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 INSTITUTE OF SOCIAL STUDIES UNIVERSITY OF ECONOMICS THE HAGUE HO CHIMINHCITY VIETNAM 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 Acknowledgement Abstract Table of content List of tables List of figures Introduction 1.1 Research problem 1.2 Research objective 1.3 Research scope 1.4 Thesis structure Literature review 2.1 Theoretical review 2.1.1 Climate change 2.1.2 Impact of climate change 2.1.3 Adaptation of people to climate change 2.1.4 Income diversification 2.1.4.1 Definition and classification of income div 2.1.4.2 Motivations of income diversification 2.1.4.3 Income diversification measurements 2.2 Empirical review 2.2.1 Impact of temperature and precipitation variat 2.2.2 Impact of high salinity intrusion to income div 2.2.3 Impact of socio-economic characteristics on in Research methodology 3.1 Analytical framework 3.2 Methodology 3.2.1 Income diversification index 3.2.2 Model specification 3.2.3 Variable description 3.3 Data sources 3.4 Salinity measurement 4.Result and discussion 4.1 Overview of the Mekong River Delta 4.1.1 Geographical position and natural conditi 4.1.2 Socio – economic conditions 4.1.3 Impact of climate change on the Mekong 4.2 Salinity intrusion in the Mekong River Delta 4.3 Descriptive statistics of variables 4.3.1 Dependent variable 4.3.2 Independent variables 4.4 Empirical results 4.4.1 Findings of the Poisson model 4.4.2 Findings of the Tobit model 4.4.3 Interpretation 5.Conclusion 5.1Conclusion 5.2Policy implications 5.3Research limitations and research directions Reference Appendix 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 o C in the 20 th 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 policies also relate to the improvement of physical infrastructure, financial subsidy, and the openness of agriculture market However, both uncertainties about determinants of income diversification and the response of farmers to climate changecould lead to the inefficiency or less efficiency of Government supporting policies Therefore, a research of climate change and income diversification could producereliable and sustainable evidences for policy makers about the impact of climate change on income diversification Based on those empirical findings, policy makers could implement policies, which are more efficient to support farmers in income diversification process Page Page 39 -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 dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitation sqr_wet > precipitation ( ( ( ( ( ( ( ( ( 1) 2) 3) 4) 5) 6) 7) 8) 9) [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 = (10) (11) (12) (13) (14) (15) (16) [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 = test wet_precipitation sqr_wetprecipitation ( 1) [diversity_index1]wet_precipitation = ( 2) [diversity_index1]sqr_wetprecipitation = ** Calculate Marginal effect: xtpoisson diversity_index1 scaled_salinity c.dry_temp##c.dry_temp c.wet_temp##c.wet_temp c.dry_precipitation##c.dry_precipitation c.wet_pre > cipitation##c.wet_precipitation `control', re cluster() 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 log log log log likelihood likelihood likelihood likelihood = = = = -1894.1399 -1665.3846 -1656.6542 -1655.4798 Fitting full model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: Iteration Iteration Iteration Iteration Iteration 4: 5: 6: 7: 8: log likelihood = -1655.1249 log likelihood = -1655.0454 log likelihood = -1655.031 log likelihood = -1655.0275 log likelihood = -1655.0267 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 Random effects u_i ~ Gamma Log likelihood = -1655.0265 c.dry_precipitation#c.dry_precipitation | c.wet_precipitation#c.wet_precipitation | _cons | 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 Expression dy/dx w.r.t : wet_precipitation : Linear p 1._at : wet_pre 2._at : wet_pre 3._at : wet_pre 4._at : wet_pre 5._at : wet_pre 6._at : wet_pre 7._at : wet_pre 8._at : wet_pre Page 41 -wet_precipitation | _at | // Tobit model: / is not a valid command name r(199); xttobit diversity_index2 scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitati > on sqr_wetprecipitation `control' , ul(1) re cluster() Obtaining starting values for full model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: log log log log likelihood likelihood likelihood likelihood log log log log log likelihood likelihood likelihood likelihood likelihood = = = = 164.51368 173.39111 173.56978 173.56995 Fitting full model: Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: = = = = = -338.06347 -270.15085 -269.09481 -269.08819 -269.08818 Random-effects tobit regression Group variable: id Random effects u_i ~ Gaussian Integration method: mvaghermite Log likelihood = -269.08818 _cons | 138.7191 - Page 42 -+ rho | -1 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) [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 = (10) (11) (12) (13) (14) (15) (16) [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 = test dry_temp sqr_dry_temp ( 1) [diversity_index2]dry_temp ( 2) [diversity_index2]sqr_dry_t test wet_precipitation sqr_wetprecipitation ( 1) [diversity_index2]wet_precipitation = ( 2) [diversity_index2]sqr_wetprecipitation = ** Calculate Marginal effect: xttobit diversity_index2 scaled_salinity c.dry_temp##c.dry_temp c.wet_temp##c.wet_temp c.dry_precipitation##c.dry_precipitation c.wet_preci > pitation##c.wet_precipitation `control', re cluster() Obtaining starting values for full model: Iteration Iteration Iteration Iteration 0: 1: 2: 3: Fitting full model: Iteration 0: Iteration 1: log log log log likelihood likelihood likelihood likelihood = = = = 164.51055 173.38895 173.56764 173.56781 Random-effects Group variable: id Page 43 Random effects u_i ~ Gaussian Integration method: mvaghermite Log likelihood = 173.56781 c.dry_precipitation#c.dry_precipitation | c.wet_precipitation#c.wet_precipitation | _cons | - left-censored observations 1,071uncensored 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 Expression dy/dx w.r.t : dry_temp : E(divers 1._at : dry_tem 2._at : dry_tem 3._at : dry_tem 4._at : dry_tem 5._at : dry_tem 6._at : dry_tem 7._at : dry_tem 8._at : dry_tem Page 44 9._at 10._at 11._at 12._at 13._at 14._at 15._at 16._at -dry_temp Page 45 .. .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... determinants on income diversification 2.1 Theoretical 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... seasonal fluvial floods and increasing the ability to keep water via optimal land and water use 2) Middle Delta – facing the heavy fresh water shortage in dry season and, droughts and ensuring