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Countries with higher GDP are not necessarily the best at converting their wealth into improvement in living standards for their citizens. Indeed, the cross-country correlation between growth rates of GDP per capita and multidimensional living standards is positive but with large variance across countries.27 In particular, only 38% of the variance in cross-country changes in multidimensional living standards can be statistically explained by GDP growth. For example, France and Germany have registered almost the same per capita GDP growth during 1995-2007, but living standards grew 1.7 times faster in France (Figure 2.5). Moreover, some countries switch positions when moving away from GDP towards multidimensional living standards. For instance, economic growth has been 2.5 times faster in Sweden than in Italy, but growth in multidimensional living standards has been about 20% less in Sweden. One element that shapes these differences is the divergence between GDP growth and the growth of average household income, influenced by structural factors such as the fiscal stance or the respective roles of the private and public sector.

–  –  –

1995-2007 Note: Calculations correspond to the median household income.

Source: OECD calculations based on OECD Annual National Accounts, OECD Income Distribution Database and OECD Health Data Base.

The contribution of changes in inequality to changes in multidimensional living standards reflects the degree to which the target group’s (equivalent) income growth deviates from average (equivalent) income growth. This is somewhat different from stating that rising inequality as such drags down multidimensional living standards. Take, for instance, a situation where the income of the target group (say, the median household) remains unchanged between two periods and only the income of the lowest decile increases. This would translate into an unchanged overall measure of multidimensional living standards, driven by two offsetting effects: a positive effect due to the rise in average income (as low-income households earn more with everyone else’s income unchanged) and a negative “inequality” effect, because median households did not benefit from the increase in average income. Yet, an overall measure of inequality, such as the Gini coefficient, would show a decline in inequality.

Furthermore, the contributions of household income, longevity, unemployment and income inequality to growth in multidimensional living standards vary across countries. For example, Germany and Austria display lower growth of multidimensional living standards than France and Australia, because the contributions of household income and employment were relatively lower over that period (Figure 2.6). Australia and Finland are the two countries that display both stronger growth in multidimensional living standards and income relative to OECD averages, combining strong household income growth with large gains in longevity (Australia) and reductions in unemployment (Finland). Conversely, Germany experienced slow household income growth and an increase in income inequality, which jointly explain a significantly lower improvement in multidimensional living standards during 1995-2007. Unemployment did not fall in four countries (Austria, Germany, Czech Republic, Portugal), and only in four countries did income disparities narrow (Belgium, Hungary, Italy, New Zealand). Multidimensional living standards have increased relatively more where they were initially lower. The convergence in multidimensional living standards has been driven by convergence in the underlying dimensions, namely incomes, unemployment, income inequality, and to a lesser extent, longevity. Overall, before the crisis, multidimensional living standards (a combined measure of median and real household income, life expectancy and jobs) grew faster than GDP per capita in most OECD countries due to falling unemployment and rising longevity. This is despite rising income inequality in some countries, which had a negative effect on growth in multidimensional living standards.

Figure 2.6.

Growth in GDP per capita and in multidimensional living standards often diverge Relative contributions of household income, longevity, unemployment and income inequality to changes in multidimensional living standards, 1995-2007 Note: Calculations correspond to the median household income.

Source: OECD calculations based on OECD Annual National Accounts, OECD Income Distribution Database and OECD Health Data Base.

The drivers of multidimensional living standards also vary for different segments of the income distribution. To illustrate these diverse outcomes, multidimensional living standards can be calculated for three different representative equivalent income references: average income, median income and income close to the lowest decile (Table 2.1). When applied to OECD countries over the period 1995-2007, calculations show the rise in multidimensional living standards was somewhat weaker for median households (those with income at the middle of the distribution) and for the poor (those households with income at the lowest decile of the distribution), at 3.8% and 3.6%, respectively, than for the “average” (mean) household. These findings show that widening income inequality affected the poorest households most adversely.

Since the economic crisis, the evolution of multidimensional living standards has taken a different turn, a fact also borne out by the work of Jorgenson and Slesnick (forthcoming).

Analysis for 30 OECD countries between 2007 and 2011, or the latest year available, shows that, on (unweighted) average, multidimensional living standards fell by 0.5% per year, with 15 countries witnessing negative growth and 5 countries (Spain, Estonia, Ireland, United States and Mexico) experiencing a fall in multidimensional living standards of more than 5% annually (Figure 2.7). The bulk of the average loss in multidimensional living standards is explained by rising unemployment, while household income remained broadly stable, except in a few countries (Estonia, Greece, Hungary, Italy, Mexico, Switzerland, United States).

–  –  –

‐5 ‐10 ‐15 ‐20 Note: Calculations correspond to the median household income.

Source: OECD calculations based on OECD Annual National Accounts, OECD Income Distribution Database and OECD Health Data Base.

Figure 2.6.

Growth in GDP per capita and in multidimensional living standards often diverge Relative contributions of household income, longevity, unemployment and income inequality to changes in multidimensional living standards, 1995-2007 Note: Calculations correspond to the median household income.

Source: OECD calculations based on OECD Annual National Accounts, OECD Income Distribution Database and OECD Health Data Base.

–  –  –

2.5. Linking policies to multidimensional living standards Measuring multidimensional living standards is a necessary first step towards providing policy analysis and advice for Inclusive Growth. The different dimensions of well-being are driven by a combination of policy and non-policy factors. GDP per capita is affected by pro-growth policies, and also by other influences, which can be exogenous, such as geography, or endogenous, such as good health or education. Similarly, health outcomes are influenced by health policies and also indirect factors, such as income and lifestyle, suggesting that there may be feedback linkages between different outcomes. The way resources are distributed can also affect equality of opportunities to participate in the production process and potentially average outcomes.

Interactions between policies and outcomes in both income and non-income dimensions are complex and important. For example, growth-oriented policies can affect the distribution of income, wealth or consumption; fiscal policies may affect environmental, health and education outcomes; higher public health spending can have potentially positive effects on employment and incomes, but it will also imply higher taxation and hence less material consumption. Such effects have been analysed in OECD work, but they have been considered either as unintended consequences of growth policies or as policy issues in their own right,28 without taking an integrated analytical and policy perspective. Recent OECD work has started to address these shortcomings by analysing the trade-offs among growth, equity and social preferences when pursuing fiscal consolidation (OECD 2013a). The ambition of the policy framework for Inclusive Growth is to bring these dimensions together in a coherent manner, where measurement, analysis and policy advice all keep an ultimate objective in sight: improving multidimensional living standards.

Against this background, the policy framework for Inclusive Growth pursues the following objectives. First, it should provide a clear link between individual dimensions of well-being and policies through both direct and indirect channels. Second, it should make explicit the main policy trade-offs and synergies so that policy makers can be better informed about relevant policy choices with respect to the different dimensions of multidimensional living standard. Finally, the framework should be sufficiently flexible to be adapted to country-specific challenges and circumstances.

Analysing the impact of policies on Inclusive Growth For the framework to be both policy-oriented and evidenced-based, a number of criteria or

constraints need to be considered. In particular:

 An understanding of the key drivers of the outcomes included in the social welfare function is critically important.

 An identification of robust empirical relationships between multidimensional living standards and policies is crucial for interpreting the effects of changes in policy on outcomes.

 The responsiveness of outcomes to policy intervention should affect the choice of variables.

The complexity of the interactions between policies and outcomes, together with data limitations, restrict the choices available. In this section, and for illustrative purposes, the analytical framework used to link outcomes to policies focuses only on income and health. Income contributes to material living standards, while health (measured in terms of life expectancy at birth) contributes to quality of life. The choice of these two dimensions is for illustrative purposes only, and an explicit link to policies can be provided not only for the jobs dimension (employment or unemployment), as discussed above, but to other dimensions as well.

The measure of income

Typically, growth-oriented policy analysis focuses on average per capita GDP as the variable of interest. For instance, OECD analysis provides country-specific recommendations on policies that would increase the long-run level of GDP per capita, analysed through a set of sub-indicators. The advantage of using per capita GDP is that it is widely available across countries and over time, and there is much empirical research linking policies and non-policy parameters to GDP. More recent OECD work (OECD 2013a) has started to look at side-effects of pro-growth policies on income inequality and the environment, but without any attempts to consolidate the aggregate welfare effects or to model potential inter-linkages between these different dimensions. In addition, in OECD (2011a), the focus has instead been on identifying policies and other drivers of shifts in the distribution of income in OECD countries.

From an Inclusive Growth perspective, household income is a more appropriate variable, as discussed above, and the use of GDP per capita as a proxy is unsatisfactory. For instance, as mentioned in Chapter 1, the widening gap between GDP and average household income observed in many OECD countries29 suggests that even as a proxy for mean living standards, GDP falls short of representing the concerns of the typical individual or household (Atkinson, 2011). Indeed, giving more prominence to household disposable income as opposed to GDP per capita was one of the main recommendations of the Stiglitz-Sen-Fitoussi Commission (2009). An even more relevant measure would be household “spendable” income, where the National Accounts measure of disposable income is adjusted to remove a number of imputed income components (Atkinson, 2012). The main difficulty is that while the link from policies to per capita GDP is well established, it is far less obvious when household income is used as a proxy. Also, the traditional growth model and production function cannot be applied directly to household disposable income as this is not well grounded in economic theory.

The approach adopted here consists of looking jointly at the two income concepts and examining the extent to which growth in GDP per capita trickles down and benefits households across different income groups. More specifically, the idea is to investigate whether household incomes – on average and along the distribution – are jointly determined by the same set of structural policies as GDP. Comparing the impact of policies on GDP as well as on household incomes and their underlying distribution may allow for identifying potential policy trade-offs and complementarities with respect to the objective of raising both efficiency and equity. The analysis uses measures of household income following the income standard approach based on the general means concept defined in Atkinson (1970)30 as developed in Foster and Szekely (2008) (Box 2.5).

Policy interactions are potentially complex even in a simple case where aggregate living standards are defined over two dimensions. This creates scope for similar policy instruments to influence living standards through multiple, sometimes offsetting, channels (Box 2.7). For instance, growth-oriented policies that successfully raise the income of the majority of households will at the same time benefit life expectancy through higher spending on health, but such benefits may be offset, as noted earlier, if growth entails severe air or water pollution. Conversely, environmental policies may result in lower GDP per capita and income but still lead to higher multidimensional living standards through improved health status and life expectancy. Similarly, health spending raises life expectancy at 65 by more than at birth. Hence, unless working life is adjusted in proportion to life expectancy, higher health spending will result in a higher ratio of retirees to the working population, thereby lowering employment rates and GDP per capita. Finally, increasing health spending may have indirect but hard-to-estimate effects on GDP, for instance through lower sick leave and disability (increasing average hours and therefore GDP per capita) or higher tax rates (lowering employment rates and therefore GDP per capita).

Box 2.7. From production to income: taking distributional effects into account In a two dimensional setting, it is important to identify the channels through which policies affect income and health outcomes. Income is defined by household disposable income and health by longevity as in the previous example.

–  –  –

To capture the drivers of income, two alternative sets of equations are considered, which differ according to

whether household income is linked directly to GDP or to its main components:

–  –  –

y is generalised average household disposable income that varies according to the weight put on where different segments of the distribution, as mentioned above; gdp, k, h, mfp and empl denote, respectively, average GDP, physical capital, the stock of human capital, multifactor productivity and the ratio of employment to working-age population; zY denotes exogenous factors such as geography; and qY is a vector of policies influencing directly GDP per capita after controlling for inputs. z  captures non-policy factors, such as corporate savings, and which can drive a wedge between GDP per capita and household incomes over a prolonged period;

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