interpreting correlative statistics table (Stata) 

My professor sent this table to me and I am supposed to interpret the results. However, I do not really know what exactly he actually computed (using Stata, which I don't have access to), I only got this table. I'm sorry if it seems like a very basic question. I assume that it is a regression analysis (student's t-distribution?) to determine the effects of "Reference Pricing" (a measure of pharmaceutical regulation) on the other variables stated.
Is this correct:
First (first row, first column), we check whether life expectancy is affected by Reference Pricing. Second (first row, second column), we check whether life expectancy is affected by Reference Pricing, even when share of 65+ years population is being controlled for. Third (first row, third column), we check whether life expectancy is affected by Reference Pricing when share of population of 65+ years and log GDP per capita is being controlled for.
Are my assumptions correct or am I completely off? What exactly is the row "constant" supposed to tell me?
Please help me interpret this, Once again: I'm sorry if it seems obvious or basic of a question; I'm a business student and have never really done this before.
 A: NB: None of this is for certain, but an educated guess based on seeing things like this many times. If you have truly never seen this before, this will probably make very little sense.
This looks like the output of estimates from a regression model:
$$\ln LE = b \cdot RP + c \cdot share + d \cdot \ln GDP + a, $$
where your professor reported the coefficients and the constant $a$ above the dashed lines. The stars next to them tell you something about their statistical significance (usually * for p<.05, ** for p<.01, and *** for p<.001). The numbers below the dashed line are most likely t-statistics (the coefficients divided by their standard errors). In each column, your professor is adding more control variables to show how the coefficient on reference pricing changes. You can find many guides on how to interpret these on-line or in your textbook. Here's one example.
I will focusing on the last column in everything that follows.
I am assuming that RP is a binary variable (RP is a form of defined contribution health benefit, where employers pay a fixed amount toward the cost of a specific health care service, and health plan members must pay the difference in price if a more costly health care provider or service is selected). When RP is in effect, life expectancy tends to go up by 1.27%, all else equal. This is a semi-elasticity.
The share variable is not logged, so the coefficient is also a semi-elasticity. For a 1 unit change in the share (which is a very big change assuming share is bounded by 0 and 1), you get a 0.629% change in life expectancy.
The GDP variable's coefficient can be interpreted as an elasticity, because both the outcome variable and GDP are both logged. So for a 1% increase in GDP per capita, you get a .0297% increase in life expectancy.
