In this paper, we attempted to generate new insights about the meaning of income inequality and about additional wealth and spatial factors that are essential for a more complete understanding of inequality. Quantitative data analysis was used to reveal (spatial) income distribution patterns between statistically representative population groups. Qualitative data were then used to describe the productive activities of these population groups, the dynamics of losses and accumulation of wealth, and how households are affected by their location (access to economic and ecological resources). Our findings bring us to the following policy and research recommendations.
5.1. Policy Recommendations
There are wide disparities in income as revealed by the following key statistics: the ratio of the lower limit of the income of the tenth decile to the upper limit of the first decile is 7.4 (P90/P10); the cumulative income of the 50% “poor” category is equivalent to that of the top 4.5%; and the 20% “rich” category earns as much as the remaining 80%. Other disparities are reflected in the sources of income, which can give an impression of how households participate in the economy. Compared to the “poor”, the “rich” earn 6.6 times more from their capital, and 3.2 times more from their labour.
In two of the four tabias, there are significant correlations between total income and distance to road (which we used as a proxy for access to economic resources) (p
< 0.05). In May Quiha, higher income households live closer to the road and thus have relatively better access to economic resources; in Hade Alga, they live further away from the road. While productivity and income benefits are often quickly attributed to better access to economic resources and a lowering of transaction costs, it seems that the geographical pattern of income distribution in these two tabias is more strongly affected by pre-existing environmental conditions, i.e., by the unaccounted income generated from the landscape and its natural endowment. In our cases, these were free water and grazing space, but there could be others such as soil fertility, forests or biodiversity. In rural Tigray, the spatial distribution of land degradation and scarcity is also likely impact on income, but in a negative way [3
A careful conclusion from these cases is that the disparities sometimes take on a statistically significant spatial dimension, sometimes they do not, but this must always be considered when seeking explanations for inequality. Our findings have implications for development policy. Interventions will have to consider different scenarios for different localities.
Income inequality is widest in Were Abaye and narrowest in Adi Kisandid. At the same time, the average income of the lowest deciles in the former is higher than in the latter. Should policy then focus on inter- rather than intra-tabia inequalities? Income inequalities in these two tabias are not spatial, but are related to the capacity to manage land. Should this situation be taken as a given and employment opportunities be developed in other sectors for those who cannot cultivate their land? Should interventions instead assist in building the capacity needed to “reclaim” their land? What interventions are best suited for targeting the generally poorer households in the more remote areas in May Quiha, or those closer by in Hade Alga? Improving access to water could be a better strategy in May Quiha. In Hade Alga, labour mobility could be improved through affordable public transport. Alternatively, interventions could also focus on asset building, although livestock might be inappropriate due to the lack of free grazing land in this particular case.
We did not explore how diversification (contributions from more income sources) correlates with access to economic and/or ecological resources. There may be important inequalities in the level of diversification, which represents an important risk minimising strategy in rural Ethiopia. Studies indicate that, compared to better off households, those in poverty tend to diversify in activities with lower marginal returns as they lack the required initial capital to engage in higher return non-farm activities [3
]. It would also be useful to explore whether there are spatial patterns in terms of agricultural versus non-agricultural income. The latter could be more responsive to distance from economic than from ecological resources.
5.2. Research Recommendations
The objective of this paper was to contribute to a better understanding of the relationships or discrepancies between income and wealth inequalities. The discipline of economics mostly abandoned measuring material wealth for more one-dimensional assessments of financial flows. An interesting step towards perceiving the difference between income and wealth has been to distinguish between the sources of income, i.e., labour versus capital. Recent evidence points to widening gaps in both—the latter being typically wider than the former [9
The generation of financial flows are in many ways coupled to the stock of wealth—even material wealth: for example, more land increases income from selling food and lowers spending on food. (On the other hand, more capital also increases spending on its upkeep.) Based on income statistics, households with more substantial amounts of physical assets could potentially be identified by their relatively higher production earnings as a proportion of total income. In other words, financial income from capital is likely to reflect to some extent the distribution of material wealth between households, and can therefore serve as a proxy for wealth distribution.
The question we set out to answer was: to what extent? Our paper points towards several sources of distortion and blind spots.
In the paper, we identified several areas of attention where such distortions can take place. For example, inequality in measurable income from capital might indicate inequality in material assets, but it fails to reveal the consequent inequality in a range of additional unaccounted benefits that might also flow from those assets. Earnings from the selling of output indeed fail to capture “unaccounted” flows of food, fuel or other self-produced goods and services to the household.
We were able to correct one such distortion when we measured the exchange value of the in-kind return that people receive from the land that they rent out. This revealed that even the “poor” earn most of their income from capital. However, a monetary valuation does not always work. A particular distortion that could not be solved in this manner was the selling of livestock, which makes households either richer or poorer, depending on the situation.
A more general problem with assigning a monetary value to unaccounted flows of self-production is that qualitative differences get lost in the calculations: the “real” value of self-produced flows is very different from their equivalent purchased flows. As Frederick Soddy [6
] remarked a long time ago:
“[M]oney incomes do not include a farmer’s consumption of his own product or a wife’s domestic duties, and even if we can estimate the money value of these, the question remains whether services of a mother to her child are economic and are to be appraised at the same money value as those of a wet-nurse. Then it is necessary to ‘go behind’ the money valuation and consider ‘real’ income as distinct from money income”.
Economist Irving Fisher [25
] was one of the first to argue for an alternative measure of income that would capture a flow of satisfaction of needs rather than simply the final costs of goods and services per unit of time.
Most importantly perhaps, is that financial accounting—no matter how comprehensive—suffers from a blind spot: it fails to help us unravel the structural causes of inequality. For this we need to dig deeper in what constitutes wealth and how it works.
Development geography, particularly through livelihood approaches, has long distinguished between different forms of capital, but leading conceptualisations do not elaborate on how to deal with financial and physical assets as entities that obey different laws [26
]. Research on these matters would benefit from bringing in a biophysical economics perspective [31
]. One of its intellectual founders remarked that, unlike physical wealth, financial wealth does not rot with old age: it grows through a flow of interest [6
]. According to this perspective, wealth is physical and gives rise to a physical flow of output, which can be directly consumed or sold to generate a financial flow of income. The physical flow of output could not be produced without access to other forms of wealth, in the form of groundwater, soil nutrients, solar energy and so on. It also could not be produced without a flow of physical labour, a substantial part of which remains formally unaccounted for.
One important manifestation of inequality is lopsided wealth accumulation. Before households can increase their stock of wealth, they need to be able to maintain existing wealth. The capacity to do this depends on the productivity of assets that these households already own, including their own labour and skills. This productivity depends, in turn, on economic resources, which might be geographically, but also politically inaccessible. Productivity also relies to a large extent on the flows of energy and matter from the physical environment, which again might be unequally distributed [6
]. This could lead to accumulation for some and “decumulation” for others through complex feedback dynamics [32
This paper is not an argument against measuring income. On the contrary, it has served us well to generate questions about the way income groups generate income. However, measuring income flows can also be a source of distortion and blind spots. This calls for alternative approach to estimate: (1) the physical assets belonging to households, their accumulation and their loss over time; (2) the physical flows of outputs from and inputs into production processes (which can be linked to their associated financial and unaccounted income flows); and (3) the flows of services from the landscape.
The inequalities associated with these bio-physical dimensions of wealth include, for example, inequality in ownership of the assets, in entitlement to the flows of services or in control over the flows of production [33
]. Although measuring these diverse dimensions of inequality is likely to face its own challenges, we need not be blinded by the confusion created by always measuring inequality based on exchange-value [6
]. Even if we cannot quantify all the dimensions as precisely as financial indicators, Carveth Read suggested “it is better to be vaguely right than exactly wrong” [34
] (p. 272).