Relationship Between Income Level and Cost of Import Goods - Literature review Example

RELATIONSHIP BETWEEN INCOME LEVEL AND COST OF IMPORT GOODS By Name Course Instructor Institution Location Date Introduction Income is the opportunity to save and invest gained by different economic entities during a particular period represented in monetary units. Economic entities or groups include the government, firms, and the households (Barr 2004). Households are groups of people who share income with an aim of consumption. Income, for homes and individuals, is obtained by summing their salaries, wages, interests’ payments, profits, rents and other categories of earnings they receive over a particular period. The Paper Seeks To elaborate on the relationship between income level and cost of import goods. In other fields like public economics, the ability of monetary and nonmonetary consumption accumulation as what is referred to as income (Barr 2004). Imports are purchases of foreign goods and services by domestic consumers. The good or service is brought to the country across its border from the external country. The country from which the right or the service is imported terms them as exports. Exports and imports are the players in international trade financial transactions. Goods imported or exported are said to be limited by export or import quotas customs authorities’ mandates. Tax may or may not be attached to the imported or exported goods. Imports exchanged according to the trade agreements of the involved countries (Joshi 2007). Effect of income level on the cost of imports According to Paul and William (2004), increase in income increases the consumers’ wish of purchasing more services and goods. The services and commodities may be imports, and hence this increases the volume of goods imported. When income level goes up, foreign goods and services’ demand also increases. This can be best explained by the marginal propensity to import, which is the ratio of change in the volume of imports to modification in the level of income. For example, assuming that taxes and government purchases are fixed, mps is calculated by taking the reciprocal of the 1- marginal propensity to consume. Assuming a marginal propensity to consume of 0.7, the marginal propensity to import will be 1/ (1-0.7) which is equal to 3.33. Marginal propensity to the import of 3.3 means that increase in increase in income by 3.3 units will lead to one unit increase in a number of import goods. The increase of goods imported means that their quantity of the imports presents in a particular country will increase and as a result their prices go down. Therefore, the increase in the level of income enhances the volume of imports and finally lowers their cost (Wiley 2013). Other factors that affect the cost imports Laws and bans in concealed and assault weapons According to Lott and Mustard(1997), countries which have issued a law to ban concealed and assault weapons have lower crime rates than those which have not yet passed regulations restricting the use of concealed and assault weapons. Lower crime rate indicates lower cases of death rates maintaining the existing and average population. As a result, international trades which involve transactions of imports are favored and demand the goods increases. Due to the growing demand for the imports, their cost and price go up in the effort to meet the excess demand. Conversely, countries which have not issued a law to ban concealed and assault weapons have been described by Lott and Mustard as victims of high crime rates causing fear and tension among the people and also causing low population growth due to deaths associated with the crime. Low population means low demand for imports (Wiley 2013). As a result, producers of the imports reduce their expectations and begin to reduce their production; these leads to low cost of imports. Consumption Use affects the cost of imports through the income. The increase in consumption increases the marginal propensity to save and as a result marginal propensity to import increases increasing the volume of imports. The increase amounting to imports leads to a drop in the price and cost of the imports. (Paul & Williams 2004) Savings The increase in savings also affects the cost of imports through the income. The increase in savings increases revenue through the multiplier and as a result, the cost of imports goes up since an increase in income leads to increase in demand for imports. The decrease in savings leads to decrease in income hence reducing the cost of imports. (Paul & Williams 2004) Tax The decreases in tax increases disposable income among the domestic consumers and as a result demand for imports increases increasing the cost of imports. Conversely, the increase in tax decreases the level of disposable income reducing the demand for imported goods and as a result, their value goes down (Paul & Williams 2004). Transfer of payments The increase in the transfer of payments increases the level of income. The increase in the level of revenue leads to increase in the quantity of import goods demanded. Due to increased demand, their cost increase. The decrease in the degree of transfer payments leads to the decrease in consumption, and hence income reduces declining demand for imported goods which finally pushes up the cost of consumption goods (Paul & Williams 2004). Poverty According to Michael P. Todaro, 2005, poverty is a low level of income received by certain individuals in the economy. A high degree of poverty means that people’s purchasing power is diminished, and hence demands for imports decreases and their cost eventually decreases. The absence of poverty among the domestic consumers indicates higher demand for imports and as a result, their cost goes up. Econometric approach of the relationship between the income and cost of imports (Michele 2010 and Brussels 2015) Country Cost of imports year 2006 in billions of euro Mean national income year 2006 in millions of euro Ginni coefficients year 2006 poverty BG CR DK DE EE IE EL ES FR IT CY LV LT LU HU NL AT PL PT SI SK FI SE UK 307.2 174.3 400.1 300.1 100.3 307.7 200.4 275.4 175.4 175.7 200.4 54.7 77.3 500.4 796 307.5 25.33 173.2 100.4 960.7 5.3 254.3 253.3 396.3 27157 7252 33547 24511 5572 27275 17157 17311 21751 21442 17247 4713 5345 44355 5337 27257 25235 7152 13255 12357 4734 23574 23525 32727 0.32 0.33 0.35 0.42 0.40 0.47 0.37 0.37 0.37 0.37 0.40 0.43 0.40 0.40 0.40 0.44 0.44 0.43 0.33 0.43 0.33 0.41 0.40 0.40 Econometric approach of the relationship between the income and cost of imports (Michele 2010 and Brussels 2015) STATA output Dependent variable: cost of the imports, C Method: least squares Date: 227th of July, 2016 Sample: 24 Included observations: 500 C=k (1) + k (2) y + k (3) g where y is the mean national income level and g is the ginni coefficient. Coefficient Standard error t-statistics Probability k(1) 257.14541 2.317341 3.5711734 0.0013 k(2) 1.352273 0.154274 2.527001 0.0024 k(3) -0.003437 1.437127 3.241342 0.0047 R-squared: 0.773541 Adjusted R- squared 0.312450 Standard error of regression: 4.341273 Sum square residual: 54.435147 Log likelihood: -14.12751 Durbin Watson d statistics: 2.000173 Akaike information criterion .3531227 Schwarz information criterion: 5.475137 F statistics: 3.451342 Probability of F statistics: 0.002310 From the above information, the data presented in the table can be compressed into an algebraic model below: C= 257.14541 + 1.352273y – 0.003437g. Interpretation of the model One unit increase in the mean national income of a country yields 1.352273 units increase in the cost of the consumption goods. One unit increase in the ginni coefficient reduces the cost of consumption goods by 0.003437 units. Interpretation of standard errors The standard errors for the coefficients of y and g are below the values of their corresponding t-statistics. This means that, the null hypothesis that they are equal to zero is rejected favoring the acceptance of the alternative hypotheses that they are not equal to zero. This confirms that they are statistically significant. Therefore they are important policy variables and one can use them for forecasting purposes. (Stephen & Deirdre 2004) Interpretation of the F statistics and its probability The null hypothesis F = 0 is rejected when probability is less than the significance level. Here, the probability of F statistics is below 0.05 and hence, we reject the null hypothesis and conclude that the joint impact of y and g on c is statistically significant. (Hughes & Andrew 1997) Interpretation of the probability column from the table Reject the null hypothesis when the probability is more than the significance level and conclude that the variable is not good in policy making. The probability values in column five are less than 0.05 hence we conclude that the null hypothesis is reject and the two variables, that is, the mean national income level of a country and the ginni coefficient which is a measure of poverty are important policy making variables since they are statistically significant. (Hughes & Andrew 1997) Tests for multicollinearity According to Pesaro, 1990, multi-co-linearity is where the independent variables are highly correlated such that they can be expressed in terms of one another. This occurs when there is correlation among the explanatory variables breaking the assumptions of Classical regression models. The data analyzed above is cross sectional and it is important to test whether it is affected by severe multicollinearity. Several methods can be used to detect this problem in this data. 1) Use of simple correlation approach Correlation is the degree by which the involved independent variables associate. Correlation can give a positive or negative answer. The sign of the answer indicates the direction of the relationship while the size tells us about the strength of multicollinearity. The answer falls between zero and one. The closer it is to one, the stronger is the relationship and the closer it is to zero, the lesser the relationship. Correlation co-efficient of zero point eight tells us that there is severe co linearity. The correlation coefficient of y and g above is 0.7. This means that multicollinearity is no severe among the observations. 2) The determinant method We use the determinant of the standardized matrix to test the severity of multicollinearity problem. When the determinant is equal to zero, we conclude that there is dependency among the columns and multicollinearity is severe. When it is equal to one, we conclude that there is no dependency among the columns and multicollinearity therefore is not severe. The determinant of the standardized matrix obtained from the above data is 0. 996530 meaning that there is no multicollinearity since the value is approximately equal to one. 3) Use of variance inflation factor VIF for the parameters under estimation which is a reciprocal of 1- R squared can also be used to test whether multicollinearity is severe. If VIF is greater than 4 we do say that multicollinearity is severe but if less than four we say that there is no severe multicollinearity problem. Here, VIF= 4.4 which is approximately 4 and hence multicollinearity problem is not severe. The above three tests confirms that multicollinearity is not severe. Tests for heteroskedasticity Heteroskedasticity is evident in a data when the variance of the error term is not constant. When data is afflicted with this problem, the estimators of the unknown variables become biased leading to a wrong R squared, F and t values. The best way to tell whether a particular model is afflicted with heteroskedasticity is to check whether there is an omitted variable, check for the presence of outliers and also investigate whether the model is well specified. (Pesaro 1997) The model derived above has some omitted variables but that does not indicate that it is afflicted with this problem. The variables affect the dependent variable through the income. Income, y, in this model then stands for all the other factors which affect the cost of imports through the multiplier effect of the income. The model does not have outliers which cause different errors hence heteroskedasticity. Model specification is linear since there is a linear relationship between the cost of imports and the level of income and the cost of imports and the level of poverty. This confirms that there is no heteroskedasticity in the data used to come up with the model above. Test for autocorrelation According to Pearl, 2000: Autocorrelation is a situation where the error terms of the observations of different cross-sectional data are serially correlated. Covariance of the data should be equal to zero, otherwise the data has autocorrelation problem. Autocorrelation affects analysis of data in many ways: 1) Causes the estimates of the standard errors not to be unbiased hence hypotheses testing becomes unreliable. 2) Makes the estimators not to have minimum variance. First order autocorrelation is most common in cross sectional data and is caused by the following factors: 1) When the model’s functional form is faulty 2) When there is an omitted variable 3) Extrapolation or averaging of data 4) When the error terms’ functional form is faulty 5) When there is error in the depended variable To detect whether the above problems are present in a data, we use Durbin Watson d test. Durbin Watson used regression to test whether autocorrelation is evident in a particular model. They used autocorrelation relationship to test the hypothesis that correlation between error terms is equal to zero. If Durbin Watson d statistic is about two, it means that the data does not have autocorrelation and hence there is no correlation among the disturbance terms. If Durbin Watson d statistics lies between two and four, it means that there is negative autocorrelation. When the Durbin Watson d statistics falls below two, we do say that the data has a positive autocorrelation but autocorrelation is worst when the test statistic is zero. (Kennedy 2003) The data analyzed in this research paper has a Durbin Watson d statistics of 2.00173. This value is very close to two. Therefore the data does not have any form of autocorrelation. This statistic also confirms that, the model is in correct functional form, the functional form of the error terms is also correct; there is no averaging of data, no observational error in the dependent variable and no important variable that is omitted from the model. Absence of the violation of the three assumptions listed above confirms that the model fits data observation well. . (Lamer 1997) Conclusion According to Karen, 2015, cost of imports has a positive relationship with the level of income of a country. Increase in the level of income of a country increases the demand for imports creating excess demand which then pushes the prices and hence the cost of imports upwards. Decrease in the level of income of a country decreases the demand for imports and the producers’ expectations go down forcing them to cut down their production and lowering the prices hence the cost of their produce. This pushes down the cost of the imports. According to Michael, (2005), the cost of imports and poverty are negatively related. Here, ginni coefficient is used indicate the level income equality. Ginni coefficient falls between zero and one. The closer it is to zero, the lower the degree of income inequality and the closer it is to one, the higher the degree of income inequality. This means that, the higher the level of ginni coefficient the higher the level of poverty and hence the lower the cost of imports of a certain country. References Lamer, EL, 1997. Let’s take the con out of Econometrics. 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