Second, the decline in the prices of farm products led to lower income from land: Farmers could make virtu- ally no gains from farming in the late 1990s.
The inequality in rural wealthdistribution can be measured with the Gini coefficient, which was 0.40 for rural personal wealth in 2002. Compared with a coeffi- cient of 0.31 in 1988, 48 rural wealth inequality clearly wid- ened from 1988 to 2002. Table 2.5 indicates that the poor- est 10 percent owned only 2 percent of the total wealth while the richest 10 percent owned as much as 30 per- cent of the total wealth in rural areas. Of all wealth items, financial assets were most unevenly distributed. The rich- est 20 percent owned 55.3 percent of the total financial assets, while the poorest 20 percent owned only 4.5 percent. The ratio between the two groups was 12.2:1.
of the incomedistribution has different effects on growth and a single inequality statistic for the entire distribution is insufficient to capture the effects of inequality on growth. Our results may therefore be seen as reflecting the impact on growth of wealth inequality at the top of the distribution.
The second prong of our analysis focuses on examining the prediction that the effect of wealth in- equality on growth depends on whether wealth has been acquired through political connections. We identify the fraction of billionaire wealth that has been generated through the use of political connections by classify- ing each billionaire into one of two categories: those who benefited from political connections and those who did not. We start by creating a dummy variable called “Political connections” and set it equal to 1 when we conclude through an extensive search on Factiva and LexisNexis using news sources from around the world that political connections had a material part to play in the success of the billionaire. We set this variable equal to 0 when we conclude that political connections have not been crucial to the billionaire’s rise to riches even though he may have had prior political connections. The criterion we use for classifying billionaires as having benefited from political connections is that our extensive review of evidence indicates that the person would not have become a billionaire in the absence of political connections that resulted in favoritism and/or explicit government support. 10 Three examples of billionaires who are classified as politically connected are provided in the Data Appendix A.3. A full classification of billionaires into the two categories of politically connected and politically unconnected is available from the authors on request.
The following chapter presents standard economic theory on consumption in macro- economics, and comment on the implications of these theories with regards to the empirical consumption function. Chapter 3 presents the most relevant empirical work on the consumption function; here I separate the presentation of the interna- tional and Norwegian research. For Norway the most relevant empirical research has been conducted after the credit liberalization in the 1980s. Chapter 4 presents the econometrical methods that I use in the thesis. Here, I first present some of the most important features of time series analysis, before explaining the concept of stationarity and cointegration. Finally, in this chapter I explain the relationship between cointegration and the error correction model (ECM). Chapter 5 briefly presents the data and the stationarity properties of the series. Then, in chapter 6 I reestimate the old consumption function from Jansen (2013) and show how this breaks down around 2009, which is the motivation for this thesis. Chapter 7 investigates whether the incomedistribution might have an eﬀect on aggregate consumption. My second model, where I split the wealth variable to see if the in- dividual wealth components have diﬀerent eﬀects on consumption, is documented in chapter 8. Finally, chapter 9 concludes.
The graph in the upper left panel of Figure 4 shows that recent wealth in 2015 is slightly negatively correlated with negative recent wealth in 2003. This suggests that negative net wealth in 2003 has, on average, become positive over the subsequent 12 years, and that taxpayers with the most negative net wealth have experienced the largest increases. One possible explanation for this observation is that people with large invest- ments in 2003 ended up with zero net wealth later on because they defaulted on their debt and went bankrupt. If we look, however, at the correlation between recent wealth in 2003 and recent income in 2015 in the upper right panel, we find that taxpayers in the first percentile of the recent wealthdistribution in 2003 are, on average, in the 66th percentile of the recent incomedistribution in 2011, which is equivalent to the mean recent income percentile of the 95th percentile of the recent wealthdistribution in 2003 in even higher recent income percentiles than in the contemporaneous graph displayed in Figure 3. This suggests that the high incomes of negative net wealth individuals increase even more over time and that their investments, thus, pay off on average. 10
8 Assets in defined benefit pensions are problematic both because of the potential not to be collected and because of back loading in benefit determination. We are less worried about the distributional consequences because most such pensions accrue to the top end of the incomedistribution and therefore do not affect lower incomes. Our analyses also ignore non-cash public sector benefits such as those provided by health, education, and the taxes used to pay for them (see Garfinkel, et al, 2006, on the latter). While these benefits are especially important for low income persons, they pale in comparison to the levels of imputed income from assets for the large majority of households, especially middle and high income units. Hence, while MCI helps us better understand the impact and importance of residual wealth and the way it affects public and private finances and inequality, it does not represent a complete accounting of all flows of income from all sources.
Ravi Kanbur, Joseph Stiglitz 18 August 2015
Growth theories traditionally focus on the KaldorKuznets stylised facts. Ravi Kanbur and Nobelist Joe Stiglitz argue that these no longer hold; new theory is needed. The new models need to drop competitive marginal productivity theories of factor returns in favour of rentgenerating mechanism and wealth inequality by focusing on the ‘rules of the game.’ They also must model interactions among physical, financial, and human capital that influence the level and evolution of inequality. A third key component will be to capture mechanisms that transmit inequality from generation to generation.
Although democracy gives equal political power for every citizen, lobbyists exert disproportionate influence on policy-makers, which makes the policy regime less democratic. Bénabou (1998) dubs the policy regimes “elitist”, when the pivotal voter group possesses more wealth than the median voter, and “populist”, when the group’s wealth is below the median. This may create biased redistributive policies that favor some interest groups at the cost of others and alter the distribution of income. Such policies include restrictions on imports and competition, various subsidies, taxes and public spending. For example, import restrictions tend to favor the poor, because they shelter domestic production from international competition. On the other hand, agricultural subsidies, for instance, may boost incomes of the rural poor, but lower disposable incomes of the urban poor. One way to take a pro-wealth bias could be to reduce competition or give export subsidies, which traditionally shift wealth from the poor to the rich. According to Bénabou’s model, the democratic regimes that have sufficiently low rates of inequality do not benefit from the pro-poor bias. The populist regimes, in contrast, reap economic benefits from redistribution, while the elitist regimes might gain from inequality. To obtain these results, however, they must fulfill certain assumptions, such as complete asset markets for the elitist regimes and incomplete asset markets for the populist regimes. For this reason, empirical evidence from real countries might give more reliable predictions about the interplay between these forces than theoretical musings.
Circumstances of the Elderly:
Income, Wealth, and Social Security
James P. Smith
How is the economic status of the elderly changing and what are their prospects for the future? My portrait tells us how well off they are on average, but also about the vast disparities that exist among them. This description includes an often neglected measure of their economic well being— the amount of wealth they control. Amazingly little is known about how much personal wealth older people have and how and what determines its distribution. But the conventional definition of household wealth ignores two critical components of wealth: the expected income flows from pensions and Social Security. For some elderly households, Social Security represents the largest part of their wealth. I conclude with some thoughts on one of the most sensitive and critical public policy issues— the necessity of reforming Social Security.
Barro (2000) finds that higher inequality tends to retard growth in poor countries and encourage growth in richer countries, and finds in a sense support for the Kuznet curve. The three main arguments in favor of a positive relationship between growth and inequality, reviewed in Aghion,et al (1999) : The first arguments is that if the growth rate is positively related to the proportion of national income that is saved, more unequal economies are bound to grow faster that economies whit a high level of distribution, since the marginal propensity to save of the rich is higher than that of the poor. The second is related to the issue of investment indivisibility. Investments often involve a large sunk coast, which pre- supposes that wealth needs to be concentrated for such investment projects to be undertaken- in the absence of well developed credit market. The third argument realizes on the effects of incentive through distribution. Beside the fact that a redistribution of wealth creates a more equalized distributionincome, if redistribution financed by income taxes, this would also diminish the incentive to accumulate wealth.
The South — a region once known for its endemic poverty — has seen substantial progress in the standard of living of its people over the past quarter-century. Family income has risen, and the middle class has mush- roomed. Like the United States as a whole, however, the South has experienced growing inequality in the distribution of income, with a widening gap between rich and poor and a persistent gap between whites and minorities. The wealth gap between white and black Southerners continues to exceed the income gap, despite gains in wealth for both races. I NCOME : M AJOR FINDINGS
Given the renewed focus on distributional issues, and motivated by dissatisfaction with the dominant paradigm of economic theory, we aim in this paper at introducing a new way of thinking about the income and wealthdistribution problem. In this respect, we will argue that the economy must be analysed as a complex adaptive system, and the network of interactions among heterogeneous agents taken into due consideration, to give distributional issues the conceptual and methodological attention they merit from the economics profession. We will also present a brief survey of parametric models belonging to the “κ-generalized” family, a fruitful set of statistical models for the size distribution of income and wealth developed by the authors over several years of collaborative and multidisciplinary research. As will be discussed in the following, this family of distributions embeds many of the theoretical challenges posed in this study while at the same time helping to address the main stylized facts of empirical income/wealth distributions – i.e. why the upper tails of the distributions are Pareto (fat-tailed) and why at lower levels of income/wealth the distributions seem to be described by a different law.
The top panel of Figure 3 clearly shows that a smaller scale of transfers increase the employment rates across the whole distribution through income e¤ects on labor supply. More importantly, note that this e¤ect is particularly stronger among the …rst and second wealth quintiles. This substantial change in the labor supply behavior of these households who hold relatively few asset holdings is driven by heightened precautionary labor-supply motives. Since the wealth poor households lack savings and are near the borrowing constraint, their consumption would become very low (especially so in the absence of government transfers) if they choose not to work. It is important to note that this concern for hitting zero consumption is relevant not only in the current period but also in the near future periods since low productivity is expected to be persistent. This signi…cantly increases their value of working relative to the value of not working, leading to a stronger incentive to work even if their productivity (or market wage) is low. This is why the upper panel of Figure 3 shows that the reduced amount of transfers induces more of the wealth poor households to work, thereby making the relationship between employment and wealth more negative.
1%) is likely to lead to different results. In essence, whether then richest person in the survey has €1 billion or €10 billion of net wealth will affect the overall distribution of wealth.
Credit Suisse recognises this problem with survey data. The “Wealth Databook” uses data on income inequality to estimate wealth inequality for countries lacking direct wealthdistribution data. vii Their methodology states that it does ‘not expect to generate accurate predictions of the number and value of holdings of high net worth individuals’ because of ‘well-known statistical regularities in the top wealth tail’. viii In their data they use statistical models to account for this variation and they augment their data with information on the wealth holdings of individuals from the rich list data reported by Forbes Magazine and other publications. ix This gives a more accurate reflection of what is happening at the extreme end of the distribution (Top 1%). However it lacks the detail of the HFCS in understanding the wealth profile of individual households.
This dissertation focuses on the economic inequality problem in Nepal, in particular, to integrate insights from other social aspects into the distribution problems of economic resources. It consists of five chapters on income and wealthdistribution in Nepal. The first Chapter presents an overview of Nepal’s history, geography, economic development, policies and problems. The aim of Chapter Two is to provide an understanding of the inequality of income for 1984 and 1996 in Nepal and to describe how income/expenditure inequality in Nepal has changed during the period 1984 and 1996. In Chapter Three we examine the inequality of wealthdistribution for 1995 and 1996 in Nepal. The Chapter four continues to investigate income and wealth inequalities using decomposition methods because they provide rigorous and powerful tools for identifying the underlying structure of income or wealth, which allow for direct interpretation of the estimated contribution in terms of the inequality index – the relative contribution of a set of population characteristics and of each income factor source that may be found within household income, expenditure and wealth. Chapter Five investigates the inequality of income in the process of development in Nepal. We first examine the Kuznets’ proposition according to which ‘the degree of inequality varies systematically with the level of income per head – initially increasing as incomes rise and then, beyond some point, decreasing, with further increases in income per head’. By considering historical, structural, institutional, political and socioeconomic issues, we offer an alternative explanation of reducing economic inequality in Nepal, with an emphasis on economic development.
interrelationship are also of interest. Moreover, information about private transfers between households and intergenerational transfers of income and wealth (gifts, inheritances) become necessary for a complete picture.
The first step of an analysis in the fields of income, wealth, and income poverty always aims at a description of the situation based on statistics on household income and wealth. More demanding, however, is the analysis of the factors that have caused the existing distributions, and which will cause changes of this distribution, and changes of the relative positions of individuals in the income and wealth hierarchy, and especially in the poverty section. That means we should like to find explanations and to make predictions. Although a comprehensive theory of the personal distribution of income and wealth does not exist, one can say that macroeconomic and demographic trends in combination with institutional arrangements interact with personal characteristics. Social and fiscal policy decisions that change the institutional arrangements work within this general setting. While information on macroeconomic and demographic developments, on institutional arrangements, and on policy decisions has to be provided by other sources, information on relevant personal characteristics of each individual should be part of the same data file from which information on the individual’s or household’s income and wealth is taken. This is necessary in cross-section household surveys as well as in household panel surveys. While simulations of the first round effects of social and fiscal policy changes usually by assumption neglect behavioral responses, the prediction of second and third round effects needs estimates of individual behavioral responses with respect to working time, consumption and savings, and changes in the portfolio structure of wealth holdings. Econometric estimates of these behavioral responses, therefore, should be based on variables that are ascertained in the same data set as the income and wealth variables. Usually, however, one has to ignore repercussions of individual behavioral changes on the macro level due to a lack of a comprehensive micro- macro model.
Some participants are aware that their perceptions are skewed or “out of touch” (n=5) often with reference to the City of London, similarly to Sherman’s (2017) wealthy New Yorkers. A hedge fund manager for instance cannot believe that the top 1 percent threshold is as “low” as I told him, adding that it is “shocking” that he views it as low. Further, a financial manager (annual income between £140-400k) feels not “particularly special or well off” but caveats that “I live in London, I work in finance, so my sample is probably skewed towards the top of that” (Interview transcript 7). Some respondents are also highly conscious of the skew at the right-tail of the distribution and actively try to make sense of it, demonstrating awareness of the increasingly differentiated and demarcated structure of economic inequality at the top (Savage, 2014). A senior executive, who was annoyed that he forgot what the median was, explains that he was told this figure at a breakfast with Financial Times correspondents just before the general election. He remembers though that the figure was “just way below what anybody would have expected” (Interview transcript 6). Participants also underestimated where their own income lies on the incomedistribution. Hence, similarly to Bamfield and Horton’s (2009) focus group respondents, my participants thought that high salaries were much more common than they are. As a result, participants generally overestimate economic inequality as measured by top income and wealth shares (as I will discuss in Chapter 8).
Therefore, an average β smaller (in absolute value) than 0.5 can be read as suggesting the existence of long-range correlation between an economy's components, like in models of the business cycle based on direct interactions 11 .
Furthermore, the negatively sloped trend of the estimated parameter γ signals that the volatility of fluctuations in countries in the lowest part of the 30 th -85 th range of the distribution has been increasing in relative terms all over the span 1960-1997, so that β has actually increased over the same period. Of course, our analysis is unsuited to ascertain whether this fact is due to an increase in the amplitude of output fluctuations in low-income countries or to a decrease of volatility in countries with higher incomes. Independent evidence (Agenor et al., 2000; IMF, 2001), however, seems to suggest that the first conjecture is likely to be the right one, probably reflecting a strengthening of the inverse relationship between income levels and vulnerability to financial and debt crisis.
This paper is concerned with the inverse relation between income inequality and economic growth.
The approach used was straightforward. We began by describing four models linking inequality to slower growth. We then showed how demographic explanations complemented each of the mainstream accounts. We assumed that age structure had an ob- vious, though indirect effect on economic growth through its impact on incomedistribution. But in- dependent of its effect on incomedistribution, our review of theory suggests that age structure has a direct impact on economic performance through its effect on credit constraints, the stock of human capi- tal, and agency costs. Stylized evidence and illustrations demonstrated how national age struc- tures heavily weighted with younger working-age cohorts could induce effects as diverse as lower po- litical participation, greater social unrest, and higher borrowing costs.
The model answers the puzzling result emphasized by Piketty (2014). As did Karl Marx, Piketty concludes that because the return on capital repeatedly exceeds the growth rate of developed economies and does not change much over time, developed economies will face ever-increasing capital stocks. Since returns to capital go mainly to the highest income groups, the distribution of income widens over time and will continue to do so. Another possibility, of course, is that capital owners either consume or donate to charity the capital output in excess of the economic growth rate, so that capital does not accumulate faster than the economy grows. The puzzle for Piketty’s conjecture is why there is no evidence anywhere that the capital stock has approached saturation. That fact opens the way for an alternative explanation of the relative constancy of the return to capital. Unlike Piketty who bases his conclusion on a comparison of the before tax income of the top 1 or 0.1 percent to before redistribution to the lowest income groups, we compare incomes available for consumption by the di¤erent income classes. Piketty’s choice greatly overstates what has happened in developed countries. Our measure is more closely related to income after