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UCSC ECON 104 - ECONOMIC GROWTH

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ECONOMIC GROWTHA. IntroductionB. Regressions: Growth rates1. Growth convergence2. More human capital implies higher growth rates3. Stability of property rights4. Fertility and population growth rate per capita.5. Government expenditures: good or bad?6. Cultural dummy variables7. Cross section or time series?8. Multiple regressionsC. Regressions: opportunistic empiricism1. Civil Liberties and Political rights2. FertilityECONOMIC GROWTH(Barro.dta)A. IntroductionEconomic theory does not have a lot of answers regarding economic growth and the answers do not tend to have empirical corroboration. The data sets tend to have few observations, so it is hard to come up with strong conclusions. Doing empirical work on macro variables often requires considerable ability to tolerate ambiguity. The Barro data set is particularly rich and offers a unique opportunity to test many far flung hypotheses. For cross country observations, this is an unusually large and comprehensive data set, yet it only has 114 countries and countries are not uniform commodities. This is considerably different from having thousands of observations of onestock price or hundreds of observations of wheat prices. So, in comparison to many microeconometric studies, the empirical results are never very persuasive and controversies remain unresolved.In this section, I follow a different format close to that of the Barro article. I consider various simple regressions first (each devoted to a different idea). Then at the end of the section on growth rates, I present the multiple regression that I would have chosen before looking at the data.B. Regressions: Growth rates1. Growth convergence-0 . 050-0 . 0250.0000.0250.0500.0750.0 2.5 5. 0 7. 5GD P 60Low income countries (other things being equal) should have higher growth rates since marginal productivity of capital is higher. That is, there should be convergence of countries over time. Past studies have not supported. this hypothesis. We can do a quick simple regression to get the flavor of such results (see the previous page for the diagram):scat(R) GR6085 GDP601If the relationship is positive, it says that countries with lower GDP in 1960 had higher growth rates from 1960 until 1985. The scatter diagram shows that there is only very mildsupport for this hypothesis.2. More human capital implies higher growth ratesThis is the endogenous economic growth model. A simple endogenous growth model is that human capital can be passed on to future generation (and not only to own children) with little cost or depreciation. Therefore human capital is under-invested unless government policy to subsidize education. If government policy promotes human capital, then there will be high growth. This says that countries with more education should growfaster. There is some evidence to support this argument. Remember that should have lagged values. That is, education rates in 1960 determine growth rates in 1970s and 1980s. Also we may want to control for quality of education. Again, we can initially try a simple regression such as the following:scat(R) GR6085 LIT601You also can just type 'scat' by itself, fill in the variable names, and click on regression.-0.050-0.0250.0000.0250.0500.0750.00 0.25 0.50 0.75 1.00GR 6085LIT60The scatter diagram shows some evidence of a positive relationship.3. Stability of property rightsIf you do not know whether investment will be returned to you because property rights are insecure, then you will not invest and there will be no growth. This explains why countries in revolution do not have high growth rates, but cannot explain differential between Japan and U.S. Again, it is useful to start with a simple regression:scat(R) GR6085 REVCOUP-0.050-0.0250.0000.0250.0500.0750.00 0.25 0.50 0.75 1.00 1.25GR 6085REVCOUPThe ocular test suggests that there is empirical support for this theory.4. Fertility and population growth rate per capita. Hypothesis: more people, lower growth rate per capita. Should you invest in more children or fewer children with higher productivity? We will not investigate this hypothesis.5. Government expenditures: good or bad? The a priori hypothesis depends on whether you are a Republican or a Democrat. May want to break down into defense and non-defense expenditures.genr GOVOTHER = HSGOV - GDE - GEETOTls GR6085 c GDE GEETOT GOVOTHERThis regression states that the growth rate of per capita GDP depends on the ratio of government expenditures on defense to GDP (GDE), the ratio of government expenditures on education to GDP (GEETOT), and the ratio of other government expenditures to GDP (GOVOTHER). The coefficients are expected to be negative for GDP, positive for GEETOT, and positive (negative) for GOVOTHER if you believe that government expenditures are efficient (inefficient).LS // Dependent Variable is GR6085Date: 6/6/93 / Time: 9:18 SMPL range: 1901 - 2018 Observations excluded because of missing dataNumber of observations: 98________________________________________________________________ VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.________________________________________________________________ C 0.0311348 0.0060915 5.1111623 0.000 GDE -0.0193424 0.0543865 -0.3556466 0.723 GEETOT 0.1327473 0.1196847 1.1091419 0.271 GOVOTHER -0.1317727 0.0332273 -3.9658007 0.000 ________________________________________________________________R-squared 0.148075 Mean of dependent 0.022032Adjusted R-squared 0.120886 S.D. of dependent 0.018518S.E. of regression 0.017363 Sum of squared resid 0.028337Durbin-Watson stat 1.796224 F-statistic 5.446134Log likelihood 260.2224The results are only very weakly supportive of the first two hypotheses.6. Cultural dummy variablesAFRICA, Latin America, ASIAN, pacific rim. Economists generally frown on the use of such data.7. Cross section or time series?This data is basically cross section.8. Multiple regressionsNow that we have some simple ideas we may want to combine them in a multiple regression:ls GR6085 c GDE GEETOT HSINVLS // Dependent Variable is GR6085Date: 6/6/93 / Time: 9:19 SMPL range: 1901 - 2018 Observations excluded because of missing dataNumber of observations: 98________________________________________________________________ VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL


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