Let's аssume thаt we wаnt tо estimate the effect оf tech innоvation on individual wealth in a nation. We can measure individual wealth in a nation with GDP per capita (i.e., GDPcapita). We can measure tech innovation with the number of patents (i.e., Patent) or the number of science journal publications (i.e., SCJournal). In addition, we want to measure the Covid-19 pandemic effect (i.e., Covid) on individual wealth as well. Therefore, we construct the dataset as follows: Patent: the number of patents in a country GDPcapita : GDP per capita = GDP/population GDPgrowth: GDP growth rate SCJournal: the number of scientific journal publications HighTechExport: the share of high-tech export in GDP Unemploy: Unemployment rate Covid: 1 data comes from 2020 (during the pandemic); 0 data comes from before the COVID-19 pandemic period. Then, we conducted correlation test among variables as follows: we want to put both the patent variable and SCJournal variable as independent variables into a linear regression. However, the high correlation between patent and SCJournal variables (i.e., 0.87) can cause _______________. In this case, we cannot trust the p-value for the coefficients of independent variables in the sample linear regression; therefore, we usually drop one of the correlated independent variables in the linear regression model. Hint: A variance inflation factor (VIF) test is a statistical tool used to measure the amount of this problem in a regression model.