The Correlation Between Socioeconomic Status and Obesity

The Correlation Between Obesity and Socioeconomic Status
Looking at previous researches about obesity, one can already see that there is a connection between socioeconomic status (SES) and obesity among individuals. Is this observation for countries? In an article published in The Economist, obesity is becoming a problem not only in the United States and United Kingdom, two of the richest nations today, but also in Mexico, Brazil, China and other countries in the developing world ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Ward", "given" : "Charlotte", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "2The Economist", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2012", "12" ] ] }, "title" : "The big picture", "type" : "article-magazine" }, "uris" : [ "/documents/?uuid=624d8e02-1716-40be-b144-7e1a950d284a" ] } ], "mendeley" : { "previouslyFormattedCitation" : "(Ward, 2012)" }, "properties" : { "noteIndex" : 0 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Ward, 2012). The report says that as of 2008, roughly one-third of the world`s total population is obese or overweight. The article also provides an explanation to the phenomenon of obesity: "Much of the shift is due to economic growth... Greater wealth means that bicycles are abandoned for motorbikes and cars, and work in the fields is swapped for sitting at a desk. In rich countries the share of the population that gets insufficient exercise is more than twice as high as in poor ones" ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "Ward", "given" : "Charlotte", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "2The Economist", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2012", "12" ] ] }, "title" : "The big picture", "type" : "article-magazine" }, "uris" : [ "/documents/?uuid=624d8e02-1716-40be-b144-7e1a950d284a" ] } ], "mendeley" : { "previouslyFormattedCitation" : "(Ward, 2012)" }, "properties" : { "noteIndex" : 0 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(Ward, 2012).
Meanwhile in a recent report by The Organisation for Economic Co-operation and Development (OECD), it was mentioned that obesity prevalence, has slowed for some countries, but it will continue to increase, though in a more modest rate. OECD accounts the slowdown of the epidemic to various legislation instituted by governments to support researches on obesity and policies for promoting healthy lifestyles. Through OECD`s report, one can already see the importance of the role of government support in order to combat obesity prevalence. Because government support requires corresponding budget to fund programs that prevent obesity, this paper assumes that just like that of the individual, a country`s socio economic status is also related to obesity prevalence.
This paper aims to address the question: How is a country`s socio economic status related to obesity prevalence in the nation?
To measure SES, this research will utilize country-level data which are categorized four: population, production and income, employment rates, literacy rate. Under production and income, two indicators will be used: GDP per capita, and real GDP growth. Literacy rate is indicated by the number of individuals aged 25 to 64 years old who have received tertiary level education. GDP, literacy and employment rates are known indicators used to determine the economic and social standing of a country.
Population will also be used as an independent variable since developmental frameworks today ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "URL" : "http://cgge.aag.org/PopulationandNaturalResources1e/CF_PopNatRes_Jan10/CF_PopNatRes_Jan108.html", "accessed" : { "date-parts" : [ [ "2013", "3", "20" ] ] }, "author" : [ { "dropping-particle" : "", "family" : "AAG Center for Global Geography Education", "given" : "", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "AAG Center for Global Geography Education", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2011" ] ] }, "title" : "Malthusian Theory of Population", "type" : "webpage" }, "uris" : [ "/documents/?uuid=5354d1c9-60b2-486c-b4cb-eed106f0ef8b" ] }, { "id" : "ITEM-2", "itemData" : { "author" : [ { "dropping-particle" : "", "family" : "UNFPA", "given" : "", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "id" : "ITEM-2", "issued" : { "date-parts" : [ [ "2005" ] ] }, "title" : "Reducing Poverty and Achieving the Millennium Development Goals", "type" : "report" }, "uris" : [ "/documents/?uuid=1c8207e0-9145-41ab-9942-0b610782ac7b" ] } ], "mendeley" : { "previouslyFormattedCitation" : "(AAG Center for Global Geography Education, 2011; UNFPA, 2005)" }, "properties" : { "noteIndex" : 0 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(AAG Center for Global Geography Education, 2011; UNFPA, 2005) tend to have lesser resources to spend for social services. Countries with higher population have to face other social issues such as poverty, social inequality, crimes and many others. When this happens, prevention of obesity is listed at the bottom of the priority list as hunger and malnutrition takes center stage. In a sense, this research assumes that there is low obesity prevalence in countries with higher population, but the connection is not a direct one. There are other factors that must be considered. For example, high population with high income may translate to high obesity as citizens in the country live a sedentary lifestyle. Moreover, those countries with low population with low income can register high obesity prevalence as citizens are not engaged in physical activities. Through analysis of available data, this research hopes to provide light to these assumptions.
Data for all indicators will be retrieved from the OECD website ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "URL" : "http://stats.oecd.org/Index.aspx#", "accessed" : { "date-parts" : [ [ "2013", "3", "20" ] ] }, "author" : [ { "dropping-particle" : "", "family" : "OECD", "given" : "", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "OECD Stat Extracts", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2013" ] ] }, "title" : "Country Statistical Profiles", "type" : "webpage" }, "uris" : [ "/documents/?uuid=ac0467ca-72a8-4726-8f65-a58dd41e633b" ] } ], "mendeley" : { "previouslyFormattedCitation" : "(OECD, 2013)" }, "properties" : { "noteIndex" : 0 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(OECD, 2013). Note that obesity prevalence data if for adults aged 25 and above.
To help answer the research question, the following hypotheses were formulated:
1. The relationship between socioeconomic status and obesity prevalence.
Ho: There is a significant relationship between a country`s SES and obesity prevalence.
H1: There is no relationship between a country`s socioeconomic status and obesity prevalence.
2. The connection between population and obesity.
Ho: There is a significant relationship between a country`s population and obesity prevalence.
H1: There is no relationship between a country`s population and obesity prevalence.
This study will focus on the top 30 obese OECD countries in 2009 because of the variety in culture and economy size of each member country. Some OECD countries are rich nations, while others are still developing. No country in OECD is a transition economy. OECD also updates its database regularly so data is also current. Table 1 below shows how data collected will be recorded.
Table 1: Dummy table for recording data


Obesity Prevalence, %

Literacy Rate, %

Employment Rate, %

Production and Income

Population (thousands)






GDP
US$b

GDP per capita, US$

Real GDP Growth, %


1

Country 1








…

Country …








3

Country 30








To make sense of the data, various statistical methods will be utilized. First, the researcher will compute for the averages and standard deviation in the four independent variables so as to describe the sample collected. Next, t-test will be performed in order to accept or reject the hypothesis. Correlational studies will also be performed to determine how much change in obesity prevalence can be expected for any unit change in any of the independent variables. Statistical testing will be done through Microsoft Excel.
When statistical analysis has been completed, the researcher will discuss how the results figure in obesity prevention. Moreover, the discussion will also mention other ways of preventing or reducing obesity through the improvement in the four independent variables mentioned.
Analysis/Discussion
Based on table 2, it is clear that average obesity prevalence in the 30 countries of study is at 18.34%, a few points higher than the usual obesity prevalence in OECD countries. Average literacy rate is 28.81% while average employment rate is 65%.. From these aggregated data, it is clear that countries in the sample are relatively well off compared to the rest of the world which is registering only a fraction (about half) of the average GDP per capita of these OECD countries ADDIN CSL_CITATION { "citationItems" : [ { "id" : "ITEM-1", "itemData" : { "URL" : "/library/publications/the-world-factbook/geos/xx.html", "accessed" : { "date-parts" : [ [ "2013", "3", "24" ] ] }, "author" : [ { "dropping-particle" : "", "family" : "CIA Factbook", "given" : "", "non-dropping-particle" : "", "parse-names" : false, "suffix" : "" } ], "container-title" : "Central Intelligence Agency World Fact Book", "id" : "ITEM-1", "issued" : { "date-parts" : [ [ "2013" ] ] }, "title" : "World", "type" : "webpage" }, "uris" : [ "/documents/?uuid=c28060b2-75f1-42c8-838d-8eed5110bc2c" ] } ], "mendeley" : { "previouslyFormattedCitation" : "(CIA Factbook, 2013)" }, "properties" : { "noteIndex" : 0 }, "schema" : "https://github.com/citation-style-language/schema/raw/master/csl-citation.json" }(CIA Factbook, 2013). Meanwhile, average real GDP growth in the sampled countries is very low at -4.46%. Note that data used for this analysis was derived from 2009 figures, a time when the United States and all its trading partners were experiencing massive recession.
Table 2: Descriptive Statistics

Obesity Prevalence, %

Literacy Rate, %

Employment Rate, %

GDP
US$b

GDP per capita, US$

Real GDP Growth, %

Population (thousands)









Mean

18.34

28.8068

65.2133

1141.342

32224.566

-4.26427

34422.97963

Median

16.95

31.25

65.2788

328.9577

32963.0206

-4.33482

10714.485

Standard Deviation

5.918037885

9.981977

7.384762

2524.825

12912.6113

3.290956

58697.8233

Sample Variance

35.02317241

99.63987

54.53471

6374743

166735531

10.83039

3445434461

A quick review of the standard deviation shows that deviation from the mean is huge. This essentially means that while mean values for obesity (as an example) is low, this may not be because there is low obesity prevalence in the 30 OECD countries, but rather, because values for obesity prevalence among the 30 countries are far apart. In fact, we know that this 30 countries in the same have the highest number of obese individuals in the world.
To compare the differences in obesity prevalence, literacy rate, employment rate, GDP, GDP per capita, GDP growth rate and population of the three, the researcher decided to run a 2-Way ANOVA test. By doing this the researcher can determine whether the differences in countries of the differences in the values of the independent variables were causing the discrepancy in the means. The results of the test may be seen in table 3 below. For the rows (country), ANOVA test yielded F=1.11614, p>.05 whic...