Econometrics Institutions and Growth - Essay Example

Running Head: s, Property Right and Colonialism in Economic Performance s, Property Right and Colonialism in Economic Performance Name: University: Date: Abstract: This paper analysis the differences in economic performance with reference to institutions and property right, previous study such as that undertaken by Acemoglu (2001) show that colonialism led to the introduction of institutions that aid in improving performance, further this paper highlights differences in economic performance and the role played by different settlers in these countries, this is evident from a study by Lange (2006) which stated that British institutions were better than those of Spain whereby British colonised territories had positive effects after colonisation. The paper shows that British settlement introduced institutions that aided in improving economic performance, also that property right plays a major role in determining economic performance. British colonies with relation to GDP level shows that British colonised countries have higher GRDP level than the other countries. Introduction: This paper focuses on the reasons why some countries are wealthier than others, studies such as Daron Acemoglu (2001) focus on colonisation and its effect on institutions. They state that colonisation led to the introduction of institutions which are present today, these institutions are therefore contributed to better economic performance. The role of better institutions in economic performance is supported by North (1973) who states that better institutions would lead to more investment in human capital and this would result into better economic performance will be realised. Besley (1995) studied the relationship between property rights and output and identified that there was strong correlation between the two variables, therefore economic performance will also depends on property rights. In this paper we focus on the role of colonisation and establishment of institutions, we also discuss the effect of property right on GDP and the differences in economic performance and the colonisers of the various countries. Previous Studies: A study by Acemoglu (2001) was based on three assumptions that the institutions introduced by the colonisers are present today, these institutions have an effect on income per capita and that colonisers chose areas with low mortality and in areas with high mortality establish extractive institutions. According to the study by Acemoglu (2001) the high mortality rate was as a result of disease such as yellow fever and malaria which was common in the tropic areas, settlers had no immune to these diseases and therefore chose not settle in these areas, settlers settled in the equatorial regions where mortality was relatively low. Therefore we focus on the effect of good institutions and property rights to discuss the differences in the economic performance, according to Douglas North (1973) better institutions and secure poverty rights would lead to more investment in human capital will lead to better economic performance. Besley (1995) also stated that economic performance depends on property rights. It is also evident that the countries considered in previous studies were colonised by a number of countries, a study by Mathew Lange (2006) showed that colonisers had differing effect on post colonial period economic development, his study compared Spain and British colonies that showed that Spain colonies had negative effects on post colonial economic development while British colonies had positive effects on post colonial economic development, some explanation or this effect is associated with the type of institutions left by the colonisers, Landes(1998) stated that a comparison of Spain and British colonies is that British left better institutions. Methodology: The first section of the paper is a description of the sample, the determination the correlation between the various variables under question to determine the nature relationship, measure of central tendencies and deviation which include mean and standard deviation of the variables we undertake econometric modelling of the variables using the linear regression model. We also use the two least square methods to estimate the relationship between colonies, mortality, institutions and output or GDP. The sample data contains 8 variables and they include the "countryn" which is the country name, variable name Africa which is a dummy variable indicating whether the country is located in Africa, the variable "latitutde" which stands latitude of the capital city, the variable "euro" is the proportion Europeans in this countries in the year1975, the variable "prot" which is a measure of the property rights, the variable "lgdp" which is the log of Gross domestic production as at 1995, the variable "logmort" which is the log of mortality rate of colonisers and the variable "coloniser " which is a dummy variable indicating whether the country was colonised by the British. Results: Sample: In this study a sample of 64 countries is used, 27 countries in this sample are located in Africa while others are located in other parts of the world, the chart below summarises the sample with reference to location: From the chart only 42.19% of the countries in the sample are located in Africa and 57.81% are located in other parts. An analysis of the colony variable shows that the while 23 of the 64 countries in our sample were colonised by the British, the chart below summarises the results: Correlation coefficients: The following table summarises the correlation coefficients between the various variables: correlation coefficient variable lgdp logmort -0.6801 Africa -0.5968 colonir 0.2607 latitude 0.4512 euro 0.6556 prot 0.7349 From the table above it is evident that the highest correlation coefficient exit between the property protection rights and LGDP, there is a negative correlation between LGDP and Africa variable and mortality. This negative value of the correlation coefficient shows that as the Africa variable increases LGDP declines, also that when Mortality variable increases LGDP also increases. Linear Regression Estimation: European Settlement and Mortality: The correlation coefficient between European Settlement and log mortality is 0.5240, this means that there is a relatively high but negative correlation between the two variables, this means that as one variable increases then the other variable declines. We estimate a model that depicts the relationship between mortality and the number of European settlers in 1975; we state the model as follows: Euro = + logmort The following table summarises the results regress euro logmort Number of obs = 64 R-squared = 0.2746 euro Coef. Std. Err. t P>t [95% Conf. Interval] logmort -12.4667 2.573551 -4.84 0.000 -17.61115 -7.322241 _cons 75.94532 12.36385 6.14 0.000 51.23034 100.6603 We therefore state our estimated model as follows: Euro = 75.94532+ -12.4667LogMort From the above model it is evident that settlement was influenced by the mortality rate, the above model depicts that an increase in logmort by one unit will reduce the number of European settlers by 12.4667 units, the R squared value for the model is 0.2746 meaning that 27.4% deviations of European settlement is explained by mortality rate. European Settlement and GDP: In this section we analyse the relationship between European settlement and LogGDP, the correlation coefficient between the two variable is 0.6556 which depicts a relatively strong and positive relationship between the two variables, this means that as one variable increases the other variable also increases, this means that European settlement would have influenced GDP levels, we state a model as follows: LGDP = + Euro The following table summarises the results: regress lgdp euro Number of obs = 64 R-squared = 0.4298 lgdp Coef. Std. Err. t P>t [95% Conf. Interval] euro .0230885 .0033772 6.84 0.000 .0163376 .0298395 _cons 7.645092 .1165237 65.61 0.000 7.412164 7.878019 We state our model as: LGDP = 7.645092+ 0.0230885 Euro From the above model it is evident that settlement GDP is influenced by the European settlement, the above model depicts that an increase in the number European settlers by one unit will increase the number of European settlers by 0.0230885 units, the R squared value for the model is 0.4298 meaning that 42.98% deviations GDP is explained by the number of European settlers. GDP and Property Rights: We now investigate the relationship between property rights and GDP level, the correlation coefficient of these variables is 0.7349 and this value depicts a strong and positive relationship between the variables, this means that as property rights variable increases then GDP level also increases, we therefore estimate a model that depicts the relationship between the variable: LGDP = + Prot The following table summarises the results: Number of obs = 64 R-squared = 0.5401 lgdp Coef. Std. Err. t P>t [95% Conf. Interval] prot .522107 .061185 8.53 0.000 .3997999 .6444142 _cons 4.660383 .4085062 11.41 0.000 3.843791 5.476976 From the above table we state our estimated model as LGDP = 4.660383+ 0.522107Euro The above model states that settlement GDP is influenced by the protection rights, the above model depicts that an increase in the property right variable by one unit will increase the number of logGDP by 0.522107 units, the R squared value for the model is 0.5401 meaning that 54.01% deviations GDP is explained by the property rights GDP, Institutions, Property Rights and British Colonies: In this section we analyse the relationship between GDP, institutions that were formed following settlement, property rights and British colonies, in this case therefore we state the model to be estimated as follows: LGDP = + 1 Prot + 2 Euro+ 3 Colony Where prot is a variable representing the property rights, euro is the number of European settlers in 1975 and colony is a dummy variable whose value is 1 if the country was colonised by British and if otherwise the dummy variable value is zero. The table below summarises the results: Number of obs = 64 R-squared = 0.6508 lgdp Coef. Std. Err. t P>t [95% Conf. Interval] prot .3924896 .0671295 5.85 0.000 .2582106 .5267685 colony -.0517702 .1791656 -0.29 0.774 -.4101547 .3066143 euro .0134848 .003106 4.34 0.000 .0072718 .0196977 _cons 5.279895 .4016526 13.15 0.000 4.47647 6.08332 Our estimated model is as follows: LGDP = 5.279895+0.3924896Prot + 0.0134848 Euro -0.0517702 Colony From the above model it is evident that the estimated model has an R square value of 0.6508 and this means that 65.08% deviations in LogGDP is explained by the above explanatory variables, the above model states that if we increase port by one unit holding all other factors constant then the LGDP level will increase by 0.3924896 units, if we hold all other factors constant and increase the number of Euro settlers by one unit then LGDP increases by 0 .0072718, finally if we hold all factors constant and the country under question was colonised by the British then the LGDP level declines by -0.0517702 Findings: From the above analysis it is evident that economic performance depends on institutions and property right in a country, another factor that determines economic performance is the type of institutions introduced by the settlers who colonised these countries, this is evident research that have shown that British institutions were better than Spain colonies. From the above discussion it is evident that mortality rate influenced settlement, this is shown by the relationship between mortality rate and European settlers in 1975, another finding is that the number of settlers in the countries influenced economic performance and this is an indication of influence of institutions on economic performance. Another finding is that the GDP level of those countries colonised by the British is much higher than those others colonised by other countries, this is depicted by correlation which is high and positive. Conclusion: From the discussion it is evident that institutions,, property rights and colony type explains the difference in wealth of nations, British are considered to have introduced better institutions that enabled better economic performance in the post colonial period, regression analysis and analysis of correlation shows the relationship between the variables. For this reason therefore institutions, property right will determine economic performance. References: Acemoglu, D., Johnson S. and Robinson J. (2001) The Colonial Origins of Comparative Development: An Empirical Investigation, American Economic Review, volume 91, page 1369 to 1401. Besley, T. (1995)Property Rights and Investment Incentives: Political Economy Journal, vol 5, page 903 to 937 Knack, S. and Keefer, P. (1995) Institutions and Economic Performance: Cross Country Tests Using Alternative Measures. Economics and Politics, vol 7, page 207 to 227 Landes, D. (1998) Wealth and Poverty of Nations: Why some are Rich and Some Poor. New York: Norton Lange, M, Mahoney, J. And Matthias, H. (1998) Colonialism and Development: A Comparative Analysis of Spanish and British Colonies, Social Science History, vol 27, page 397 to 423 North, D. And Thomas, R. (1973). The Rise of the Western World: New Economic History. Cambridge: Cambridge University Press Appendixes: Correlation: Africa latitude euro prot lgdp logmort colonir Africa 1.0000 latitude -0.2462 1.0000 euro -0.5068 0.6140 1.0000 prot -0.3753 0.3806 0.5012 1.0000 lgdp -0.5968 0.4512 0.6556 0.7349 1.0000 logmort 0.5917 -0.4507 -0.5240 -0.5268 -0.6801 1.0000 coloniser -0.1123 0.3130 0.1748 0.3942 0.2607 -0.3038 1.0000 Regression: 1 Euro = + logmort regress euro logmort Source SS df MS Number of obs = 64 F( 1, 62) = 23.47 Model 15182.8029 1 15182.8029 Prob > F = 0.0000 Residual 40114.9587 62 647.015463 R-squared = 0.2746 Adj R-squared = 0.2629 Total 55297.7616 63 877.742247 Root MSE = 25.436 euro Coef. Std. Err. t P>t [95% Conf. Interval] logmort -12.4667 2.573551 -4.84 0.000 -17.61115 -7.322241 _cons 75.94532 12.36385 6.14 0.000 51.23034 100.6603 Regression 2: LGDP = + Euro regress lgdp euro Source SS df MS Number of obs = 64 F( 1, 62) = 46.74 Model 29.4781877 1 29.4781877 Prob > F = 0.0000 Residual 39.1035308 62 .630702109 R-squared = 0.4298 Adj R-squared = 0.4206 Total 68.5817185 63 1.08859871 Root MSE = .79417 lgdp Coef. Std. Err. t P>t [95% Conf. Interval] euro .0230885 .0033772 6.84 0.000 .0163376 .0298395 _cons 7.645092 .1165237 65.61 0.000 7.412164 7.878019 Regression 3: LGDP = + Prot Source SS df MS Number of obs = 64 F( 1, 62) = 72.82 Model 37.0420118 1 37.0420118 Prob > F = 0.0000 Residual 31.5397067 62 .508704946 R-squared = 0.5401 Adj R-squared = 0.5327 Total 68.5817185 63 1.08859871 Root MSE = .71324 lgdp Coef. Std. Err. t P>t [95% Conf. Interval] prot .522107 .061185 8.53 0.000 .3997999 .6444142 _cons 4.660383 .4085062 11.41 0.000 3.843791 5.476976 Regression 4: LGDP = + 1 Prot + 2 Euro+ 3 Colony Source SS df MS Number of obs = 64 F( 3, 60) = 37.28 Model 44.6335431 3 14.8778477 Prob > F = 0.0000 Residual 23.9481754 60 .399136256 R-squared = 0.6508 Adj R-squared = 0.6333 Total 68.5817185 63 1.08859871 Root MSE = .63177 lgdp Coef. Std. Err. t P>t [95% Conf. Interval] prot .3924896 .0671295 5.85 0.000 .2582106 .5267685 colony -.0517702 .1791656 -0.29 0.774 -.4101547 .3066143 euro .0134848 .003106 4.34 0.000 .0072718 .0196977 _cons 5.279895 .4016526 13.15 0.000 4.47647 6.08332 Read More