# The Effect of Rainfall on Economic Growth in Thailand: A Blessing for Poor Provinces
## 4 Results and Discussion
### Main Economic Growth Results  
Table 2 examines how precipitation affects the GPP per capita growth. Table 2, Column 1 shows a negative statistically significant relationship between precipitation and growth on average across the 76 provinces. In Column 2, we interacted precipitation with a dummy for a province being \"poor\", which was defined as the region with the lowest GRP, with the first and the second regions being ranked; namely, the Northeast and North, respectively (Office of the National Economic and Social Development Board of Thailand, 2015). The interaction coefficient between the \"poor\" dummy and precipitation was positive and strongly statistically significant indicating substantial heterogeneity between the poor and rich regions. This evidence indicated that precipitation tended to affect poor  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Obs** & **Mean** & **Std. Dev.** & **Min** & **Max** \\ \hline Gross provincial product (GPP) per capita growth & 1596 & 0.023 & 0.067 & \(-\)0.39 & 0.43 \\ Agriculture per capita growth & 1596 & 0.013 & 0.110 & \(-\)1.65 & 0.86 \\ Industry per capita growth & 1596 & 0.020 & 0.139 & \(-\)0.96 & 0.80 \\ Service per capita growth & 1596 & 0.025 & 0.060 & \(-\)0.32 & 0.59 \\ Farming per capita growth & 1596 & 0.014 & 0.108 & \(-\)1.67 & 0.57 \\ Mining per capita growth & 1596 & 0.027 & 0.368 & \(-\)2.32 & 2.58 \\ Hotel per capita growth & 1596 & 0.048 & 0.219 & \(-\)1.35 & 1.45 \\ Public per capita growth & 1596 & 0.009 & 0.154 & \(-\)0.88 & 0.85 \\ Health per capita growth & 1596 & 0.051 & 0.120 & \(-\)1.72 & 1.12 \\ Population growth rate & 1596 & 0.006 & 0.012 & \(-\)0.06 & 0.15 \\ Annual average rainfall (100 mm/year) & 1596 & 1.295 & 0.665 & 0.29 & 5.39 \\ Annual average temperature (\({}^{\circ}\)C) & 1596 & 27.479 & 1.045 & 21.06 & 29.60 \\ \hline \hline \end{tabular} Note: All growth variables are in natural logs, except population growth rate.  
\end{table}
Table 1: Summary statistics of the variables.  
regions greater than rich areas. According to the coefficient value, the effect of a 100 mm variation in precipitation had an influence on the economic growth in poor regions greater than in rich areas by 3.37%. This implied that precipitation in the poor areas had more importance than rich areas in Thailand.  
Table 2, Column 3 added temperature as a control variable, which showed that controlling the temperature did not substantively affect the precipitation estimates. Column 4 then added population growth as a control variable with the results again showing that controlling population growth did not affect the precipitation estimates and also did not affect the interaction term between precipitation and poor dummy province with the poor coefficient remaining unchanged. This evidence suggested that poorer regions were the locus of substantial positive precipitation effects. All the results were robust since temperature and population growth were used as the control variables, which did not affect precipitation, and when considering the coefficient, it did not change across all the models. Finally, when comparing Columns 4 and 5, using different estimators, the results of the coefficient remained unchanged and the standard error (SE) of the FGLS estimator was less than the OLS (Ordinary Least Squares) estimator with a fixed effect at all specifications. This evidence showed that the FGLS estimation could solve the heteroscedasticity problem as well as had a lower SE than the OLS estimation, thus resulting in reliable results.  
The results of the point estimates in Table 2 for the overall models showed higher precipitation had reduced GPP growth rate, and a robust negative effect between average precipitation and GPP per capita growth in Thailand. By contrast, precipitation stimulated strong significant economic growth in poor provinces while temperature had a negative impact although this result was not statistically significant.