APPENDIX I. PROOF OF RESULTS
APPENDIX II. THE EQUILIBRIUM OF THE OPEN ECONOMY MODEL
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)| false Sachs, Jeffrey, and Xavier Sala-i-Martin, 1992, “ Fiscal Federalism and Optimum Currency Areas: Evidence for Europe from the United States,” in Establishing a Central Bank: Issues in Europe and Lessons from the U.S., ed.by ( Matthew Canzoneri, Vittorio Grilli, and Paul R. Masson Cambridge, Massachusetts: Cambridge University Press).
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“We are grateful to Robert Barsky, Susanto Basu, Junko Koeda, John Laitner, Gary Saxonhouse, Alonso Segura, John Thornton, and participants in several seminars for helpful comments and suggestions.”
In public economics, see Musgrave (1959) stressing stabilization as one of the roles the government should play.
For the imperfect risk sharing in the presence of asymmetric income shocks, see Asdrubali, Sorensen, and Yosha (1996). Cochrane (1991) also shows imperfect consumption insurance in his empirical research.
These studies measure the size of government based on various fiscal data, including government expenditure, tax revenue, and total transfer in percent of GDP.
Region-specific fixed effects and year-specific effects are often correlated with the government size and uncertainty of an economy, which makes statistical inference on the relationship between government size and uncertainty difficult in the previous cross-sectional studies that use the variance of GDP growth rates.
A shock to nominal exchange rate would be one of the most intuitive examples, which will affect export and import sectors in opposite directions while its total effect on aggregate income would be mitigated through aggregating these two sectors.
As in a simple Keynesian model, we assume the presence of underutilized factors of production. This approach is similar to Rodrik (1998), which relies on the variance-covariance structure of the components of GDP for each country.
The assumption on the covariance structure does not make any difference to the partial relationship between government size and the intensity of intersectoral fluctuation. It was adopted for clarity of exposition.
For example, the government spends x percent of the total government spending to sector i if sector i’s steady state income is x percent of the total steady state income of the economy. However, we can still have qualitatively same results with much weaker assumption that the proportion of government spending allocated to sector i,
For analytical convenience, we assume that the number of workers in each sector is constant over time.
There are many studies on the effects of government spending on real activities. In a macroeconomic perspective, distortionary taxes or government-expenditure programs are often pointed out as a primary source of real costs of government. For detail, see Barro (1990, 1991) among others.
The second-order condition suggests that
Due to the immobility of labor, factor price (wage) equalization does not hold in this model.
Actually, the condition that we propose in Result 5 is not the only case that supports the result. Unless the magnitude of shocks is extremely different and/or the intermediate good shares are drastically different in the two sectors, we will have a positive relationship between openness to trade and intersectoral fluctuation.
For most of oil-importing countries, oil price shocks are a good example of this type of shocks.
The demand for infant industry protection is greater especially when labor is immobile due to technical reasons.
Lagged measure of openness is often used to resolve the endogeneity problem of openness. However, as Rodrik (1998) mentioned, it does not fully get around this problem.
The first stage regression shows that sectoral productivity difference has a negative effect on openness to trade.
Rodriguez and Rodrik (2001) provide an excellent survey on this issue while maintaining a critical view.
Rodrik et al (2004) control for the quality of institutions using IVs that are recently developed by Acemoglu et al (2001). Their results indicate that the quality of institutions “trumps” everything else. In addition to the insignificance of trade, conventional measures of geography have, at best, weak direct effects on income once institutions are controlled for, although geographic factors have a strong indirect effect by influencing the quality of institutions.
The trend component of log of real per capita GDP is computed using Hodrick-Prescott Filter with a smoothing parameter of 400.
This exclusion provides an opportunity to check the endogeneity of INC and other useful information. First, if INC is endogenous, the 2SLS estimation of equation (7) with this exclusion will produce different but unbiased estimates. If estimates do not change much, INC would not be endogenous at least in our sample. Second, exclusion of a relevant variable causes misspecification problems in equation (8), but at least the primary variable of interest, ASY, does not seem to be correlated with INC. So we can still obtain useful information with the exclusion while checking its validity.
The Gini coefficients are from Government Financial Statistics (2001, IMF). The OLS regression uses 125 observations, and it controls year-specific and region-specific effects. A negative coefficient implies that more open economies tend to have more equitable income distribution.
The argument made for the identification of equations (7) and (8) implicitly introduces the following equation into the system of equations: OPNit = cj + yt +γ1·ASYit + γ2·POPit + γ3·LNDit + γ4·INCit + γ5·PRDFFit +υit where υ is error term.
All other measures of employment and output in STAN dataset produce qualitatively the same empirical results, while ‘total employment’ and ‘value added at current price’ provide more observations than any other measure does.
Community social and personal services, which are a part of government expenditure, are excluded in the calculation of intersectoral income fluctuation. The study also tested the sensitivity of the empirical findings with respect to the inclusion or exclusion of ‘community social and personal services’ and ‘mining and quarrying’. There were no significant differences in the empirical results.
We can reflect the industry-concentration index in the definition of PRDFF. However, it does not make any significant difference in the empirical results. It should be noted that in the construction of ASY, one of required conditions is the unbiasedness of ASY, which is satisfied by reflecting the concentration index.
The use of the deviation squared does not make any critical difference in the estimation results.
This study classifies 15 OECD countries into 5 regional groups: (1) Austria, Belgium, Netherlands, (2) Italy, Spain, (3) Australia, Canada, United Kingdom, United States, (4) Denmark, Finland, Norway, Sweden, and (5) Japan, Korea.
In our single equation estimation, three different covariance matrices of error terms were considered: homoskedastic (equivalent to IV approach), heteroskedastic, and within-country homoskedastic (but heteroskedastic across countries) covariance. However, this consideration does not contribute to significant differences in either the magnitude or the statistical significance of the estimates, so we report the standard errors based on the homoskedasticity assumption for brevity
This result is derived from the following calculation: (.11*.121)/.018=.739.
This result is derived from the following calculation: (.03+1.35*.03)*.288/.018=1.128
Since we expect the partial effect of terms of trade shock to be zero in a closed economy, the intercept of −0.69 in this equation may be puzzling. It is important to remember that this relationship can be the 1st order approximation of a nonlinear equation.
Our estimate 0.16 means that per capita government expenditure rises by (1+0.16/GOV) percent with a 1percent increase in per capita income, suggesting the elasticity of 1.45.
We can examine the effect of population density on government size, by imposing the restriction that the absolute values of the coefficients on POP and LAND are identical. All the estimation results of equations (7) and (8) hardly change by imposing this restriction. The coefficient on population density is .043 and it is highly significant.
A formal test for the relevance of IV approach, which is often called “Hausman Test,” is also performed. The test results confirm that the difference between OLS and IV estimates is statistically significant in all cases. The test statistics are available upon request.
As we can see from the high significance and the large differences between instrumented and uninstrumented estimates, the estimation results do not seem to suffer from the presence of weak instrument. In the case of equation (8), the F-statistic for first stage regressions is well above the threshold of 10 suggested by Staiger and Stock (1997), which is relevant when only one endogenous variable is included as a regressor.
Government size has a ‘negative’ influence on intersectoral fluctuation while the latter has a ‘positive’ effect on the former
The average EXP is 35.1 percent, which is the sum of NSTEXP (13.6 percent) and STEXP (21.5 percent).