Effect of Culture on Economic Outcomes - Assignment Example

Applied Econometrics Question By first regressing the equation ln (y) = a + bx + cx2 + dz + e.ln (w) Ẏ = β0 + β1x + β2 x2 + β3z + β4 ln(w) Assuming N β0 = ln (y)/N – (β1 + β2) Ẍ + β3Ẑ + β4 ln(w)/N d ln(y)/dx = β1 + β2x d ln(y)/dz = Zβ3 + β4 ln (w) d ln(y)/d ln(w) = β4 The change in each of the variables in the right hand side would have a positive direct effect on the left hand side i.e Y. Upon the regression, the resultant relationship between the variables and the regressed is positive hence, indicating that a fixed effect on the data. Question 2 a. In the performance of the regression equation, it is essential to consider the assumptions of the ordinary least square (OLS). One such key assumption is the assumption of zero conditional mean. As a critical assumption, it would be argued to imply that there is no omitted variable bias and that the simultaneity problem does not exist. In typical situations, the prospect is that a regression would deliver consistent or unbiased estimates if the zero conditional mean assumption holds (Jones, 2008.p.201-211). However, on the contrary if a biased estimate or relationship is delivered, then the regression would be considered as having not been under the zero conditional mean assumption. Therefore, in a regression equation in which the coefficient of wealth is found to be positive, the reason for getting biased estimates would be because the zero conditional mea assumption failed to hold. As is noted in the case, the expectation of an exogenous rise in the level of wealth (meaning positive), would result in a decrease or decline in the number of hours that one has to work (Wooldridge, 2011). b. The use of instrumental variables estimation is always an important way of trying to remedy the problem of omitted variable bias. Therefore, it can be designated as a good way for satisfying assumptions in the independent variable. In the given case of a dummy variable D having two key independent variable assumptions, it would be essential to refer to D as good due to the following reasons. First, D as an instrumental variable can be stated fairly simply given that its main ingredients are the two key assumptions which have been applied in the derivation of the independent variable estimators. Subsequently, considering the identification problem that is always experienced in regression equations, it would appropriate to consider the dummy variable as a good instrumental variable because it is correlated to the variables wealth and labor supply and uncorrelated to the error term (u). In a real sense, running a normal regression in the setting set in the study of only the labor supply and wealth variables, plus an error term, would likely lead to an upward generation of biased estimates of the true effect of the labor supply on wealth of individuals. However, with the two assumptions of D being uncorrelated to the error term and correlated to wealth, a person would likely take up more labor hours so as to increase their wealth status. In an equation form, this can be exemplified as: Wealth = β0 + β1 (Labor supply) + error term (u) Let y = wealth; and X = labor supply y = β0 + β1X + u D satisfies the two key assumptions in the sense that it applies the two in the evaluation of the regression equation without having to render the regression estimation as being biased; thus failing to meet up with the zero conditional mean assumption (Fox & Fox, 2008.p.145-153). In running two reduced form regression forms to obtain causal effects of wealth on labor supply in this case, since wealth had been indicated as exogenously rising, D is endogenous hence, can be excluded from the main equation of interest (Asteriou & Hall, 2011.p.125-135). In the first reduced form, it is the endogenous variable that will be regressed on the instrument. Putting a focus on the covariance of D and y; cov (D, y) = cov (D, β0 + β1X + u) = β1cov (D, X) + cov (D, u) Given that the OLS estimation delivers on consistent estimates of π, and since D is uncorrelated with u, X = π0 + π1D + u. In the second reduced form, y = α0 + α1D + w; in this case, the OLS is noted to have delivered a consistent estimate of α1 in which D is uncorrelated to the w. In establishing the help of the reduced forms, β1 = δy/δx = δy/δD x δD/δx the equation above now provides the indirect form of the least squares. When run together as a result of the independent variables, X = π0 + π1D + u. with π1 > 20. In the second form, y = α0 + α1D + w suggesting that an increase in the labor supply will result in increased wealth for those individuals who have won big in the lottery; thus, α1 > 0 With the estimates from these reduced forms, the instrumental variable effect for wealth on labor supply would be β1 = π1/ α1. PART B a. The Effect of Culture on Economic Outcomes The author uses the data on immigrants’ entry to reveal their impact on the economic factors. The view is linked to the culture that the immigrants impose on the taste, preferences and priorities on the economy. The differences between the cultures in the region will imply different buying and selling of products in the markets (Fernández, 2006). In analyzing the impact of culture on economic outcomes, the author categorically seeks to answer the question on the impact of immigrants on the economic outcomes of the country. The outcomes of the empirical research reveals that the impact of the immigrants are portrayed in the imposed tastes beliefs, priorities and backgrounds to affect the buying, selling and an entire economy of the region. b. Positive relationship Figure 4 gives the evidence on the impact and role played by the country of origin. The country of origin affects the tastes and preferences of the people in the understanding of the cultures and economic development in the long run (Fernández, 2006). This was linked to the possibility of the influences that female born in countries where the level of LFP in the 1990 tended to work less in the US in 1970. The position of the workers’ level within the females tends to be of a direct link to the cultures of the countries of origin. The regression results provide the information on different issues associated with cultures as affecting the markets. Therefore, there is a positive relationship between female LFP in the parents’ origin country and the LFP in the second generation of women of women in 1970 (Fernández, 2006). c. Interpretation of elasticity In table 2.2. the elasticity rate is at 0.056. This indicates a value for education for women by a positive rate of the same amount. As such, being that the education variable is a missing variable, it could result in the regression model being biased thus violating the zero conditional mean assumption of unbiasness. Particularly, there would be an upward biased estimate in the column (i) of the table thus, an indication that the source of bias in the case may be a real case of concern. For instance, with the female level in the 1990 year being elastic at 0.056, this indicates that a lower estimation would result in the regression equation taking on a fixed effect nature. d. The Effect of GDP on working Hours The GDP impacts directly the number of labor forces of the subsequent generations of the females whose countries of origin determines the level of hours worked. The effect of the GDP in the countries of origin should be considered to have effects on the level of work on female LFP in the US. This is because the GDP affects the wages of the workers, which is as well influenced by the microeconomic developments and changes in the tax systems. The changes in the countries of residence may have positive or negative changes in the family status, school, pension ages and acquisitions and spousal income (Fernández, 2006). All these affect the need to work in different countries. The data on medication is also affected by the GDP and the impact of the country of origin underlines all the possible changes in the working hours of the female immigrants. e. The inclusion of the SMSA The inclusion of the SMSA in the analysis of the data in the regression is used to include the possible effects of the social disorders in the regression analysis. That is the variables in the explanation of the constancy in the factors compared in the regression. The importance of including the other possible factors is to affect the outcome in the female LFP; thus, create a constant out of the possible effects of these factors to affect the outcomes of the tested variables (Fernández, 2006). The levels of crimes in the city within the given states are factors that influence the level of working hours. This is linked to the possibility that crimes lessen the affluence of the persons in these regions and would motivate females employed to work for longer hours. The inconsistency of employment in the cities where the statistics were done or the level of unemployment in these regions contributes to the level of working hours. The moment one gets some job on contract; she tries to maximize the time spent to do the work. This is intended to increase the amount of wages gotten from the contract since the uncertainty of when to get the next job would be untold and unrevealed. These factors are included in the regression analysis to help in getting the most realistic relationship between the working hours and the impact of the culture of the country of origin. f. The Weaknesses of the Analysis The evidence provided in the work provides to a little extent the impact of culture on the female LFP. The weaknesses related to the work are; the author did not include the possible effects of the external economic factors like the GDP, interest rates, inflation and availability of resources as probable factors to affect female LFP. The conditions, terms of employment and the wages of the females are not included as having possible effects in the female LFP. The author did not include all the possible factors that may affect the female LFP; thus, the analysis is not that perfect for the claims of the impact of culture on female LFP (Fernández, 2006). References List ASTERIOU, D., & HALL, S. G. (2011). Applied econometrics. Basingstoke [England], Palgrave Macmillan. FERNÁNDEZ, R. (2006). Women, Work and Culture. New York City, CEPR, NBER. FOX, J., & FOX, J. (2008). Applied regression analysis and generalized linear models. Los Angeles, Sage. JONES, A. (2008). Applied econometrics for health economists: a practical guide. Abingdon, Radcliffe. WOOLDRIDGE, J. (2011). Introductory econometrics. [S.l.], South-Western. Read More