Good afternoon, I am an undergraduate student. I am trying to estimate the effects of natural disasters on agricultural product exports for the period 2000-2020 and I have 27 countries in my dataset and which form around 259 trading pairs between them. I have taken aggregated product export values from the BIMTS dataset provided by OECD, and the yearly disaster metrics are count variables from EMDAT. Aside from this, I also have some of the usual variables that are kept in gravity models, such as GDP of the countries, distance, RTAs etc.
I am using PPML model as it is recommended for trade data. For my model, I am mainly interested in the outcome for the natural disaster variables for exporters and importers. I have also filtered a number of major natural disaster variables based on criteria in relevant literature. I am mainly having issues with the coefficients that Stata is returning for the disaster variables in the models. I first ran the following command:
which returned the following result:
what is confusing me is the positive coefficients for number of disasters as it does not make any intuitive sense. Afterwards, I took importer and exporter fixed effects and pair-wise fixed effects as well using the following command:
Which had the following results
In this case, the coefficients for GDP became negative, and there were also some sign changes in the disaster variables. I also tried running the model with just the number of major disaster variables (excluding the overall number of disaster variables), and the results for that variable came out positive and insignificant. As a result, I am somewhat confused about what the underlying issue in my model, command or data could be. I would appreciate any sort of insight on this matter from experts. Thank you very much for your time. I am also sharing a sample of my dataset below:
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I am using PPML model as it is recommended for trade data. For my model, I am mainly interested in the outcome for the natural disaster variables for exporters and importers. I have also filtered a number of major natural disaster variables based on criteria in relevant literature. I am mainly having issues with the coefficients that Stata is returning for the disaster variables in the models. I first ran the following command:
Code:
ppmlhdfe ln_export_value num_of_dis_exp l.num_of_dis_exp l2.num_of_dis_exp num_of_major_dis_exp l.num_of_major_dis_exp l2.num_of_major_dis_exp l3.num_of_major_dis_exp num_of_major_dis_imp num_of_dis_imp ln_gdp_exp ln_gdp_imp ln_pop_exp ln_pop_imp ln_distcap outdegree indegree rta wto comlang_off comcol hegemony_relationship
Code:
PPML regression No. of obs = 4,921
Residual df = 4,899
Wald chi2(21) = 3020.22
Deviance = 11212.80631 Prob > chi2 = 0.0000
Log pseudolikelihood = -15929.8341 Pseudo R2 = 0.1383
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| Robust
ln_export_value | Coefficient std. err. z P>|z| [95% conf. interval]
----------------------+----------------------------------------------------------------
num_of_dis_exp |
--. | .0045309 .0013589 3.33 0.001 .0018675 .0071943
L1. | .0043782 .0014044 3.12 0.002 .0016256 .0071308
L2. | .0041736 .0013842 3.02 0.003 .0014606 .0068866
|
num_of_major_dis_exp |
--. | -.0018753 .0021146 -0.89 0.375 -.0060199 .0022693
L1. | -.004591 .0023506 -1.95 0.051 -.009198 .0000161
L2. | -.0057954 .0023962 -2.42 0.016 -.0104918 -.001099
L3. | -.004473 .0018278 -2.45 0.014 -.0080555 -.0008905
|
num_of_major_dis_imp | -.0008275 .0016083 -0.51 0.607 -.0039798 .0023247
num_of_dis_imp | -.0014102 .0009266 -1.52 0.128 -.0032263 .0004058
ln_gdp_exp | .0806123 .0043619 18.48 0.000 .0720632 .0891614
ln_gdp_imp | .0961636 .0038321 25.09 0.000 .0886527 .1036744
ln_pop_exp | -.0369827 .0056349 -6.56 0.000 -.0480269 -.0259385
ln_pop_imp | .0332621 .0053604 6.21 0.000 .0227559 .0437682
ln_distcap | -.1153456 .0068734 -16.78 0.000 -.1288173 -.1018739
outdegree | .0061965 .0003727 16.63 0.000 .005466 .006927
indegree | -.0015845 .0004009 -3.95 0.000 -.0023702 -.0007988
rta | .0170505 .0085625 1.99 0.046 .0002684 .0338327
wto | .1001379 .0196778 5.09 0.000 .0615701 .1387056
comlang_off | .1015086 .010186 9.97 0.000 .0815444 .1214728
comcol | .0344936 .0237892 1.45 0.147 -.0121324 .0811195
hegemony_relationship | -.0189402 .0176511 -1.07 0.283 -.0535358 .0156554
_cons | -1.195602 .1773664 -6.74 0.000 -1.543233 -.8479699
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Code:
ppmlhdfe ln_export_value num_of_dis_exp l.num_of_dis_exp l2.num_of_dis_exp num_of_major_dis_exp l.num_of_major_dis_exp l2.num_of_major_dis_exp l3.num_of_major_dis_exp num_of_major_dis_imp num_of_dis_imp ln_gdp_exp ln_gdp_imp ln_pop_exp ln_pop_imp ln_distcap outdegree indegree rta wto comlang_off comcol hegemony_relationship, absorb(exporter_id importer_id pair_id) vce(cluster dist)
Code:
HDFE PPML regression No. of obs = 4,921
Absorbing 3 HDFE groups Residual df = 300
Statistics robust to heteroskedasticity Wald chi2(17) = 103.21
Deviance = 6669.246421 Prob > chi2 = 0.0000
Log pseudolikelihood = -13658.05416 Pseudo R2 = 0.2612
Number of clusters (dist) = 301
(Std. err. adjusted for 301 clusters in dist)
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| Robust
ln_export_value | Coefficient std. err. z P>|z| [95% conf. interval]
----------------------+----------------------------------------------------------------
num_of_dis_exp |
--. | -.0005491 .0008931 -0.61 0.539 -.0022994 .0012013
L1. | .0004038 .0008164 0.49 0.621 -.0011962 .0020038
L2. | .0001799 .000816 0.22 0.826 -.0014195 .0017792
|
num_of_major_dis_exp |
--. | .0002221 .0013716 0.16 0.871 -.0024661 .0029103
L1. | .0000312 .0014169 0.02 0.982 -.0027459 .0028083
L2. | -.0001937 .0014434 -0.13 0.893 -.0030228 .0026353
L3. | .0003116 .0010321 0.30 0.763 -.0017113 .0023345
|
num_of_major_dis_imp | -.0034234 .0019005 -1.80 0.072 -.0071484 .0003016
num_of_dis_imp | .000843 .0005096 1.65 0.098 -.0001558 .0018417
ln_gdp_exp | -.073666 .0595342 -1.24 0.216 -.1903509 .0430189
ln_gdp_imp | -.2679774 .1121216 -2.39 0.017 -.4877317 -.0482232
ln_pop_exp | .7049349 .3080901 2.29 0.022 .1010893 1.30878
ln_pop_imp | 1.122893 .3021418 3.72 0.000 .5307059 1.71508
ln_distcap | 0 (omitted)
outdegree | .003352 .0015725 2.13 0.033 .0002699 .006434
indegree | .0005123 .0012268 0.42 0.676 -.0018921 .0029168
rta | -.0274514 .0169421 -1.62 0.105 -.0606574 .0057546
wto | .1950682 .0623619 3.13 0.002 .0728412 .3172952
comlang_off | 0 (omitted)
comcol | 0 (omitted)
hegemony_relationship | 0 (omitted)
_cons | -21.95647 5.045151 -4.35 0.000 -31.84479 -12.06816
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Code:
* Example generated by -dataex-. For more info, type help dataex clear input int year str3(iso3_o iso3_d) byte(comlang_off comcol hegemony_relationship num_of_major_dis_exp num_of_major_dis_imp) float(outdegree indegree ln_gdp_exp ln_gdp_imp ln_pop_exp ln_pop_imp ln_distcap ln_export_value) 2000 "ARG" "AUS" 0 0 0 0 0 4.161259 .467287 26.70468 27.4952 17.432196 16.761465 9.372289 12.50696 2001 "ARG" "AUS" 0 0 0 1 0 4.954784 .36824 26.65816 27.51449 17.443174 16.774303 9.372289 12.782493 2002 "ARG" "AUS" 0 0 0 0 1 4.37543 .431357 26.54335 27.55414 17.453869 16.785679 9.372289 15.13579 2003 "ARG" "AUS" 0 0 0 1 0 5.614964 .664061 26.62863 27.58393 17.464201 16.797182 9.372289 16.6069 2004 "ARG" "AUS" 0 0 0 0 0 5.949921 .585038 26.71473 27.62601 17.47434 16.807873 9.372289 14.39857 2005 "ARG" "AUS" 0 0 0 0 0 6.934795 .662563 26.79861 27.66058 17.484615 16.820045 9.372289 14.071252 2006 "ARG" "AUS" 0 0 0 0 1 6.278434 .769001 26.876 27.67981 17.494898 16.83354 9.372289 14.274624 2007 "ARG" "AUS" 0 0 0 0 1 10.481373 .927276 26.96354 27.7172 17.504808 16.851791 9.372289 16.364223 2008 "ARG" "AUS" 0 0 0 0 1 15.12983 1.062732 27.00179 27.75324 17.514938 16.87183 9.372289 15.582834 2009 "ARG" "AUS" 0 0 0 0 2 7.046975 .957076 26.941866 27.77078 17.525536 16.892439 9.372289 15.48392 2010 "ARG" "AUS" 0 0 0 0 3 12.42004 1.12896 27.038624 27.796535 17.536098 16.907995 9.372289 15.802192 2011 "ARG" "AUS" 0 0 0 0 1 16.01985 1.407277 27.096586 27.82164 17.546747 16.92189 9.372289 15.753852 2012 "ARG" "AUS" 0 0 0 0 0 15.847264 1.399465 27.086294 27.862133 17.557024 16.939348 9.372289 15.90519 2013 "ARG" "AUS" 0 0 0 1 1 15.52329 1.335947 27.110146 27.885664 17.566954 16.95656 9.372289 15.526612 2014 "ARG" "AUS" 0 0 0 0 1 11.49686 1.497158 27.08457 27.908653 17.57727 16.971476 9.372289 15.776915 2015 "ARG" "AUS" 0 0 0 0 1 11.272637 1.39542 27.11183 27.931126 17.587744 16.985868 9.372289 15.958762 2016 "ARG" "AUS" 0 0 0 1 1 12.623044 1.427795 27.089737 27.960325 17.597431 17.001488 9.372289 15.413207 2017 "ARG" "AUS" 0 0 0 0 1 11.991758 1.63659 27.11853 27.98168 17.606245 17.017956 9.372289 15.631765 2018 "ARG" "AUS" 0 0 0 1 1 11.104562 1.588034 27.091454 28.009457 17.614473 17.032915 9.372289 15.154465 2019 "ARG" "AUS" 0 0 0 0 2 15.508174 1.69718 27.070656 28.029797 17.621584 17.047691 9.372289 14.659573 2020 "ARG" "AUS" 0 0 0 0 3 14.567545 1.746344 26.96743 28.029797 17.62643 17.060024 9.372289 15.162526 2021 "ARG" "AUS" 0 0 0 0 1 19.196016 1.728934 27.067146 28.04973 17.62909 17.061434 9.372289 15.329987 2000 "ARG" "BGD" 0 0 0 0 2 4.161259 .667196 26.70468 25.14803 17.432196 18.717403 9.727705 16.23662 2001 "ARG" "BGD" 0 0 0 1 4 4.954784 .844089 26.65816 25.197554 17.443174 18.734074 9.727705 15.488054 2002 "ARG" "BGD" 0 0 0 0 2 4.37543 .771186 26.54335 25.23517 17.453869 18.749508 9.727705 12.1258 2003 "ARG" "BGD" 0 0 0 1 1 5.614964 1.123751 26.62863 25.281475 17.464201 18.764061 9.727705 9.103163 2004 "ARG" "BGD" 0 0 0 0 4 5.949921 1.292155 26.71473 25.328436 17.47434 18.777676 9.727705 16.769659 2005 "ARG" "BGD" 0 0 0 0 3 6.934795 1.214314 26.79861 25.396095 17.484615 18.790283 9.727705 18.097454 2006 "ARG" "BGD" 0 0 0 0 1 6.278434 1.624914 26.876 25.459465 17.494898 18.801893 9.727705 17.489979 2007 "ARG" "BGD" 0 0 0 0 4 10.481373 2.106431 26.96354 25.527287 17.504808 18.81265 9.727705 18.025837 2008 "ARG" "BGD" 0 0 0 0 1 15.12983 2.246869 27.00179 25.5908 17.514938 18.822636 9.727705 17.927904 2009 "ARG" "BGD" 0 0 0 0 2 7.046975 2.747937 26.941866 25.63592 17.525536 18.831953 9.727705 16.32062 2010 "ARG" "BGD" 0 0 0 0 2 12.42004 3.618653 27.038624 25.69308 17.536098 18.84072 9.727705 14.31912 2011 "ARG" "BGD" 0 0 0 0 2 16.01985 4.364283 27.096586 25.753704 17.546747 18.849804 9.727705 18.058853 2012 "ARG" "BGD" 0 0 0 0 3 15.847264 3.57821 27.086294 25.817017 17.557024 18.859388 9.727705 17.637295 2013 "ARG" "BGD" 0 0 0 1 1 15.52329 4.259815 27.110146 25.87656 17.566954 18.868773 9.727705 18.533525 2014 "ARG" "BGD" 0 0 0 0 2 11.49686 4.262775 27.08457 25.93275 17.57727 18.877884 9.727705 18.241816 2015 "ARG" "BGD" 0 0 0 0 2 11.272637 4.614795 27.11183 25.996265 17.587744 18.886822 9.727705 17.787113 2016 "ARG" "BGD" 0 0 0 1 2 12.623044 2.859848 27.089737 26.0656 17.597431 18.895746 9.727705 17.72174 2017 "ARG" "BGD" 0 0 0 0 2 11.991758 4.98801 27.11853 26.13044 17.606245 18.904255 9.727705 18.670645 2018 "ARG" "BGD" 0 0 0 1 0 11.104562 5.250591 27.091454 26.19973 17.614473 18.912464 9.727705 18.152012 2019 "ARG" "BGD" 0 0 0 0 3 15.508174 6.251867 27.070656 26.276226 17.621584 18.920929 9.727705 18.316277 2020 "ARG" "BGD" 0 0 0 0 2 14.567545 6.97281 26.96743 26.310514 17.62643 18.929293 9.727705 18.734407 2021 "ARG" "BGD" 0 0 0 0 2 19.196016 8.909783 27.067146 26.375755 17.62909 18.937443 9.727705 18.540874 2000 "ARG" "BRA" 0 0 0 0 1 4.161259 2.160713 26.70468 27.804974 17.432196 18.97467 7.755339 20.933893 2001 "ARG" "BRA" 0 0 0 1 1 4.954784 1.752217 26.65816 27.813343 17.443174 18.987705 7.755339 20.84301 2002 "ARG" "BRA" 0 0 0 0 1 4.37543 1.770926 26.54335 27.84613 17.453869 19.000118 7.755339 20.583223 2003 "ARG" "BRA" 0 0 0 1 1 5.614964 2.160287 26.62863 27.854164 17.464201 19.01192 7.755339 20.7335 2004 "ARG" "BRA" 0 0 0 0 2 5.949921 1.720203 26.71473 27.9162 17.47434 19.02322 7.755339 20.599745 2005 "ARG" "BRA" 0 0 0 0 0 6.934795 1.689548 26.79861 27.94583 17.484615 19.03418 7.755339 20.54539 2006 "ARG" "BRA" 0 0 0 0 1 6.278434 2.299382 26.876 27.98168 17.494898 19.044762 7.755339 20.881567 2007 "ARG" "BRA" 0 0 0 0 2 10.481373 3.137951 26.96354 28.04313 17.504808 19.054886 7.755339 21.12944 2008 "ARG" "BRA" 0 0 0 0 3 15.12983 4.126668 27.00179 28.094755 17.514938 19.064466 7.755339 21.29766 2009 "ARG" "BRA" 0 0 0 0 3 7.046975 3.038094 26.941866 28.088446 17.525536 19.073421 7.755339 20.85564 2010 "ARG" "BRA" 0 0 0 0 1 12.42004 4.226881 27.038624 28.16165 17.536098 19.08183 7.755339 21.08443 2011 "ARG" "BRA" 0 0 0 0 3 16.01985 5.293038 27.096586 28.202 17.546747 19.08997 7.755339 21.49753 2012 "ARG" "BRA" 0 0 0 0 2 15.847264 4.753578 27.086294 28.21881 17.557024 19.098085 7.755339 21.41978 2013 "ARG" "BRA" 0 0 0 1 1 15.52329 5.585747 27.110146 28.2516 17.566954 19.10619 7.755339 21.11085 2014 "ARG" "BRA" 0 0 0 0 2 11.49686 4.802975 27.08457 28.25696 17.57727 19.114254 7.755339 20.86071 2015 "ARG" "BRA" 0 0 0 0 1 11.272637 3.54652 27.11183 28.21881 17.587744 19.12217 7.755339 21.039684 2016 "ARG" "BRA" 0 0 0 1 0 12.623044 4.861575 27.089737 28.184906 17.597431 19.12979 7.755339 21.262035 2017 "ARG" "BRA" 0 0 0 0 1 11.991758 4.042827 27.11853 28.202 17.606245 19.137074 7.755339 21.16939 2018 "ARG" "BRA" 0 0 0 1 0 11.104562 4.115983 27.091454 28.21881 17.614473 19.14391 7.755339 21.327425 2019 "ARG" "BRA" 0 0 0 0 1 15.508174 4.196014 27.070656 28.22986 17.621584 19.150427 7.755339 21.30669 2020 "ARG" "BRA" 0 0 0 0 1 14.567545 4.017918 26.96743 28.196335 17.62643 19.15622 7.755339 21.134523 2021 "ARG" "BRA" 0 0 0 0 4 19.196016 5.230016 27.067146 28.24079 17.62909 19.160475 7.755339 21.53823 2000 "ARG" "CHN" 0 0 0 0 8 4.161259 6.428595 26.70468 28.671297 17.432196 20.956474 9.865993 20.09719 2001 "ARG" "CHN" 0 0 0 1 12 4.954784 6.952546 26.65816 28.749435 17.443174 20.96374 9.865993 20.56557 2002 "ARG" "CHN" 0 0 0 0 14 4.37543 7.131054 26.54335 28.83699 17.453869 20.97044 9.865993 20.071836 2003 "ARG" "CHN" 0 0 0 1 15 5.614964 11.70507 26.62863 28.933933 17.464201 20.97667 9.865993 20.968706 2004 "ARG" "CHN" 0 0 0 0 10 5.949921 16.70713 26.71473 29.02974 17.47434 20.982607 9.865993 20.96224 2005 "ARG" "CHN" 0 0 0 0 19 6.934795 18.242695 26.79861 29.139534 17.484615 20.98849 9.865993 21.331245
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