Exam 2: Trade and Growth: the Empirical Evidence

Augmenting the regression equation derived from the production function, g_Y = a + b₁ g_K + b₂ g_L, by adding other possible explanatory variables in order to create the regression equation g_Y = a + b₁ g_K + b₂ g_L + b₃Z₁ + ... + b_n+2Z_n, the coefficients of the Z variables tell us the effect of the Z variables on:

All of the following types of empirical techniques have found asignificantly positive relationship between trade and growth except:

In using Leamer's sensitivity analysis, Levine and Renelt are trying to deal with what weakness(es) in statistical analysis?

The formal statistical procedure of regression analysis has several advantages over the simple visual observation method, including:

As of date, the most significant challenge to the positive empirical results linking trade and growth is the issue brought up by Rodriguez and Rodrik, namely that:

Regression analysis is useful in pointing out relationships among variables, but it:

In distinguishing exactly how trade impacts growth, Wacziag (2001) employs a simultaneous equations model and finds three significant channels through which trade impact growth. Which of the following is not one of those channels?

The average coefficient estimates for qualitative studies find a ___ relationship between openness and economic growth.

Hall and Jones estimates of the levels of technology across countries show that the levels of technology:

The Feder's (1982) model and findings suggest all of the following reasons for why trade has a positive impact on growth except:

The estimated value of a coefficient in a linear regression equation is statistically significant if it is:

Regression analysis is:

The following is an example of a linear regression equation:

In a statistical study where he tests 62 variables that potentially affect the rate of economic growth, Xavier Sala-i-Martin finds that:

Which of the following is not a way to deal with simultaneity bias in economic models?

The sources of growth equation decomposes the rate of growth of real output into:

Levine and Renelt run repeated regressions to examine the effect on economic growth of a large number of potential explanatory variables, and they find:

The Cobb-Douglas production function Y = AK^αL^1-α can be converted into the regression equation:

According to cross-section, time-series, and per-capita income regression summaries, a one-percentage increase in exports is significantly associated with roughly a _ increase in economic growth:

According to Barro's simple linear regression relating data on the levels of 1973 real per capita GDP and post-1973 rates of growth,