FiFoSiM -an integrated tax benefit microsimulation and CGE model for Germany[END_REF] describe a combined tax-benefit, CGE model for Germany. [START_REF] Colombo | Linking CGE and Microsimulation Models: A Comparison of Different Approaches[END_REF] presents a methodological overview of the main approaches to combined CGE-microsimulation modelling, exemplified on fictitious data. The invited introductory note to this issue, by Andreas Peichl, summarises the key methodological points, with reference to the more recent literature and the development of alternative approaches.

Suggestions for further readings.

Combined CGE-microsimulation papers do not make it to the International Journal of Microsimulation only. They also appear, with regular frequency, in journals like Economic Modelling.

Debowicz (2016) provides a nice comparison of the behavioural and non-parametric approaches to CGE-microsimulation modelling, where a distinction is made whether the microsimulation component incorporates a parametric behavioural or non-parametric responses. Quite interestingly, he finds that both approaches are consistent with the data, supporting the view that the importance attributed by and large in the economic profession to behavioural responses might be, at least for some applications, overstated. Obviously, whether this is the case or not can be evaluated only ex-post, after having allowed for the more complex feedbacks in the model. Other recent CGE-microsimulation papers also appeared in Economic Modelling are Verikios andZhang (2013, 2015) and [START_REF] Breisinger | Simulating economic growth effects on food and nutrition security in Yemen: a new macro-micro modeling approach[END_REF].

RICHIARDI Editorial

On a different note, our Associate Editor Deborah Schofield highlights the opportunity for microsimulation modelling in genomics. Genomics is an emerging field of clinical medicine which moves from the old paradigm of aggregation of diseases into large groups such as cardiovascular disease, often defined as headline disease groups or national health priority areas. This aggregate approach to medicine is rapidly being redefined as our growing understanding of the human genome proves that these large disease groups are, in fact, made up of many much rarer conditions.

The field of genomics and personalised medicine will revolutionise modern medicine [START_REF] Dzau | Aligning incentives to fulfil the promise of personalised medicine[END_REF]. It also calls for a form of modelling that can capture individual genetic differences and response to therapy -a purpose to which microsimulation is eminently suited. Conversely, the risk of using aggregate models of grouped diseases with crude averaged impacts as inputs can produce highly erroneous results, further confounded if based on assumptions rather than the quality of data microsimulation models typically reply upon [START_REF] Doble | The promise of personalised medicine[END_REF].

RICHIARDI Editorial

Verikios G and Zhang X-g (2013) 'Structural change in the Australian electricity industry during the 1990s and the effect on household income distribution: a macro-micro approach', Economic Modelling, 32, 564-575. Verikios G and Zhang X-g (2015) 'Reform of Australian urban transport: A CGEmicrosimulation analysis of the effects on income distribution ',Economic Modelling,44,[7][8][9][10][11][12][13][14][15][16][17]


John Cockburn

Hélène Maisonnave Edehn

Véronique Robichaud

Luca Tiberti
email: luca.tiberti@ecn.ulaval.ca

INTERNATIONAL MICROSIMULATION ASSOCIATION Fiscal Space and Public Spending on Children in Burkina Faso 1

Keywords: Child poverty, Education, Computable General Equilibrium, Micro-simulation, Burkina Faso JEL classification: I32, D58, C50, O55

Despite experiencing high growth rates in recent decades, Burkina Faso remains a poor country. Poverty among children is particularly worrying, as it has long term (and often irreversible) effects on individuals. Furthermore, it can be transmitted from one generation to the next and significantly reduce economic growth. To address this issue requires fiscal and budgetary policies supporting health, education and improving the household economy. At the same time, the Burkina Faso's budget deficit has grown in recent years as a result of various crises, which have COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso hit the country.

This study uses a macro-micro model to evaluate three different policy interventions aimed at reducing child poverty in Burkina Faso under given budgetary constraints. The results indicate that increased public education spending helps raise school participation and promotion rates. This increases the supply and education level of skilled workers, leading to a reduced incidence and depth of both monetary and caloric poverty. A cash transfer scenario reduces child poverty even more.

INTRODUCTION

Burkina Faso has experienced consistently high economic growth over the last two decades. Despite a relatively solid economic performance, Burkina Faso remains a poor country and lags significantly in terms of infrastructure and human development. Since the beginning of the 2000s, Burkina Faso has made commendable efforts to promote development and to reduce poverty by developing a strategic framework for the fight against poverty (CSLP). However, assessments of the implementation of the CSLP indicate that it was largely undermined by the weakness of sectoral policies and insufficient budget allocations (Ministry of Economy and Finance 2010).

Child poverty results in huge social and economic losses with long-term effects on individuals and future generations. Intergenerational effects are not limited to poor children, as child poverty also significantly reduce economic growth for society as a whole. For example, child malnutrition imperils the acquisition of basic knowledge and thus productivity as an adult. Public social spending can improve child wellbeing, in the long run contributing to the economic growth of a nation.

However, governments face budget constraints and need to make difficult choices concerning allocations of their limited budget between many pressing needs. Moreover, the sustainability and impacts of public spending on different programs depend on the financing mechanism(s) put into place.

The goal of this paper is to evaluate the impacts of three different scenarios that increase public spending on children under given budget constraints. We use a macro-micro framework to quantify the impacts of these scenarios on different education and poverty indicators.

The rest of the paper is organised as follows: the next section consists of an analysis of the poverty and the types of vulnerability and risks affecting children, in addition to a brief review of the existing social protection framework, in Burkina Faso. This is followed by two sections, which respectively present a literature review and the methodology used. The simulation results are then discussed before we conclude.

THE SITUATION OF CHILDREN IN BURKINA FASO

As in many developing countries, in Burkina Faso children are more likely to be living in poverty than adults. [START_REF] Batana | Analyse de la situation de la pauvreté et de la vulnérabilité de l'enfant et de la[END_REF] find that children are 20 percent more likely than adults to live in poverty in developing countries.

Major progress has been accomplished in child education in Burkina Faso since 2000, particularly with respect to the supply of and access to universal primary education. The gross enrollment ratio in primary school went from 40% in 2000 to 87% in 2013. However, the rapid increases in the number of registered students, following the elimination of school fees for primary education, constitute a significant challenge with respect to education quality. Rapid population growth also puts pressures on the system's capacity. In effect, average per capita public spending has not stopped falling since 2003 and student-teacher ratios have barely budged (World Bank, 2009).

In Burkina Faso, a large share of individuals are excluded from health care, particularly in rural areas. Major improvements have nevertheless been achieved: child vaccination objectives were largely exceeded and the number of health centres and personnel have both increased rapidly.

Despite significant progress in health coverage, with a reduction in the average distance to reach a Health and Social Promotion Centre (CSPS), nearly one in ten children dies before the age of five in Burkina Faso 2 . Furthermore, more than one-third also suffer from delayed height and weight growth.

In terms of nutrition, stunting declined from 43.1% of children under the age of five in 2003 to 35.1% in 2009, with more noteworthy progress in rural areas. The share of children suffering from wasting also fell by nearly half, although the level still remains "severe" according to WHO norms.

A strategic plan for 2010-2015 was adopted in order to reduce hunger and illness linked to nutritional deficiency diseases.

LITERATURE REVIEW

The pursuit of Burkina Faso's Poverty Reduction Strategy and the recent global Sustainable Development Goals (SDGs) requires for more detailed study of the impacts of public policies and social reforms. The studies reviewed below evaluate the consequences of fiscal reforms and public spending on vulnerable populations (poor households, orphans and other vulnerable children, etc.).

Child wellbeing is affected by fiscal and budgetary policies, notably through public services such as health and education, and improving the household economy [START_REF] Waddington | Linking Economic Policy to Childhood Poverty: A review of the evidence on growth, trade reform and macroeconomic policy[END_REF]. A few pioneering studies are based on evaluation methods for fiscal and budgetary reforms and policies.

Ahmad and [START_REF] Stern | The Theory of Reform and Indian Indirect Taxes[END_REF] use benefit incidence analysis to evaluate the impacts of fiscal reforms on wellbeing and marginal benefit incidence analysis to assess the benefits associated with tax reforms.

This approach has seen numerous applications, including contributions on child poverty, e.g. [START_REF] Yitzhaki | Welfare Dominance and the Design of Excise Taxation in the Côte d'Ivoire[END_REF], [START_REF] Yitzhaki | Welfare dominance: An application to commodity taxation[END_REF], [START_REF] Mayshar | Dalton-improving tax reform: When households differ in ability and needs[END_REF], [START_REF] Ray | Development economics[END_REF],

and [START_REF] Makdissi | Consumption Dominance Curves: Testing for the Impact of Indirect Tax Reforms on Poverty[END_REF].

COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso

Bibi and [START_REF] Duclos | Réformes Fiscales et Réduction de la Pauvreté: Application sur des Données Tunisiennes (Fiscal reforms and poverty reduction: An application to Tunisian data)[END_REF] extend benefit incidence analysis to identify the direction of impacts of fiscal reforms when the objective is to reduce poverty. Their approach, illustrated with Tunisian data, consists in deriving the cost-benefit ratio of an increase in a consumption tax by minimizing a poverty indicator.

Recent fiscal reforms in Africa following trade liberalization have rekindled interest for analysis of the impacts of fiscal reform on social welfare [START_REF] Sahn | Estimating the Incidence of Indirect Taxes in Developing Countries[END_REF][START_REF] Chen | A Quest for Revenue and Tax Incidence[END_REF][START_REF] Rajemison | Indirect tax in Madagascar: Estimations using the inputoutput table[END_REF][START_REF] Alderman | Poverty issues for zero rating VAT in South Africa[END_REF]. These analyses are particularly interesting as they can be used to analyse fiscal changes induced by new policy regimes, as well as their impacts on the most vulnerable populations.

Benefit incidence analysis has the advantage of requiring little data, making it relatively easy to carry out [START_REF] Ray | Development economics[END_REF]. This is particularly suitable for developing countries where little data is available.

However, it is limited to measuring the direct effect of the fiscal reform and thus ignores indirect effects that the reform may have on individuals and their behavioral responses.

Some authors [START_REF] Glewwe | Investigating the Determinants of Household welfare in Côte d'Ivoire[END_REF][START_REF] Gertler | The willingness to pay for medical care: Evidence from two developing COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso countries[END_REF], aiming to address the shortcomings of this approach, developed econometric estimations of the impact of fiscal policies on wellbeing by controlling for other variables that may influence the estimations.

The two preceding approaches (marginal incidence and econometric analysis) are partial equilibrium, and thus do not capture the feedback effects induced by other sectors or actors in the economy. These effects need to be considered for major public policy reforms with economy-wide impacts.

Computable general equilibrium (CGE) models are the most comprehensive tool to study the impacts of such policy reforms. They have been used to analyze the impact of a variety of public policies (trade liberalization, fiscal reforms, increased public spending) in developing countries. In order to capture household-level impacts, some such studies combine the CGE model with a microsimulation model [START_REF] Decaluwé | Measuring poverty and inequality in a Computable general equilibrium framework[END_REF]Cogneau andRobillard, 2001 and[START_REF] Waddington | Linking Economic Policy to Childhood Poverty: A review of the evidence on growth, trade reform and macroeconomic policy[END_REF][START_REF] Cockburn | Trade Liberalisation and Poverty in Nepal: A Computable General Equilibrium Micro Simulation Analysis[END_REF][START_REF] Bourguignon | Representative versus real households in the macroeconomic modeling of inequality[END_REF][START_REF] Boccanfuso | Un modèle CGE-Multi-Ménages Intégrés Appliqué à l'économie sénégalaise (An integrated multi-household CGE model applied to the COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso Senegalese economy)[END_REF].

The literature provides few applications of CGE models to Burkina Faso [START_REF] Balma | Public education spending and poverty in Burkina Faso: A calculable general equilibrium approach (Dépenses publiques d'éducation et pauvreté au Burkina Faso: une approche en Équilibre Général Calculable)[END_REF][START_REF] Sawadogo | Impact de l'expansion économique et commerciale de la Chine sur la croissance et l'emploi au Burkina Faso: une analyse en équilibre général calculable[END_REF]. [START_REF] Gottschalk | Analyzing Fiscal Space Using MAMS: An Application to Burkina Faso[END_REF] use the MAquette for MDG Simulations (MAMS) model, developed by [START_REF] Lofgren | MAMS: An Economy-wide Model for Analysis of MDG Country Strategies[END_REF], to simulate fiscal space and how it could be used to finance the pursuit of the MDGs. They identify three mechanisms to create fiscal space: establish priorities in public spending, increase foreign borrowing, or increase government revenues. In terms of uses of this space, they analyse an increase in spending on health, education and infrastructure. The authors show that there are trade-offs, for example between impacts on monetary poverty and health/education impacts, between the different spending scenarios. There are also trade-offs between financing mechanisms in terms of the reliance they create on domestic and foreign sources of financing. Investment in infrastructure is shown to not only beneficial for growth, but also for attainment of the MDGs. Health and education spending may run into structural constraints, notably the fact that learning and the training of personnel takes time. Our study complements the literature by contributing a macro-micro analysis that specifically deals with child poverty and schooling.

THE MODELLING FRAMEWORK AND DATA

To evaluate the impact of public policies on child well-being, we use a combined CGE (macro)

and microsimulation (micro) model. The CGE model captures the macroeconomic effects of the policy and takes into account budgetary constraints. The micro model complements provides information on the specific impacts of the various reform scenarios on children. A detailed description of both models is provided in [START_REF] Cockburn | Fiscal Space and Public Spending on Children in Burkina Faso[END_REF]. Here, we will briefly present their main characteristics.

The macro model

The dynamic computable general equilibrium (CGE) model we use is based on the standard PEP 1-t model [START_REF] Decaluwé | The PEP standard computable general equilibrium model single-country, recursive dynamic version PEP-1-t[END_REF]. To account for country-specific characteristics as well as schooling and health components, we made the following modifications.

First we changed the household demand function from an LES in the standard PEP 1-t model to a two-level nested function. The first level represents demand for each food product and for an aggregate non-food product. Demand in this first level is characterized by an AIDS (Almost Ideal Demand System)-type function [START_REF] Deaton | An Almost Ideal Demand System[END_REF], which allows us to better account for cross-price elasticities. Note that price and income elasticities were estimated econometrically using the 2009 Integrated Survey on Burkinabe Household Living Conditions (EICVM). Demand for the "non-food aggregate" is then allocated among its components following a Cobb-Douglas function.

To model schooling decisions, we essentially follow the approach proposed by [START_REF] Lofgren | MAMS: An Economy-wide Model for Analysis of MDG Country Strategies[END_REF]. We specify the following five educational behaviours: o the primary entry rate, i.e., the share of children of age 6 to enter primary school o the promotion rate, i.e., the share of students who successfully finish a given year in an academic cycle. This rate covers both students who pass a non-terminal year in a cycle and those who pass the final year of the academic cycle in question o the repetition rate, i.e., the share of students repeating a given year o the dropout rate, i.e., the share of students who drop out of school in a given year o the transition rate, i.e., the share of students who, once they have completed an academic cycle, pursue further studies at a higher cycle All of these shares are calibrated for the base year and then determined endogenously by the model.

Following the MAMS model, the primary entry rate, the promotion rate and the transition rate are determined by a logistic function. The repetition and dropout rates are assumed constant. These three behaviors are influenced by several arguments such as an education quality index (computed as the total spending budget at each cycle of schooling divided by the number of students enrolled in the cycle), the stock of capital in the respective education sectors, the infrastructure capital stock in the economy, the wage differential between skilled and unskilled workers, a health indicator represented by maternal mortality (MDG4), the price of education and, finally, per capita consumption. The value of each of these arguments is determined endogenously within the model.

We assume that each of these arguments play a role in student behavior. For instance, an improvement in the education quality index or capital stock in the education sectors will positively impact the different behaviours regarding education.

The model also accounts for two health-related objectives: maternal mortality (MDG4) and child mortality (MDG5). For these two indicators, we use a logistic function to describe their evolution.

In the same spirit as the preceding logistic function, the evolution of these two indicators is determined with the help of numerous arguments: per capita public health spending, the level of skills in the population, the value of MDG7 (the percentage of the population with access to drinking water). This last argument is not explicitly modelled: it is an exogenous variable. The final argument of this function is real per capita consumption, which in some form represents the wealth of households.

The micro-model

In order to evaluate the distributive effects of social policies, the CGE results serve as inputs to The monetary values of household consumption of various purchased or auto-consumed food products are converted into quantities using available information on prices in the geographically nearest market. These quantities are then converted into individual-level figures (per adult equivalent) by dividing by the household equivalence scale. These are converted into calories using nutritional tables, then compared to minimum caloric needs of 2 450 kilocalories per day per adult equivalent in order to measure caloric poverty. This measure allows us to calculate the incidence of caloric poverty among children. Changes in food prices and household incomes both affect the quantities of food products consumed, and thus caloric consumption.

The data used

We built a SAM for 2009 using the input-output table of that year (Automated Forecasting Instrument (IAP), Burkina Faso). While other SAMs have already been published for Burkina Faso, they do not include major changes in recent years. Indeed, growth in the mining sector has been COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso substantial, particularly its exports. Also, we wanted the data, particularly data linked to the public budget, to reflect the current situation of the economy. The SAM includes two types of workers (skilled and unskilled), one household, 24 products and 17 sectors.

We separate the education system into two levels. Level 1 education includes students in primary school and those in the first cycle of secondary. Students who complete the first level of secondary school are considered as skilled workers when they enter the labour market. However, students who do not manage to complete this level enter the labour market as unskilled workers. Level 2 education includes students in the second level of secondary school and students enrolled in higher education.

The different shares for the above-mentioned student behaviors were calibrated for the base year

PRESENTATION OF SCENARIOS AND RESULTS

The reference scenario

First, we build a reference scenario, also called the Business As Usual (BAU) scenario. Between is based on IMF forecasts (International Monetary Fund, 2011): increases in foreign demand for cotton and minerals, a reduced deficit-to-GDP ratio, an increase in direct taxes on households and firms, and constant public spending as a share of GDP. On this basis, our model produces, in the reference scenario, an evolution of GDP similar to the forecasts in the IMF document.

The IMF forecasts only go to 2015, so the reference scenario assumes that policies implemented by 2015 will be maintained in following periods. Also, the increase in demand for various export products matches the population growth rate as of 2015, implying that foreign demand will continue to grow, but at a slower pace compared to the 2010-2015 period. All of these base assumptions are maintained throughout all simulation scenarios to allow the results to be compared to the reference scenario.

The simulation scenarios

The simulation scenarios represent three policies that target child well-being and take account of the financial constraints of the Burkina Faso government. The first scenario involves an increase in current spending on education (Spending). The second scenario introduces an education price subsidy (Subsidy). The third scenario is based on a cash transfer to households with a child aged 0-5 (Transfer). In all cases, the same total amount of money is injected into the economy, making it easier to compare the three scenarios. Each of these scenarios is financed by extending the timeframe to reduce the deficit-to-GDP ratio 3 . The deficit thus grows relative to the reference scenario, and the government achieves its goal of progressively reducing the deficit-to-GDP ratio by half over ten years, rather than the anticipated five years. It should be noted that the policy is temporary (10 years) in every case. By 2020, public policies return to those forecasted in the reference scenario.

Analysis of results

These different interventions, while equivalent in terms of public spending, will have significantly different impacts in terms of education outputs, GDP and children poverty.

Impact on education

Student behaviors, such as school participation and promotion rates, are affected by different factors (cf. Section 4.1). The different policy interventions, in turn, affect these factors differently:

an increase in education spending has a direct impact on the education quality index; a school fees subsidy acts primarily by reducing the relative price of education; cash transfers essentially increases household real per capita consumption.

School fees subsidies have a larger beneficial impact on primary school entry but also increase the dropout rate, whereas increasing education spending has a smaller positive impact on entry but simultaneously reduces the dropout rate (Figures 1 and2). Indeed, subsidizing school fees causes more six-year-old children to join the education system. However, since public spending on education is constant in this scenario, the higher number of students leads to a reduction in the quality of education services received by each student and therefore increases the dropout rate.

Under the "spending" scenario, the education quality index increases, thus favouring both primary school enrollment and the duration of studies.

Cash transfers to households increase household real per capita consumption directly. However, our simulation results indicate that this has a negligible impact on education decisions. The different indicators are thus essentially the same as in the reference scenario (Figures 1 and2).

Impact on labour market and growth

Given the reduction in dropout rates, it is not surprising to find that the supply of skilled workers increases most under the spending scenario. Despite higher entry rates, higher dropout rates in the subsidy scenario imply that the number of skilled workers -those who complete the first level of secondary school -increases by just 3 000 (Figure 3). Given the limited impacts of a cash transfer policy on education decisions, it is unsurprising that it has a negligible impact on the number of skilled workers (less than 400 new workers). As a result, the impacts on growth of these two scenarios are barely perceptible (Figure 4). All of the scenarios have a similar impact on the deficit-to-GDP ratio (solid line in Figure 5): it increases during the period of increased spending and then returns near to reference values. Given that, compared to the reference scenario, the end-of-period ratios are slightly higher in the spending scenario and slight lower in the subsidy and cash transfer scenarios, the end-of-period debt-to-GDP ratios (broken line) are greater for the latter. These results can be explained by the increased economic growth in the spending scenario, which increases the denominator of the debt-to-GDP ratio, as income-to-GDP ratios (dotted line) are practically identical in all scenarios.

COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso to the various crises that have hit the country. There are strong pressures to rapidly reduce this budget deficit, but there are grave concerns about how this will be achieved.

In this paper, through a combined CGE-micro simulation model, we analysed three temporary public spending policies: an increase in education spending, a school fees subsidy and a cash transfer to households with children under the age of five. The amount injected into the economy is the same in each of these three cases, making it easy to compare the three scenarios. In all scenarios, the injections are reduced gradually in order to attain the same deficit-to-GDP ratios as a reference -business-as-usual -scenario, but over a slightly longer period: ten years, instead of five. Compared to the reference scenario, the results indicate that higher public education spending increases school participation and promotion rates, leading to an increased supply of skilled workers and higher economic growth, while also reducing the incidence and depth of both monetary and caloric poverty. The school fees subsidies have more differentiated impacts on education, with a larger beneficial impact on primary school entry but a partially offsetting decline in education quality and resulting promotion rates. As a result, the supply of skilled workers and economic growth are practically unaffected. By freeing up part of the household budget for other ends, this type of intervention has a slightly stronger effect on poverty than public spending. Cash transfers have a minimal impact on educational behaviours, and thus on the supply of skilled workers and economic growth, but they directly increase household income thus significantly reducing the incidence and severity of poverty.

In summary, if the objective is to achieve improved educational and economic performance, the best approach would be to increase public education spending. However, cash transfers to families would be more suitable if reducing child poverty is instead prioritized.

  the microeconomic analysis at the level of individual households. Behavioural models are estimate COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso so that we can then use the results (prices, employment, revenues) of the CGE to simulate the effects of implementing different social policies on two essential aspects of the living conditions of children: monetary and caloric poverty. Education (level 1 school entry, level 1 graduation and entry to the second level of study) and child mortality behavioural models are also used to estimate certain elasticities used in the macro model. The education models is also used to identify the new sample of skilled and unskilled individuals in each year of the simulation period. All these models affect real household consumption, which is the variable used to simulate the effect of the different proposed policy scenarios on child well-being. A child is defined as monetarily poor if s/he lives in a household where consumption per adult equivalent, deflated by appropriate temporal and spatial price indices, is below the monetary poverty line. To analyze the level of monetary poverty among children in the initial situation and in the different scenarios, we use the headcount and poverty gap indicators for children aged 0-17. These two indicators respectively measure the percentage of children living in poverty and the average gap of consumption per adult equivalent relative to the poverty line. In this study, we use the monetary poverty line of 142 481 FCFA estimated in May 2011 by the World Bank. Total real consumption per adult equivalent is the principal indicator we use to evaluate the effects of changes brought about by the government interventions simulated in the macro component. This indicator is influenced by consumption prices and household income (wage rates and employment, income from independent work and auto-consumption, as well as education attainments -i.e. skilled and unskilled workers).

  using data published in the annual report on education statistics (Ministry of Primary Education and Literacy, 2010; Ministry of Secondary and Higher Education and Scientific Research, 2009). The total population of Burkina Faso in 2009 and the population growth rates are both taken from the Population Division, Population Estimates and Projections Section (UNDESA) database for 2009-2033. As for the values relating to the MDGs, we take the values published in SCADD (2010).The elasticities of the education-related behavior and those on household consumption were estimated econometrically using the EICVM 2009 survey (National Institute of Statistics and Demography, 2009) following the approaches presented above. As for the elasticities of the child mortality model, estimates were made based on matched data from the DHS survey (which provides the information needed to calculate mortality) and the EICVM survey (which includes data on household consumption). A logit model of child mortality is estimated using the DHS survey using only variables found in both surveys. This is then used to predict the probability of death among children under the age of 5 in the EICVM survey. We then used the EICVM data to estimate an OLS model of the probability of death as a function of per capita consumption -which is only available in the EICVM survey -and other variables. This is used to estimate the elasticity of the probability of death with respect to per capita consumption. For elasticities related to trade and in the production function, we have taken the values used by[START_REF] Balma | Public education spending and poverty in Burkina Faso: A calculable general equilibrium approach (Dépenses publiques d'éducation et pauvreté au Burkina Faso: une approche en Équilibre Général Calculable)[END_REF]

  2009 (the base year in the model) and 2033 (the last year simulated in the model), the BAU scenario COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso

Figure 3 Figure 4

 34 Figure 3 Supply of qualified workers index (reference = 100)

Figure 5

 5 Figure 5 Evolution of fiscal ratios to GDP (reference = 1)

Figure 6

 6 Figure 6 Variation of poverty incidence (P0) and poverty gap (P1) for monetary and caloric poverty (in percentage points) with respect to reference scenario

  , ROBICHAUD, TIBERTI Fiscal Space and Public Spending on Children in Burkina Faso

  Assumptions for the three simulation scenarios

	2010 2011 2012 2013 2014 2015 2016 2017 2018 2019	Spending increase (in % of education spending) 25% 23% 21% 16% 15% 14% 11% 11% 5% 5%	Subsidy (CFA per student) 24,501 23,982 23,380 18,954 18,414 17,867 13,820 13,340 6,436 6,208	Transfer (CFA per child 0-5 years) 17,823 17,463 17,135 14,026 13,790 13,568 10,668 10,498 5,166 5,085

COCKBURN, MAISONNAVE, ROBICHAUD, TIBERTI

Fiscal Space and Public Spending on Children in Burkina Faso

Table

1

We thank Lacina Balma, Yiriyibin Bambio and Hervé Jean-Louis Guène for their research assistance. Sarah Hague, Sebastian Levine, Leonardo Menchini, a technical committee of the government of Burkina Faso, as well as Jingqing Chai and colleagues from the Policy and Practice division of UNICEF, provided extremely useful comments. This study was financed by UNICEF-Burkina Faso through the Partnership for Economic Policy (PEP), which receives funding from the Department for International Development (DFID) of the United Kingdom (or UK Aid), and the Government of Canada through the International Development Research Center (IDRC).


Post Doc

Hélène Maisonnave
email: hmaisonnave@hotmail.fr

Bernard Decaluwé

Margaret Chitiga

Does South African Affirmative Action Policy Reduce Poverty? A CGE Analysis

Keywords: Computable general equilibrium model, top down analysis, South Africa, poverty, inequality, labor market JEL Classification: D58, E27, I32, O11, O55

This paper presents a computable general equilibrium model (CGEM) able to measure the impacts of the affirmative action policy set up in South Africa. In order to decrease inequalities inherited from the former regime, the government encourages firms to employ Historically Disadvantaged Persons (HDP). Through this study, we evaluate the impact of this policy on employment, poverty and inequality. To evaluate impacts on poverty and inequality, we use a CGE Top Down approach. The paper analyses two scenarios; the first one deals with the impact of affirmative action on skilled jobs. The second scenario adds to the previous by including semi skilled workers in the simulation. Both of these scenarios show a deep decrease in unemployment as well as a fall of poverty for each population groups.

INTRODUCTION

After the collapse of the Apartheid regime, South Africa faces difficult issues, notably in the labour market and on poverty levels. Indeed, during this regime, the labour market was racially organised, skilled labour was reserved for White people, and unskilled labour for other population groups, in particular African. This segmentation on the labour market has led to huge inequality and poverty differences among the population. Aware of this situation, the South African government committed itself to decrease inequalities in the labour market, and proposed in 1998 the Employment Equity Act, an incentive for firms to hire Historically Disadvantaged Persons (HDP) as skilled workers. What are the implications of changing skilled shares for firms? Does the setting of this policy lead to a decrease in terms of poverty and inequality levels?

This paper analyses the impacts of this affirmative action policy on labour market and poverty and inequality using a Computable General Equilibrium Model (CGEM). We focus on the impacts of this policy on economic growth and income distribution. To capture impacts on poverty and inequality levels, we use a Top Down approach.

Four sections follow this introduction. First, relevant information explaining the South African context is presented, followed by a brief review of the South African CGEM. The CGE model results are then presented. A final section concludes.

APARTHEID LEGACIES: A SEGMENTED LABOUR MARKET AND STRONG POVERTY AND INEQUALITY DIFFERENCES AMONG POPULATION

Under the Apartheid regime, race was the relevant factor to obtain a skilled job. Whites were systematically advantaged compared to other population groups. This segmentation was reinforced by several laws 4 . With the end of the regime, discriminatory laws were abolished. However, as shown in [START_REF] Burger | Returns to Race: Labour market, Discrimination in Post Apartheid South Africa, Stellenbosch Economic Working Paper[END_REF], the South African labour market is faced with long run effects of an inadequate education system5 , international isolation, and firms' capital intensive policies.

One of the main issues in South Africa is the high levels of unemployment whether defined broadly or narrowly. In 2003, 30% of the active population was unemployed according to the strict definition, whereas 42% was unemployed if we use the broad one, [START_REF] Kingdon | Unemployment in South Africa, 1995-2003[END_REF]. These rates are not uniform among population groups. Indeed, the White unemployment rate (4.1%) is nearly seven times lower than African's (Table 1). Unemployment is a structural issue in South Africa because there is a mismatch between an excess of unskilled workers, and a shortage of skilled people, that firms are looking for. The South African labour market has not created enough jobs between 1995 and 2004, and thus, it was not able to absorb the labour force growth. Indeed, [START_REF] Oosthuizen | The post apartheid labour market: 1995-2004[END_REF] shows that the South Africa economy did not create enough jobs. Moreover, [START_REF] Pauw | Graduate unemployment in the face of skills shortages: a labour market paradox[END_REF] show that unemployment for skilled jobs has increased due to the mismatch of students' skills and what firms look for.

According to [START_REF] Kingdon | Unemployment in South Africa, 1995-2003[END_REF], unemployment rate has increased between 1995 and 2003 for each population group. [START_REF] Oosthuizen | The post apartheid labour market: 1995-2004[END_REF] explains this increase as being due to the high propensity for the population to enter the labour market and the weak job creation.

In the same study, [START_REF] Kingdon | Unemployment in South Africa, 1995-2003[END_REF] estimate that in 1995, an African person was around 30 percentage points more likely to be unemployed than a White person with the same characteristics. This probability has risen by 6.5 percentage points in 2003 [START_REF] Kingdon | Unemployment in South Africa, 1995-2003[END_REF]). [START_REF] Burger | Returns to Race: Labour market, Discrimination in Post Apartheid South Africa, Stellenbosch Economic Working Paper[END_REF] analyse the employment gap for population groups between 1995 and 2003. They find that White-Black employment gap increased strongly between 1995 and 2000 and then stabilised. They use the Oaxaca-Blinder technique to decompose this gap and find that unexplained component did not decline in the post apartheid period. They suggest that the fact that there is no decrease in the unexplained component could mean that affirmative action is not really successful in reducing discriminatory practices. However, they moderate their finding by explaining the important role for the quality of education.

The post apartheid government has implemented a series of remedial measures, notably an affirmative action policy to reduce inequalities in the labour market and the Black Economic Empowerment (BEE) that aims for a better distribution of productive capital8 .

In 2004, government decided to go further because changes were very slow in the labour market. It created a scorecard where firms that have good results in the scorecard can apply for government offers. These offers represent 12% of the South African GDP, (Lettre d'Afrique du Sud et Australe, French Trade Commission, (April, 2007)). To have a good mark in the scorecard, a firm has to have equitable ownership which includes previously disadvantaged people, and to have a significant proportion of black persons in the executive management. Employment equity component is another part of the scorecard. Thus, not being compliant with the law means losing an opportunity to get government offers.

Moreover, the situation in the labour market has direct implications on poverty and inequality levels. As pointed out by [START_REF] Burger | Returns to Race: Labour market, Discrimination in Post Apartheid South Africa, Stellenbosch Economic Working Paper[END_REF], there is a persistent association between race and poverty. Indeed, Africans are still poorer that other population groups. [START_REF] Leibbrandt | Measuring recent Changes in South African Inequality and poverty using 1996 and 2001 Census Data[END_REF] show an increase of poverty between 1996 and 2001, whatever the poverty line chosen (Table 2). [START_REF] Van Der | Trends in poverty and inequality since the political transition[END_REF] have shown that poverty has recently declined due to the increase of government subsidies, but remains at a high level. [START_REF] Chitiga | La protection du secteur des textiles et la pauvreté en Afrique du Sud: une analyse en équilibre général calculable dynamique micro-simulé, Cahier de recherche MPIA[END_REF] compute poverty levels using a low poverty line (322R/m/h) and a high poverty line9 (593R/m/h), and they find respectively a headcount of 52.6 and 70.4. [START_REF] Leibbrandt | Measuring recent Changes in South African Inequality and poverty using 1996 and 2001 Census Data[END_REF])p.20, using Census (1996, 2001) [START_REF] Leibbrandt | Measuring recent Changes in South African Inequality and poverty using 1996 and 2001 Census Data[END_REF] also find that the inequality level has risen between 1996and 2001. Hoogeveen and Ozler (2004) and [START_REF] Seekings | Income inequality after apartheid[END_REF] find results in the same direction. [START_REF] Seekings | Income inequality after apartheid[END_REF] focus on the fact that intra group inequality has risen, whereas inter group inequality has declined as shown in Table 3. [START_REF] Whiteford | Understanding Contemporary Households Inequality in South Africa[END_REF] and Income and Expenditure Survey (2000), and Seekings et al (2004) (pg 11) As we can see, poverty and inequality levels remain very high. Poverty and inequalities levels are greater for the African group. The government believes that in order to decrease these levels, it is necessary to facilitate its policies of Affirmative Action. This paper investigates to what extend the government policy of Affirmative Action, specifically, employing HDP, decreases inequality and poverty in South Africa.

LITERATURE REVIEW

Many CGE models have been built for the South African economy, but to our knowledge, no one deals with affirmative action issues. Most of the models contribute to analysing the impact of trade liberalisation or world price negative shock impacts. Thus, [START_REF] Gelb | Modeling the South African Economy-Real Financial interactions, Macro Economic Research Group[END_REF] developed a dynamic one sector CGEM to evaluate the impact of a negative external shock and the setting of a program of government stimuli. Moreover, many experts of the World Bank have developed CGEM [START_REF] Van Der Mensbrugghe | Prototype Model for Single Country Real Computable General Equilibrium Model, Development Prospects Groups[END_REF][START_REF] Van Der Mensbrugghe | Prototype Model for Single Country Real Computable General Equilibrium Model, Development Prospects Groups[END_REF], Devarajan and [START_REF] Devaradjan | Economywide and Distributional Impacts of an Oil Price Shock on the South African Economy[END_REF]) to understand the impacts of trade liberalisation and increases in public spending. [START_REF] Thurlow | A standard Computable General Equilibrium Model for South Africa[END_REF] propose a standard CGE modelling framework for South Africa.

Cockburn et al (2005) evaluate gender discrimination on labour market after trade liberalisation. They develop a CGE that takes into account household's home production. They find that trade liberalisation has a better impact on men's salaries than on women's, due to the sectoral employment repartition. [START_REF] Thurlow | Has Trade liberalization in South Africa affected men and women differently? DSGD Discussion Paper[END_REF] finds that trade liberalisation has affected men and women differently and that it has worsened inequality in the country.

Heraut ( 2005) analyses the impact of trade liberalisation using all the information contained in household's survey. He finds that whatever the closure, neoclassical or Keynesian, trade liberalisation seems to be pro-poor. Employment creation in the formal sector seems to be the cause of this decrease on poverty. In terms on inequality, intra group inequalities decrease whereas inter group increase.

McDonald and Van Schoor (2005) and Essama-Nssah et al. (2007) use a CGE macro-micro framework to understand the structural and distributional consequences of oil price increases for South Africa. [START_REF] Fofana | Oil Prices and the South African Economy: Macro-Meso-Micro Modelling Approach[END_REF] follow this approach and show that oil price increases would have negative effects on the economy and welfare. [START_REF] Chitiga | La protection du secteur des textiles et la pauvreté en Afrique du Sud: une analyse en équilibre général calculable dynamique micro-simulé, Cahier de recherche MPIA[END_REF] analyse the impact of protection in textile sector on poverty levels, using a dynamic micro simulation CGEM. They find that increasing protections in this sector is not good for the whole economy, welfare decreases and poverty increases.

4

DATA USED

We To compute poverty index, we use Foster, Greer and Thorbecke (FGT) (1984) indexes. We take [START_REF] Simkins | What happened to the distribution of income in South Africa between 1995 and 2001?[END_REF] poverty line, of 800 rands per household per month. At the base year, we find the indexes indicated in Table 5. We are also interested in inequality levels. To evaluate this, we use Gini index as well as Theil index. At the base year, we found a Gini index equals to 0.66 for the whole population. Each poverty and inequality index is computed with Distributive Analyse Stata Package DASP, [START_REF] Arrar | DASP: Distributive Analysis Stata Package[END_REF]). 2001). We have changed some assumptions for this model. First, our model has two production factors, capital and labour, but to take into account South Africa's specificities, we have disaggregated labour by population group and skill levels so that we end up with twelve different labour categories. Each activity uses both production factors. However, we assume that there is unemployment on each labour market. We also assume that capital is mobile across sectors such that we have a middle run analysis.

We have also disaggregated households such that we have households by population groups (African, Coloured, Indian and White). Then, we have specified households' consumption with a Linear Expenditure System (LES) of Stone Geary (1954) function, and we assume that transfers between institutions are significant. Finally, we consider that South Africa can not export as much as it wants by introducing an export demand function with determined elasticity.

The CGE model has 25 activities. The production function technology is assumed to be of constant returns to scale and is presented in a five-level production process. At the first level, output is a Leontief inputoutput of value added and intermediate consumption.

At the second level, a CES function is used to represent the substitution between an aggregate labour and capital. At the third level, composite labour demand is also a Cobb Douglas function of three different labour skills (skilled, semi skilled and low skilled). To take into account the South African labour market characteristics, we have disaggregated each sectoral labour demand by population group. We know that, for historical reasons, job opportunities for same skilled level are not the same for a White worker as for another race. This systematic bias does not only answer to economical factors (workers productivity, wage rates etc.). To take into account these elements, we assume that firms keep constant their share of workers given their population group. Thus, in technical terms, we assume that for each skilled level, race composition is determined by a Leontief function. In that way, a skilled White worker can not be a substitute to another skilled worker. These shares are assumed fixed and are the result of social behaviours unexplained by strictly economic factors. Figure 1 gives the labour market structure. We know that South Africa is faced with unemployment issues. Actually, unemployment rates are different given population groups and skilled levels. Following [START_REF] Blanchflower | An Introduction to the Wage Curve[END_REF], we assume that at equilibrium, there is an equilibrium wage rate compatible with the unemployment rate. The authors show the existence of an empirical relation linking wage rates and unemployment rates. This wage curve is stable econometrically, whatever the country or socio-economic category. The relation shows a negative slope between unemployment rates and wage rates. We will follow this approach for t, a 10% crease in the unemployment rate leads to a 1% decrease in wages.

of their bacco's consumption even though it is not essential consumption. nment income less its consumption and ansfers paid to other institutions. our twelve labour markets. [START_REF] Kingdon | How flexible are wages in response to local unemployment in South Africa?[END_REF] have pointed out a wage curve for South Africa, meaning that wages are decreasing strongly when unemployment rate increase. They find the same result as OECD countries, and specifically tha in As we mentioned previously, we have four households. The model distinguishes three sources of income, labour income (salaries and wages), capital income and transfers income from institutional sectors (households, firms, government and rest of the world) .Household's income is composed of different labour income and transfers received from other institutions. Note that African households are the only ones who receive transfers from other population groups. Available households' income is equal to households' income net of income taxes and transfers paid to other institutions. Households' savings is a fixed proportion of its available income, adjusted by an adjustment variable. This adjustment variable is added to assure savings investment equilibrium. In other words, if an agent's savings is decreasing, the adjustment will come from households. They are forced to save (or dis save). Household sectoral consumption is modelled by a Linear Expenditure System and subject to its budget constraint. Consequently, sectoral consumption depends on total consumption, specified minimum consumption levels and the vector of consumer prices. Minimum consumption represents the volume of a product that a household wants to maintain to keep its standard living unchanged, Decaluwé et al (2001). This minimum reflects habits of the household and corresponds to consumption needs that are not affected by price changes. For instance, smokers can have a minimum level to Firm's income is equal to their share in sectoral capital incomes plus transfers from other institutions. After paying income tax and transfers to other institutions, the remaining income constitutes firm's savings. Government income is composed of direct taxes paid by households and firms, indirect taxes on domestic sales, import tariffs, transfers from the rest of the world and a share of capital income. Government consumption spending is on the non tradable sectors' production (in volume) multiplied by their prices.

Government savings is equal to gover tr

The relationship between the rest of the world and the domestic economy is determined by the substitutability between imported and domestic goods on the consumption side, and by the substitutability between the domestic and international markets on the production side. The relative prices of foreign goods determine the allocation of supply and demand between the domestic and international markets. To qualify exports and imports behaviour, we use the traditional approach of CGE modelling. We assume that South Africa is a small open economy, meaning that it does not have any influence on world rices.

is represented by a Constant Elasticity of Transformation (CET) by ports and transfers paid abroad, net of exports and transfers received. that transfers between institutions are exogenous, as well as factors s income or in its real consumption will lead to an crease in its welfare.

RESULTS OF THE POLICY

-1: Affirmative action applied to skilled workers:

p
The model allows for imperfect substitution in consumption and production between domestic and foreign goods. Consumers have a choice between local or imported products. Their choices are specified by a Constant Elasticity of Substitution (CES) function following the Armington assumption. This function specifies that products are not perfectly substitutable, and that strong quality differences can exist depending on where the product is from. The substitution elasticities vary across sectors, with lower elasticities reflecting greater differences between domestic and imported goods. The demand for imports relative to domestically produced goods is derived from cost minimization subject to the CES function. Substitution possibilities exist also between production for domestic or foreign markets. This decision of producers function.

We have assumed that South Africa is a small country. However, we reject the hypothesis that South Africa has an infinite exports demand for its products. Indeed, we assume that South African producers can't sell as much as they want on international markets. To sell more on these markets, South African producers will have to be more competitive than other producers. Thus, export supply is constrained by export demand, which is assumed to have a finite elasticity, reflecting the competitiveness of local products on the international market. Finally, the current account balance is obtained im

We assume that the real exchange rate is our numeraire. Then, world prices are fixed, following the assumption that South Africa is a small country. We assume supply.

To capture the impact of affirmative action policy on poverty and inequality, we assume that the current account balance is fixed, as well as total investment in volume as well as government production in volume. In that case, an increase in household' in 6

The aim of the simulation is to find the impact of an increase of the shares of historically disadvantaged people in skilled positions. We increase African's skilled share by 10%, and both Coloured and Indian by 5% each. As we have shown in Table 4, initial shares of these three population groups were very low.

Due to this policy, we expect skilled labour demand to increase for each population group except for White. Indeed, the results show that labour demands for HDP increases, with the amount depending on sector. We can point out that increases in government and gold sectors are less important in terms of magnitude than in other sectors. Actually, as we have already mentioned, the shares of HDP in these two sectors were not as low as in other sectors. The impact on White skilled labour demand is surprising. One expects a decrease in the demand but we find that skilled labour demand for Whites increases except in government and gold sectors. However, the decrease in these two sectors is such that total labour demands for skilled White decreases (-0.17%). Total skilled labour demand increase by 0.68%. Semi skilled total labour demand and unskilled labour demand increase respectively by 0.22% and 0.26%. This leads to an increase in total labour demand, of 0.37%.

These impacts on the labour demand also have consequences on unemployment and wages for workers. Unemployment rates are decreasing, except for White skilled workers, and wages follow the opposite direction10 . For instance, unemployment rate for African skilled workers falls by 9.1%, which is a huge decrease. If we consider sectoral wages, we would not be surprised that sectoral wages for unskilled and semi skilled are increasing. However, skilled sectoral wage is decreasing, due to the strong decline of the White wage.

We know that labour demand is increasing in most of the sectors, so we can expect an increase in production leading, ceteris paribus, to an imbalance on the goods market. Production is indeed increasing in all sectors except gold and government. Due to initial shares (higher than in others) in the gold sector, this policy is expensive for the gold sector that has to fire people. For the government sector, we assume that its production is constant in volume. This imbalance on the goods market leads to a decrease in producer prices. Due to the production increase in most of the sectors, and the decrease of producer local price, it is relatively more attractive for producers to export their production. Actually, only the gold sector is decreasing its exports (-0.21%), due to the decrease of its production. Thus this policy boosts supply.

We expect households' income to increase. As explained above, it is composed of transfers and incomes from production factors. Households consumption is increasing as well as direct taxes paid to government. Firms also see their income increasing, because of the increase of capital rate of return. Government's income is increasing due to the increases of direct taxes received and taxes on products. Its saving increases by 0.3%.

We assumed that households savings were linked to an adjustment variable in order to realise investment-savings equilibrium12 , in case one agent's savings would decrease. Here, each institution's savings is increasing (or constant for foreign savings), so households' saving is decreasing. Households' welfare is increasing, due to the rise of their income and the decline of prices. If we look at Table 8, we find that poverty decreases for each population group. However, Gini and Theil indexes are slightly increasing. Gini and Theil indexes both increase by 0.01%. Within group inequality contributes 0.864413 and between group 0.289397. Within group poverty has slightly decreased whereas between groups poverty has increased.

6-2: Affirmative action applied on skilled and semi skilled workers:

In this scenario, we are interested to analyse what would be the effects of a stronger affirmative action policy. We analyse an increase of 5% of African semi skilled share, combined with the previous scenario. Knowing that unemployment rate for semi skilled Africans is relatively high, we expect this policy to have stronger effects on poverty and inequality. On total labour demand, the impact of this policy is stronger than in the previous scenario; total labour demand increases by 0.73%. As shown in Table 9, consequences of this policy on labour demand are very positive. All labour demands are increasing except skilled white and semi skilled white but that was expected. Due to the increase of total labour demand, we expect that unemployment rates are decreasing. Compared to the previous scenario, decreases in unemployment rates are deeper. Wages will follow the opposite direction to unemployment rates for each labour category as expected. As far as sectoral wages are concerned, unskilled mean sectoral wage is increasing, whereas both semi skilled and skilled sectoral wages are decreasing. Indeed, for these two categories, the decline in White wage leads to a decline in the mean sectoral wage. For skilled, it is more the initial repartition that leads to a fall, whereas for semi skilled, it is the deep decrease of white wages (-2.63%). In terms of production, we expect the same effects as in the previous scenario: production is rising except for gold sector (-0.28%) whose increases in African wages are too important. In most of the sectors, prices are decreasing.

Concerning institutions, household's income is increasing, and so is their consumption expenditure. They benefit from an increase of their income and also from a decrease of local prices. They will increase their consumption, as well on local and foreign markets. Government's income is increasing (+0.38%) due to the rise of direct, indirect and import taxes. Moreover, it reduces its current deficit. What are the impacts of this reinforced affirmative action policy on poverty and inequality levels? As shown in Table 11, poverty is decreasing more in this scenario for each population group. Gini coefficient increases very slightly, but a little more than in the previous scenario. This result is reinforced by Theil index; which is also increasing.

Within group inequality decreases but between group inequality increasing more such that total inequality is rising.

CONCLUSION AND POLICY RECOMMENDATIONS

The paper estimated the effects of the affirmative action policy both on unemployment rates, poverty and inequality levels in South Africa. It presented two scenarios, the first one deals with a better repartition of skilled workers in each activity, improving historically disadvantaged peoples' shares.

The second scenario adds to this policy an increase of African semi skilled shares in each activity.

Results found show that this policy is very encouraging in terms of unemployment and poverty reduction, two main problems in South Africa. Indeed, unemployment rates are decreasing for most of the labour categories as jobs are created. These findings are stronger in the second scenario. In terms of poverty, FGT computations show that poverty is decreasing for each population group, and the decrease is deeper in the second scenario. There is however a very slight increase of inequality.

Figure

  Figure 1: The labour market structure

Table 1 :

 1 Status of people aged 15-65 years old on the labour market by population group in 2001 6 (%)

		African	Coloured	Indian	White	Total
	Employed (full and part time)	27.8	46.1	49.2	61.4	33.7
	Unemployed (strict definition)	28.1	17.1	10.0	4.1	24.0
	Non economically active 7	44.1	36.9	40.9	34.5	42.3
	Source: Statistics South Africa, Census, 2001

Table 2 :

 2 Poverty levels by population groups:

		Headcount	Poverty gap	Headcount	Poverty gap
			ratio		ratio
	Poverty line : 2$/d/hab.	1996	1996	2001	2001
	African	0.34	0.14	0.35	0.14
	Coloured	0.1	0.03	0.13	0.04
	Indian	0.03	0.01	0.03	0.01
	White	0.01	0.0	0.01	0.0
	Poverty line : 250R/mois
	African	0.62	0.38	0.67	0.39
	Coloured	0.34	0.16	0.41	0.19
	Indian	0.11	0.05	0.14	0.06
	White	0.03	0.02	0.04	0.02
	Source :

Table 3 :

 3 Gini coefficients' in households income repartition, 1995Gini coefficients' in households income repartition, -2000

	1995		2000
	African	0.56	0.61
	White	0.44	0.46
	Coloured	0.50	0.55
	Indian	0.47	0.5
	Total	0.65	0.69
	Source: Census data from

  use the same Social Accounting Matrix asCockburn et al (2005) based on 2001 data. We took the same elasticity and Frisch parameter as them. To calibrate unemployment rates, we use the Labor Force Survey(2001). Given our specific study, we present in Table4the disaggregation of skilled workers by race for each activity. Analysing this Table, we find that except in the government sector, White workers represent the main share of skilled labour. Indeed, their share is between 66.6% in gold sector and 84.3% in real estate. In the government sector, the difference between races is less important, Africans represent 35.6% of total skilled workers and Whites 51.8%. What we want to analyse is an increase of these share for each population group except White in the first scenario.

Table 4 :

 4 Skilled labour disaggregation by race for 2001

		African	Coloured	Indian	White	Total skilled
	SECTORS
	Agriculture (AGRI)	16.2	6.1	2.2	75.5	100.0
	Coal industries (COAL)	19.1	1.3	2.0	77.6	100.0
	Gold Industries (GOLD)	31.2	0.9	1.3	66.6	100.0
	Others mining (OTHMIN)	18.4	3.4	1.9	76.3	100.0
	Food Industries(FOOD)	13.8	7.3	6.3	72.6	100.0
	Textile Industries(TEXT)	10.0	17.4	14.8	57.8	100.0
	Footwear industries (FOOTWEAR	10.4	7.3	7.3	75.0	100.0
	Petroleum(PETROL)	10.6	5.7	6.4	77.3	100.0
	Other non metallic mineral products	13.6	5.5	4 .3	76.6	100.0
	(OTHNON)
	Basic Iron/Steel(STEEL)	10.5	4.0	3.8	81.7	100.0
	Electrical machinery(ELEC)	12.3	6.9	7.5	73.3	100.0
	Radio (RADIO)	9.9	7.7	7.3	75.1	100.0
	Transport equipment (TRANSEQ)	11.3	7.3	6.7	74.7	100.0
	Other manufacturing(OTHMAN)	12.1	7.9	7.5	72.5	100.0
	Electricity(ELECT)	14.9	4.5	4.8	75 .8	100.0
	Water(WAT)	13.9	3.2	6.5	76.4	100.0
	Construction (CONSTR)	13.7	5.9	4.6	75.8	100.0
	Trade (TRADE)	14.1	7.8	7.6	70.5	100.0
	Hotels-Restaurants (HOT)	19.5	5.3	5.4	69.8	100.0
	Transports services (TRANSSER)	12.1	4.6	5.4	77.9	100.0
	Communications (COM)	15.8	8.9	5.0	70.3	100.0
	Financial Intermediation (FININT)	11.7	5.2	4.4	78.7	100.0
	Real Estate (REALE)	8.3	4.0	3.4	84.3	100.0
	Business Activities (BUSAC)	10.5	4.4	4.6	80.5	100.0
	General Government (SERN)	35.6	8.4	4.2	51.8	100.0
	Source: Own computations from SAM (2001)

Table 5 :

 5 FGT indexes at the base year(2001)

	Groups #	Headcount	Depth	Severity
	African	0.39	0.15	0.07
	Coloured	0.11	0.03	0.02
	Indian	0.03	0.01	0.01
	White	0.01	0.00	0.00
	TOTAL	0.32	0.12	0.06
	Source: Own computations

Table 6 :

 6 Theil index at the base year(2001)

		Entropy index
		Within	0.864436
		Between	0.289268
		TOTAL	1.153704
		Source: Own computations
	5	THE MODELLING FRAMEWORK
	Our model is inspired by the pedagogical model EXTER developed by
	Decaluwé et al (

Table 7 :

 7 Impact on unemployment rates

		Base year 11 level (in %)	Level after policy (in %)
	Skilled African	13.9	9.1
	Skilled Coloured	1.9	1.5
	Skilled Indian	5.3	4.4
	Skilled White	0.6	0.8
	Semi Skilled African	41.6	41.5
	Semi Skilled Coloured	19.1	19.0
	Semi Skilled Indian	16.5	16.4
	Semi Skilled White	7.7	7.6
	Unskilled African	28.5	28.4
	Unskilled Coloured	20.4	20.2
	Unskilled Indian	13.8	13.7
	Unskilled White	11.0	10.9

Table 8 :

 8 Impact on poverty using FGT indexes (en %)

	Groups #	Headcount	Depth	Severity
	African	-0.36	-0.46	-0.42
	Coloured	-0.41	-0.70	-0.74
	Indian	-0.01	-0.37	-0.48
	White	-1.5	-0.45	-0.38
	TOTAL	-0.37	-0.47	-0.53

Table 9 :

 9 Impact on labour demands

		Variation of labour demand (in %)
	Skilled African	+5.59
	Skilled Coloured	+0.37
	Skilled Indian	+0.95
	Skilled White	-0.17
	Semi Skilled African	+5.1
	Semi Skilled Coloured	+0.65
	Semi Skilled Indian	+0.66
	Semi Skilled White	-2.63
	Unskilled African	+0.33
	Unskilled Coloured	+0.30
	Unskilled Indian	+0.28
	Unskilled White	+0.24
	Total labour demand	+0.71

Table 10 :

 10 Unemployment rates

	Base year		Scenario 2
	Skilled African	13.9	9.1
	Skilled Coloured	1.9	1.5
	Skilled Indian	5.3	4.4

Table 11 :

 11 Impact on poverty

	Groups #	Headcount	Depth	Severity
	African	-0.58	-0.69	-0.68
	Coloured	-1.37	-1.03	-1.09
	Indian	-0.01	-0.99	-0.70
	White	-1.67	-0.65	-0.58
	TOTAL	-0.60	-0.70	-0.80

See[START_REF] Van Der Berg | The present as a legacy of the Past: The labour market, Inequality and Poverty in South Africa[END_REF] for an historical review.

The education system was also racially organized: Whites could attend the best schools whereas other population group studied in poor quality schools. Thus the system created very few skilled workers and a lot of unskilled workers. South Africa suffers now from skills shortage.

As our SAM is based on 2001, we will focus on this year.

Includes students, the retired and people that choose not to work

See for example[START_REF] Burger | Returns to Race: Labour market, Discrimination in Post Apartheid South Africa, Stellenbosch Economic Working Paper[END_REF] for an explanation of BEE

Hoogeven and Ozler (2004) find that an appropriate poverty level for South Africa is between this space

At the base year, all wages are equal to one, meaning we have indexes of wage. We do not take into account the differential in productivity between workers nor differential in their wages

Unemployment rates at the base year are calibrated using the Labour ForceSurvey (2001)

We assume that total investment in volume is fixed