CONCLUSIONS: MAJOR FINDINGS FROM THE LITERATURE SURVEY AND IMPLICATIONS FOR FURTHER RESEARCH

19.6.1 A Summary of Findings and Propositions from the Overview of Studies Providing Multicausal Explanations

This section summarizes the main findings presented above from the most important recent studies that provide multicausal explanations and provides a combined analysis of the relative weights of the various arguments set out in Section 19.5.

1. structural macroeconomic sectoral changes

2. globalization and technology change

3. labor market and other relevant institutions

4. politics and political processes

5. tax/transfer schemes

6. demographic and other microstructural changes

As indicated in Section 19.1, we may think of the above factors as “underlying” causes of inequality change. On the second level, there are elements within each of the six broad groups (such as FDI, technology, trade, etc., for globalization or such as unionization, unemployment benefits, employment protection legislation, etc., for labor market insti­tutions). This second group could be included under the umbrella of “proximate causes” of inequality or “hints” at causes.^{[320] [321] Finally, there is a third level, on which the various authors operationalize their models, that is, where they chose the appropriate variables for their models, which are, in most cases, necessarily second-best proxies of the second-level factors. In what follows, we summarize the results of the level of abstraction represented by the first level. While doing that, we also report findings for the interactions between the effects of the various variable groupings as far as they are available.}

As for the major hypothesis of structural macroeconomic sectoral changes (i.e., sector bias and sector dualism, as proposed by Kuznets), the evidence is inconclusive.

As for the debate on globalization versus technology, there has been a move away from trade-focused explanations to technology explanations during the 1990s. In the 2000s, several authors changed track from their earlier views that the effect of trade on inequality was modest at best (Krugman, 2007; Scheve and Slaughter, 2007). They now suggest that trade-induced phenomena such as outsourcing may have had a more significant effect on income distribution than formerly assumed. That said, while under the pure aspect of trade costs, off-shoring all tasks that are technically off-shorable may indeed be possible, this will not always make sense from a business point of view, espe­cially when transaction costs and economies of scope are taken into account; the assumed effect of a surge in off-shoring may therefore be exaggerated, as argued by Lanz et al. (2011).

At the same time, technological change now is more often understood as endogenous and interacting with trade. More generally, the key issue today is no longer identifying which trade or technological change was the main culprit in increasing inequality, but rather to identify the channels through which these two operate and interact in their effect on inequality (see Chusseau et al., 2008).

The effect of education—human capital accumulation—on inequality is not linear and, because of different composition and wage premium effects at different times, can first be disequalizing and then equalizing, analogous with the Kuznets process.

While it is widely recognized that institutions matter, the weight attached to this fac­tor in econometric studies has long been limited. A majority of (but not all) studies finds significant negative associations, in particular with wage inequality, through direct or indirect effects of union density/coverage, wage coordination/centralization and EPL. Checchi and Garcia-Penalosa (2005) and the OECD (2011) found the weakening of employment protection and the decline in unionization increased wage dispersion, mostly having effects at the lower ends of the distribution of wages. It has, however, also been emphasized that when observed in a broader context (i.e., concentrating on com­bined employment and dispersion effects of institutional changes), the results were incon­clusive because employment and inequality effects of institutional change tended to net each other out (OECD, 2011). Also, Checchi and Garcia-Penalosa (2008) suggested that the combined effects of institutions on factor income inequality are weak, whereas the income distribution effects of high tax wedges (which could be expected to serve larger redistribution to favor lower segments of the labor markets) also has controversial effects (high-wage workers are able to pass on tax burden to their employers, while the overall tax wedge effects can contain considerable unemployment increases).

All in all, it is shown that for inequality trends, developments in political processes are of key importance. How preferences of the electorate are recognized, processed and translated into policies (which, in turn, shape labor market and welfare state institutions) do play an important role in redistributive institutions and, ultimately, in inequalities.

The identification of the Left and the Right may easily turn out to be problematic, especially when representation of the various labor market segments is taken into account (Rueda, 2008). Given the fact that parties sometimes pick up interests of insiders (such as active earners) as opposed to the interests of outsiders (such as the inactive earners and the unemployed), redistributive outcomes might come about as results of sometimes contra­dicting tendencies of redistribution from the rich to the poor and of legislation to support the interest of the insiders of the labor markets.

When analyzing actual redistribution processes, the definitions of the pre- and post­redistribution inequality (in other words, the accounting framework in which the redis­tribution processes are understood and interpreted) has been identified as crucial to the measurement of the effects of redistribution (Whiteford, 2008; Immervoll and Richardson, 2011; Kenworthy and Pontusson, 2005). It also has been emphasized that redistribution might have a number of second-order effects. The results of redistribution analyzes have shown that redistribution reduces inequality overall in all OECD countries, although to a varying extent, depending on concrete institutional settings.

The redistributive effectiveness of the two sides (taxes and benefits) has been shown to be different: cash transfers (in all countries but the United States) are estimated to have much larger first-order effects on inequality than taxes (Whiteford, 2008; Immervoll and Richardson, 2011).^[323] Among public social transfers, public pension programmes achieve the largest redistribution; however, the interpretation and evaluation of these differs and is dependent on the chosen perspective of Robin Hood or piggy bank welfare states.

There are second-order effects of redistribution, such as those resulting from behav­ioral adjustment on the contributor side (taxpayers) or the recipient side (social assis­tance beneficiaries). Some studies are able to show the existence of second-order responses, the magnitude of which, however, seems to be relatively small (Doerrenberg and Peichl, 2012). The measured effects of taxation on labor supply (which is clearly an important area of potential behavioral repercussions) imply that social embeddedness of institutions is noticeable. Studies by Blundell et al. (2011) highlighted that behavioral elasticities for women are larger with regard to both deci­sions about entering the labor markets (extensive margin) and changing work efforts on the labor markets (intensive margins).

An important aspect in redistribution research is how the change in size and tech­niques of tax transfer schemes have contributed to changes in overall inequality. As highlighted by the OECD (2011), changes in redistribution can be seen as causal factors for increasing inequality during the period before the breakout of the economic recession in 2008. The redistributive power of the welfare state was weakened in the period between the mid-1990s to mid-2000s.

The social context can also be captured by the effects of changing demographic com­position (by age, household types, etc.) and ofchanging demographic behavior (house­hold formation, assortative mating, etc.) on inequality. While the (composition) effects of ageing and of household composition are estimated to have an inequality-increasing effect (Lu et al., 2011; OECD, 2011; Peichl et al., 2010), the results of some of the dis­cussed behavioral trends (assortative mating) are less clear-cut, but in general also are shown to have an effect on inequality change, mostly as disequalizing effects. Some scholars present the results of the “incomplete revolution” of women’s changing role in labor markets and in families as equalizing within the households (because of more equal divisions of domestic labor) but disequalizing among households (because of differ­ential behavioral reactions of women with higher and lower status [Esping-Andersen, 2009]). Taken together, when modelling the inequality effects of changes in demo­graphic composition and behavior on the one hand and labor market related changes on the other, the OECD (2011) concludes that the former seems to explain much less of the increase in inequality than the latter.

In a nutshell, this is what we found at the first level of factors identified at the begin­ning of this section (and in the diamonds of Figure 19.1). To give a brief summary assess­ment of the results found in the studies published over the past 10-15 years, Figure 19.3 provides an idea of the direction of causal factors of inequality that were identified. This summary remains qualitative and cannot be based on quantitative assessment because the multitude of studies use various and different methodologies, estimation methods and data, as well as varying country coverage. Further, it is in part our own subjective assess­ment. As a convention, positive/negative association means disequalizing/equalizing. “Significance” has to be understood here (and elsewhere in the text) as a statistically sig­nificant association, notwithstanding the relative size of a coefficient. “Inconclusive” means that roughly as many studies report (significantly) positive as negative effects. Fur­ther, this assessment is based as much as possible on studies covering the restricted sample of OECD/EU countries.

Figure 19.3 Drivers of inequality: a qualitative summary of results for OECD countries reported in recent studies. EPL, employment protection legislation;FDI, foreign direct investment;UB, unemployment benefit.

A first glance at Figure 19.3 reveals that inconclusiveness prevails for many possible drivers of inequality, that is, the large number of recent empirical, cross-country studies report contradicting results, which can often but not always be traced back to different country samples, time periods, data and methodological specifications. In particular, for those factors for which there are more complete and fairly direct measures at hand (such as measures of trade openness or financial openness), there is little clear effect reported, whereas for factors where more proxy-type measures need to be used (such as technol­ogy), there seem to be more significant findings. One is tempted to detect some sort of Heisenberg principle: the sharper we can measure a variable, the less effect will be found.

As mentioned above, the summary assessment in Figure 19.3 refers to findings on the different level-one factors separately. To show and interpret the relative strength of the various findings, one would need to refer to studies with a true multivariate design, that is, those covering not only a multitude of countries but also a sufficient number of variables representing each of the first-level factors in the models. Because of the complexity of methodological and data requirements, none of the studies attempts to cover all of the first-level factors simultaneously, but a few studies in our literature review were able to cover a multitude of the factors mentioned above.

One of the few examples is OECD (2011), which makes an attempt to study the interactions between four groups of factors: (i) globalization (captured both by trade and financial openness); (ii) SBTC; (iii) institutional and regulatory reforms; and (iv) changes in employment patterns.^[324] When explaining the relative weights of these factors within a common analytical framework,^[325] the authors conclude that globalization (trade, FDI, financial liberalization) had little effect on wage inequality trends per se once institutional factors are accounted for. However, globalization processes put pressure on policies and institutional reforms to deregularize labor and product markets. Such insti­tutional and regulatory reforms were primarily aimed at promoting growth and produc­tivity, and while they had a positive effect on employment, at the same time they have been associated with increased wage inequality in many countries. What concerns the role of technology development in the period is that it was mostly beneficial for the highly skilled workers, a trend that resulted in larger wage disparities. However, increases in human capital (via mostly large-scale expansion of higher education in most OECD countries) offset much of the drive towards rising inequality.

Another example is Cornia (2012), who examined the explanatory factors of the declining inequality trends in Latin American countries. Among “proximate” causes of inequality, he investigated changes in both factorial and personal distributions of income caused by endowments of unskilled labor, human capital, physical capital, land and nonrenewable assets; their rates of returns also were taken into account. State inter­vention was measured by taxes and transfers received by households. Household-level income components enter the equation (similar to GIRE), together with macro-level variables such as dependency rates and activity rates. Overall inequality (measured by Gini coefficients) was decomposed into a weighted average of six factors (six different types of income). Results then were put into a broader framework, and changes in prox­imate causes are interpreted within the frame of changes in underlying causes (these include external conditions such as exports or capital flows, macrovariables related to the balance of payments, nonpolicy endogenous factors such as fertility and activity trends, dependency ratios, etc.), educational achievements and policy factors (related to taxes and transfers policies, wages, labor markets, economic and social policies, etc.). The major conclusion of the paper is that the decline in inequality in Latin America was most importantly due to the reversal of the skill premium (resulting from a massive increase of secondary enrolment), a decrease in the supply of unskilled labor, a return to collective bargaining and an increase in minimum wages. Other factors such as the improvements in external economic conditions or the endogenous changes in depen­dency and activity rates played only a minor role in inequality reversal.

A third noticeable example for an attempt to create a broad based modelling of inequality change is Mahler (2010), who sought to explain the determinants and effects of government redistribution on inequality, mostly focusing on the role of taxes and transfers and on the distributive effect of wage bargaining institutions and minimum wages. He tested five alternative explanations from the literature: the median voter argu­ment, the PRT, the political institutional approach, the labor unions approach and the globalization approach. Government redistribution was found to be positively related to pregovernment inequality (as the MR argument predicts), to the level of electoral turn­out, to unionization rate and to the presence of proportional electoral systems. Further, a relatively egalitarian distribution of earnings was found to be positively associated with the degree of coordination of wage bargaining. On the other hand, no significant rela­tionship has been found for the measures of globalization in his models.^[326] The study also does not find support for the government partisanship hypothesis (share of cabinet posi­tions held by Left parties).

These three examples are quoted here in more detail because they help show how far the various multivariate analyses can take us in understanding the relative weights of the various drivers of inequality. However, for a more encompassing GIRE-type specifica­tion and a proper test of it, still better data and larger country coverage are awaited.

19.6.2 Lessons on Methods and Models

We started this chapter with the aim to provide a thorough survey of what international (i.e., cross-country) studies can tell us about the drivers and underlying causes of income inequality with regard to levels and, in particular, trends. In the sections above, we were able to demonstrate how much progress has been made in terms of data availability and use for the countries in the joint set of the EU and the OECD (despite all remaining deficiencies of secondary data sets). A rich literature of studies of various drivers of inequality and their results have been discussed in the chapter. Yet, for the answers to some of the most important questions formulated at the outset, the jury is still out. These relate to

— the influence of the time coverage and geographical coverage of inequality data

— a more precise identification of the relative weights of factors (drivers) of inequality

— the comparability and accuracy of model estimates

Below we discuss these three aspects in turn.

The articles reviewed in this chapter reveal that there have been quite spectacular developments in data infrastructures for the research on earnings and income inequality. Elements of this development can be summarized as follows:

— First and foremost, some new, large, comparative data collection exercises began. The most prominent one is the EU-SILC, produced annually for all of the member states of the EU and some non-EU countries. This data exercise encompasses a combination of ex ante and ex post harmonized data collection activities (Atkinson and Marlier, 2010).

— The collection of inequality variables in secondary data sets (most recently, the OECD Income Distribution Database, for example) has been accelerated and standardized and moved to annual reporting. In addition, some new secondary data sets have been built (of which the GINI project has most recently provided a rich data set for 30 countries and 30 years; Toth, 2014).

— For some of the countries, a historical data collection exercise started, which contrib­utes to a much better understanding of long-term trends in inequality (see, e.g., Atkinson and Morelli, 2014 or the long-run data series of the World Top Incomes Database developed by Alvaredo et al.)

In sum, the data situation improved greatly in the past few decades and even since the publication of Volume 1 of the Handbook of Income Distribution (Atkinson and Bourguignon, 2000). Simon Kuznets could now perhaps count on a situation where not 5% but maybe 50% of the analysis comes from data and only the other half (rather than 95% in 1955) of the analysis has to rely on speculation. Nevertheless, there are still deficiencies in the data front that impose serious limits on analysis and on a better under­standing of the dynamics of inequality from a cross-country perspective.

While there are some data sets covering a large number of countries, there are a few truly longitudinal data sets covering long periods but only a few countries. However, researchers wishing to analyze inequality developments using comparable long-term series of country data will have to make serious compromises.^[327] These types of compro­mises regard coverage (N), the number of data points (t) per country and their combi­nations as well.

The vast majority of studies reviewed is based on unbalanced panels because they cover different time periods for each country. That means that t has a variance across the cases. If this variance is nonrandom, the estimates may be biased. When missing years correlate in a systematic way with the dependent variable, estimates risk being biased. In addition, for income inequality estimates, annual time series are not available for most countries and in general not in secondary data sets. Most of the studies summarized in Annex Table A19.1 look at a time period of about 20-30 years, but the number of obser­vations per country differs greatly, from around 3 up to 20.

How serious the issue of unbalanced panels is also depends on the nature of the research question: for some tests of questions, a large N may compensate for a small t, for example, when testing the effect of institutional change (in which case the over-time variance in short periods will be negligible). In other cases, for example, when looking at the effect of macroeconomic changes (where year-to-year fluctuations may be not neg­ligible), it may not.^[328]

As we have shown in Sections 19.5.1-19.5.6 (roughly corresponding to the six major “diamonds” in Figure 19.1 representing six different groups ofpotential drivers ofinequal- ities), studies of inequality identified significant effects of globalization and technical change, of political structures, of redistributive expenditures and some demographic com­position changes. However, most models following the structure of Equation (19.3) (GIRE) are partial in the sense that they ask how variable group X affects inequality when controlled for variable groups Z or Q variables. This sometimes can misguide readers when interpreting the relevance of the results. All in all, in the literature there are rare attempts to provide weights to various significant factors; many leave complementary variable sets among the group of omitted variables or assume them to be absorbed by fixed effects.

As an example, studies analyzing the effects of globalization on inequality typically con­trol for sectoral composition of the economy or sometimes for institutional variables (such as unionization or employment protection) but still leave out a great number of variables that could help control for demographic or education structure, for political processes or for redistribution. Similarly, analyses focusing on, for example, politics do account for party structures, electoral systems, voter turnout patterns and the like, sometimes controlling for demographic composition of societies, and so on. However, they also remain “rough,” omitting too many variables (related to globalization, sectoral divisions, etc.) and thereby keeping a large part ofthe unexplained variance in the dark (or gray).

However, when trying to enrich the variable sets on the right-hand side ofthe GIRE, we run into problems similar to those of growth regressions. This does not come as a surprise because the structure of inequality regressions and those of growth regressions is similar, with just different left-hand variables. As indicated in the literature on eco­nomic growth regressions (see Mankiw, 1995; Temple, 2000; Eberhardt and Teal, 2009), part of the problem of inconclusiveness of results stems from a very simple fact: too small a number of countries, too many competing explanations and too short a time series with not many comparable definitions. Mankiw (1995) lists three of these problems: the problem of simultaneity, the problem Ofmulticollinearity and the problem of degrees of freedom. For inequality regressions, each of these holds equally.

Simultaneity refers to the fact that right-hand variables are, in many cases, not exog­enous but products of the same third (sometimes unobserved) factor, which determines inequality, and the chosen right-hand-side variable as well. This problem can also be called the endogeneity problem or reverse causality. Should we find that inefficient redis­tribution in a country fails to produce the expected inequality reduction, it might easily be that both government inefficiency and the large market income inequality are a prod­uct of a third factor, such as bad governance and or distrust in the given country (also on this issue see Robinson, 2009).

Multicollinearity has a similar origin. In many of the models the right-hand variables are correlated. A high level of taxes, for instance, will correlate with high levels of expenditures, especially in countries with higher levels ofstate employment (which in itself may have a lower level of inequality within this sector). Also, a higher share of more educated people may cor­relate with higher employment in education, where wage bargaining is more centralized. Inequality regressions need to face these multicollinearities, and researchers need to be inno­vative in trying to find proper ways to decrease the level of multicollinearity problems.

The third aspect is related to the potential number of explanatory variables. The trade­off here can be summarized as follows. For partial regressions, there may be too much unexplained variance left for the omitted variables. For more comprehensive regressions, the small number of observations limits the options. Given the fact that cross-country comparisons usually cover only a limited number of countries, the increase in the number of independent variables also is constrained. As Mankiw (1995) puts it, “there are too few degrees of freedom to answer all the questions being asked” (p. 306). For a better under­standing of how inequalities evolve in a cross section of countries, more data points are needed—but for this we cannot have more countries, only time observations.

Furthermore, with the current amount of information at hand, not all of the complex mechanisms and channels that affect the distribution of earnings and incomes will show up in aggregate inequality regressions. Therefore, attempts to better specify the GIRE need to be complemented with more analysis of the constituent parts of these channels.

A final but important lesson relates to the disciplinary composition of inequality researchers. In our review we covered literature from economics, sociology and political sci­ence. Our most important lesson from this was that these disciplines have something to tell and to learn from each other. To share knowledge and discuss results, a common language is needed. As we have seen from scrolling though the literature, it is starting to exist.

As Atkinson and Brandolini (2009) put it, “valuable lessons can be learned but that we require: an integrated approach to theory and estimation; a proper specification of the data employed; and techniques to address the deficiencies of the underlying data” (p. 442). This will help decrease the level of speculation in inequality research—what Kuznets estimated to be 95% and we estimate now to be around 50% because of the fast development of inequality research in the past few decades.

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-country income distributions—cont'd

Continued

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-country income distributions—cont'd

Continued

Annex Table A19.1 Summary of multivariate analyses of determinants of crosscountry differentials of Withimcountrv income distributions—cont'd

Institutions

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-country income distributions—cont'd

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-country income distributions—cont'd

Political Processes

Continued

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-countrv income distributions—cont'd

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-country income distributions—cont'd

Continued

Annex Table A191 Summary of multivariate analyses of determinants of cross-country differentials of within-roιιntrv income distributions—cont'd

Annex Table A19.1 Summary of multivariate analyses of determinants of cross-country differentials of within-country income distributions—cont'd

ACKNOWLEDGMENTS

The authors are grateful to Tony Atkinson, Francois Bourguignon and Andrea Brandolini for their useful comments and suggestions on earlier drafts of this chapter. The authors also thank Wen-Hao Chen, Tim Goedeme, Alexander Hijzen, Marton Medgyesi and Pieter Vanhuysse for comments and advice on the lit­erature. Numerous comments at the Paris author’s conference organized in preparation of this book in April 2013 are much appreciated. The authors also thank Anna B. Kis and Eszter Rekasi for their research assis­tance. They have no responsibility for any remaining errors. The views expressed are not necessarily those of the institutions with which the authors are affiliated.

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