Inequality as a brake on technological progress, productivity and growth

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1 Link between inequality and technology 
 
This chapter reviews the state of the art on inequality and technology and the causes and 
the consequences of their intersection. In particular, the determination and the 
development over time of technology and inequality are introduced in paragraph 1.1. In 
paragraph 1.2 the connection between inequality and wage distribution is shown. In 
conclusion, the impact of inequality on technology is highlighted in the last paragraph 
with some details on the opposite side of the relation.  
 
1.1 Definition and evolution of inequality and technology over time 
  
Before proceeding to expound inequality and technology and to discuss their recent trends 
and their particular kind of relationship, let us recall some reason why it is important to 
study inequality.  
First of all, the point must be made very clearly that equality has an ethical value. It is a 
concept devoid of mere quantitative calculation, closely related to humanity, solidarity, 
social inclusion and the fair reallocation of scarce resources. At the same time, the 
economic rationale cannot be ignored: the importance of high-income brackets, wealth 
acquired through inheritance, functional distribution of income between capital and 
labour have led to a structural transformation of the economic system (Pianta, Franzini, 
2016).  
Furthermore, the definition of the element on which the whole analysis revolves, 
inequality, is complex and may differ in relation to the variable taken as a benchmark 
(income, consumption, wealth, capabilities and happiness). Each of these focal variables 
has several advantages and drawbacks in the study of inequality and their choice depends 
on the research question. Most of them are quite difficult to be measured, especially in 
international comparisons, due to methodological complexities and data limits. Income is 
the monetary or non-monetary flow of a stock of wealth and, given the objective of this
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paper, it seems to be one of the variables to focus on more because it is easier to quantify 
despite some difficulties to compute all its components.  
With this in mind, we can start by observing how household disposable income inequality 
varies across OECD countries through the Gini index (Fig. 1). The Gini index is a measure 
of disparities of a particular distribution, it is often used as a concentration index to 
measure income or wealth inequality. It varies between 0 and 1: the smaller the Gini 
coefficient, the more equal is the distribution. In this case, it refers to household disposable 
income that consists of earnings, self-employment and capital income and public cash 
transfers; income taxes and social security contributions paid by households are deducted.   
 
Income inequality 
 
Fig. 1 OECD (2021), Income inequality (indicator) 
 
If we compare the Gini index across these different countries, this graph underlines the 
presence of low values of income inequality in most Nordic European countries and 
Canada (Gini lower than 0.3) and high values for the others. In particular, the highest 
values characterize Latin American countries. In addition, a more explanatory framework 
can be provided if we consider changes in disposable income inequality over time. Figure
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2 shows what happened to OECD countries from the 1980s to 2011 in terms of income 
inequality.  
 
 
Fig. 2 Note: Income refers to disposable household income, corrected for household size.  
Source: OECD Income Distribution Database (IDD). 
 
According to OECD data, income inequality is at the highest level in the last half-century. 
However, the values of disposable income inequality differ a lot across countries. 
Inequality first started to grow in the 1970s and 1980s in the United Kingdom, the United 
States and Israel. From the late 1980s onwards, the increase in income inequality became 
more widespread. The 1990s and early 2000s witnessed a widening gap between poor and 
rich also in traditionally low-inequality countries, such as Germany and some zones of 
North Europe. If we focus on Italy, the data prior to the sixties are very heterogeneous and 
not very precise because it was not until 1963-64 that the Italian Central Statistical Office 
(Istat) produced the first official statistics on the distribution of income. According to these 
first observations and comparisons with other studies, there has been stability or a 
moderate decline of income inequality after the Second World War; a sharp fall in the 
1970s and fluctuations around a flattened trend since the early 1980s (Brandolini, 1999). 
Instead, during the early 1990s, income inequality and poverty grew rapidly and it can be 
seen in fig. 3.
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Fig. 3 Source: Growing Unequal?, OECD 2008. Income is disposable household income adjusted for 
household size 
 
 Something has been changed again when the Great Recession began: if we mean EU15 
countries during the crisis between 2008 and 2011, the disposable income inequality 
sharply fell in the United Kingdom, more slightly in Southern Europe and it increased in 
the Continental and Nordic countries. Just to get an idea of more recent levels of 
inequality, we can mention 2018 data. In that year, the Gini coefficient for the EU-27 was 
30.4%. The highest income divergences between the EU Member States were recorded in 
Bulgaria, Latvia, Lithuania and Romania. However, even Italy, Spain, Luxembourg, 
Greece, Portugal, Germany and Estonia have been characterized by a Gini coefficient 
above the EU-27 average of 30.4%. At the other end of the range, income was more evenly 
distributed in Czechia, Slovenia and Slovakia, where the Gini coefficient was less than 
25.0% (Eurostat). Today, the median income of the richest 10% of the population is about 
nine times that of the poorest 10%, compared to seven times 25 years ago.  
Another widely cited way to visualize changes in income inequality is the Growth 
Incidence Curve introduced by Ravallion and Chen by plotting the percentile-specific rate 
of income growth in a given period of time. A downward-sloping GIC indicates that 
growth contributes to equalize the distribution of income (i.e., growth rate in income
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decreases as quintile increases), whereas an upward-sloping GIC indicates non-equalizing 
growth (i.e., growth increases as quintile increases). When the GIC is a horizontal line, 
inequality does not change over time, and the rate of growth experienced by each quantile 
is equal to the rate of growth in the overall mean income (Ravallion and Chen, 1993).  
Lakner and Milanovic's Elephant curve apply the GIC to the global distribution of income 
to measure changes in global personal inequality. Their original work was born as an 
attempt to measure global inequality: each individual enters the computation with their 
actual income regardless of her country. The Elephant graph refers to each single decile of 
income and allows to identify the trend of the corresponding incomes from 1988 to 2008: 
the tail of the elephant, made of poor, shows growth close to zero; the back, i.e. the most 
consistent income development, peaks towards the median; the base of the trunk reveals 
acute stagnation; and finally, the tip of the trunk, which represents the richest decile of the 
population, has a peak that indicates strong growth in the income of the wealthiest (fig. 4) 
(Lakner and Milanovic, 2016; Boldrini et al., 2018). 
 
 
Fig. 4 Source: The American Prospect, using data provided by Branko Milanovic 
 
However, this framework has been partially corrected by Kharas and Seidel, as the 
original graph by Milanovic and Lakner is affected by the inclusion in 2008 of countries
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whose data were not available in 1988. For example, these include Congo, Kenya, 
Tanzania, Russia, Ukraine and Vietnam which are low-income countries, and Singapore, 
Norway and Luxembourg which are considered to be rich. The “balanced” sample (made 
up only of the countries included in both periods) consists of 60 countries instead of the 
original 130 and it manages to capture only 77% of the global population. Analysing so 
few countries would seem to betray the idea of "globalization", but it is useful for 
consistently examining the dynamics of incomes in the countries taken into consideration. 
In this case, there is a more sustained development for each level of income, and an 
increase in the growth rate for the two deciles at the tail and the base of the elephant's 
trunk. The rest nevertheless remains unchanged. Furthermore, a supplemental 
modification due to the two economists is to shift the sample for five-year forward and to 
increase the "balanced" sample of 17 countries. This experiment is interesting because we 
see if what is observed depends on the period and the sample (Fig. 5). Indeed, the profile 
of the elephant undergoes small changes: it is extremely sensitive to economic cycles; 
therefore, the historical period is relevant, and, secondly, the inclusion of low-income 
countries which have shown rapid growth affects positively on global growth. Moreover, 
the trunk disappears: in the highest percentiles are the most developed countries, which 
are also those most affected by the financial crisis of 2008 and by the European crisis of 
2011. The weak growth is influenced both by these events and by the inability of the 
countries to recover quickly (Kharas, Seidel, 2018).
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The Elephant with a consistent and larger sample (1993-2013) 
 
Fig. 5 Source: Kharas & Seidel, 2018 
 
If we consider inequality trends and the performance of technological development, and, 
if we analyse the mechanisms underlying their close connection, we can see that 
technological progress and inequality are strictly related. There are many drivers of 
inequality: technology is one of these. Even if it is a phenomenon in which numerous 
limits of explanation are found, especially because it fails to explain the direct impact on 
inequality "within", it remains an important determinant if we consider the sources of 
inequality. At the same time, technology, economic growth and innovation are also 
defined by the level of inequality. Tendentially, the relationship between inequality and 
technology can be considered two-way since they influence each other. Even technology, 
like inequality, has experienced different rates of growth and decrease depending on the 
willingness to pay for innovation in research and development. Certainly, it founds its 
maximum expression in the so-called "Industrial Revolutions" which brought about 
innovations that radically changed technological production, human interaction and 
above all the labour market. The First Industrial Revolution (1750-1875) undoubtedly 
represents an epochal turning point and mainly concerned textile manufacturing and 
steam energy. The Second Industrial Revolution began in 1875 and it was marked by the 
introduction of electricity and oil as new sources of energy. At the beginnings of the XX 
century, the average level of wages grew to a greater extent, and the per capita income of 
industrialized countries increased despite the simultaneous large increase in population.