Determinants of income diversification and its effects on household income in rural Vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF INCOME DIVERSIFICATION AND ITS EFFECTS ON HOUSEHOLD INCOME IN RURAL VIETNAM BY HO THI NGOC DIEP MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, JANUARY 2013 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF INCOME DIVERSIFICATION AND ITS EFFECTS ON HOUSEHOLD INCOME IN RURAL VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By HO THI NGOC DIEP Academic Supervisor: Dr HA THUC VIEN HO CHI MINH CITY, JANUARY 2013 ACKNOWLEDGEMENTS With these words, I would like to express my sincere thank to all who have supported me during my journey to complete the Master program in general and this thesis in particular First of all, I would like to thank the Board of Faculties of the University of Economics of Ho Chi Minh City and International Institute of Social Studies (ISS) for providing me with all fruitful and precious academic knowledge during the master program The Thesis could not have been designed and completed without the support from my Professors in the University of Economics and ISS I would like to show my deepest gratitude to Dr Ha Thuc Vien and Dr Tran Tien Khai for all their invaluable comments and fruitful guidance from the very beginning of the formatting of the topic My special thanks go to Dr Ha Thuc Vien for his academic supervision, inspiration through the progress of my thesis writing In addition, I would like to express my sincere thank to Dr Pham Khanh Nam, Mr Nguyen Van Dung for their guidance on technical issues used for the analysis of the thesis Last but not least, I am deeply indebted to my family members: my parents, my parents – in law, my sisters for all their understanding and supports during my study I would like to express my special thanks to my mother and my motherin-law, who have helped me to take care of my little baby so that I can concentrate on my thesis Finally, I would like to thank my husband who is always besides me, encouraging and helping me with daily life, so that I can spend most of my time on completing the thesis TABLES OF CONTENTS Acknowledgements List of tables, figures Abstracts CHAPTER INTRODUCTION 1.1 Problem Statement 1.2 Research objectives 1.3 Research questions CHAPTER LITERATURE 2.1 Concepts and measures of income diversification 2.2 Theoretical framework 2.3 Determinants of income diversification 2.4 Previous studies on income diversification in Vietnam 10 CHAPTER DATA AND RESEARCH METHODOLOGY 12 3.1 Data 12 3.2 Research methodology 12 3.2.1 Classification and calculation of income sources 12 3.2.2 Indicators of income diversity 13 3.2.3 The method of analysis 15 3.3 Chapter remarks 16 CHAPTER FINDINGS AND DISCUSSION 17 4.1 Patterns and trends in income diversification 17 4.1.1 Diversity of income sources 17 4.1.2 Diversification as a shift to non-farm activities 21 4.1.3 Diversification as commercialization production 24 4.2 Econometric results and discussion 29 4.2.1 Expected sign of determinants of income diversification 31 4.2.2 Determinants of income diversification (number of income sources) 33 4.2.3 Determinants of income diversification (Simpson index of diversity) 35 4.2.4 Determinants of income diversification (share of non-farm income in total income) 38 4.2.5 Impact of income diversification on total income of household 42 4.3 Chapter remarks 45 CHAPTER CONCLUSIONS AND RECOMMENDATIONS 46 LISTS OF TABLES AND FIGURES TABLES Table Measures of diversity in income sources and Simpson index of diversity in rural areas by regions across years 18 Table Measures of diversity in number of income sources and Simpson index of diversity in rural areas by income quintile across years .20 Table Share of non-farm income in total income by income quintile across years 23 Table 4 Measure of commercialization by regions and year 26 Table Measure of commercialization by income quintile and year 28 Table Descriptive statistics for the dependent and independent variables 29 Table 4.7 Hypotheses regarding impact of independent variables on measures of income diversification 31 Table Determinants of income diversification (NIS) 36 Table Determinants of income diversification (SID) and (NFS) 37 Table 10 Summary of results on determinants of income diversification .41 Table 11 Impacts of income diversification on total income of household 44 FIGURES 2.2 The Sustainable Livelihood Framework……………………………… Trends in income composition of rural households……………………22 Share of nonfarm income in total income of rural households……… 23 Share of output sold or bartered by region and year………………… 25 ABSTRACT Income diversification has been a special attention of researchers, especially in developing countries It is a means to increase household’s income and reduce risks of income volatility of each income source However, the patterns and trends of income diversification vary from country to country and from region to region This research aims at examining the determinants of income diversification among rural households in Vietnam and the impact of diversification on household’s total income in order to decide appropriate policy responses Based on Vietnam Household Living standard surveys in 2002, 2004, 2006, 2008 and 2010, the descriptive analysis on a variety of concepts of diversification shows that the diversification in rural areas is very common and tends to increase over time For instance, a number of income sources among rural households goes up to from 4.08 in 2002 to 4.28 in 2010 The analysis also indicates the growing importance of non-farm activities Nevertheless, the extent of diversification is not the same between the rich and the poor The poorer tend to have more income sources than the richer while the richer is much more diversified in terms of share of income from non-farm activities than the poorer The econometric analysis uses methods of Poisson regression in the model of number of income sources and Tobit regression in the model of SID and NFS The regression results show that socio-economic status and access to formal financial market both have positive impact on the number of income sources pursued by households and the Simpson index of diversification Interestingly, it is found that the access to financial markets has negative effect on the share of non-farm income The accessibility of infrastructure is also an important determinant of income diversification The evaluation of reverse impact of diversification on household’s total income confirms that all of the three indicators of income diversification: a number of income source, share of non-farm income and Simpson index of diversity have positive impact on household’s total income It implies that households try to increase their income by pursuing multiple income strategy, expanding their income generating out of agricultural activities and maintaining the balance among different income activities CHAPTER INTRODUCTION 1.1 Problem Statement Income diversification among rural household in developing countries has called for substantial attention of scholars in development economics It is referred to the allocation of resources among different income generating activities, both on-farm and off-farm, according to Abdulai and Crolerees (2001) There are several motives for households to diversify their income sources Households tend to carry out the income diversification because of the need to manage risks, to secure a smooth flow of income, to allocate the surplus labor or to respond to different kinds of market failures such as insurance and credit market imperfection (Ellis, 1998) Given the potential role of income diversification in stabilizing income and alleviating rural poverty, governments in several developing countries are increasingly interested in promoting diversification And Vietnam with more than 70% of the population lives in rural areas is not an exception Since 1986, the Government has implemented various policies with an aim to developing multi-sector economy, renovating the economic structure and stabilizing the social economic environment including improving people’s living standards and opening the country’s economy to the rest of the world For rural development, some specific objectives raised are to create more jobs, to raise agricultural and rural industry-related income, and to develop services and offfarm activities In other words, these policies are designed to directly or indirectly stimulate the process of income diversification in Vietnam in general and in rural areas in particular With the nation-wide renovation, Vietnam has gained some remarkable achievements in economic development and poverty reduction, with the annual economic growth rate of 6-8 percent since the early 1990 and the poverty rate falling from 58% in 1993, 29% in 2002, 15.5% in 2006 to 14.5% in 2008 and 14.23% in 2010 (GSO) Part of income growth and poverty reduction is undoubtedly due to diversification among households both into higher value crops and into non-crop activities such as livestock raising, and non-farm activities Though income diversification plays such an important role in the early stage of rural transformation, the patterns of rural income diversification may vary across countries and regions (Ellis, 1998) Hence, it is necessary to identify the determinants of income diversification of specific countries and regions as it helps government have appropriate policy response to support the rural areas While there are very few empirical studies about the income diversification issue in Vietnam, the research on the impact of diversification on household income is even rarer Based on the empirical studies and data from Vietnam Household Living Standards Survey 2008 (VHLSS 2008), this paper is aimed at determining the factors that affect the ability to carry out income diversification among households in rural Vietnam and to measure the impacts of diversification on household incomes 1.2 Research objectives The paper is to investigate determinants of income diversification among households in rural areas of Vietnam, and measure the difference in the level of impact of these factors among economic and geographical regions This paper also aims at examining the reverse effects of income diversification on the household income 1.3 Research questions Specifically, the paper tries to address the following questions: - What are the determinants of income diversification in rural Vietnam at household level? - How does the level of income diversification differ among rural regions within Vietnam? Period that a road is passible (road_pass) 0.0014 0.0012 0.0065** 0.0031 0.0278*** 0.0052 -0.0264** 0.0116 North East 0.0059 0.0091 -0.0857*** 0.0201 North West -0.0354** 0.014 -0.0921*** 0.0311 North Central Coast 0.0189** 0.0085 -0.2062*** 0.0194 South Central Coast -0.0202** 0.0103 -0.0422** 0.0214 Central Highlands -0.0792*** 0.0125 -0.3267*** 0.0303 Southeast -0.0958*** 0.0106 -0.0684*** 0.023 Mekong River Delta -0.0590*** 0.0089 -0.1497*** 0.0201 Income quintile 0.0027 0.0074 0.0926*** 0.0177 Income quintile -0.0035 0.0082 0.1393*** 0.0191 Income quintile -0.0175* 0.0091 0.1688*** 0.0207 Income quintile -0.0101 0.011 0.2160*** 0.0237 0.3284 0.0253 -0.0295 0.0601 Access to formal credit (credit) Geographical regions Income quintile 2008 _cons N 6058 6058 R2 -0.2416 0.1672 F – statistics 19.69 68.15 Note: (*) 91 left-censored observations at SID=1 (**) 1826 left-censored observations at NFS=1 *, **, *** Coefficients are significant at the 10%, 5%, 1% level respectively Source: Vietnam Household Living Standard Survey (VHLSS) 2008 4.2.4 Determinants of income diversification (share of non-farm income in total income) This model is to examine the determinants of the share of income generating from non-farm activities, including the non-farm wage labor and the selfemployment in non-farm enterprises As shown in Table 4.9, the ethnic minority household head earns 15.78 percent points less from non-farm 38 activities than the Kinh household head, holding other variables constant This implies that the minority have fewer opportunities to access to non-farm wage jobs, which may be explained that most minority groups live in remote or mountainous areas where the condition is more convenient for the agricultural activities than non-farm ones Age of household head is significantly and negatively associated with the non-farm income share, and so is the male household head compared to female household head in earning from non-farm activities As expected, average education of household members is significantly and positively related to the share of non-farm income, indicating that education provides households with knowledge and skills required for skilled wage-jobs in non-farm field, as well as for establishing and managing household’s own non-farm enterprises Large size households with fewer older and fewer children members have more likelihood to diversify their income out of agricultural activities In other words, the more working-age adults the household has, the larger proportion of income earning from non-farm activities This could be explained that with more working-age people, a household can have more labor involve in non-farm activities due to the decrease in the marginal return of farm output to the labor input The availability of electricity and tap water is strongly and positively related to the share of income from non-farm activities Controlling other variables, household with electricity has higher share of 8.02 percent points of non-farm income compared to household without electricity Similarly, this figure for tap water is 10.10 percent points The variables of market access such as distance to a daily market and access to main roads are negatively and significantly associated with the share of income from non-farm activities This is due to higher transaction and transportation costs born by self-employment enterprises of households living far from roads 39 and market centers Similarly, the longer period that a road is passable, the higher share of non-farm income the household in that area has This highlights the importance of market access in the share of income from non-farm activities It is surprising to note that access to formal credit is significantly and negatively correlated with the share of non-farm income This may be because the fact that households living in rural areas use most of their credit from formal financial organizations to invest in agricultural activities rather than in non-farm ones It is also shown that comparing to the reference region (the Red River Delta), after controlling other variables, all other regions have lower share of income from non-farm activities, especially the North Central Coast, Central Highlands with 20.62 percent points and 32.67 percent points lower respectively These two regions with their own characteristics facilitating more for farm related activities than non-farm ones One more important note is that the rich have higher share of their income generating from non-farm activities than the poor which is supported by the econometric results related to the income quintiles in Table 4.9 The richest group of households earns 21.60 percent points more from non-farm activities than the poorest group, holding other variables constant This means that household economic transformation is closely linked with income growth and economic development Table 4.11 summarizes the analysis results regarding to determinants of different indicators of income diversification Among independent variables, education, household size, farm size and access to electricity are the ones which have the consistent positive influence on all of the three measures of diversification in question Education is the proxy of human capital which is very important in taking up complicated wage-earning jobs as well as selfmanaging business Education also broadens the opportunity of households in pursuing various activities to earn income, hence having the positive impact on 40 the number of income sources and also helps to gain the balance among different income sources Household size is an indicator of labor available for production and taking part in non-farm activities such as non-farm wage job Households headed by Kinh people tend to specialize more in non-farm activities while households headed by minority people are likely to stretch to more activities for income earning and to maintain the balance among these income sources Age of household head which stands for experience and management skills is positively correlated with the number of income sources and the SID, and therefore not much concentrating on the non-farm activities The infrastructure such as the distance to a car road and the period that a road is passable significantly affect the level of diversity into non-farm activities The distance of the household from a car road reduces the number of income sources as well as the Simpson index of diversity due to higher transaction cost and transportation cost Access to formal credit enables households to diversify their income sources and gain the balance among these sources Nevertheless, it has negative relation with the share of non-farm income, which suggests that rural household tend to use the credit investing into agricultural production like livestock, fishing and forestry, ect rather than into non-farm business Table 10 Summary of results on determinants of income diversification Number of Non-farm Simpson Index income sources share income of diversity (NIS) (NFS) (SID) Kinh people headed - + - Age + - + Male headed + - + Education + + + Household size + + Dependency ratio - Independent variable Farm_size + 41 + Access to electricity + + - Access to tap water - + Distance to daily market + - Distance to a car road - - Period a road is passable Credit Region Income quintile - + + - + NW+, SCC+, NE-, NW-, NW-, SCC-, MRD+, CH-, NCC-, CH-, CH-, SE-, SE- SE-, MRD- MRD-, NCC+ Quintile 2+ Quintile 2,3,4,5 + Quintile 4- 4.2.5 Impact of income diversification on total income of household The second part of this section examines the impact of diversification on households’ total income by analyzing three models in which household’s total income is taken as dependent variable and diversification indicators are included as explanatory variables As mentioned above, in order to control for the problem of endogeneity, we use the Instrumental Variables (IV) method two stage least squares (2SLS) In which, the income diversification indicators are the endogenous variables and instruments for them include education, access to formal credit and the household size These variables influence the household’s total income through their impacts on the income diversification The three models are summarized as below: Y1 = f (NIS, ethnicity, age, gender, dep_ratio, electric, tapwater, market_dis, road_dis, road_pass) Y2 = f (NFS, ethnicity, age, gender, dep_ratio, electric, tapwater, market_dis, road_dis, road_pass) Y3 = f (SID, ethnicity, age, gender, dep_ratio, electric, tapwater, market_dis, road_dis, road_pass) 42 In which: Y1, Y2, Y3 is household’s total income in model 1, model 2, model respectively NIS, NFS, SID are income diversification measures, which are considered endogenous variables with the instrumental variables: education, credit and household size The other variables in the three equations are all exogenous variables The regression results in Table 4.11 show that all of the three diversification measures have significant and positive impact on household’s total income Specifically, each additional source of income increases household’s total income by 47,877,000 VND on average, holding other variables constant (column 1) Column and show that an increase of 10 percent in the share of non-farm income will bring household an average rise of 17,630,000 VND in total income while the same percent increase in the Simpson index of diversity helps to increase the household’s total income by 14,127,900 VND, after controlling other variables In short, regardless of indicators, income diversification has a significant and positive influence on household’s total income, which is in line with Sustainable Livelihood theory and consistent with findings by Babatunde and Qaim (2009), or by Minot et al (2006) in the context of Vietnam This supports the idea that diversification is a strategy chosen by household to increase their income 43 Table 11 Impacts of income diversification on total income of household Variable NIS (1) 47,877*** (5,422) NFS Total income (2) 1,763*** (162) SID Kinh ethnicity of household head (ethnicity) Age of household head Male household head (gender) Dependency ratio (dep_ratio) Farm_size Access to electricity (electric) Access to tap water (tapwater) Distance to a daily market (market_dis) Distance to a car road (road_dis) Period that a road is passible (road_pass) Geographical regions North East North West North Central Coast South Central Coast Central Highlands Southeast Mekong River Delta (3) 28,904*** (3,583) -264*** (72) -3,498 (2,360) 2,637* (1,378) 0.34** (0.16) 3,370 (4,388) 8,201*** (2,932) -767*** (222) 1,039* (552) -56 (411) -14,636*** (3,494) 407*** (82) 9,672*** (2,354) 2,749** (1,316) 0.95*** (0.16) -5,918* (3,477) -16,301*** (3,594) 529** (208) 1,408** (615) -497 (371) 141,279*** (44,585) 13,221*** (3,073) -260*** (85) 3,484 (2,159) 1,582 (1,124) 0.50*** (0.15) 1,231 (3,287) 4,615* (2,710) -31 (174) 708 (472) 148 (292) -10,176*** (3,239) -24,920*** (5,202) -11,890*** (2,774) -11,122*** (3,337) 432 (4,098) 28,052*** (3,829) -380*** (3,456) 9,260*** (3,316) 10,168** (4,515) 19,576*** (3,869) 1,120 (3,578) 37,155*** (5,761) 20,704*** (3,970) 31,908*** (4,622) -6,742*** (2,487) -389 (3,914) -11,427*** (2,275) -484 (3,041) 7,128 (5,149) 24,650*** (5,530) 14,392*** (3,908) 44 Income quintile 2008 Income quintile Income quintile Income quintile Income quintile _cons Observations 2,983 (2,059) 13,823*** (2,207) 29,553*** (3,578) 54,150*** (4,524) -160,605*** (19,284) 6,058 -4,177* (2,246) -2,559 (2,739) 5,668 (3,626) 22,319*** (4,281) -62,494*** (10,363) 6,058 4,824*** (1,323) 12,843*** (1,453) 27,849*** (3,002) 48,493*** (3,832) -50,506*** (16,119) 6,058 Note: *, **, *** Coefficients are significant at the 10%, 5%, 1% level respectively Source: Vietnam Household Living Standard Survey (VHLSS) 2008 4.3 Chapter remarks Descriptive results show that household in rural Vietnam tend to obtain their income from a variety of sources, in which income from non-farm activities play an increasing importance to the total household income overtime However, the level of income diversification is very different among income quintile The poor has a tendency to be more diversified in terms of the number of income sources but less diverse in the share of non-farm income in a comparison to the rich The econometric results indicate the significant impact of a range of social econometric factor on the income diversification of household such as: education, farm-size, access to market, access to credit… The study of the impacts of income diversification on household total income confirms the significantly positive relationship between diversification and income regardless of indicators used 45 CHAPTER CONCLUSIONS AND RECOMMENDATIONS In this paper, we examine the patterns of income diversification among households in rural Vietnam by taking into consideration various indicators of diversification The results show that the majority of households in rural Vietnam have fairly diversified in income sources On average, each household has from 3.5 (VHLSS 2008) to 4.3 (VHLSS 2010) sources of income Besides, the share of income generating from non-farm activities in household’s total income tends to grow over time, from 27.4 percent in VHLSS 2002 to reach 37.1 percent in VHLSS 2010 However, households not have the same level of diversification It depends on a variety of socio-economic characteristics of the households as well as the geographical regions in which the household is located Besides the characteristics of the household head such as age, gender and ethnicity which are found to have significant effect on household’s income diversification, education is one of the most consistent variables which significantly and positively influence all the three indicators of diversification in question This emphasizes the importance of education in enabling household to diversify their income Household and farm size are also found to be positively and significantly correlated with the share of income from non-farm activities in total income Non-farm income is an important component of total household’s income is impacted by a number of different factors, especially the infrastructure such as the access to electricity, access to tap water, the availability and the quality of car road, etc in the living area The diversification among household also varies across geographical regions This may be explained by the specific characteristics of each region The poorest regions such as North East and North West tend to extend to more activities to earn their income than other regions as a means to increase income and reduce the income variation On the opposite way, regions which have 46 advantageous conditions for non-farm economic development such as Red River Delta, South East are the ones which have the highest proportion of income generating from non-farm activities in total income compared to other regions In conclusion, pursuing multiple income source strategy is very common among geographical and econometric regions as well as among households of different income quintile and tends to increase in diversity level over time This is shown in the descriptive statistic part of this paper However, the diversity degree is varied depending on regions and income quintiles The poorer have a tendency to be more diversified in terms of number of income sources than the richer This suggests that diversification is a mean to reduce risks of variation of a certain income source However, in terms of non-farm income, the poor are much less diversified than the rich for the fact that the poor often face more constraints compared to the rich The econometric analysis reconfirms that households have unequal abilities to diversify their income due to the difference in the endowment of different types of capital among them The socioeconomic characteristics such as education, household size, farm size, the availability of electricity, accessibility to credit and good infrastructure conditions influence the level of income diversification However, it is surprising to note that the access to credit has the negative effect on the diversification of household into non-farm activities This suggests that households use the credit mostly to invest into agricultural activities rather than non-farm ones, which implies that there is a lack of opportunities for households to join the non-farm activities in rural areas The regression analysis also point out that the diversification level has significantly positive effect on the total income of households In other words, rural households may increase their income by pursuing the diversification strategy Based on the above findings, the following policy recommendations are emphasized: 47 Firstly, it is essential for government to improve the education system in general and promote the development of education in rural areas in specific for the fact that education is a significant factor that can help household in rural areas to gain knowledge and skills required for different income-generating activities, especially in non-farm sectors Only by this way can households increase their income and minimize the income uncertainty From the government’s perspective, it is a means to improve livelihood of its citizens and reduce the poverty rate in rural regions as well as in the whole country Secondly, in order to enhance the ability of households in diversifying their income, the government should improve the investment in infrastructure, including roads, electricity, water, telecommunications, both in terms of quantity and quality The improvement in infrastructure will help to reduce transport and transaction costs for households in rural areas Moreover, it also makes it easier for households to approach more job opportunities in non-farm sectors in urban areas Thirdly, the financial market in rural areas should be improved to finance the production of households in an effective way It requires the study and execution of the new scheme, in order to meet the loan needs of households and to maintain the sustainable development of the financial system Fourthly, along with improving the formal financial market, it is vital to train households the way to properly invest into agricultural production like crops, fisheries and livestock, as well as providing the technical support for these activities through agricultural extension programs This can be done by recruiting and training the qualified staff to handle the extension programs Furthermore, it is necessary to create more convenient conditions for households to invest in non-farm activities based on the comparative advantages of the regions This should be carried out in close accompany with the expansion of market for output 48 Last but not least, the government policies or programs to foster the income diversification should pay special attention to the poor in remote and mountainous areas who encounter much more constraints than the rich From the households’ perspective, the education should be considered a very important factor and paid high attention Only by improving the level of education can households and individuals have good opportunities to diversify their income and better the total income in professional non-farm jobs and in setting up and managing family’s own non-farm businesses Although the income diversification in terms of NIS is good in enabling households to increase income and reducing the risk of variation in income, it is not always encouraged to take income diversification Under some certain circumstances, it is better to specialize in specific activities, which household has the comparative advantages At the time this paper is being worked on, the VHLSS 2010 results have not fully officially announced, therefore, it bases its econometric analysis on VHLSS 2008 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