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October 2, 2013 Lisbon, Portugal EUROMOD Research Workshop

October 2, 2013 Lisbon, Portugal EUROMOD Research Workshop. More targeting, less redistribution? An enquiry into the role of policy design Ive Marx, Lina Salanauskaite, Gerlinde Verbist Centrum voor sociaal beleid Herman Deleeck , U niversiteit antwerpen. Outline. Background & aim

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October 2, 2013 Lisbon, Portugal EUROMOD Research Workshop

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  1. October 2, 2013 Lisbon, PortugalEUROMOD Research Workshop More targeting, less redistribution? An enquiry into the role of policy design Ive Marx, Lina Salanauskaite, Gerlinde Verbist Centrum voorsociaalbeleid Herman Deleeck, Universiteitantwerpen

  2. Outline Background & aim Methodology Results Conclusions

  3. Background & aim

  4. Paradox of redistribution Korpi and Palme (1998) : “the more we target benefits at the poor... [], the less likely we are to reduce poverty and inequality”. Recent contests of this observation: Kenworthy (2011), Brady and Bostic (2012), Marx et al. (2013). Source: Kenworthy (2011)

  5. Targeting: concept • Targeting = a policy design instrument • Korpi and Palme (1998): • targeting = “excluding the better-off citizens” • Kenworthy (2011), • “targeted transfers are directed [] to those with low incomes and assets, whereas universal transfers are provided to most or all citizens”. • Whiteford (2010): • “a means of determining either eligibility for benefits or the level of entitlements for those eligible”. ....

  6. Targeting: measurement • Korpi and Palme (1998): • The degree of low-income targeting or • “Index of the degree of targeting of transfers” , also called • “ Index of targeting of transfer income” • = the extent to which budgets used for redistribution go to those defined as poor. • = "index of concentration”. • Concentration coefficient: • twice the area between the concentration curve and the line of equality (the 45-degree line); • similar to GINI, but its ranking variable (e.g. disposable income) and the variable of interest (e.g. social transfers) are different.

  7. Concentrationcoefficient • Bounded between –1 and 1; some thresholds: • -1.0 - the poorest person (based on the chosen income distribution) gets all the transfer income; • 0 – everybody gets equal absolute amounts of transfers; • [-1; 0] – a strong pro-poor distribution of social transfers; • = GINI index value - all individuals get the same proportion of transfers; • [0; GINI index value] - weakly pro-poor distribution of transfers; • > GINI index value - pro-rich distribution of social transfers; • +1.0 –the richest person gets all the transfer income. • Some properties: • Unless ranking is affected, a change in the degree of income inequality does not affect the concentration index measure. • Invariant to multiplication by any scalar; however sensitive to changes based on any linear (i.e; adding a constant to the variable) transformation of the variable of interest.

  8. Redistribution • Absolute reduction in incomeinequality: • GINImarket-GINIdisposable • Relativereduction in incomeinequality: • (GINImarket-GINIdisposable)/GINImarket

  9. Targeting and redistribution • Korpi and Palme (1998): • “Without specifying the functional form or all other relevant factors, []… final redistribution is a function of : • degree of low-income targetingx redistributive budget size; • Trade-off: the greater the degree of low-income targeting, the smaller the redistributive budget. •  Paradox of Redistribution. • Note: properties of concentration coefficient.

  10. Aim How do changes in concentration coefficient of social transfers relate to redistribution? To what extent could we place an equality sign between the concentration coefficient and targeting as a policy design instrument? Two first focus countries: Ireland and Lithuania Years: 2007-2012. Today’s presentation: selected results.

  11. METHODOLOGY

  12. Country selection: EU-SILC data 2006 2010

  13. Targeting and redistribution • A link between GINI and CI: • Using Rao (1969) decomposition of income inequality, • GINI=sum(s*CI)  sum of “contributions” of diverse concentration coefficients to the overall inequality. • Redistribution (absolute reduction): • RE = GINIMI-GINIDI • = GINIMI - sst*CIST - smi*CIMI-stax*CITAX • where s is a relative share of income component in disposable income, so that ∑ si= 1.

  14. Methodology • Empirical analysis: • We use microsimulation model EUROMOD (version F6.36), in order to assess: • policy design change impact on redistribution and concentration coefficient of social transfers; • the redistribution impact of changing market income distribution and changing socio-demographic structures. • Simulation scenarios: • Ireland: 2008 dataset, plus 2007-2011 policies (baselines and reformed policies); • Lithuania: 2008 and 2010 datasets, plus 2007-2012 policies (baselines and reformed policies).

  15. Selected Results

  16. Lithuanian baselines 2008 data, policyyears 2007-2012 2010 data, policyyears 2009-2012

  17. Ireland baselines Data: 2008

  18. RE: decomposition by factors (1) • Here and further on: focus on two years 2008 & 2009. • Overall observations: • Higher redistribution level in Ireland than in Lithuania: • both due to GINIMI > in Ireland; • and GINIDI < in Ireland. • Progressivity of market income is rather similar in both countries. • Stronger pro-poor distribution of social transfers and taxes in Ireland.

  19. RE: decomposition by factors (2) • Changes from 2008 to 2009: • Redistribution increases in both countries  decrease in GINIDI(market income distribution remains stable). GINIDI component changes: • Similar changes regarding progressivity and relative shares of social transfers – CI reduces, whereas the relative share of social transfers increases; • Similar slight drop in CI of market income and taxes; • Diverging roles of relative shares of market income and taxes: a small drop in Lithuania, but an increase in Ireland.

  20. More detailed look - Lithuania • What policy changes are the main triggers of lower targeting degree? • A reminder: CI increases to 0.072 from 2008 to 2009 (data the same). • Increased targeting is due to the universal child benefit becoming means-tested (Sc. 1). No other big influences by changes in other transfers (Sc. 2). • Loweered targeting due to changes in tax and SIC structures (Sc. 3). • Influence of uprating factors – a major jump in a few dimensions (Sc. 4). • Policy design and targeting: country’s social transfers become more targeted, however other policy changes are main drivers of reducing targeting degree.

  21. More detailed look - Ireland • What policy changes are the main triggers of becoming more universal? • A reminder: CI increases from -0.055 to - 0.026 given a change in policies from 2008 to 2009 (same data). • No big influence by changes in means-tested or non means-tested benefits. • Biggest CI increase driven by “Public Sector Pension Related Deduction” (Sc. 1). • Even bigger role is played by changes in the tax policy: e.g. an introduction of a special income levy (Sc. 2). • Uprating factors also have a strong role (Sc. 3). • Policy design and targeting: the change in targeting degree is not directly related to means-testing of benefits.

  22. Some extras - Lithuania • 2007 to 2008 : a big jump in CIst in Lithuania is mainly caused by: • Changes in non-means tested benefits, and particularly parental leave policies. • Interestingly: a change in replacement rate ratio has a CI lowering, whereas other changes in the rules (i.e. benefit duration) has a CI value increasing effect. • An expanded provision of a universal child benefit  higher targeting! • Tax policy change (reduced flat rate tariff)  higher targeting. • The influence of uprating factors is strong and of CI increasing value. • 2009-2010: none of the implemented partial policy changes seem to have a major influence on CI only their interactive effect makes a difference. This shows how interactive and complex policy changes are. • 2011 – 2012: no major role of uprating factors. • Puzzle: a change in constants (i.e. pension age, CYI, maximum limit on contributory parental benefits) has a lowering effect on GINImi.

  23. Conclusions

  24. Conclusions • Targeting and redistribution: • An increase in CI of social transfers does not necessarily have a the same directional effect – and if any - on the RE index. Other components could be of higher importance: e.g. CI of market incomes, changing structure of disposable incomes, etc. • A move in CI of social transfers and redistribution could also be due to changing socio-economic structures, not policy changes. • CI indication of lower or higher targeting is not necessarily linked to targeting – as policy design instrument. • Actually, reverse effects could be observed: e.g. an expansion of the universal child benefit provision in Lithuania associated with higher targeting, as indicated by CI. • Country effects are highly diverse.

  25. Further research is on the way…. Thank you!

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