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Climate Change Impacts on Yield and Financial Performance of Agro-Plantation Companies in Malaysia

In Malaysia, there is a declining trend in agricultural productivity and crop yields due to various climate events in the recent years. Therefore, this study aims to examine the impacts of climate change, especially El Nino and flood, on the financial performance of Malaysian agro and plantation firms. The study used a panel data set on 33 Malaysian agro and plantation firms listed in Bursa Malaysia for the period of 2003 to 2016. A panel of regression models including GMM, Pooled OLS, Random Effect and Fixed Effect were used to analyze the data. The results show that both the El Nino and flood have significant negative impact on the firms’ financial performance as measured by ROA and ROE. The findings indicate that climate change results in reduction of agricultural production which reduces revenue and consequently the profit of the agro and plantation firms. The study findings might help the firm managers as well as policy makers to take into consideration the environmental factors that affect the overall financial health of the firms and take appropriate adaptation and mitigation policies to climate change at firm level and macro level in the country.


Introduction
The performance of a firm might be affected by different factors. Climate change could be among the factors inhibiting productivity of the firms especially in agricultural sector. Climate is the statistics of weather of a country over the long period measured by the amount of precipitation, temperature, relative humidity, flood and drought (Alam, et al., 2017). The climate has been changing over time with a rapid change recorded in the recent years. For example, the world annual average temperature of 0.70 degree Celsius is more than what was recorded in both the twentieth and nineteen centuries (Kalra et al., 2007). The recent climate change such as flood and drought destructed the crops and reduced agriculture production (Ibrahim & Alam, 2016). Needless to say, however, a perfect temperature and rainfall enhance the growth of crops which increases their yield.
In spite of the significant contribution of the agriculture sector to the economic growth of many countries, it is affected by climate change damaging several crops and causing a decline in the farm produce. Evidently, the flash floods or hurricanes disrupt the agricultural activities. In addition, the drought caused by El Nino is a major threat to crops yield. Although, El Nino Southern Oscillation (ENSO) is a climate event that originates in the Pacific Ocean, it affects the global weather through droughts and floods (Kovats et al., 2003). The previous studies revealed that the El Nino is a recurrent weather phenomenon that takes place approximately every two to eight years and remains for twelve to eighteen months (Kovats et al., 2003;Moy, et al., 2002). Moreover, a number of studies had been conducted to assess the impacts of climate change on agricultural production (Aydinalp & Cresser, 2008;Bosello & Zhang, 2005;Collier, et al., 2008;Rosenzweig et al., 2002). The findings of these studies revealed that climate change has negative impacts on the agricultural production and crop yield. The decline in crop production could be one of the reasons of the declining firm's profitability.
Agriculture sector plays a significant role in Malaysian economy. On average, 10% of the country's total GDP comes from the agricultural production. Moreover, Malaysia is a leading exporter of palm oil and rubber around the world. However, in the recent years, there is a declining trend in agricultural productivity and crop yields due to various climate events such as flood, drought, El Nino and so on. In Malaysia, the increasing seasonal temperature related to El Nino from 2015-2016 caused a sharp decline in agricultural production (Kamil & Omar, 2016). It was reported that production in agricultural sector reduced from RM94.14 billion in 2015 to RM93.58 billion in 2016(UNDP, 2017. Decrease in agricultural production due to climate change might be one of the possible reasons of declining profitability and performance of the agricultural firms in the country. However, the direct impacts of climate change on financial performance of agricultural firms are not clearly known. To the researchers' knowledge, no study has been conducted yet to assess the relationship between climate change and agricultural firms' performance in the Malaysian scenario. Therefore, this study aims to fulfill the research gap by assessing the impacts of climate change on financial performance of agro and plantation companies in Malaysia. The study is the first academic attempt to identify the climatic factors that affect the financial performance of agro and plantation firms in Malaysia.

El Nino and Firm Performance
El Nino Southern Oscillation (ENSO) is a climate event that originates in the Pacific Ocean and impacts global weather. El Nino phenomenon is the most potential source of climatic variability (Berry & Kozaryn, 2008). It causes frequent droughts and floods (Kovats et al., 2003). El Nino could be a reason of less productivity in agro-based firms in a country. The study by Cashin, et al. (2017) found that El Nino has negative impact on real economic activity in South Africa, Philippines, Peru, Indonesia, Brazil, and Australia. On the other hand, it has positive impact on real economic activity in USA, Thailand, Singapore, Europe, Chile, China, Canada, and Argentina. Therefore, this study considers the following hypothesis: H1: There is statistically significant relationship between El Nino and firm's financial performance (ROA & ROE)

Flood and Firm Performance
Flash flood can occur suddenly and cause hazards such as landslides, damage to infrastructure, mud flows and even death (Collier, 2007). These hazards impact directly to the agricultural production and quality of the product and subsequently affects firm's performance. For example, the flood in the Yangtze basin adversely affected crops production and damaged land and houses; consequently, China faced huge economical losses (Piao, 2010). In Thailand, the 2011 flood affected 69 provinces of the country, and damaged 16,668.55 square km of agricultural area and 9,859 factories (Ministry of Agriculture and Cooperatives, 2012). Total damage and loss were estimated to be $1,318 m. in agriculture, livestock and fishery sectors (Poaponsakorn & Meethom, 2013). The study also reported that the 2011 flooding had a large negative impact on the farm profits of some middle-income households in the flooded provinces. In Bangladesh, the average loss in the national paddy crop was estimated to be nearly 6.4% per year due to floods, typhoons and droughts (FAO, 2011). Therefore, this study develops the following hypothesis: H2: There is statistically significant relationship between flood and firm's financial performance (ROA & ROE)

Leverage and Firm Performance
A good number of studies have been conducted to assess the relationship between leverage and firm's financial performance. Some studies found positive relationship between leverage and firm's financial performance while others revealed negative relationship between them. However, some other studies reported that leverage does not affect firm's performance. In other words, there is no impact of leverage on the financial performance of a firm. Clifford and Lindsey (2016) conducted study on S&P 1500 firms from 1996 to 2005. They found a positive and significant relationship between leverage and ROA. Similar findings were obtained by other studies (Davydov, 2016;Roy, 2016). The study by Kwong (2016) employed a sample of 680 Malaysian non-financial firms for the period of 2003-2012 and found positive and significant relationship between leverage and ROE. It indicates that firms with higher leverage can generate more profit. This finding is supported by a number of studies around the world (Castro et al., 2010;Azeez, 2015;Elyasiani & Zhang, 2015). (2014) conducted a study on 105 insurance companies in Romania using panel data from 2008 to 2012. They found that leverage is negatively associated with ROA. The negative result indicates that usage of leverage (i.e. financing firm's activities through debt capital) rather than issuing equity capital results in increased browning and magnifies the bankruptcy risk in the event of unexpected losses which causes reduction in firm's performance. Some other studies also found negative influence of leverage on ROA (Anderson & Reeb, 2003;Nguyen & Nguyen, 2015;Chang & Boontham, 2017;Lim, et al., 2017). On the other hand, a study on the firms listed in Indian Stock Exchange found significant negative relationship between leverage and ROE (Roy, 2016). Negative relationship between leverage and ROE suggests that increase in leverage tends to decrease firm's profitability and vice versa. Similar results were reported by a number of studies around the world (Sami et al., 2011;Mirza & Javed, 2013;Yu, 2013;Liu et al., 2015;Nguyen & Nguyen, 2015, Siddik et al., 2017Atan et al., 2018).

Burca and Batrinca
However, another study on all listed firms in India found that leverage is not a significant determinant of firm's financial performance measured by ROA (Chaudhuri et al., 2016). Some other studies also found that there is no significant linkage between leverage and ROA (Heffernan & Fu, 2010;Ekholm & Maury, 2014;Muhamed et al., 2014). Similar with this line, a number of studies revealed that there is no impact of leverage on the firm's financial performance measured by ROE (Heffernan & Fu, 2010;Ahmad et al., 2012;Muhamed et al., 2014;Zouari & Taktak, 2014). Based on the above discussion, this study considers the following hypothesis: H3: There is statistically significant relationship between leverage and firm's financial performance (ROA & ROE)

Firm Size and Firm Performance
Firm size is an important determinant of firm's profitability. Generally, large firms are more diversified, more able to utilize advance technology and well overseen. Therefore, it is hoped that the firm size can affect a firm positively and probably boost firm performance (Margaritis & Psillaki, 2010). However, previous studies reported mix findings about the relationship between firm size and firm performance.
Lewandowski (2017) conducted a study consisting of 1640 companies over the period of 2003-2015. The study discovered a positive and significant impact of firm size on ROA of the companies. Some other studies also found a positive and significant effect of firm size on ROA because big firms are well risk diversified, better in expenses management, and have complex information system (Hudaib & Haniffa, 2006;Burca & Batrinca, 2014;Nguyen & Nguyen, 2015;Nimtrakoon, 2015;Daher & Saout 2015;Clifford & Lindsey, 2016;Lim et al., 2017). Similar with this line, Siddik et al. (2017) examined the linkage between capital structure and performance of 22 Bangladeshi banks over a 9-year period (2005-2014) and found a positive relationship between firm size and ROE. Almost similar findings were observed by a number of previous studies (Castro et al., 2010;Mirza & Javed, 2013;Liu et al., 2015;and Nguyen & Nguyen, 2015).

Liquidity and Firm Performance
Liquidity is defined as the firm's ability to fulfill its short-term obligations. Liquidity is considered as availability of internal fund and an important factor of investment (Hoshi et al., 1991). Moreover, liquidity management is concerned itself with the allocation of how much wealth should be in hand and how much should be invested in alternative financial assets (Tobin, 1958). Thus, firm's liquidity level might be an important determinant of firm performance.
During the past few decades, numerous studies have been carried out to assess the impact of liquidity on the firm's financial performance. The study by Rachdi (2013) employed a sample of large number of Tunisian commercial banks over the period of 2000-2010 and revealed significant and positive relationship between liquidity and firm financial performance as measured by ROA. In Bangladesh, Rahman et al., (2015) investigated the determinants of bank profitability and revealed the similar findings. In line with these studies, Heffernan and Fu (2010) found that liquidity is significantly and positively related with ROE in the banking sector of china. Almost similar findings were observed by other studies (Rachdi, 2013;Rahman et al., 2015). The findings of these studies indicate that firms with high level of liquidity generate more profit.
On the other hand, Davydov (2016) examined the effect of public and bank debt financing on firm's financial performance. The researcher found a negatively association of liquidity with firm's ROA. Adams and Buckle (2003) also found a significant and negative relationship between liquidity and ROA. In line with these studies, Mirza and Javed (2013) and Gurbuz et al. (2010) identified that liquidity negatively and significantly affects firm's ROE. They argued that high liquidity means firm holding too much cash on hand that could make more profit if it was invested properly.

Data Collection
We conducted a retrospective secondary-data based study and used the annual data on agro and plantation firms in Malaysia for the period of 2003 to 2016. All the agro and plantation related firms in Malaysia are listed under the plantation sector in Bursa Malaysia. During the study period, a total of 43 companies were registered under the plantation sector. However, due to unavailability of long time series data of newly registered companies, this study took into consideration only 33 companies for the collection of data between 2003 and 2016. A panel data set of 462 firm-year observations (i.e. 33 plantation firms x 14 years data) were collected from DataStream and Bursa Malaysia. Data for the climatic events (i.e. El Nino and flood) were extracted from various sources such as Climate Prediction Center of USA, World Bank, Department of Statistics, Malaysia and Newspapers.

Measurement of Variables
Measurement of the various variables used in this study is discussed in the following subsections.
Financial Performance Variables: This study used accounting based measurements, mainly Return on Assets (ROA) and Return on Equity (ROE) to quantify the financial performance of the study firms. ROA and ROE have been widely used in the previous literatures to measure firm's financial performance (Burca & Batrinca, 2014;Ekholm & Maury, 2014;Chang & Boontham, 2017). ROA measures the overall effectiveness of a firm in generating profits with its available assets (Nimtrakoon, 2015). On the other hand, ROE measures the return earned on common stockholders' investment (i.e. equity capital) in the firm (Muhamed et al., 2014).

ROE = Net income ÷ Book value of the total equity
Electronic copy available at: https://ssrn.com/abstract=3901303 Climate Variables: This study used El Nino and flood to quantify the climate change. El Nino Southern Oscillation (ENSO) is a climate event originated in equatorial zone of the Pacific Ocean which affects the atmospheric circulation worldwide and is associated with droughts and floods (Kiladis & Diaz, 1989;Kovats et al., 2003). On the other hand, flood is a natural disaster which can cause extensive damages in a country, particularly the agricultural production. The flash flood occurs suddenly and causes hazards such as landslides, damages to infrastructure, mud flows and even death (Collier, 2007). For this study, El Nino and flood have been considered as dummy variables, where 1 indicates that the event happens in a particular year and 0 otherwise.

Control Variables:
This study also considered few control variables such as financial leverage, firm size and liquidity in the regression models. The leverage ratio is a measure of optimal capital structure of a firm based on total debt by total assets (Sami et al., 2011;Chi & Su, 2017). This study used total assets as a proxy of the firm size (Adams et al., 2005;Burca & Batrinca, 2014). Liquidity refers to how quickly a firm's asset or security can be converted into cash without losing real value of the asset. One of the basic measures of a firm's liquidity is current ratio which is calculated as current assets by current liabilities (Davydov, 2016;Rachdi, 2013;Muhamed et al., 2014).

Econometric Model
Initially, this study employed the pooled OLS (POLS), fixed effect (FE) and random effect (RE) models to test hypotheses of the study. However, all of these methods have some limitations. The POLS enacts that intercept and slop coefficient of all cross-sections are same and denies the heterogeneity that may exist among the entities (Bollen & Brand, 2010). The FE model assumes that the individual effect of is correlated with the predictor variable while the RE model assumes no correlation. Hence, the error term in RE becomes ( + ), whereby is the specific RE element for the group which is similar to except that with , for every group there is a single draw that is considered in the regression identically for each time (Wooldridge, 2006;Gujarati & Porter, 2010).
Furthermore, due to the limitations of the panel static models of POLS, FE and RE, this study used the system generalized method of moments (GMM) model developed by Arellano and Bond (1991) to address the potential endogeneity problem commonly found in the dynamic panel models. The panel data sets normally exhibit the characteristics of both heteroskedasticity and autocorrelation. Thus, application of suitable instruments for the regressors can be a way out to the problem. Hence the system GMM model employed for estimation in this study is capable of overcoming the limitations of the panel static models as well as handling both the endogeneity and serial correlation problem found in most of the panel models including the FE and pooled OLS models.
The Operational models of this study is: Moreover, to find the best fit model from POLS, FE and RE, this study relies on Haussmann specification test. In case of GMM, the system GMM combines a set of equations in the first difference with suitable lags as instruments. Therefore, the validity of the instruments used can be evaluated using the Sargan test of over-identifying restrictions while the Arellano-Bond test of AR(2) displays the presence/absence of the second order autocorrelation in the model.

Correlation Analysis
The correlation matrix shows that only firm size is positively correlated with ROA and ROE (Table 1). However, leverage, liquidity, El Nino and flood are negatively correlated with ROA and ROE.

Regression Analysis
Among the diagnostic test to determine the best fit model, the post estimation results of the system GMM explain the Sargan test of over-identifying restrictions and the test of second order serial correlation ( Table 2). As presented, the probability values of the Sargan tests imply that the null hypothesis of valid instruments cannot be rejected. Similarly, the AR(2) probability values show the absence of second order serial correlation of the error term suggesting the system GMM would be a consistent model. Moreover, based on the Haussmann test, fixed effect (FE) model was found the appropriate model out of POLS, FE and RE. Therefore, this analysis only reported the output of FE and the system GMM. Table 2 shows that El Nino has a negative and statistically significant impact on the ROA and ROE based on GMM model, but no significant effect under FE model. The findings suggest that climate change affects negatively the firms' profitability as measured by ROA and ROE. The finding of the present study is in line with the results of Cashin et al., (2017) where El Nino was identified as an important cause of decrease in the farm produce. Similarly, the flood has highly negative and significant impacts on both ROA and ROE under the GMM and FE model. Piao et al, (2010) also revealed the similar finding. It indicates that irrespective of the indicators of firm performance (i.e. ROA or ROE), flood causes an adverse effect on farm produce. Therefore, overall it can be inferred that climate change results in reduction of agricultural production which reduces revenue and consequently the profit of the agro and plantation firms. The study revealed that firms' characteristics also have impact on the performance of the agro and plantation firms in Malaysia (Table 2). It was found that the relationship between liquidity and the indicators of firm performance (i.e. ROA and ROE) is negative and statistically significant under the GMM model while it is negative but not statistically significant under the FE model. It indicates that holding high volume of liquidity reduces the return of the firm. The negative impact of liquidity on firm performance goes in line with the findings of Davydov (2016) and Mirza and Javed (2013). However, the study revealed positive impacts of firm size on firm performance using the GMM model. A number of previous studies also found positive relationship between firm size on firm performance (Abiodun, 2013;Doğan, 2013;Sritharan, 2018). In addition, the impact of financial leverage on ROA and ROE were found to be negative and statistically significant under the GMM and FE model. The negative impact of financial leverage on the firm performance was also reported in the study by Clifford and Lindsey (2016). The findings indicate that the firms that are highly leveraged have lower chance to earn sufficient return to meet their financial obligations.

Conclusions
The present study aims to examine the impacts of climate change on the financial performance of Malaysian agro and plantation firms. The results show that the climate change variables (i.e. El Nino and flood) have a significant impact on firm's financial performance measured by -0.361 -0.208 Note: The values in parentheses are standard errors and the notation ***, ** and * denote significance level at 1%, 5% and 10% respectively ROA and ROE. The negative impacts of flood and El Nino imply that climate change has an adverse effect on agricultural production and subsequently on revenue and profitability of the firms. The study also revealed a significant relationship between firm's characteristics (such as financial leverage, firm size and liquidity) and firm's profitability (i.e. ROA and ROE). Therefore, it can be concluded that both the climate change and firm's characteristics affect significantly the financial performance of the agro and plantation firms in Malaysia.
To our knowledge, no study has been conducted yet to assess the impacts of climate change on financial performance of Malaysian agro and plantation firms. Therefore, the present study might be considered an important contribution to the existing literature. Moreover, the findings of the study would be beneficial to the top-level management of the agro and plantation firms to identify the factors that affect the financial performance of their firms. Therefore, the managers can be cautious about the environmental factors that might affect the overall financial health of the firms and can take proper decision and apply corrective measures to enhance the firm's profitability.
Despite a significant contribution to the existing literature, this study has several limitations which create the scope for future research. Firstly, the study was limited to 33 Malaysian agro and plantation firms listed in Bursa Malaysia due to unavailability of data of a number of public listed plantation firms in the country. Therefore, further research can be conducted by including the private plantation firms which might increase the number of observations as well as acceptability of findings. Secondly, the study is confined to only the Malaysian agro and plantation firms. Thus, future research can be carried out in other countries of the South-East Asian region which might broaden the scope of the study as well as generalize the study findings. Finally, future research can be carried out to include other climatic factors to find out other possible determinants of performance of agro and plantation firms in the country.