# The Effect of Rainfall on Economic Growth in Thailand: A Blessing for Poor Provinces
## 4 Results and Discussion
### Economic Sector Results
Considering the interaction between precipitation and the poor dummy province, the results showed that precipitation stimulated the agricultural sector in both of these poorer regions at the sector level (refer to Table 3, Panel A) and the subsector level (refer to Table 4, Column 1). This is because most farming income is generated from these poorer regions, which are also rather dry. This finding was consistent with previous studies, which found that rainfall had positive impacts on the economic growth of poor and arid countries that were reliant on rainfed agriculture ([PERSON] and [PERSON] 2015; [PERSON] 2012; [PERSON] 2014; [PERSON] and [PERSON] 2011; [PERSON] et al. 2018). In addition, the Mekong River is a major source of water for both northern and northeastern regions. Nevertheless, in 2010, both regions suffered the worst drought in 20 years due to the Mekong River falling to its lowest level for 50 years ([PERSON] 2011). This evidence supports the assertion that rainfall is highly beneficial to Thailand's poorer regions due to their limited water supplies. Footnote 1: Farming is defined as agriculture, hunting and forestry (refer to Table 4). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{6}{c}{Dependent Variable} \\ \hline  & **Agriculture** & **Industry** & \multicolumn{2}{c}{**Services**} \\ \cline{2-6}  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** \\  & **Farming** & **Mining** & **Hotel** & **Public** & **Health** \\  & **Growth** & **Growth** & **Growth** & **Growth** & **Growth** \\ \hline Precipitation (100 mm/year) & \(-0.0609\)*** & \(0.0997\)** & \(0.0620\)** & \(-0.0725\)*** & \(-0.0284\)* \\  & (0.0133) & (0.0474) & (0.0277) & (0.0156) & (0.0148) \\ Precipitation interact with & & & & & \\ Poor province dummy & \(0.0610\)*** & \(-0.0573\) & \(-0.120\)*** & \(0.0469\) & \(0.00800\) \\  & (0.0215) & (0.0762) & (0.0447) & (0.0253) & (0.0238) \\ Temperature (Celsius) & \(-0.0129\) & \(0.0345\) & \(0.0680\)** & \(-0.0173\) & \(-0.0136\) \\  & (0.0123) & (0.0441) & (0.0257) & (0.0145) & (0.0137) \\ Temperature interact with & & & & & \\ Poor province dummy & \(0.00665\) & \(-0.0525\) & \(-0.0646\)** & \(0.0252\) & \(0.0153\) \\  & (0.0121) & (0.0432) & (0.0251) & (0.0142) & (0.0134) \\ Population growth & \(-0.604\) & \(-1.925\) & \(-0.721\) & \(-0.132\) & \(-1.083\)*** \\  & (0.3216) & (1.1448) & (0.6690) & (0.3781) & (0.3568) \\ Constant & \(0.237\) & \(0.605\) & \(0.0630\) & \(-0.128\) & \(0.127\) \\  & (0.1843) & (0.6467) & (0.3836) & (0.2167) & (0.2045) \\ Observations & \(1596\) & \(1596\) & \(1596\) & \(1596\) & \(1596\) \\ Log likelihood & \(1367.865\) & \(-500.816\) & \(253.853\) & \(1121.597\) & \(1209.817\) \\ \hline \hline \end{tabular}  
* 1. Standard errors in parentheses; 2. Time and provincial fixed effects were included (coefficient not reported); 3.
* Significant at 10%, *
* Significant at 5%, **
* Significant at 1%, **
* Significant at 0.1%; 4. The results were reported only for subsectors where the effect of precipitation is statistically significant; 5. Subsector definitions (refer to Table 4).  
\end{table} 
Table 4: Economic subsector results. Sample: 76 provinces (1995–2015); Estimation method: FGLS. Secondly, precipitation was found not to impact the industry sector (refer to Table 3, Panel B) although when considering the four industry subsectors, only the mining subsector was positively influenced by precipitation. The evidence showed that a 100 mm rise in precipitation was to increase the mining growth rate by 9.97% (refer to Table 4, Column 2). Our hypothesis was that certain mining activities used rainwater during the production process; such as, during salt extraction. Thirdly, rainfall was found to have a negative impact on the service sector (refer to Table 3, Panel C) although certain subsectors of service were stimulated by precipitation. Ten subsectors were considered in total.