Income Structure and Distribution

Summary

Driver description
Interactions within the Social Domain
Interactions with the Economy Domain
Interactions with the Environment Domain
Interactions with the Technology Domain

Driver description

  • “Income distribution reflects the nature and extent of inequalities in the income of individuals or households in a given society or subgroups within society. The concept may also be applied to geographical units.” (Ref: CO_5054)
  • “Measurements of income distribution include poverty measures, either identified as absolute or relative poverty. Absolute poverty measures the number of people living below a certain income threshold or the number of households unable to afford certain basic goods. Relative poverty, which is related to the concept of income inequality, measures the extent to which a household’s financial resources fall below an average level of income threshold for that economy.” (Ref: CO_5054)
  • “There are many ways to characterise income distribution and inequality (...) Aggregate measures such as the Gini coefficient and the Atkinson family of inequality measures summarise income inequality in society in a single number. (...) The Gini-coefficient is perhaps the most popular measure of inequality. The coefficient varies between 0, which reflects complete equality and 1, which indicates complete inequality (one person has all the income or consumption, others have none).” (Ref: CO_5054)
Figure 1‑15 Interpersonal income inequality: Gini idex across EU member states, 2007

Source: Social Mobility and Intra-Regional Income Distribution Across EU Member States (Ref: CO_5054)

  • “The main source of income for individuals and households in the EU is earnings from employment.” (Ref: CO_5026)
  • “EU-15 countries have greater spatial inequality in per capita income and unemployment rates, two common indicators of individual living standards in high-income countries.” (Ref: CO_5028)
  • “Inequality in earnings has risen in the majority of member states in recent decades. In particular labour’s share of value added has fallen especially among the low paid. This means that employment no longer provides a guarantee against poverty and exclusion. One third of working adults are in poverty, implying the need to strengthen policies aimed at working poor.” (Ref: CO_5026)
  • “Furthermore, an increasing proportion of European workers have experienced a decline in total income – wages plus social contributions.” (Ref: CO_5026)
  • “As findings from INEQ, EQUALSOC, and LoWER3 show, both earnings and incomes inequalities have increased in recent decades for most EU states. The level of inequality varies between different MS. For example, inequality declines marginally in Belgium but rises significantly in the UK. The main source of rising inequality for many states is the increased share of income accruing to more affluent households, those in the top quintile – (the top 20 % of incomes).” (Ref: CO_5026)
Figure 1‑16 Trends in household income inequality (Gini coefficient)

 

Source: Why socio-economic inequalities increase? Facts and policy responses in Europe (Ref: CO_5026)

  • “The projects show that a combination of factors (including economic restructuring associated with the move towards a knowledge economy, labour market change and redistributive policies of welfare states) account for these increases in inequality in the last two to three decades.” (Ref: CO_5026)
  • “Hoffmeister (2006) found in a study of European countries that more than a fifth (21.6%) of overall inequality was attributed to the income gap between the western and eastern halves of the EU. The differences between countries (within the areas) account for only 1.3 percent, and the differences between the regions (within the countries) for less than 1 percent of overall inequality. Three quarters of the inequality was attributed to income differences between people living in the same NUTS1 region.” (Ref: CO_5054)

 

Figure 1‑17 Interpersonal income inequality: Gini idex across EU member states, 2007

Source: Social Mobility and Intra-Regional Income Distribution Across EU Member States (Ref: CO_5054)

 

  • “In Europe, 17 families out of 100 were considered at risk of poverty in 2007. In addition to this conventional form of poverty, new forms of social exclusion and poverty are emerging: “infrastructure-poor” (eastern Europe); “feminisation of poverty”, mainly among single, immigrant mothers (southern Europe); “immigrant poverty” (central Europe and other countries); “young people at risk of poverty” (eastern Europe); “the vulnerable elderly” (eastern and western Europe), among other forms.” (Ref: CO_5027)
  • “Cities in particular have many low-income communities – this trend will increase as much of the world’s future population growth will be occurring in Asian and African cities.” (Ref: CO_5018)
  • “In some countries that recently joined the EU, the level of urban poverty is underestimated. A World Bank study found that peri-urban dwellers, homeless and internally displaced people and refugees are consistently underrepresented or omitted entirely from surveys. The appearance of forms of slums in the periphery of big cities is a new – and still not well recognised or documented – phenomenon. The World Bank study found that most poverty analyses fail to differentiate among urban settlement types, and as a result, “the better off capital cities conceal the degree of poverty in secondary cities.”(Ref: CO_5027)
  • “Passenger traffic is very much related to both personal income and travel time. People are willing to travel, and are disposed to spend a certain amount of their income (i.e. 10 to 15% of their personal income) and of their time (i.e. 1h per day) for travel.” (Ref: CO_5048)
  • “For passengers, the key element in transport is its cost in relation to people’s personal income. If personal income rises, rising transport costs are not a substantial problem.” (Ref: CO_5048)

Interactions within the Social Domain

Migration flows

  • “Migrants move for higher wages, greater education opportunities, or a better quality life.” (Ref: CO_5028)
  • “The pool of potential migrants is likely to remain large given prevailing wage differentials between poor and rich countries, three to four times those triggering the mass migration of Europeans to North America in the late-nineteenth century.” (Ref: CO_5028)

Households structure and distribution

  • “Using the 2005 EU-SILC data, Lelkes et al. (2009) examined the distribution of (net) household incomes in all 27 EU Member States and analysed the determining factors of inter-personal income inequality. Decomposing total income inequality by population subgroups using the MLD[1] index, the authors investigated the impact of the following factors on income distribution: age of the head of the household; household structure; the education and the employment status of the head of the household; work intensity of the household[2]; and, the degree of urbanisation in the household’s place of residence. Four categories for the age of household head were used: 18–35 years; 36–49 years; 50–64 years; and, over 65 years. Household structure was grouped into five categories: a) households with a working-age head (between 18 and 64 years of age) with no children; b) with one child; c) with two children; d) with three or more children; e) households with a retirement-age head. The ‘work intensity’ of the household was defined taking into account the total number of months worked by all household members compared with the number of total workable months. The results of the decomposition show that with the exception of few countries, differences in the ages of household heads accounted for less than 5% of total inequality. Such differences in age were more important in the Nordic countries (DE, FI and SE) as well as CY and EE where there are significant income differences between different age groups, particularly between those of working age and retired. The authors suggest that the explanatory power of household structure (with children or no children) is relatively high in CZ, CY and IE. Differences in the number of children account for 8% of the total inequality in CZ and 8% in the UK, where the average income of the families with three or more dependent children is less than two-thirds of the mean income of childless households. The income differentials by household structure are explained by the differences between households headed by a working-age person or by a retired person. However, in contrast, Brandolini, A. and G. D’Alessio (2001) concluded that differences in population structure (household size; age and sex of household head) do not explain many of the large differences in income inequality observed across countries.” (Ref: CO_5054)

[1] Mean Log Deviation

[2] Work intensity of the household is defined taking into account the total number of months worked by all household members, related to the number of total workable months

Car ownership

  • “Income is an important factor for car ownership and thus for the level of trip making overall, and for motorised trip making in particular.” (Ref: CO_5048)
  • “Observed that households that rely exclusively on non-motorized modes of transport and public transportation spend only about 3 to 5 percent of their income on travel; that percentage rises to 10 to 15 percent for people who own at least one motor vehicle.” (Ref: CO_0059)
  • “Per capita automobile ownership and mileage tend to increase rapidly over the range of $3,000 to $10,000 (2002 U.S. dollars), when vehicle ownership increases twice as fast as per-capita income, but at higher income levels growth rates levels off and eventually reach saturation. International analysis indicates that per capita automobile ownership peaks at about $21,000 (1996 U.S. dollars) annual income, and levels off or even declines with further wealth. Using U.S. data, Holtzclaw found that vehicle travel increases strongly with annual income up to about $30,000, but then levels off and declines slightly with incomes over $100,000.” (Ref: CO_5047)
  •  “As incomes rise, there will be a shift towards a demand for transport. In 2005, vehicle ownership was about 11 cars per 1,000 capita in China and about 6 cars per capita in India, compared with a global average of 111 cars per 1,000 capita. Recently, car ownership rate in China has been growing at a rate of 12% per year, while in India it has been growing at 9% per year.” (Ref: CO_0159)

Urbanisation

  • “Urban economists have long understood that several fundamental forces drive the spatial growth of cities. These forces include the growth in household income (which raises the demand for living space), the reduction in commuting costs due to transport improvements (which eases suburban access) and rising city populations.” (Ref: CO_4014)
  • “Increasing urbanisation (an increasing weight of the urban relative to the rural population within a region) is negatively associated with income inequality (Rodríguez-Pose, and Tselios, 2009a). However, different results were found by Hoffmeister (2006) who examined income differences between people living in the same (NUTS1) region across the EU. The regions characterised by high levels of interpersonal income inequality in Europe are usually the ones that incorporate the capital of a country or regions marked by high rates of agglomeration (such as Ostösterreich in AT, Hamburg and Berlin in DE, London in the UK, Centralny in PL). One of the explanations for this contradiction could stem from the fact that increasing weight of the urban relative to the rural population means a decreasing income inequality for the whole of the population in a region, but this does not apply to the disparities within the working population which may increase (Rodríguez-Pose, and Tselios, 2009a). Urbanisation might increase the local economic prosperity and income per capita for the local population but at the same time, it can trigger a higher earnings dispersion between the skilled employees and the less skilled, those who work in the advanced industries versus the more traditional, low paid industries.” (Ref: CO_5054)
  • “There is a strong correlation between poverty and urban mobility, but its extent is not sufficiently well known or quantified. The time and money that the poor must spend meeting basic mobility needs keeps low-income families from accumulating the assets that would lift them out of poverty.” (Ref: CO_0163)

Planning

  • “Mobility systems must work for rich and poor alike, to ensure no-one is shut off from goods, services and employment opportunities. There are currently 4 billion people around the globe on low incomes[1].” (Ref: CO_5018)
  • “One of the most significant transport challenges the world faces is how to offer reliable, safe, and affordable choices of transport to an expanding number of poor people. The income-transport gap is the gap between those that can afford transport choices and those with no transport choices. Dealing with the income-transport gap will be essential in the megacities of the future, in order to help manage pollution and environmental degradation.” (Ref: CO_0159)

[1] WRI, The Next Four Billion: “Low-income” is defined as earning less than $3,000 in local purchasing power.

Tourist flows

  • “As millions of people are to become “middle-class” in Asia in the next 20 years, Europe will become a theme park for extra-EU visitors: between 300 and 600 million Asian tourists will be travelling yearly to Europe. This estimate implies a large growth of transcontinental flights, especially towards consolidated tourist areas such as Paris and London, but also towards Italy, Barcelona and Berlin.” (Ref:CO_5048)

Change of lifestyle and values

  • “The stimulus provided by cheap air links to the real estate business of secondary housing. Increasingly, medium to high-income households have been buying second residences outside their home countries. Commonly, the households are from the wealthier European countries (notably the United Kingdom) that have turned their attentions to Southern Europe, which is considerably cheaper and has much better weather conditions all year round. The Northern European households travel regularly (on a weekly basis or twice per month), using LCA cheap fares, to their secondary residences located in Southern Europe to spend a weekend. Naturally, this business has stimulated the real estate business in these regions.” (Ref: CO_5037)

Education

  • “Educational inequality (unequal distribution of education level completed) is associated with higher income inequality within a region (Rodríguez-Pose, and Tselios, 2009a). Analysing income data at national level Lelkes et al. (2009) demonstrate that education in general accounts for income inequality to a greater extent than age and household structure: differences in education can account for up to 30% of income inequality in PT and around 20% in HU. In LT, PL, CY, LU and SI, education seems to matter at both lower and upper ends of the distribution.” (Ref:CO_5054)
  • “Low-income households are often more exposed to environmental pollution than middle- and high-income households and thus are also more vulnerable.” (Ref: CO_5009)

Health

  • “Low-income households are often more exposed to environmental pollution than middle- and high-income households and thus are also more vulnerable.” (Ref: CO_5009)

Interactions with the Economy Domain

Availability of public and private resources and investments in the transport sector

  • “Just as wealthier consumers tend to purchase more expensive vehicles for greater performance, comfort and prestige, wealthier cities tend to invest in higher quality public transit systems that offer superior service. In developed countries, cities with higher incomes tend to have better transit systems which result in higher per capita transit ridership rates.” (Ref: CO_5047)

Fiscal policy

  • “In order to reduce the inequality which is one of the causes of the crisis tax policy must be used more vigor­ously, for example, by raising the top tax rate and rein­troducing the wealth tax.” (Ref: CO_0235)

Interactions with the Environment Domain

Pollution levels and emissions standards

  • “Using US data, Huntington (2005) found that, after allowing for (...) technology shifts, the positive relationship between emissions per head and income per head has remained unchanged.” (Ref: CO_2024)

Interactions with the Technology Domain

Technology development in general and innovation diffusion

  • “Technological changes at the regional level create demand for innovative products, better human capital and skilled labour. Therefore, technological change is usually associated with greater earnings inequality. Perugini and Martino (2006) show that the technology-driven innovation (strongly correlated with R&D expenditure) is positively associated with income inequality.” (Ref: CO_5054)

New vehicles design

  • “Everyone in the mobility sector will have to design tailored mobility solutions that meet these poor people’s needs.” (Ref: CO_5018)
  • “Worldbike is an international network of professionals in the bicycle industry, who work on creating affordable bike transportation and income-generating opportunities for the poor. The Chop‘N Drop bike is an open-source design, which is shipped to small-scale manufacturing facilities or skilled individuals in the developing world, who then construct the bike locally.” (Ref: CO_5018)

Impacts on Mobility and Transport

  • “Another social factor of potential relevance for transport policy has to do with the distribution of economic resources and the level of inequality. Household income correlates strongly with GDP per capita and could be thought of as a proxy to the latter for the estimation of transport demand. The average values of household income or GDP per capita, however, say little about how the latter is distributed within the population. As economic measures increase in importance in the transport policy domain and given the gradual deregulation of public transport services, the assessment of levels of inequality is likely to gain in significance for transport politics.” (Ref: CO_2041)

Increased demand and mileage

  • “(...), transport accounts for a certain percentage of each person’s disposable income, and this percentage seems to remain rather stable but at different levels in different countries. Since transport is becoming less expensive, relative to most people’s revenue, and faster, transport demand is growing in terms of number of trips and total length, even though the natural and cultural thresholds may remain basically stable.” (Ref: CO_5048)
  • “Studies have shown that TTB[1] per traveler is typically higher at lower incomes (Roth and Zahavi, 1981). (A ``traveler'' is defined in travel surveys as someone making at least one motorized trip on the day of the survey.) The poor face more constraints on their choice of living locations and transport modes and thus find it more difficult to optimize travel times. The share of travelers to total population is lower in low-income societies (...).” (Ref: CO_0001)

[1] Travel Time Budget

  • “(...) the time spent in motorized modes (TTBmot) rises with income and mobility as people shift from slow non-motorized modes to motorized travel.” (Ref: CO_0001)
  • “On average, people spend a constant share of money on traveling; rising income leads nearly directly to rising demand for mobility, which we demonstrate historically. A person also spends a constant share of time for travel on average; as total mobility rises, travelers shift to faster modes to remain within the fixed travel time budget of 1.1 h per person per day. In addition to these constant budgets, travel behavior is also affected by the path dependence of infrastructures, landuse constraints, and the development of niche markets.” (Ref: CO_0001)
  • “(...) the rise in TMB[2] from 3-5 to 10-15% with increasing motorization might allow for more rapid growth in mobility because a larger fraction of income is devoted to travel. In practice, it appears that rising travel money budgets are offset completely by the rising unit cost of travel as travelers shift from public modes (bus, railroads) to private automobile. Thus even at low mobility, there is a direct relationship between rising income and rising mobility.” (Ref: CO_0001)

[2] Travel Money Budget

  • “The world’s population reached 6 billion in 2000 and will be around 9 billion in 2050. Coupled with rising incomes this will lead global mobility to expand strongly through 2050. If infrastructure and energy prices allow, there will be around 3 to 4 times as much global passenger mobility (passenger-kilometres travelled) as in 2000 and 2.5 to 3.5 as much freight activity, measured in ton-kilometres.” (Ref: CO_4024)

Increased demand for faster transport modes

  • “The fixed travel money budget requires that mobility rises nearly in proportion with income. Covering greater distances within the same fixed travel time budget requires that travellers shift to faster modes of transport. The choice of future transport modes is also constrained by path dependence because transport infrastructures change only slowly.” (Ref: CO_0001)
  • “Studies show that income is closely related to people's value of time, in other words, how much in monetary terms people think their time is worth. Transport services which allow a reduction in journey time (e.g. cars, high-speed rail and aviation) are therefore strong candidates for growth from this respect.” (Ref: CO_5031)

Increased demand for air transport

  • “Economic factors, in particular disposable income, air ticket price and price of competing modes (e.g. high-speed rail) are often regarded as key determinants of air traffic demand (see Hanlon, 2007).” (Ref: CO_5031)
  • “For example, in the UK, the statistics provided by the Civil Aviation Authority (CAA) indicate a strong relationship between the frequency of leisure flying and the socio-economic characteristics of passengers with higher income households as well as singles, childless couples, and those with properties abroad taking more flights.” (Ref: CO_5054)
  •  “IATA (2008) calculates that for a 1 % rise in income (typically measured in GDP per capita), demand for air travel in developed economies increases by 1.5 % for short haul, and 1.7 % for long haul.” (Ref: CO_5031)
  • “Air travel probably continues to increase at high incomes.” (Ref: CO_5047)
  • “The growth in income is likely to put an upwards pressure on demand for air travel, especially with regards to leisure travel. A downturn in the economy with a reduction in income would have the opposite effect, which is currently being observed temporarily as a result of the global economic slowdown.” (Ref: CO_5031)

Poverty complicates mobility

  • “Poverty complicates mobility and lack of transport options complicates poverty. The poor do not travel less, they just travel under worse conditions. Lack of transport options hamper access to employment, as well as contribute to weakened social networks.” (Ref: CO_0163)