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
 A: I think that, in general, there is far too much concentration on p-values and significance and far too little on effect sizes, so I applaud the idea of showing tables without p-values. 
There are a number of problems with over-reliance on p values, but one of them is alluded to (almost in reverse) in @Rob 's answer, above: People who don't understand the model may rely on the p-value. This may give them a false sense of understanding.
The particular case that you show is a sensitivity analysis. Here, the use of p-values seems even less appealing than typically: The goal of a sensitivity analysis ought to be to see how much the parameter estimates change, not whether they change from "significant" to "non-significant"; the sensitivity analysis shows that democracies and non-democracies have quite different values on some parameters. That's important. The p-value of those parameters is much much less important.
A: With more complex models, it may be difficult or impossible to calculate p-values. In these cases, t-values are often given instead (if you can provide a link to the paper, we can have a look if p-values could have been calculated). An example are mixed models, where the absence of p-values is a topic of constant debate (see http://glmm.wikidot.com/faq for more information). However, if the model is simple enough to calculate reliable p-values, I personally would want to provide them to the readers - especially in applied areas where readers may have trouble judging the models without them.
I do agree with @peter-flom that ideally, the focus should be on effect sizes. However, in my experience the only way to convince an applied scientist that a result should not be considered significant is by simply saying "my calculations yielded that it is not statistically significant". When I suggested that the effect size was not e.g. biologically relevant, they usually were not very responsive ("but it's significant!"). So I would use p-values to convince an applied reader not to pursue a line of thought further without more evidence, rather than to use p-values to try to prove something meaningful is happening. And of course, the biggest effect size still is not enough if it might very well be pure chance.
