Income diversification and the role of non-farm activities : A case of rural Vienam

I t' INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM VIETNAM- NETHERLANDS PROGRAMME FORMA IN DEVELOPMENT ECONOMICS INCOME DIVERSIFICATION AND THE ROLE OF NON-FARM ACTIVITIES: A CASE OF RURAL VIETNAM By LEVINHHOA MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, MAY 2011 ~· r ~ UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FORMA IN DEVELOPMENT ECONOMICS INCOME DIVERSIFICATION AND THE ROLE OF NON-FARM ACTIVITIES: A CASE OF RURAL VIETNAM A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS Academic Supervisor: Dr HA THUC VIEN HO CHI MINH CITY, MAY 2011 ACKNOWLEDGEMENTS First of all, I appreciate and sincere gratitude to the Vietnam Netherlands Program for • granting me an academic course to pursue the Master of Art in Development Economic I would like to give my sincere thank to my supervisor professor Dr Ha Thuc Vien, who has straight accepted to guide me in this thesis He supervised me with enthusiasm, efficiency and pragmatism, and always provided me with ideas and guidance During my thesis, I have benefited from the assistance of Mr Nguyen Trung Hieu, Mrs Nguyen Thi Kim Cue (Vietnam- Netherlands Programme for M.A in Development Economics) I want to thank Mrs Nguyen Thi Kim Cue for her very pertinent comments and frankness I've really appreciated Mr Nguyen Trung Hieu availability for technical help as well as for guiding me towards a higher level in econometrics Professor Peter Calkins has also commented on an earlier version of this thesis I would also like to express my sincere thanks to all of you HCMC, Summer 2011 LE VINHHOA ECLARATION declare that "INCOME DIVERSIFICATION AND THE ROLE OF NONARM ACTIVITIES: CASE OF VIETNAM" is my own work, that it has not been ubmitted to any degree or examination at any other universities, and that all the ources used or quoted are indicated and acknowledged by complete references HCMC, April 2011 LE VINHHOA 11 ABSTRACT his thesis examines how diversification level of household income in Vietnam The suits show a trend in increasing number of income resources The fact that one third f the households in the studied have not engaged yet in any form of economic iversification (if exclude money transfer), both wage and non-farm activities ontribute to an increased average total household income Increasing rural income trongly relies upon the development of non-farm activities, including the evelopment of a local rural industry, tourism as well as migration Non-farm ctivities are a part of rural economy in Vietnam today and it is a significant income ontributor for many rural households Non-farm income has good impacts on ousehold income and agricultural sector is not the leading employment sector anymore 111 TABLE OF CONTENTS CKN WLEDG EMENTS i ECLARATI N ii BSTRACT iii ABLE OF CONTENTS iv 1ST OF TABLE vi 1ST OF FIGURES vii HAPTER 1: INTRODUCTION 1 Problem statement The objectives of study 3 Research question Structure of thesis HAPTER II: LITERATURE REVIEW Definitions: Factors effect to income diversification Household non-farm activities 3.1 Declining a share of agriculture in GDP and labor 3.2 Increasing role of non-farm activities in household economy Empirical Literature 11 CHAPTER III: RESEARCH METHODOLOGY 18 Model specification-dependent variable 18 Model specification-independent variables 21 Econometric Model 22 CHAPTER IV: DATA ANALYSIS AND DISCUSSIONS 26 Data description 26 IV Descriptive statistics 28 Household's income diversification 30 Income diversification by the number of income sources 31 3.2 Measurement of income share diversity 33 Roles of non-farm activities in Vietnam's rural household economy 36 Econometric evidence 40 HAPTER V: CONCLUSIONS AND RECOMMENDATIONS 45 Conclusions and recommendations .45 Limitations 46 EFERENCES 48 PPEND IX 52 v LIST OF TABLE Table 4.1: Structure of family income in the 2008 survey 26 Table 4.2: Descriptive statistics (N = 9189) 28 Table 4.3: Structure of employed population by kind of economic activity 30 Table 4.4 Trends in income diversification, by the number of income sources 32 Table 4.5: Trends of income diversification, by income shares 35 Table 4.6: Income of household with and without non-farm income 38 Table 4.7: Detailed non-farm activities of household 39 Table 4.8: Status of Training and Education of household 39 Table 5.1: Regression results of diversification index 41 Table 5.2: Regression result ofhousehold income 40 VI LIST OF FIGURES Figure 2.1: Share of labor and GDP in agriculture Figure 3.1: Distribution of the Shannon equitability index 21 Figure 4.1: Family income and the number of family income sources 31 Figure 4.2: Number ofhouseholds and number of income sources 31 vii CHAPTER I: INTRODUCTION Problem statement A renovation process of Vietnam, commonly known as "Doi Moi", was officially launched in 1986 and has spent about two and haft decades The country has transformed from an economy centrally planned economy to a dynamic market One of the fundamental reforms was the legalization of most forms of private economic activities (including households and businesses), and the removal of price controls on almost all products and services Most of the reforms affected the rural sector, where farmers were given a greater freedom in the choice of their production, and price distortions were slowly diminished Agriculture directly benefit to the majority of Vietnam's population whose livelihoods are closely dependent on small-scale agricultural self-sufficiency in rural areas (Benjamin and Brandt, 2004) Higher yields of rice and other crops after have allowed Vietnam to become the second largest rice exporter without expansion in rice area and reduction in domestic consumption while it had been a rice importer in the mid-eighties (Minot and Goletti, 2000) As farmers were given the choice of their agricultural production with new exporting possibilities, other kind of crops started to be grown, such as pepper or rubber Vietnam has become the second largest coffee producer in the world, and production and export of fruits and vegetables have risen dramatically over this period And part of the income growth is undoubtedly due to diversification into other -= activities such as aquaculture, livestock, and non-farm activities with substantial tructural changes towards more industry and services a~ditional member m a household increases the probability of undertaking d"versification by a factor of 0.09 Table 5-1: Probit Regression results of diversification index none diver age ~ge2 ~istance edu edulev numedu lg_ender dependency house size land _p_ c training vi !!age dens region cons -0.06102 -0.21442 0.106196 -6.74E-06 0.283806 4.79E-05 Robust Std Err 0.011991 0.000109 0.000936 0.03272 0.015563 0.044223 0.072228 0.094159 0.024204 4.72E-06 0.106385 6.17E-05 0.030517 0.069143 0.241109 0.535129 0.46881 0.368943 0.370325 -0.33365 0.104069 0.157093 0.114777 0.163011 0.173362 0.130965 0.10504 0.342819 Coef 0.065174 -0.00066 -0.00175 -0.02551 0.032327 -0.0489 z 5.44 -6.02 -1.87 -0.78 2.08 -1.11 P>lzl -0.84 -2.28 0 0.061 0.436 0.038 0.269 0.398 0.023 4.39 -1.43 2.67 0.78 0.154 0.008 0.438 0.29 0.44 2.1 3.28 2.7 2.82 3.53 -0.97 0.769 0.66 0.036 0.001 0.007 0.005 0.33 [95% Con f 0.041673 -0.00087 -0.00359 -0.08964 0.001825 -0.13557 -0.20258 -0.39897 0.058756 -1.6E-05 0.075294 -7.3E-05 -0.17345 -0.23875 0.016151 0.215634 0.129026 0.112257 0.16445 -1.00556 Interval] 0.088675 -0.00044 8.04E-05 0.038621 0.062829 0.037778 0.080548 -0.02987 0.153635 2.52E-06 0.492317 0.000169 0.234488 0.377039 0.466067 0.854624 0.808595 0.625629 0.576201 0.338265 Second, household with members has training in non-farm work is associated with a much higher probability of diversification index with the factor of 0.28 These results ~an be explained by increasing returns to scale in household chores for households with a larger size and more labor availability that makes it easier for them to let some !members engage in others activities Studies of Dercon and Krishnan ( 1996) in Ethiopa and of Tanzania and Micevska and Rahut (2008) in India find similar results Third, the presence of old members strongly reduces the likelihood of households to participate in migration (and to a lesser extent to engage in diversification): a higher 41 ependency ratio of the household reduces the labor availability for migration As for he decision to diversify agricultural production, more arable land per adult also ncreases the likelihood of farm diversification As a general rule, local non-farm ecision is driven by households' asset position rather than by human/social capital or ousehold composition esults of Liner regression on diversification index is shown in Table 5-2 (see ppendix for more details) Basing on these results, several discussions might be resented as following: Table 5-2: Liner Regression results of diversification index diver index age age2 distance edu edulev numedu gender dependency house size land p c training village dens regiOn cons Coef 0.496044 -0.0039 -0.03639 -0.28528 0.682239 0.237208 0.407125 -2.10355 0.393301 -0.00062 4.976478 0.000159 Robust Std Err 0.132329 0.001229 0.00843 0.306865 0.155369 0.349843 0.720861 0.907021 0.183138 0.00012 0.791587 0.000419 -5.01766 -10.7401 -1.86441 1.071866 -5.28486 -4.38463 -5.64842 28.55018 0.986004 1.567864 0.943379 1.072059 1.349849 1.184064 0.967929 3.567531 (1) Demographic factors: t 3.75 -3.18 -4.32 -0.93 4.39 0.68 0.56 -2.32 2.15 -5.15 6.29 0.38 -5.09 -6.85 -1.98 -3.92 -3.7 -5.84 Household 0.002 0.353 0.498 0.572 0.02 0.032 0 0.705 [95% Con f 0.236635 -0.00631 -0.05292 -0.88684 0.377663 -0.4486 -1.00601 -3.88162 0.034288 -0.00085 3.424696 -0.00066 0 0.048 0.317 0 0 -6.95057 -13.8136 -3.71376 -1.02974 -7.93103 -6.7058 -7.54589 21.55659 P>lti s1ze has a positive Interval] 0.755454 -0.00149 -0.01986 0.316281 0.986816 0.92302 1.820259 -0.32548 0.752315 -0.00038 6.52826 0.000981 -3.08475 -7.66652 -0.01506 3.17347 -2.63869 -2.06346 -3.75095 35.54377 effect on the diversification index The larger household size, diversify trend of household 42 mcrease Gender of household head have not significant on diversification index and cannot give any comment about it Age of household head is significant and show that when the header older, they had more experience in life and lead household to have more income resource (2) Education: Education lever of household member which have a statistically significant effect plays a key role in household diversification, the higher level of education they are, the more diversification trend This finding is similar to results of other studies conducted by Corral and Reardon (200 1), Yunez and Taylor (2001) and de Janvry and Sadoulet (2001) Form their empirical studies, they come to conclusions that that more educated households earn more overall income but not more farm income The overall impact of education on income is positive and large The decrease in the share of income from wage employment is explain by the higher level of education, people had trend to go to big city and find a better job or join in non-farm activities to get more income More education they attain, the more total income they earn Thus, better education enables them with more opportunities of highly paid jobs Datt and Jolliffe (2005) suggest that education variable is a strong determinant of household living standards, in both rural and urban areas They find some substitutability between education and land ownership Furthermore their results show that adult's education has a strong positive effect on household welfare in rural and urban areas Likewise El-Laithy, Lokshin and Banerji (2003) find that education is the factor that mostly affects households' escape ; from poverty (3)Household assets and community variables: Per capita landholding very small reduces diversification index With regard to community level variables, 43 village population density increases diversification index This is a reasonable and easy to understand when density of village increase, not enough land for agriculture and then villager need to find new source of income The variable distance from village to nearest urban center not have significant but still indicated that households living far from urban center, income of household in village decrease 44 HAPTER V: CONCLUSIONS AND RECOMMENDATIONS Conclusions and recommendations his study was motivated to review current status of diversification level of household i come and examine the role of non-farm income in total income of Vietnam's ousehold The analysis reported here was based on cross-section drew on a amework that conceptualized diversification as a product of household capacity ariables and "incentives to diversify" First, the fact that one third of the households the studied have not engaged yet in any form of economic diversification (if xclude money transfer), both wage and non-farm activities contribute to an increased verage total household income The empirical evidence in chapter showed that over an increasing movement in number of household income's source, specially in richest household Second, non-farm activities are a part of rural conomy in Vietnam today and it is a significant income contributor for many rural ouseholds, especially in many parts of Vietnam, where villages in mountainous egions are characterized by land scarcity, increasing household income strongly relies pon the development of non-farm activities, including the development of a local ral industry, tourism as well as migration The income quintiles results also show hat despite the vast differences in the levels of development, in geographical onditions and in the institutional structure, non-farm income is importance source elps to increase total household income Non-farm income has positive impacts on ousehold income and agricultural sector is not the leading sector anymore n order to increase family income, households follow two strategies, in some cases his is a mixture of both First, they increase number of income sources, primarily 45 tl om the self-employment activities Second, family members can increase family i come more if they work in different sectors of rural economy Conditions for s ccess are based on the ability to increase access to non-farm activities for all ouseholds, particularly for households with little human, land The econometric alysis shows that key determinant of success is well functioning labor markets in oth rural and urban areas, including education which most affect wage income and t aining for non-farm income egarding education and training, government intervention through the lowering of ducation and training costs is also required Although the average level of education ttainment has increased over time, Vietnam is lagging far behind of its Asian eighbors in terms of both investment in rural education and educational attainment nd remains quite low in skill labor The results confirm that better educated people re able to take more wages earning jobs and having training make them get more ·ncome from non-farm activities People in the rural area can be learning and training njob skill for increased wage opportunities as well as non-farm activities that leads to mproved social well-being, household income and satisfactory livelihoods Together ith the need for higher investment in rural education, the results also suggest that on he supply side, efforts must be done in urban areas to give better access to skilled obs to migrant people If migrants were to be given an equal access to urban skilled obs as compared to urban residents, higher expected returns to education and training ould probably pull more educated people out of farm jobs Limitations his thesis has some limitations With 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365 51 APPENDIX distance diver_index summarize age age2 edu edulev numedu gender dependency ouse_size land_p_c training village_dens Max Min Std Dev Mean Obs variable I + -age 6504 49.46817 13.72242 16 97 age2 6504 2635.376 1485.211 256 9409 edu 6504 1.337946 1.345635 12 edulev 6504 986716 966165 12 6504 8085793 1.085226 numedu + -gender dependency distance diver_index house_size 6504 6504 6504 6504 6504 2021833 2946816 39.25969 41.94158 4.198493 4016591 316826 35.70457 23.47788 1.681861 0 0 1 446 99.85677 15 land_p_c training illage_dens 6504 6504 6504 1897.226 1646679 661.8885 4340.304 3709091 749.9889 0 3461 126675 15661.4 + 52 probit none_diver age age2 distance edu edulev numedu hquse_size land_p_c training village_dens i.region , vee >/(robust) 'fration ,, I I I I • "' eration eration eration eration log log log log log 1: 2: 3: 4: pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood pseudolikelihood = = = = = none_diver I = Number of obs Wald chi2 (19) Prob > chi2 Pseudo R2 -933.83116 Coef dependency -1017.5992 -937.15031 -933.84164 -933.83116 -933.83116 Pfobit regression Lbg pseudolikelihood gender Robust Std Err z P>lzl = = = = 6504 138.09 0.0000 0.0823 [95% Conf Interval] + age age2 distance edu edulev numedu gender dependency house_size land_p_c training illage_dens • ,, ~ I I region 1 I I I I I I I I I I I I I I I I I I cons I 0651739 -.0006555 -.0017534 -.025508 0323269 -.0488981 -.0610157 -.2144217 1061956 -6.74e-06 2838056 0000479 0119907 0001089 0009356 0327197 0155627 0442234 0722278 0941592 0242044 4.72e-06 1063852 0000617 5.44 -6.02 -1.87 -0.78 2.08 -1.11 -0.84 -2.28 4.39 -1.43 2.67 0.78 0.000 0.000 0.061 0.436 0.038 0.269 0.398 0.023 0.000 0.154 0.008 0.438 0416725 -.0008689 -.0035871 -.0896374 0018245 -.1355743 -.2025795 -.3989703 0587558 -.000016 0752944 -.0000731 0886753 -.0004421 0000804 0386214 0628293 0377781 0805482 -.0298731 1536354 2.52e-06 4923169 0001688 0305174 0691426 2411086 5351287 4688104 3689429 3703253 1040685 1570926 1147765 1630106 1733624 1309648 1050403 0.29 0.44 2.10 3.28 2.70 2.82 3.53 0.769 0.660 0.036 0.001 0.007 0.005 0.000 -.173453 -.2387532 0161507 2156337 1290263 1122566 1644502 2344879 3770385 4660665 8546236 8085945 6256292 5762005 -.3336476 3428187 -0.97 0.330 -1.00556 3382648 c ~ '~ 53 regress diver_index age age2 distance edu edulev hquse_size land_p_c training village_dens i.region if > lnone_diver ==1 , vce(robust) gender Number of obs F( 19, 6247) Prob > F R-squared Root MSE Linear regression - dependency 6267 29.19 0.0000 0.0890 21.439 ' iver_index I Coef Robust Std Err t P>ltl [95% Conf Interval] -+ -age age2 distance edu edulev numedu gender dependency house_size land_p_c training illage_dens 4960444 -.0039031 -.0363908 -.2852792 6822394 2372084 4071246 -2.103547 3933013 -.0006172 4.976478 0001589 1323285 0012292 0084304 3068649 1553687 3498427 7208607 9070205 183138 0001199 7915866 0004191 3.75 -3.18 -4.32 -0.93 4.39 0.68 0.56 -2.32 2.15 -5.15 6.29 0.38 0.000 0.002 0.000 0.353 0.000 0.498 0.572 0.020 0.032 0.000 0.000 0.705 236635 -.0063128 -.0529173 -.8868399 3776633 -.4486035 -1.00601 -3.881619 0342879 -.0008524 3.424696 -.0006627 7554537 -.0014933 -.0198642 3162814 9868156 9230203 1.820259 -.3254753 7523147 -.0003821 6.52826 0009805 region -5.017661 -10.74007 -1.864411 071866 -5.28486 -4.38463 -5.648417 9860037 567864 9433789 072059 1.349849 1.184064 9679286 -5.09 -6.85 -1.98 00 -3.92 -3.70 -5.84 0.000 0.000 0.048 0.317 0.000 0.000 0.000 -6.950567 -13.81363 -3.713758 -1.029739 -7.931028 -6.705803 -7.54589 -3.084754 -7.666521 -.0150639 3.17347 -2.638691 -2.063458 -3.750945 28.55018 3.567531 8.00 0.000 21.55659 35.54377 I I I I I I I I cons I • ,~' , l , , numedu ' 54 •