Irrigation and rice production evidence in vietnam

UNIVERSITY OF ECONOMICS ERASMUS UNVERSITY ROTTERDAM HO CHI MINH CITY INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IRRIGATION AND RICE PRODUCTION: EVIDENCE IN VIETNAM BY LE HUU NHAT QUANG Academic Supervisor: DR LE VAN CHON MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, December 2016 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IRRIGATION AND RICE PRODUCTION: EVIDENCE IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By LE HUU NHAT QUANG Academic Supervisor: DR LE VAN CHON HO CHI MINH CITY, December 2016 ABSTRACT This thesis investigates the relationship between irrigation and rice production in the case of Vietnam Further analyses are conducted to figure out the differences in impact on rice yield among water sources and explore whether effect of using canal for irrigation varies across locations on commune canal system The employed dataset is VARHS 2014, which contains information on rice production, inputs use at household level, characteristics of plot managers, quality and irrigation condition of plots, and commune data on infrastructure The thesis uses a cross-section data model with cluster-specific fixed effect and cluster-robust standard error Research results show that irrigation plays an important role in rice production However effect of irrigation varies across water sources, and only effects of using water from canal and river or spring for irrigation are significant Benefit of irrigation by canal to rice production is not higher than that of using water from river or spring Moreover, there is inequality in water distribution of commune canal system Keywords: irrigation, canal, rice production, Vietnam i ACKNOWLEDGEMENT Wholeheartedly, I would like to thank Dr Le Van Chon, my supervisor, for being patient, giving me useful advice and frank comments Without him, I certainly cannot complete my thesis I am also extremely grateful to Dr Truong Dang Thuy and Dr Pham Khanh Nam for their advice when I get stuck in building research ideas In addition, I really appreciate all the hard work, advice and encouragement that Mr Do Huu Luat did to help me during the time I doing my thesis I would like to say thank to Mr Le Van Thang, Mr Nguyen Son Kien, Mrs Hoang Quynh Trang and Mrs Vu Thi Thuong for encouraging and helping me to complete my thesis Finally, from the bottom of my hearth, I would like to say thank to my parents for being there when I need them ii ABBREVIATIONS BLUE Best linear unbiased estimator CRVM Cluster-robust variance matrix CSFE Cluster-specific fixed effect CSRE Cluster-specific random effect FGLS Feasible Generalized Least Square GLS Generalized Least Square OLS Ordinary Least Square VARHS Vietnam Access to Resources Household Survey VIF Variance Inflation Factor CONTENTS CHAPTER 1: INTRODUCTION 1.1 PROBLEM STATEMENT 1.2 RESEARCH OBJECTIVES 1.3 RESEARCH QUESTIONS 1.4 SCOPE OF THE RESEARCH 1.5 THESIS’S STRUCTURE 10 CHAPTER 2: LITERATURE REVIEW 11 2.1 DEFINITION OF IRRIGATION 11 2.2 THEORETICAL LITERATURE 11 2.2.1 Role of irrigation in agriculture 11 2.2.2 Properties of rice plant and water need for rice production 13 2.2.3 Water sources .15 2.2.4 Analysis model .15 2.2.5 Cobb-Douglas production function 17 2.2.6 Other production functions 19 2.3 EMPIRICAL LITERATURE 19 2.4 HYPOTHESIS TESTING 22 CHAPTER 3: DATA AND METHODOLOGY 24 3.1 DATA SOURCE .24 3.2 MODEL SPECIFICATION 24 3.2.1 Building model 24 3.2.2 Constructing variables 25 3.3 ESTIMATION STRATEGY 30 3.3.1 Cluster-specific random effect model 33 3.3.2 Cluster-specific fixed effect model .35 3.3.3 Choosing between CSRE and CSFE 36 CHAPTER 4: EMPIRICAL STUDY IN IMPACT OF IRRIGATON ON RICE PRODUCTION 37 4.1 VIETNAM RICE PRODUCTION AND PUBLIC IRRIGATION SYSTEM 37 4.2 RICE PRODUCTION AND IRRIGATION OF FARMERS IN VARHS 2014 39 4.3 EFFECT OF IRRIGATION ON RICE PRODUCTION IN RURAL VIETNAM .42 CHAPTER 5: CONCLUSION AND IMPLICATION .48 5.1 MAIN FINDINGS 48 5.2 POLICY IMPLICATION 49 5.3 LIMITATION 49 REFERENCES 51 APPENDICES 57 TABLE OF FIGURES Figure 2.1: Determinants of irrigation frequency 13 Figure 2.2: Analytical framework .23 Figure 4.1: Rice productivity in 2014 of some countries 37 Figure 4.2: Vietnam's annual rice production .38 Figure 4.3: Vietnam's annual harvested area .38 LIST OF TABLES Table 3.1: Variables and expected sign 28 Table 4.1: Area cultivating rice, ratio of irrigation and ratio of polyculture by number of season growing rice 40 Table 4.2: Average rice productivity by irrigation and water sources 41 Table 4.3: Test results of choosing among OLS, CSRE, and CSFE 42 Table 4.4: Cluster-specific fixed effect regression of log household's rice production .47 vii CHAPTER 1: INTRODUCTION 1.1 Problem statement Rice can be grown in a wide range of weather conditions, so that many countries have grown rice, especially in Asia with about 90% rice production of the world (Maclean, Hardy, & Hettel, 2013) In Vietnam, rice is one of the main crops, ratio of land cultivating rice to total land growing cereals is up to 86% (Vietnam GSO, 2015) In 2014, Vietnam was ranked fifth in the world for rice production, after China, India, Indonesia, and Bangladesh (FAOSTAT) From 1990 to 2014, rice productivity in Vietnam increased by nearly 100% (FAOSTAT) This success is contributed by many factors such as application of new varieties, technology and extension of irrigation system Until 2011, Vietnam has 254180 km of canal serving for irrigation of 85.5% land cultivating rice (Ha, Nguyen & Nguyen, 2015) However, it also shows many disadvantages in operation, such as weak management, lacking of maintaining and dredging, and low quality of water (Tran, 2016) This raises the questions about the role of irrigation, and impact of irrigation system in Vietnam on rice production There are some papers related to these issues which are conducted in Vietnam (Walle, 2003; Biltonen, Hussain & Tuan, 2003; Ut, Hossain & Janaiah, 2000), but these studies use dataset in the stage of 1991-2000 Meanwhile, from 2000 to nowadays there have been many changes in Vietnam irrigation system and rice production The role of irrigation on agriculture and poverty alleviation has been research objective of many economists A large number of studies were conducted in many countries Almost research results show that irrigation helps to increase crops’ productivity and is a solution to reduce poverty (Hussain & Hanjra, 2004) Nonetheless, some of them suggest that impact of irrigation varies across system and even across locations on canal system (Biltonen, Hussain & Tuan, 2003; Jin, Jansen and Muraoka, 2012; Hussain et al., 2006; Hussain et al., 2004) Moreover, these results are different among countries and regions Hence, it is necessary to investigate the effect of irrigation system on rice production in the case of Vietnam In this thesis, employed dataset is VARHS 2014, which is the latest secondary data and provides very specific information related to agricultural operation of households in Vietnam rural F test checks whether there is difference in impact of locations on canal system on rice production ( 1) ln_land_size_head - ln_land_size_tail = F( ( 1) 0.61 0.4339 ln_land_size_middle - ln_land_size_tail = F( ( 1) 1, 327) = Prob > F = 1, 327) = Prob > F = 2.93 0.0879 - ln_land_size_head + ln_land_size_middle = F( 1, 327) = Prob > F = 0.44 0.5098 1.2 OLS with cluster-robust variance matrix estimator Dependent variable: log household's rice production Model Model Model beta t-statistic beta t-statistic beta t-statistic Irrigation Area of irrigated land (log) 0.051 (7.32)*** Area of irrigated land at head end canal (log) 0.032 (6.78)*** Area of irrigated land at middle canal (log) 0.032 (7.51)*** Area of irrigated land at tail end canal (log) 0.023 (5.82)*** Area of land irrigated by canal (log) 0.037 (5.89)*** Area of land irrigated by river or spring (log) 0.022 (4.79)*** 0.018 (4.36)*** Area of land irrigated by lake or pond (log) 0.004 (0.35) -0.001 (-0.11) Area of land irrigated by other sources (log) 0.000 (0) -0.006 (-0.58) Inputs Area of cultivated land (log) 0.475 (10.82)*** 0.482 (10.81)*** 0.479 (10.19)*** 10 Household members' working days to produce rice (log) 0.169 (5.48)*** 0.169 (5.53)*** 0.167 (5.29)*** 11 Cost of hiring labor (log) 0.012 (3.85)*** 0.013 (4)*** 0.011 (3.27)*** 12 Cost of fertilizer (log) 0.039 (3.27)*** 0.046 (3.77)*** 0.048 (3.73)*** 13 Cost of seed (log) 0.153 (6.2)*** 0.152 (6.03)*** 0.145 (5.6)*** 14 Cost of renting asset, machinery, equipment, means and cattle (log) 0.008 (2.25)** 0.008 (2.1)** 0.007 (1.76)* 15 Productive assets (log) 0.009 (2.82)*** 0.010 (2.95)*** 0.011 (3.11)*** Farmer's characteristics 16 Average age of plot managers (log) 0.011 (0.37) 0.011 (0.35) 0.003 (0.11) 17 Average schooling years of plot managers (log) 0.060 (5.01)*** 0.064 (5.05)*** 0.069 (5.28)*** 18 Male plot managers -0.053 (-2.8)*** -0.053 (-2.79)*** -0.059 (-2.8)*** 19 Male and female plot managers 0.008 (0.24) 0.010 (0.34) 0.021 (0.66) Other's factors 20 Ratio of land with better quality than average in the village 0.023 (0.38) 0.025 (0.4) 0.029 (0.42) 21 Number of cultivated plots (log) 0.046 (1.78)* 0.038 (1.41) 0.036 (1.29) 22 Visit agriculture extension 0.068 (3.27)*** 0.067 (3.1)*** 0.069 (3.03)*** 23 Ratio of land hit by disaster -0.138 (-4.27)*** -0.143 (-4.42)*** -0.143 (-4.53)*** 24 Constant 1.185 (5.2)*** 1.181 (5.04)*** 1.302 (5.45)*** Number of observations 2285 2285 2082 Source: calculated by author Other control variables include commune’s infrastructure (log of distance to main road, log of distance to district center, and percent of concrete commune roads) and dummy variables of provinces * Significant at 10%; ** significant at 5%; *** significant at 1% Standard error is clustered at commune level 61 1.3 FGLS with cluster-robust variance matrix estimator Dependent variable: log household's rice revenue Model beta t-statistic Model beta t-statistic Model beta t-statistic Irrigation Area of irrigated land (log) 0.047 (7.1)*** Area of irrigated land at head end canal (log) 0.026 (5.68)*** Area of irrigated land at middle canal (log) 0.027 (6.53)*** Area of irrigated land at tail end canal (log) 0.021 (5.69)*** Area of land irrigated by canal (log) 0.033 (5.61)*** Area of land irrigated by river or spring (log) 0.022 (5.03)*** 0.018 (4.23)*** Area of land irrigated by lake or pond (log) 0.005 (0.41) -0.001 (-0.07) Area of land irrigated by other sources (log) 0.001 (0.06) -0.006 (-0.61) Inputs Area of cultivated land (log) 0.479 (10.55)*** 0.487 (10.58)*** 0.483 (9.94)*** 10 Household members' working days to produce rice (log) 0.165 (5.6)*** 0.167 (5.63)*** 0.167 (5.47)*** 11 Cost of hiring labor (log) 0.012 (4.11)*** 0.013 (4.21)*** 0.011 (3.54)*** 12 Cost of fertilizer (log) 0.039 (3.39)*** 0.044 (3.75)*** 0.045 (3.68)*** 13 Cost of seed (log) 0.134 (5.8)*** 0.132 (5.65)*** 0.127 (5.28)*** 14 Cost of renting asset, machinery, equipment, means and cattle (log) 0.006 (1.75)* 0.006 (1.66)* 0.005 (1.37) 15 Productive assets (log) 0.009 (2.79)*** 0.009 (2.88)*** 0.010 (2.85)*** Farmer's characteristics 16 Average age of plot managers (log) 0.008 (0.32) 0.009 (0.33) 0.001 (0.02) 17 Average schooling years of plot managers (log) 0.047 (3.79)*** 0.049 (3.88)*** 0.060 (4.43)*** 18 Male plot managers -0.047 (-2.54)** -0.046 (-2.5)** -0.057 (-2.69)*** 19 Male and female plot managers -0.002 (-0.08) 0.002 (0.07) 0.007 (0.26) Other's factors 20 Ratio of land with better quality than average in the village 0.029 (0.5) 0.033 (0.56) 0.034 (0.51) 21 Number of cultivated plots (log) 0.053 (2.13)** 0.047 (1.76)* 0.048 (1.77)* 22 Visit agriculture extension 0.060 (3.17)*** 0.058 (2.98)*** 0.059 (2.84)*** 23 Ratio of land hit by disaster -0.133 (-4.22)*** -0.133 (-4.17)*** -0.131 (-4.15)*** 24 Constant 1.361 (5.86)*** 1.381 (5.88)*** 1.495 (6.2)*** Number of observations 2285 2285 2082 Source: calculated by author Other control variables include commune’s infrastructure (log of distance to main road, log of distance to district center, and percent of concrete commune roads) and dummy variables of provinces * Significant at 10%; ** significant at 5%; *** significant at 1% Random effects and standard error are clustered at commune level TEST RESULTS 2.1 Test for cluster effect: choosing between CSRE and OLS without cluster effect 2.1.1 Two-sided LM test suggested by Breusch and Pagan (1980) Model Breusch and Pagan Lagrangian multiplier test for random effects ln_production[commune,t] = Xb + u[commune] + e[commune,t] Estimated results: Var ln_prod~n e u Test: Var(u) = sd = sqrt(Var) 1.009284 1251438 0540137 1.004631 3537568 2324084 chibar2(01) = Prob > chibar2 = 1801.81 0.0000 Model Breusch and Pagan Lagrangian multiplier test for random effects ln_production[commune,t] = Xb + u[commune] + e[commune,t] Estimated results: Var ln_prod~n e u Test: sd = sqrt(Var) 1.009284 1271861 0537701 1.004631 3566315 2318838 Var(u) = chibar2(01) = Prob > chibar2 = 63 1613.52 0.0000 Model Breusch and Pagan Lagrangian multiplier test for random effects ln_production[commune,t] = Xb + u[commune] + e[commune,t] Estimated results: ln_prod~n e u Test: Var sd = sqrt(Var) 9727354 1298976 0546363 9862735 3604131 233744 Var(u) = chibar2(01) = Prob > chibar2 = 776.54 0.0000 2.1.1 One-sided LM test suggested by Moulton (1987) P(Z≤2.33) = 99% Model 1: 2365 ��1 = 3060.036 ( − 1) ≈ 30.015 × (44847 − 2365)1/2 491.045 30.015 > 2.33 ��Reject H0 Model 2: 2365 2953.197 ��2 = ( − 1) ≈ 28.404 × (44847 − 2365)1/2 496.271 28.404 > 2.33 ��Reject H0 Model 3: 2159 2016.282 ��3 = ( − 1) ≈ 19.705 × (38171 − 2159)1/2 451.684 19.705 > 2.33 ��Reject H0 2.2 Test for cluster effect: choosing between CSFE and OLS without cluster effect Model Fixed-effects (within) regression Group variable: commune Number of obs Number of groups = = 2365 341 R-sq: Obs per group: = avg = max = 6.9 50 within = 0.7488 between = 0.8326 overall = 0.7630 corr(u_i, Xb) F(16,2008) Prob > F = 0.2451 ln_production Coef Std Err t P>|t| [95% Conf Interval] 040352 4783494 1662538 0113262 0353684 1202812 0031571 005349 0073893 0370907 -.0363364 -.0011991 0436053 0646194 0553925 -.1290195 1.484605 0041859 0179098 0165338 0028593 0057813 0100401 0028614 0028119 0216692 0114713 0186844 0276622 0437297 0193401 0184321 0260837 1280002 sigma_u sigma_e rho 3894805 35375675 54795428 (fraction of variance due to u_i) F(340, 2008) = 5.63 0.000 0.000 0.000 0.000 0.000 0.000 0.270 0.057 0.733 0.001 0.052 0.965 0.319 0.001 0.003 0.000 0.000 374.18 0.0000 ln_land_size_irrigated ln_land_size ln_working_day ln_labor ln_fertilizer ln_seed ln_rent_equi_cattle ln_productive_assets ln_manager_avg_age ln_manager_avg_edu rice_manager_male rice_manager_male_female land_good_quality_ratio ln_cultivated_plot_num visit_agri_extension land_hit_disaster_ratio _cons F test that all u_i=0: 9.64 26.71 10.06 3.96 6.12 11.98 1.10 1.90 0.34 3.23 -1.94 -0.04 1.00 3.34 3.01 -4.95 11.60 = = 0321428 4432257 1338286 0057186 0240303 1005911 -.0024544 -.0001656 -.0351073 0145938 -.0729792 -.0554488 -.0421551 0266907 0192443 -.1801735 1.233577 Prob > F = 0.0000 0485612 513473 1986789 0169338 0467064 1399714 0087687 0108635 0498858 0595876 0003064 0530505 1293657 1025481 0915406 -.0778656 1.735632 Model Fixed-effects (within) regression Group variable: commune Number of obs Number of groups = = 2365 341 R-sq: Obs per group: = avg = max = 6.9 50 within = 0.7451 between = 0.8251 overall = 0.7558 corr(u_i, Xb) F(19,2005) Prob > F = 0.2330 Coef ln_land_size_canal ln_land_size_irrigated_river ln_land_size_irrigated_lake ln_land_size_irrigated_w_other ln_land_size ln_working_day ln_labor ln_fertilizer ln_seed ln_rent_equi_cattle ln_productive_assets ln_manager_avg_age ln_manager_avg_edu rice_manager_male rice_manager_male_female land_good_quality_ratio ln_cultivated_plot_num visit_agri_extension land_hit_disaster_ratio _cons 0274487 0207112 0090838 002252 4854077 1681948 0115753 0380806 118702 0029873 0056253 0067278 0389148 -.0358296 0019469 0464301 0599732 0537754 -.1262131 1.50658 003713 0035214 0086625 0095613 0180892 0166807 0028873 0058183 0101357 0028985 0028394 0218637 011601 0188469 0279975 0441513 0196455 0186406 0263177 1294971 sigma_u sigma_e rho 39540812 35663154 55142516 (fraction of variance due to u_i) F(340, 2005) = 5.58 t 7.39 5.88 1.05 0.24 26.83 10.08 4.01 6.55 11.71 1.03 1.98 0.31 3.35 -1.90 0.07 1.05 3.05 2.88 -4.80 11.63 P>|t| 308.50 0.0000 ln_production F test that all u_i=0: Std Err = = 0.000 0.000 0.294 0.814 0.000 0.000 0.000 0.000 0.000 0.303 0.048 0.758 0.001 0.057 0.945 0.293 0.002 0.004 0.000 0.000 [95% Conf Interval] 020167 0347304 0138052 0276171 -.0079046 0260722 -.0164991 0210031 4499321 5208834 1354814 2009081 0059128 0172377 0266702 0494911 0988244 1385795 -.0026972 0086718 0000568 0111938 -.0361501 0496058 0161635 0616661 -.0727911 0011319 -.0529603 0568541 -.0401571 1330173 0214455 0985008 0172184 0903323 -.1778259 -.0746003 1.252617 1.760543 Prob > F = 0.0000 Model Fixed-effects (within) regression Group variable: commune Number of obs Number of groups = = 2159 328 R-sq: Obs per group: = avg = max = 6.6 49 within = 0.7336 between = 0.8086 overall = 0.7460 corr(u_i, Xb) F(21,1810) Prob > F = 0.2549 ln_production Coef Std Err = = t 0192708 0219669 0158821 0164203 0014842 -.0039184 4798211 1728087 0102942 0384423 1120808 0022381 005522 -.0001624 0491586 -.0476536 0104066 0461176 0633567 0525153 -.119983 1.641513 0041928 0033265 004413 0034171 0089797 0099139 0191315 0175294 0030397 0062075 0105903 0030963 0030362 0233623 0124153 0200378 0300124 0477453 0210737 0197895 028123 1382312 sigma_u sigma_e rho 40990813 36041309 5639883 (fraction of variance due to u_i) F(327, 1810) = 5.10 0.000 0.000 0.000 0.000 0.869 0.693 0.000 0.000 0.001 0.000 0.000 0.470 0.069 0.994 0.000 0.018 0.729 0.334 0.003 0.008 0.000 0.000 [95% Conf Interval] ln_land_size_head ln_land_size_middle ln_land_size_tail ln_land_size_irrigated_river ln_land_size_irrigated_lake ln_land_size_irrigated_w_other ln_land_size ln_working_day ln_labor ln_fertilizer ln_seed ln_rent_equi_cattle ln_productive_assets ln_manager_avg_age ln_manager_avg_edu rice_manager_male rice_manager_male_female land_good_quality_ratio ln_cultivated_plot_num visit_agri_extension land_hit_disaster_ratio _cons F test that all u_i=0: 4.60 6.60 3.60 4.81 0.17 -0.40 25.08 9.86 3.39 6.19 10.58 0.72 1.82 -0.01 3.96 -2.38 0.35 0.97 3.01 2.65 -4.27 11.88 P>|t| 237.30 0.0000 0110475 0154427 0072271 0097184 -.0161275 -.0233623 442299 1384287 0043326 0262677 0913103 -.0038346 -.0004328 -.0459822 0248089 -.0869532 -.048456 -.0475241 0220253 0137027 -.1751399 1.370404 Prob > F = 0.0000 0274941 0284912 0245372 0231223 019096 0155254 5173432 2071887 0162558 0506169 1328513 0083108 0114768 0456575 0735084 -.008354 0692691 1397593 1046881 091328 -.0648261 1.912622 2.3 est for validity of CSRE: Choosing between CSRE and CSFE 2.3.1 Official Hausman test Model Coefficients (b) (B) a b ln_land_~ted ln_land_size ln_working~y ln_labor ln_fertili~r ln_seed ln_rent_eq~e ln_product~s ln_manager~e ln_manager~u rice_m~_male ric~e_female land_good_~o ln_cultiva~m visit_agri~n land_hit_d~o 040352 4783494 1662538 0113262 0353684 1202812 0031571 005349 0073893 0370907 -.0363364 -.0011991 0436053 0646194 0553925 -.1290195 0484388 517583 154163 013199 0465256 1287318 0086644 0095948 0191942 0599988 -.0425099 -.0043547 0801844 0049112 048544 -.1619439 (b-B) Difference sqrt(diag(V_b-V_B)) S.E -.0080868 -.0392336 0120907 -.0018728 -.0111573 -.0084505 -.0055073 -.0042459 -.0118049 -.0229081 0061734 0031555 -.0365791 0597082 0068484 0329244 0008702 0074513 0037564 0006979 0008117 0020226 0006697 000676 0006292 0029514 0030901 0026761 0033736 0080008 0040303 0065383 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(16) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 784.97 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) Model Coefficients (b) (B) a b ln_land_s~al ln_land_~ver ln_land_s~ke ln_l~w_other ln_land_size ln_working~y ln_labor ln_fertili~r ln_seed ln_rent_eq~e ln_product~s ln_manager~e ln_manager~u rice_m~_male ric~e_female land_good_~o ln_cultiva~m visit_agri~n land_hit_d~o 0274487 0207112 0090838 002252 4854077 1681948 0115753 0380806 118702 0029873 0056253 0067278 0389148 -.0358296 0019469 0464301 0599732 0537754 -.1262131 0348331 021593 0059647 0024529 5273152 1550098 0138201 0509887 1269343 0084624 0102555 0189405 0624784 -.0418215 -.0021933 083983 -.003442 0440817 -.1629832 (b-B) Difference -.0073844 -.0008818 0031191 -.0002009 -.0419074 013185 -.0022448 -.0129081 -.0082323 -.0054751 -.0046302 -.0122126 -.0235636 0059919 0041402 -.0375529 0634152 0096936 0367701 sqrt(diag(V_b-V_B)) S.E .00079 0073378 003652 0006981 0008702 0019909 0006292 0006651 0028452 0028804 0022774 0021248 0079879 0039607 0063546 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(19) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 345.03 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) Model Coefficients (b) (B) a b ln_land_s~ad ln_land_s~le ln_land_s~il ln_land_~ver ln_land_s~ke ln_l~w_other ln_land_size ln_working~y ln_labor ln_fertili~r ln_seed ln_rent_eq~e ln_product~s ln_manager~e ln_manager~u rice_m~_male ric~e_female land_good_~o ln_cultiva~m visit_agri~n land_hit_d~o 0192708 0219669 0158821 0164203 0014842 -.0039184 4798211 1728087 0102942 0384423 1120808 0022381 005522 -.0001624 0491586 -.0476536 0104066 0461176 0633567 0525153 -.119983 0259705 0256142 0165607 0158265 -.003234 -.0050048 5314026 1541943 0130965 0540461 1240828 0080703 0107852 0105755 069659 -.0490088 0083994 0830536 -.0117418 0476751 -.1662743 (b-B) Difference -.0066997 -.0036473 -.0006786 0005938 0047183 0010864 -.0515815 0186144 -.0028023 -.0156038 -.012002 -.0058323 -.0052632 -.0107379 -.0205003 0013552 0020071 -.036936 0750985 0048403 0462913 sqrt(diag(V_b-V_B)) S.E .0010679 0008768 0008839 0077348 0035999 0006589 0009911 0016925 0006911 0006908 0029074 0026483 0026976 0044608 008196 0039117 00682 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(21) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 241.78 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 2.3.2 Robust Wald test for auxiliary regression suggested by Wooldridge (2010) Model Test of overidentifying restrictions: fixed vs random effects Cross-section time-series model: xtreg re robust cluster(commune) Sargan-Hansen statistic 180.083 Chi-sq(16) P-value = 0.0000 Model Test of overidentifying restrictions: fixed vs random effects Cross-section time-series model: xtreg re robust cluster(commune) Sargan-Hansen statistic 184.385 Chi-sq(19)P-value = 0.0000 Model Test of overidentifying restrictions: fixed vs random effects Cross-section time-series model: xtreg re robust cluster(commune) Sargan-Hansen statistic 220.140 Chi-sq(21) P-value = 0.0000 2.4 Multicollinearity test (VIF) Model Variable VIF ln_land_size ln_working_day ln_seed ln_fertilizer rice_manager_male_female ln_cultivated_plot_num ln_land_size_irrigated ln_labor ln_manager_avg_edu ln_rent_equi_cattle rice_manager_male land_hit_disaster_ratio ln_manager_avg_age ln_productive_assets visit_agri_extension land_good_quality_ratio 3.04 2.42 2.06 1.50 1.36 1.33 1.33 1.28 1.27 1.23 1.22 1.14 1.11 1.09 1.09 1.02 Mean VIF 1.47 1/VIF 0.329377 0.413868 0.484714 0.667339 0.733066 0.752405 0.754046 0.782679 0.789449 0.810258 0.81651 0.877929 0.902755 0.917964 0.921489 0.976448 Model Variable ln_land_size ln_working_day ln_seed ln_land_size_canal ln_land_size_irrigated_river ln_fertilizer ln_cultivated_plot_num rice_manager_male_female ln_manager_avg_edu ln_labor ln_rent_equi_cattle rice_manager_male land_hit_disaster_ratio ln_land_size_irrigated_lake VIF 3.10 2.42 2.07 1.85 1.59 1.46 1.38 1.37 1.29 1.28 1.26 1.23 1.15 1.12 1/VIF 0.322164 0.412561 0.483969 0.539175 0.627803 0.685807 0.722177 0.729403 0.772569 0.782857 0.795145 0.813427 0.870578 0.893432 ln_manager_avg_age ln_productive_assets visit_agri_extension ln_land_size_irrigated_w_other land_good_quality_ratio 1.11 1.11 1.09 1.05 1.03 Mean VIF 1.47 0.901679 0.90349 0.916819 0.949933 0.972634 Model Variable VIF ln_land_size ln_working_day ln_seed ln_land_size_middle ln_cultivated_plot_num ln_land_size_irrigated_river ln_fertilizer rice_manager_male_female ln_manager_avg_edu ln_labor ln_rent_equi_cattle ln_land_size_head rice_manager_male ln_land_size_tail land_hit_disaster_ratio ln_productive_assets ln_manager_avg_age visit_agri_extension ln_land_size_irrigated_lake land_good_quality_ratio ln_land_size_irrigated_w_other 3.04 2.38 1.98 1.61 1.48 1.41 1.40 1.36 1.35 1.28 1.26 1.26 1.23 1.21 1.17 1.12 1.12 1.10 1.08 1.04 1.03 Mean VIF 1.42 1/VIF 0.329 0.420 0.506 0.621 0.676 0.709 0.713 0.735 0.741 0.782 0.791 0.796 0.811 0.825 0.858 0.894 0.896 0.907 0.928 0.965 0.969 2.5 Heteroskedasticity test for CSFE model Model Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (341) = Prob>chi2 = 1.9e+33 0.0000 Model Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (341) = Prob>chi2 = 3.6e+32 0.0000 Model Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (328) = Prob>chi2 = 8.3e+33 0.0000 ... lowland rice, rainfed lowland rice, deepwater rice, floating rice, and upland rice (Maclean, Hardy, & Hettel, 2013) Particularly, lowland rice is characterized that rice fields are bunded and. .. survey is conducted in 12 provinces which include provinces in Northern, provinces in Central Coastal, provinces in Central Highlands, and province in Mekong River Delta For each province, some communes... ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS IRRIGATION AND RICE PRODUCTION: EVIDENCE IN VIETNAM