\r\n\t \r\n\tMoreover, the application of modern methods in the developing world will help attract new investments, whether domestic or international from developed countries to developing countries. These actions will create new jobs for young people in terms of exploration projects and thus increase the living standard of people living in poor countries and help increase national income. \r\n\t \r\n\tWith regard to the problem of desert reclamation in these countries, the results of the application of these methods can help in finding new groundwater sites that can be used for drinking and agriculture, and create new urban communities. \r\n\t \r\n\tFinally, it can be concluded that these methods play an important and essential role in combating poverty in developing countries in the world. At the end, the book chapters will help the readers to know about Geophysics and it's applications.
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Essa",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9984.jpg",keywords:"Land, Gravity, Radiometry, Particle Swarm Optimization, Evolutionary Algorithms, Simulated annealing, Engineering problems, Environmental problems, Ground motion monitoring, Oil and gas exploration, Mineral exploration, Geothermal activity",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 16th 2019",dateEndSecondStepPublish:"November 6th 2019",dateEndThirdStepPublish:"January 5th 2020",dateEndFourthStepPublish:"March 25th 2020",dateEndFifthStepPublish:"May 24th 2020",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,editors:[{id:"102766",title:"Prof.",name:"Khalid S.",middleName:"S.",surname:"Essa",slug:"khalid-s.-essa",fullName:"Khalid S. Essa",profilePictureURL:"https://mts.intechopen.com/storage/users/102766/images/system/102766.jpeg",biography:"Dr. Essa received his B.Sc. (1997), his M.Sc. (2001) and his Ph.D. (2004) in Geophysics from the Faculty of Science, Cairo University. He joined the staff of Cairo University in 1997 and was appointed a Research Professor of potential field methods in the Department of Geophysics in 2014. He has authored more than 50 technical papers, affiliated to post-doctoral visits to Strasbourg University, France in 2018, Charles University in Prague, Czech in 2014 and in Geoscience Department, Western Michigan University, USA in 2006 and served as an Editor and external reviewer for many journals. He attended several International Geophysical Conferences in the USA, Australia, and France. Dr. Essa has supervised and still supervises many M.Sc. and Ph.D. students in geophysics. He is a member of different geology and geophysics associations around the world. 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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53514",title:"An Influence of Relative Income on the Marginal Propensity to Consume: Evidence from Shanghai",doi:"10.5772/66785",slug:"an-influence-of-relative-income-on-the-marginal-propensity-to-consume-evidence-from-shanghai",body:'\n
\n
JEL: D11, D12
\n
1. Introduction
\n
Consumption represents a key determinant of economic thought in many ways, not so much for its immense practical significance, but rather because it de facto represents the essence of economics itself, the essence of the issue of infinite needs and finite resources. Both in terms of microeconomics, that consumption hypotheses are always necessarily based on, and within a macroeconomic approach the widely accepted theory of consumption of mainstream economics seems to be very well formulated and developed and as such it has remained virtually unaltered for nearly 60 years. But is this theoretical concept entirely accurate and complete? Could not even here be one of the major determinants of the general consumption function omitted? Now these questions are a starting point of this chapter.
\n
Since the 1950s of the twentieth century, the approach of permanent income theory and lifecycle hypothesis has prevailed in professional circles of economic theory. This mainstream view of the basic economic laws determining household consumption is in professional economic texts established to such an extent that the different approaches are practically not visible. However, this does not mean that there are not any alternative hypotheses of consumer behavior. We can find many critical perspectives on the standard theory of consumption, but often it is only a solution of narrowly focused issues, the pieces of a mosaic of complex alternative theory, that as a whole remains fragmented across countless of professional studies as poited out by Ackerman (1997). And if this comprehensive theory arose after all, still it was ignored for various reasons. And that is exactly also the case of Duesenberry’s relative income hypothesis—consumer concept, based on the idea of interdependent utility, which has the potential with theoretical way to challenge a complete validity of the consumption theory of mainstream economics, and ultimately and primarily to significantly enrich the basic pattern of generally accepted consumption function of life cycle-permanent income hypothesis (LC-PIH) (Mason, 2000).
\n
Income and price are the key determinants of consumer choice as for mainstream economics. Relative income hypothesis, however, points out the fact that if the consumer is also affected by consumption habits of his surroundings, then the income itself must be seen in two ways: in absolute and in relative terms. From these two concepts of the basic economic determinant of general consumption function, it stems also two channels of influence on the total amount of consumption. An absolute concept of income implies a direct effect, already well known from the Keynesian consumption function. Higher disposable income will lead to a proportionately greater amount of consumer spending. A variable of disposable income then figures in the functional form of consumer equation simply as the independent variable directly explaining the level of consumption. While the relative concept of income, at least according to the principles of Duesenberry’s hypothesis, implies an indirect effect. Higher disposable income will lead to higher position of household across income distribution and according to interdependent concept of utility and consumption also to a lower value of the marginal propensity to consume (MPC). The decline of MPC then, as an element transforming disposable income into consumption, negatively affects the ultimate level of consumption. The position of household in income distribution is then represented in the consumption pattern as an independent variable, which indirectly through MPC affects the level of consumption.
\n
The problem is that while the absolute (direct) income effect is a well-known matter and the virtually undisputed, relative (indirect) income effect remains often completely ignored by professional economic communities, whether in the form of Keynesian consumption function or access of LC-PIH. It is true that every relevant and really applicable model must be extracted of elements that have not a major impact on it. However, is also the relative income effect the insignificant element, which should be completely removed out of the consumption function without a trace? Is the interdependent concept of utility from the consumption a matter totally irrelevant? If so, then this whole work is a pointless effort.
\n
To explore this matter, we use data from China, which has been undergoing significant structural changes recently. The shift from investment and export-oriented economy into consumption-oriented economy is one of the biggest changes. Although the consumption contribution to the country’s GDP is still lower than in all developed countries, the change of government’s policy (hence the whole economy) is significant. Such development turns the attention of researchers to the consumption and its determinants.
\n
Unfortunately, only few research studies have been carried out in this field earlier. There are several studies analyzing the factors influencing very low consumption rates in China (see for example Horioka and Wan, 2007; Yang et al., 2011) or studies generally describing the consumption determinants on the macro level as Guo and Papa (2010). Many studies also focus on inequality of income distribution as a factor affecting the consumption (see Lou and Li, 2011). However, none of these studies mention the relative income as one of the possible consumption determinants. The interdependence of consumers seems to be analyzed much more by marketing specialists, see for example the studies of Zhang and Kim (2013); Yu (2014). These studies can provide useful insight into the relations among the consumers, but they cannot provide any evidence of the influence of “keeping up with the Joneses” effect on the final general consumption function.
\n
The aim of this work is to identify, describe, and prove an influence of relative income on the marginal propensity to consume using data of urban residents of Shanghai and thus to prove the correctness of Duesenberry’s hypothesis.
\n
2. Relative income hypothesis
\n
“Professor Duesenberry’s study of the impact of budgetary and aggregative empirical consumption data on the received theory of consumer behavior is one of the most significant contributions of the postwar period to our understanding of economic behavior" written in his review by Arrow (1950, p. 906), his time respected neoclassical economist and later Nobel prize laureate in economics.
\n
The relative income hypothesis is fundamentally built on criticism of established neoclassical preconditions for the creation of demand and Keynesian theory of consumption based on them. The main and fundamental idea with which Duesenberry (1949) comes to the field of knowledge in order to confront these established relationships of mainstream economics is a complex social concept of consumer and revision of Veblen’s demonstration effect (Veblen, 1899), which the author gives a particular dimension through income distribution of households.
\n
We can find two fundamental propositions in the work of James Duesenberry, let\'s say postulates, on which the theory of relative income stands and which are the basis for its further implications (Palley, 2010, p. 6):
\n
“The strength of any individual’s desire to increase his consumption expenditure is a function of the ratio of his expenditure to some weighted average of the expenditures of others with whom he comes into contact.”
“The fundamental psychological postulate underlying our argument is that it is harder for a family to reduce its expenditure from a higher level than for a family to refrain from making high expenditures in the first place.”
\n
The real foundation of the new model is, however, the first claim. The author himself called this effect as keeping up with the Joneses or the effect of relative income. The principle is mainly simple. The consumer is not isolated from others, he lives in a world where he every day meets his friends, colleagues, family, his neighbors, and so on. And not only he meets them, especially he is confronted with their consumption. He sees what they buy, what they spend for, by what they form their standard of living, and their position in society. He sees what Veblen saw in his theory, the so-called pompous ("pointless") consumption. Unlike Veblen (1899), for the majority of the population, these consumer expenditures are not pointless, because it allows them to reach the intangible social values—a status. And that is what this is about. Our consumer shall see how people around him buy goods for their ceremonial value, before his eyes they increase the value of their status, strengthen their social position and even he does not want to be left behind. Therefore, if the consumer belongs to low-income households (his disposable income (YD) is under the society-wide weighted average (Y¯D)), then he spends more of his disposable income just to demonstrate that he can afford it, just to catch up with social status of others. His MPC is then relatively high. Conversely, high-income households (whose YD is above the society-wide weighted average) usually already have valuable status, therefore they have not such a motivation to "catch up with someone", they do not have to spend so much of their income and vice versa they save more, simply because they can afford it. So we come to the first simple implication:
\n
MPC1>MPC2>...>MPCnE1
where the higher value of the index n stands for a household with a higher value of relative disposable income (YRD), most simply expressed as:
\n
YRD=YDY¯DE2
Put simply marginal propensity to consume can be written as a negative functional dependence of relative (disposable) income, as similarly shows (Palley, 2010):
\n
MPC=c(YRD)0<c<1;c`<0E3
The total amount of household consumption C is then given by the product of disposable income and the marginal propensity to consume, which is not constant now (as naively assumes the mainstream theory of consumption), but it depends on the position of the entity in the curve of income distribution:
\n
C=c(YRD)⋅YDE4
Plain view on the derivation of the final rule of general consumption function, especially on the relationship between MPC and YRD (Eq.(3)), can logically evoke questions like: Is not such a general notation too trivial? Would it be possible at this point to express the dependence of marginal propensity to consume on relative disposable income in particular functional form? We find in the later part of this work that the real version of this relationship is not such a trivial matter, it depends on the number of other factors and it simply cannot be expressed in the general shape like this. There are only a number of methods by which this relationship can be approximated into particular form. One of these ways is, as we could see, the central theme of this work.
\n
3. Methods and data
\n
The first thing we need to realize at this point is that the marginal propensity to consume of households does not change due to the amount of disposable income, but depending on the relative disposable income, as shown in Eq. (3).This is essentially a central idea of discussed hypothesis, a key contribution to the debate on the form of consumption function. As literally written by Alvarez-Cuadrado and Long (2011, p. 1489): "For any given relative income distribution, the percentage of income saved by a family will tend to be unique, invariant, and increasing function of its percentile position in the income distribution. The percentage saved will be independent of the absolute level of income. It follows that the aggregate saving ratio will be independent of the absolute level of income." An important factor is that although the MPC and therefore also APC of households differ substantially across the Lorenz curve of distribution of disposable income (which we can figure out even with simplest common sense, but no longer with the standard theory of consumption), it is this way only because of the effect of relative income, which does not exist at the aggregate level. Average propensity to consume for the whole economy is then constant in the long term, thus the relative income hypothesis is entirely consistent with the observation presented by Kuznets et al. (1946) 70 years ago.
\n
Whatever is the strength of the effect of relative income throughout income distribution in the society, the MPC for every household, more precisely a category to which the household belongs, is always given by a functional relationship due to its relative disposable income. And as it is well known that the function generates for any given situation only one result, therefore, each type of household also has only one marginal propensity to consume. Maybe the above sounds trivial and like a commonplace, but it is important to realize that the MPC of different groups of households does not change over time ceteris paribus, it is independent on the absolute amount of income and so it has for each YD a constant value. But first and foremost, as the previous lines try to imply and how sadly Palley (2010) himself, whose model we use as a basis, forgot to mention, the above applies to types of households, to the categories to which they belong, not to individual households themselves and their individual consumption functions. This is a fundamental difference!
\n
The biggest shortcoming of the standard model of consumption in the form of LC-PIH can therefore be seen in a constant characteristic of value of marginal propensity to consume for all kinds of income categories. To refute this erroneous assumption is then precisely the goal of the following analysis.
\n
3.1. Methods
\n
Let us recall at this point that the main motive of this work is to prove an influence of relative income on the value of its marginal propensity to consume, particularly by formulation of a specific form of its possible functional form. The term of relative income thus still remains the key concept for us. From the principle point of view it is de facto quantification and therefore the possibility of mathematical-economic interpretation of the issue of household’s position in the distribution of disposable income. From the definitional point of view, it is a ratio of disposable income to the society-wide weighted average, as shown in Eq. (2). Now we have only left to specify precisely the variable of Y¯D. From the perspective of the principles of relative income hypothesis, it seems to be the best solution to set the weights as the average numbers of household members in the given income category, which would epitomize the best a frequency of individual income cases in society. However, due to limited data source we have to settle for determining variable Y¯D as the simple arithmetic average of disposable incomes for considered income categories. Therefore, this point can be considered as a necessary simplification given by the availability of empirical data and potentially a weaker place of the following analysis, but not weak enough to make it impossible to achieve the stated objective.
\n
For actual try of expressing a specific form of assumed functional dependence, we use a regression analysis by estimation of regression coefficients using the least squares method. Due to the nature of the input data, in particular the limited number of statistically measured income categories (small number of observations), the classical regression could lead to distorted results, therefore, will use panel regression.
\n
The general formula of the required univariate linear regression model depends on whether we use a panel regression method for fixed or random effects. Which of these panel regression methods is more suitable for expression of wanted dependency will be shown up by Hausman’s test at a later stage of the analysis, so it is necessary now to still consider both the options. In the case of using fixed effects the regression equation is given by:
\n
MPCi,t=αi+β⋅YRDi,t+ui,tE5
where MPCi,t is the marginal propensity to consume for the category i at time t, αi is the level constant (an intercept) for the ith income category, the product of YRDi,t is relative disposable income for the ith category at the time t and the regression coefficient β expressing the sensitivity of the marginal propensity to consume to the relative disposable income. Variable ui,t symbolizes the random component. In a more detailed breakdown, the level constant αi for each category is divided into two subfolders, where:
\n
αi=β0+γiE6
where β0 is the basic level constant to which it applies β0=α1. A constant γi is then an added fixed impact for given income category for i∈{2;…;I}, where I is the number of categories. By simply rewriting αi according to Eq. (6) we get new more detailed form of the general expression of wanted regression equation using fixed effects:
\n
MPCi,t=β0+γi+β⋅YRDi,t+ui,tE7
Since in the case of using the fixed effects method (for a given entity) we subsequently need also to verify the appropriateness using time fixed effects, it is necessary to consider other, 1 order of magnitude more detailed breakdown of level constants αi, which could now be broken down to the given shape:
\n
αi=β0+γi+τtE8
where for newly level constant it applies the condition β0=α1 only for t=1 and where τt is an added fixed impact due to the time period for t∈{2;…;T}, where T stands for the number of such time periods. Moreover, by a new rewriting αi in Eq.(5) we can write down a general expression of wanted regression equation using fixed effects for given categories and time:
\n
MPCi,t=β0+γi+τt+β⋅YRDi,t+ui,tE9
For regression estimation based on random effects the wanted relationship is characterized more simply and clearly in the form:
\n
MPCi,t=α+β⋅YRDi,t+ui,t+εi,tE10
Where newly α represents a level constant for all categories, ui,t is a random component between categories and εi,t is a random component within an income category.
\n
Either way, an important prerequisite of any possible resulting variations of panel regression is a negative value of the coefficient β, because according the principles of Duesenberry’s hypothesis with increasing relative disposable income the marginal propensity to consume must necessarily decline, as demonstrated by Eq. (3).
\n
3.2. Data
\n
The prerequisite of negative linear dependency of MPC on YRD is tested here using the example of data for the budgetary situation of urban households in China\'s Shanghai; therefore, all the input data for the aforementioned analysis were taken from the database of the Shanghai Municipal Statistics Bureau (2016). The original input data are annual statistics between the years 2000 and 2014, which resulted in essentially two time series, which are further divided into five another subfolders. Followed 15 observations are then basically written in two variables:
\n
YD = average nominal disposable income of household per capita in CNY,
C = average nominal consumption of household per capita in CNY.
\n
As can be seen, we work with the mean values per person. For better demonstration of the validity of Duesenberry’s hypothesis, this procedure is certainly preferable. An important finding is also mentioned in the secondary division of basic variables. Indicators YD and C are both equally divided into five other subfolders reflecting income and consumption situation of different types of households arranged ascendingly by quintiles of disposable income. Finally, we register 10 input time series here, divided into five panels by the types of income categories. Indicators directly entering the subsequent panel regressions are YRD calculated according to Eq. (2) and APC expressed by formula:
\n
APC=CYDE11
It is then necessary at this point to realize that we work with income categories here (not with individual households), for which the value of APC is independent on YD and in the absence of an intercept it is at any point equal to MPC. That is why we could use this simple equivalence, where MPC values are substituted by the average propensity to consume. In conclusion, we note that although the original input data in this study are nominal expression of consumption and disposable income, but due to the relative nature of indicators MPC and YRD the unwanted effect of changes in the price level is to be fully canceled out anyway.
\n
4. Results
\n
Chart 1 is used for preliminary visual assessment of the expected dependence. Although the linear dependence of both followed quantities is quite obvious at this point, only a graphical analysis is obviously not enough for us. The aim here is to mathematically approximate this relationship by the regression equation.
Chart 1.
Visual assessment of linear dependence of MPC and YR D.
\n
Before it can be proceeded to the actual final estimate of regression parameters of mentioned dependency, it is necessary for the panel nature of the data to decide whether it should be used as a method of fixed or random effects, in other words, whether there are differences significant enough in the wanted functional relationship between the categories that they must be captured in a separate level constant just for each category. This dilemma is unambiguously solved by executed Hausman’s test when its results indicate that a suitable panel regression in this case is in the method of random effects, at least at the 5% significance level, which we also use for further analysis.
\n
The results of the final panel regression using random effects are summarized in Tables 1 and 2. In this final estimation of the desired functional form, we use a robust method of estimation of standard error using White\'s estimator, thereby the model was protected against a possible autocorrelation and heteroskedasticity. An important finding is that considering the inclusion of only one explanatory variable, a relatively high value of the coefficient of determination was achieved, which indicates that approximately 57% of the variability of MPC was explained just by YRD. This fact then clearly confirms the main initial assumption about the influence of relative disposable income on the marginal propensity to consume.
\n
\n
\n
\n\n
\n
\nNumber of observations\n
\n
75
\n
\n\n\n
\n
\nF(10, 149)
\n
36.69
\n
\n
\n
Prob > F\n
\n
0.000
\n
\n
\n
R2
0.575
Table 1.
Estimation of Eq. (10) using panel regression with random effects, part 1.
Source: own calculations and processing in Stata 12.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
\nMPC\n
\n
Coefficient
\n
Robust Std. Err.
\n
\nz\n
\n
\nP>|z|
\n
[95% Conf. Interval]
\n
\n\n\n
\n
\nβ\n
\n
−0.155
\n
0.026
\n
−6.06
\n
0.000
\n
−.206,−.105
\n
\n
\n
\nα\n
\n
0.912
\n
0.037
\n
24.75
\n
0.000
\n
.84,.984
\n
\n\n
Table 2.
Estimation of Eq (10) using panel regression with random effects, part 2.
\nSource: own calculations and processing in Stata 12.
\n
There is no doubt that the model as a whole is statistically significant, as well as the regression coefficient and level constant. The key element—the wanted regression coefficient β achieves exactly according to our expectations a negative value, which cannot be influenced nor by potential standard error. The resulting model corresponds to an initial economic theory and predicts that a change in the relative disposable income by 0.1 also changes the value of the marginal propensity to consume of any income category in the opposite direction by 0.0155.
\n
In conclusion, let us emphasize that the result of Hausman’s test significantly influenced (and positively) the very predictive ability of the resulting model. The final use of the random effects method means that the regression relationship between the MPC and YRD can be expressed in a fully general way and elegantly by only one equation (which could not be possible using fixed effects) and therefore it does not depend on what income category we are situated. The final functional dependence of the marginal propensity to consume on relative disposable income has then a following form:
\n
MPCi,t=0.912−0.155⋅YRDi,t+ui,t+εi,tE12
\n
5. Conclusion
\n
The primary goal of this work was to find and prove an influence of relative (disposable) income on the value of marginal propensity to consume. To achieve this goal, we have used primarily a panel regression for data from the Chinese province of Shanghai. There is no doubt that relative income affects the marginal propensity to consume, which concurrently means that validity of “keeping up with the Joneses” effect (“keeping up with the Wangs” as we say in the context of China) is finally proved. And as indicated by the relatively high value of the coefficient of determination (relative to one explanatory variable), this dependence must become a new key factor of the general consumption function.
\n
The mainstream theory of consumption, mainly represented by the concept of LC-PIH, assumes a constant value of MPC for all types of income categories. However, as it is shown by the results of our study, this assumption can no longer be considered realistic. Marginal propensity to consume remains unchanged in relation to disposable income only for a given income category, not for individual households. If the income situation of household changes, it will shift to the new income category and at the same time it will fix the new value of MPC. Household consumption function then does not have a constant slope (opposed to the consumption function of income categories) as mistakenly assumed by the mainstream theory of consumption, but it is under a concave characteristic. This is occurring due to the effect of relative income, it is appropriate at this point to emphasize again that the mainstream microeconomics distinguishes only between the income and substitution effect. Duesenberry’s theory, as well as the conclusions of this study, requires to add further subdivision and so to distinguish between the income effect of absolute (direct) and relative (indirect).
\n
Although the impact of relative income on the marginal propensity to consume was unequivocally confirmed, the issue of its precise nature still remains open. Approximation of followed dependency, of course, depends on a functional form, which is used for it, and here utilized linear function is certainly not the only option. Moreover, it may not even be the most appropriate. It is important to realize that, at least in terms of statistics, there is not only one correct and objective functional form, this is only just what we define it. And the definition of a new, elegant and more convenient functional relationship of MPC and YRD better and more accurately describing consumer behavior of households so remains the motive for further scientific research.
Acknowledgments
\n
This chapter was supported by a grant from Students Grant Project EkF, VŠB-TU Ostrava within the project SP2016/112 and within Operational Programme Education for Competitiveness—Project No. CZ.1.07/2.3.00/20.0296.
\n',keywords:"relative income, marginal propensity to consume, Duesenberry’s hypothesis, interdependent utility, consumption function",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/53514.pdf",chapterXML:"https://mts.intechopen.com/source/xml/53514.xml",downloadPdfUrl:"/chapter/pdf-download/53514",previewPdfUrl:"/chapter/pdf-preview/53514",totalDownloads:850,totalViews:238,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,dateSubmitted:null,dateReviewed:"November 9th 2016",datePrePublished:null,datePublished:"February 1st 2017",readingETA:"0",abstract:"This chapter deals with the question whether there is a relationship between the marginal propensity to consume and the status of the household in income distribution represented by a relative income. If so, then the current assumption of mainstream theory of consumption about the constant marginal propensity to consume could no longer be considered realistic and it will be necessary to take the element of relative income as a new key determinant of general consumption function. The aim of this work is to identify, describe, and prove an influence of relative income on the marginal propensity to consume using data for urban residents of Shanghai and to prove the correctness of Duesenberry’s relative income hypothesis. To achieve this goal, we use a panel regression, through which the results clearly confirm the validity of the initial hypothesis about the existence of functional dependence of the marginal propensity to consume on the relative income and so it fully supports the idea of interdependent concept of utility and consumption.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/53514",risUrl:"/chapter/ris/53514",book:{slug:"proceedings-of-the-2nd-czech-china-scientific-conference-2016"},signatures:"Ondřej Badura, Tomáš Wroblowský and Jin Han",authors:[{id:"200987",title:"Prof.",name:"Jaromir",middleName:null,surname:"Gottvald",fullName:"Jaromir Gottvald",slug:"jaromir-gottvald",email:"jaromir.gottvald@vsb.cz",position:null,institution:null}],sections:[{id:"sec_1",title:"",level:"1"},{id:"sec_2",title:"1. Introduction",level:"1"},{id:"sec_3",title:"2. Relative income hypothesis",level:"1"},{id:"sec_4",title:"3. Methods and data",level:"1"},{id:"sec_4_2",title:"3.1. Methods",level:"2"},{id:"sec_5_2",title:"3.2. Data",level:"2"},{id:"sec_7",title:"4. Results",level:"1"},{id:"sec_8",title:"5. Conclusion",level:"1"},{id:"sec_9",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Ackerman, F. (1997). Consumed in Theory: Alternative Perspectives on the Economics of Consumption. Journal of Economics Issues. 31(3): 651–664. ISSN 0021-3624.'},{id:"B2",body:'Alvarez-Cuadrado, F. and N. V. Long. (2011). The Relative Income Hypothesis. Journal of Economic Dynamics and Control. 35: 1489–1501. DOI: 10.1016/j.jedc.2011.03.012.'},{id:"B3",body:'Arrow, K. J. (1950). Income, Saving and the Theory of Consumption Behavior. By James S. Duesenberry (book review). American Economic Review. 40(5): 906–911. ISSN 0002-8282.'},{id:"B4",body:'Duesenberry, J. S. (1949). Income, Saving, and the Theory of Consumer Behavior. Cambridge: Harvard University Press. ISBN 978-0674447509.'},{id:"B5",body:'Friedman, M. (1957). A Theory of the Consumption Function. New York: Princeton University Press. ISBN 0-691-04182-2.'},{id:"B6",body:'Guo, K. and N. D. Papa (2010). Determinants of China\'s Private Consumption: An International Perspective. IMF Working Paper, WP/10/93.'},{id:"B7",body:'Hall, R. E. (1978). Stochastic Implication of the Life Cycle – Permanent Income Hypothesis: Theory and Evidence. Journal of Political Economy. 86(6): 971–987. ISSN 0022-3808.'},{id:"B8",body:'Horioka, C. Y. and J. Wan (2007). The Determinants of Household Saving in China: A Dynamic Panel Analysis of Provincial Data. Journal of Money, Credit and Banking. 39(8): 2077–2096.'},{id:"B9",body:'Kuznets, S., L. Epstein and E. Jenks (1946). National Product Since 1869. New York: National Bureau of Economics Research. ISBN 0-87014-045-0.'},{id:"B10",body:'Mason, R. (2000). The Social Significance of Consumption: James Duesenberry\'s Contribution to Consumer Theory. Journal of Economic Issues. 34(3): 553–572. ISSN 0021-3624.'},{id:"B11",body:'Modigliani, F. and R. Brumberg (1954). Utility Analysis and the Consumption Function: An Attempt and Integration. In: Kurihara, Kenneth (ed.), Post-Keynesian Economics. New Brunswick: Rutgers University Press. ISBN 978-0415607896.'},{id:"B12",body:'Palley, T. I. (2010). The Relative Income Theory of Consumption: A Synthetic Keynes-Duesenberry-Friedman Model. Review of Political Economy. 22(1): 41–56. DOI: 10.1080/09538250903391954.'},{id:"B14",body:'Shanghai Municipal Statistics Bureau (2016). Shanghai: SMSB. [20. 3. 2016]. Available at: http://tjj.sh.gov.cn/'},{id:"B15",body:'Veblen, T. (1899). The Theory of the Leisure Class: An Economic Study of Institutions. New York: MacMillan.'},{id:"B16",body:'Lou, F. and X. Li, (2011). An Empirical Analysis of Income Disparity and Consumption in China. Frontiers of Economics in China. 6(1): 157–170.'},{id:"B17",body:'Yang, D. T., J. Zhang, et al. (2011). Why Are Saving Rates So High in China? NBER Working Paper. No. 16771, Cambridge: National Bureau of Economic Research.'},{id:"B18",body:'Yu, D. (2014). Motivations of Luxury Consumption in America vs. China. Graduate Theses and Dissertations. Iowa State University, Ames, Iowa, USA, Paper 13854.'},{id:"B19",body:'Zhang, B. and J. Kim (2013). Luxury Fashion Consumption in China: Factors Affecting Attitude and Purchase Intent. Journal of Retailing and Consumer Services. 20(1): 68–79.'}],footnotes:[{id:"fn1",explanation:"A theoretical approach to consumption, based on original works: Modigliani and Brumberg (1954) and Friedman (1957). In the case of adding an element of rational expectations, then it can be primarily referred to the so-called random walk model, as defined by Hall (1978)."},{id:"fn2",explanation:"As you can see, for simplicity, there is described a mechanism of functioning at only two types of households: high income and low income. This is however only a demonstration of the principle, which otherwise could be applied to any number of categories (social classes), as shown in Eq.(1)."},{id:"fn3",explanation:'The indicator may only be relative compared with another value. But an aggregate scale only shows one type of household—the "aggregate" one. Therefore, disposable income has nothing to be compare with, respectively, is equal to the average disposable income. After substituting into Eq. (2), YRD is always equal to one and whether the MPC inferred form it takes any value, it will be constant throughout the progress of consumption function. And because it is linear and based on the origin of coordinates, the average propensity to consume is also constant taking equality MPC = APC.'},{id:"fn4",explanation:"Widely appreciated and respected study that using macroeconomic data from the US for nearly 70 years proves that even during rapid long-term growth of real income, the average propensity to consume had not virtually changed, just as the autonomous component of consumption would not exist. This discovery thus de facto entirely denies a validity of Keynesian consumer theory in the long run."},{id:"fn5",explanation:"Changing values of MPC in time could in our case characterize only one thing, a change in the distribution of disposable income, thus de facto enlargement or reduction of income inequality."}],contributors:[{corresp:"yes",contributorFullName:"Ondřej Badura",address:"Ondrej.badura@vsb.cz",affiliation:'
Shijiazhuang University of Economics, East HuaiAn Road, Shijiazhuang, Hebei, China
Shijiazhuang University of Economics, East HuaiAn Road, Shijiazhuang, Hebei, China
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Concrete Pavements Using FRP Bars",slug:"load-transfer-coefficient-of-transverse-cracks-in-continuously-reinforced-concrete-pavements-using-f",totalDownloads:643,totalCrossrefCites:1,signatures:"Chunhua Hu and Liang Chen",authors:[{id:"200987",title:"Prof.",name:"Jaromir",middleName:null,surname:"Gottvald",fullName:"Jaromir Gottvald",slug:"jaromir-gottvald"}]},{id:"53355",title:"Protection of Buildings at Areas Affected by Mining Activities",slug:"protection-of-buildings-at-areas-affected-by-mining-activities",totalDownloads:629,totalCrossrefCites:0,signatures:"Pavlina Mateckova, Martina Janulikova and David Litvan",authors:[{id:"200987",title:"Prof.",name:"Jaromir",middleName:null,surname:"Gottvald",fullName:"Jaromir Gottvald",slug:"jaromir-gottvald"}]},{id:"53356",title:"High-Speed Railway Tunnel Hood: Seismic Dynamic Characteristic Analysis",slug:"high-speed-railway-tunnel-hood-seismic-dynamic-characteristic-analysis",totalDownloads:738,totalCrossrefCites:0,signatures:"Wang 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1. Introduction
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Modern humans usually spend their free time with technical devices (car, computer, smartphone, remote controller, etc.) or passive entertainment such as watching television, surfing the Internet, all of which moves them away from daily activities requiring movement. Thereby, people progressively lose the need to move at all and be physically active. Be it workplace, fun or rest, contemporary humans, therefore, spend most of their time in an unhealthy environment. Such way of sedentary lifestyle faces each person with a number of risk factors, above all with hypokinetisia [1]. It has been determined that most non-infectious diseases have prevalence at either high level or constantly rising. Despite a growing advancement in the last 30 years, coronary diseases, or more precisely coronary artery disease, remains the number one cause of death in Serbia, equally present among both males and females alike [2, 3]. The lack of physical activity has been recognized as one of the main causes of these diseases, and is considered as the primary risk factor, alongside with smoking, high cholesterol, and high blood pressure (hypertension) [4].
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Obesity figures as one of—if not the—consequence of such lifestyle/insufficient physical activity that represents a well-known cause of early death [5], hypertension, coronary heart disease, lower quality of life and sleeping problems [6], and the World Health Organization rightfully deals with it as a global epidemic [7]. Therefore, obesity has become one of the biggest public health problems in the world [8] and it could develop into the leading public health problem in this century [9]. Since obesity can be defined as an excessive body fat content, and overweight as an excess of body mass relative to height [10], it can be closely related to the estimation of body composition which is very important not only for the determination of nutritional status in health conditions and in disease [11], but also in various fields such as nutrition, medicine, and sports sciences [12, 13].
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1.1 Contemporary lifestyle
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The lifestyle of certain population groups, especially of young people, can lead to eating habits and insufficient physical activity that behave as risk factors in chronic diseases [14]. Modern societies then become characterized by the inactivity and sedentary lifestyle [15], which in turn gets reflected in the greatest amount of body fat, overweight, and obesity among the population, which are associated with a higher degree of risk of adverse health events and higher mortality [16, 17]. Young adults from 18 to 30 years of age are often in transition, graduating from high school, going to college, starting a new job, getting married, and forming a family. Often, these transitions are accompanied by potentially detrimental changes in lifestyle such as decreased physical activity, poor eating habits, increased alcohol consumption, and other unfavorable risk behaviors [18, 19, 20]. Nutrition and physical activity are of the utmost importance for health promotion. Eating habits and attitudes toward physical activity shape the lifestyle of an individual to the greatest extent, thus determining health, that is, the most common diseases of a modern man [21]. Regular physical activity has many health benefits for adults [22, 23]. But, surveillance data indicate that there is an age-related decline in physical activity and that females are less active than males [23, 24]. Only 16% of female participants of the National Health Interview Survey (NHIS) aged between 18 and 29 reported an adequate amount of vigorous physical activity [23]. Longitudinal data from young adults further attests to the decline in physical activity in this age group [25, 26]. A significant drop in physical activity and increased sedentary habits largely influence the relation between body composition factors.
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1.2 Body composition
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Body composition is the term that defines the phenomenon of the biological-material composition of the body, that is, the set of substances that constitute the materially manifest structure of the human body [27]. The macro-level composition of the human body is represented by four biologically measurable segments of matter:
water, as liquid;
the fat component, as the basic reserve of energy;
the mineral component, as the solid body component; and,
the protein component, as the basis for the contractile component responsible for locomotion, that is, movement [28].
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In addition to these basic components of body composition, index indicators can be defined with the task of determining the relation between individual elements or even segmented relations between the same elements.
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In existing research, the BMI has been presented as one of the crucial and basic parameters of obesity. The BMI presents the ratio of body high and body weight and is the simplest and most commonly used measure for determining body state of observed population and samples. Even though the BMI does not allow a detailed insight into the state and mutual relations of structural components such as the overall amount of fat or the distribution of fat in particular segments of the body, which can vary significantly within a normal values of body mass index [29], it has been shown that the values of this index had great influence on inflammatory and lipid markers (cardiovascular biomarkers) in a research that included a large number of women [30]. On the basis of results published by the Serbian Institute for Public Health, adult population in Serbia is among the highest worldwide by the number of persons and deaths from heart and blood vessels conditions, metabolic and malignant diseases, etc. [31, 32].
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Owing to technological advancements, it is nowadays simple, fast, reliable, reproducible, and non-invasive to systematically follow morphological characteristic. Various methods are available to estimate or directly measure body composition. Measuring body composition with electric multichannel bioimpedance is a new generation technology that enables the direct measurement of the basic components of body composition. In addition to that, InBody 720 provides valid data in the simplest and non-invasive manner [27, 33].
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Body fat, that is, the percentage of body fat, is the only component of body composition that has the tendency of increasing almost through entire lifespan [34, 35]. In addition to the biological influences related to aging [36], the change of the lifestyle of modern humans (reduced physical activity and increased energy intake) have caused an enormous increase of body fat to be one of the basic determinants of health or illness. As Gába et al. argue [37], the prevention of excessive gain in body mass-fat has become a public health priority in the developed countries, and in Serbia alike [38]. In contrast, the reduced muscle components of body composition are inevitable side effects of aging, whereby body mass remains the same or increases on the expense of fat [34, 39].
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Besides the basic elements gained through a direct measurement of body composition, such as body fat, muscle mass, a very important scientific and medical role belongs to index indicators like muscle mass index and fat tissue index (SMM, FFMI, or FMI) [40].
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Following the changes of body composition with different age groups is important not only for controlling the current status, but also for determining the trends of changes of the overall mass or particular parts of the given structure. At present, there is an increasing importance and need to systematically follow relevant indicators of body composition of sportspersons, persons exercising for recreational purposes or persons who do not exercise alike [41, 42, 43].
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The aim of this article is to determine the influence of lifestyle and living habits to the characteristics of body composition among young females in Serbia. It is well-known that the habits one acquires from childhood to early adulthood are very important, since they present a fundamental personal foundation for a lifestyle one has in adulthood and maturity. Thus, this particular age group is a turning point for the final adoption of healthy or unhealthy life habits. Our secondary aim is defining a multidimensional model of dependence between body composition, lifestyle, and nutrition, which would enable programming of optimal patterns of behavior in both spaces, as a planned corrective measure.
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2. Methodology
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2.1 Subject samples
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This research included 248 females aged 18–29. The group comprised professionals, students of the University of Belgrade, and secondary school students. The average age of young females was 24.40 ± 3.34 years. The research was conducted in accordance with the “Declaration of Helsinki for recommendations guiding physicians in biomedical research involving human subjects” [44] and with the permission of the Ethics Committee of the Faculty of Sport and Physical Education, University of Belgrade. Each subject was well informed about the purpose of the study, and all invitees agreed to participate.
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2.2 Body composition measuring method
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Body composition measurement was done by multisegmental bioelectrical impedance analysis (BIA). We relied on a professional measurement equipment—In Body 720 Tetrapolar 8-Point Tactile Electrode System (Biospace, Co., Ltd.) and DSM-BIA method (direct segmental multi-frequency bioelectrical impedance analysis). BIA is a widely used standard method for determining whole body composition and segmental lean mass measurements. InBody [45] body composition analyzer has high test-pretest reliability and accuracy (ICC 0.9995) [46]. Compared with DXA as a golden standard, the interclass correlation coefficient of BIA was between 0.96 and 0.99 in the normal-weight population [47]. All measurement had been performed between 2013 and 2016, in the morning hours. The procedure and course of analysis have been described in previous studies [33, 48].
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2.3 Variables
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We used a validated questionnaire [49] in order to collect data about lifestyle, living habits, and physical activity of young females. The participants were given five questions about their physical activity, with four close-ended answers to each question. Each response was validated from 0 to 3, which thus gave the maximum total score of 15. The higher scores indicated healthier behavior.
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Questions:
do you exercise? (0—never; 1—occasionally, 2—only seasonally; 3—regularly)
how often? (0—never; 1—1–2 h per week, 2—3–4 h per week; 3—more than 4 h per week)
how do you spend your free time? (0—watching TV, listening to music, using computer, reading books; 1—shopping, 2—walking; 3—practicing sport)
how much time do you spend behind a computer? (0—over 6 h per day; 1—5–6 h per day, 2—2–4 h per day; 3—1–2 h per day)
how would you describe your daily lifestyle? (0—too sedentary; 1—sedentary, 2—moderately active; 3—very active)
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Body composition variables:
BH—body height, expressed in cm;
BW—body weight, expressed in kg;
BMI—body mass index, expressed in kg/m2;
BFM%—body fat mass percentage, calculated as: (BW/BFM) × 100, expressed in %;
SMM%—skeletal muscle mass percentage, calculated as: (BW/SMM) × 100, expressed in %;
VFA—visceral fat area, expressed in cm2;
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Index:
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FFMI—fat-free mass index, presented as fat-free mass (FFM) relative to body size, calculated as: FFM/BH2, expressed in kg/m2;
FMI—fat-mass index, presented as Body Fat Mass (BFM) relative to body weight, calculated as: BFM/BH2, expressed in kg/m2;
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Point score:
BCS—body composition score, a mathematical model with the highest degree of prediction has been selected on the basis of regression analysis (a more detailed description has been provided in Section 3);
LSS—lifestyle score, representing the sum of points scored on the basis of the five questions, with the maximal sum of 15.
3.1 Results of descriptive statistics among young females
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Table 1 provides the results of descriptive statistics of body composition.
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Figure 1. provides the distribution of results of the correspondents’ responses in relation to physical activities habits.
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Mean ± SD
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%cV
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Min.
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Max.
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95% confidence interval for mean
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Lower bound
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Upper bound
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BH
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169.89 ± 6.95
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4.09
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151.70
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193.70
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169.020
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170.759
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BW
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64.44 ± 11.74
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18.23
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44.60
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143.70
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62.969
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65.906
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BMI
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22.42 ± 4.00
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17.85
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17.40
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48.63
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21.922
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22.924
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BFM%
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24.14 ± 8.69
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36.00
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5.90
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55.28
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23.057
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25.231
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SMM%
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41.11 ± 4.58
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11.13
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24.80
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50.70
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40.555
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41.700
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VFA
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51.83 ± 32.09
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61.91
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11.80
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254.50
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47.817
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55.844
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FFMI
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16.45 ± 1.35
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8.24
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12.63
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22.23
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16.276
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16.615
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FMI
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5.87 ± 3.28
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55.91
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1.93
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26.40
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5.463
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6.284
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LSS
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9.29 ± 3.72
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40.05
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0
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15
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8.821
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9.751
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BCS
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50.00 ± 16.67
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33.33
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−5.10
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84.30
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47.914
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52.083
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Table 1.
Basic descriptive indicators of body composition among young females.
BH—body height; BW—body weight; BMI—body mass index; BFM—body fat mass; BFM%—body fat mass percentage; SMM—skeletal muscle mass; SMM%—skeletal muscle mass percentage; VFA—visceral fat area; FFMI—fat-free mass index; FMI—fat-mass index; LSS—lifestyle score; BCS—body composition score.
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Figure 1.
Percentage of the responses given to each question from the questionnaire.
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3.2 Results of regressive analysis among young females
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On the basis of results gathered via multidimensional modeling and regressive analysis, we selected a mathematical model with the highest degree of prediction of the optimal model of our correspondents’ body composition. Within this model, body composition score (BCS) presents a criterion variable, while body composition variables (BW, BMI, FMI, SMM%, BFM%, VFA, FFMI) present the most discriminative variable of body composition that makes up a predictive part of the defined model.
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Body composition score has been given quantitatively, that is, by a numerical value calculated in the following way: all body composition variables applied here were subjected to the factorial analysis, and on that basis a reduced set of singular variables that statistically best describe correspondents’ body composition has been selected. For each correspondent, BCS has been selected on the basis of specification equation that has the following form:
Correlation between lifestyle and body composition among young females.
p < 0.05.
p < 0.01.
BW—body weight; BMI—body mass index; BFM%—body fat mass percentage; SMM%—skeletal muscle mass percentage; VFA—visceral fat area; FFMI—fat-free mass index; FMI—fat-mass index; BCS—body composition score.
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Statistically, most prominent correlations were found between lifestyle score and body composition score (r = 0.505, p < 0.01), while the highest negative statistically relevant correlation was between the body fat mass percentage and lifestyle score (r = 0.408**).
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Figure 2 presents the relation between body composition score and lifestyle score, which is explained by applying the method of mathematical modeling. The change of trend of relation between body composition score and lifestyle score has been defined by the following ratio:
y = 2.26x + 28.99
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Figure 2.
Linear regression of body composition and lifestyle scores.
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In relation to the model body composition score, we could claim that the intercept stood at 28.99, while the trend of change (curve inclination) was defined by the coefficient of regressive constant of 2.26. In other words, this means that the increase of lifestyle score by 1 point led to the increase in the value of body composition score by 2.26 points on average, that is, to its rise for as much as 2.3%.
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On the basis of the value of determination coefficient (R2 = 0.255), we conclude that 25.5% of the overall variability of body composition score results was determined by lifestyle score, that is, by the variability of an independent variable. The rest of variability of 74.5% has not been explained by the regression model, that is, it is influenced by other factors.
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4. Discussion
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Within this study, we aimed to explore the relationship between physical activity and body composition among young females. The results showed expected correlation. Specifically, most results (SMM%, BFM%, VFA, FMI, FFMI) showed statistically relevant correlation (p > 0.001) with lifestyle variables, with lifestyle score being a somewhat stronger predictor in comparison to other variables.
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This cross sectional study determined the existence of correlation between physical activity and certain parameters of body composition, as well as of the constructed mathematical model of body composition score for young women.
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Our correspondents had normal (some say optimal) weight according to BMI (22.42 kg/m2). A large scale sample (n = 1023) of female students of the University of Belgrade aged 21.3 years on average, average BMI was 20.7 kg/m2 [50]. This difference could be explained by the fact that our correspondence are not only older by 3 years on average, but also by the fact that our sample includes working women, women who gave birth in addition to students, which can influence their health habits related to physical activity, nutrition, etc.
\n
According to cutoffs % fat Heyward [51], young women from our research belong to the category of above average fat occurrence, which is unfavorable considering the likely increase in body fat during aging, especially related to the menopause [52]. Gába and Přidalová [53] applied the same method of measuring body composition among Czech women of the same age group (18–30 years). The correspondents had lower values of all variables that have fat component, that is, BM, BMI, %BFM, and VFA. There is a clear correlation between excess body fat, which is a basic characteristic of obesity and increased mortality among different ethnic groups around the world. Yet, it is clinically relevant that obesity is heterogeneous, and there are individual differences in regional fat distribution, especially in visceral adipose tissue [54]. There is a strong relation among visceral fats, metabolic syndrome, and the most common chronic non-infectious diseases of contemporary humans [55]. Enzi et al. [56] found that among lean or obese young women, subcutaneous abdominal fat dominates in relation to abdominal visceral fat, both measured by CT at the upper renal pole. There are no clearly defined standards for the visceral fat content. According to the measuring technique applied here, a VFA of 100 cm2 [45] is considered as a risk, but this needs to be taken with caution as further studies are needed. Among our correspondents, the average value was 51.83 ± 32.09 cm2, while it was lover by 10 cm2 in the study of Gába and Přidalová [53].
\n
Fat-free mass index is the indicator indexed according to the body height (FFMI—kg/m2) and is considered as not only a good indicator of the body composition of healthy, but also ill persons [57]. FFMI retains stable values among young and middle-aged women and men, and then drops after 74 years of age. Average FFMI value of our correspondents (16.45 ± 1.35 kg/m2) corresponded to the values obtained by the research of Kyle et al. [57] (14.6 and 16.8 kg/m2) and belonged to the category of normal and high values. According to the these authors, BMI increase that comes with aging has been complemented with the increase in BF and FFM, and thereby with FFMI.
\n
Fat-mass index (FMI) is significantly higher among sedentary than physically active men than women, and the difference rises with age [57]. Average FMI value of our correspondents (5.87 ± 3.28 kg/m2) corresponded to the values obtained by the research of Kyle et al. [57] (3.9–8.1 kg/m2) and belong to the category of normal.
\n
\n
4.1 Lifestyle
\n
Out of the maximum score of 15 for physical activity, our correspondents had 9.29 ± 3.72, which can be considered to be a moderate activity. Close to 50% of them said to be active over 4 h per week, which could be said to correspond to the recommendation of 30 min of exercising each day [58]. Still, only a third has been continuously active throughout the year, which is near to the percentage of those who described their lifestyle as very active. Half of the correspondents spend their free time in sedentary activities (using the phone, computer, watching TV, reading and the like).
\n
With regard to the mathematical model, the results of the combined influences of individual variables of body composition showed that FFMI and SMM% (4.894 vs. 1.353) had the greatest influence on the point score of body composition, while BW = 0.092 had the lowest influence (please see the formula). Apparently, taken overall variability of the point score, the most influential to the optimal model of body composition were those variables that define fat-free muscle mass dependent on longitudinality (FFMI), or the percentage of muscle tissue in the organism independent of voluminosity (SMM%). As both cases apply to contractile components, that is, body components most responsible for motorical quality, that is, movement, the results clearly showed that fat-free body structure was the most sensitive body component for defining the optimal body composition within the defined optimal body model among young females from the investigated sample. In addition, the two abovementioned most sensitive variables of the model are body components that are directly developed by physical activity, regardless of that activity being endurance training or resistance or weight training. Thereby, it has been directly shown that young females were no different in relation to the occurrence of body fat in the body—as a variable directly linked to diet and sedentary lifestyle, but dominantly differed by the occurrence of contractile component—as a variable directly dependent of the amount of physical activity or exercising.
\n
\n
\n
4.2 Correlation between lifestyle score and body composition score
\n
Lifestyle score (LSS) in this research statically significantly correlated with all the tested variables of body composition, as well as with the calculated score of body composition (BCS), except with weight (Table 2 and Figure 2). The correlation coefficient was the highest between LSS and BFM%, SMM%, and BCS (r = −0.408, 0.461, and 0.505, respectively, Table 2). The vast amount of previous studies about the influence of various modalities of physical activity and exercising on BW, BMI, and the occurrence of body fat among females applied to overweight/obese women, which were either middle-aged or postmenopausal. Donnelly et al. [59] conducted a cognate research that included young, sedentary, overweight/obese women. As they persuasively showed, controlled exercising lasting 16 weeks showed significant effects on preserving the existing BW and reducing total and visceral fat among them in comparison to the physically inactive control group. An optimal exercising among women with sedentary habits, regardless of the modality (high or low intensity, aerobic, or resistance training) significantly reduces body fat [60, 61, 62]. The aerobic training was shown to be more effective than resistance training at improving visceral and liver fat and also abdominal subcutaneous fat among women and men of various age [62]. Therefore, the positive effect of aerobic exercising on reducing visceral fat among overweight/obesity subjects is connected with the improved insulin resistance and cardio-metabolic health [55, 63]. Even though our research did not involve the modality of physical activity practiced by young correspondence, the results showed statistically relevant correlation between their physical activity and VFA and FMI (Table 2).
\n
Higher level of muscle mass in body composition significantly correlates with a number of health parameters [64]. It is also known that exercising, and especially resistance training, leads to an increase in muscle mass [60, 65]. Our research showed prominent correlation of SMM% with all variables of physical activity, in the first place with LSS (r = 0.461, p < 0.05) (Table 2).
\n
Kyle et al. [57] found that physical activity increased FFMI by 0.32 kg/m2 among men and women combined. In addition to that, the effect of age on FFMI was −0.007 kg/m2 among physically active men and women. Our research confirmed significant correlation of physical activity and FFMI (r = 0.242, p < 0.01) by analyzing a general level of dependence between BCS, as an optimal modeled score of body composition, and LSS, as a score of lifestyle in relation to the level of physical activity and sedentary habits. On the basis of our results, it could be claimed that each standardized value, that is, each point more for improving lifestyle and physical activity, leads to increase in optimizing body composition of young females by 2 points, that is, it doubles (Figure 2).
\n
\n
\n
\n
5. Conclusions
\n
This research set the task to investigate the correlation between lifestyle and body composition among young females. The results showed the existence of significantly relevant correlation among a large number of variables. This chiefly applies to the correlation between BFM% and SMM% in comparison with lifestyle score (r = −0.408, p < 0.01; r = −0.461, p < 0.01). As far as the relation between body composition and lifestyle point score is concerned, that correlation stood at the level of r = 0.505, p < 0.05. In addition, on a general level of correlation between BCS and LSS conducted by applying the method of mathematical modeling, it has been shown that the body score would rise by 2 points, that is, twice, if lifestyle score rises by 1 point. This is a clear evidence that a particular attention in further research on these subjects should be given to people’s lifestyle and habits, and that increase in physical activity and making it into a regular, daily part of one’s lifestyle, brings serious health benefits and reduces several risks arising from the contemporary sedentary habits.
\n
\n
Acknowledgments
\n
The chapter is a part of the project Effects of applied physical activity on the locomotive, metabolic, psychosocial, and educational status of the population of Republic of Serbia, number III47015, funded by the Ministry of Education and Science of the Republic of Serbia—Research Projects Cycle 2011–2016.
\n
\n',keywords:"mathematical model, point score, questions, linear regression, index indicators",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68536.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68536.xml",downloadPdfUrl:"/chapter/pdf-download/68536",previewPdfUrl:"/chapter/pdf-preview/68536",totalDownloads:68,totalViews:0,totalCrossrefCites:0,dateSubmitted:"August 29th 2018",dateReviewed:"May 23rd 2019",datePrePublished:"September 10th 2019",datePublished:null,readingETA:"0",abstract:"The aim of this study is to determine if certain lifestyle and habits influence the characteristics of body composition among young females in Serbia. The research included 248 randomly chosen females between 18 and 29 years of age. Data about physical activity were collected via validated questionnaire. In determining body composition, we relied on the instrument InBody 720, which enabled us to define the variables: body height (BH), body weight (BW), body fat mass percentage (BFM%), skeletal muscle mass percentage (SMM%), and visceral fat (VFA). In addition, we determined variables indexed for body height (BMI, FFMI, and FMI). On the basis of the results of regression analysis, we selected a mathematical model with the highest degree of prediction for body composition (BSC) = −64.554 + (0.092 × BW) + (−0.107 × BMI) + (−1.001 × FMI) + (1.353 × SMM%) + (−0.626 × BFM%) + (−0.079 × VFA) + (4.894 × FFMI). Our correspondents had normal BMI, above average % BFM, VFA—50.8 cm2, FFMI in the range of normal and high and normal FMI. The score of physical activity (LSS) stood at the moderate level (9.29 ± 3.72). LSS statistically correlated significantly with all tested variables of body composition, except with BW. The highest degree of correlation has been between LSS in relation to BFM% and SMM% (−0.408 and 0.461, respectively).",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68536",risUrl:"/chapter/ris/68536",signatures:"Ćopić Nemanja, Đorđević-Nikić Marina, Rakić Slađana, Maksimović Miloš and Dopsaj Milivoj",book:{id:"7949",title:"Cardiorespiratory Fitness",subtitle:null,fullTitle:"Cardiorespiratory Fitness",slug:null,publishedDate:null,bookSignature:"Dr. Hasan Sozen",coverURL:"https://cdn.intechopen.com/books/images_new/7949.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"161402",title:"Dr.",name:"Hasan",middleName:null,surname:"Sözen",slug:"hasan-sozen",fullName:"Hasan Sözen"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Contemporary lifestyle",level:"2"},{id:"sec_2_2",title:"1.2 Body composition",level:"2"},{id:"sec_4",title:"2. Methodology",level:"1"},{id:"sec_4_2",title:"2.1 Subject samples",level:"2"},{id:"sec_5_2",title:"2.2 Body composition measuring method",level:"2"},{id:"sec_6_2",title:"2.3 Variables",level:"2"},{id:"sec_6_3",title:"Index:",level:"3"},{id:"sec_8_2",title:"2.4 Statistical analyses",level:"2"},{id:"sec_10",title:"3. Results",level:"1"},{id:"sec_10_2",title:"3.1 Results of descriptive statistics among young females",level:"2"},{id:"sec_11_2",title:"3.2 Results of regressive analysis among young females",level:"2"},{id:"sec_12_2",title:"3.3 Results of correlation analysis among young females",level:"2"},{id:"sec_14",title:"4. Discussion",level:"1"},{id:"sec_14_2",title:"4.1 Lifestyle",level:"2"},{id:"sec_15_2",title:"4.2 Correlation between lifestyle score and body composition score",level:"2"},{id:"sec_17",title:"5. 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Exercise and Sport Sciences Reviews. 2014;42(2):53\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ćopić Nemanja",address:"nemanjacopic@yahoo.com",affiliation:'
Institute of Sport, Tourism and Service, South Ural State University, Russia
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