# Significance: p-value or t-value?

I'm writing an economics thesis and I've found many econometric papers in which I found regression tables without the p-value. They only show the coefficient of the independent variable and its t-value, like in the example image.

My econometrics knowledge is very elementary, but I thought that the most important thing to evaluate the significance of a variable was the p-value. Searching on the web, I've found that sometimes $t-value>2$ is considered as a good value for significance. Is that right? Can I say that when $t-value>2$ the coefficient is statistically significant?

Edit: after Rob's suggestion, here's a link to the paper

• Why not give both? At the 5% level, a $t$ is strictly only significant for $|t|>2$ if the degrees of freedom is $>60$. – Glen_b Aug 21 '13 at 9:35
• That's why I don't understand these tables. Why do they give only the $t$ value and say that it's significant, even if they don't show the p-value – Luigi Aug 21 '13 at 9:47
• They give the number of observations, and the number of explanatory variables is (hopefully!) made clear, so you could work out the degrees of freedom, and hence the p-value. The critical value for 13 d.f. (the smallest one there if the listed variables are the only ones) is 2.16, so all but one of the coefficients that exceed 2 is actually significant. – Glen_b Aug 21 '13 at 9:53