The Fall and Rise of Inequality: Disaggregating Narratives

Austrian Economics: The Next Generation

ISBN: 978-1-78756-578-4, eISBN: 978-1-78756-577-7

ISSN: 1529-2134

Publication date: 3 December 2018


In this chapter, I attempt to extend insights regarding statistical aggregates from scholars, such as Hayek (1931) and Mises (1947), to the topic of inequality. Using the work of Lindert and Williamson (2016), I show that a disaggregation of inequality into some of its many subcomponents alters our reading of its evolution. While I only work with stylized facts from the field of economic history, and the authors argues that the promising implications derived from disaggregation militate in favor of more effort being directed toward decomposing the evolution of inequality.



Geloso, V. (2018), "The Fall and Rise of Inequality: Disaggregating Narratives", Horwitz, S. (Ed.) Austrian Economics: The Next Generation (Advances in Austrian Economics, Vol. 23), Emerald Publishing Limited, pp. 161-175.

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In the field of macroeconomics, there is a tradition (not shared widely enough though) of being skeptical of aggregates. Macroaggregates like total output, monetary indicators, and inflation measures may conceal rather than reveal important microfoundations crucial to understanding any event or phenomenon (Hayek, 1931; Mises, 1947).1 This concealment could be the result of mixing too much information into one estimate, or it could also be the result of a design which may be suited ideally to one very precise question, but which may not be used efficiently for other purposes.2 When this concealment takes place, incomplete or false conclusions may be reached.

Measures of inequality are, by definition, aggregates. They encompass countless individual persons or households in order to create an image of the distribution of wages, earnings, compensation, consumption or total income. Contained within these distributions are different forms of inequalities which, when aggregated, are concealed from our sight.

The increase in inequality that has taken place in the United States since the 1970s has generated important debates within academic circles. Many scholars have weighed in with their personal qualms regarding measurements of inequality and the interpretation of the movements of these measurements (Atkinson, 2015; Burkhauser, Larrimore, & Simon, 2012; Burkhauser, Feng, Jenkins, & Larrimore, 2012; Chetty et al. 2016; Corak, 2016; Lindert & Williamson, 2016; Piketty & Saez, 2003; Reynolds, 2006, 2012). From these debates, a broad pattern of the evolution from the late nineteenth century to the present day has been agreed upon: from the high points of the nineteenth century, inequality fell steadily up until the 1950s at which time it reached a plateau and only began to increase after 1975. Another point of agreement is that the decline and low-plateau phases, described by Lindert and Williamson (2016, p. 194) as the “Great Leveling,” were largely the result of the rise of redistributive policies.

The intuition behind the prudence of using aggregates in macroeconomics has generally centered around business cycle analysis. This intuition has rarely been extended to the subject of income distribution and inequality. This should be remedied. The study of inequality, like that of the business cycle, can be foiled by overreliance on aggregates. In fact, the rise of mechanistic models and interpretations of the impacts of inequality on society (Piketty, 2014; Wilkinson & Pickett, 2011) where inequality measures are taken to represent the same thing regardless of context is reminiscent of macroeconomic models used prior to the econometrics credibility revolution. In this chapter, I disaggregate the macroinequalities into smaller components in order to analyze the evolution of the pattern described above.

These smaller components, which I will dub microinequalities, are related to the evolution of the gender and ethnic earnings gaps, regional differences in incomes and prices, and the effects of immigration. In the light of this more focused analysis, I propose a different interpretation of the fall and rise of inequality.

Rising wages and earnings for women and black Americans relative to male and white Americans until the early 1970s were heavy contributors to the Great Leveling. (This appears to have reversed at some point between 1970 and 1980, see notably Kopczuk, 2015 for a summary.) Simultaneously, convergence in regional incomes (adjusted for regional prices), resulting in the rise of the Southern states, contributed to the leveling of living standards within the United States. Finally, this was also a period in which immigration was more restrictive which had the effect of limiting increases in inequality.3 All these factors were unrelated to the rise of redistributive policies. As such, I argue that government policies, on net, had a limited influence. On closer inspection it appears that a large part of the Great Leveling would have materialized regardless of state intervention (albeit, not its entirety).

Since 1970, reversals on the front of the relative economic conditions of blacks have been observed which, in concert with moderate increases in immigration levels and minor divergence in living standards between states, have increased inequality. This post-1970 increase in inequality appears in a dramatically different light since it occurred simultaneously with a significant increase in state intervention.

The Great Leveling, Its Reversal and Disaggregation

The history of American inequality suggests the colonial era was a low point for economic inequality (Hanson Jones, 1980; Lindert & Williamson, 2016; Main, 1977). However, during the Antebellum era, there was a steady increase in income inequality (Buettinger, 1978; James & Thomas, 2000; Lindert & Williamson, 2016; Steckel & Moehling, 2001). The Civil War leveled off inequality and it either increased modestly, or remained stable through 1910 (Lindert & Williamson, 2016; Steckel & Moehling, 2001; Stelzner, 2015). Afterwards, the Great Leveling described by Lindert and Williamson began. By all accounts, inequality fell dramatically up to the late 1960s (see also Kuznets, 1953; Smiley, 1994). So much so, that the share of total income represented by the top 1% of tax units fell back to the level observed in the 1860s (Stelzner, 2015, p. 896). It was only in the 1970s that inequality began inching back up, and the increase picked up steam in the 1980s to the point that, by 2010, income inequality measures were back to the high level observed at the beginning of the twentieth century.

Many scholars are tempted to describe the causes of the evolution in mechanical terms. Piketty (2014) ascribed it to the simple equation that g < r where g represents the growth rate and r the return on capital. This implies that capital-owners constantly obtain returns greater than economic growth and thus a growing share of all incomes. Moreover, in these mechanical narratives, government actions affecting redistribution generally account for the lion’s share of the explanation regarding the evolution of inequality. 4 While wars can reorganize the composition of the capital stock in ways that level inequality, scholars like Piketty argue that tax policy is the most efficient channel for permanent reduction of inequality.

In general, this mechanical description is the norm with one striking exception within the mainstream, that of Lindert and Williamson (2016). In their work on the economic history of American inequality, they eschew the temptation to ascribe general laws of inequality. Rather, they propose that an understanding of inequality requires a multidimensional cluster of concepts: immigration, relative factor price, discrimination, regulation, international trade, urbanization, labor force participation, skill-biased technological change, market integration, and many others (Geloso, 2016, p. 3). In essence, they lay out all the theoretical tools that may be relevant to the analysis of inequality, and there are many. They argue that in a given context, one theoretical tool may not be relevant to explain the evolution, but it may hold relevance in another context.

In fact, their latest book is the culmination of a long string of publications on the many facets and intricacies of both the history and economics of inequality. For example, alongside Timothy Hatton and Williamson (2005) considered the role that international migration played in domestic inequality. Later, Williamson (2011) tied the evolution of inequality to deindustrialization and terms of trade during the late nineteenth century. Heavily referencing the work of Kuznets (1955) who emphasized the role of internal population migration and urbanization as forces behind the evolution of inequality, Williamson (2002) also devoted considerable attention to the role played by urbanization and the urban–rural real wage gap. As for Lindert (2004), he devotes two volumes to documenting the many aspects of welfare state policies and their effects. He also documents the role of rent-seeking (along with Kristov, Lindert, & McClleland, 1992) in redistribution processes, as well as demographic changes related to reproductive behavior (Lindert, 1978). In fact, one particularly important article produced by Lindert (alongside Hoffman, Jacks, & Levin, 2002) created group-specific price indices to measure “real” inequalities that account for the role of relative price changes affecting different layers of the income distribution differently. Taken as a whole, each of these works (and the list is not exhaustive) represent microfoundations to the macroeconomic topic of inequality.

As such, Lindert and Williamson are using an approach to economic history very similar to that advocated by Ludwig von Mises in Theory and History (1957 [2005]). The core argument of Theory and History is that axiomatic statements can be applied to historical events. The goal of historians and economic historians is to sort out which “theory” applies to a clear event that has already occurred. Two theories can be axiomatically true. However, they can compete to negate each other, one may be minimal or irrelevant in a context, or they may even amplify each other. Each theory has microfoundations (i.e., why some actions were taken instead of other actions) that must be studied to ascertain the relevance of each. To explain which of these two axiomatic statements applies to the event (and in what dosage), one needs data, quantitative and qualitative. In Human Action (1947), Mises applies this logic of decomposition by arguing for the necessity of examination of innate differences (134), differences in effort (134, 158), institutions (158, 287–289, 737–739), and constraints in the immediate environment (158).

Implicitly, as soon as one disaggregates to consider each of the microinequalities, one ends up going to microfoundations. As such, Lindert and Williamson end up accidentally following Mises’s footsteps. Each source of inequality is explained by microfoundations that cannot be fully captured in aggregated statistics.

By definition, such efforts to avoid mechanistic interpretations of inequality require the use of disaggregation. The implication of the Lindert, Williamson, and Mises approach is that one must constantly look at the subcomponents of inequality, dividing it into its many sources and considering contextual forces in order to form sound interpretations. While one can disagree with some of their interpretations (or policy recommendations), this approach makes it easy to see the salient points of contention.

The Role of Geography, Race, Gender and Immigration to 1970

To explain the transition from the high plateau of income inequality of the late nineteenth century to the low point of the 1970s, Lindert and Williamson disaggregate their explanations into five broad categories. The first, and most easily explained, relates to the role of redistributive policy. This era was marked by a progressive increase in the size of government (Beito, 1989; Higgs, 1987; Lindert, 2004) which included a wide variety of redistributive or public insurance schemes as well as the introduction of progressive taxation. 5 However, some have argued that tax policy may not be as efficient in the reduction of inequality as initially believed (Scheidel, 2017). This should not be surprising, as tax policy, like other policy realms, is prone to rent-seeking (Tullock, 1967). Actors can seek exemptions, special treatments or unfavorable treatments against rivals in ways that benefit them. There is great evidence of this especially in the 1920s (Smiley & Keehn, 1995) as witnessed by the large number of deductions and exemptions that existed. However, since most of our measures of United States inequality from 1917 to today (Piketty & Saez, 2003) emerge from tax records, the measures themselves are sensitive to this problem. This has been explicitly demonstrated by recent research in which corrections are made to adjust for changes in the manner incomes are reported (Armour, Burkhauser, & Larrimore, 2014; Auten & Splinter, 2017; Auten, Splinter, & Nelson, 2016; Bricker, Henriques, Krimmel, & Sabelhaus, 2017; Mechling, Miller, & Konencny, 2017). All these corrections show that the recent trend in inequality has been overestimated. Although evidence is more limited for the pre-1960 era, there is some evidence that the same occurred (Smiley, 1998, 2000). It is also worth downplaying the role that government policies overall may have played, as some of them may have been regressive. For example, tax policy has been shown to have been regressive in the 1930s (Folsom, 2009; Renaghan, 1984), while Katznelson (2005) and Rothstein (2017) showed that numerous government programs explicitly discriminated against blacks who tended to be poor even though their taxes served to finance the policy that benefitted others.

The other four channels (geography, gender, race, and immigration) require some explanation. Two of these factors, geography, and race, are intimately related. A significant share of the high level of income inequality in the United States prior to the 1970s emanated from differences between regions of the country. Lindert and Williamson (2016) found that during the colonial era the national-level Gini coefficient was higher than the state-level Gini coefficients because of the large income differences between regions. By the late nineteenth century, this feature was not as pronounced, but there remained substantial regional income differences as can be seen through the coefficient of variation in Fig. 1 at the point associated with 1880. That high-level of regional difference progressively collapsed through the last decades of the nineteenth century and continued falling right through to the 1970s (see notably Michener & McLean, 1999). One important contributing factor for convergence between the different states was the mobility of capital which progressively moved to where the returns were greater, exactly as standard growth theory would suggest (Turner, Tamura, & Mulholland, 2013). However, a substantial portion of the convergence resulted from the migration of Southern individuals, especially black Americans, to the North (Boustan, 2016; Wright, 1987, p. 174), which had an important effect on convergence between the North and the South. This migration had the double effect of reducing inequality both between regions and between ethnic groups. Throughout the period, the migration of blacks to the North created a convergence in the earnings of blacks to whites nationwide (although the gap remained constant in the North, it fell dramatically in the South). The estimates conflict (see notably Higgs, 1977 vs Margo, 2016), but the trend is unmistakable: the income gap between whites and blacks fell. Higgs estimated that the per capita income ratio of blacks to whites was slightly below 25% while Margo pointed to a slightly higher proportion closer to 30%. By 1940, Higgs estimated a ratio of 34% while Margo placed it at 38%. By 1970, the proportion made available through census data placed the proportion north of 55%. A gap remained, but it had nonetheless diminished appreciably and it would have acted as a significant force in favor of reducing overall inequality.

Fig. 1. 
Coefficient of Variation, Average Income per Capita at the State Level, 1880–1975.

Fig. 1.

Coefficient of Variation, Average Income per Capita at the State Level, 1880–1975.

Simultaneous with the convergence between ethnic groups and regions was the convergence between genders. As was documented by Goldin (1990), the wage gap between men and women shrank progressively from the beginning of the twentieth century up to the 1970s, while the labor force participation rates of all women (married and single) increased dramatically. Among the contributing factors, changes in state policy did have an impact (Oreffice, 2007), but many important contributors to the relative improvement of the economic condition of women were mundane: the contraceptive pill (Bailey, 2006), running water (Cardia, 2008), household appliances (Coen-Pirani, León, & Lugauer, 2010), and technological changes affecting the workplace (Phillips, 1982). All these forces would have encouraged the shift from the family economy to the market economy (Rotella, 1980), which, in turn, would have compressed income inequality.

The remaining element of importance is that of immigration. By definition, immigration will increase inequality. Unless entrants into a country have an income distribution identical to that of the overall population, immigrants increase inequality by pure mathematical definition. Unskilled immigrants earning low wages swell inequality at the bottom. Allowing only high-skilled immigrants does the same if it swells inequality at the top. In fact, as soon as an immigrant diverges from the median, he is likely to increase inequality. As such, if the immigrant share of the total population increases, then inequality increases as well. Conversely, a reduction in the immigrant share of the population will tend to reduce inequality. This has nothing to do with economics, it is a purely mathematical fact. 6 In the late nineteenth century, there were very high levels of immigration relative to the population (Hatton & Williamson, 2005) and this was logically a contributing factor to the high level of inequality (Steckel & Moehling, 2001; see also Card, 2009 for modern day estimates). However, from the early twentieth century up to the 1970s, there were significant restrictions on immigration (relative to the late nineteenth century levels): the immigrant share of the population fell from 13.2% to 4.7% between 1920 and 1970. All by itself that would have been an important contributor to the reduction of inequality in the United States.

The Role of Geography, Immigration and Race Since 1970

Since 1970, gender differences have continued to shrink (Goldin, 2014). This channel has actually acted in favor of minimizing inequality within the country. However, the other channels have reversed themselves and are now contributors to inequality.

On regional convergence, Young, Higgins, and Levy (2008) have found no evidence of sigma-convergence (i.e., reduction in the dispersion of levels of income across regions) for 3,000 United States Counties between 1970 and 1998. In fact, they found some evidence suggesting the presence of sigma-divergence between those counties. Other researchers (Ganong & Shoag, 2017) have also found such a reversal in terms of regional integration. Of great importance in the measurement of this regional disintegration (albeit modest) is the role of regional price disparities. There are significant price differences across the United States: prices in New York are 15 percent higher than the national price level while those in South Dakota are 13 percent lower than the national price level (Aten, Figueroa, & Martin, 2013). Thus, there are important adjustments to make for regional prices in order to properly account for “real” differences in incomes. The problem is that while regional prices were converging along with incomes prior to 1940 – which amplified the regional convergence forces prior to that point–prices have begun to diverge again since (Fig. 2). This amplifies regional differences and thus contributes to pushing inequality upwards.

Fig. 2. 
Coefficient of Variation of Regional Prices in the United States (1880–2012).

Fig. 2.

Coefficient of Variation of Regional Prices in the United States (1880–2012).

While this modest regional divergence occurred, there were also modest increases in the size of the immigrant population of the United States. David Card (2009) found that 5% of the increase in inequality in the United States stemmed from an increase in the size of the immigrant labor forces. A similar result has been found in Canada by Moore and Pacey (2003). They found that once immigrants were excluded from censuses between 1981 and 1996, the level of inequality in Canada was lower and it increased at a slower rate.

As for inequalities between ethnic groups, the rate of earnings convergence between blacks and whites has slowed down since the 1970s (Margo, 2016). However, the official statistics are misleading on this. Since they are based on surveys including individuals in the workforce or outside institutions, black Americans who are in jail are excluded. Because the number of incarcerated people has been growing (relative to the population), and because they tend to be poorer than those who do not end up in jail, this affects the statistics on earnings through a cream-skimming effect in surveys. The distribution of measured black wages and incomes consists only of those with the top wages and incomes in the black population. When scholars like Pettit (2012), Lyons and Pettit (2011), and Western and Pettit (2005, 2010) attempted to make corrections for this problem, they found that there were signs of reversals in black–white convergence in the United States, which indicates that this is acting as a long-term force contributing to increases in inequality.

The Implications of Disaggregation

Through disaggregation of the underlying components of inequality, we find a wide variety of causes. There are some more that could be discussed, 7 but in the case of the United States the few forces discussed here are heavy contributors. Had we not disaggregated, the impact of these forces may have been hidden to the naked eye. However, now that they are seen, they force us to draw implications that would otherwise have been elusive, but which can now be seen plainly.

The first implication is that the decrease in inequality from the late nineteenth century to the 1970s is largely the result of non-governmental forces. After all, the migration of blacks from the South to the North can hardly be painted as the intentional result of governmental policy. In fact, most convergence between ethnic groups prior to the 1960s occurred in spite of government action. Not only were the Jim Crow laws in action in order to openly discriminate against black Americans, but the first welfare programs in the United States (those of the New Deal and Fair Deal of the 1930s and 1940s) were created in a deeply discriminatory manner (Katznelson, 2005; Rothstein, 2017). Convergence between regions also resulted from largely non-governmental forces; the migration of capital from the North to the South, as well as better integration of markets which permitted the realization of arbitrage opportunities. As for convergence between men and women, as indicated previously this resulted mostly from mundane forces related to technological changes. Thus, an important share of the Great Leveling must be attributed to non-governmental forces. In fact, these forces could even have been more significant had it not been for governmental policies hindering them, as, for example, in the case of the Jim Crow laws (Halcoussis & Lowenberg, 1998; Roback, 1984, 1986, 1989) and laws concerning the legal and fiscal status of women (McCaffery, 1997).

The second implication is that the portion of the decrease that resulted from government action resulted in part from the imposition of immigration restrictions. True, the rise of the welfare state during the Progressive Era would have been a factor, but the importance of this channel should not be oversold. For example, the commonly used Piketty and Saez time series for the share of total income captured by the top 10% shows a reduction in income inequality during the 1930s. This conflicts with other evidence whereby most of the tax changes for the 1930s are found to be fiscally regressive (Folsom, 2009; Renaghan, 1984). Another example is the role of income taxation in discouraging work by women by unevenly favoring single-earner families (McCaffery, 1997). To this we must also add that there was the extension of the income tax to lower and lower levels of income from the 1930s to the 1950s. As such, it is hard to identify the amplitude of the decrease in inequality caused by tax and redistribution policies. The large reduction up to the 1970s would have been influenced by immigration restrictions. While this would have reduced inequality, it is hard to praise such an outcome. As indicated above, immigration is bound to increase inequality unless the distribution of immigrants is identical to the distribution of the host population. Since most studies find that (generally) immigration has a positive effect on wages through increased demand effect and greater scope for specialization (Clemens & Hunt, 2017; Ottaviano & Peri, 2012; Peri, 2017), it is doubtful that immigration is harmful to the lower rungs of the income ladder. Inequality thus only increases because we added more rungs at the bottom or at the top. The reduction in inequality up to the 1970s was thus in part a composition effect. And it came with a cost to potential immigrants who were deprived of the opportunity to move to where their incomes could have been greater (Clemens, 2011). Indirectly, this means that restricted immigration implied lower inequality in the United States, but more inequality globally. By opposition, a part of the increase in inequality in the United States since 1970 would have resulted from increased immigration. But this would have been accompanied by a global reduction in inequality.

The third implication is that since the 1970s, many of these forces have reversed. In many cases, the reversals were caused by government policies. In the case of the divergence between ethnic groups, the “War on Drugs” is a strong explanatory force. As indicated, when corrections are made to account for the imprisoned population when measuring wages and incomes, we find that there is divergence between blacks and whites in the United States . Given that blacks are predominantly those who end up in jail (relative to their weight in the population) for drug-related offenses, this explains the role that this set of government policies (it would be presumptuous to assert that it the “War on Drugs” is one unified and consistent policy) contributes to the surge in inequality. As for differences between states, housing, and zoning policies that restrict the supply of housing are strong government-produced forces that push up inequality. First of all, the restrictions in supply generally tend to benefit homeowners who already present in the market when the restrictions are implemented. Because the restrictions reduce the elasticity of supply, any increase in demand implies that the value of their property increases more than it otherwise would. This represents equity gains for them which appear in the form of (largely tax-exempt) capital gains when the property is sold. Secondly, since low-income individuals are generally renters, restrictions on supply imply that the cost of housing will go up more for them than for homeowners. This means that their consumption basket is getting more expensive relative to the consumption of homeowners. This pushes up inequality by pushing down the real incomes of individuals on the lower rungs of the income ladder. Thirdly, it limits mobility (Cebula, 2014) by creating a barrier to movement. More importantly, if restrictions on housing supply are applied in high-productivity areas, it limits the ability of individuals in low-productivity areas to earn more by moving to high-productivity areas (Ganong & Shoag, 2017; Hsieh & Moretti, 2015). Basically, these restrictions allow already established individuals to capture the gains of productivity in one area. This explains why some scholars find a large role for housing in the measurement of inequality (Albouy & Zabek, 2016; Rognlie, 2016). In those two cases, the trends in inequality result from government-produced forces.

The final, and probably most depressing, implication is that the increase in inequality since the 1970s has occurred in spite of a greater scope for government redistribution (Geloso, 2016). Indeed, government spending on education, health care and social welfare (relative to GDP) is up since the 1970s. While it could be that inequality would have been at even higher levels without these interventions, in light of the earlier points, it seems that these efforts at reducing inequality were at odds with other government policies which increased it. This last implication is akin to saying that the left hand is unaware of the actions of the right hand, which leads to contradictory actions.

All of these implications are logical derivations from an attempt to disaggregate inequalities as Lindert and Williamson (2016) do. The relative strength of each component would have be carefully assessed and measured to produce a ranking of their relevance. It is telling that, with the recent exception of Lindert and Williamson (2016), no scholar since Simon Kuznets (1953) in the 1950s has attempted to do this disaggregation in order to better interpret the surge in inequality. Many take the aggregate measure that is inequality at face value and provide little interrogation of its subcomponents. Important interpretative nuances go unobserved and, as a result, improper policy responses are proposed. However, by incorporating the Austrian skepticism of aggregates, it becomes possible (though arduous) to discern these nuances. The relative strength and direction of changes in each subcomponent of inequality provides us with the ability to disentangle the many elements of relevance in the inequality debate. More efforts toward disaggregating inequalities according to these forces (and many others also at play) would provide much clarity in the debate over causes, effects, and (especially) responses.


In this chapter, I surveyed the economic history of inequality in the United States with the intention of highlighting the relevance of incorporating Austrian insights regarding statistical aggregates into the debates over inequality. Largely inspired by Lindert and Williamson (2016), I disaggregated inequality into some of its many components (immigration related, regional, ethnic, gender, etc.) and showed that a different interpretation of the U-curve of United States income inequality can be gleaned. Rather than being a function of government actions, many of the forces that acted to reduce inequality up to the 1970s were more or less mundane and unrelated to government. In fact, some of these forces would have been stronger had it not been for government interference. I have also argued the post-1970 increase in inequality to be in part the result of government policies negating the effects of redistributive policies.

By virtue of disaggregation, I painted a different portrait of inequality and its evolution, a portrait which differs from most popular (and, to a lesser extent, scholarly) interpretations of inequality. Obviously, this portrait is not perfect and it relies heavily on a considerable literature from the field of economic history. Ideally, one would attempt to provide an empirical decomposition of these forces to assess the past evolution of inequality. While this would imply a tremendous amount of work for the enterprising scholar, the rewards would be equally tremendous as it would provide a wider and deeper perspective on American inequality.



Hayek (1931, p. 277) pointed out, in a rebuttal to J. M. Keynes’s A Treatise on Money, that “aggregates conceal the most fundamental mechanisms of change.” While he was referring to macroeconomic debates, his admonishment can be easily extended to inequality. Indeed, the citation need only be modified to say that aggregate measures of inequality conceal the sources of changes and the proper interpretation of these changes.


For example, price indices are well known to be designed to capture the evolution of price levels given a certain consumption basket. This measure may be an efficient deflator for capturing the evolution of living standards. However, it may not – because of the manner in which it is constructed – be adequate for the purposes of measuring inflation in the process of studying the effects of monetary policy. This is why the Federal Reserve uses the Personal Consumption Expenditures deflator as a monetary policy tool instead of the Consumer Price Index.


Immigration tightening occurred from the 1910s onwards but the apex of these restrictions was reached in the 1920s. The loosening began in the 1970s. This can be seen by charting the share of the population in the census that reports another country than the United States as birthplace which follows a U-curve from 1910 to 2010.


There are other explanations that are less mechanistic that Piketty’s. For example, Scheidel (2017) argues that increases in inequality generally correlate (he is careful about attribution) with subsequent wars or disasters that will level these inequalities. Nevertheless, this is a mechanistic explanation as well which implies regularities in the form of increases up to a point where shocks brings the level crashing down. Non-mechanistic explanations of inequality need rely only on microeconomic concepts to explain non-random changes. As argued below, the relevance of different microeconomic concepts to particular event will affect the evolution and interpretation of inequality.


Now, I personally disagree with the weight they attribute to state-redistribution in the process, but it would be senseless to assert that state redistribution had no effects. As I have expressed elsewhere (Geloso 2016), I am skeptical of generally attributed to that channel in comparison with the four others.


It is necessary to point out that there is nothing wrong with this form of inequality. First of all, it implies that there is global reduction in inequality since those who move do so because they gain relative to their prior situation. Thus, a migrant from nineteenth century Norway to the United States increased inequality in his new country but since he earned more than if had he remained in Norway, the income distribution encompassing the individuals within Norway and the United States became more equal. Secondly, in such a situation, the wages of everybody who was there prior to the arrival of immigrants could increase in equal proportions–which would normally imply no increase in inequality. However, because the immigrants swell one end of the distribution more than the other, there will appear to be an increase in inequality.


Population aging could be added to this. Research research by Almås and Mogstad (2012) and Almås et al. (2011) confirms that a substantial share of the increase in economic inequality results from population aging which creates a composition bias in the population. See also Schirle (2012) on the case of the United States, which removed one eighth of the increase in inequality through a correction for composition bias resulting from aging.


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