Constantin, Laura-Gabriela

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450  INSIGHTS ON DIVERSIFYING AGRICULTURE SYSTEMIC RISK ACROSS REGIONS – APPROACHING THE ROMANIAN CASE   Laura-Gabriela CONSTANTIN The Bucharest University of Economic Studies, Bucharest, Romania E-mail: constantinlauragabriela@gmail.com   Summary: The scope of the research is to reveal several insights on diversifying systemic risk across the eight Romanian development regions for the wheat culture. In order to pursue the scope of paper, the research is threefold and encompasses a series of results as follows: (1) tracking the specialized literature and based on crop yield data, there is developed an area yield index that resembles the payoff of a derivative contract; (2) further, by employing parametric and nonparametric measures, there is quantified the dependence of the payoffs across the eight development regions and (3) the investigation is complemented with cluster analysis for determining natural and statistically significant grouping of the Romanian development regions considering the values of the index. The main findings reflect mostly a low potential of diversifying risk at regional level.  Keywords: agriculture systemic/catastrophe risk, risk management, area yield index   1. Selective research grounds – short insights  There is generally acknowledged that, besides asymmetrical information (Skees and Reed, 1986), systemic risk is one of the most important impediments when establishing a market for private crop insurance (Miranda and Glauber, 1997; Duncan and Myers, 2000; Shen and Odening, 2013). Following these arguments, the specialized literature focused on investigating whether systemic risk stands as an obstacle for crop insurance for specific markets and on developing methods and tools for mitigating this particular risk (Wang and Zhang, 2003; Xu et al., 2010; Okhrin et al., 2013; Shen and Odening, 2013). Considering the importance of the agriculture sector in Romania, developing risk management tools for this sector could enhance capital allocation, at macro level, and even out the revenues of the SME and big companies, at micro level, while standing as a key component for pursuing corporate competitive advantages at EU level. Therefore, the present paper revolves around investigating the potential of diversifying systemic risk across the eight Romanian development regions for the wheat culture by employing an area yield index.  2. Data and methodology  The base sample considered within this research consists in wheat crop yield data collected from 1990 until 2013 for the eight Romanian Development Regions (North-West – VI, Centre – VII, North-East – I, South-East – II, South-Muntenia – III , Bucharest – Ilfov – VIII, South-West Oltenia – IV, and West – V). Following the specialized literature (Shen and Odening, 2013, 6), for each of these regions, there were determined area yield indices as a deviation of realized area yield from a strike level considered as the wheat long-term average of the area yield. The index resembles the payoff structure of a derivative contract (put option), being computed as a product between: (1) the maximum value between zero and the difference between the strike level actual area yield and (2) the tick value (the average of the annual minimum prices on the Romanian representative markets). In order to account for various DOI: 10.17626/dBEM.ICoM.P00.2015.p083 451  diversification potentials, the strike level was considered as follows: (1) at regional level (for each of the eight regions), at macro-regional level (for each of the four Romanian macro-regions) and at country level (national coverage).  Further, in order to check for the risk diversification potential, the analysis consisted in using dependence measures both through a parametric (Pearson Correlation) and a nonparametric approach (Kendall's tau_b and Spearman Rho) to account for the association of the payoffs across the eight development regions. The examination was consolidated by employing cluster analysis considering the long-term values of the mean and the standard deviation of the index as a proof of the diversification pattern. In order to attain the natural and statistically significant grouping of the Romanian development regions, the hierarchical cluster analysis was applied, considering the Squared Euclidian for distance measure and the Ward’s method.  3. Main findings and results   In Table 1, Table 2, and Table 3 there are displayed the results of the association analysis (for space reasons, there are presented the Pearson Correlation and Kendall's tau_b values, while the Spearman Rho values, similar to the former ones, are available at the author, on demand).   Table 1: Dependence analysis – regional approach Correlation Coefficients Region VI VII I II III VIII IV VPearson Correlation VI 1 ,762 ** ,421 * ,151 ,146 ,192 ,256 ,879 ** Kendall's tau_b 1 ,633 ** ,348 * ,295 ,248 ,325 ,309 ,826 ** Pearson Correlation VII ,762 ** 1 ,690 ** ,258 ,270 ,386 ,354 ,754 ** Kendall's tau_b ,633 ** 1 ,558 ** ,295 ,324 ,391 * ,507 ** ,649 ** Pearson Correlation I ,421 * ,690 ** 1 ,688 ** ,725 ** ,776 ** ,626 ** ,439 * Kendall's tau_b ,348 * ,558 ** 1 ,436 * ,370 * ,447 * ,524 ** ,332 Pearson Correlation II  ,151 ,258 ,688 ** 1 ,908 ** ,844 ** ,648 ** ,136 Kendall's tau_b ,295 ,295 ,436 * 1 ,700 ** ,690 ** ,558 ** ,281 Pearson Correlation III ,146 ,270 ,725 ** ,908 ** 1 ,957 ** ,847 ** ,094 Kendall's tau_b ,248 ,324 ,370 * ,700 ** 1 ,837 ** ,538 ** ,322 * Pearson Correlation VIII ,192 ,386 ,776 ** ,844 ** ,957 ** 1 ,836 ** ,147 Kendall's tau_b ,325 ,391* ,447 * ,690 ** ,837 ** 1 ,459 ** ,368 * Pearson Correlation  IV ,256 ,354 ,626 ** ,648 ** ,847 ** ,836 ** 1 ,284 Kendall's tau_b ,309 ,507 ** ,524 ** ,558 ** ,538 ** ,459 ** 1 ,426 * Pearson Correlation V ,879 ** ,754 ** ,439 * ,136 ,094 ,147 ,284 1 Kendall's tau_b ,826 ** ,649 ** ,332 ,281 ,322 * ,368 * ,426 * 1 Correlation is significant at the: ** - 0.01 level (2-tailed).; * - 0.05 level (2-tailed). Source: Authors’ contribution (developed in SPSS-IBM) 452  The results for the analysis developed at regional level (see Table 1) reflect that all the dependence measures are positive.  While, as expected, the highest dependence is between the indices for the Bucharest - Ilfov and South Muntenia Regions, the lowest one is between South Muntenia and West Regions. Lower association values (below 0.2) are registered between the following regions: South-Muntenia and North-West, South-East and North-West, South-East and West, Bucharest - Ilfov and North-West, and Bucharest - Ilfov and West. The results for the research developed at macro-regional level (see Table 2) highlight, however, a different state of facts as there are identified two negative correlation coefficients (through the parametric approach): for the South Muntenia and West Regions and for the Bucharest - Ilfov and West Regions. Though positive, lower association values (below 0.2) are recorded between the following regions: South-East and North-West, South-Muntenia and North-West, South-East and West, and South-West Oltenia and West. The nonparametric measures generally support the Pearson correlation results.   Table 2: Dependence analysis –macro regional approach Correlation Coefficients Region VI VII I II III VIII IV V Pearson Correlation VI 1 ,762**,420* ,149 ,144 ,209 ,313 ,630**Kendall's tau_b 1 ,668**,348* ,295 ,248 ,325 ,432 ** ,712**Pearson Correlation VII ,762** 1 ,690** ,258 ,270 ,396 ,413 * ,585**Kendall's tau_b ,668** 1 ,539** ,320 ,348*,415 * ,575 ** ,671**Pearson Correlation I ,420*,690** 1 ,687**,724**,770 ** ,635 ** ,367 Kendall's tau_b ,348*,539** 1 ,436*,370*,447 * ,447 ** ,467*Pearson Correlation II  ,149 ,258 ,687** 1 ,909**,843 ** ,642 ** ,106 Kendall's tau_b ,295 ,320 ,436* 1 ,700**,690 ** ,580 ** ,239 Pearson Correlation III ,144 ,270 ,724**,909** 1 ,955 ** ,830 ** -,031 Kendall's tau_b ,248 ,348*,370*,700**,837 ** ,571 ** ,181 Pearson Correlation VIII ,209 ,396 ,770**,843**,955** 1 ,809 ** -,022 Kendall's tau_b ,325 ,415*,447*,690**,837** 1 ,485 ** ,267 Pearson Correlation  IV ,313 ,413*,635**,642**,830**,809 ** 1 ,181 Kendall's tau_b ,432**,575**,447**,580**,571**,485 ** 1 ,398*Pearson Correlation V ,630**,585** ,367 ,106 -,031 -,022 ,181 1 Kendall's tau_b ,712**,671**,467* ,239 ,181 ,267 ,398 * 1 Correlation is significant at the: ** - 0.01 level (2-tailed).; * - 0.05 level (2-tailed). Source: Authors’ contribution (developed in SPSS-IBM)  453  The country level analysis (see Table 3) stresses a similar situation to the macro-regional one from two perspectives: (1) there are also insulated two negative correlation coefficients for the same pairs of development regions and (2) positive and low association values (below 0.2) are recorded between the same pairs of regions (however, there is also recorded such a value for an additional pair: Bucharest - Ilfov and North-West.  Table 3: Dependence analysis – country approach Correlation Coefficients Region VI VII I II III VIII IV VPearson Correlation VI 1 ,731** ,391 ,162 ,059 ,117 ,251 ,616**Kendall's tau_b 1 ,621**,449** ,324 ,272 ,345 * ,353 * ,598**Pearson Correlation VII ,731** 1 ,718** ,273 ,243 ,387 ,360 ,564**Kendall's tau_b ,621** 1 ,540** ,324 ,235 ,418 * ,486 ** ,567**Pearson Correlation I ,391 ,718** 1 ,684**,704**,763 ** ,612 ** ,345 Kendall's tau_b ,449**,540** 1 ,450** ,326 ,475 ** ,343 * ,290 Pearson Correlation II  ,162 ,273 ,684** 1 ,895**,832 ** ,669 ** ,152 Kendall's tau_b ,324 ,324 ,450** 1 ,710**,690 ** ,605 ** ,327 Pearson Correlation III ,059 ,243 ,704**,895** 1 ,956 ** ,833 ** -,046 Kendall's tau_b ,272 ,235 ,326 ,710** 1 ,832 ** ,599 ** ,198 Pearson Correlation VIII ,117 ,387 ,763**,832**,956** 1 ,819 ** -,045 Kendall's tau_b ,345*,418*,475**,690**,832** 1 ,507 ** ,182 Pearson Correlation  IV ,251 ,360 ,612**,669**,833**,819 ** 1 ,128 Kendall's tau_b ,353*,486**,343*,605**,599**,507 ** 1 ,432*Pearson Correlation V ,616**,564** ,345 ,152 -,046 -,045 ,128 1 Kendall's tau_b ,598**,567** ,290 ,327 ,198 ,182 ,432 * 1 Correlation is significant at the: ** - 0.01 level (2-tailed).; * - 0.05 level (2-tailed). Source: Authors’ contribution (developed in SPSS-IBM)  The main findings of the cluster analysis are presented in Figure 1. As displayed in figures 1.1 (regional approach), 1.2 (macro-regional approach), and 1.3 (country approach), according to the average values (mean) and volatility (standard deviation) of the wheat area yield indices, the eight development regions group in two main groups irrespective of the analysis level (a first cluster comprises the following regions: South-East, South-Muntenia , Bucharest – Ilfov, South-West Oltenia, while a second one encompasses: the North-West, Centre, North-East, and, West regions). This clustering pattern supplements the dependence analysis reflecting that systemic risk could be diversified between the western and centre regions, on one side, and the southern and eastern regions on the other. 454  Figure 1: Area yield index based clusters 1.1. Regional level strike          1.2. Macro-regional level strike            1.3. Country-level strike  Source: Authors’ contribution (developed in SPSS-IBM)  4. Concluding remarks  The association analysis reflects a rather low potential of diversifying risk across the regions for the wheat culture, the correlations being mostly positive. However, when varying the reference strike at macro-regional and country level, there is envisaged a week diversification opportunity which is enforced by the negative correlations between three regions (two from the southern part of the country, and one from the western one). In addition, the cluster analysis enforces the results of the dependence research as, on the long term, the regions are clustered in two groups reflecting a diversification prospective between the southern and eastern regions, on one side, and the western and centre regions, on the other. As further research, there could be considered other cultures as well as other dependence measures.  5. Acknowledgement  This work was cofinanced from the European Social Fund through Sectoral Operational Programme Human Resources Development 2007-2013, project number POSDRU/159/1.5/S/142115 „Performance and excellence in doctoral and postdoctoral research in Romanian economics science domain”   References  1. Duncan, J. and Myers, R.J. (2000): Crop insurance under catastrophic risk, „American Journal of Agricultural Economics”, 82(4), p. 842-855. 2. Miranda, M.J., Glauber, J.W., 1997: Systemic risk, reinsurance, and the failure of crop insurance markets. „ American Journal of Agricultural Economics”, 79(1), p. 206–215. 3. Okhrin,O., Odening, M. & Xu, W. (2013): Systemic Weather Risk and Crop Insurance: The Case of China, „Journal of Risk and Insurance”, 80(2), p. 351–372 4. Skees, J.R., Reed, M.R., 1986: Rate making for farm-level crop insurance: Implications for adverse selection., „American Journal of Agricultural Economics”, 68(3), p. 653–659. 5. Shen, Z. and Odening, M. (2013): Coping with systemic risk in index-based crop insurance, „Agricultural Economics”, 44(1), p. 1-13. 6. Wang, H. and Zhang, H. (2003): On the Possibility of a Private Crop Insurance Market: a Spatial Statistics Approach, „Journal of Risk and Insurance”, 70, p. 111-124. 7. Xu, W., Filler, G., Odening M. and Okhrin, O. (2010): On the systemic nature of weather risk, „Agricultural Finance Review”, 70(2), p. 267–284. 

Insights on diversifying agriculture systemic risk across regions – approaching the Romanian case

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