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103 Seiten, Note: 1,7
List of Figures
List of Tables
List of Abbreviations
SECTION 1: Introduction
SECTION 2: Theoretical Background
2.1.1 Measurement of Income Inequality
2.1.2 Limitations of the Gini coefficient
2.1.3 Measurement of Economic Growth
2.3 Theory on Economic Growth and the Distribution of Income
SECTION 3: Literature Review on Economic Growth and Income Inequality
3.1 Income Inequality: The Necessary Evil for the Sake of Economic Growth?
3.2 The impact of Income Inequality on Economic Growth
3.3 Long-term Implications of Income Inequality on Economic Growth
3.4 Relationship between Income Inequality and Economic Growth in China
SECTION 4: Income Inequality in China
4.1 Extend of Income Inequality in China
4.2 Income Inequality and Insufficient Consumption
4.2 Overview of Policy Measures by the Chinese Government
SECTION 5: Drivers of Income Inequality in China
5.1 Economic Opening Process
5.2 The Urban-Rural Income Gap
5.3 Educational Inequality
5.4 Regional Inequality
5.5 Inequality of Wealth
5.6 Demographic Change
5.7 China’s Taxation System
SECTION 6: Policy Recommendations
6.1 Taxation Reforms
6.2 Fiscal Policies
SECTION 7: Conclusion
Figure 1: Lorenz Curve
Figure 2: Consumer Spending as a Function of Disposable Income
Figure 3: Income Inequality in China, 1978 to 2017
Figure 4: Pre-tax National Income Share by Income Groups, 1978 to 2015
Figure 5: Gini for Selected Developing Countries, 2011
Figure 6: Lorenz Curve for China, 2013 and 2017
Figure 7: Urbanization Rate in China, 1978 to 2018
Figure 8: Urban-Rural Disposable Income Ratio, 1978 to 2017
Figure 9: Urban and Rural Household Income Decomposition, 2011
Figure 10: Urban-Rural Disposable Income Ratio by Region in China, 2013 to 2017
Figure 11: Income Structure of Eastern, Central, and Western Households in China, 2011
Figure 12: Wealth Shares of Different Income Percentile Groups, 1994 to 2015
Figure 13: Tax Revenue Sources of Germany, OECD and China in 2016
Figure 14: Gini Coefficients of Selected Developing Countries, 2016
Table 1: Household Saving Rate, 2011 (in RMB)
Table 2: Share of Chinese Households that Saved, 2011
Table 3: Share of Chinese Households that Saved by Income Group, 2011
Table 4: Urban Households that Saved by Assets and Income Group, 2011
Table 5: Rural and Urban Household Income Decomposition, 2011 (in RMB)
Abbildung in dieser Leseprobe nicht enthalten
Income inequality is an issue of concern that has persisted in economic debates since long back in history, with different researchers from various fields exploring this subject with distinct interpretations and conceptualizations starting with the ancient Greek philosophers to modern day philosophers, economists, and politicians, income inequality had been extensively explored. According to the International Monetary Fund, income inequality has elevated itself into one of the most devastating issues that countries are currently facing, with the gap between the wealthy and the poor being experienced at significantly high rates in both developing and advanced economies (Dabla-Norris, Kochhar, Suphaphiphat, Ricka, & Tsounta, 2015). This does not exclude developed countries since the World Economic Forum recently pointed to a global income inequality trend as the distribution of income have worsened for 17 out of 22 OECD member countries since 1980 (Schwab, 2018).
Since adoption of the open policy and implementation of extensive economic reforms in 1978, China has experienced rapid economic growth. The rate of economic growth has increased uninterruptedly, and the country has significantly competed with other countries on the international front, coming up as one of the emerging economies that have shown the highest growth over the past few decades. The country’s economic growth from 2000 to 2010 averaged 9.15 percent, a number that made China the second largest economy in the globe (NBS, 2012). With such growth, it is expected that persons across the country would enjoy the fruits. Nevertheless, like it has been in other economies, China’s economic growth has been accompanied by major increases in its income inequality. The country’s income inequality in the 1980’s was among the most equal, a trend that has changed over the last three decades, with income inequality growing to become one of the global highest (Xie & Zhou, 2014). Yang (1999) even coined China the country that has seen the fastest increase in income equality for which comparable data is available since 1978.
As much as some countries such as the United States are highly developed in many aspects, they also suffer from high levels of income inequality. For instance, also Germany has been harshly criticized by the IMF in his most recent country status report for its increased income and, in particular, its wealth inequality. The latter is now deemed as one of the fastest growing in the developed world (Dao, Perry, Klemm, & Hebous, 2019). Since we are looking at wealthy countries, this bags the question whether income inequality is in fact the reason for economic growth or a necessary outcome of it.
In this context, some may argue that an extremely equal society would lack incentives needed to spur economic growth – note, why should someone invest in its education and training if the benefits in terms of pay are low? Besides, rising income inequality can be consistent with welfare arguments provided that all income groups experience even little positive income growth. This is the case for China ever since 1978 where all income groups alike experienced large income gains, albeit greater ones for the upper income groups that eventually led to the hike in income inequality (NBS, 2018). As a result, absolute poverty as measured by the poverty headcount ratio has been almost entirely eradicated (Sicular, Li, Yue, & Sato, 2017). Consequently, measured by the income and consumption increase, this could be described as pareto-efficient. This is certainly a field of tension among scholars with those arguing in favor of income inequality taking the stance, among others, that income disparities are essential drivers of economic growth.
Nevertheless, staying with the above argument, even if the lower income-groups recorded positive income-growth this would still imply that they got relatively poorer compared to the rest of the society thereby they have likely recorded losses in participation and opportunities. This circumstance may entail various implications and spill-over effects such as on the level of health or crime that may question pareto consistency as we will learn in this thesis. More importantly, high rates of inflation often accompanied by high economic growth may quickly eliminate these income gains for the case that real wage increases are not considered – albeit inflation is a national average parameter, not necessarily capturing price increases for low-income households alike.
As Sampson (2016) argues, inequality within a society may be accompanied with lack of upward mobility, stagnation of wages, and hollowing out of the middle class resulting in a concentration of poorly distributed quality of education, violence and poverty, all of which cause a nationwide impact on general well-being and on the economy. In addition, there are ethical and philosophical reasons for resentment towards inequality per se since there should be no different treatment with respect to one’s access to economic resources. However, some may argue that individuals are accountable for outcomes of their actions and choices they made over the course of their life. Under some circumstances this may be correct, in the majority of cases, though, the unfair treatment starts with the day they are born. Two children can have two entirely different chances in life solely based on which family they are being raised in. Ravallion (2016) refers to that as the level of inequality in opportunities that he deems the most crucial factor to assess the negative impact of income inequality on economic growth. Therefore, calls for equal distribution of income also bring forth questions regarding the extent to which income distributions could be described as being equal (Li & Gibson, 2013). Hence, there is a conceptual issue of contention concerning whether a trade-off exists between equality and efficiency (Wang, Wan, & Yang, 2014). This paper will thus shed light to the various characteristics and sources of income inequality in China and demonstrate their interrelations on economic growth.
The Gini coefficient is employed to determine the level of income inequality. In China, the Gini coefficient surged from a mere 0.27 in 1980 to its peak of 0.49 in 2009, while a modest decline or stabilization respectively can be observed until the most recent estimation in 2017 (Chen, Pu, & Hou, 2018; NBS, 2018). As much as there are contentions regarding these estimations by the official Chinese statistical agency and the fact that various sources lead to different results, it is beyond question that income inequality in the country has reached great heights and has become an issue of concern. The inequality has been widely noticed among the Chinese citizens, with most of them acknowledging that it has affected their lives. According to a national survey in 2012, Chinese identified economic-inequality as the most critical social problem that they faced in comparison to other social issues such as unemployment and corruption (Wu, et al., 2013).
Historically, income inequality in China has been mainly driven by two major factors, including access and extend to public resources and the emergence of market forces in the wake of major opening up reforms (Li, Sato, & Sicular, 2013). From a conceptual point of view, both of these factors have the capacity to offset or re-enforce the impact of each other. In the course, they developed into several dimensions of inequality drivers manifested in extensive urban-rural inequality, cross-regional inequality, inequality of education, wealth inequality or demographic changes - among others.
This paper is aimed at establishing China’s current degree of income inequality, while identifying various forces and drivers behind these changes since the economic opening process in 1978/79. This paper further aims at exploring the relationship between income inequality in China and the country’s economic growth, as well as build upon these findings by proposing adequate policy recommendations.
Demonstrating the significance of studying the increasing income inequality in China, the author argues that the growth of China’s economy could be affected by a further increase in income inequality in the future. In this case, the author will base his argument on the view of Murphy, Shleifer and Vishny (1989) exploring implications of further income inequality on economic growth from the perspective of domestic demand. In that regard, the paper will seek to demonstrate that under consideration of the combination between the domestic demand perspective and the unique economic structure of China, a further increase in income inequality could reduce the country’s economic growth.
This thesis starts by providing an understanding of definitions used in this paper, introducing data sources applied, its underlying limitations as well as provide theoretical background regarding what economic growth theory suggests in the context of income inequality. In order to assess the relationship between income inequality and economic growth, this study uses a literature review approach beginning in Section 3 to explore the mechanisms behind income distribution and economic growth first in a broad context and subsequently specifically in the case of China. Findings from the literature review will then be complemented with national Chinese data on consumptions and savings patterns in Section 4.2. Subsequently, Section 4 and 5 will portray major insight of the development of income inequality since adopting economic opening reforms in 1978, thereby identifying its trends and sources that relentlessly drove income disparities within the society. Ultimately, Section 6 will provide policy recommendations based on the previous findings established.
This dissertation addresses the following research questions:
1. What is the relationship between economic growth and income inequality in general and with respect to China? What are likely implications of higher levels of income inequality on economic growth? (Section 3 & 4.2)
2. How did income inequality evolve over time? How did Chinese policy makers address this issue? (Section 4)
3. What are the drivers and the reasons for income inequality changes in China over the past two decades until today? (Section 5)
4. What policy recommendations may be undertaken to address this issue? (Section 6)
Max Lorenz (1905) developed a theory explaining income distribution. The theory compared the income distribution among households of a population over a period of time. According to Lorenz, a perfect equality refers to a case in which all households within the society receive the same income , whereas a perfect inequality represents a case in which a single entity receives all the income while the rest of the population receive none. Since both perfect equality and perfect inequality cannot be actualized, Lorenz curve presents a framework based on which the distribution of income within a given population can be understood (Lorenz, 1905). The curve is therefore supposed to always lie below the perfect equality line (Clark, 1992).
The Gini coefficient has been developed based on the Lorenz curve. The Gini refers to the ratio of the area between the line of perfect equality and the Lorenz curve. Therefore, as Figure 1 illustrates, from the areas denoted as B and the area C, the Gini can be calculated by the ratio of B to (B + C), which ought to be a value between 0 and 1. A lower value of the Gini coefficient is indicative of a relatively equal income distribution, while a higher value would suggest a comparatively unequal distribution.
The Gini measure will be henceforth employed to indicate the extend of inequality of net disposable income within certain groups of households along with the personal or size distribution of income. Besides the Gini and Lorenz curve, there are several other concepts of measuring income inequality such as the Theil index or decile ratios, which will not be specifically addressed in this thesis (World Bank, n.d).
Figure 1: Lorenz Curve
Abbildung in dieser Leseprobe nicht enthalten
Source: Authors’ depiction
There are two main concepts describing income inequality: By means of the personal income or size distribution and the functional distribution of income. While the latter focuses on the functional sources of income of an economy overall, the former groups individuals or households in accordance to their income share within the observed society into percentiles (Piketty, 2014). Both concepts will be of great importance for the following sections to come.
Most developing countries have Gini coefficients ranging from about 0.35 to 0.50, while most developed countries fall between 0.22 and 0.32, demonstrating that developing countries often have more unequal distribution of income than its developed counterparts. Why this is, that will be elaborated in the course of this thesis.
Definition of Income
Income which can be understood as the potential command over consumption. It is used as the primary variable when measuring inequality rather than the actual consumption. In that context, disposable income, i.e. post-tax deductions and including transfers, is applied to measure income inequality, if not indicated otherwise, rather than market income which can be defined as income prior taxation and any redistribution. In some cases, disposable income may include or exclude rent or in-kind income, which makes comparability harder for one and may also be used by governments to delude the public over progress by changing definitions accordingly.
However, one must note that income is incapable of accurately reflecting the respective individuals’ access to an acceptable standard of living which may be affected by several other factors such as insecurity, health, legal rights or education (Lipton & Ravallion, 1995). This assumption will be of importance for the impact of income inequality on economic growth as we will see.
The Gini index as a measure of comparing income inequality between countries entails various caveats. First, income can be calculated based on household level income or based on the individual level whereby income may be defined differently as well. The common disposable income consideration does, for instance, not capture possible education, healthcare or housing subsidies of low-income households – or in agricultural-centered subsistence-based economies, income may be received by non-monetary means also distorting the bigger picture. Thus, differences in the methodology of the Gini calculation may limit cross-country comparisons to that extend (Chitiga, Owusu-Sekyere, & Tsoanamatsie, 2014). Worth noting that this limitation is clearly reflected in the major variations that exists for the estimations of China’s income inequality1 (World Bank, 2019; Solt, 2019).
Second, the income of the informal sector is most commonly disregarded when comparing income levels from the different income percentiles. Particularly for developing countries this grey sector forms a considerate, if not predominant, share of the total economic output further limiting the validity of the Gini measurement as used today (Osberg, 2016). This aspect is evidently of notable interest also with respect to China and often leads to the upward correction of official data.
Moreover, the Gini as a relative measure is unable to reflect absolute improvements in income in a sense that even for countries where all income groups have experienced increased income and thereby reduced extreme poverty, the Gini could still rise. Consequently, this would be in violation to the Pareto improvement principle. Similarly, in a scenario where all income groups in a given society record decreases in income the Gini coefficient could indicate an improvement of the level of income inequality (Osberg, 2016).
Lastly, in addition, demographic differences are not specifically captured by the Gini so that countries with a larger share of students or retirees whose principle source of income are pensions2 are likely to show higher Gini coefficients (Chitiga, Owusu-Sekyere, & Tsoanamatsie, 2014). The former is of particular relevance when considering China’s income inequality given the consequences sustained low fertility rates had on China’s demography.
Nonetheless, the Gini coefficient along with the Lorenz Curve are the most commonly accepted tools portraying income inequality among scholars in that field (World Bank, n.d.). Therefore, these are being employed for this study while bearing the above-mentioned limitations in mind.
The most common mean to account for economic growth is the real change in total output of goods and services within a country referred to as GDP. Additional measures include income per capita as well as consumption per capita (Cho, Kim, & Rhee, 2014). The economic growth rate may be influenced by microeconomic factors such as human and natural resources, technological and capital resources as well as macroeconomic factors such as level of inequality, education, employment, population growth. the health status as well as the institutional setting of an economy (Barro, 2000). This study used gross domestic product (GDP) per capita as the measure of economic growth, to investigate the relationship between economic growth and income inequality with the other macroeconomic factors such as education, employment, health expenditures as control variables. Since GDP is dependent on sheer limitless variables within and outside the given economy, measurement errors are very likely for example by means of the omitted variable bias.
While one measures economic growth, many actually intend to measure societal progress or quality of life. This is an interesting field of study, although not subject to this thesis. For reasons of simplicity, the author assumes the positive correlation of economic growth and positive societal progress. Besides, the well-regarded doubt whether GDP reflects development, there are several more. For instance, the fact that it does not capture environmental damages and resource depletion among others exemplify the lack of sustainability the output measure fails to express (Stiglitz, Sen, & Fitoussi, 2010). Also, the fact that the value of unpaid work, such as home improvements or the household is not taken into account by the GDP which are considered to make up a substantial share of the economy.
The bulk of data on income inequality was obtained from data compiled by Chen, Pu and Hou (2018) - which is also listed in the SWIID database -, the NBS’ Statistical Yearbooks (2011-2018), the World Income Inequality Database (WID) as well as the China Household Finance Survey with survey data from 2010 and 2011. Notably additional data was obtained from the World Bank Gini Index database and OECD stats. Minor data in that respect was used from the CIA World Book and additional authors.
Note, since the China Household Finance Survey (CHFS) is regarded as delivering a uniquely detailed dataset collected in China, the author decided to exploit these survey results, despite its relatively old period of study, as one of the important sources looking deeper into China’s current inequality - with more information given further below. A popular alternative would have the survey results provided by the China Household Income Project (CHIP) which are freely available upon confirmed registration.
Raw data for the graphs and figures illustrated will be steadily provided in the Appendix, i.e. Appendices A to L.
China Household Finance Survey (CHFS) with survey data from 2011 compiled by Gan et al. (2014)
The CHFS was first launched in 2010 by the Chengdu based South Western University of Finance and Economics and is considered unique in its extend providing detailed information on Chinese household income, expenditure, assets, debts as well as wealth levels (Gustafsson, Li, & Sato, 2014). Another advantage of this data source is seen in complimentary quarterly interviews on income, employment and expenditures assigned every two years of the main survey regularly providing updates (Gustafsson, Li, & Sato, 2014). The survey covered 29.500 individuals from 8.400 households.
Since the CHFS has been first launched 2010 and did not regularly provide open data access for their findings, comparability over time is limited. Survey data for 2013 turned out to be not that extensive in its reach compared to previous publications, so that this thesis principally made use of the sample year 2011. Also, data for most recent survey conducted in 2017 is currently not freely available for researchers outside of the University SWUFE. Given the perhaps outdated data, besides possible underlying survey biases, this constitutes a caveat for looking into China’s todays income inequality dynamics.
National Bureau of Statistics of China (NBS) – China Statistical Yearbooks
The NBS first began releasing proper estimates of Gini coefficients in 2002 via there China Statistical yearbooks in per-capita terms. These estimates are based on surveys conducted with both urban and rural residents. Data is complemented with personal income records to correct for biases (Jain-Chandra, et al., 2018).
Weaknesses include the fact that several changes in their income and population definition make data sets not consistently comparable over time (Chen, Pu, & Hou, 2018). Importantly, income did not include in-kind compensations and benefits such as for housing, medical, pension and unemployment or even bonus employer payments (Gustafsson, Li, & Sato, 2014). According to Chen, Pu and Hou (2010), this would lead to an underestimation of the urban-rural income gap since, as they claim, the urban residents receive considerably more public benefits than rural households do. Moreover, urban and rural households were previously defined based upon their hukou household registration classification (note, a proper definition of hukou will be given at a later stage). Th authors stress that this neglects particularly rural migrants working in cities, which causes several statistical distortions. However, there have been major statistical changes in China since 2013 that corrected several caveats, albeit not all. As a side effect, this made comparisons over time (even) less accurate (Gustafsson, Li, & Sato, 2014).
Despite its limitations, data from the NBS and their China Statistical Yearbooks can be considered as the most pivotal data source to calculate the Chinese Gini – particularly for those years where no other large nationwide household survey data is available (Chen, Dai, Pu, Hou, & Feng, 2010).
Chen, Pu, & Hou (2018)
Five Chinese Professors and researchers point to several caveats in their literature review on the official income data that is widely used to calculate the Gini. They identify the data provided by the NBS as underestimated as intra-group income inequality in not reflected in the grouped data samples they commonly state (Chen, Pu, & Hou, 2018). Thus, urban-rural income ratio needs to be regarded as too low. Raw data was not provided before 2006 other than limited income grouping data. Hence their motivation to come up with adjusted Gini figures for the years since the economic opening reforms that started 1978.
By combining survey data from the Department of Rural and Social Economic of the NBS and the Urban Socio-Economic Survey, first 7.500 distinct Gini coefficients have been computed and consequently combined so that Gini coefficients in per-capita terms for the period observed could be estimated. These estimates are listed within the SWIID.
General limitations of the data employed
General reservations of the data sources include the quite universal issue of an underreporting of income. This is true especially for high-income households that generally tend to have better access to the informal sector or bribes.
In addition, there is a huge variance of different estimates in the case of China (e.g., World Bank; CHFS; NBS; OECD; Chen, Pu and Hou; Ravallion all reporting different estimates for a given year). This will surely increase the chance of interpreting flawed numbers and likely limit the comparability of data that originates from different sources.
According to the classical economic growth models of Harrod, Domar and Solow, in order to experience an increase in the GDP the investment must exceed the amount necessary to compensate for the depreciated capital. Hence, the level of savings and investments play a crucial role in understanding economic growth (Gallo, 2002). A savings rate increase, while all other variables are held constant, would thus lead to economic growth. That is one of the conclusions than can be made based the early Harrod-Domar model (Sampson, 2016). However, quite apparently those additional variables are changing and not constant. This is the case for instance for the capital output rate or the population growth. Here, the variables are assumed exogenous for economic growth. Although, it is not as simplistic as that since not only growth processes influence the development of all those variables but also the central savings rate may be affected by income per capita, cultural factors and, in fact, how income is distributed among the different layers of the population (Gallo, 2002). If the benefits of growth are distributed unequally to higher shares to income groups of the upper tail, then the overall savings rate is predicted to increase given the expected higher saving propensity of the higher income groups. However, if the higher share of the income growth is granted to the group of individuals that have a lower propensity to save and thus a higher consumption rate, then the overall savings rate is expected to fall as a consequence of growth (Gallo, 2002). This could harm subsequent growth as argued by some scholars in the context of economic growth and income distribution.
In sharp contrast to the Harrod-Domar model, which assumes constant returns, the Solow model predicts that savings rate has no long run effect on the growth rate. As capital accumulation is not able to stabilize the per capita growth, the effect is only of short-term nature. As ultimate sustained source of economic growth, scholars later incorporate the exogenous technology variable where technological progress increases labor productivity (Ranis, 2004).
Since technological factor plausibly seems to be rather endogenous, the so-called New Growth Theories brought forward the concept of human capital that is integrated into the Solow Model with the savings divided into human and physical capital. This makes constant returns to capital possible even if physical capital shows diminishing returns. Similar to the initial Harrod-Domar model, this brings the savings rate back into the arena for explaining long-run growth (Gallo, 2002). More recent literature, from the 1990s, brings in play a relationship via political and economic factors. Economic factors predict a relationship between income distribution and the level of domestic demand as well as the existence of imperfect credit markets. Similarly, Galor and Zeira (1993) establish that initial income inequality may lead to different human capital investments choices in the presence of imperfect borrowing markets. This, in turn, affects the level of investments and GDP growth. Countries with high income inequality will have less households that possess the financial means to investment in human capital as compared to their more equal counterparts (Galor & Zeira, 1993). Alesina and Perotti (1993) on the other side, stress the importance of political factors based on their findings that income inequality may fuel social-political unrest in the country which in turn negatively affects investment. They therefore likewise establish the link to GDP growth via observing the level of investment as instrumental variable (Alesina & Perotti, 1993).
Yet, in order to predict the influence of savings and investments, the variables cannot be understood as simple aggregate levels. It seems plausible to assume that certain sectors in the economy require more investment than others, which for that reason contribute more to the growth process than the remainders do. Dual economy-based models consider the manufacturing sector in the forefront of generating growth, thus seeing inequality as an assumption for economic growth. These models are being presented by certain scholars when arguing in favor of increasing income inequality for developing countries with the Lewis Model perhaps being one of the most influential. In this Lewis (1954) constructs a theory where a traditional (i.e., agricultural from rural areas) and a modern (i.e., industrial based in urban areas) sector coexist. Initially there is an abundance of labor in the agricultural sector and for that reason low real wages. Therefore, industrialization can be easily fueled by sheer unlimited and cheap labor supply. The economy then eventually reaches the so-called Lewis turning point once the rural labor surplus is exhausted (Lewis, 1954). As a consequence, real wages for the unskilled industrial sector exhibit sharp gains, which in turn enables further growth until a labor surplus is reached again. The basic assumption underlying is the abundance of labor supply of an economy until the Lewis turning point and after that regained as long as balanced growth policies by the state are adopted. Consequently, the Lewis Model assumes, due to the existence of two very different sectors in the economy, the initial unequal distribution of income that worsens the stronger the modern or industrial sectors grows until the Lewis turning point has been reached and wages of skilled and unskilled labor narrowing due to the incurring initial labor shortage, which in turn enables major improvements in terms of income inequality.
As much as the Lewis Model has been praised in its role of understanding economic transformation processes in developing countries, it also received several criticism, such as, among others, the circumstance that an economy eventually suffers from food shortages resulting in an economic turmoil or that labor cannot be endlessly abundant (Piazza, 2014). On the other hand, Ranis (2004) puts into perspective that an open market condition would make food imports seem more than plausible. Ranis (2004) concludes that as much as the Lewis Model has lost major significance for economists when researching about matters of developed countries, it is yet extensively used in parts of the developing world to explain economic growth and labor dynamics - which we will see in the course of this thesis in the case of China.
Yet, these classic views of economic development above stand in contrast with the alternative neoclassical models that evolved in the 1960s (Ge & Yang, 2010). To take the work of Schultz (1964) as an example, who considers wages to be assigned by market forces rather than being set from above. Also, what changed to the Lewis model is that the industrial sector can only attract labor at the expense of losing agricultural output. Moreover, the rise in real wages is a steady rather than a sudden process, while a turning point, as opposed to Lewis, does not exist (Ge & Yang, 2010; Schultz, 1964). Consequently, the neoclassical theory relies on elasticities of substitution and production functions, so that it stresses the importance of the market in allocating resources and hence views regional inequality as a temporary phenomenon (Wei, 2015)
Contrary, the Keynesian economics assumes the marginal propensities to save as crucial. In Nicholas Kaldor model the economy consists of two classes: workers and capitalists, where workers have a lower and capitalists have a higher propensity to save (Galbraith, 2001). Thereby assuming that investment to overall income is of exogenous nature and not dependent on shifts of the savings rate. Under full employment condition, the sole possible distribution of income between profits and wages can be achieved when saving equals investment (Gallo, 2002). Therefore, an increase in investment makes a corresponding hike in savings necessary, whereby capitalists as the more substantial savers require higher profits. That in turn results in a higher price level (Galbraith, 2001). Thus, the Kaldor model suggests a positive correlation between income inequality and economic growth. In the following decades the model was confronted with several critique concerning its restriction to only two classes or that the investment being exogenous.
The before mentioned theoretical concepts were aimed at providing answers on the functional distribution of income as opposed to the personal income distribution. Even though the neoclassical theory provides some insights on the personal income distribution through the differences in the factor endowment, it does not seem to be enough explaining rampant Gini coefficients in developing countries such as in China. Also, it fails to explain how the differences in endowments were created in the first place. One may classify the theories on personal income distribution into those theories that assume that income inequality is mainly a result of voluntary choice (e.g., Milton Friedman’s Individual Choice Theory) to those in which institutions and inheritance are central (e.g., Acemoglu’s and Robinson’s work on institutional persistence in Latin America), while representatives of the other belief that either incomes are genetically determined or that inequality outcomes are assigned by chance and luck (Wei, 2015).
The next section will be fully devoted to the relationship of economic growth and income inequality first in general and subsequently in context to China.
Official Statistics show that there has been a global increase in inequality when it comes to the distribution of income over the past two decades (Schwab, 2018). Academic discussions, political debates, and popular press commonly react to this trend by regarding such an increase in inequality as a problem that should be addressed using redistributive policies. Nevertheless, there are some scholars who disagree with this notion, arguing that inequality is not necessarily a problem and, hence, it does not need policies directed at addressing those. In that respect, these scholars point out that there is more need to develop policies that would address poverty (Lyubimov, 2017; Saez & Zucman, 2016). Based on this premise, some researchers argue that some of the changes in the society result in an increase in the incomes of higher income individuals, without affecting the incomes of the bottom end of the income distribution. Such a change is in line with the Pareto principle given that it improves the incomes of the high-income individuals, while incomes levels of the low-income individuals remain unchanged or also grow respectively - albeit at a slower pace than the upper income groups do (Jenkins, 2017).
Piketty forms one of the authors who presented significant arguments against equality in income, positing that income inequality is bound to continually increase within a given population with economic development and that this is not necessarily a bad sign (Lyubimov, 2017). Central to Piketty’s argument is the aspect of capital. In his argument, Piketty postulated that capital is more unevenly distributed as compared to labor income, an aspect that significantly affects household income, thus hugely contributing towards income inequality (Piketty, 2014). Piketty noted that capital comprises of various assets including intellectual property, financial capital, equipment, real estate, and land, among others (Lyubimov, 2017). As such, with capital being mostly owned by a small percentage of individuals, such individuals are able to accumulate more wealth within an economy as a result of the relatively higher income that is gained from capital. The fact that such capital is held by a small fraction of the population significantly contributes towards such individuals making large gains in terms of capital income (Wade, 2014). After such individuals make gains from their capital, they are able to further invest in assets and to increase their income (Jones, 2015). For example, individuals who have high capital income are able to hire those who have a better understanding of asset management, allowing them to increased investment returns. On the contrary, households with little amounts of assets at their disposal do not have as much opportunities and are thus forced to entirely rely on the traditional financial services and labor income, thus receiving lesser returns (Lyubimov, 2017).
Capital that has been accumulated is inherited, hence remains within the small percentage of the population (Wade, 2014). Moreover, the wealthiest group of the population may be largely represented by professionals, who include managers earning labor income. For that reason, Piketty considers the rise in income inequality as an inevitable and crucial element of capitalism, with reductions in the past, especially during the World War period, being associated with shocks.
Many scholars pondered how the evidently higher Gini for most developing countries, in particular for emerging economies, as compared to developed countries can be explained. The Kuznets model seemed to provide a plausible theoretical framework to that – other extended it, such as Fields (2019) with his theory on different paths of modern sector enlargements. The US-American economist Simon Kuznets was among the first that developed a theory on the development of income inequality. Kuznets (1955) provided an explanation to that with his inverted U hypothesis that predicts that economic growth decreases income equality in the first stages of economic development process (Jauch & Watzka, 2016). After a certain stage has been reached, an abating income inequality is expected alongside waning economic growth. The modern sector where productivity and labor wages are high is the engine of economic growth which, however, is incapable of providing job opportunities for all. Therefore, always leaves individuals, that are still part of the traditional sector, behind leading to increased income inequality (Jauch & Watzka, 2016). Yet, technology and human capital levels eventually increase in the wake of persistent economic growth which, in turn, creates more job opportunities and a large middle class. This middle class, in turn, eventually demands for redistribution through taxation and public spending, thereby reducing income inequality. This inverted ‘U’ pattern described, predicts the evolution of the income inequality alongside economic growth depending on which stage the respective country is (Jauch & Watzka, 2016).
The Kuznets model and further models based on it, provide several implications for developing or transitioning countries. First, income inequality is temporal and a prerequisite as the economy is transforming until that point where inequality reduces again. Second, emerging economies can overcome income inequality by taking a path leaned on today’s rather equal developed countries. While Kuznets model and his inverted U-hypothesis could be empirically confirmed for a large set of countries, such as from Thornton (2001), Kuznets’ hypothesis has also received plenty of criticism, including Thomas Piketty’s argument that inequality is progressive and that it for it to be brought under control, there is need for an internationally coordinated policy to be developed (Lyubimov, 2017).
Another point of view that may help explaining why some countries tend to be stuck in a high level of inequality while not experiencing significant growth, such as in many Latin American countries the case, is what Ravallion (2016) refers to as inequality in opportunities. Depending on the degree of income inequality of impeding a certain share of the population from access like to credit or to education determines how inequality translates to growth reduction. Thus, the experienced income inequality manifests itself in excluding specific groups within the society, be it solely based on material wealth, racially or gender etc., the more potential and consequently economic growth is being lost.
Whether or not Kuznets model may be applicable, fact is that todays most developed economies with large redistribution schemes in place feature low levels of Gini (note, that excludes Anglo-Saxon countries). Also true is that these countries once had a worse distribution of income. Therefore, what may be summarized is that high rates of economic growth for a country in transition comes along with increases of income inequality. What the exact mechanisms behind are, though, that has still not been agreed upon. Furthermore, it is widely agreed upon that income inequality is a necessary consequence of a market economy, however how much inequality could or should be accepted is still subject to discussion. More on this discussion will be presented in the following subsections.
From a practical perspective, the relationship between the extent to which income is equally distributed within a population and economic growth is not linear. More importantly, numerous non-observed parameters surely lead to omitted variable biases for studies conducted in that field. Hence, various empirical studies have produced quite distinctive results. Previously, it was believed that income inequality was positively related with growth in the economy, given that proper incentives are provided to individuals to stimulate such growth. Between the 1950s and 1960s, researchers argued that the propensity of high income individuals to make higher savings meant that income inequality results in increased levels of investments, hence positively affecting the growth in the economy (Benhabib, 2003). In a review of 45 countries between 1966 and 1995, Forbes established that there is a positive relationship between the level of income inequality within a country and its growth in terms of the economy (Forbes, 2000).
Nonetheless, by the 1990s, a myriad of empirical studies provided a different position. In a study conducted by Knell (1999) to establish the relationship that existed between income distribution using the Gini coefficient and per capita GDP growth rate, the researcher noted that between 1960 and 1985, the annual growth rate was reduced by between 0.3 percent and 0.6 percent as a result of a 10 percentage points rise in the Gini coefficient. In a different study, Herzer and Vollmer (2012) examined the impact that income inequality had on the GDP per capita of 46 countries between 1970 and 1995, with the researchers concluding that a rise in income inequality negatively affected GDP growth. Significantly, the researchers established that such a relationship was uniform across both developed and developing countries, as well as across both democratic and non-democratic countries (Herzer & Vollmer, 2012). Nonetheless, as they conducted their literature review, the authors established that some studies point to a positive relationship between economic growth and income inequality (Herzer & Vollmer, 2012).
In a study conducted by Barro (2000), the author investigated the relationship between income distribution and economic growth within the period of 1965 and 1995 across 84 countries. In his findings, the author concluded that no significant relationship between them could be observed. Nevertheless, with the categorization of the countries under rich and poor categories, there were changes in the results. In this case, the poor countries, including those that had a real GDP per capita below $2,070 (in 1985 dollars), demonstrated a negative relationship between income inequality and economic growth (Barro, 2000). On the other hand, the group consisting of rich countries demonstrated a positive relationship between the variables. Ostry, Berg and Tsangarides (2014) conducted a review of previous studies on the relationship between income inequality and economic growth. The researchers found that most studies establish a negative relationship between the variables, with economic growth being dampened by an increase in income inequality (Ostry, Berg, & Tsangarides, 2014). The findings of their own empirical study also demonstrated a negative impact of income inequality on economic growth. The authors were quick to point out that a number of studies were found to hold the contrary position (Ostry, Berg, & Tsangarides, 2014). Moreover, an OECD study conducted by Cingano (2014), an IMF study by Berg and Ostry (2013), and an study from Standard & Poors (2014) all demonstrated the negative implications of increasing income inequality. Furthermore, United Nations reports and studies, including the United Nations Development Programme 2013 report on inequality, regularly showcasing the growth-dampening implications of income inequality (UNDP, 2013a).
Research does not provide adequate evidence concerning whether there is a shift in the direction of the implications of income inequality on economic growth between growth dampening and growth promotion depending on its magnitude. According to a study by Cornia et al., the authors argued that increased income inequality resulted in economic growth up to a 0.3 Gini coefficient value, while a growth dampening effect of income inequality on economic growth was realized at a Gini coefficient value above 0.45 (Cornia, Odusola, Bhorat, & Conceição, 2017). Earlier, in 2001 Cornia and Court’s policy brief, revealed that a growth-promoting effect was bound to be realized at a Gini coefficient of between 0.25 and 0.4 (Cornia & Court, 2001). On the contrary, the Korea Institute for International Economic Policy’s authors Cho, Kim and Rhee (2014) engaged in a study to explore the relationship between income inequality and economic growth between 1980 and 2007, with their findings revealing that income inequality’s dampening effect may be realized at a lower Gini coefficient value of 0.245 (Cho, Kim, & Rhee, 2014).
Furthermore, there are distinct implications and spill-over effects of income inequality with an indirect impact on the rate of economic growth. These may include negative effects on the health level due to competition and overwork (Rowlingson, 2011). The negative impact that income inequality has on health is further reiterated in a study by Pickett and Wilkinson (2015), in which the authors established a causal relationship between income inequality and health and wellbeing of the population (Pickett & Wilkinson, 2015). Similarly, Reiss (2013) finds that income inequality can be linked with mental illness. One might argue that these negative health effects might lead to lower productivity and hence lower economic growth.
Other important links to economic growth, has been made with respect to the promotion of crime on the economy through a lower level of foreign direct investment, tourism or due to, in general, higher transaction costs of running a business. According to the neoclassical theory, it is could be rational for relatively poor individuals of a given country to commit a crime since their benefits and costs differ from the rest of the society such as the higher relative benefits (Danziger & Wheeler, 1975). For instance, Rufrancos, Power, Pickett and Wilkinson (2013) confirms neoclassical theory and established positive relationship between income inequality and crime. The authors reviewed 17 studies analyzing the association between the two variables using time-series data. Their findings suggested that there was an increase in property crime as a result of an increase in income inequality, in particular a high sensitivity of the rates of violent crime, including robbery and homicide, in reference to income inequality (Rufrancos, Power, Pickett, & Wilkinson, 2013). Other implications of income inequality may include the tendency of having less political stability which implications on the level of FDI or tourism such as argued by Alesina and Perotti (1993).
Collectively, these findings demonstrate that starting at a relatively high Gini coefficient is likely to have a dampening effect on economic growth (Standard & Poor's, 2014). Nonetheless, one should note that such an average value is based on an analysis of data from various countries that, among others, have reached distinctive levels of growth in terms of their economies, had different levels of income or the causes of growth were entirely different. Evidence suggests that the economic development status of each economy may be pivotal in determining the extent at which there is a shift from growth-promoting effects of income inequality to growth dampening effects.
In the discussion in the previous section, this paper carried out a comparative static analysis, comparing the various levels of income inequality and GDP growth across various countries. However, in this section the long-term implications of increased income inequality on economic growth is examined. Likewise, the implications that those may have on both demand and supply side of GDP are explored, including their resulting interactions.
When it comes to the supply side of the GDP, increased income inequality could lead to weakening of the production potential of the economy, especially in relation to human capital (Cingano, 2014). In cases where individuals within an economy feel that increased input into the economy does not pay as a result of the largest share of the national income being distributed among a small portion of the general population, then they are likely to lose interest in making investments in human capital including their own education. Improvements in human capital, in terms of quality, are critical for any economy to realize economic growth (Galor & Zeira, 1993). Increased income inequality grows into a serious challenge in cases where individuals are largely dissatisfied that they end up leaving their country (Bernstein, 2013). Empirical evidence shows that cross-border mobility is more common among well-qualified, young individuals, resulting in the affected countries being exposed to the risk of brain drain and capacity to experience economic growth. A country whose human capital is weakened as a result of the reaction of individuals to increases in income inequality experiences a reduction in its ability to experience low economic growth in the long-term (Bernstein, 2013). Heightened income inequality could also result in impairments of the human capital within an economy by impeding the access of low-income individuals to the healthcare system. From a general perspective, reduced investment in healthcare and education results in reduced formation of human capital, ultimately resulting in impeded growth in the economy (Daguet, Colombier, & Baur, 2015).
In addition, weaknesses in the supply side may result from the use of income concentration to amass economic power that could be applied to exert political influence (Daguet, Colombier, & Baur, 2015). As such, one would expect high-income individuals within an economy to propose tax reductions or subsidies. Reduced state revenues would subsequently require that reductions must be made in terms of state expenditure and investments directed at education and infrastructure. With the public services being undersupplied, the productive apparatus of the state is weakened, hampering economic growth (Bernstein, 2013).
Lastly, high market income inequality could result in a case in which broad redistribution of income by the state becomes necessary (Fenig, Mileva, & Petersen, 2013). For this to be achieved, increased state revenue levels are required. As such, high levels of income security in such situations call for an expansion of public debt or an increase in tax. Increases in tax bar taxpayers from receiving performance incentives, resulting in capital flight and brain drain. As a consequence, the affected economy experiences diminished volumes of investment, which in turn leads to declines in the economy’s capital stock and economic growth (Fenig, Mileva, & Petersen, 2013). A similar outcome occurs in cases where increased state borrowing leads to an increase in interest rates, hence suppressing investments from the private sector (Fenig, Mileva, & Petersen, 2013).
From the demand side of the GDP, increased income inequality has a reducing effect on the demand for services and goods. In cases where high income inequality results in a small portion of the population who make up the high-income households, there occurs an increase in savings across the population, resulting in reduced demand, due to the decline that takes place in the consumption ration of the individuals with increased levels of disposable income. By taking Germany as an example given the high quality of statistical data available, such a relationship can be observed in Figure 2 below. The income group with a monthly net household income of less than 1,300 Euro even has an average negative saving rate indicating the use of loans to extend their level of consumption above their income and higher saving rates the higher the household income gets.
1 E.g., for 2015: World Bank estimates 0.386, while China’s statistical agency suggests 0.462, the SWIID even 0.469.
2 For that reason, some studies do only include households or individuals in the age range between 18 – 64 such as the OECD.
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