Solidarity Responsibility Job Incentive Scheme Master Thesis Daan Struyvenv3

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Master thesis of Daan Struyven on employment incentives for Belgian Decentralized Entitities

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Solidarity Responsibility Job Incentive Scheme Master Thesis Daan Struyvenv3

  1. 1. FACULTÉ DES SCIENCES SOCIALES ET POLITIQUES/ SOLVAY BRUSSELS SCHOOL OF ECONOMICS AND MANAGEMENT MEMOIRE Présenté en vue de l'obtention du Master en Ingénieur de gestion, à finalité spécialisée Combining solidarity and responsibility by improving employment incentives for Belgian decentralized entities Daan Struyven Directeur: Professeur Mathias Dewatripont Commissaires: Professeurs Estelle Cantillon, François Rycx & Ariane Szafarz Experts : Professeurs Rudi Vander Vennet & Magali Verdonck Président du jury : Professeur Bruno Van Pottelsberghe Année académique 2008-2009
  2. 2. ACKNOWLEDGEMENTS It is a great pleasure to thank the people who made this thesis possible. First, I am very grateful to my supervisor, Mathias Dewatripont, for proposing the research topic, for steering me in the right direction and for providing his thoughtful and frequent feedback with energy, ambition, humor and perfectionism. He allowed me to discover, taste and enjoy research. I would like to thank the committee members Estelle Cantillon and Ariane Szafarz, for staying in the jury for this thesis, although I switched to a topic which might be less at the centre of their research interest. I would like to thank Estelle Cantillon for her feedback of my initial thesis work, which was highly appreciated by the professors of the Indian Institute of Management Bangalore. I am very grateful to the other committee members François Rycx, Rudi Vander Vennet and Magali Verdonck for their helpful comments and suggestions. I am grateful to Didier Baudewijns, Koen Hendrickx, Dirk Horelbeeke and Luc Masure. They provided me with and explained me the rich HERMREG model and database developed by the Planning Office. I would like to express my full gratitude to Jean-Paul Abraham, Koen Algoed, Laurent Bouton, Michael Castanheira, Antonio Estache, Paul De Grauwe, Catherine Dehon, Renaud Foucart, Wim Moesen, Geert Noels, Jan Smets, Guy Tegenbos, Bruno Vanderlinden, Paul Van Rompuy and David Veredas for their insights, feedback and suggestions. Those stimulating interactions made the time fly. I also thank my friends Alexandra, An, Fiona, François, Henri, Koen, Maarten, Pieter, Thomas K. and Yana for their friendship. My friends Jérôme, Nicolas and Thomas D. provided me with very smart feedback, which I highly appreciated. Aakash and Nicolas deserve a special mention for the incredible time we spent in India. I will not forget their support when I took the difficult decision, on the exotic beach of Pondichéry, to switch my thesis topic in December 2008. I would like to thank my parents, Ine and Hans, for their unconditional love and support. They are my sounding board and rock. Finally, I would like to thank my sister Heleen and my brother Robbert. Heleen drilled my layout skills. Robbert allowed me to put this work in perspective, despite all the passion which drove me along this journey. ii
  3. 3. ABSTRACT The job bonus malus is a hot topic on the Belgian political agenda. But could it be useful? And could it work in practice? First, we show that a job bonus malus could be useful for the Belgian federation because it could lead to better activation incentives. Better activation incentives could lead to higher employment rates since governments tend to react to financial incentives. Today Regions bear the financial pain of activation without any significant financial gain. Activating an unemployed costs the Regions around €21,800 and brings in around €3,200 in Flanders, €1,200 in Wallonia and €1,100 in Brussels. Significant activation gains could lead to decentralized policy reactions in fields such as education and active labor market policies. These policies have a high job creating potential according to our international and Belgian literature review. Second, we show that the job bonus malus could be effective. To be effective, coupling benchmarking of regional employment rate variations to mobility premiums is an attractive option. Such a scheme could be fair and hard to manipulate. Such a scheme could significantly raise activation incentives while controlling budget risk and fostering interregional cooperation. We work out and simulate the budget impact of several schemes to generate a maximum of lessons for policymakers. Covered schemes include Belgian and international benchmarking, various targets and various incentivization sizes. Key words: Belgium, incentives, federalism, solidarity, responsibility, employment rate, job bonus malus, risk. iii
  4. 4. TABLE OF CONTENT ACKNOWLEDGEMENTS ............................................................................................................................ii ABSTRACT ................................................................................................................................................iii TABLE OF CONTENT ................................................................................................................................. iv LIST OF TABLES ....................................................................................................................................... vii LIST OF CHARTS ....................................................................................................................................... ix SUMMARY ............................................................................................................................................... xi 1. INTRODUCTION ........................................................................................................................... - 1 - 2. RATIONALE FOR JOB BONUS MALUS .......................................................................................... - 6 - 2.1. Today activation incentives are limited at the decentralized level ................................. - 6 - 2.1.1. Federal return.............................................................................................................. - 6 - 2.1.2. Regional return ............................................................................................................ - 9 - 2.1.3. Community Return .................................................................................................... - 13 - 2.2. Employment rates have to converge upward for sustainability Belgian social model . - 16 - 2.2.1. Status-quo baseline course of employment rates is not an option .......................... - 16 - 2.2.2. Current economic crisis and ageing cost urge for upward employment rate convergence .............................................................................................................................. - 17 - 2.2.3. Credible perspective of upward employment rate convergence might contribute to alleviate Belgian political deadlock ........................................................................................... - 18 - 3. LESSONS FROM CONTRACT THEORY ......................................................................................... - 19 - 3.1. Job bonus malus raises four issues contract theory can shed light on ......................... - 19 - 3.2. Five incentive models illustrating issues in bonus malus design................................... - 20 - 3.2.1. Principal-agent baseline model with observable actions .......................................... - 20 - 3.2.2. Principal-agent model with uncertain results from observable actions .................. - 20 - 3.2.3. Principal-agent model with uncertain results from hidden actions ......................... - 21 - 3.2.4. Principal-agent multitasking model.......................................................................... - 22 - 3.2.5. Principal-multiple agents model and relative performance evaluation ................... - 23 - 3.3. Key Lessons.................................................................................................................... - 24 - 3.3.1. Job externalities create an opportunity for a win-win incentive scheme ................. - 24 - 3.3.2. Uncertain results raise a risk aversion issue.............................................................. - 24 - 3.3.3. Imperfectly observable actions raise a moral hazard issue ...................................... - 24 - 3.3.4. Multiple tasks raise an effort-substitution issue ....................................................... - 25 - 3.3.5. Multiple agents create an opportunity for relative performance evaluation ........... - 25 - 4. WHICH PERFORMANCE INDICATORS COULD BE DESIGNED? ................................................... - 26 - 4.1. Scheme design questions, scheme features and scheme trade-offs ........................... - 26 - 4.1.1. Five answers to five questions .................................................................................. - 26 - 4.1.2. Three trade-offs......................................................................................................... - 28 - 4.2. Options to overcome trade-offs .................................................................................... - 30 - 4.3. Analysis features of schemes proposed by academics and politics .............................. - 31 - 4.3.1. The two Van Rompuy proposals................................................................................ - 32 - 4.3.2. The Van der Linden proposal..................................................................................... - 32 - 4.3.3. The Vandenbroucke-Marcourt proposal ................................................................... - 33 - 4.4. Shortlist of three considered performance indicators .................................................. - 33 - 4.5. Translation of three performance indicators into financial formula’s .......................... - 34 - 4.5.1. Absolute total employment rate variations .............................................................. - 34 - 4.5.2. Relative total employment rate variations................................................................ - 35 - 4.5.3. Absolute total employment rate variation controlled for external factors with regression .................................................................................................................................. - 35 - 5. EMPIRICAL ANALYSIS OF HOW WELL SHORTLISTED INDICATORS WORK ................................. - 36 - 5.1. Objectives of empirical analysis .................................................................................... - 36 - iv
  5. 5. 5.2. Exploring risk reduction potential with descriptive statistics ....................................... - 37 - 5.2.1. Cumulative variations less risky than yearly variations............................................. - 37 - 5.2.2. Strong Flanders-Wallonia correlation provides support for yearly and cumulative Belgian benchmarking ............................................................................................................... - 38 - 5.2.3. Strong mutual Flanders-Wallonia interaction employment rate variations provides support for yearly Belgian benchmarking ................................................................................. - 39 - 5.2.4. Strong correlation between variations of three neighbors, Flanders and Wallonia provides support for cumulative neighbor benchmarking........................................................ - 40 - 5.2.5. Brussels has relatively uncorrelated and volatile employment rate variations ........ - 41 - 5.3. Reducing risk of yearly employment rate variation indicator ....................................... - 42 - 5.3.1. Benchmarking ............................................................................................................ - 42 - 5.3.2. Estimating impact external factors with regressions ................................................ - 43 - 5.4. Reducing risk of cumulative employment rate variations............................................. - 46 - 5.5. Reducing shortlist to empirically effective indicators ................................................... - 47 - 5.6. Fine-tuning shortlisted and empirically effective indicators ......................................... - 48 - 5.6.1. Business cycle and Brussels ....................................................................................... - 48 - 5.6.2. Demographics ............................................................................................................ - 48 - 5.6.3. Cooperation issues and mobility premiums .............................................................. - 49 - 5.6.4. A more precise employment rate? ............................................................................ - 50 - 6. BRUSSELS AND THE JOB INCENTIVE SCHEME............................................................................ - 51 - 6.1. Brussels has a special labor market ............................................................................... - 51 - 6.1.1. Intense economic interactions in the Brussels Metropolitan Area ........................... - 51 - 6.1.2. International city with atypical job demand sector structure .................................. - 52 - 6.1.3. Skill mismatch and language challenge ..................................................................... - 52 - 6.2. Relatively uncorrelated and highly volatile employment rate ...................................... - 54 - 6.3. Risk reduction techniques fail ....................................................................................... - 54 - 6.4. Policy options for indicators for Brussels ...................................................................... - 55 - 6.4.1. Interactions with hinterland require cooperative scheme ....................................... - 55 - 6.4.2. Atypical job structure suggests focus on private jobs ............................................... - 56 - 6.4.3. Skill mismatch and language challenge suggest partial community returns ............ - 56 - 6.4.4. Failure classical risk reductions suggests international city benchmarking or smaller incentivization size .................................................................................................................... - 56 - 7. DECENTRALIZED POLICIES CAN INCREASE EMPLOYMENT RATES SIGNIFICANTLY .................... - 57 - 7.1. Policies impact employment ......................................................................................... - 57 - 7.2. Which policies are decentralized? ................................................................................. - 59 - 7.2.1. Job-related competencies ......................................................................................... - 59 - 7.2.2. Education ................................................................................................................... - 60 - 7.3. Impact and potential of active labor market policies.................................................... - 61 - 7.3.1. Impact ........................................................................................................................ - 61 - 7.3.2. Potential Belgian decentralized entities .................................................................... - 64 - 7.4. Impact and potential of education policies ................................................................... - 66 - 7.4.1. Impact ........................................................................................................................ - 66 - 7.4.2. Potential .................................................................................................................... - 67 - 7.5. Conclusion ..................................................................................................................... - 69 - 8. TARGETS ON FINANCIALLY NEUTRAL PATH .............................................................................. - 70 - 8.1. Planning Office HERMREG baseline scenario ................................................................ - 71 - 8.1.1. How does the HERMES-HERMREG system work? ..................................................... - 71 - 8.1.2. Which demographical projections underpin the HERMREG forecasts? ................... - 73 - 8.1.3. What would HERMREG baseline scenario forecast if financial crisis is integrated? . - 74 - 8.1.4. Lessons from HERMREG forecasts ............................................................................ - 76 - v
  6. 6. 8.2. Which realistic scenario’s can we imagine for Flanders? ............................................. - 77 - 8.3. Which speed of interregional convergence do we target for Brussels and Wallonia? . - 77 - 8.4. Different problem definitions lead to different families of targets .............................. - 77 - 8.5. National relative performance targets .......................................................................... - 78 - 8.6. Lisbon absolute performance targets ........................................................................... - 79 - 8.7. Semi Danish-style absolute targets ............................................................................... - 80 - 8.8. European relative performance targets ........................................................................ - 80 - 8.9. Reality checks of targets and scenario selection ........................................................... - 81 - 8.9.1. Employment rate variations European Regions in 1999-2007 .................................. - 81 - 8.9.2. Employment rate variations European countries in 1995-2007 ............................... - 82 - 8.9.3. Scenario selection with reality check ........................................................................ - 83 - 8.10. Conclusion with visualization of four selected scenario’s ............................................. - 86 - 9. SIZE OF INCENTIVIZATION ......................................................................................................... - 89 - 9.1. Definition of incentivization size coefficients................................................................ - 89 - 9.2. Factors one might look at to fix incentivization size coefficients.................................. - 90 - 9.3. High-powered Regional incentivization size .................................................................. - 92 - 9.4. Medium-powered Regional incentivization size ............................................................ - 93 - 9.5. Low-powered Regional incentivization size................................................................... - 94 - 9.6. Advantages and disadvantages of the three incentivization size options .................... - 94 - 10. SIMULATING THE IMPACT OF THE SCHEME .............................................................................. - 95 - 10.1. Budget Impact without policy link................................................................................. - 96 - 10.2. Impact scheme on net cost of increasing employment rates with activation policies - 103 - 11. CONCLUSION ........................................................................................................................... - 107 - 12. BIBLIOGRAPHY ......................................................................................................................... - 109 - 12.1. Books ........................................................................................................................... - 109 - 12.2. Articles in journals ....................................................................................................... - 110 - 12.3. Articles in newspapers................................................................................................. - 112 - 12.4. Articles in books .......................................................................................................... - 112 - 12.5. Unpublished documents ............................................................................................. - 114 - 12.6. Databases .................................................................................................................... - 117 - 13. APPENDICES............................................................................................................................. - 118 - 13.1. Newspaper articles related to this thesis .................................................................... - 118 - 13.1.1. Article scan from De Standaard ............................................................................... - 118 - 13.1.2. Article website screen shot from Le Soir ................................................................. - 119 - 13.2. Community budget response to GDP increases .......................................................... - 122 - 13.3. Descriptive statistics of Regional employment rate variations ................................... - 125 - 13.4. Risk reduction results of benchmarking of yearly employment rate variations ......... - 128 - 13.5. Risk reduction results of benchmarking of cumulative employment rate variations . - 132 - 13.6. Benchmarking of weighted average indicator for Brussels ......................................... - 136 - 13.7. Methodological challenges in assessing the impact of active labor market policies .. - 138 - 13.8. Modeling the impact of education on growth ............................................................ - 139 - 13.9. Benchmarking education potential of Belgian communities ...................................... - 143 - 13.10. Functioning of the HERMES forecasting model........................................................... - 146 - 13.11. HERMREG baseline forecasts in May 2007 before the crisis ...................................... - 149 - 13.12. European relative performance targets ...................................................................... - 150 - 13.12.1. Catching-up with employment levels of best similar Regions ............................ - 150 - 13.12.2. Increasing employment levels at same speed as best similar Regions ............... - 151 - 13.13. Has Regional growth become more labor intensive? ................................................. - 152 - 13.14. Governments react to financial incentives: brief literature review ............................ - 155 - vi
  7. 7. LIST OF TABLES Table 2.1:Average annual cost per unemployed person and its components 1987-2002(in €1000) . - 7 - Table 2.2:Growth rate and break-down of annual cost of an unemployed person 1987-2002(in %) - 7 - Table 2.3:Unemployed budget cost estimated with a linear regression on the basis of the effective value in 1987 and the price evolution in 1987–2007 ......................................................................... - 8 - Table 2.4:Unemployed budget cost in 2002-2007 estimated with a linear regression(in €1000) ...... - 8 - Table 2.5:Shares of revenue categories in Belgian GDP in 2007(in %)................................................ - 9 - Table 2.6:Regional budget impacts when Regional GDP increases by €100 in 2007(in €)............... - 10 - Table 2.7:Regional budget impact associated with one activation in 2007(in €1000 ) .................... - 11 - Table 2.8:Net Regional returns on activation through ALMP expenses in 2007(in € 1000) ............ - 12 - Table 2.9:Community budget impacts if Regional GDP increases with €100 in 2007(in €) ............. - 13 - Table 2.10:Community budget impacts associated per activated unemployed in 2007(in €1000). - 13 - Table 2.11:Community-, Regional- and federal budget impacts per activation in 2007(in €1000) .. - 14 - Table 2.12:Shares of Community-, Regional- and federal budget impacts in total return associated with activation of an unemployed in 2007(in %) .............................................................................. - 15 - Table 2.13:Expected evolution Regional employment rates until 2013 including crisis impact(in percentage points)............................................................................................................................. - 16 - Table 4.1:Main features of most concrete academic and political scheme proposals ..................... - 31 - Table 5.1:Standard deviation of 5-year and yearly employment rate variations ............................. - 38 - Table 5.2:Correlations between Regional employment rate variations in 1981-2007 ..................... - 38 - Table 5.3:Results regressions of Regional yearly employment rate variations ................................ - 39 - Table 5.4:Correlations between 5-year employment rate variations of Belgian Regions and their neighbor countries ............................................................................................................................ - 40 - Table 5.5:Results external factor regressions ................................................................................... - 45 - Table 6.1:Sector weights in employment in Brussels and its benchmarking Regions in 2007(in %) - 52 - Table 6.2: Employment qualification structure in Brussels and its peers in 2007(in %) ................... - 52 - Table 7.1:Correlations between unemployment rate and policy variables for 1982-2003 .............. - 58 - Table 7.2:Actors currently involved in main job-related competencies in Belgium ......................... - 60 - Table 7.3:National active labor market expenses compared to passive labor market expenses and compared to GDP in 2007(in %) ........................................................................................................ - 64 - Table 7.4:Effect of an increment of one year of schooling on the probability of being unemployed for an average citizen at working age in 2004(in %) ............................................................................... - 66 - Table 8.1:Regional demographic projections: percentage change on 2000 figures in the number of residents between 15 and 64 years old over the period 2000-2060(in %) ....................................... - 73 - Table 8.2:Expected impact of financial crisis on national number of working residents(in 1000) ... - 75 - Table 8.3:Expected evolution of the Regional number of working residents at horizon 2013 ........ - 75 - Table 8.4:Expected evolution Regional employment rates at horizon 2013(in percentage points) - 76 - Table 8.5:Regional employment targets under a slow national convergence scenario ................... - 78 - Table 8.6:Regional employment targets under a fast national convergence scenario ..................... - 78 - Table 8.7:Regional employment targets under a slow Lisbon scenario ............................................ - 79 - Table 8.8:Regional employment targets under a fast Lisbon scenario ............................................. - 79 - Table 8.9:Regional employment targets under a slow semi Danish scenario................................... - 80 - Table 8.10:Regional employment targets under a fast semi Danish scenario .................................. - 80 - Table 8.11:Statistics of average yearly employment rate variations of 332 EU Regions 1999-2007(in percentage points)............................................................................................................................. - 82 - Table 8.12:Statistics of average yearly employment rate variations of 20 European countries for 1995-2007(in percentage points) ...................................................................................................... - 83 - Table 8.13:Reality check metrics for rejected scenario’s .................................................................. - 84 - Table 8.14:Reality check metrics for accepted scenario’s................................................................. - 85 - Table 9.1:Total returns per activated resident with high-powered incentives(in €1000) ................ - 92 - vii
  8. 8. Table 9.2:Total returns per activated resident with medium-powered incentives(in €1000) .......... - 93 - Table 9.3:Total returns per activated resident with low-powered incentives(in €1000) ................. - 94 - Table 13.1:Community budget impacts if Regional GDP increases with €100 in 2007(in €) ......... - 124 - Table 13.2:Regional yearly employment rate variations descriptive statistics in 1981-2007......... - 125 - Table 13.3:Regional five-year employment rate variations descriptive statistics in 1981-2007 .... - 126 - Table 13.4:Correlations between employment rate variations of Belgian Regions and neighbor countries in 1984-2007.................................................................................................................... - 126 - Table 13.5:Standard deviations of Regional yearly employment rate variations with Belgian benchmarking .................................................................................................................................. - 128 - Table 13.6:Standard deviations of Regional employment rate variations with international benchmarking .................................................................................................................................. - 128 - Table 13.7:Standard deviations of Regional five year employment rate variations 1985-2007 with Belgian Benchmarking ..................................................................................................................... - 132 - Table 13.8:Standard deviations of Regional five year employment rate variations 1988-2007 with international benchmarking ............................................................................................................ - 132 - Table 13.9:Correlations between variation of weighted average of employment- and job-rate of Brussels and variation of employment rates in Flanders and Wallonia for 1981-2007 .................. - 136 - Table 13.10: Risk level of weighted average indicator with a 25% weight for 1981-2007 ............. - 137 - Table 13.11: Risk level of weighted average indicator with a 50% weight for 1981-2007 ............. - 137 - Table 13.12: Risk level of weighted average indicator with a 75% weight for 1981-2007 ............. - 137 - Table 13.13:Education public spending per student in 2006(in €).................................................. - 143 - Table 13.14:Distribution of students in last two degrees of secondary education per education type in 2001(in %)........................................................................................................................................ - 143 - Table 13.15:PISA results in 2006 ..................................................................................................... - 143 - Table 13.16:Percentage of students with at least one year delay in sixth year in 2003-2004 ....... - 143 - Table 13.17:Share of residents knowing language(s) in 2006 ......................................................... - 144 - Table 13.18:Distribution of students per group of university studies for 2000-2001 .................... - 144 - Table 13.19:Shanghai Academic ranking of World Universities in 2008 ......................................... - 145 - Table 13.20:Number of patents per million inhabitants in 2002 .................................................... - 145 - Table 13.21:Expected evolution Regional growth rates at horizon 2013(%) .................................. - 149 - Table 13.22:Expected evolution Regional number of working residents at horizon 2013 ............ - 149 - Table 13.23:Expected evolution Regional employment rates at horizon 2013 .............................. - 149 - Table 13.24:Regional employment targets under a European peer level caching up scenario ...... - 150 - Table 13.25:Regional employment targets under a European peer speed caching up scenario.... - 151 - Table 13.26:Flanders’ elasticities of employment rate with respect to GRP for 1980-2007 .......... - 153 - Table 13.27:Brussels’ elasticities of employment rate with respect to GRP for 1980-2007........... - 153 - Table 13.28:Wallonia’s elasticities of employment rate with respect to GRP for 1980-2007 ........ - 154 - viii
  9. 9. LIST OF CHARTS Chart 5.1:Regional yearly employment rate variations 1981-200(in percentage points) ............... - 41 - Chart 6.1:Number of jobs and number of working residents as percentages of the working age population in Belgian Regions in 2007 .............................................................................................. - 51 - Chart 6.2:Required training for job offers in Brussels, Wallonia and Flanders in 2007(in %) ........... - 53 - Chart 6.3:Regional employment rate per training level in 2007(in %).............................................. - 53 - Chart 7.1:Impact of active labor market programs (right scale) on effect (left scale) of benefits on unemployment rate in 20 OECD countries........................................................................................ - 62 - Chart 7.2:Breakdown of national active labor market expenses in 2006(in %) ................................ - 64 - Chart 7.3:Breakdown of Regional active labor market expenses in 2007(in %) ............................... - 65 - Chart 8.1:Distribution of average yearly employment rate variations of 332 EU Regions 1999-2007(in percentage points)............................................................................................................................. - 81 - Chart 8.2:Distribution of average yearly employment rate variations of 20 European countries 1995- 2007(in percentage points) ............................................................................................................... - 82 - Chart 8.3:Targeted business-cycle adjusted average employment rate levels under Lisbon scenario’s 2008-2035 ......................................................................................................................................... - 87 - Chart 8.4:Targeted Business- cycle adjusted average employment rate levels under Danish scenario’s 2008-2035 ......................................................................................................................................... - 88 - Chart 10.1:Bonus malus incentive flows for Flanders with slow Semi-Danish targets and medium- powered premiums ........................................................................................................................... - 97 - Chart 10.2:Bonus incentive flows for Flanders with slow Semi-Danish targets and medium-powered premiums .......................................................................................................................................... - 98 - Chart 10.3:Bonus malus incentive flows for Wallonia with slow Semi-Danish targets and medium- powered premiums ........................................................................................................................... - 99 - Chart 10.4:Bonus incentive flows for Wallonia with slow Semi-Danish targets and medium-powered premiums ........................................................................................................................................ - 100 - Chart 10.5:Marginal impact of schemes with different incentivization sizes on net ALMP cost to achieve employment rate variations for Flanders .......................................................................... - 105 - Chart 10.6:Marginal impact of schemes with different incentivization sizes on net ALMP cost to achieve employment rate variations for Wallonia .......................................................................... - 106 - Chart 13.1:Regional yearly employment rates 1981-2007(in %) ................................................... - 125 - Chart 13.2:Regional five-year employment rate variations 1985-2007(in percentage points)..... - 126 - Chart 13.3:Sector shares in Regional GRPs in 2007(in %) ............................................................... - 127 - Chart 13.4:Effective and relative yearly employment rate variations of Brussels with Belgian interregional benchmarking 1981-2007(in percentage points) ...................................................... - 129 - Chart 13.5:Effective and relative employment rate variations of Brussels with neighbor benchmarking 1984-2007(in percentage points) .................................................................................................... - 129 - Chart 13.6:Effective and relative yearly employment rate variations of Flanders with Belgian interregional benchmarking 1981-2007(in percentage points) ...................................................... - 130 - Source: HERMREG Planning Office .................................................................................................. - 130 - Chart 13.7:Effective and yearly relative employment rate variations of Flanders with neighbor country benchmarking 1984-2007(in percentage points) ............................................................... - 130 - Chart 13.8:Effective and relative employment rate variations of Wallonia with Belgian interregional benchmarking 1981-2007(in percentage points) ............................................................................ - 131 - Chart 13.9:Effective and relative employment rate variations of Wallonia with neighbor country benchmarking 1984-2007(in percentage points) ............................................................................ - 131 - Chart 13.10:Effective and relative five year employment rate variations of Brussels with Belgian interregional benchmarking 1985-2007(in percentage points) ...................................................... - 133 - Chart 13.11:Effective and relative five year employment rate variations of Brussels with neighbor benchmarking 1984-2007(in percentage points) ............................................................................ - 133 - ix
  10. 10. Chart 13.12:Effective and relative five year employment rate variations of Flanders with Belgian interregional benchmarking 1985-2007(in percentage points) ...................................................... - 134 - Chart 13.13:Five year effective and relative employment rate variations of Flanders with neighbor country benchmarking 1988-2007(in percentage points) ............................................................... - 134 - Chart 13.14:Effective and relative five year employment rate variations of Wallonia with Belgian interregional benchmarking 1985-2007(in percentage points) ...................................................... - 135 - Chart 13.15:Five year effective and relative employment rate variations of Wallonia with neighbor country benchmarking 1988-2007(in percentage points) ............................................................... - 135 - Chart 13.16: A flowchart of the HERMES model ............................................................................. - 147 - x
  11. 11. SUMMARY The job bonus malus has been set on the Belgian political agenda by 120 Belgian economists via a text discussed in De Standaard and Le Soir on 26th of January 2008. In December 2008 Walloon and Flemish Ministers of Labour and Economy J.C. Marcourt and F. Vandenbroucke formulated a job bonus malus proposal. The idea behind the job bonus malus is simple. Belgian Regions and/or Communities, the so-called decentralized entities, receive boni when jobs are created. With these boni, public policy investments in jobs are(partially) paid back. This pay-back perspective could lead to more and better policy investments and to more jobs. Two questions arise. First, could the job bonus malus be useful? Second, how could the job bonus malus work in practice? The job bonus malus could be useful to improve activation incentives. Better activation incentives could lead to higher employment rates because governments react to financial incentives such as tax base elasticities, financing of own expenditures and bail-outs by other governments. Higher employment rates are necessary to fund the social security and the ageing cost and to get the Belgian political federation out of its current political, budgetary and economic deadlock. Today Regions bear the financial pain of activation without any significant financial gain. Activating an unemployed costs the Regions around €21,800 and brings in around €3,200 in Flanders, €1,200 in Wallonia and €1,100 in Brussels. Significant activation gains could lead to decentralized policy reactions. Decentralized education-, active labor market- and economic policies have a high job creation potential. This potential is shown by our international and Belgian literature review. With a year of extra schooling, the average Walloon citizen at working age decreases his probability of being unemployed by around 17.2%. The job bonus malus could be effective. To be effective, coupling benchmarking of regional employment rate variations to mobility premiums might be an attractive scheme option. Such a scheme could be fair and hard to manipulate. Such a scheme could also significantly raise activation incentives while controlling budget risk and fostering interregional cooperation. Employment rate variations measure an objective and desirable end: increasing the activity of the working age population. Employment rate variations are hard to manipulate. Employment rate variations are fair both for Regions with weaker and stronger starting positions. Initially weaker Regions are not penalized for bad past performances because only progress matters. xi
  12. 12. Initially stronger Regions are not penalized for good past performances since targets could be set at relatively less ambitious levels. We define targets such that Regions receive no bonus or malus if they achieve certain employment rate variation targets. Targets can be set on the basis of Lisbon objectives, Belgian interregional convergence paths or European benchmarking. European benchmarking could be based on leading countries such as Denmark. Suppose Flanders bridges 50% of the employment rate gap with Denmark at horizon 2020. Suppose that Brussels and Wallonia achieve this level in 2030. To follow this Semi-Danish path, Brussels, Flanders and Wallonia should join the 17%, 42% and 20% fastest European employment rate climbers. Budget impact simulations show that schemes based on Semi-Danish or Lisbon targets are ambitious for Wallonia and Brussels. To protect Wallonia and Brussels against the vicious circle, one could limit incentive flows to boni. Coupling employment rate variations of a certain Region to financial incentive flows, could significantly improve activation incentives. The size of the incentive flows could be set such that Regions can finance investments with scheme boni or such that total budget returns on activation are equally shared between the federal and Regional level. Equal return sharing is possible if Flanders, Wallonia and Brussels receive around €10,800, €13,700 and €12,700 per activated resident. Budget risk could be controlled by Belgian or international benchmarking. By comparing the employment rate variations of two similar Regions, part of the business cycle risk is filtered out. Indeed, similar Regions are exposed to the same international business cycle. Evaluating employment rate progress since the previous year and evaluating cumulative employment rate progress are two attractive options. The first option reduces the risk of a vicious circle and provides long-run activation dividends to the federal budget. The second option encourages long-run Regional investments. If policymakers opt for assessing yearly employment rate variations, then Belgian benchmarking of Flanders and Wallonia relative to one another reduces the budget risk. Risk is then reduced by around 55% and 47%. If policymakers opt for cumulative employment rate variations, then Belgian and neighbor benchmarking appear as two effective techniques to reduce the risk supported by Flanders and Wallonia. For Brussels, benchmarking is not the magical solution to reduce risk. Cooperation could be fostered by mobility premiums. Mobility premiums reward the activation of neighbor residents. Employment rate incentives reward Regions for the activation of their own residents. Mobility premiums are useful in Brussels where around 304,000 commuters go working. To conclude, the job bonus malus could lead to higher activation incentives and higher employment rates. Employment rate variation benchmarking and mobility premiums combine solidarity and responsibility by stimulating activation and by fostering cooperation while controlling budget risk. xii
  13. 13. 1. INTRODUCTION Today, Belgium is at a triple cross-road. Employment, the sustainability of the Belgian social security system and the accountability of Regions and Communities are at stake. Here are a couple of facts to draw the picture of this triple cross-road. According to employment forecasts, no Belgian Region will reach the Lisbon 70% employment rate target in 2015. Today, a job gap separates Flanders, Wallonia and Brussels. The employment rate in Flanders is 11.1 and 8.8% above the values in Brussels and Wallonia. This gap is expected to widen by 1.2 and 1.5% at horizon end 2013, except if new measures are taken. And in the current turbulent economic context, expected job losses total 37,000 in 2009 and 53,000 in 2010. The sustainability of the Belgian social security is at stake. The High Council of Finance forecasts a deterioration of the structural deficit up to 5% in 2015. Between 2018 and 2050, ageing-related extra costs will rise to 4.2% of GDP. It took 192 days to form an interim government after the federal elections of 10th of June 2007. Until now, very few measures have been taken to overcome the opposition between, on the one hand, Flemish calls for more decentralized policy autonomy and financial accountability and, on the other hand, a Frenchspeaking “non” coupled to the wish to maintain interpersonal and hence interregional solidarity. In terms of accountability of the decentralized entities, a job incentive paradox is ingrained within the Belgian federation. On the one hand, decentralized entities possess more and more policy levers to create jobs and to activate residents. Think about active labor market and regional economic policies for the Regions and education for the Communities. On the other hand, when a resident is activated, more than 82% of the financial budget returns on activation are reaped by the federal level. Indeed, it is the federal level which collects taxes and social contributions and who pays unemployment benefits. The Belgian federation faces a triple challenge. First, it has to lift up regional employment rates to get its budget back on track and to finance ageing costs. Second, the federation has to get out of the political stalemate, which seems quite irresponsible in the light of the dramatic current economic and financial crisis. Third, it has to improve financial accountability and incentives for decentralized entities to create jobs and foster growth. Within the context of this triple challenge, 120 Belgian economists put the principle of the job incentive scheme on the agenda via a text discussed in De Standaard and Le Soir of 26th January 2008. Walloon and Flemish Ministers of Labor and Economy -1-
  14. 14. J.C. Marcourt and F. Vandenbroucke formulated a proposal for a job incentive scheme in December 2008. The core idea behind the job incentive scheme is relatively simple. If decentralized entities can share in financial returns on activation, then they can win back part of their public policy investments in jobs. This pay-back perspective could foster the quality and quantity of these investments, which might ultimately result into higher employment rates and safeguard interpersonal and interregional solidarity. The main questions of this thesis are the following: “Is it possible to implement such a job incentive scheme? Could it work in practice? What are the main questions, trade-offs and difficulties which arise when one wants to design such a scheme? Could such a scheme enhance the credibility of an upward employment rate convergence scenario as one of the key steps to lift up employment rates, to get out of the current political deadlock, to finance the ageing cost and to solve the job incentive paradox?” This research question might seem relatively ambitious. Therefore, we think it is crucial to limit the scope of this thesis. In this thesis we do not cover three main questions related to our topic. First, we do not discuss the institutional structure of Belgium. Therefore, we do not assess whether the current distribution of competences, incomes, expenditures, assets and liabilities between the federal, regional and community level is optimal. Second, we only study employment incentives for Belgian political entities. We do not cover labor law-related employment incentives for employers and employees. Third, we do not compare the advantages and disadvantages of the job incentive scheme with the advantages and disadvantages of tax decentralization, another big issue on the political agenda. Our target audience is double. We target on the one hand academics and on the other hand citizens, media and policy makers1. But how to deal with this duality? Because of the scientific intentions of this master thesis, we aim to help and inform decision-makers by proposing facts, trade-offs and elements of solutions. But since we cannot discuss, test and simulate an infinity of schemes, we have to make choices at various points. But clearly, this does not mean that we advocate these options. The main idea is to draw a maximum of lessons from the simulations. We want to be policy relevant. Therefore, the proposed schemes and their statistical verifications cannot be too complex. All empirical work and all computations have to be considered as first cuts. 1 In this perspective we summarized some of our idea’s in the newspaper articles Struyven(2009a) and Struyven(2009b). The articles in De Standaard and Le Soir can be found in Appendix 13.1. of this thesis. -2-
  15. 15. We now introduce the 10 chapters of this work in a nutshell. In chapter 2, we explain the rationale for a job incentive scheme. First, we quantify the job incentive paradox. Today, if Regions invest in resident activation, the federal level reaps the financial benefits with a budget return of €29,000 per activated resident. Regions, which have to invest approximately €21,800 in active labor market policies to activate one resident, only get back a very small amount. Regional total incomes only increase by around €3,200 in Flanders, €1,200 in Wallonia and €1,100 in Brussels when respectively Flemish, Walloon or Brussels’ residents get a job. Second, we argue that the job incentive scheme might contribute to the upward convergence of employment rates. This upward convergence seems absolutely necessary in the light of the budgetary, the ageing and the political challenges faced by the Belgian federation. In chapter 3, we briefly discuss the main models of the theory of incentives to formulate five key lessons for the incentive scheme. First, job externalities create a win-win opportunity. Indeed, more decentralized incentives and better efforts can increase employment rates. Higher employment rates reduce the federal budget costs of unemployment while increasing the decentralized incomes trough incentive boni. Second, we underscore the necessity to study schemes which maximize incentives while controlling budget risk for decentralized entities. Third, we argue that the incentive scheme should not insure decentralized entities against bad results which are the consequence of bad efforts. Entities should only be protected against bad luck. Fourth, we argue that performance should be coupled to global indicators rather than to specific indicators. Excessively specific indicators might induce decentralized entities to neglect important non-rewarded tasks. Fifth, we argue that the exposure of the three Regions and their peers to the international business cycle creates an opportunity for benchmarking. Benchmarking could filter out risk for a given level of incentives. In chapter 4, we discuss the economic advantages of assessing global results rather than efforts or specific results. We present the trade-offs of penalizing versus rewarding only, absolute versus relative performance evaluation and of assessing yearly rather than long-run results. We analyze the salient features of schemes proposed by Van Rompuy, Van der Linden and Vandenbroucke and Marcourt. We work out formula’s for schemes based on absolute and relative performance evaluation of employment rate variations. Chapter 5 leverages the HERMREG regional employment database. The database goes back to 1980. We test how well certain performance indicators might improve incentives, how they might limit risk exposure and how they might avoid any unintended consequences. If one opts for yearly variations, then evaluating Flanders’ and Wallonia’s performance relative to one another brings down risk by -3-
  16. 16. respectively 55% and 47%. This is the consequence of the 89% correlation between the yearly employment rate variations of Wallonia and Flanders. If one opts for assessing cumulative variations, then Belgian benchmarking and France-Netherlands-Germany neighbor benchmarking are two effective risk reduction techniques for Flanders and Wallonia. For Brussels, benchmarking with other Belgian Regions or with our neighbor countries, does not seem to reduce risk significantly. Chapter 6 shows that Brussels has a special labor market. The special labor market is characterized by the daily inflow of around 304,000 commuters, an atypical sector structure, a skill mismatch and a language challenge. For Brussels, benchmarking is not the magical solution to reduce risk. The scheme could take into account the specificities of Brussels. Mobility premiums could foster commuting. Reinvestments of boni into education could tackle the skill and language challenges. Chapter 7 summarizes the important potential of decentralized entities to increase employment rates. Policy changes explain around 74% of the cross-country differences in unemployment changes. Active labor market, regional economic and education policies are largely decentralized. Various empirical studies show that active labor market policies can have a positive net impact on employment rates. The impact increases if the right interventions, such as counseling schemes, are well-designed, accompanied with other positive measures and regularly evaluated. Belgian Regions could increase the job impact of their active labor market policies. Therefore, they could invest more in training rather than in direct job creation. Education stimulates employment both directly and indirectly by fostering growth and innovation. One year of extra schooling decreases directly the probability of being unemployed by around 17.2% for the average Walloon citizen at working age. Chapter 8 lists several options for employment rate variation targets. We define targets such that Regions receive no bonus or malus if they achieve certain employment rate variation targets. Targets could be set on the basis of Lisbon objectives, Belgian interregional convergence paths or European benchmarking. European benchmarking could be based on leading countries such as Denmark. Suppose Flanders bridges 50% of the employment rate gap with Denmark at horizon 2020. Suppose that Brussels and Wallonia achieve this level in 2030. To follow this Semi-Danish path, Brussels, Flanders and Wallonia should join the 17%, 42% and 20% fastest European employment rate climbers. Chapter 9 discusses the potential size of incentivization. To fix the size of incentivization, policymakers might look at several factors. These factors are related to risk and to budgets. Major risk-related factors are the current exposure of decentralized budgets to (business cycle) risk and the level of risk aversion. Major budget-related factors are the budget sizes and the potential for -4-
  17. 17. decentralized to generate cash in bad days. In bad days cash could be generated through borrowing, taxation or cost-cutting. The size of incentivization could be set such that Regions can finance investments with scheme boni or such that total budget returns on activation are equally shared between the federal and Regional level. Equal sharing of the return on activation between the federal and Regional level is possible if Flanders, Wallonia and Brussels receive around €10,800, €13,700 and €12,700 per activated resident. The budget- and job impacts of some schemes are simulated in chapter 10. Simulations aim to draw a maximum of lessons rather than to confirm or reject any options. Budget impact simulations show that schemes based on Semi-Danish or Lisbon targets are ambitious for Wallonia and Brussels. If one wants to protect Wallonia and Brussels against the vicious circle risk, limiting incentive flows to boni is an option. For the job impact simulations, we do not assume a precise policy reaction because this would be to speculative. We nevertheless argue that decentralized Belgian entities would react to financial activation incentives created by job bonus schemes. This arguing is based on significant international empirical evidence of government reactions to various financial incentives such as tax base elasticities, financing of own expenditures by other governments and bail-outs by other governments. Therefore, we present a range of job impact figures triggered by a range of active labor market policy reactions for several incentivization sizes rather than estimating one precise job impact figure for a given incentivization size. In chapter 11, we repeat the two take-away points of this work. First, the job bonus malus could lead to higher activation incentives and higher employment rates. Second, coupling benchmarking of employment rate variations to mobility premiums could be an attractive option. This option could combine solidarity and responsibility by stimulating activation and fostering interregional cooperation while controlling budget risk. -5-
  18. 18. 2. RATIONALE FOR JOB BONUS MALUS In this chapter we argue that a job bonus malus might be useful for the Belgian federation for at least two reasons. First, it might improve financial returns, gained by decentralized governments when they activate their residents. We show in 2.1. that these activation returns are low today, compared to the federal returns and compared to the decentralized cost of activation. Second, these better incentives might lead to more and more effective public decentralized investments in active labor market policies and education and might, ultimately, result in an upward convergence of the Regional Belgian employment rates. Indeed, we argue in 2.2. that employment rates have to converge upward to finance the social security and to ensure the sustainability of the Belgian federation. 2.1. TODAY ACTIVATION INCENTIVES ARE LIMITED AT THE DECENTRALIZED LEVEL We compute in 2.1. financial returns on job creation for the federal, Regional and Community budgets in respectively 2.1.1., 2.1.2. and 2.1.3. The Regional and Community activation returns result from the positive budget response to an increase in Regional GDP, which is associated to job creation. We discuss the aggregate incentive policy implications of those returns in 2.1.4. 2.1.1. FEDERAL RETURN 2.1.1.1. Federal budget cost per unemployed person in 2002 The Planning Office defines the federal cost per unemployed as an average cost based on average values of unemployment benefits, average wages and average tax rates. The evaluation is quite simple: the budget cost is the sum of the unemployment benefit cost2 for an average employee in the private sector with an average wage and the opportunity cost in terms of taxes3 (both direct and indirect taxes) and in terms of social contributions (both employee and employer contributions). The Planning Office estimates the total cost in 2002 at €25,700 per unemployed person. 2 This unemployment benefit covers complete unemployment assessed on the basis of the average yearly amount and the average unemployment duration as communicated by O.N.E.M.-R.V.A. 3 The loss in terms of taxes is the difference between on the one hand direct and indirect taxes paid if the persons would have been working and on the other hand taxes paid during unemployment. -6-
  19. 19. Table 2.1:Average annual cost per unemployed person and its components 1987-2002(in €1000) ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Total cost 16.4 16.2 16.6 17.6 18.5 19.5 20.3 20.8 21.1 21.6 22.2 22.7 23.5 24.1 24.9 25.7 Unemployment benefit 5.1 5.2 5.4 5.5 5.9 6.0 6.3 6.4 6.6 7.0 7.1 7.3 7.5 7.7 8.0 8.5 Loss social contributions 7.2 7.0 7.4 7.9 8.3 9.1 9.3 9.4 9.4 9.5 9.8 9.8 10.2 10.3 10.6 10.9 Loss taxes 4.1 4.0 3.9 4.2 4.3 4.5 4.7 5.1 5.1 5.2 5.3 5.5 5.8 6.1 6.3 6.3 Source: De Bresseleers et al(2004) Table 2.2:Growth rate and break-down of annual cost of an unemployed person 1987-2002(in %) Share in total cost ‘87 Share in total cost ‘02 Growth rate ‘87-‘02 Total cost 100.0 100 3.0 Unemployment benefit 30.9 33.0 3.5 Loss social contributions 44.1 42.6 2.8 Loss taxes 25.0 24.5 2.9 Source: De Bresseleers et al(2004) 2.1.1.2. Federal budget cost of an unemployed in 2007 with inflation regression The most recent estimate of the federal budget cost of an unemployed person goes back to 2002. Therefore we have to estimate the value for 2007 on the basis of the price evolution4. We obtain a very strong empirical linear relationship between the Consumer Price Index (base 1996) and the budgetary cost of unemployment with a R squared of more than 99%: = −€9,575 + €315 ∗ 1996, + (2.1) 4 We opted for this regression after obtaining less good estimates of the budget cost between 1987 and 2002 by multiplying the budget cost of 1987 with the price index: the estimation errors are quite large (up to 12.0% in 2002) despite a very strong correlation between the CPI and the budget cost of 99.6%. The reason is that budgetary cost increased faster ,at a growth rate of 3.0%, than the CPI which increased at 2.1% on average. -7-
  20. 20. With this regression the estimation errors are limited and vary in a range from -1.56 % in 1999 to +3.88 % in 1987 between 1987 and 20025. With relationship (2.1) we estimate the total budget cost in 2007 at €29,000 per year per unemployed person. Table 2.3:Unemployed budget cost estimated with a linear regression on the basis of the effective value in 1987 and the price evolution in 1987–2007 ‘87 ‘88 ‘89 ‘90 ‘91 ‘92 ‘93 ‘94 ‘95 ‘96 ‘97 ‘98 ‘99 ‘00 ‘01 ‘02 Total cost(K€) 16.4 16.2 16.6 17.6 18.5 19.5 20.3 20.8 21.1 21.6 22.2 22.7 23.5 24.1 24.9 25.7 Estimated cost(K€) 15.8 16.1 16.9 17.8 18.7 19.3 20.1 20.8 21.3 21.9 22.4 22.7 23.1 23.9 24.8 25.3 Estimation error(%) -3.88 -0.54 1.31 1.16 0.61 -0.82 -1.00 0.05 0.85 1.33 1.06 0.33 -1.56 -0.57 -0.48 -1.37 Source: De Bresseleers et al (2004)and own computations Table 2.4:Unemployed budget cost in 2002-2007 estimated with a linear regression(in €1000) 6 2002 2003 2004 2005 2006 2007 25,33 25,89 26,63 27,64 28,30 28,99 Source: De Bresseleers et al (2004) and own computations 5 The value of the t-statistic of the price index coefficient equals 3.91. 6 The value for 2002 is the value computed by the Planning Office, whereas values beyond 2002 are own estimations on the basis of linear regression (2.1). -8-
  21. 21. 2.1.2. REGIONAL RETURN Since Regional incomes are not directly coupled to employment but to GDP, we first compute the impact of employment on GDP. Then we estimate the Regional budget return on activation. 2.1.2.1. Regional GDP variation associated with employment rate variation We compute the impact of the Regional employment rate on the Regional GDP7 without taking into account the multiplier-effect8. We argue that activation leads to the payment of an average wage. The wage payment results into an increase of GDP according to the extra revenues approach9 to compute GDP. The idea behind this extra revenues methodology to compute GDP is the following. Any extra sales, resulting from extra production, allow to pay capital- (Yk) and labor (Yl)revenues, to save for capital expenses (through accounting Depreciations- Dep) and to pay indirect taxes net of subsidies (Ti-Subs): = + + + − (2.2) Thus, coming back to wages, the increase of GDP (Δ GDP) can be estimated on the basis of the increase of wages ( ) if we suppose a locally constant unitary elasticity between GDP and wages: = ∗ (2.3) Wages represent 50.2 % of GDP in 2007 whereas they represented 52.4% in 2002. Table 2.5:Shares of revenue categories in Belgian GDP in 2007(in %) Labour Revenues 50.2 Gross Profit margin 38.5 Indirect Taxes (net of Subsidies) 11.3 Gross Domestic Product 100 Source : Institute for National Accounts Bresseleers et al(2004) compute a gross average wage for private workers (i.e. manual and intellectual workers). For 2002, the average gross wage is estimated at €25,040 per worker. We estimate GDP growth associated with activation at €47,814 per worker in 2002 with equation (2.3). 7 Noticing that employment and GDP are jointly determined. 8 Estimating correctly the multiplier-effect of job creation on GDP seems very difficult from a statistical point of view because of the bicausality of growth and employment. 9 There are three ways to assess GDP growth. One can measure extra production (value added), extra expenditures (consumption, investments, public expenses and the net expense of foreign goods) and extra revenues generated by extra production. Here, we focus on the third extra revenues approach. -9-
  22. 22. On the basis of GDP- and price evolutions in 2002-2007, we estimate GDP growth associated with activation at €58,417 per worker in 2007. 2.1.2.2. Regional budget impacts associated with Regional employment rate variation Table 2.6 illustrates how Regional budgets react to changes in Regional GDP as computed by Algoed (2009)10. The total impact gives the combined response of the three types of Regional revenues: i) personal revenue tax revenues transferred ii) additional funds of new expenditure competences and iii) Regional tax revenues. Table 2.6:Regional budget impacts when Regional GDP increases by €100 in 2007(in €) Impact budget Fla Impact budget Wal Impact budget Bru GDP increase of 100 € in Bru 1.34 0.84 1.88 GDP increase of 100 € in Fla 5.55 1.39 0.38 GDP increase of 100 € in Wal -0.90 2.01 0.39 Source: Algoed(2009) We distinguish volume-, substitution- and solidarity grant effects. There are volume effects since the personal revenue tax grant and the grant for new expenditures competences are indexed to Belgian GDP. Own tax revenues are also linked to Regional GDP. Substitution effects arise because changes in the personal revenue tax revenue in Region i increase the personal revenue tax transfer for Region I, which affects negatively the funds at the disposal of Regions j and k. Finally, a change in a Region’s GDP, affects solidarity transfers, which are based on the relative divergence of the Region’s tax revenues. Table 2.6 teaches us that Belgian Regions only win €1.8811 up to €5.55 when their Regional GDP increases with €100. Knowing that GDP growth associated with activation equals approximately €58,417 per worker in 2007 and with the results of Table 2.6, we compute the Regional budget returns on activation in Table 2.7. Regional returns per activated resident are limited and vary between €1,100 in Brussels and €3,200 in Flanders. 10 Algoed assumes that the GRP-elasticity of personal tax revenues and the GDP-elasticity of own tax revenues both equal one. 11 The limited return for Brussels and Wallonia arises because the solidarity grant, received by the weaker Region, reduces since the personal tax revenue gap between the weaker Region and the national average, which is the driver of the solidarity grant, reduces. The negative impact of an increase of the Walloon GDP on Flanders’ budget arises because the negative substitution effect dominates the positive volume effect. - 10 -
  23. 23. Table 2.7:Regional budget impact associated with one activation in 2007(in €1000 ) Budget Fla Budget Wal Budget Bru Bru 0.8 0.5 1.1 Fla 3.2 0.8 0.2 Wal -0.5 1.2 0.2 Source: Own computations 2.1.2.3. Regional active labor market expenses Since we now know how much activation brings in for a Region, let us now turn to how much activation costs for a Region. We compute this cost with Estevão(2003). We assume the three Regions to increase their active labor market policy (ALMP) expenses with a certain amount resulting into a total12 Regional employment rate variation with one percentage point. We consider this investment as a recurring investment with an immediate and certain return and compare this investment cost with the yearly recurring Regional returns from a one percentage point employment rate increase. Hence, we obtain a yearly net return. We assume that the ALMP-employment rate relationship in Estevão is verified in the three Regions in 200713. Estevão defines as dependent variable the share of the working age population employed in the business sector14. The main explanatory variable concerns the ALMP expenditures. He computes total ALMP expenditures as a share of GDP. ALMP expenditures cover expenditures on public employment services and administration, labor market training, youth measures, subsidized employment and measures for the disabled. ALMPs are normalized by GDP and not by unemployment15. He tests the following equation, where X is a vector of control variables16 capturing changes in institutions and the business cycles, Y a vector of year dummies to control for common shocks, C a vector of country dummies, and ε the error term, for fifteen17 industrial countries between 1993 and 2000: , = 1 × , + 2 × , + 3 × + 4 × + , (2.4) 12 Estevão has studied empirically the link between ALMP-expenses and business employment whereas we are interested (because of data constraints) in total employment. Since total employment is the sum of public and private employment, we make the cautious assumption that public employment grows at the same speed as business employment because of the ALMP expenses. 13 Estevão did not publish the coefficient values for Belgium with respect to the country dummies or interaction terms. It would be interesting to know if the Belgian employment rate responds differently to ALMP-expenses than the rates in other countries. But the dataset (8 datapoints per country) seems relatively small to estimate these country dummies. 14 The restriction to the business sector avoids overestimating importance of ALMPs. 15 Countries with lower employment rates spend relatively more in ALMPs but these effects are attenuated by controlling for institutions, other country-specific factors and economic shocks. He applies this normalization to avoid positive bias toward estimating a positive effect of ALMPs unemployment. 16 As main control variables, he proposes per capita GDP, technological growth, extent of economic openness and the share of GDP spent on passive labor market policy (PLMPs). 17 The fifteen countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Netherlands, Norway, New Zealand, Spain, Sweden, the United Kingdom and the United States. - 11 -
  24. 24. We notice in (2.4) that the employment rate during the year t of Region i (BEit) only depends on the ALMP-expenditures during the year t of Region i (ALMP it ). In a more sophisticated lead and lag model, we could also imagine adding as explanatory variables the ALMP-expenditures of previous years (ALMP it-1 ,ALMP it-2,…). In (2.4), current ALMP expenditures (ALMP it ) also capture the effects of ALMP-expenditures in the past (ALMP it-1, ALMP it-2, …), given the strong correlation between current and past expenditures. One can expect a positive effect of past ALMP-expenditures on the employment rate today since activated residents probably continue working for more than one year since the day of training, activation, coaching, etc given their higher human capital. By making the assumption of a permanent18 ALMP-increase and by computing yearly immediate and certain returns, we do not have to make any assumption on the depreciation rhythm or the risk profile of past stocks of ALMP-investments. We have a model determining employment rates and wages19. Here is Estevão’s key result20: “If the share of ALMP expenditures in GDP increases with one percentage point, then the business employment rate increases with 1.88 percentage points.” Therefore, applying this result at a macro-level, if Belgium wants to lift up its employment rate by 1 percentage point, then it should increase its ALMP expenditures from €4.5 Billion to €6 Billion. At a micro-level, assuming equal costs across Regions, €21,800 should be invested in ALMP expenses to activate one unemployed, which would otherwise not have found a job, in every Region anno 2007. 2.1.2.4. Regional net returns Table 2.8 is clear. Regions are not encouraged to invest in ALMP expenses. Activating one unemployed gives a net cost of €18,600 in Flanders, €20,700 in Wallonia and € 20,700 in Brussels. Table 2.8:Net Regional returns on activation through ALMP expenses in 2007(in € 1000) Entity Activation of one Activation of one Activation of one unemployed in Fla unemployed in Wal unemployed in Bru Bru Reg 0.2 0.2 1.1 Bru Reg (incl. ALMP cost) 0.2 0.2 -20.7 Fla Reg 3.2 -0.5 0.8 Fla Reg (incl. ALMP cost ) -18.6 -0.5 0.8 Wal Reg 0.8 1.2 0.5 Wal Reg (incl. ALMP cost) 0.8 -20.7 0.5 Source: Own computations based on Estevão(2003) 18 The assumption regarding the permanent character of this increase seems realistic since the bonus malus scheme aims achieving permanent and structural changes in the policies of decentralized entities. 19 Many of the expected effects of ALMPs on employment occur through variations in wages, which are also a function of ALMPs. So, wages are excluded from the employment rate specification and the estimated effect of ALMPs on employment rates should already incorporate shifts in wage setting. 20 This result is statistically significant at a 5% level and has been obtained with an Ordinary Least Squares method. The t- stat is also significantly different from zero and equals 4.08. - 12 -
  25. 25. 2.1.3. COMMUNITY RETURN Again, like for the Regions, we first compute the impact of activation on GDP, which influences on its turn the budget of the Community. 2.1.3.1. Impact of Regional GDP on Community budgets We compute the impact of Regional GDP-increases on Community budgets in a methodology similar to Algoed(2009). The methodology is based on partial derivatives of Community budget components with respect to GDP. We base our computations in appendix 13.2. on the clear overview of the four types of revenues of the French and Flemish Communities21 in Van der Stichele and Verdonck(2001): the value added tax (VAT)-grant, the personal revenue tax (PIT)-grant, the Radio-TV fee and Government funding in respect of foreign students. Table 2.9 teaches us that Communities do not gain much money from GDP increases. Table 2.9:Community budget impacts if Regional GDP increases with €100 in 2007(in €) Impact Budget Fla Comm Impact Budget Fre Comm Increase GDP Fla with 100 € 1.82 0.18 Increase GDP Wall with 100 € -0.25 2.05 Increase GDP Bru with 100 € 1.16 0.92 Source: Own computations based on Conjunctuurnota 2008, Projet de loi de Finances pour l’année 2008 : “Exposé des Motifs” and NBB, Belogstat figures. 2.1.3.2. Community budget impacts associated with Regional employment rate variation without multiplier effect Table 2.10 teaches us that financial incentives for Communities to activate residents in the Region of jurisdiction, are very low and vary between €1,200 and €500 per activated resident. Table 2.10:Community budget impacts associated per activated unemployed in 2007(in €1000) Entity Activation of one Activation of one Activation of one unemployed in Fla unemployed in Wal unemployed in Bru Fla Comm 1.1 -0.1 0.7 Fre Comm 0.1 1.2 0.5 Source: Own computations based on Conjunctuurnota 2008, Projet de loi de Finances pour l’année 2008 : “Exposé des Motifs, HERMREG and NBB, Belogstat figures. 21 To keep it simple, we neglect the German Community and also the transfers between French Community and Brussels- Capital Region (Cocof) and Walloon Region linked to transfers of competences. - 13 -
  26. 26. 2.1.4. Total returns Table 2.11:Community-, Regional- and federal budget impacts per activation in 2007(in €1000) Number Entity Activation Activation one Activation one one unemployed in unemployed in unemployed Wal Bru in Fla (1) Fla Reg 3.2 -0.5 0.8 (2) Fla Reg (incl. ALMP cost ) -18.6 -0.5 0.8 (3) Fla Comm 1.1 -0.1 0.7 (4) Wal Reg 0.8 1.2 0.5 (5) Wal Reg (incl. ALMP cost) 0.8 -20.7 0.5 (6) Bru Reg 0.2 0.2 1.1 (7) Bru Reg (incl. ALMP cost) 0.2 0.2 -20.7 (8) Fre Comm 0.1 1.2 0.5 (9)=-(1)-(4)-(6) Fed (Financing Law Reg) -3.0 0.7 -1.2 (10)=-(3)-(8) Fed (Financing Law Com) -1.2 -1.1 -1.2 (11) Fed unemployment cost 29.0 29.0 29.0 (12)=(9)+(10)+(11) Fed net 24.8 28.7 26.5 (13)=(2)+(3)+(5)+(7)+(8)+(12) Total Return (incl. ALMP) 8.5 8.8 8.3 (14)=(1)+(3)+(4)+(6)+(8)+(12) Total Return (excl. ALMP) 30.3 30.6 30.1 Source: 0wn computations 22 Lines 1,4 and 6 reflect the impact of Regional GDP increases, associated with activation, on Regional budgets . 23 Lines 2,5 and 7 are the same Regional figures but we take into the account the activation cost of one unemployed based on Estevão(2003). Lines 3 and 5 are the Community returns based on own computations regarding the impact on Community incomes of Regional GDP increases associated with activation. Line 11 is gives the total federal budgetary cost of an unemployed person which is an up-dated figure of De Bresseleers et al (2004). 22 The Regional GDP increase, associated with the activation of an unemployed, is based on an up-date of De Bresseleers et al(2004). Regional budget impacts of Regional GDP variations are based on Algoed(2009). 23 ALMPs consist mainly in training, targeted subsidies to job creation, public employment services and other expenditures aimed at promoting employment. Non targeted policies to lower labor costs are not included in this definition, as they are considered general macroeconomic policies. - 14 -
  27. 27. Table 2.12:Shares of Community-, Regional- and federal budget impacts in total return associated with activation of an unemployed in 2007(in %) Channel Entity Activation of one Activation of one Activation of one unemployed in unemployed in unemployed in Fla Wal Bru Indirect: GDP,PIT* - Fla Reg 10.7 -1.7 2.6 Financing Law Indirect: GDP,PIT* - Fla Comm 3.5 -0.5 2.3 Financing Law Indirect: GDP,PIT* - Wal Reg 2.7 3.8 1.6 Financing Law Indirect: GDP,PIT* - Bru Reg 0.7 0.7 3.6 Financing Law Indirect: GDP,PIT* - Fre Comm 0.4 3.9 1.8 Financing Law Direct: Unemployment Federal 82.0 93.7 88.1 benefits, taxes, social contributions Regions, Communities, Total Return 100.0 100.0 100.0 Federal Source: Own computations *The abbreviation PIT stands for personal income taxes which increase in GDP. In the column “Channel”, we distinguish direct returns of activation through higher taxes, social contributions and lower unemployment benefits and indirect returns through the positive impact of activation on GDP which on its turn drives Regional incomes via the Financing Law. What are the incentive implications of the summary results in tables 2.11 and 2.12? Taking into account the ALMP-cost, the three Regions have a limited financial interest in investing to increase the employment rate. Excluding the ALMP-cost, the financial incentives for Wallonia and Brussels are not significant (respectively 3.8 and 3.6% of the total return). The incentives for the Flemish Region represent 10.7% of the total return. Moreover, the returns are not direct neither transparent because the Financing Law is (i) complex and (ii) linked to Regional GDP and not to Regional employment. The same story holds for the Communities: incentives for the Flemish and French Communities represent 3.5 and 3.9% of the total return. The federal level is clearly the biggest winner when Regional employment rates increase, especially when residents in the lagging Regions are activated. The federal returns are important, direct and transparent. We clearly showed that today, activation incentives are limited at the decentralized level. Let us now turn to the second main argument for the bonus malus: employment rates have to convergence upward in Belgium both for socio-economic and political stability reasons. - 15 -
  28. 28. 2.2. EMPLOYMENT RATES HAVE TO CONVERGE UPWARD FOR SUSTAINABILITY BELGIAN SOCIAL MODEL 2.2.1. STATUS-QUO BASELINE COURSE OF EMPLOYMENT RATES IS NOT AN OPTION Table 2.13 provides us with two key messages. First, no Belgian Region has reached nor will reach the Lisbon 70% employment rate targets in 2013, considered as hurdle rate to finance the ageing cost. Second, the current employment rate gap (11.1 percentage points between Flanders and Brussels and 8.8 percentage points between Flanders and Wallonia) is expected to widen with 1.2 and 1.5 percentage points at horizon end 2013 at unchanged policies.24 If we want to close this interregional gap, while at the same time bringing all Regions to sustainable Lisbon target conform employment rate levels, then we have to achieve this upward employment rate convergence. Let us now turn to the socio-economic and political arguments for this upward convergence. Table 2.13:Expected evolution Regional employment rates until 2013 including crisis impact25(in percentage points) Employment Employment Average yearly variation Average yearly variation rate end ‘07 rate end'13 employment rate '08-'13 employment rate '10-'13 Bru 55.7 56.0 0.06 0.33 Fla 66.8 68.3 0.25 0.40 Wal 58.0 58.0 0.00 0.20 Source: Own computations based on Planning Office(2008) 24 Although the Planning Office does not explain the widening employment rate gap between Flanders and the two other Regions, we can outline the main reasons. For Brussels, demographics play an important role. In Brussels, the active population grows with 3.6 percentage points between 2007 and 2010 whereas the active population in Flanders only grows with 1.5 percentage point. On the other hand, the number of working residents is expected to grow faster is Brussels with a compounded average growth rate of 1.35% versus 0.89% in Flanders between the end of 2007 and the end of 2013 in the baseline scenario which includes the impact of the financial crisis. For Wallonia, the main reason is the lower GRP growth driven by an unfavorable sector structure. Indeed, the fastest growing sectors over the period 2007-2013, in the baseline scenario drafted before the financial crisis, namely market services and construction with growth rates of 2.3% and 3.3% have relatively low shares in Wallonia’s GRP (respectively 38.4% and 5.7%) compared to the relatively high shares of those sectors in Flanders’ GRP (respectively 43.3% and 6%). The second reason for the lower employment rate growth in Wallonia is demographic: the active population is expected to grow with 2.3% over 2007-2010 compared to 1.5% in Flanders. 25 We estimate the number of lost jobs at 66,000 in Belgium in 8.1.3. on the basis of Planning Office data and we suppose they are (definitively) lost in 2009. - 16 -
  29. 29. 2.2.2. CURRENT ECONOMIC CRISIS AND AGEING COST URGE FOR UPWARD EMPLOYMENT RATE CONVERGENCE In May 2009, The European Commission estimated the Belgian public deficit for 2009, at unchanged policies, at 6.1% of GDP. For the first time since 2002, public debt would exceed Belgian GDP in 2010. In March 2009, the High Council of Finance estimated the structural budget deficit26 at 2.7% of GDP. At unchanged policies, the structural deficit might deteriorate up to 5% in 2015 due to the rising ageing costs, increasing at 0.5 to 0.6% of GDP every two years, and due to the interest rate snowball effect. Between 2018 and 2050, ageing-related extra costs will rise to 4.2% of GDP. This estimate depends on critical assumptions concerning demographics, employment rates and productivity gains. The High Council of Finance and the Ageing Commission stress the need to increase the employment rate to finance our social security by increasing the financing base while reducing the number of recipients of unemployment benefits. In the current turbulent economic content, many workers fall into unemployment. The job bonus malus can incentivize decentralized entities to coach, train and educate them to avoid them falling into structural unemployment and to become victims of hysteresis effects. To conclude, the deteriorating situation of Belgian public finances, both in the short as in the medium run, urges for significant employment rate increases, as necessary condition to finance the ageing cost. 26 The structural deficit assesses the underlying public financial result, independent of temporary or reversible factors such as the business cycle, international relative price levels or one-shot budget measures. The structural deficit is assessed on the basis of the estimation of the cyclical deviation of effective GDP from the GDP growth trend line, which is the so-called output gap. - 17 -
  30. 30. 2.2.3. CREDIBLE PERSPECTIVE OF UPWARD EMPLOYMENT RATE CONVERGENCE MIGHT CONTRIBUTE TO ALLEVIATE BELGIAN POLITICAL DEADLOCK Since the 2007 elections, the political debates about institutional reforms have been characterized by immobility. Once can represent this deadlock as a stylized opposition between, on the one hand, Flemish calls for more decentralized policy autonomy and financial accountability and, on the other hand, a Frenchspeaking “non”, coupled to the wish to maintain interpersonal and hence interregional solidarity. But is solidarity necessarily at odds with responsibility? No, as outlined by Dewatripont(2009), a credible job bonus malus might “ensure that federated entities manage to gradually converge so that solidarity is not forever unidirectional as far as cross-regional flows are concerned”. Dury et al(2008) quantify how interregional employment rate upward convergence could reduce or even eliminate North-South transfers. If employment rates converge to 68.1% in the three Regions at horizon 2030, then financial interregional transfers will change dramatically. Flanders would turn from a net-payer (1.9% of Belgian GDP in 2007) into a net-receiver (0.2% of Belgian GDP in 2030). Brussels would pay a transfer equal to 1.1% of Belgian GDP in 2030, compared to 0.1% today. The interregional transfers, received by Wallonia, would drop from 2.1% of GDP in 2007 to 0.9% of GDP in 2030. To conclude, the job bonus malus could enhance the credibility of this convergence scenario as one of the key steps to get out the current political deadlock, which seems quite irresponsible in the light of the dramatic current economic and financial crisis. - 18 -

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