Hello everyone,

for my thesis i am currently investigating the effects of emissions on health on a regional basis. the dependent variable is bicategorical which takes the value 0 (if health is good) and 1 (if health is bad) with the exception of emissions and capita_gdp every variable is categorical:

here is an exemplary regression:

Code:
 
 probit health i.year i.region##emissions age educ smoker gender urban capita_gdp, robust  nofvlabel allbaselevels  Probit regression                               Number of obs     =     67,041                                                 Wald chi2(64)     =    5850.28                                                 Prob > chi2       =     0.0000 Log pseudolikelihood = -43026.965               Pseudo R2         =     0.0660  -------------------------------------------------------------------------------------                 |               Robust     health      |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval] --------------------+----------------------------------------------------------------            year |              1  |          0  (base)              2  |  -.0236149   .0290446    -0.81   0.416    -.0805412    .0333115              3  |  -.0552885   .0343119    -1.61   0.107    -.1225386    .0119615              4  |  -.7498958   .0521191   -14.39   0.000    -.8520474   -.6477442                 |          region |              1  |          0  (base)              2  |   .3424928   .1944582     1.76   0.078    -.0386383     .723624              3  |   .6631291    .343445     1.93   0.054    -.0100107    1.336269              4  |   1.005453   .1809361     5.56   0.000     .6508251    1.360081              5  |   .5202438   .2705144     1.92   0.054    -.0099547    1.050442              6  |    .853456   .2053275     4.16   0.000     .4510215     1.25589              7  |   -1.32784   1.329886    -1.00   0.318    -3.934369    1.278688              8  |   .2074103   .5587633     0.37   0.710    -.8877457    1.302566              9  |   .8778635   1.005655     0.87   0.383    -1.093184    2.848911             10  |    .614019   .2058646     2.98   0.003     .2105317    1.017506             11  |   1.103564   .2395228     4.61   0.000     .6341078     1.57302             12  |  -.9928198   1.189953    -0.83   0.404    -3.325084    1.339444             13  |   .2024027   .3014841     0.67   0.502    -.3884953    .7933008             14  |   .8510637   .1966648     4.33   0.000     .4656078     1.23652             15  |  -.4685238   1.062594    -0.44   0.659    -2.551171    1.614123             16  |   .1222191   .4271317     0.29   0.775    -.7149435    .9593818             17  |   1.777416   .9296525     1.91   0.056    -.0446694    3.599502             18  |   .7016812   .3960197     1.77   0.076    -.0745032    1.477866             19  |   .2164103   .2324297     0.93   0.352    -.2391436    .6719642             20  |  -.8683004   2.079837    -0.42   0.676    -4.944707    3.208106             21  |   .6094313   .1969787     3.09   0.002     .2233601    .9955025             22  |   .4586692   .2175369     2.11   0.035     .0323048    .8850336             23  |   .1376296    .316405     0.43   0.664    -.4825129    .7577721             24  |   .8800929   .2139805     4.11   0.000     .4606989    1.299487             25  |   .5008748    .181908     2.75   0.006     .1443417    .8574079             26  |   .7885192   .2055236     3.84   0.000     .3857004    1.191338             27  |   .8370192   .2066431     4.05   0.000     .4320061    1.242032             28  |   .0342872   .3383975     0.10   0.919    -.6289597     .697534                 |       emissions |   .2331187   .0475761     4.90   0.000     .1398713    .3263662                 | region#c.emissions|              1  |          0  (base)              2  |  -.1763598   .0473856    -3.72   0.000    -.2692338   -.0834858              3  |   .0902526   .3483855     0.26   0.796    -.5925705    .7730757              4  |  -.2545669   .0436166    -5.84   0.000    -.3400539   -.1690798              5  |  -.1903919   .0525988    -3.62   0.000    -.2934837   -.0873002              6  |  -.2595892   .0565328    -4.59   0.000    -.3703914    -.148787              7  |   .3660934   .3615611     1.01   0.311    -.3425534     1.07474              8  |  -.1810636   .0873587    -2.07   0.038    -.3522836   -.0098436              9  |  -.2360667   .2817683    -0.84   0.402    -.7883225     .316189             10  |  -.2362498   .0452001    -5.23   0.000    -.3248403   -.1476593             11  |  -.2986525   .0606014    -4.93   0.000    -.4174291    -.179876             12  |   .4210453   .4355456     0.97   0.334    -.4326084    1.274699             13  |  -.1393217    .063414    -2.20   0.028    -.2636109   -.0150324             14  |  -.2428271   .0452505    -5.37   0.000    -.3315166   -.1541377             15  |  -.1078827   .1281398    -0.84   0.400     -.359032    .1432667             16  |  -.1121361   .0991541    -1.13   0.258    -.3064746    .0822024             17  |  -.3670531   .1360779    -2.70   0.007    -.6337609   -.1003453             18  |   -.241021   .1572069    -1.53   0.125    -.5491408    .0670988             19  |  -.2128744   .0452858    -4.70   0.000    -.3016328   -.1241159             20  |    .103139   .4313025     0.24   0.811    -.7421983    .9484763             21  |   -.217597   .0532092    -4.09   0.000    -.3218851   -.1133089             22  |  -.1796928   .0509009    -3.53   0.000    -.2794568   -.0799288             23  |  -.1510797   .0529603    -2.85   0.004    -.2548799   -.0472795             24  |  -.2589344   .0509662    -5.08   0.000    -.3588264   -.1590425             25  |   -.231851   .0448358    -5.17   0.000    -.3197276   -.1439745             26  |  -.2411263   .0442314    -5.45   0.000    -.3278182   -.1544344             27  |  -.2452313   .0465597    -5.27   0.000    -.3364867    -.153976             28  |  -.0563099   .1191566    -0.47   0.637    -.2898525    .1772328                 |             age |   .1085835   .0049886    21.77   0.000      .098806    .1183609            educ |  -.1802489   .0107034   -16.84   0.000    -.2012272   -.1592707          smoker |    .080728   .0145963     5.53   0.000     .0521198    .1093362          gender |  -.2019473   .0145416   -13.89   0.000    -.2304483   -.1734463           urban |  -.1362217   .0112233   -12.14   0.000    -.1582189   -.1142245      capita_gdp |  -8.36e-06   .0000194    -0.43   0.667    -.0000464    .0000297           _cons |  -.4987429   .1638654    -3.04   0.002    -.8199132   -.1775726 -------------------------------------------------------------------------------------
My question is, how can I exactly interpret the coefficients of emissions and the interaction of region.c#emissions on the dependent variable ? To my understanding the coefficient of emissions for region 1 is the base level and the coefficient of emissions in region 2 is lower than region 1 by -.176 ?

Also, can I make inferences about the overall effects of emissions on health?