Climate change and income diversification in the mekong river delta a panel data analysis

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM –THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS CLIMATE CHANGE AND INCOME DIVERSIFICATION IN THE MEKONG RIVER DELTA: A PANEL DATA ANALYSIS BY NGUYEN THI TUYET NGA MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, December 2016 UNIVERSITY OF ECONOMICS HO CHIMINHCITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS CLIMATE CHANGE AND INCOME DIVERSIFICATION IN THE MEKONG RIVER DELTA: A PANEL DATA ANALYSIS A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN THI TUYET NGA Academic Supervisor: PHAM KHANH NAM HO CHI MINH CITY, December 2016 ACKNOWLEDGEMENT I would first like to thank my thesis supervisorDr Pham Khanh Nam of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics He consistently allowed this paper to be my own work, but steered me in the right direction whenever he thought I needed it I would like to express my gratitude to the VNP officers who were involved in mythesis processby updating thesis schedule and providing good conditions for my research process Without their passionate participation, the thesis process could not have been successfully conducted Finally, thanks are also due to my classmates for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them Thank you Nguyen ThiTuyetNga Ho Chi Minh City, December 2016 Page i ABSTRACT The main objective of this study is to analyze the impact of climate change and other socio – economic determinants on the income diversification strategy in Mekong River Delta The data set is drawn from the VHLSS 2010,2012 and 2014, while climatic data including temperature and precipitation are extracted from the statistics website of The Ministry of Agriculture and Rural Development (MARD) Salinity data is collected from the Vietnam Institute of Meteorology, Hydrology and Climate Change.Findings of the study show that farmers have tendency to diversify their activities to reduce risk of crops failure when there is increasing temperature in dry season and precipitation in wet season However, it is recognized that those relationships are non-linear Diversification behavior is discovered not to be sensitive with the salinity intrusion and other climate variables Regarding socio-economic determinants, the household labor ratio and land area holding are found to be positively correlated with the income diversification, while educational qualification has the negative effect A male household head would more likely to diversify their activities to disperse risk of climate change than female head From this result, many policies are recommended in order to support farmers to access an effective diversification strategy, helping them to response actively to climate change consequences Page ii TABLE OF CONTENT Chapter Page Acknowledgement i Abstract ii Table of content iii List of tables v List of figures vi Introduction 1.1 Research problem 1.2 Research objective 1.3 Research scope .3 1.4 Thesis structure Literature review 2.1 Theoretical review 2.1.1 Climate change .4 2.1.2 Impact of climate change 2.1.3 Adaptation of people to climate change .5 2.1.4 Income diversification 2.1.4.1 Definition and classification of income diversification 2.1.4.2 Motivations of income diversification 2.1.4.3 Income diversification measurements 2.2 Empirical review 10 2.2.1 Impact of temperature and precipitation variation .10 2.2.2 Impact of high salinity intrusion to income diversification 12 2.2.3 Impact of socio-economic characteristics on income diversification 14 Research methodology 16 3.1 Analytical framework 16 3.2 Methodology 17 3.2.1 Income diversification index 17 3.2.2 Model specification 18 3.2.3 Variable description .20 Page iii 3.3 Data sources 24 3.4 Salinity measurement 26 Result and discussion 28 4.1 Overview of the Mekong River Delta 28 4.1.1 Geographical position and natural conditions 28 4.1.2 Socio – economic conditions 28 4.1.3 Impact of climate change on the Mekong River Delta .29 4.2 Salinity intrusion in the Mekong River Delta .31 4.3 Descriptive statistics of variables 34 4.3.1 Dependent variable 34 4.3.2 Independent variables 35 4.4 Empirical results 41 4.4.1 Findings of the Poisson model 41 4.4.2 Findings of the Tobit model .44 4.4.3 Interpretation 45 Conclusion 50 5.1Conclusion .50 5.2Policy implications 51 5.3Research limitations and research directions 52 Reference 53 Appendix .57 Page iv LIST OF TABLES Table 3.1.Variable description 21 Table 4.1.Descriptive statistics 34 Table 4.2.Results of the panel Poisson model and panel Tobit model 42 Page v LIST OF FIGURES Figure 3.1.Analytical framework .16 Figure 3.2.Salinity stations in Mekong River Delta 27 Figure 4.1.GDP share per sector in Mekong River Delta 29 Figure 4.2.Regional Division of Mekong River Delta .31 Figure 4.3.The salinity intrusion map of Mekong River Delta 32 Figure 4.4.Income shares of households in Mekong River Delta 35 Figure 4.5.Precipitation in Mekong River Delta 37 Figure 4.6.Temperature in Mekong River Delta 38 Figure 4.7.Salinity at stations in Long An and Ca Mau – Bac Lieu 40 Figure 4.8.Marginal effect of precipitation in wet season 44 Figure 4.9.Marginal effect of temperature in dry season 45 Figure A.1.Salinity at stations in Mekong River Delta 59 Page vi CHAPTER 1: INTRODUCTION 1.1 Research problem Climate change has been the most controversial issue in the world due to its significant impacts on many aspects of society and economy in which agriculture is the most vulnerable sector The variationof climate conditions is reflected through temperature rising, abnormal precipitation, droughts, or floods Thoseare the main reasons for insects, diseases, and crop failures (Zerihun, 2012) Climate change is generally harmful for crop production, but indeed, the impact is much diversified (IPCC, 2014).Specifically, higher temperature shortens the growing period of rice, leading to rice yield reduction; however, in some study the increased CO2 from the pollution has supported the photosynthesis process of some crops such as maize and wheatresulting in a better productivity of cereals In Japan, the increase of 1oC in the 20th century has resulted in the drop of wheat, vegetables, milk, and egg production In Russia, the potential production of major crops is acknowledged to fallby 50% on average due to the climate change.On the other side, climate change does notgive identical effects on agriculture sector in different areas in the world due to alternative natural conditions and specific socio-economic characteristics of each region All demographic properties and adaptive solutions of people in an area are the main factors, which determined the vulnerability to climate change.In spite of diversified impacts,it is undeniable that climate change has severely affected food security all over the world Being the second biggestrice exporter in the world just after Thailand, Vietnam has 90% of exported production derived from the Mekong River Delta Located nearby the final branches of Mekong River before converging into the ocean, Mekong River Delta is the wide fertile area, which is appropriatefor rice paddy cultivation and is known as the biggest rice granary in Vietnam However, in recent years, Mekong River Deltaisseriously exposed to threat of climate change, which is clearly shown in high saline intrusion in coastal areas, droughts, and the shortage of fresh water in dry season, resulting in the restriction of arable land.In particular, according to the projected climate scenario in 2100 of the Ministry of Agriculture and Rural Development, if the sea level risesby 1meter, an approximate of 40% arable land could be sunk in salt water The yield shortfall has significantly caused the Page income loss for farmers, theincrease of poverty, and the social insecurity at the same time However,overcoming all difficulties of natural conditions, Mekong River Delta still keeps a stable development rate of production In order to deal with environmental challenge, farmers have applied many solutions to adapt with climate change and improve their lives In those solutions, income diversification is considered as an effective response to climate change (Smit et al., 2000; Bryan et al.,2011) Specifically, income diversification helps farmers to reduce the risk of crop failureand increase household’s total revenue (Zerihun, 2012;Haiwang et al., 2015).Income diversification process is understood as the way in which farmers participate in manyactivities to generate incomefor their households For example, household’s income sources could stem fromgrowing varieties of rice, fruits and other cereals; livestock breeding; aquaculture rearing; or non-farm activities Although income diversification is observed in various levels, researchers still concern aboutthe drivers of income diversification Several studies suggested that drivers are temperature, drought, salinity,price change, and institutional change Understanding separate channels that lead to the farmer’s behavior on diversifying income is important since it allows policy makers to know what to focus on in their policies for farmers Moreover, drivers of income diversification in the Mekong River Delta could be different from other areas and in the world where evidences could be found.Specific evidences for the Mekong River Delta are what policy makers need In Vietnam, income diversification process, which is considered as an effort to reduce thethreatens of climate change, is favorablyrecommended for farmers by Vietnamese Government.Besides,Government 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Agricultural Systems 116 (2013): 7-15 Page 33 APPENDIX Tiền Giang Salinity, g/L 25 20 15 10 2010 2011 2012 2013 2014 2015 2014 2015 Year Vàm Kênh Hòa Bình Salinity, g/L Bến Tre 25 20 15 10 2010 2011 2012 2013 Year Lộc Thuận Bình Đại An Thuận Sơn Đốc Bến Trại Hương Mỹ Page 34 Salinity, g/L Trà Vinh 14 12 10 2010 2011 2012 2013 2014 2015 Year Hưng Mỹ Trà Vinh Trà Kha Cầu Quan Sóc Trăng Salinity, g/L 20 15 10 2010 2011 2012 2013 2014 2015 Year Trần Đề Đại Ngãi Sóc Trăng Thạnh Phú Page 35 Kiên Giang Salinity, g/L 20 15 10 2010 2011 2012 2013 2014 2015 Year Xẻo Rơ Gò Quao Rạch Giá Figure A.1 Salinity at stations in Mekong River Delta Page 36 LOG FILE - -name: log: D:\Google Drive\Tuyet Nga\MY BIG THESIS\Test model\File data regression\Success regression\For submission - Nguyen Thi Tuyet Nga > - VNP21\logs\log file.log log type: text opened on: 20 Nov 2016, 16:17:58 use final_data.dta xtset id year panel variable: time variable: delta: id (strongly balanced) year, 2010 to 2014, but with gaps unit loc control "hh_size hh_labor_ratio migration education gender age land_ha" // Poisson model: / is not a valid command name r(199); ** Do Hausman Test to choose the most appropriate model: xtpoisson diversity_index1 `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dr > yprecipitation sqr_wetprecipitation , re Fitting Poisson model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -1655.2474 -1655.0338 -1655.0335 -1655.0335 = = = = = = = = = = = -1894.1417 -1665.3907 -1656.6608 -1655.4867 -1655.1319 -1655.0524 -1655.0379 -1655.0345 -1655.0337 -1655.0335 -1655.0335 Fitting full model: Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: log log log log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood Random-effects Poisson regression Group variable: id Number of obs Number of groups Random effects u_i ~ Gamma Obs per group: Log likelihood Wald chi2(16) Prob > chi2 = -1655.0335 = = 1,071 362 = avg = max = 3.0 = = 128.99 0.0000 -diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval] -+ -hh_size | 0043159 0137782 0.31 0.754 -.0226888 0313206 hh_labor_ratio | 1683662 0905751 1.86 0.063 -.0091578 3458902 migration | 0419248 0364741 1.15 0.250 -.0295631 1134126 education | -.0751008 0187117 -4.01 0.000 -.111775 -.0384265 Page 37 gender | 1667806 0507943 3.28 0.001 0672255 2663357 age | 0012656 0015894 0.80 0.426 -.0018495 0043807 land_ha | 0380917 0051055 7.46 0.000 0280851 0480983 scaled_salinity | -.0035491 0032377 -1.10 0.273 -.0098949 0027968 dry_temp | 5.436609 8.158859 0.67 0.505 -10.55446 21.42768 wet_temp | -16.22262 14.00837 -1.16 0.247 -43.67852 11.23328 dry_precipitation | 0005638 0017785 0.32 0.751 -.0029221 0040496 wet_precipitation | 0080996 0028302 2.86 0.004 0025525 0136467 sqr_dry_temp | -.1024101 1506563 -0.68 0.497 -.397691 1928709 sqr_wet_temp | 2908038 2512028 1.16 0.247 -.2015447 7831522 sqr_dryprecipitation | -2.87e-06 5.07e-06 -0.57 0.572 -.0000128 7.07e-06 sqr_wetprecipitation | -.0000175 6.12e-06 -2.85 0.004 -.0000295 -5.47e-06 _cons | 153.7981 256.2877 0.60 0.548 -348.5166 656.1127 -+ -/lnalpha | -16.47073 284.1286 -573.3526 540.4112 -+ -alpha | 7.03e-08 00002 9.9e-250 5.0e+234 -LR test of alpha=0: chibar2(01) = 0.00 Prob >= chibar2 = 1.000 est sto re xtpoisson diversity_index1 `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dr > yprecipitation sqr_wetprecipitation , fe note: groups (7 obs) dropped because of only one obs per group Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -809.66846 -807.99749 -807.99727 -807.99727 Conditional fixed-effects Poisson regression Group variable: id Log likelihood = -807.99727 Number of obs Number of groups = = 1,064 355 Obs per group: = avg = max = 3.0 Wald chi2(16) Prob > chi2 = = 3.35 0.9996 -diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval] -+ -hh_size | 0064639 0308044 0.21 0.834 -.0539115 0668394 hh_labor_ratio | 0523888 2197282 0.24 0.812 -.3782706 4830481 migration | 0300864 0482235 0.62 0.533 -.0644299 1246028 education | 0334917 1367977 0.24 0.807 -.234627 3016103 gender | 0677677 3751553 0.18 0.857 -.6675233 8030586 age | 0014319 0360825 0.04 0.968 -.0692886 0721523 land_ha | 0149515 0136784 1.09 0.274 -.0118576 0417607 scaled_salinity | -.0038368 0202574 -0.19 0.850 -.0435405 035867 dry_temp | 5.342075 9.54153 0.56 0.576 -13.35898 24.04313 wet_temp | -3.154381 24.05328 -0.13 0.896 -50.29795 43.98919 dry_precipitation | 0002433 0026125 0.09 0.926 -.0048771 0053636 wet_precipitation | 0026639 0040137 0.66 0.507 -.0052027 0105306 sqr_dry_temp | -.0986839 1764474 -0.56 0.576 -.4445144 2471467 sqr_wet_temp | 0585224 4325856 0.14 0.892 -.7893298 9063745 sqr_dryprecipitation | -8.24e-07 7.47e-06 -0.11 0.912 -.0000155 0000138 sqr_wetprecipitation | -5.41e-06 8.52e-06 -0.63 0.526 -.0000221 0000113 - est sto fe hausman fe re Note: the rank of the differenced variance matrix (11) does not equal the number of coefficients being tested (16); be sure this is what you expect, or there may be problems computing the test Examine the output of your estimators for anything unexpected and possibly consider scaling your variables so that the coefficients are on a similar scale Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fe re Difference S.E -+ Page 38 hh_size | 0064639 0043159 002148 0275512 hh_labor_r~o | 0523888 1683662 -.1159774 2001915 migration | 0300864 0419248 -.0118383 031546 education | 0334917 -.0751008 1085924 135512 gender | 0677677 1667806 -.0990129 3717008 age | 0014319 0012656 0001663 0360475 land_ha | 0149515 0380917 -.0231402 0126898 scaled_sal~y | -.0038368 -.0035491 -.0002877 019997 dry_temp | 5.342075 5.436609 -.0945336 4.947101 wet_temp | -3.154381 -16.22262 13.06824 19.55316 dry_precip~n | 0002433 0005638 -.0003205 0019136 wet_precip~n | 0026639 0080996 -.0054356 0028459 sqr_dry_temp | -.0986839 -.1024101 0037262 0918496 sqr_wet_temp | 0585224 2908038 -.2322814 3521753 sqr_drypre~n | -8.24e-07 -2.87e-06 2.04e-06 5.48e-06 sqr_wetpre~n | -5.41e-06 -.0000175 0000121 5.93e-06 -b = consistent under Ho and Ha; obtained from xtpoisson B = inconsistent under Ha, efficient under Ho; obtained from xtpoisson Test: Ho: difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 8.34 Prob>chi2 = 0.6822 ** Choose the random effect model: xtpoisson diversity_index1 `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dr > yprecipitation sqr_wetprecipitation, re cluster() Fitting Poisson model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -1655.2474 -1655.0338 -1655.0335 -1655.0335 = = = = = = = = = = = -1894.1417 -1665.3907 -1656.6608 -1655.4867 -1655.1319 -1655.0524 -1655.0379 -1655.0345 -1655.0337 -1655.0335 -1655.0335 Fitting full model: Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: log log log log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood Random-effects Poisson regression Group variable: id Number of obs Number of groups Random effects u_i ~ Gamma Obs per group: Log likelihood Wald chi2(16) Prob > chi2 = -1655.0335 = = 1,071 362 = avg = max = 3.0 = = 128.99 0.0000 -diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval] -+ -hh_size | 0043159 0137782 0.31 0.754 -.0226888 0313206 hh_labor_ratio | 1683662 0905751 1.86 0.063 -.0091578 3458902 migration | 0419248 0364741 1.15 0.250 -.0295631 1134126 education | -.0751008 0187117 -4.01 0.000 -.111775 -.0384265 gender | 1667806 0507943 3.28 0.001 0672255 2663357 age | 0012656 0015894 0.80 0.426 -.0018495 0043807 land_ha | 0380917 0051055 7.46 0.000 0280851 0480983 scaled_salinity | -.0035491 0032377 -1.10 0.273 -.0098949 0027968 dry_temp | 5.436609 8.158859 0.67 0.505 -10.55446 21.42768 wet_temp | -16.22262 14.00837 -1.16 0.247 -43.67852 11.23328 dry_precipitation | 0005638 0017785 0.32 0.751 -.0029221 0040496 wet_precipitation | 0080996 0028302 2.86 0.004 0025525 0136467 sqr_dry_temp | -.1024101 1506563 -0.68 0.497 -.397691 1928709 Page 39 sqr_wet_temp | 2908038 2512028 1.16 0.247 -.2015447 7831522 sqr_dryprecipitation | -2.87e-06 5.07e-06 -0.57 0.572 -.0000128 7.07e-06 sqr_wetprecipitation | -.0000175 6.12e-06 -2.85 0.004 -.0000295 -5.47e-06 _cons | 153.7981 256.2877 0.60 0.548 -348.5166 656.1127 -+ -/lnalpha | -16.47073 284.1286 -573.3526 540.4112 -+ -alpha | 7.03e-08 00002 9.9e-250 5.0e+234 -LR test of alpha=0: chibar2(01) = 0.00 Prob >= chibar2 = 1.000 ** Test the significance of the variable: test `control' scaled_salinity dry_temp wet_temp sqr_dry_temp sqr_wet_temp sqr_dryprecipitation sqr_wet > precipitation ( 1) ( 2) ( 3) ( 4) ( 5) ( 6) ( 7) ( 8) ( 9) (10) (11) (12) (13) (14) (15) (16) dry_precipitation wet_precipitation [diversity_index1]hh_size = [diversity_index1]hh_labor_ratio = [diversity_index1]migration = [diversity_index1]education = [diversity_index1]gender = [diversity_index1]age = [diversity_index1]land_ha = [diversity_index1]scaled_salinity = [diversity_index1]dry_temp = [diversity_index1]wet_temp = [diversity_index1]dry_precipitation = [diversity_index1]wet_precipitation = [diversity_index1]sqr_dry_temp = [diversity_index1]sqr_wet_temp = [diversity_index1]sqr_dryprecipitation = [diversity_index1]sqr_wetprecipitation = chi2( 16) = Prob > chi2 = 128.99 0.0000 test wet_precipitation sqr_wetprecipitation ( 1) ( 2) [diversity_index1]wet_precipitation = [diversity_index1]sqr_wetprecipitation = chi2( 2) = Prob > chi2 = 8.21 0.0165 ** Calculate Marginal effect: xtpoisson diversity_index1 scaled_salinity c.dry_temp##c.dry_temp c.dry_precipitation##c.dry_precipitation c.wet_pre > cipitation##c.wet_precipitation `control', re cluster() c.wet_temp##c.wet_temp Fitting Poisson model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = -1655.2403 -1655.0268 -1655.0265 -1655.0265 = = = = = = = = = -1894.1399 -1665.3846 -1656.6542 -1655.4798 -1655.1249 -1655.0454 -1655.031 -1655.0275 -1655.0267 Fitting full model: Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: log log log log log log log log log likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood likelihood Page 40 Iteration 9: Iteration 10: Iteration 11: log likelihood = -1655.0265 log likelihood = -1655.0265 log likelihood = -1655.0265 Random-effects Poisson regression Group variable: id Number of obs Number of groups Random effects u_i ~ Gamma Obs per group: Log likelihood Wald chi2(16) Prob > chi2 = -1655.0265 = = 1,071 362 = avg = max = 3.0 = = 129.00 0.0000 diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval] + -scaled_salinity | -.0035484 0032375 -1.10 0.273 -.0098938 002797 dry_temp | 5.442579 8.160219 0.67 0.505 -10.55116 21.43631 | c.dry_temp#c.dry_temp | -.1025196 1506811 -0.68 0.496 -.3978492 19281 | wet_temp | -16.27622 14.02314 -1.16 0.246 -43.76107 11.20862 | c.wet_temp#c.wet_temp | 2917645 2514663 1.16 0.246 -.2011005 7846294 | dry_precipitation | 0005633 0017784 0.32 0.751 -.0029224 0040489 | c.dry_precipitation#c.dry_precipitation | -2.86e-06 5.07e-06 -0.57 0.572 -.0000128 7.07e-06 | wet_precipitation | 0080959 0028312 2.86 0.004 0025468 0136449 | c.wet_precipitation#c.wet_precipitation | -.0000175 6.12e-06 -2.85 0.004 -.0000295 -5.45e-06 | hh_size | 0043187 0137781 0.31 0.754 -.0226859 0313233 hh_labor_ratio | 168364 0905751 1.86 0.063 -.0091599 3458879 migration | 0419269 0364738 1.15 0.250 -.0295603 1134142 education | -.0750974 0187117 -4.01 0.000 -.1117716 -.0384232 gender | 1667777 0507943 3.28 0.001 0672228 2663326 age | 0012656 0015894 0.80 0.426 -.0018495 0043807 land_ha | 0380908 0051054 7.46 0.000 0280844 0480973 _cons | 154.4647 256.5812 0.60 0.547 -348.4252 657.3546 + -/lnalpha | -17.66624 255.5196 -518.4755 483.143 + -alpha | 2.13e-08 5.43e-06 6.7e-226 6.7e+209 LR test of alpha=0: chibar2(01) = 5.4e-05 Prob >= chibar2 = 0.497 ** Draw marginal effect graph of Precipitation in the wet season: marginsplot Variables that uniquely identify margins: wet_precipitation margins, dydx(wet_precipitation) at( wet_precipitation =(107.14(30)345.29)) Average marginal effects Model VCE : OIM Number of obs = 1,071 Expression : Linear prediction, predict() dy/dx w.r.t : wet_precipitation 1._at : wet_precip~n = 107.14 2._at : wet_precip~n = 137.14 3._at : wet_precip~n = 167.14 4._at : wet_precip~n = 197.14 5._at : wet_precip~n = 227.14 6._at : wet_precip~n = 257.14 7._at : wet_precip~n = 287.14 8._at : wet_precip~n = 317.14 Page 41 | Delta-method | dy/dx Std Err z P>|z| [95% Conf Interval] + -wet_precipitation | _at | | 004355 0015416 2.82 0.005 0013335 0073764 | 0033075 0011894 2.78 0.005 0009762 0056387 | 00226 0008503 2.66 0.008 0005934 0039265 | 0012125 0005489 2.21 0.027 0001366 0022884 | 000165 0003869 0.43 0.670 -.0005934 0009234 | -.0008825 0005178 -1.70 0.088 -.0018975 0001325 | -.00193 0008103 -2.38 0.017 -.0035182 -.0003418 | -.0029775 0011468 -2.60 0.009 -.0052252 -.0007298 // Tobit model: / is not a valid command name r(199); xttobit diversity_index2 scaled_salinity dry_temp wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitati > on sqr_wetprecipitation `control' , ul(1) re cluster() wet_temp dry_precipitation Obtaining starting values for full model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = 164.51368 173.39111 173.56978 173.56995 = = = = = -338.06347 -270.15085 -269.09481 -269.08819 -269.08818 Fitting full model: Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: log log log log log likelihood likelihood likelihood likelihood likelihood Random-effects tobit regression Group variable: id Number of obs Number of groups Random effects u_i ~ Gaussian Obs per group: Integration method: mvaghermite Integration pts = 12 Log likelihood Wald chi2(16) Prob > chi2 = = 47.59 0.0001 = -269.08818 = = 1,071 362 = avg = max = 3.0 -diversity_index2 | Coef Std Err z P>|z| [95% Conf Interval] -+ -scaled_salinity | 0003955 0020019 0.20 0.843 -.0035282 0043193 dry_temp | -5.094096 2.920753 -1.74 0.081 -10.81867 6304745 wet_temp | -4.931524 6.094254 -0.81 0.418 -16.87604 7.012994 dry_precipitation | -.0003053 0006958 -0.44 0.661 -.0016691 0010585 wet_precipitation | -.0022184 001155 -1.92 0.055 -.0044822 0000454 sqr_dry_temp | 0948993 0539556 1.76 0.079 -.0108516 2006503 sqr_wet_temp | 0878975 1094411 0.80 0.422 -.1266032 3023982 sqr_dryprecipitation | 1.14e-06 1.98e-06 0.58 0.563 -2.73e-06 5.02e-06 sqr_wetprecipitation | 4.81e-06 2.46e-06 1.96 0.050 -6.47e-09 9.63e-06 hh_size | -.0089625 0072813 -1.23 0.218 -.0232336 0053087 hh_labor_ratio | -.047354 0497081 -0.95 0.341 -.1447801 0500721 migration | -.0157082 0147405 -1.07 0.287 -.0445989 0131826 education | 0508484 0129138 3.94 0.000 0255379 076159 gender | -.1413014 0345128 -4.09 0.000 -.2089452 -.0736576 age | -.0010515 001133 -0.93 0.353 -.003272 0011691 land_ha | -.003443 0036456 -0.94 0.345 -.0105882 0037023 _cons | 138.7191 107.0625 1.30 0.195 -71.11951 348.5577 -+ -/sigma_u | 2459029 0138871 17.71 0.000 2186847 273121 /sigma_e | 1954196 0060676 32.21 0.000 1835274 2073118 Page 42 -+ -rho | 6129133 0312161 5505751 6724636 -0 left-censored observations 775 uncensored observations 296 right-censored observations ** Test the significance of the variable: test scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitation sqr_wetprecipitat > ion `control' ( 1) ( 2) ( 3) ( 4) ( 5) ( 6) ( 7) ( 8) ( 9) (10) (11) (12) (13) (14) (15) (16) [diversity_index2]scaled_salinity = [diversity_index2]dry_temp = [diversity_index2]wet_temp = [diversity_index2]dry_precipitation = [diversity_index2]wet_precipitation = [diversity_index2]sqr_dry_temp = [diversity_index2]sqr_wet_temp = [diversity_index2]sqr_dryprecipitation = [diversity_index2]sqr_wetprecipitation = [diversity_index2]hh_size = [diversity_index2]hh_labor_ratio = [diversity_index2]migration = [diversity_index2]education = [diversity_index2]gender = [diversity_index2]age = [diversity_index2]land_ha = chi2( 16) = Prob > chi2 = 47.59 0.0001 test dry_temp sqr_dry_temp ( 1) ( 2) [diversity_index2]dry_temp = [diversity_index2]sqr_dry_temp = chi2( 2) = Prob > chi2 = 7.05 0.0295 test wet_precipitation sqr_wetprecipitation ( 1) ( 2) [diversity_index2]wet_precipitation = [diversity_index2]sqr_wetprecipitation = chi2( 2) = Prob > chi2 = 3.86 0.1448 ** Calculate Marginal effect: xttobit diversity_index2 scaled_salinity c.dry_temp##c.dry_temp c.dry_precipitation##c.dry_precipitation c.wet_preci > pitation##c.wet_precipitation `control', re cluster() c.wet_temp##c.wet_temp Obtaining starting values for full model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood = = = = 164.51055 173.38895 173.56764 173.56781 log likelihood = log likelihood = 173.56781 173.56781 Fitting full model: Iteration 0: Iteration 1: Random-effects tobit regression Group variable: id Number of obs Number of groups = = 1,071 362 Page 43 Random effects u_i ~ Gaussian Obs per group: = avg = max = 3.0 Integration method: mvaghermite Integration pts = 12 Log likelihood Wald chi2(16) Prob > chi2 = = 41.25 0.0005 = 173.56781 diversity_index2 | Coef Std Err z P>|z| [95% Conf Interval] + -scaled_salinity | 0004745 0013884 0.34 0.733 -.0022467 0031957 dry_temp | -3.97197 2.255179 -1.76 0.078 -8.392039 4481002 | c.dry_temp#c.dry_temp | 0739636 0416519 1.78 0.076 -.0076726 1555999 | wet_temp | -3.129728 4.598307 -0.68 0.496 -12.14224 5.882788 | c.wet_temp#c.wet_temp | 0556771 0825489 0.67 0.500 -.1061158 21747 | dry_precipitation | -.0002633 0005265 -0.50 0.617 -.0012952 0007686 | c.dry_precipitation#c.dry_precipitation | 9.19e-07 1.49e-06 0.62 0.538 -2.01e-06 3.84e-06 | wet_precipitation | -.0014003 0008433 -1.66 0.097 -.003053 0002525 | c.wet_precipitation#c.wet_precipitation | 3.04e-06 1.80e-06 1.69 0.091 -4.87e-07 6.57e-06 | hh_size | -.0065029 0053121 -1.22 0.221 -.0169145 0039087 hh_labor_ratio | -.0536531 0355123 -1.51 0.131 -.1232558 0159497 migration | -.0161259 0116518 -1.38 0.166 -.038963 0067112 education | 0291604 0085039 3.43 0.001 0124931 0458276 gender | -.0845108 0228651 -3.70 0.000 -.1293257 -.0396959 age | -.0005967 0007589 -0.79 0.432 -.0020842 0008907 land_ha | -.0002609 0027889 -0.09 0.925 -.0057271 0052054 _cons | 98.29724 80.98127 1.21 0.225 -60.42313 257.0176 + -/sigma_u | 1595978 0085215 18.73 0.000 142896 1762996 /sigma_e | 1643946 0044296 37.11 0.000 1557128 1730763 + -rho | 485198 032434 4220835 5486863 - left-censored observations 1,071 uncensored observations right-censored observations **Draw marginal effect graph of Temperature in the dry season: marginsplot Variables that uniquely identify margins: dry_temp margins, dydx(dry_temp) predict(e(.,1)) at(dry_temp=(26.4(.1)27.9)) Average marginal effects Model VCE : OIM Number of obs = 1,071 Expression : E(diversity_index2|diversity_index2|z| [95% Conf Interval] -+ -dry_temp | _at | | -.0454592 0385408 -1.18 0.238 -.1209977 0300793 | -.0357579 0340743 -1.05 0.294 -.1025422 0310264 | -.0257697 029286 -0.88 0.379 -.0831692 0316299 | -.0155774 0243432 -0.64 0.522 -.0632891 0321343 | -.0052632 0194858 -0.27 0.787 -.0434547 0329283 | 0050923 0151324 0.34 0.736 -.0245665 0347512 | 0154081 012097 1.27 0.203 -.0083015 0391177 | 025603 011548 2.22 0.027 0029693 0482368 | 0355953 0136889 2.60 0.009 0087655 0624252 10 | 0453018 0173658 2.61 0.009 0112655 0793382 11 | 0546389 0215662 2.53 0.011 0123699 0969079 12 | 0635211 0257609 2.47 0.014 0130307 1140116 13 | 0718633 0296639 2.42 0.015 0137232 1300035 14 | 0795809 0330912 2.40 0.016 0147232 1444385 15 | 0865916 0359075 2.41 0.016 0162143 156969 16 | 0928187 0380072 2.44 0.015 018326 1673114 Page 45 ... review 2.1.1 Climate change Climate change is defined as: A change in the state of the climate that can be identified by changes in the mean and/ or the variability of its properties, and that persists... agricultural activity due to harmful effect of climate change, maintain and improve the living standard of farmers 1.3 Research scope This study employs apanel data analysis for 362 households in the Mekong. .. climate change Then, the chapter provides a brief theory on the motivations of income diversification, an effective adaptation behavior to climate change and approaches to measure diversification