Development in Different Global Economies - Research Paper Example

The Development with Bad Geography Research question According to different scholars, the of development in different global economies is dependent on the land’s terrain and geographic locations. It is evident that African economies differ in economic development from the American, European, and Asian economies. With reference to the US geographic factors, studies show that the Northern part of the nation reflects positive economic growth in the level of per capita income and infrastructure development compared to the Southern region. According to a research conducted on the African continent by Nunn Nathan and Puga Diego, it is evident that the effects of continent’s geography have influenced the economic development of some of the nations while that of other nations has influenced a slump in the economic growth (Nunn 2). After reviewing a study sample of 190 countries from the developed, developing, and the underdeveloped categories, it is evident that countries from the African Sub-Saharan region show a reduced domestic product compared to others in the region and to those in the European region. Through the application of geography hypothesis, the study indicates a historical change of events in the African region following the advent of slavery, colonization, and adoption of the western political institutions. Developing and least developed countries in the East and Southern Asia, America, and Africa are located between tropics of Cancer and Capricorn contrary to the rich nations, which are generally situated in temperate geographical regions (Durante 170). The controversial aspect is presented through the first illustration named Figure 1. The figure depicts changes in the GDP between 1975 and 2009 in countries around the Sub Saharan region of Africa and Europe. The countries located in Europe display a better performance than those in the African region. Figure 1 establishes the authenticity of the hypothetical (Dinkelman 3079). The aspect contrasts to the second figure named “Figure 2,” because it develops an orientation to evaluate countries within the tropical region, but still argues that the geography hypothesis might not lead to the implementation of the correct measure of economic growth and outcomes in development. Scholars argue out on the concept of dependency on economic growth rate by a country based on its geography as a tool known as the economic geography approach. Researchers have been evaluating the geography hypothesis for several decades and they have established that the economic geography a dormant subject. Currently, it is evident that an issue of the kind could stimulate an active debate because the majority researchers argue on the opinion that poverty and worsening standards of living in tropical region should not be related to geographical locations and further to the infectious disease (Dinkelman 3089). The thought professed therein is the governing institutions to such states that are ton blame for the imminent failures to establish strategic measures to empower people in the environment. The discussion provokes the understanding that the measure is vital in identifying the background of the shortcomings as it presents the understanding by showing that one group of people relate prosperity in a country’s GDP and GNP ration to the geography hypothesis, while the opponent group groups of people relate it to the performance of the institutions (Fenske 1365). The geography hypothesis’ proposing team acclaims that those located in unfavored geographic zones might remain poorer compared to the ones situated in conditions favored by the right geographic location (Nunn 7). On the other hand, it is realistic that the opponents’ argue out that good political institutions can propel the mitigation process of the worse and threatening aspects of bad geography. Variables and data types The explanatory survey accrued the information that the tropical climate’s inhabitants are lazy and rarely engage in tedious tasks. Further, the population inhabiting the regions oppose any political institutions; hence, economic and chances in growth rate face failures in the region as explained through the rule of law approach. Still on the same subjects, different groups of other researchers claim countries situated within the confines of the region usually present a reduced or rather inadequate infrastructure and landlocked characteristics; hence, such nations face higher costs of transportation, a practice that in turn threatens the soundness of growth for the individual population of skilled and unskilled workers. With a view of Montesquieu’s statement, it is arguable that prosperity and poverty evolve in related locations of the world. Poverty of the region contributes to the absence of proper public health care. ` The review establishes a descriptive approach to establish the cause and effect relationship in a nation with a neutral and equality-bound rule of law will have prosperity while with bad rule of law will have poverty. The argument is that the rule of law prevails to be rated an independent variable; hence, rendering prosperity a dependent variable. An attachment of the relationship in infrastructure and public health, which are correlative variables, spurs the qualitative approach to affect various aspects instead. The descriptive approach discusses the research question (Dinkelman 3094). The use of quantitative analysis approaches provokes an understanding of the relationship; Prosperity = ∫ (rule of law, infrastructure, public health). The regression practices are conducted on data collected through observations by a leading organization. The dataset is divisive of the global nations underlying in the chosen regions; for example, the Central and South Asia is 1, Eastern Europe is 2, East Asia and Pacific islands is 3, Latin America and Caribbean is 4, Middle East and North Africa is 5, Sub Saharan Africa is 6, and West Europe, USA, and Canada is 7. Countries from the identified regions denote a variable through continuous and dichotomous type data. Table 1 illustrates the variables. Table1. Description of Variables Name of the Variables Type of the Variables Data type Notations GDP per capita, 2009 (rgdpch2009) Dependent Continuous Y Rule of Law, 2009 (rulelaw2009) Independent Continuous X1 Landlocked (landlocked) Dummy independent Dichotomous X2 Age dependency ratio, 2009 (agedep09) Independent Continuous X3 Infant mortality rate, 2009 (infmor09) Independent Continuous X4 Regression models The dependent variable Y is GDP per capita in 2009 ; it shows prosperity of countries of the world. Independent variables are selected to reflect the argument. Partly, the argument presents the geographic hypothesis as a factor that dictates prosperity of nations; the economies inhabiting the tropics are destined to have poverty and disease infinitely. The multiple regression method evaluates parameters of the equation y = β0 + β1x1 + β2x2 +….. + βnxn + e (“Multiple Regression with Many Predictor Variables” 1) . The controversial approach argues out that, the problems are associated to the tropical nations and that they can be resolved through rule of law approach. Therefore, the application of the 3 models stimulates the inclusion of different independent variables to reflect both sides. GDP per capita, 2009 = ∫ (rule of law, landlocked, age dependency ratio, Infant mortality rate); or Y = b0+b1*X1+b2*X2+b3*X3+b4*X4 Regression analysis A Scatter plot in Figure 3 indicates that the significance of a positive correlation between rule of law and GDP factors to economic propulsion despite the challenge of geography. The nations depicting the highest values of rule of law tend to have a higher GDP ration compared to those with the lowest values in the rule of law. Table 2 is a representation of the regression analysis results of the first model 1. Figure 3. Scatter plot Table 2 Regression output of model 1 Coefficients: Estimate Std. Error t-Value Pr(>|t|) Intercept 11912 566 21.42 4.19e-45*** rgdpch2009 9781 570 17.16 1.23e-35*** Signif. Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*” 0.05 “.” 0.1 “ “ 1 Residual standard error: 5595517575 on 134 degrees of freedom Multiple R-Squared: 0.687 Adjusted R-Squared: 0.685 F-statistics: 295 on 1 and 135 DF, p-Value: 1.23 e-35 Regression equation of model 1 is Y = b0+b1*X1 or Y = 11912+9781*rgdpch. The equation’s slope indicates the escalation of the GDP at $9781 per unit increase in the rule of law factor. The coefficient b1 has standard error 566, t-statistics of 21.42 and p-value of 4.19 e-35 (Nunn 12). Therefore, it is statistically arguable that at significance level α = 0.05 as p < 0.05 (“Hypothesis Test for Regression Slope” 1). The implication is that the effect of rule of law on GDP, 2009 is too strong to have occurred only by chance. Table 3 Regression output of model 2 Coefficients: Estimate Std. Error t-Value Pr(>|t|) Intercept 11981 621 19.29 2.33e-40*** rgdpch2009 9757 580 16.83 9.58e-35*** landlocked -356 1413 -0.25 8.01e-01’ ‘ Signif. Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*” 0.05 “.” 0.1 “ “ 1 Residual standard error: 559284307 on 133 degrees of freedom Multiple R-Squared: 0.687 Adjusted R-Squared: 0.683 F-statistics: 146 on 2 and 133 DF, p-Value: 2.59 e-34 Therefore, the implied regression equation of model 3 is Y = b0+ b1X1 + b2X2 + b3X3 + b4X4 , or Y = 15416 + 9107*rgdpch2009 – 266*landlocked – 56*agedepo9 – 9*infmor09. The adjusted R2 stirs the thought that 68.9% GDP is a variability explained other competing variables used of the analysis (Dinkelman 4009). The p-value of F-test statistics informs that parameters jointly statistically significant at level α = 0.05 as p < 0.05 and b4states individual statistics, which are insignificant at significance level α = 0.05 as p Table 2. Effect of variables on real gross domestic product per capita (GDP), 20009 Variables Model 1 Model 2 Model 3 Rule of law 9781 (570) 9757 (621) 9107 (750) Landlocked -356 (1413) -266 (1413) Age dependency ratio -55 (60) Infant mortality -9 (36) Intercept 11912 (556) 11981 (621) 15416 (2954) R-Squared 0.687 0.687 0.698 N 136 136 135 Standard errors are in parenthesis Conclusion The conclusion is that the approach professes that the values presented between -2.5 and 2.5. have higher values and reflect better rule of law. The model 1 receives a new variable that considers the effect of infrastructure (Durante 171). Thus, we create model 2. It uses the concept that landlocked country suffers because of absence of infrastructure to the sea. The landlocked variable can have two values 0 or 1; 1 for landlocked countries and 0 for others. The study uses infant mortality rate (Infmor09) and age dependency ratio (agedep09) to reflect influence of public health. The data acrued from observations presents Denmark with 1.87 values for rule of law produced GDP in the amount $33,909 compared to Chad with -1.53 values for rule of law produced GDP that is equal to $1,277. According to the regression equation Y = 11981 + 9757*rgdpch2009 – 356*landlocked demonstrates that holding other variable unchanged, landlocked countries would make $356 less GDP than countries that are not landlocked. The infant mortality variable presents the number of infants dying before reaching one year of age per 1,000 births in a given year. It implies that when all other variables remain unchanged each of these two variables will reduce the GDP value, and the age dependency variable expresses the percentage of the younger population as less than 15 and older than 64 of the employed population. The regression equation of model 2 demonstrates that parameters of these two variables are negative However, the numerical values of b3 and b4 depict that the effect is negligible. Model 2 demonstrates that geography is not the destiny; the rule of law overpowers the geography hypothesis. Authenticity of model 2 is illustrated in Table 5. Table 3 Model 3 example Nations Rule of law approach Landlocked Age dependency Infant mortality GDP 2009 Tanzania -0.43 1 106% 79 per 1,000 $1,152 Denmark 1.87 0 53% 3 per 1,000 $33,909 Works cited Dinkelman, Taryn. "The effects of rural electrification on employment: New evidence from South Africa." The American Economic Review (2011): 3078-3108. Durante, Ruben. "Risk, cooperation and the economic origins of social trust: an empirical investigation." (2010). Fenske, James. "Does land abundance explain African institutions?." The Economic Journal 123.573 (2013): 1363-1390. Nunn, Nathan. The importance of history for economic development. No. w14899. National Bureau of Economic Research, 2009. Read More