Redistributive effect, progressivity and differential tax treatment: Personal income taxes in twelve OECD countries

Journal of Public Economics 72 (1999) 73–98

Redistributive effect, progressivity and differential tax treatment: Personal income taxes in twelve OECD countries Adam Wagstaff a , *, Eddy van Doorslaer b , Hattem van der Burg b , Samuel Calonge c , Terkel Christiansen d , Guido Citoni e , i ¨ Ulf-G. Gerdtham f , Michael Gerfin g , Lorna Gross h , Unto Hakinnen , j k i l ¨ Jurgen John , Paul Johnson , Jan Klavus , Claire Lachaud , Jørgen Lauridsen m , Robert E. Leu g , Brian Nolan n , Encarna Peran o , r ´ Carol Propper p , Frank Puffer h , Lise Rochaix q , Marisol Rodrıguez , g s j Martin Schellhorn , Gun Sundberg , Olaf Winkelhake a

School of Social Sciences, University of Sussex, Brighton, BN1 9 QN, UK Department of Health Policy, Erasmus University, 3000 DR Rotterdam, The Netherlands c Department of Econometrics, University of Barcelona, 08034 Barcelona, Spain d Centre for Health and Social Policy, Odense University, 5000 Odense, Denmark e Department of Public Economics, Universita` La Sapienza, Rome, Italy f Stockholm School of Economics, Stockholm, Sweden g Department of Economics, Bern University, 3012 Bern, Switzerland h Department of Economics, Clark University, Massachusetts, MA 01610, USA i National Research and Development Centre for Welfare and Health, 00531 Helsinki, Finland j GSF National Research Center for Environment and Health, 85764 Neuherberg, Germany k Institute for Fiscal Studies, London WC1 E 7 AE, UK l URA 934, University C. Bernard, Lyon I, 69623 Villeurbanne Cedex, France m Department of Economics, Odense University, 5000 Odense, Denmark n Economic and Social Research Institute, Dublin 4, Ireland o Department of Public Economics, University of Barcelona, 08034 Barcelona, Spain p Department of Economics, Bristol University, Bristol BS8 1 TN, UK q LEGOS, University Paris Dauphine, 75016 Paris, France r Department of Economic Policy, University of Barcelona, 08034 Barcelona, Spain s Department of Economics, Uppsala University, 751 20 Uppsala, Sweden b

Received 1 July 1997; received in revised form 1 February 1998; accepted 8 June 1998

*Corresponding author. [email protected]

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Abstract This paper decomposes the redistributive effect of the personal income taxes (PITs) of twelve OECD countries into four components: (i) an average rate effect, (ii) a departurefrom-proportionality or progressivity effect, (iii) a horizontal equity effect and (iv) a reranking effect. The product of (i) and (ii) indicates the vertical redistribution associated with the PIT and the sum of (iii) and (iv) indicates the impact on the distribution of income of differential tax treatment. The average tax rate is found to be low in France and high in the Nordic countries, and the PIT is found to be most progressive in France, Ireland and Spain, and least progressive in Denmark and Sweden. Taking (i) and (ii) together, Denmark and the US achieve broadly similar levels of vertical redistributive effect. Differential treatment is found to have a much smaller effect on income redistribution (as a proportion of redistributive effect) than the vertical redistribution caused by progressivity, though there are differences between countries. These differences appear to be due principally to a different emphasis on deductions, such as tax deductibility of mortgage interest payments and insurance premiums, and on local income tax.  1999 Elsevier Science S.A. All rights reserved. Keywords: Redistributive effect; Progressivity; Horizontal equity; Reranking; Personal income tax JEL classification: D31; H23; H24

1. Introduction A standard approach to measuring the impact of a tax on the distribution of income is to take the difference between the pre-tax and post-tax Gini coefficients. Aronson et al. (1994)-hereafter AJL-have recently shown that this measure of redistributive effect depends on four factors: the average tax rate, the progressivity of the tax, the extent to which households with similar incomes are treated unequally (classical horizontal inequity) and the extent of any reranking in the move from the pre-tax income distribution to the post-tax income distribution.1 In this paper we apply the decomposition developed by AJL to the personal income tax (PIT) systems of twelve OECD countries: Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Spain, Sweden, Switzerland, the UK and the US. The data are taken from household surveys conducted during the late 1980s and early 1990s. 1 Cf. also Aronson and Lambert (1994). See Lambert and Ramos (1995), (1997) and Lambert (1995) for more on decomposing redistributive effect, including decompositions based on measures of income inequality other than the Gini coefficient.

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Our focus on the PIT, and the consequent exclusion of all other taxes, including social insurance contributions, is inevitably somewhat arbitrary. It might be argued that one ought also to include social insurance contributions and indirect taxes, on the grounds that countries that raise relatively little revenues from the PIT often have correspondingly higher social insurance contributions or correspondingly higher indirect taxes. But, following this line of argument does not lead to any firm conclusion. Why not include corporate income taxes and property taxes? Why not include an allowance for government borrowing? Furthermore, since countries vary in their reliance on public finance in areas such as education, health care and housing, why, in an international comparison, should one focus exclusively on public finance? Aside from the problem of drawing a logical boundary around a broader income redistribution analysis, there are huge difficulties in undertaking such an analysis. And at least one can reasonably argue that policy-makers appear to accept that income redistribution is a proper policy goal of the PIT, whilst the same cannot be said of many other taxes. The international evidence to date on the redistributive effect of the PIT is limited, as is the international evidence on the aforementioned determinants of redistributive effect. A number of studies present international evidence on inequality in pre-tax and post-tax incomes,2 and several studies have presented international comparisons of progressivity of direct taxes; 3 few of these, however, focus just on the PIT and instead include at least employee social insurance contributions along with the PIT, if not transfers as well. Rather more is known about international variation in average personal income tax rates.4 Next to nothing appears to be known, however, about the comparative performance of PIT systems in terms of horizontal inequity and reranking. This is a serious gap in our knowledge, given that horizontal equity appears to feature prominently amongst policy-makers’ objectives in this area and reranking is thought by many to be a

2 Atkinson et al. (1995a), (1995b), (1995c) present evidence on inequality in market income and disposable income, but the gap between market income and disposable income reflects not just PIT but also payment of employee social insurance contributions, as well as the receipt of occupational pension payments, social insurance cash transfers, universal cash transfers and social assistance payments. Smeeding and Gottschalk (1997) also present evidence on inequality in market incomes and in disposable incomes. 3 Studies of tax progressivity include Bishop et al. (1993), (1990); Formby et al. (1984); Kakwani (1977); O’Higgins et al. (1990); Rosenberg (1989) and Zandvakili (1994). Most of these cover social insurance as well as the PIT. 4 For example, the OECD (1990) and Messere (1993) both provide overviews of PIT rates in the OECD countries. 5 See Kaplow (1989) and Lambert (1995) for interesting discussions of the role of horizontal equity in tax design.

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relevant factor to consider when appraising the merits of alternative PIT schemes.5,6 The paper is organised as follows: Section 2 summarises the AJL decomposition method; Section 3 outlines the data and variable definitions; Section 4 presents our empirical results; Section 5 seeks to shed some light on the question of why horizontal inequity and reranking may vary across the PIT systems covered in the paper; and the final section- Section 6-presents some conclusions.

2. The AJL decomposition of the redistributive effect of the income tax Denote by RE the difference between the pre-tax Gini coefficient, GX , and post-tax Gini coefficient, GX 2 T . AJL show that where not all households on a given income pay the same amount of tax, RE can be decomposed as follows

S

D

g RE ; GX 2 GX 2T 5 ]] KT 2 12g ;V 2 H 2 R

Oa G x

F(x)

2 [GX2T 2 CX2T ] (1)

where g is the tax rate, KT is the progressivity index proposed by Kakwani (1977) computed on the assumption that everyone faces the same tax schedule, aX is the product of the population share and post-tax income share of households with income x, GF ( x) is the Gini coefficient for post-tax income for households with pre-tax income x and CX 2T is the post-tax concentration index obtained by ranking households first according to their pre-tax income and then within each group of pre-tax equals by their post-tax income. V measures the inequality reduction that would have obtained if there had been no differential tax treatment. H measures the extent of classical horizontal inequity-i.e. the unequal treatment of equals.7 It is computed as a weighted sum of the post-tax income Gini coefficients GF ( x) of 6 The issue of reranking has proved somewhat controversial. Several papers, including those of Atkinson (1980); Plotnick (1981) and King (1983), equate reranking with horizontal inequity, but other authors, including Berliant and Strauss (1985); Kaplow (1989); Le Grand (1987) and Aronson et al. (1994), have questioned this practice, arguing that reranking refers to the treatment of unequals not equals. Several authors have discussed the issue of whether the initial ranking ought to be accepted as fair, with some, including Le Grand (1987), suggesting that it may in fact be one of the equity goals of the PIT to alter the ranking of households. The AJL decomposition used in this paper implicitly accepts the initial ranking as equitable. Lerman and Yitzhaki (1995) question this and develop an alternative decomposition which weights income changes by the post-tax ranking. This, however, is open to the objection that the post- tax ranking may not be considered fair either. 7 Throughout we refer, like AJL, to H as ‘‘horizontal inequity’’. It might, however, be more appropriate to call it ‘‘horizontal inequality’’, since some horizontal inequality may actually be considered equitable by policy-makers.

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households with pre-tax income x, these Gini coefficients being zero only if there is no differential tax treatment of equals. R measures the extent of reranking in the move from the pre-tax distribution to the post-tax distribution by comparing the post-tax Gini coefficient with the post-tax concentration coefficient.8 If there is no reranking, R is zero. Positive values of H do not necessarily imply positive values of R, but in the absence of marginal tax rates in excess of 100%, reranking can only arise through differential tax treatment. Hence positive values of R do imply positive values of H. H and R are always non-negative. In the case of a progressive tax KT is positive and differential tax treatment, as reflected in positive values of H and R, reduces the redistributive effect of the tax.

3. Data and variable definitions The surveys used are listed in Table 1. With the exception of the Spanish survey, which is a sample drawn from tax files, these are all household surveys that aim to achieve close to 100% coverage of the non-institutionalised population. Our surveys are not always the same as those in the Luxembourg Income Study (LIS) database though the majority of them are now included in the LIS Wave III database (cf. Atkinson et al., 1995a,b,c; De Tombeur and Ladewig, 1994). Differences are indicated in Table A1 in the Appendix. In only two cases (Ireland and Italy), is the survey used in the present paper the same as that used in the LIS-based publications (cf. Smeeding and Gottschalk, 1997). In several cases (e.g. the UK, France, Germany), the survey used here is the same as that in the LIS database but is for a more recent year. In the seven cases where different surveys than the ones at LIS were used, the surveys used here seem to be at least as suited to the task in hand as that held at LIS. The sampling unit in our surveys is the household, except in the cases of Sweden and Switzerland, where it is the individual, and Spain, where it is the tax unit. The unit of analysis throughout is the family or household. Typically, then, our unit of analysis is not the tax unit: in Denmark, Finland, the Netherlands and Sweden, the individual is the tax unit; in Germany, Ireland and the UK (and in Italy, Spain and the US on an optional basis), taxation is on a joint basis; in France and Switzerland, the family is the tax unit. By choosing a unit of analysis that is typically not the tax unit, we run the risk of producing a confusing picture of horizontal inequity and reranking — non-zero values of H and R may be due simply to our failure to choose the ‘‘correct’’ unit of analysis. On the other hand, however, it allows us to use same unit of analysis throughout and one that is, from a social welfare perspective, arguably the most relevant. 8

This is similar to the measure of reranking proposed by Atkinson (1980) and Plotnick (1981), though these authors interpret the index as a measure of horizontal inequity.

Year

1987 1990 1989 1988

1987

1991 1990 1990

1990 1992 1993 1987

Country

Denmark

Finland France

Germany

Ireland

Italy

Netherlands

Spain

Sweden Switzerland UK US

Level of Living Survey Living Conditions Survey Family Expenditure Survey National Medical Expenditure Survey

Ministry of Finance sample of tax files

Survey of Income Distribution, Poverty and Use of State Services Survey of Household Income and Wealth Family Budget Survey

Income and Expenditure Survey

Household Expenditure Survey Family Expenditure Survey

Household Expenditure Survey

Survey Name

Table 1 Details of surveys and samples

Institute for Social Research National Science Foundation Central Statistical Office Agency for Health Care Policy & Research

Netherlands Central Bureau of Statistics Institute for Fiscal Studies (IEF)

Bank of Italy

Economic and Social Research Institute

Central Statistical Office (Danmarks Statistik) Statistics Finland National Institute of Statistics & Economic Studies (INSEE) German Federal Statistical Office

Institution conducting survey

Individual Individual Household Household

Tax unit

Household

Household

Household

Household

Household Household

Address

Sampling unit in survey

Family Tax unit Household Household

Family

Household

Household

Household

Household

Household Household

Household

Unit of analysis

5274 9524 7000 12649

21233

2767

8188

3924

42982

8258 8679

2232

Sample size

No Yes No Yes

No

Yes

Yes

Yes

No

No No

Yes

Sample weighted?

Private households only surveyed

Sample drawn from tax files. Probably covers around 80% of population, but excludes families under tax threshold Persons interviewed aged 18–75 Persons interviewed aged 201

Private households only surveyed

Tax data partly simulated using TBM. Difficulties in obtaining accurate PIT data from a nonlongitudinal survey. Private households only surveyed

Private households included

Comments on survey and sample

78 A. Wagstaff et al. / Journal of Public Economics 72 (1999) 73 – 98

Survey-data obtained via questionnaire and income tax files Income tax files Survey

Survey

Survey Estimated

Survey

Income tax files Income tax files Income tax files Survey Survey

Denmark

Germany

Ireland

Italy

Netherlands

Spain Sweden

Switzerland UK US Incomes of self-employed under-reported

Files provided incomes of all adult members of interviewee’s household

Gross income estimated from disposable income. Incomes of self-employed probably underreported Income in excess of Dfl 200,000 have been set at Dfl 200,000 in the tape provided by CBS. This affects 0.8% of the sample

Compares well with tax reports

Incomes appear to be under-reported in survey compared to National Accounts by the order of around 20%. The problems is especially pronounced amongst the self-employed Survey sought information on incomes of all persons living in interviewee’s household

Comments on income data a

Income tax files Estimated Estimated

Income tax files Income tax files

Survey

Estimated

Survey

Survey

Survey-data obtained via questionnaire and income tax files Income tax files Survey

Source of tax data

Files provided income tax payments of all adult members of interviewee’s household Data very accurate Estimated using IFS tax-benefit model Federal personal income tax estimates (over 80% of the total) estimated very accurately as most of the necessary information is recorded in the survey. State and local income taxes estimated less accurately-estimates do not reflect differential treatment across e.g. states.

Taxes larger than Dfl 100,000 have been set at Dfl 100,000 in the tape provided by CBS. This affects 0.1% of the sample

Tax refunds for 1987 (as recorded in 1988) used to simulate 1988 net tax payments (i.e. after 1988 refunds in 1989) Taxes estimated for around 20% of sample who did not report them All taxes estimated using ISPE’s tax-benefit model

Church tax excluded

Comments on tax data a

These comments fall well short of a full assessment of the merits of the income and tax data. It seems likely, for example, that incomes are under-reported relative to national accounts in most countries. This issue has been more extensively researched in some countries than others.

a

Finland France

Source of income data

Country

Table 2 Sources of data on incomes and income taxes

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The data on incomes recorded in the surveys have been obtained either by means of questionnaire or from tax files (Table 2). The income concept used is gross income defined here along the lines of the LIS (cf. e.g. Smeeding et al., 1985). This is somewhat narrower than the definition used by the OECD (1990) in its study of tax progressivity, in that it excludes fringe benefits and other employer social insurance contributions, imputed income from owner-occupancy, and capital gains.9 As the authors of the OECD study emphasise, however, countries vary widely in the way they value these items; indeed, some countries do not value them at all, as is the case with imputed income from owner occupancy in countries where it is exempt from income tax. For the sake of comparability, it seems sensible therefore to exclude these items.10 The income taxes included are all personal income taxes, irrespective of the level of government at which they are levied and are calculated net of any tax credits. Social insurance contributions are excluded, as are any taxes on fringe benefits such as employer contributions to pension schemes, taxes on capital gains and taxes on the imputed income from owner-occupancy.11,12 In the case of Italy, and for an unknown proportion of the German data, the tax data are estimated taxes that households ought to have paid rather than what they actually may have paid.13,14 In the case of the US, they are simulated tax payments and are therefore

9 For the most part, the progressivity estimates in the OECD study are, in any case, based on income subject to taxation (a narrower definition of income) rather than on gross income. Results for gross income are reported by the OECD only for Denmark, Finland and the US. 10 As the OECD (1990) acknowledges, the definitions of income used in its progressivity analysis are not fully comparable across countries (p. 24). 11 This is not true of Finland, where social insurance contributions have been included, or Spain where taxes on imputed income are included. 12 Whether excluding these latter items makes sense is debatable. On the one hand, they do contribute towards personal income tax revenues in many countries and might therefore be sensibly included. On the other hand, our definition of gross income excludes fringe benefits, imputed rent and capital gains. Furthermore, those countries that do not tax imputed rent (such as the UK) often have an alternative tax (the UK, for example, has the so-called Property Tax) and one might argue that these alternatives ought to be viewed as personal income taxes for the purpose of the present exercise. But that would take us beyond the personal income tax system and, as pointed out in the Introduction, it is not clear then where one should stop. 13 It might be argued that the results obtained using the estimated tax payments are of more interest than those obtained using the actual tax payments on the grounds that the progressivity, horizontal inequity and reranking that emerges is that which is intended to emerge by the tax legislation. 14 In the case of Italy, both the personal income tax and the gross income were estimated using the tax-benefit model developed by the Istituto di Stato per la Programmazione Economica (ISPE) (Di Biase et al., 1995).

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neither actual nor expected tax payments.15 For all other countries, data on actual tax payments were obtained either by means of questionnaire or from the tax authority’s tax files (cf. Table 2). To help assess the effect of our using estimated taxes rather than actual taxes, we have included, in the sensitivity analysis reported in Table 4, results for the UK using both recorded tax payments and tax payments that have been estimated using the tax-benefit model (TBM) of the Institute for Fiscal Studies (IFS). We have equivalised both income and income tax payments using the same equivalence scale in each country. In effect, our analysis of horizontal equity will show the consonance of each country’s tax system with the chosen equivalence scale.16 We have used the scale proposed by AJL, with the parameters u and F both set equal to 0.5.17 To compute the values of the vertical redistribution and reranking terms, V and R, one has to construct artificial groups of pre-tax equals, since in an expenditure survey no two households are likely to have exactly the same pre-tax income. We have employed one of the three intervals used by AJL in their analysis of their 1990 / 91 data (£5.00 per week).18 This range was converted into other currencies at prevailing purchasing power parity exchange rates and adjusted for local inflation. Having decided on pre-tax groupings, the extent of reranking, R, can be computed as the difference between the pre-tax Gini coefficient, GX2T and the pre-tax concentration index, CX2T . The counterfactual 15

Since the National Medical Expenditure Survey did not provide direct information on taxes, it was necessary to estimate the amount that would have been paid in taxes by a given household. The three primary categories of tax to be estimated were federal income tax, and state and local taxes. The task of estimating federal income tax was made easier and more accurate by the inclusion of a number of tax related questions in the final round of the NMES survey. These concerned filing status, number of dependants, type of tax form used, amount of deductions claimed, and other information that allowed a reconstruction of the tax return based on the income reported in various categories. For the minority of households which did not respond to the tax questions, tax estimates were made using standard deductions. Tax adjustments for those items such as alimony payments and IRA plans, which are not reported in the NMES data were simulated by random assignment of amounts to potentially eligible tax filers as a function of income and household characteristics to match the actual distribution as reported in the Internal Revenue Service’s Individual Income Tax Returns 1987 and based on a sample of actual tax returns. These data were also used to benchmark taxes and various tax items by household income level. The lack of geographic detail any finer than Census region made it impossible to calculate state-specific tax payments. Instead, a table of state and local tax payments as a function of income was constructed for each Census region using population weighted averages of 1985 and 1991 state tax rate data as reported in Citizens for Tax Justice (1991). The state and local tax estimates therefore do not reflect any differences in treatment of households of the same income within a given Census region. 16 Some results were also obtained using country-specific equivalence scales. See Section 5.3 below. 17 These values were found by AJL to minimise H in the case of the UK’s personal income system. It need not, of course, be the case that this is true of other countries’ tax systems. 18 Results for all countries were obtained with the other intervals used by AJL. As in AJL’s analysis, the results differed with respect to the relative sizes of H and R, but their sum was much less sensitive to the choice of interval.

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Kakwani index, KT , can then be computed on grouped observations using the groups of pre-tax ‘‘equals’’. The degree of horizontal inequity, H, can then be computed as a residual.

4. Empirical results Our main results are reported in Table 3 and Fig. 1. Ireland, Spain, the UK and the US emerge as the countries with the most unequal pre-tax income distributions. Finland and Germany have the most equal pre-tax income distributions.19 Broadly speaking, the ranking of countries in terms of income inequality is the same after-tax as before-tax: France and Italy swap positions, as do Finland and Germany, but the differences are small.20 Table 3 reveals that there Table 3 Redistributive effect in 12 OECD countries

Denmark (1987) Finland (1990) France (1989) Germany (1988) Ireland (1987) Italy (1991) Netherlands (1992) Spain (1990) Sweden (1990) Switzerland (1992) UK (1993) US (1987)

Tax data GX

GX 2T

RE

g

KT

V (%)

H (%) R (%)

A A A A/E A E A A A A A E

0.2703 0.2253 0.3065 0.2312 0.3418 0.3009 0.2517 0.3694 0.2608 0.2541 0.3768 0.3673

0.0320 0.0432 0.0154 0.0279 0.0452 0.0239 0.0329 0.0389 0.0396 0.0174 0.0352 0.0376

0.2966 0.2188 0.0620 0.1108 0.1540 0.1354 0.1487 0.1397 0.3270 0.1210 0.1421 0.1370

0.0938 0.1644 0.2717 0.2433 0.2685 0.1554 0.1977 0.2545 0.0891 0.1528 0.2278 0.2371

123.8% 106.7% 116.6% 108.5% 108.2% 102.0% 104.9% 106.1% 109.3% 120.7% 107.1% 102.6%

1.9% 1.0% 1.9% 1.3% 1.0% 0.4% 0.7% 0.4% 1.5% 1.7% 0.9% 0.4%

0.3023 0.2685 0.3219 0.2591 0.3870 0.3248 0.2846 0.4083 0.3004 0.2716 0.4121 0.4049

21.9% 5.7% 14.8% 7.3% 7.3% 1.6% 4.2% 5.7% 7.8% 19.0% 6.3% 1.9%

Notes: Source of tax data: A5actual payments, E5estimated / modelled payments. Variable definitions: GX and GX 2T are pre-tax and post-tax Gini coefficients, RE is the difference between them, g is the average tax rate, KT is Kakwani’s progressivity index computed on the assumption that all households face the same tax schedule, V (%), H (%) and R (%) are the values of V, H and R (see Eq. (1)) expressed as proportions of RE. 19 The Ginis for pre-tax income reported here are not comparable to those reported in, for example, Atkinson et al. (1995a), (1995b), (1995c), as their distributions refer to primary income and market income, not to gross income. Furthermore, the years are different. 20 The Ginis for post-tax income reported here are not comparable to those reported in, for example, Atkinson et al. (1995a), (1995b), (1995c), as their distributions refer to disposable income (i.e. gross income net of both PIT and employee social insurance contributions). Furthermore, the years are different. Despite this, it is perhaps worth noting that the rankings are broadly similar. The exceptions are Switzerland and the UK, but the survey years for these two countries are quite different in the two studies — the Swiss survey used in the study by Atkinson et al. is 10 years older than that used in this study, and the UK’s survey is seven years older. Income inequality is known to have increased in the UK over the period in question.

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Fig. 1. RE, V, g and K for PIT for selected OECD countries.

is no link between pre-tax income inequality and the degree of redistribution brought about by the PIT: Sweden and Finland both have a low level of inequality in pre-tax incomes and yet their PIT systems achieve a higher level of income redistribution than any other country apart from Ireland. The rays in Fig. 1 correspond to different values of the average tax rate, g; given the relationship between vertical redistribution, V, and the Kakwani index, KT , moving anti- clockwise around the chart results in higher average tax rates. This variation reflects differences in the overall tax burden between countries, as well as the relative importance of personal income tax, other direct taxes and indirect taxes. The relative unimportance of the PIT in France and its relative importance in the Nordic countries is well known.21 Fig. 1 reveals a cluster of countries, comprising Ireland, Italy, the Netherlands, Spain, the UK and the US, which all have values of g around 0.15. Fig. 1 and Table 3 show that the PIT is most progressive in France, Ireland and Spain, and least progressive in Sweden and Denmark. Our high progressivity index for the French personal income tax system contradicts what appears to be the conventional wisdom in France and the low progressivity of the Nordic systems also goes against the popular belief of highly progressive tax structures in these countries.22 It is also interesting to note that despite the progressivity-reducing tax 21 In 1989, the PIT raised only 11.4% of total tax revenues in France, compared to 39.3% in Sweden, 47.4% in Finland and 52% in Denmark (cf. Messere, 1993, p. 48). 22 Cf. Malabouche (1991).

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reforms in the UK and the US during the 1980s, the PIT in these countries is still fairly progressive by international standards.23 Fig. 1 also illustrates how the average tax rate, g, and the Kakwani index, KT , interact to determine vertical redistribution, V. The PIT systems in Denmark and the US, for example, achieve broadly similar values of V, but Denmark achieves it through a combination of a high average tax rate and a low level of progressivity, whilst the US achieves it through a combination of a medium average tax rate and a relatively high level of progressivity. Vertical redistribution, V, is highest in Ireland and the Nordic countries, but by international standards, V is also high in Spain, the UK and the US. The degree of vertical redistribution is a good deal lower in France and Italy. Also evident from Fig. 1 are the discrepancies between redistributive effect, RE, and vertical redistribution, V, caused by non-zero values of the measures of horizontal inequity, H, and / or reranking, R. In most countries, the discrepancy is fairly small, implying that, in terms of its impact on the distribution of income, differential tax treatment is far less important than progressivity. There are, however, some noteworthy differences across countries. By international standards, the sum of horizontal inequity and reranking is relatively large in Denmark and Ireland, and-albeit to a lesser extent-in Finland and Sweden. As a proportion of redistributive effect, V is largest in Denmark, France and Switzerland: thus, in Denmark, for example, the PIT would be 24% more redistributive in the absence of differential tax treatment. In Italy, the Netherlands, the UK and the US, the discrepancy between V and RE is, by international standards, small, in both absolute and proportional terms. Since the gap between redistributive effect and vertical redistribution varies across countries, it is inevitable that the ranking of countries by RE is not identical to the ranking by V. Thus, for example, in the absence of differential tax treatment, the PIT would have been more redistributive in Denmark than it would have been in the UK and the US. As it is, since differential tax treatment is a good deal higher in Denmark than in the UK and the US, the actual redistributive impact of the PIT in Denmark is a good deal lower than that of the PIT systems of the UK and the US. Table 3 also shows that horizontal inequity, H, is larger than reranking, R, in all countries, but as AJL emphasise, the relative values of H and R (but less so their sum) depend on the interval chosen. Specifically, H falls and R rises as the interval used to identify equals is reduced. Given this, it is probably unwise to attach too much importance to the relative values of H and R, and instead to focus on their sum as a measure of the gap between actual and potential redistribution.

23

See e.g. Giles and Johnson (1994) on the progressivity consequences of the PIT reforms of Britain’s last Conservative government.

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5. Accounting for the empirical results The obvious question arises: What factors account for the cross-country differences in the components of redistributive effect, RE? A number of issues seem worth exploring.

5.1. Reported versus estimated taxes The first is the possibility that the differences may in part be spurious due to the fact that in some countries, taxes are estimated or simulated tax payments, whilst in others they are recorded tax payments. Table 4 shows inter alia the effect for the UK of using TBM-estimated tax payments instead of the recorded tax payments. The estimated average tax rate, g, is marginally lower when the TBM is used, but the value of the Kakwani index, KT , is a good deal higher — the PIT in the UK is less progressive in practice than it is in theory. Furthermore, as AJL found in their analysis, the discrepancy between redistributive effect, RE, and vertical redistribution, V, is a good deal smaller with the TBM than it is with the survey data; indeed, the discrepancy is almost eliminated altogether. This might be interpreted as suggesting that the discrepancy between RE and V in the survey data may have more to do with factors such as mis-reporting of income and taxes and incorrect assessment of tax liabilities than with genuine differential treatment in the PIT structure. But it also needs to be borne in mind that TBMs may fail to capture the full complexity of the income tax structure and therefore the TBM results may underestimate the discrepancy between RE and V in the tax structure. What is certainly true is that cross-country comparisons need to be undertaken with caution when the tax payments in some countries are reported tax payments and in others are estimated payments. In a rather crude way, we attempt to take this into account in what follows.

5.2. Local versus central government income taxes Another issue worth exploring is the differential emphasis on local and central government income taxes. Table 4 shows that the income tax levied by central governments of the three Nordic countries and Switzerland is more progressive (KT is higher) than the income taxes levied by lower levels of government but generates a smaller amount of revenue (the average tax rate, g, is lower). The difference in KT is especially pronounced in the case of Sweden, though here too the difference in g is also particularly pronounced. More interestingly, from the point of view of the present exercise, differential tax treatment is lower in the central government’s PIT systems in these countries than in the income tax systems of the other levels of government (V, as a percentage of RE, is lower). This

86

Table 4 Additional RE results Specification

GX

GX 2 T

RE

g

KT

V (%)

H (%)

R (%)

UK

Recorded (FES) Estimated (TBM) All PIT Central income tax Local income tax All PIT Central income tax Local income tax All PIT Central income tax Local income tax All PIT Federal income tax Cantonal income tax Communal inc. tax AJL equiv. scale Finnish equiv. scale AJL equiv. scale French equiv. scale AJL equiv. scale OECD equiv. scale AJL equiv. scale Swedish equiv. scale

0.4121 0.4004 0.3023 0.3023 0.3023 0.2685 0.2685 0.2685 0.3004 0.3004 0.3004 0.2716 0.2716 0.2716 0.2716 0.2685 0.2513 0.3219 0.3290 0.4083 0.4261 0.3004 0.2959

0.3768 0.3547 0.2703 0.2844 0.2904 0.2253 0.2386 0.2596 0.2608 0.2798 0.2892 0.2541 0.2650 0.2652 0.2668 0.2253 0.2108 0.3065 0.3124 0.3694 0.3902 0.2608 0.2563

0.0352 0.0457 0.0320 0.0178 0.0118 0.0432 0.0298 0.0088 0.0396 0.0206 0.0113 0.0174 0.0065 0.0063 0.0048 0.0432 0.0405 0.0154 0.0166 0.0389 0.0360 0.0396 0.0396

0.1421 0.1418 0.2966 0.1376 0.1598 0.2188 0.0968 0.1220 0.3270 0.0782 0.2488 0.1210 0.0198 0.0534 0.0478 0.2188 0.2139 0.0620 0.0636 0.1397 0.1392 0.3270 0.3264

0.2278 0.2858 0.0938 0.1193 0.0707 0.1644 0.2853 0.0684 0.0891 0.2485 0.0390 0.1528 0.3336 0.1280 0.1067 0.1644 0.1660 0.2717 0.2820 0.2545 0.2360 0.0891 0.0895

107.1% 101.7% 123.8% 106.6% 113.6% 106.7% 102.6% 107.7% 109.3% 102.2% 114.8% 120.7% 103.3% 113.9% 111.4% 106.7% 111.5% 116.6% 115.4% 106.1% 106.2% 109.3% 109.5%

0.9% 0.6% 1.9% 1.1% 2.5% 1.0% 0.7% 2.0% 1.5% 0.8% 3.6% 1.7% 1.0% 2.3% 2.8% 1.0% 1.6% 1.9% 2.1% 0.4% 0.5% 1.5% 1.6%

6.3% 1.2% 21.9% 5.6% 11.9% 5.7% 1.9% 5.7% 7.8% 1.5% 11.1% 19.0% 2.4.% 11.6% 8.6% 5.7% 9.9% 14.7% 13.3% 5.7% 5.7% 7.8% 7.9%

Denmark

Finland

Sweden

Switzerland

Finland France Spain Sweden

Notes: Variable definitions: GX and GX 2T are pre-tax and post-tax Gini coefficients, RE is the difference between them, g is the average tax rate, KT is Kakwani’s progressivity index computed on the assumption that all households face the same tax schedule, V (%), H (%) and R (%) are the values of V, H and R (see Eq. (1)) expressed as proportions of RE.

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87

undoubtedly reflects the geographical variation in non-central government income tax rates in these countries.24

5.3. Local versus common equivalence scales A third factor that may account for some of the international variation in differential tax treatment is the possible failure of the common equivalence scale used in this study to capture local differences in ‘‘needs’’ between households as taken into account in the country’s tax system. Table 4 shows, however, that for the most part, it makes little difference to the measured extent of vertical redistribution (expressed as a proportion of RE) whether one uses a local equivalence scale or the common scale. These countries’ PIT systems are no more consonant with their own equivalence scale than they are with the AJL scale when u and F are both set at 0.5. The only country where the choice of equivalence scale makes an appreciable difference is Finland, but here use of the local equivalence scale makes V even larger relative to RE.

5.4. The role of the tax system The final factor we explore is the tax system itself. Our income tax variable captures net income tax liabilities, which we denote by T n . These are simply the difference between gross tax liabilities, T, and tax credits, C: thus T n 5 T 2 C.

(3)

We can write T as T 5 s[x 2 E 2 A 2 D(x)] 5 s[z 2 A 2 D(x)] 5 s( y),

(4)

where s(?) is the rate schedule, x is gross income, E is exempt income, A denotes allowances, D(.) denotes deductions, z denotes income subject to taxation and y is taxable income. Thus the rate schedule is defined over taxable income, which is the difference between income subject to taxation and the sum of allowances and deductions, income subject to taxation being simply gross income less exempt income. Table 5 summarises some salient features of the PIT systems of the countries in the study. The table reveals appreciable differences across countries under each heading. These differences are likely to translate into differences in the average tax rate, g, the progressivity index, KT , the index of horizontal inequity, H, and the measure of reranking, R. Our concern in this sub-section is to see what can be said empirically about these links. The analysis that follows is of a broad-brush nature and, given the limitations of the data, the results should be seen as being nothing more than suggestive. 24

In Denmark, it also reflects the local property tax levied within the PIT system.

None

Ireland (1985/6)

Step function with 3 brackets, rates being 35%, 48% and 58%.

Zero-rated bracket, then 20%, then quadratic function rising to 55%.

None

France (1987)

Finland (1985)

Germany (1987)

Rate structure

Central government’s income tax is step function with four brackets, rates being 22%, 34%, 40% and 46%. Tax base for each of the four rates is different. County and local income taxes are proportional above a threshold. Central government income taxes account for a little under 50% of total PIT revenues. Standard non-income related tax Central government’s income tax is step credits for disability, dependent function with zero-rated bracket, then children and alimony. TCs equal to (in 1990) 6 further brackets, with rates 2% of NTLs. going from 9% to 43%. Local income tax proportional, with rates varying from 14% to 19%. In 1990, central and local taxes accounted for 43% and 57% respectively of total PIT revenues. Standard income-related and Step function with zero-rated bracket, non-income related tax credits-avoir then 12 further brackets, with rates fiscal. TCs equal to 25% of NTLs. going from 5% to 65%.

Basic relief given predominantly through TCs. All standard nonincome-related. TCs equal to 34% of NTLs.

Denmark (1987)

Country/Year Tax credits

Table 5 Institutional background Exemptions

Allowances

All social insurance transfers exempt, except for invalidity and injury transfers which are partially exempt. Foreign pensions partially exempt. Alimony and other private transfers exempt. Unemployment transfers, sickness transfers and family allowance exempt; invalidity and injury transfers partially exempt, as are other transfers.

Interest on government bonds, certain savings accounts, pensions savings accounts and other bank accounts exempt. Family allowance and dwelling allowance exempt; other social insurance transfers partially exempt. Exempt income 18% of gross income. Unemployment and sickness transfers taxed; other social insurance transfers exempt.

Deductions

Various standard income-related items, including basic allowance, and familyrelated and age-related allowances. Together make up 24% of ISTT. Nonstandard items include mortgage and other interest payments and make up 6% of ISTT.

Only standard income-related item is work-related expenses, accounting for 2% of ISTT. Variety of non-standard items, largest of which is mortgage and other interest payments. All non-standard items make up 22% of ISTT.

Basic allowance. Allowances in respect of age, dependent children and dependent relatives. Allowance for social security contributions. Other allowances. Allowances 37%. % of ISTT.

No income-related standard items. Nonstandard items include work-related expenses, insurance and mortgage interest, and comprise 5% of ISTT.

Standard income-related items include basic allowance and family-related and age-related allowances. Comprise 28% of ISTT. Nonstandard items negligible. Family-related, age-related and No income-related standard items. Nonother allowances. Allowances 7% standard items include work-related expenses of ISTT. and social security contributions, and comprise 18% of ISTT.

Family-related, age-related and other allowances. Allowances 1% of ISTT.

None

Interest on pension savings None accounts exempt. Invalidity and injury transfers partially exempt; Family allowance, dwelling allowance and other social transfers exempt. Exempt income 6.5% of gross income.

88 A. Wagstaff et al. / Journal of Public Economics 72 (1999) 73 – 98

Standard income-related and nonincome related tax credits. TCs equal to 1% of NTLs.

None.

Non-standard and non-income related tax credits. TCs equal to 2% of NTLs.

Standard income-related and non-standard income-related tax credits-earned income credit, child care credit and business- related credits. TCs equal to 2% of NTLs.

Sweden (1987)

Switzerland (1992)

UK (1986/7)

USA (1988)

Unemployment transfers taxed; other transfers partially exempt. Exempt income 10% of gross income.

Unemployment transfers subject to tax; sickness transfers partially taxed; other major transfers exempt.

Means-tested benefits exempt.

Unemployment and sickness transfers subject to tax; others exempt. Alimony and other private transfers exempt.

Unemployment, sickness and invalidity allowance exempt.

Basic, family and age-related Income-related standard items negligible. allowances. Other allowances. Non-standard items include work-related Allowances 34% of ISTT. expenses (for self-employed), insurance premiums and mortgage interest. Account for 9% of ISTT. Basic, family and age-related No income-related standard items. Nonallowances. Allowances 25% standard items include mortgage interest, of ISTT. medical and dental expenses, real estate taxes, charitable contributions and miscellaneous itemised deductions. Account for 14% of ISTT.

Family allowances.

Basic allowance. Allowances 9% of ISTT.

None.

No income-related standard items. Nonstandard items include insurance expenses and mortgage interest, and comprise 5% of ISTT. Standard income-related items include workrelated expenses and social security contributions. These account for 11% of ISTT. Non-standard items include mortgage interest, other interest and partly exempt income, and account for 9% of ISTT. Standard income-related items include workrelated expenses and social security contributions. Account for 5% of ISTT. Non-standard items include mortgage interest and others, accounting for 2% of ISTT. Income-related standard items include family and age-related allowances, and work-related expenses. Account for 4% of ISTT. Non-standard items include insurance premiums, and mortgage and other interest. Account for 12% of ISTT. Social security contributions tax deductible, as are mortgage interest payments and health insurance premiums.

Source: Columns 1 and 3–5 from OECD (1990), except Switzerland for which details were taken from national sources. Column 2 from OECD (1990), Messere (1993) and national sources. Year indicated is year to which OECD information relates, unless indicated otherwise (e.g. in column 2). ISTT is income subject to taxation. Under ‘‘allowances’’ are include all items classified by OECD as ‘‘non-income-related standard allowances’’. Under ‘‘deductions’’ are included items classified by OECD as ‘‘income-related standard allowances’’ and ‘‘non-standard allowances’’.

Central government’s income tax is step function with (in 1987) five marginal rates (11%, 15%, 28%, 35% and 38.5%). State and local income tax structures vary, some being flat rate, others being progressive. Federal income tax accounts for 82% of US PIT revenues.

Progressive structure at federal, cantonal and local levels. Account for 27%, 40% and 32% of income tax revenues respectively. Rates vary by canton and by commune within cantons. Step function with (in 1993) 3 steps, rates being 20%, 25% and 40%.

Central government’s income tax is step function with 4 steps. County and local income tax proportional above threshold.

Basic relief given predominantly Step function, with zero-rated bracket, through TCs. Standard and non-standard then 16 brackets, going from 25% to 56%. TCs. TCs equal to 41% of NTLs.

Family allowance transfer exempt; Allowances for work-related other major transfers subject to tax. expenses. Other allowances. Allowances 1% of ISTT.

Step function with (in 1992) 3 brackets, rates being 13%, 48% and 60%.

None.

Invalidity or injury allowance and family allowance exempt.

Step function with 7 brackets, with rates going from 10% to 50%.

Spain (1987)

Basic relief given predominantly through TCs. Standard income-related and non- income-related. TCs equal to 20% of NTLs. Netherlands None. (1988)

Italy (1985)

A. Wagstaff et al. / Journal of Public Economics 72 (1999) 73 – 98 89

87 85

87 87 86 85 88 87 87 92 87 88

Denmark Finland

France Germany Ireland Italy

Netherlands Spain Sweden Switzerland UK

USA

87

90 90 90 92 93

89 88 87 91

87 90

Year of survey – 89, 90; exemption removed on bond interest, earned income allowance removed, and reduction in mortgage interest relief – – 89 89, 90; reduction in mortgage interest relief and entertainment allowances 90; limits on deductions – – – 88; limits on company tax-free cars and entertainment expenses 87

Base widening

Note: Base-widening reforms are those that increase aggregate taxable income-e.g. reforms that reduce exemptions, allowances or deductions.

Year to which Table 5 refers

Country

Table 6 Recent tax reforms in selected countries

87

90 89 87, 88, 89 – 88

87, 88, 89 90 89, 90, 91 89

87 89, 90

Reductions in high rates

87

90 89 87 – 88

– 89

–

– 89

Fewer brackets

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A problem that immediately arises is that the descriptions in Table 5 relate, unless indicated otherwise in the cell of the table in question, to the year specified in the left-hand column of the table. This is not necessarily the system in the year to which the survey data used in the empirical analysis relate. In the absence of any comparable international data on the structure of PIT systems other than the OECD’s latest report underlying Table 5, the best that can be done is to indicate what has happened between the two years in terms of tax reforms. Table 6 summarises the major changes to these countries’ PIT systems and indicates when they occurred. As is apparent, a number of countries made changes to their PIT systems in between the year to which the information in Table 5 refers and the year of the survey - an issue we return to later. Several authors have discussed, from a theoretical standpoint, the influence of tax credits, the rate structure, allowances and deductions on KT .25 In principle, the values of KT of the previous section could be decomposed to show the contributions to the progressivity of the PIT of the rate structure, allowances, deductions, exemptions and tax credits. This exercise has not been undertaken in this paper, since the data are insufficiently detailed to permit such an analysis. A decomposition along these lines has, however, recently been undertaken for fifteen OECD countries by Wagstaff and van Doorslaer (1997) using data from the OECD (1990).26 Unlike the present paper, that study looks only at progressivity. Our concern in this paper lies more with the impact of the tax system on H and R, rather than with its impact on KT . Lump-sum tax credits will leave H and R unaffected, as will income-related tax credits providing they are a fixed percentage of income. If tax credits depend in part on non-income characteristics, however, they will inevitably affect H and R. The rate structure s(?) can also contribute positively towards H and R if non-central governments set varying tax rates. Exempting certain types of income from taxation will also have effects on H and R, since households on similar gross incomes may end up being taxed differently and households with different pre-tax incomes might change positions in the move from the pre-tax to post-tax income distributions. Ceteris paribus, we would expect H and R to be a high percentage of RE in countries exempting a high proportion of gross income from taxation. Lump-sum allowances, A, will not affect H and R, but those components of A that are related to non-income characteristics, such as age, may well result in households on similar incomes 25

¨ See e.g. Pfahler (1990); Lambert (1993); Keen et al. (1996) and Delipalla and Papapanagos (1996). 26 The values of KT reported by Wagstaff and van Doorslaer (1997) are not directly comparable to those reported here for a variety of reasons: the data are mostly for different years; the unit of analysis there is the tax unit; tax units are ranked by income subject to taxation or taxable income, not gross income; taxes in the case of the US are only central government taxes; both income and taxes refer only to the tax unit (mostly the individual); and the data are grouped. In the cases of Denmark, Finland, Italy, Spain and Sweden, the values of KT are remarkably close, but the results for the other countries-especially the US-are quite different.

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being treated differently and / or may result in households changing positions in the move from the pre-tax to post-tax income distributions. Income-related deductions will not generate horizontal inequity or reranking providing they are a fixed proportion of income (cf. OECD, 1990, p.20), but if they depend on, say, the extent of work-related expenses or the extent of social security contributions, then they may contribute to both H and R. Other-or ‘‘non-standard’’-deductions may affect both H and R. In order to try to see how far empirically cross-country variation in tax structures can account for cross-country variation in RE-V, we performed a simple linear regression, in which we regressed V (expressed as a proportion of RE) on a variety of variables.27,28 The first was a dummy variable, TBM, taking a value of one if the taxes were predicted by a TBM. It seemed likely, given the discussion of Section 5.1, that the coefficient on this would be negative. The second variable was another dummy variable, TF, taking a value of one if the data on taxes (and possibly incomes) were from tax files. We hypothesised that such data would contain less ‘‘noise’’ and would lower V as a proportion of RE. The third variable we included was the average value of tax credits as a proportion of NTLs, the data for this variable — denoted below by TC /NTL — being taken from Table 5. The expectation is that the coefficient on this variable will be non-negative. The fourth variable we included expressed the average value of allowances as a proportion of ISTT, which we denote below by A /ISTT. We expected the coefficient on this variable to be positive. The fifth and sixth variables included were respectively income-related standard deductions and non-standard deductions, both expressed as a proportion of ISTT. We expected the coefficients on both these variables, denoted below by IRSD/ISTT and NSD/ISTT, to be positive. The final variable was a dummy, taking a value of one if the country in question has a local PIT, denoted below by LOC. The sample of countries excluded Switzerland, for which no data on the last four variables were available, but included the UK’s FES and TBM results separately. A regression along these lines is evidently somewhat heroic, but, it seems the best way of exploring the determinants of cross-country differences in differential treatment, given that crude inter-country comparisons are inevitably hampered by confounding factors.29 The estimated regression equation — with t-values in parentheses and an R 2 of 0.852 — was:

27

Similar conclusions are reached if V-RE is regressed on these variables. The t-value on TBM becomes larger in absolute size, while the t-value on TC /NTL becomes smaller. 28 We also explored the influence of the tax unit, the hypothesis being that the discrepancy between RE and V would be highest in countries in which the individual is the tax unit. No support was found for this hypothesis and the tax unit variable was subsequently dropped from the regression. 29 Denmark and Italy, for example, both rely heavily on tax credits, but the Italian value of V /RE may well be affected by the use of a TBM to estimate tax payments and the Danish value of V/ RE may well be affected by the existence in Denmark of a local income tax.

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V/RE 5 2 0.063 TBM 2 0.030 TF 1 0.288 TC/NTL 1 0.108 A/ISTT 1 0.390 IRSD/ISTT (1.71)

(0.58)

(2.56)

1 0.969 NSD/ISTT 2 0.012 LOC. (2.93)

(0.80)

(1.65)

(49)

(0.26)

The results lend some support to the observation earlier that TBMs give rise to lower values of V /RE, though the t-value is below the critical value at conventional significance levels. The coefficient on TF is of the expected sign, but its t-value is very small. The positive and significant coefficient on the tax-credit variable implies that higher average tax credit rates in these countries are associated with higher discrepancies between V and RE. Higher allowance rates are also associated with higher values of V /RE, but the effect is not significant. The coefficients on IRSD/ISTT and NSD/ISTT are also positive, as expected, and that on NSD/ISTT is significant. Surprisingly, the coefficient on LOC is negative, though not significant. In terms of both coefficient size and statistical significance of the coefficients, the most important of the allowance and deductions variables is the NSD variable — tax systems with a high proportion of ISTT taken up by non-standard deductions, such as mortgage and other interest relief, do indeed apparently, other things equal, have a high discrepancy between V and RE. This seems logical. What is more surprising — and of potential interest to policy-makers in the non-Englishspeaking world — is that the effect of income-related standard deductions should be larger (and closer to achieving statistical significance) than the effect of allowances. Apparently, at a given income, there is less variation in allowances (at least in the English-speaking countries) than in the deductions used in the Nordic and social insurance countries such as France, Germany and the Netherlands. To what extent do the reforms that have taken place between the years of the survey and the years to which the institutional data refer affect these results? As is clear from Table 6, much of the reform activity has been directed at the rate structure. Such changes ought not to have any direct effect on the explanatory variables in Eq. (4), but this is not true of the base-widening reforms. Where such reforms involve reductions in exemptions, and hence increases in income subject to taxation, the denominators of A /ISTT, IRSD/ISTT and NSD/ISTT will all be too small. By contrast, reforms that reduce allowances or deductions will reduce the numerator of the relevant variable. In general, then, it will be the case that the values we have used of A /ISTT, IRSD/ISTT and NSD/ISTT will be unduly small for countries that have introduced base-widening reforms between the year to which Table 5 refers and the sample year. This is true of seven of the twelve observations underlying Eq. (4), though given the information in Table 6, not all of the three variables in question will be affected by measurement error in all seven cases. The effect of these measurement errors is likely to be an introduction of bias into all seven coefficients in Eq. (4). Nothing can be said about the direction of this bias or, of course, its magnitude. Given this, the results should be seen as suggestive and as a first and somewhat crude attempt to track the links

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between the characteristics of a country’s tax system and the degree of differential treatment emerging at the end of the day. The scope for producing harder evidence on this issue will, of course, increase as the OECD and other international organisations increase the frequency with which they produce comparable PIT data.

6. Conclusions and discussion Our conclusions can be summarised briefly. Broadly-speaking the countries with a high degree of pre-tax income inequality are also those that display a high degree of inequality in post-tax incomes, notably Ireland, Spain, the UK and the US. Likewise the countries with a low degree of pre-tax income inequality also tend to have a low degree of inequality in post-tax incomes, notably Germany, Sweden and Finland. Nonetheless, there is considerable variation across countries in the redistributive impact of the PIT, ranging from a 4.8% reduction in the pre-tax Gini coefficient in France to a 16.1% reduction in Finland. The latter is all the more surprising in view of the low level of pre-tax income inequality in Finland. Countries vary in the ways their PIT achieves vertical redistribution. For example, the Danish and US systems achieve broadly similar levels of vertical redistributive effect, but in the case of Denmark it is due to-by international standards-a high average tax rate coupled with a fairly low level of progressivity, whilst in the US it is due to a combination of a medium average rate and a medium-to-high level of progressivity. Like AJL, we find that differential treatment, as reflected in non-zero values of the horizontal inequity variable, H, and the reranking variable, R, does not reduce the redistributive effect of the PIT a great deal. In most countries, the PIT would have been only 5–10% more redistributive in the absence of differential treatment. There are, however, variations across countries, with the PIT systems in the UK and US displaying low values of H and R, and France and Denmark displaying high values. Our regression results suggest that, ceteris paribus, countries with PIT systems relying heavily on tax credits and making extensive use of non-standard deductions, such as mortgage interest tax relief and relief for other interest payments, are more likely to exhibit high discrepancies between actual and potential redistributive effect. Interestingly, allowances appear to be associated empirically with far less differential treatment than deductions. As emphasised in the last section, these regression results ought to be interpreted with a good deal of caution due to inadequate data. Bearing this caveat in mind, our results suggest that the rate-structure and base-widening reforms that have been implemented in several countries during the late 1980s and early 1990s (cf. Table 6) are likely to have had opposing effects on RE.30 The former have typically-although not always-involved fewer rates and a 30

Cf. fn 23 on these reforms.

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reduction of the top and higher rates of income tax. These changes are likely to have reduced the value of Kakwani’s progressivity index, KT , as well as the average tax rate, g. As a result, vertical redistribution, V, is likely to have fallen. The base-widening reforms, by contrast, with the reduction of reliefs on mortgage and other interest payments, reduced exemptions, and so on, are likely to have reduced both H and R, thereby narrowing the gap between actual and potential redistributive effect. Clearly, it would take a further empirical analysis along the lines undertaken above to determine which effect has, in each country, dominated.

Acknowledgements This paper derives from the project ‘‘Equity in the finance and delivery of health care in Europe’’ (known as the ECuity Project), which was funded by the European Community’s Biomed I programme (contract BMH1-CT92-608) and coordinated by Eddy van Doorslaer and Adam Wagstaff. We are grateful to the EU for financial support. We are also grateful to the other participants in the ECuity Project and to Nanak Kakwani and Peter Lambert for their contributions to the research leading up to this paper. We alone are responsible for any errors.

Poverty

1989 Family Budget Survey 1988 Income and Expenditure Survey 1987 Survey of Income Distribution and Poverty

France Germany Ireland

1990–91 Household income and expenditure

1990 Level of Living Survey

Sweden

1987 National Medical Expenditure

US

Note: LIS surveys listed are not necessarily the ones used in Atkinson et al. (1995a), (1995b), (1995c).

Source: De Tombeur and Ladewig (1994).

Survey

1991 Family Expenditure Survey

1993 Family Expenditure Survey

UK 1991 Current Population Survey

1982 Income and Wealth Survey

Switzerland 1992 Living Conditions Survey

1987 Income Distribution Survey

survey

1987 Survey of Income and Programme

1990 IEF income tax file sample

Income and Wealth

Income and Wealth Spain

1991 Bank of Italy Survey of Household

1991 Bank of Italy Survey of Household

available at LIS)

liabilities.

variables required to estimate federal income tax

detailed data on incomes, income sources, and

does not contain data on tax payments. Both contain

With exception of 1979 CPS, the CPS, like the NMES,

Same survey (Wave III)

Different survey

Different survey

Different survey

Different survey

Same survey (in Wave III)

Same survey (Wave II)

Same survey, but different year (IES 1988 not

panel survey, and transfer income survey

Same survey (Wave III)

estimate.

rate. IDS gives slightly higher income inequality

IDS has larger sample and lower non-response

Same survey (wave III)

Comments

Three surveys: IES 1983, Socioeconomic

1989 Family Budget Survey

1990 Income Distribution Statistics (IDS)

Netherlands 1992 Household Expenditure Survey Users

Italy

1987 Survey of Income Distribution and

1990 Household Expenditure Survey

Finland

1987 HES

1987 Household Expenditure Survey

Denmark

LIS survey(s)

Survey

Country

Table A1. Surveys used in present paper and LIS (Wave III) surveys.

Appendix 1 96 A. Wagstaff et al. / Journal of Public Economics 72 (1999) 73 – 98

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