RENT-TYPE INCOMES AND DISTRIBUTIONAL INEQUITY

RENT-TYPE INCOMES AND DISTRIBUTIONAL INEQUITY

Aleksei Izumov

Dr., University of Louisville, Louisville, USA and Visiting Professor, Novosibirsk State University,

Russia, Novosibirsk

ABSTRACT

Traditional metrics of income distribution inequity focus on inequality and emphasize the “unearned” incomes or rents accruing to the richer segments of population. In this paper we argue that current emphasis on economic rents captured by the wealthy produces an incomplete representation of unearned incomes and propose to expand the analysis of inequity by including economic rents accruing to segments of the poor and the working population.  We further propose to consider the sum of all rents taken in proportion to the GDP – the rent load - as an aggregate measure of economic inequity. We argue that in contrast to metrics of inequality, such as Gini coefficient, the rent load is a better indicator of unfairness in income distribution.

Keywords: Inequality, inequity, income distribution, economic rents.

Rents as unearned incomes

The modern interpretation of rent-based theory of incomes uses the standard neoclassical model of the perfectly competitive capitalist economy, where every factor of production is paid its marginal product.  A rent-based concept of “unearned incomes” first proposed by the Fabian school [19; 25] and elaborated by Sorensen [20; 21] posits that unearned incomes occur when economic rent, defined as income in excess of marginal productivity, is appropriated by an economic agent [21, p. 1532]:

R_i = AV_i - MP_i                                                                                                                                    (1)

where R_i is economic rent on asset i, AV_i - actual value of income received on an asset i, MP_i – marginal productivity of an asset, or income on this asset that would be received under perfectly competitive market conditions. 

Assets in (1) include labor to allow for the fact that some workers can receive economic rents when compensated above their marginal productivity. Economic rents can also include income not related to any productive effort, such as welfare payments. In such cases MP_i = 0.

The advantage of the rent-based approach to unearned incomes is that it allows analyzing and, in principle, estimating all unearned income flows in the economy, without a particular emphasis on just one of them as Marxist theory does.

In a market capitalist economy, the main source of rents is transfers from the income of labor or, more specifically, income of private sector economy workers. These transfers result mainly from the underpayment of wages and additional taxes imposed on workers. Based on the direction of rent transfers they can be classified into three categories: top, bottom and horizontal rents [11].

Top rents (R^T) are rents that accrue to upper income segments of the population, such as payments to managers and bankers in excess of their productivity, including “corporate welfare” and “golden parachutes.”

Bottom rents (R^B) include all broadly defined public-welfare payments not funded by recipients’ prior contributions. They include rents arising from fraud and abuse, such as welfare freeloading by people who otherwise can generate their own income but choose not to.

Horizontal rents (R^H) include payments to some labor-force groups in excess of what would prevail under competitive market conditions. Recipients of horizontal rents are mostly middle-income. One prominent example of horizontal rents is excessive compensation of public-sector workers.

In the contemporary capitalist economy many if not most rents are either directly or indirectly based on laws and regulations issued by the government.  Rents are also made possible by informational and bargaining power asymmetry between economic agents as in the case of corporate management vs. shareholders, politicians vs. taxpayers, trade-union members vs. non-members, etc. [1; 17]. Some of the bottom and horizontal rents can be attributed to non-economic factors. These include, for example, public sympathy for some social or professional groups such as the poor, school teachers, or farmers.

Importantly, rents described above are re-distributive in nature, as their benefits accrue to some economic groups of the society while their costs are born by other groups. That distinguishes these costs from costs of rent-seeking born by all of the economy and is reflected in a slower growth of GDP [2; 6; 13; 23].  

All three types of economic rents can be estimated and summed up as a total “rent load” of the economy:

R_t = ∑ R^T_t + ∑ R^B_t +∑ R^H_t                                                                                                   (2)

where R_t is an estimate of money value of all economic rents in time period t; R^T_t, R^B_t, R^H_t  are money values of top, bottom and horizontal rent flows.   The total sum of all rents in the economy measured in proportion to the GDP—call it rent load ratio (RL_t) can be interpreted as the quantitative measure of economy’s distributional inequity:

RL_t = (∑ R^T_t + ∑ R^B_t +∑ R^H_t ) / GDP                                                                                          (3)

Estimates of rent flows

Computation of all rents circulating in the economy is probably impossible. However, one can estimate some of the largest rent flows. To estimate them, we propose integrating existing methodologies of rent evaluation.

Thus, the estimates of the top rents can draw on the literature on bankers’ and top executives’ compensation, specifically estimates of gaps between their compensations and productivity. An influential recent study of the US finance industry found that between 30% and 50% of income of its million workers represents a premium not explained by education, skills and experience [15, p. 1603]. Based on these rates, the total annual overpayment of employees in the US finance and banking industry was estimated to be close to $50 billion [26, p. 66-67]. For CEOs of US corporations outside of finance the rate of overpayment is possibly similar. This is indirectly supported by the fact that CEOs compensation for comparable size firms in the US is two times higher than in Europe and the multiple is even larger in comparison to compensation of managers in Japan, China and other countries [15; 16]. Assuming the 40% rate of overpayment (mid-point between 30% and 50%) the rent collected by top managers of the US large and medium-sized corporations is close to $20 billion. For bankers and CEOs taken together the top rent amounts to some $70 billion (see Table 1).

Estimating the value of bottom rents can be done by analyzing welfare programs. The key problem here is separating the redundant and fraudulent components that can qualify as unfair overpayments.  Such estimates exist for unemployment benefits. A recent study of the US Department of Commerce concludes that unemployment benefits overpayments due to fraud and abuse represent 11.5% of all payouts [24]. The Budget and Economic Outlook. Washington, D.C. 

Table 1.

Estimated value of some rents in the US economy

Sources: [3; 4; 5; 7; 8; 9; 10; 14; 15; 16; 22; 24]. Note: Data are estimates for 2010-2019 period.

Based on this overpayment ratio, the total annual volume of bottom rents consisting of fraudulently claimed welfare, disability and unemployment benefits payments reaches about $100 billion (see Table 1).  Combined with rents generated through fraud and abuse in other government-managed social programs, such as Medicare and Social Security, the total of bottom-up rents is likely much larger.

In horizontal rents category the most massive examples are probably overpayments of government workers and trade union members. For an estimate of the former we used recent official reports on the U.S. public-private-sector pay gap [3; 7].  According to them controlling for education, skills, age, work experience, professional occupation and other observable characteristics, per hour compensation of federal government workers is 17% above that of private sector workers [3].  For state government workers, it is between 3-10% and for local government - 13-18% [7, p. 233]. Based on these estimates the total annual amount of rent collected by government workers is close to $200 billion (see Table 1). One can similarly estimate the trade union rents based on estimates of the trade-union compensation premium and trade union membership. Various studies report that controlling for skill and education, the average trade-union compensation premium in the US is between 12-14% of non-union pay [4]. With trade-union members in the private sector numbering about 7 million the trade-union rent is about $50 billion. The estimated annual total of horizontal rents is therefore about $255 billion.

The above estimates do not cover all of economic rents. The complete accounting of unearned incomes in the economy should also include incomes from criminal activity, bribes, monopoly rents, as well as a certain part of incomes from the informal economy and unpaid taxes.  The extent of these financial flows is hard to estimate, but is can be quite substantial. For instance, the annual sales of illegal drugs in the US is estimated at $150 billion [12], while the underpayment of taxes is estimated at up to $600 billion per year [22].

Rent load as a measure of distributional inequity

On theoretical level, the sum of rent-type incomes taken in proportion to the GDP of a country reflects the extent of unfairness in income distribution in the economy. As such it can be compared to measures of income inequality, which reflect the degree of differentiation of incomes, but are blind to the question of whether incomes are earned or unearned.  

Let us consider the most popular metric for measuring income inequality - the Gini coefficient. The Gini measures inequality as the area between a perfect curve of income equality, and an economy’s Lorenz curve.

Similarly to other metrics of inequality  the Gini coefficient includes both the “fair” (justified) and the “unfair” (unjustified) parts.  The former reflects inequality based on differences in effort, skills, work time and qualification of economy’s participants fair. The latter – inequality based on factors outside of economy’s participants control – such as working in a “clean” or corrupt economic system. The Gini does not account for differences in earning ability based on age, hours worked, education, sectoral differences, household characteristics, regional effects, and other observable factors. Computed based on pre-tax (market) incomes the Gini also fails to account for the non-monetary benefits accruing to the recipients of public welfare programs, including public housing, subsidized healthcare, nutrition, education, etc. As a result, estimation of inequality based on market incomes generates the “gross” Gini coefficient which overstates consumption-based inequality.

Figure 1. Income inequality contrasted with income inequity

However, even Gini adjusted for taxes and welfare does not reflect the level of fairness in income distribution. For example, in 2019, the level of market-incomes-based Gini in the US was 0.48. Adjusted for redistribution of incomes via taxes and welfare programs, the “consumption-based” Gini in the same year was 0.36. Yet, this adjustment, while giving a more realistic picture of inequality does not indicate improvement in income distribution fairness. Why? Because a significant part of the post-tax redistribution represents bottom and horizontal rents, a good part of which is unjustified, thus contributing to inequity rather than reducing it.  In other words, the Gini coefficient and the Lorenz curve, while indicating inequality of income distribution can misrepresent the degree of its fairness or unfairness. Figure 1 illustrates this point in the stylized form.

Here the total level of inequality is pictured as a circle, measured for example by Gini. A certain part of this circle, say two-thirds represents “fair” part based on  effort and skills of economy participants. The remaining part (one-third) represents “unfair” part of inequality.

Theoretically these fair and unfair parts of inequality can be separated and quantified. Problem is that in practice such separation is very difficult if not impossible. And even if it was possible, it would be an indirect and incomplete indicator of unfairness. In contrast to that, the rent load is a direct measure of unfairness and is more easily quantifiable. The situation where the rent load ratio equals zero corresponds to the theoretical inequality level in a situation where all incomes are distributed fairly based on productive efforts of all of the economy participants.

In substance, the rent load ratio RL, or the sum of rents as a proportion to the GDP reflects the same aspect of the economy as the unfairness component of inequality metrics. However, it captures not just unfair incomes contributing to higher inequality (top rents) but also unfair incomes that may reduce inequality (bottom rents) as well as unfair incomes that can be inequality neutral (horizontal rents). In contrast to metrics of inequality such as Gini, the rent load ratio captures all of the unearned incomes. It directly measures unfairness in income distribution and therefore is a better indicator of economic inequity.

Conclusions

In this paper we argued that current emphasis on economic rents captured by the wealthy produces an incomplete representation of economic unfairness and proposed to include in unearned incomes economic rents accruing to segments of the poor and the middle class.  We then considered the sum of all rents taken in proportion to the GDP – the rent load ratio - as an aggregate measure of economic inequity. In contrast to standard metrics of income inequality, such as Gini coefficient, the rent load ratio is a direct and more easily quantifiable measure of unfairness in income distribution. By calculating the entirety of the top, bottom and horizontal rents one can arrive at an empirical evaluation of all incomes that are earned unfairly. The resulting rent load ratio can therefore serve as a direct indicator of economic inequity.

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