Ken Boyer
EC499
Effect of Gross Domestic Product on Road Safety in South Korea
Abstract
Road carnage has increasingly become a major source of death all over the globe. Many countries lose a significant number of pedestrians, passengers, drivers, cyclists and motorcyclists on the roads, highways and superhighways. This trend is particularly alarming in South Korea where loss of lives due to road accidents has become very worrying. The country is indiscriminately losing many people to this menace. The most unfortunate thing about this scenario is the fact that some of the country’s productive population is also consumed in the process. These individuals are important members of the country who contribute immensely to the economy.
Background of the Study
Statistical figures from the World Health Organization (WHO) indicate thatroad traffic related accidents culminated into an estimated 1.24 million deaths globally in the year 2010, declining from about 1.26 million in the year 2000. Approximately 50% of all road accident related casualties comprise of pedestrians, bicycle cyclists and motorcyclists. It was also reported that 59% of deaths are associated with people aged between 15 and 44 years. In terms of gender, men are worst affected: of the 4 reported deaths, 3 are male deaths (WHO, 2013).
According to Perel, Ker, and Blackhall(2007), approximately 35 million people become disabled and 1.2 million people succumb to their injuries due to road traffic crashes annually, with 90 percent of the deaths taking place in low and middle income countries (LMICs). South Korea is one of fastest growing developed countries.
A global overview indicated that Africa and South-East Asia exhibited the largest percentage (92%) of road traffic death rates. In terms of country category, there was an average of 18 deaths per 100 000 people. The figure had marginally decreased from 20.8 deaths per 100 000 people in the year 2000 (WHO, 2013).
Problem Statement
The issue of road accidents is not an emerging one. Road accidents continue to contribute significantly to the crude death rates of the country. Several measures have been put in place to reduce this trend but the number of road traffic related deaths remains on the upward trend. The constant increase of these deaths has been related to changes in the GDP of the country over the years.
Objective of the Study
The main aim of thisstudy was to understand the effect of GDP on road safety.It analyzed whether indeed changes in the GDP of the country is related to the number of road traffic crashes that are reported. This study intends to be corroborative of studies done by other researchers.
Literature Review
According toSmeed(1949), there is a relationship between traffic fatalities and traffic congestion when the number of motor vehicle registration and population of the country are considered. As a result, an increase in the number of vehicles increases traffic on the roads and this increases the risk of accidents.This theory has compounded into Smeed’s Law of road safety. This study tested whether indeed this law holds.
Adams (1987) foundSmeed’s law to be valid with some parameters alterations.He argued that Smeed’s computation of number of deaths on the basis of vehicles per population was less accurate than the computation using vehicles per miles.
Jacobsen (2003) set out to examinedata on population level in order to make comparisons about the frequency of motor vehicle collisions resulting in injuries given different amounts of walking and cycling.He was observed with more people walking or cycling, motorists are more cautious about their driving. Road users are therefore less likely to get hit. Consequently, policies that encourage road use by pedestrians and bicyclists are an effective way to enhance road safety.
Elvik&Vaa(2004) cite research work conducted by Oppe (1991) which observed that “in countries that are highly motorized, the long term development of traffic fatalities follows a law-like pattern determined by growth of motorization and the decline of the fatality rate per vehicle kilometer of driving”. The shift from the increasing to the declining trend was exhibited in many countries.
The change from the increasing to the decreasing trend could be observed in several countries. Kopits and Cropper used 1985 international prices to argue that $ 8600 is the income amount at which the risk of traffic fatality risk initially diminishes is, regardless of the manner in which the time trends are specified. This was the estimated income amount achieved by nations such as South Korea in 1994, New Zealand in 1968, Austria, the United Kingdom, and Belgium in the initial years of 1970s, , and (Kopits and Cropper, 2005).
Collection of Data for the Study
Data collection for this data entailed gathering own information on four important variables. These variables were the Gross Domestic Product (GDP), the number of registered vehicles, the number of road traffic crashes for an eleven year period between the year 2000 and the year 2010. This data was compiled and analyzed as below.
Data Analysis
This data that was collected during the study was analyzed using regression and correlation analysis. The results of simple regression analysis involved showing the relationship between only two variables: the dependent and one independent variable. The simple regression analysis conducted involved testing if there was a relationship between the number of traffic accidents and the Gross Domestic Product of the country. The assumption here was that an increase in the GDP of the country implied an increase in GDP per capita. This led to an increase in the number of cars bought and registered.
The multiple regression analysis involved analyzing the relationship between the dependent variable and a number of independent variables. This involved understanding why change in number of cars registered and road traffic accidents could be attributed to change in GDP.
Regression Analysis
Regression is an important tool of analysis in research work. It is concerned with finding out the causal relationships that exist between two or more variables: how a change in the independent variable(s) affects the dependent variable. In regression analysis, symmetry is important, that is, there is a distinction between the dependent variable and the independent variable(s). Causality is important: the independent variable(s) must influence the dependent variable and not vice versa. Only the dependent variable is assumed to be statistical or random but the independent variable(s) are assumed to be fixed.
Simple Regression Analysis
In simple regression analysis, the research studied the effect of only one independent variable on the dependent variable. Simple regression was carried out to determine if there exists a relationship between GDP (endogenous variable) and registered vehicles (exogenous variable). The model parameters are indicated in the table below:
The table above indicates that if the number of registered vehicles in the year is zero, the GDP of the country would be $ US 4 684 560. If the number of registered vehicles is taken to affect the GDP of the country, then a unit change in the number of registered vehicles will affect GDP of the country by $ US 462.50.
Multiple Regression Analysis
The multiple regression analysis aimed at finding the model parameters where there were more than one explanatory variables. The multiple regressions that were conducted were based on the number of car crash (dependent variable) and Year & GDP (independent variables). The complete multiple regression model is illustrated in the table below:
The p-value for intercept was computed as4.16096E-01. This meant that coefficient was significant at 0.05 significance level. This implied that the null hypothesis was “not rejected”.
The p-value for the coefficient of Year 4.10416E-01was also significant at 0.05. Thus, null hypothesis was “not rejected”.
The p-value for the coefficient of GDP 2.64738E-01was also significant at 0.05. Similarly, null hypothesis was “not rejected”.
The hypothesis tests revealed that the null hypothesis was “not rejected”. The number of crashes across the period was affected by the GDP of the country. The model was significant because all the three p-values calculated were lower than 0.05.
Correlation Analysis for Single-variable Model
Correlation analysis deals with finding out the strength or the degree of linear association between variables. From the table below:
Multiple R: this is referred to as the coefficient of correlation. It was computed as 9.84624E-01. This means that there exists a strong positive correlation between GDP and the number of registered cars.
R square: This is the coefficient of determination otherwise known as the goodness of fit. It was calculated as 9.69485E-01. This figure indicated that the number of registered cars explained 96.95% of all changes experienced in GDP. The 3.05% that is unexplained are accounted for by other independent variables.
Adjusted R square: it was calculated as 0.966095. It is the coefficient of determination adjusted to degrees of freedom. It implies that 96.61% of all changes in GDP the number of registered are accounted for by the number of registered cars.
Correlation Analysis for Multi-variate Model
This is illustrated by the table below:
Multiple R for multiple regression analysis was 7.38342E-01.This indicated that there was a strong positive relationship between the exogenous variables (Year & GDP), and the number of car crash.
R square calculated was equal to 5.45149E-01. According to the results of the multiple regressions, 54.5149% of all changes in number of car crashes can be explained by the GDP of South Korea for the year between 2000 and 2010.
Adjusted R square was 4.31436E-01. If all other factors are held constant, 43.1436% of the changes in number of car crashes over the years are explained by the GDP when adjusted for degrees of freedom.
Hypothesis Testing
Theresearch study would "reject the null hypothesis" if the p-value turned out to be less than the significance level of 0.05.The hypothesis test of this study was that if income goes up, then number of car crash will increase too.
The p-value for intercept was computed as 4.2336E-05. This meant that coefficient was significant at 0.05 significance level. This implied that the null hypothesis was “not rejected”.
The p-value for the coefficient of GDP= 3.97E-08 was also significant at 0.05. Similarly, null hypothesis was “not rejected”.
Conclusion
In summary, this research paper observed that the number of the crashes recorded over the eleven years (2000-2010) is related with the GDP of the country. Basically, as the GDP of the country increased over the years, the number of road traffic accidents was seen to rise. Therefore, there exists a direct linear relationship between the number of road traffic accidents and the GDP of the country over the years (2000-2010). The results of this study supported Smeed’s law of road safety.
Work Cited
World Health Organization."Global status report on road safety 2013". (2013). Geneva.
SmeedR. "Some statistical aspects of road safety research". Journal of the Royal Statistical Society. Series A (General) (Journal of the Royal Statistical Society. Series A(General), Vol. 112, No. 1) 112 (1): 1–34. (1949). Print
Transport Forum "Towards Zero, Ambitious Road Safety Targets and the Safe System Approach". OECD. (2008). Print
Elvik, R., Vaa, T. The handbook of road safety measures, Amsterdam, Elsevier, 1078 p. (2004).
Adams, J.G.U. Smeed’s law: Some Further Thoughts, Traffic Engineering and Control, February 1987, pp. 70-73. (1987).
Perel, P., Ker, K., and Blackhall, K. Road safety in low‐ and middle‐income countries: a Neglected research area. United Kingdom: BMJ Publishing Group(2007). Print.
Kopits, E., Cropper, M. Traffic fatalities and economic growth, Accident Analysis and Prevention. Vol. 37, Issue 1 January 2005, pp. 169-178.(2005).
Jacobsen P.L. Safety in numbers: more walkers and bicyclists, safer walking and bicycling. Injury Prevention, pp. 205-209. (2003).
