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Non-farm Income, Diversification and Welfare: Evidence from Rural Vietnam

Luu Duc Khai

Central Institute for Economic Management

Christina Kinghan and Carol Newman Trinity College Dublin

Theodore Talbot

University of Copenhagen

Abstract Many emerging economies are characterized by an on-going structural transformation in economic activity that involves graduation from traditional forms of agriculture. This paper contributes to the active area of research on the household-level determinants of diversification and its welfare consequences for household members. We use data from the Vietnam Access to Resources Household Survey (VARHS) from 2008, 2010, and 2012 to study the extent to which rural households in our sample diversify away from own-farm agriculture into waged employment and operating a household enterprise. Consistent with macroeconomic aggregates indicating a significant and on-going shift in the shares of labor allocated, and national value attributed, to agriculture, we observe significant levels of diversification. Moreover, we find this diversification is welfare enhancing, albeit with significant heterogeneity across geographic areas and by household characteristics. In addition to evaluating observable household-level information that is associated with the observed transition out of wholesale agricultural specialization, we provide policy recommendations to enable more households to diversify and increase their welfare gains from diversification. Acknowledgements We are grateful to participants in various seminars at the Central Institute for Economic Management (CIEM) in Hanoi for useful comments and suggestions. We would like to sincerely thank the effort invested by the survey teams of the Vietnamese Institute of Labor Science and Social Affairs (ILSSA). Financial support under the Business Sector Programme Support (BSPS) by Danida is gratefully acknowledged. The usual caveats apply.

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1. Introduction Many developing countries continue to struggle to raise growth rates of real income, but a significant minority of emerging economies have succeeded in generating significant (albeit not always stable) growth. A common feature of the convergence of these low-income countries is a fundamental change in the pattern of economic activity, as households reallocate labor from traditional agriculture to more productive forms of agriculture and modern industrial and service sectors. The combination of this large-scale shift in labor allocation and the resulting change in the composition of economic output are collectively referred to as the structural transformation of an economy. Vietnam’s economy is undergoing a large-scale transformation of this kind. Nearly 45% of the country’s exports in 2002 were crude oil and rice; this share had fallen to under 20% by 2012. Similarly, agriculture’s share of value added went from around 25% in 2000 to less than 22% in 2012, while over the same time frame the share of GDP attributed to the industrial sector grew from 36% to more than 40% (WDI, 2012). This transformation manifests itself at the household level in a diversification away from traditional own-farm agriculture towards other activities (which, of course, may still be part of the agricultural value chain). Despite achieving an average 6% growth over the last decade in PPP-adjusted USD, in 2012 the agricultural sector (defined to include forestry and fisheries) contributed about a fifth of national value-added and employed 47% of the overall labor force. Examining just the allocation of labor in rural areas, table 1 shows a significant shift in the patterns of employment across Vietnam over the last decade. Table 1: Pattern of employment in Vietnam 2001-2011

Agriculture, Forestry, and

Fisheries Industry and Construction Services

2001 2006 2011 2001 2006 2011 2001 2006 2011

National Average 79.61 70.41 59.59 7.36 12.46 18.4 11.51 15.95 20.52

Red River Delta 77.26 60.48 42.63 10.5 20.36 31.26 11.67 18.31 25.18

Northern Midland and Mountain Areas

91.15 86.5 79.74 2.27 4.33 8.48 6.33 8.81 11.47

North Central and Central Coastal Areas

80.28 71.95 62.64 6.93 11.16 15.52 11.36 15.73 20.47

Central Highlands 91.94 88.38 85.28 1.55 2.52 3.04 6.22 8.84 11.42

South East 58.46 49.06 36.07 16.06 23.37 31.45 20.02 24.43 28.5

Mekong River Delta 79.23 71.81 62.17 7.83 9.74 14.33 12.64 16.89 21.33

Source: Authors’ calculations from General Statistics Office: Agriculture, Forestry, and Fisheries Census, 2001, 2006, and 2011.

Indeed, Vietnamese policymakers prioritized this shift away from own-farm agriculture into modern productive sectors: the national five-year Socio-Economic Development Plan (2006-2010) endorsed by the Communist Party of Vietnam at the 10th Party Congress, for example, calls for “… [promoting] the restructuring of rural labor to rapidly reduce the proportion of employees in agriculture and increase the proportion of employees in industry and services.” Despite rapid urbanization, rural areas remain the focus of this transition: with national population crossing the 90 million mark in 2013, the number of rural households increased by 1.58 million between 2006 and 2011. The combination of large-scale macroeconomic structural transformation and microeconomic diversification make Vietnam a unique environment in which to study the dynamics and

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effects of diversifying labor allocation and investment away from traditional own-farm agriculture. Studying the extent of diversification and the effects of diversification on welfare is immediately relevant to national economic policy. It also provides evidence that may be useful to policymakers in several other structurally-similar countries that are now negotiating the process of industrial transition while remaining primarily agricultural and rural. This paper explores the pattern of diversification in rural Vietnam between 2008 and 2012. We examine whether households that have diversified their economic activities are better off than households who have remained in specialized agricultural production. We focus on the extent to which there are heterogeneous welfare effects for households with different characteristics and examine how exposure to shocks impacts on the choice to diversify and its effects. We analyze factors that determine the transition out of specialized agriculture and the choice of economic activities of diversified households. Our primary objective is to provide an evidence base for informing policy on the transformation process now occurring throughout rural Vietnam. The paper is structured as follows. In Section 2 we provide an overview of the related literature focusing in particular on experiences of diversification into non-farm activities in other developing countries. Section 3 describes the data while our empirical analysis is presented in Section 4. Section 5 concludes the paper with a summary of our key findings and recommendations for policy and future research. 2. Related Literature The impact of income diversification on the livelihoods of the rural poor has been well documented in the literature. In what follows, we emphasize some key findings of relevance to the discussion presented in this paper. In general, research into the role of diversification into off-farm income generating activities has concluded that: i) diversification has the potential to increase the incomes and welfare of the poorest and most vulnerable; and ii) that it also has the potential to increase income inequality given that the least wealthy households tend to diversify into low return activities in contrast to wealthy households who invest in more productive activities. These key findings help to motivate the analysis of rural households in Vietnam presented in this paper. Owusu et al. (2010) highlight the potential for diversification to improve the living standards of households in Northern Ghana due to the low productivity of agriculture resulting from poor agro-ecological factors. They empirically examine the impact of non-farm activity on household income and food security status and find that non-farm work has a positive and statistically significant effect on both. However, as highlighted by Abdulai and Crole-Rees (2001), poorer households tend have fewer opportunities to participate in non-farm activity and hence have less diversified incomes. In their study of rural households in Mali they find that a lack of capital seems to act as a constraint in the development of non-farm activities and makes it difficult for poorer households to diversify away from subsistence agriculture. Those living nearer markets have higher levels of education or those with greater land holdings are more likely to diversify their income, highlighting the significance of entry constraints in explaining households’ diversification decisions. Bezu et al. (2012) examine whether non-farm employment leads to higher consumption expenditure growth in Ethiopia. They also examine whether non-farm employment is pro-poor by estimating its impact on expenditure growth separately for rich and poor households.

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Using household data over a 10-year period they find a strong positive relationship between a household’s non-farm income share and its subsequent expenditure growth, highlighting that households engaged in non-farm activities experience higher growth in expenditure and that this growth rate increases as the level of investment in non-farm employment increases. The positive relationship between the share of non-farm income and expenditure growth holds for both poor and well-off households. However, they find that relatively wealthier households benefit more from off-farm activity than poorer households concluding that diversification into non-farm enterprises may increase rural income inequality. Lay et al (2008), focus further on the diversification of household resources into low or high return activities by examining whether a dichotomous non-agricultural sector exists in a poor rural area of Western Kenya. They define high return activities as those requiring special skills, particular assets and a clean and healthy appearance such as vehicle repair, hair dressing and beauty, hotels and restaurants and NGO’s or international organizations. All remaining non-agricultural activities are classified as low return, such as enterprises run by one household member only. This classification of activity is done to reflect survival-led versus opportunity-led income diversification. They find that 55% of households diversify into some form of non-farm activity and interestingly highlight how more than a third of households undertaking a high-return activity in the non-agricultural sector also pursue some low-return activity. They find that the share of low-return non-farm income falls with the education level of the household head and the size of landholding. Moreover, they find that wealth barriers to high-return activities do exist. Lay et al.’s (2008) findings also reinforces the conclusion of Bezu et al (2012), that given the concentration of high-return activity among already wealthy households, it is possible that diversification may aggravate inequality. Further support for this argument is provided by the evidence in the literature on the link between diversification and shocks. Kijima et al. (2006) find evidence in Uganda that poorer households respond to shocks by being ‘pushed’ into low return non-farm jobs. Wealthier households, in contrast, are better able to cope with shocks as they are more likely to earn income from salaried employment or high-return self-employed business opportunities thus exacerbating income differentials in the longer run. In contrast, Van Den Bery and Kumbi (2006) find in their study of diversification in Oromia, the largest state in Ethiopia, that if barriers to entry to the non-farm sector are low, diversification will have an equalizing effect on the distribution of income. The authors highlight the positive role that diversification into non-farm activities can have on income levels in poor rural areas. Similarly, Schindler and Giesbert (2012) examine welfare dynamics among rural households in Mozambique. They find that drought has a negative impact on a household’s asset accumulation but households in which at least one member has regular non-farm work experience less adverse asset growth from a drought than those without non-farm wage opportunities suggesting that income diversification has a positive impact in the aftermath of an exogenous shock. The authors conclude that non-farm labor market opportunities are an important means of mitigating the effects of drought in the short term. Barrett et al (2005) undertake an analysis in Cote d’Ivoire, Kenya and Rwanda with the aim of informing the debate on the determinants of patterns of asset allocation and income sourcing observed across individual studies of diversification. The authors emphasize the role of household heterogeneity in both constraints and incentives and how this impacts on the

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income diversification patterns of households, with constraints not only restricting certain types of diversification but also compelling diversification into low return activities. They emphasize the role of shocks in particular in driving households into non-farm employment as an ex-post response to risk. In general, however, the authors find that livelihood strategies that include non-farm income sources, and in particular activities that exclude unskilled labor, are associated with higher income and greater upward earnings mobility. Households that do not have access to non-farm activity and also do not have productive non-labor assets often derive their full income from on-farm agriculture and this dependence often leads to these households being trapped in poverty. This again highlights the importance of diversification into non-farm activities for incomes in rural areas. However, they also find evidence of rural market failures that force the poorest households to undertake non-farm activity with low returns, whereas wealthier households are more able to enter high return activities. In summary, the literature exploring the diversification of economic activities in low income countries highlights the importance of the rural non-farm economy for income generation that benefits the least well off. The evidence for inequalities, however, whereby less well-off households tend to diversify into low-return activities while those with better endowments of assets and higher incomes are able to take advantage of high-return non-farm opportunities, highlights the need for country specific research to establish the type of diversification taking place and its impact on households. In this paper we focus on the livelihoods of rural households in 12 provinces of Vietnam and document how they have evolved over the 2008 to 2012 period. Our aim is to establish the extent to which structural transformation is occurring and whether the diversification of livelihoods out of agriculture into other economic activities enhances household welfare. We also examine which households diversify and whether there are heterogeneous effects in the impact on welfare of the transformation process. 3. Data and Summary Statistics Our data come from three rounds (2008, 2010 and 2012) of the Vietnam Access to Resources Household Survey (VARHS).1 The survey instrument provides detailed information about the incomes, assets, and access to public services and other resources of rural households in 12 provinces in Vietnam.2 While the full dataset includes over 3,000 households for the purpose of this paper we focus on the balanced panel (i.e. the same households surveyed in each year) of 1,873 households.3 Table 2 documents the economic activities that households in our sample were engaged in during the 2008 to 2012 period. We consider three types of economic activities, agriculture, waged employment and household enterprises, and their interactions. In 2008, agriculture is the main activity with the majority of households specialized in agricultural production. Between 2008 and 2010, however, a major structural transformation took place with

                                                            1 The survey was developed in collaboration between the Development Economics Research Group (DERG), Department of Economics, University of Copenhagen and the Central Institute of Economic Management (CIEM), the Institute for Labor Studies and Social Affairs (ILSSA) and the Institute of Policy and Strategy for Agriculture and Rural Development (IPSARD), Hanoi, Vietnam. 2 The survey is implemented at the same time of year in each round to ensure that responses are internally consistent and to avoid issues relating to the seasonality of income and other types of household behavior. 3 See CIEM (2009), CIEM (2011) and CIEM (2012) for a comprehensive descriptive report of the data gathered in each round of the survey.

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diversification of economic activities into waged employment. While the majority of households continued to engage in the agricultural sector, many expanded the range of activities they were engaged in. Few switched out of agriculture completely. This is evident from the tiny proportion of households that engage in waged employment and household enterprises with no agricultural activities. Table 2: Economic activities of households 2008-2012 % hhs

Agriculture only

Agriculture + Labor

Agriculture + Enterprise

Agriculture + Labor + Enterprise

Labor only

Enterprise only

Labor + Enterprise

No activity

2008 56.01 12.23 12.87 8.97 1.44 3.47 0.59 4.43 2010 23.92 41.75 12.44 10.04 3.36 2.83 2.30 3.36 2012 21.30 44.26 9.50 10.25 4.86 2.94 1.97 4.91 n = 1,873

Table 3 shows the change in the proportion of income coming from each type of economic activity. Of particular note is the decline in the proportion of income from agricultural sources. Between 2008 and 2010 a notable increase in the importance of waged income is observed but this declines slightly in importance between 2010 and 2012 when household enterprise income makes a higher contribution to the total income of households. Table 3: Proportion of income by economic activities of households 2008-2012 % Agriculture Labor Enterprise 2008 35.29 45.90 18.80 2010 20.39 64.33 15.28 2012 22.01 58.62 19.37 n = 1,481. Households that report zero positive total income from these sources in any given year are excluded from this table leading to a reduced sample size.

Table 4 presents an occupation transition matrix for the period 2008 to 2010 and from 2010 to 2012. It illustrates the movement of households between different occupation types over time. For example, in 2008 1,029 households were in the agricultural sector with no other economic activities. Of these households only 32.65% stayed in agriculture in 2010 while the rest diversified. Slightly over half (50.44%) diversified by combining agriculture with waged employment while just over 8% combined agriculture with some form of non-farm non-wage activity. In contrast, of the 220 households that were involved in agriculture and waged employment in 2008, over half (54.55%) stayed in this occupation grouping while over 11% diversified further by adding an enterprise activity. An additional 25.45% specialized to focus on agricultural production only. Of those households in agricultural production and with an enterprise activity in 2008 (239 households in total) over a third continued with these activities in 2010 while 24.27% diversified further by adding a waged activity. Another 24.27% switched from a household enterprise to waged employment. Similar transitions are observed for households that have all three activities in 2008 (166 households in total). Most shifted between different combinations of agriculture, waged employment and enterprise activities. For the other categories, those without agricultural activities to begin with, there was less transition between groups but it should be noted that the sample size is much smaller for these groups. Between 2010 and 2012 there is less transition between different types of activities although a move away from agriculture is still evident. In 2010 only 417 households were exclusively engaged in agricultural activities. Of these, half continued to exclusively engage in agricultural production while 36% diversified by engaging in waged employment; only a small proportion of households diversified by engaging in household enterprise activities.

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Most households involved in agricultural activities and waged employment in 2010 (771 households) continued with activities in both sectors in 2012 (69.39%). In contrast, households involved in some form of enterprise activities appear to have been more mobile in transitioning between occupation categories. This suggests that there may be greater stability associated with waged employment that is less evident for households that are engaged in enterprise activities leading them to shift more between economic activities. This is not surprising given the additional risks that are incurred when households are self-employed and the higher variance in returns to enterprise activities. Table 4: Occupation transition matrix 2008-2012 2010 Ag only Ag + Lab Ag + Ent Ag + Lab

+ Ent Lab only Ent only Lab +

Ent

2008

Ag only (n=1,029) 32.65 50.44 8.26 7.09 1.36 0.00 0.19 Ag + Lab (n=220) 25.45 54.55 5.00 11.36 2.73 0.00 0.91 Ag + Ent (n=239) 10.46 24.27 33.47 24.27 2.09 3.35 2.09 Ag + Lab + Ent (n=166) 8.43 39.76 29.52 15.66 0.00 4.82 1.81 Lab only (n=18) 11.11 5.56 0.00 0.00 44.44 27.78 11.11 Ent only (n=60) 1.67 6.67 8.33 3.33 6.67 43.33 30.00 Lab + Ent (n=10) 0.00 10.00 20.00 0.00 0.00 30.00 40.00

2012 Ag only Ag + Lab Ag + Ent Ag + Lab

+ Ent Lab only Ent only Lab +

Ent

2010

Ag only (n=417) 50.36 36.21 6.71 3.60 2.40 0.48 0.24 Ag + Lab (n=771) 14.53 69.39 5.58 7.00 2.59 0.65 0.26 Ag + Ent (n=229) 17.47 21.40 35.37 21.40 0.00 3.93 0.44 Ag + Lab + Ent (n=186) 11.29 37.63 12.37 34.41 2.69 0.00 1.61 Lab only (n=62) 4.84 29.03 3.23 1.61 58.06 0.00 3.23 Ent only (n=50) 4.00 2.00 0.00 6.00 16.00 58.00 14.00 Lab + Ent 0.00 7.32 2.44 7.32 14.63 17.07 51.22

It is clear from the descriptive statistics presented in Tables 2 to 4 that rural households in Vietnam increasingly rely on different activities to generate income. The aim of this paper is to explore the extent to which this leads to improvements in welfare. We consider three different indicators of welfare: income, food consumption and wealth. Income is calculated as the total income of households from all sources and expressed in real 2012 prices. Adjustments are also made for provincial price differences. Food consumption is measured as the value of food items consumed in the previous month. It is based on a select number of representative items. Food consumption is a more widely used measure of household well-being due to measurement errors in income data gathered from household surveys. This is particularly the case when income is generated from a number of different sources as in our case (Deaton, 1997; Fox, 2013). Household wealth is an important covariate of income measures and determinant of consumption. Because this wealth is generally embodied in a large number of physical assets, we follow the established practice (popularized by Filmer and Pritchett (2001)) of constructing an asset-based wealth measure that summarizes households’ asset holdings based on weights calculated by Principle Component Analysis (see McKay and Tarp (2011) for details of the procedure in the context of VARHS data; here, we use a subset of the assets used in the calculation of the index presented in that paper). The resulting index is a stock measure rather than the flow represented by real income captured directly by the survey instrument and included in the set of conditioning information. The specific assets used in the calculation of this index are: number of plots of land, total land area owned (square meters), total irrigated area owned (square meters), area of main dwelling (square meters) and whether the dwelling has a good light source, toilet, or water source, and the number of cows, buffalo,

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pigs, chicken, color TVs, DVD players, telephones, motorcycles, bicycles, pesticide sprayers, and cars the household owns. Summary statistics for each of these variables are provided in Table 5 along with a disaggregation across households engaged in different types of income generating activities. Overall, household income increased significantly between 2008 and 2010 in real terms. However, between 2010 and 2012 we observe very little increase overall. This can be explained by the very high rate of inflation in Vietnam during this period. Despite the stagnation in incomes, average monthly food expenditure increased steadily over the sample period as did overall wealth. The better off households in all time periods are those with a household enterprise. This holds across all welfare measures. Households that are specialized in agriculture do the worst on each measure in each year. This suggests that diversification out of agriculture is associated with higher levels of welfare, although causal inferences cannot be made on the basis of these descriptive statistics. Identifying the nature of the relationship between diversification and welfare outcomes is the focus of our empirical analysis presented in Section 4. Table 5: Welfare measures, group-wise means Income Food consumption Assets 2008 Total 56,574 1,195 0.004 Ag Only 49,650 1,139 -0.147 Ag + Lab 47,820 932 0.034 Ag + Ent 68,930 1,447 0.303 Ag + Lab + Ent 80,825 1,387 0.333 Lab Only 50,528 951 -0.056 Ent Only 80,232 1,631 0.351 Lab + Ent 85.591 1,239 0.313 2010 Total 76,596 1,209 0.222 Ag Only 52,219 917 -0.084 Ag + Lab 69,243 1,198 0.294 Ag + Ent 103,568 1,402 0.317 Ag + Lab + Ent 99,635 1,605 0.529 Lab Only 81,448 1,264 0.260 Ent Only 177,142 1,639 0.409 Lab + Ent 106,794 1,548 0.690 2012 Total 76,762 1,540 0.450 Ag Only 60,622 1,254 0.260 Ag + Lab 72,769 1,598 0.483 Ag + Ent 88,126 1,582 0.602 Ag + Lab + Ent 87,915 1,847 0.653 Lab Only 101,972 1,940 0.547 Ent Only 175,844 1,990 0.859 Lab + Ent 103,865 1,848 0.956 n = 1,873

4. Empirical Analysis 4.1 The welfare effects of diversification Identifying a causal relationship between diversification of economic activities and welfare is complicated by a number of confounding factors, particularly when income or wealth is the outcome variable of interest. First, there may be self-selection of households into more productive activities. In other words, richer or wealthier households may choose to diversify rather than diversification in itself leading to higher levels of incomes or wealth. Second, as highlighted above, measurement errors in income are commonplace in household surveys of

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this kind, particularly with multiple income sources. This will further complicate the identification of unbiased coefficient estimates in a standard regression model. Given these constraints we focus on consumption as our outcome measure of interest and control for past wealth to address the self-selection problem. As such to determine the impact that diversification has on welfare we consider the following regression model:

ittiitititititit eταWealthβIncomeβC 15141321 XβXβSβ (1)

The key variables of interest are the sources of income of households. They are included in the vector itS in the form of dummy variable indicators of the various categories described

above with households that are involved in agriculture only (i.e. specialized agriculture) forming the base category. The vector itX includes time varying household characteristics,

namely, household size, household size squared, whether the household head is female, age of the household head, age squared, the education level of the household head, the number of children in the household, whether the household is of Kinh ethnicity, whether the household is born in the commune and whether the household is classified as poor by the authorities. Current period wealth is also included as a control variable within this vector. An additional complication with this specification is the need to control for current period income of households which is collinear with the sources of income and with the other control variables. If we assume that the generation of income is a dynamic process, in that past values will determine future values, the lag of income and the lag of other time varying household characteristics (included in 1itX ) should serve as adequate controls. The model includes

household fixed effects, iα , which means that identification of the impact of diversification

of livelihoods on welfare comes from within-household variations overtime controlling for income, wealth, time varying household characteristics and past values of income and wealth. The latter is included as a control for selection. Time dummies, tτ , are also included and ite

is the statistical noise term. Summary statistics for each all variables included in the analysis are presented in Table A1 of the Appendix. The results for the core variables of interest from the main model given in equation (1) are presented in Table 6.4 Table A2 of the Appendix details the full set of results. Our dependent variable is the log of real consumption per capita. Making the per capita adjustment is particularly important in this model given that diversification and food consumption will be related to the size of the household. Adjusting to per capita consumption overcomes potential bias in the estimates of the impact of diversification on welfare. We also include household size and its square to control for the fact that there may be economies of scale associated with food consumption in larger households. A log transformation is used to reduce the impact of outliers and for ease of interpretation of the parameter estimates. Columns (1) to (4) show that households that are diversified are better off than households that are specialized in agriculture. In particular, when all control variables are included (column (4)) we find that households that are engaged in agriculture with some other type of activity, waged employment, a household enterprise or both, have higher levels of consumption per capita than those that are engaged in agricultural production only. The coefficient estimates suggest that compared with households that are fully specialized in agricultural production, the fully diversified households do the best with consumption levels

                                                            4 We exclude households that report having no economic activities.

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per capita that are almost 23% higher than households specialized in agriculture, followed by households that are engaged in agriculture and enterprise activities with consumption levels per capita that are almost 17% higher, while households engaged in agriculture and waged employment have consumption levels per capita that are 8% higher. Households with no agricultural activities are no different in welfare terms to those that are specialized in agriculture. Table 6: Impact of diversification on household welfare (1) (2) (3) (4) Ag + Lab 0.036

(0.038) 0.084** (0.038)

0.083** (0.038)

0.083** (0.038)

Ag + Ent 0.162*** (0.053)

0.173*** (0.050)

0.168*** (0.051)

0.168*** (0.051)

Ag + Lab + Ent 0.182** (0.053)

0.227*** (0.052)

0.227*** (0.052)

0.227*** (0.052)

Lab Only -0.034 (0.107)

0.018 (0.098)

0.036 (0.095)

0.037 (0.095)

Ent Only -0.026 (0.139)

-0.005 (0.126)

0.006 (0.125)

0.006 (0.124)

Lab + Ent -0.032 (0.115)

-0.017 (0.110)

-0.015 (0.111)

-0.014 (0.111)

HH characteristics No Yes Yes Yes Current income controls No No Yes Yes Selection control No No No Yes Time dummies Yes Yes Yes Yes R-squared 0.037 0.359 0.360 0.365 Number of households 1,835 1,835 1,835 1,835 Number of observations 3,588 3,588 3,588 3,588

Note: Household fixed effects model estimated in each case. Standard errors clustered at the household level are presented in parentheses. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level. The results in Table 6 represent our baseline findings on the benefits of diversification on welfare as measured by real per-capita food consumption. However, because household consumption is an autoregressive process, we ensure the robustness of our results on diversification and welfare by directly estimating a model of the form:

ittiitititit eCC 121 XβSβ (2)

A challenge in estimating this model is that including household fixed effects requires the assumption that the within-transformed lagged dependent variable is orthogonal with respect to the within-transformed errors, an assumption that is mechanically violated.5 We take this bias into consideration by taking advantage of the fact that the effect of diversification on per-capita consumption is “bracketed” by a model that includes household fixed effects only and a model that includes lagged consumption only (see Angrist and Pischke (2008) for a detailed discussion of this property). Our objective in estimating this model is to ensure the point estimates reported in Table 6 of the effect of the various modes of diversification on

                                                            5 It is possible to estimate the effect of a lagged dependent variable on an outcome of interest with fixed effects if we adopt alternative approaches such as using prior lags of the dependent variable as instruments, but these approaches (like that pioneered by Arellano and Bond (1991)) are more appropriate in panels with longer time dimensions or if we are able to accept restrictive assumptions about the exogeneity of “earlier” observations of per-capita consumption.

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welfare are robust to directly including lagged consumption in our conditioning information set.6 The results are presented in Table 7 (Table A3 of the Appendix shows the full set of fitted coefficients and associated test statistics) and support our initial conclusion: the effect of agriculture combined with other income-earning activities remains significantly and positively associated with real consumption (see columns (2) and (3)). Table 7: Impact of diversification on household welfare, LDV and FE estimates

(1) (2) (3)

Original model LDV FE

Ag + Lab 0.083** 0.054** 0.084** (0.038) (0.024) (0.038)

Ag + Ent 0.168*** 0.115*** 0.173*** (0.051) (0.031) (0.051)

Ag + Lab + Ent 0.227*** 0.163*** 0.227*** (0.052) (0.031) (0.052)

Lab Only 0.037 0.186*** 0.018

(0.095) (0.048) (0.098)

Ent Only 0.006 0.173*** -0.005

(0.124) (0.059) (0.127)

Lab + Ent -0.014 0.064 -0.017 (0.111) (0.059) (0.111)

HH Fixed Effects Yes No Yes Lagged consumption No Yes No Current income controls Yes No No Selection control Yes No No

R-squared 0.36 0.44 0.36 Number of households 1,835 1,835 1,835 Number of observations 3,588 3,588 3,588

Note: LDV indicates lagged dependent variable. FE indicates the household fixed effects model. The model estimated in column (3) excludes current income and the selection control so that the set of control variables are comparable to those included in column (2). Standard errors clustered at the household level are presented in parentheses. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level.

In Table 8 we disaggregate the diversification of economic activities further separating out households that moved out of specialized agriculture between survey rounds from other types of diversified households.7 We find that the transition out of specialized agriculture is welfare enhancing. The per capita consumption of households that move from being engaged in agricultural production only into other types of production activities is 10% higher than those who remain specialized (column (1)). When this is disaggregated by type of activity we find that this result is driven by those households that diversify by adding an enterprise activity or adding both an enterprise activity and waged employment to their portfolio of production activities. Of the non-transition households those that are diversified also perform better, particularly those that are involved in both labor and enterprise activities.

                                                            6 Food consumption recorded by VARHS is based on recall of the month before the interview. As such, each observations on consumption for a particular household is far apart in time. 7 The full set of results are presented in Table A4 of the Appendix.

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Table 8: Impact of diversification out of agriculture on household welfare (1) (2) Transition out of Ag 0.101***

(0.038)

Of which: Into Ag+Lab 0.067

(0.043) Into Ag+ Ent 0.204***

(0.077) Into Ag+Lab+Ent 0.180**

(0.083) Into Other 0.079

(0.072) Activities of non-transition hhs: Ag + Lab 0.115

(0.114) 0.121

(0.114) Ag + Ent 0.193*

(0.116) 0.200* (0.116)

Ag + Lab + Ent 0.313** (0.118)

0.319*** (0.118)

Lab Only 0.092 (0.154)

0.096 (0.154)

Ent Only 0.069 (0.171)

0.075 (0.171)

Lab + Ent 0.053 (0.167)

0.060 (0.167)

HH characteristics Yes Yes Current income controls Yes Yes Selection control Yes Yes Time dummies Yes Yes R-squared 0.369 0.368 Number of households 1,835 1,835 Number of observations 3,586 3,586

Note: Household fixed effects model estimated in each case. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level. 4.2 The determinants of diversification We now turn our attention to exploring the characteristics of households that transition out of agriculture. To do so, we estimate a probit model of the probability that a household transitions out of agriculture in a given time period as described in equation (3).

piiiii πshockαlagWealthαlagIncomeαlagΦTransP 43211 Xα (3)

Where iTrans takes a value of one if a household moved from specialized agricultural

production to some other combination of economic activities and zero otherwise. The matrix X is as before, a matrix of time varying household characteristics, and is included at a lag. Income and wealth are also included at a lag. The variable ishock is an indicator for whether

the household suffered a natural shock in the previous two years and pπ are province

dummies to capture regional variations in the transition out of specialized agriculture. The function .Φ is the standard normal cumulative distribution function, a standard assumption of the probit model, and is used to ensure that the predicted values of the model lie within a 0-1 interval and so are consistent with interpreting the left hand side variable as a probability. Here we estimate the model separately for the cross-section of households in 2010 and 2012 respectively. We do not include household fixed effects as we are interested in the variation

13

between households in any given time period rather than the variation within households overtime. We estimate the model conditioning on households that were specialized in agricultural production in the previous period. The coefficients of the probit model do not have an obvious interpretation and so instead we present marginal effects which can be interpreted as the impact of a one unit change in the independent variable on the probability of a household transitioning from specialized agriculture. The results are presented in Table 9. The characteristics that are significant in determining the probability that a households transitions out of specialized agriculture vary somewhat across the years. Of particular note is the difference in the impact of income; in 2012, higher income households are less likely to leave specialized agriculture while in 2010 income is not a significant determinant. This suggests that diversification in the Vietnamese case is not driven by higher income households. In 2010, female headed households are more likely to transition while in 2012 the gender of the household head is not a significant determinant. Natural shocks, which include landslides, typhoons, storms, drought, pest infestation, crop disease and avian flu, have a positive impact on the probability of transitioning out of specialized agriculture in 2010 but have no significant impact in 2012. This suggests that, at least in 2010, diversification into other activities might be a mechanism that households use to cope with shocks that affect agricultural production (see Wainwright et al. (2011) for a full analysis of the role of diversification in helping households to manage risks using the VARHS data). Provincial differences are also evident.8 We find that households in Ha Tay (the base category) are most likely to move out of specialized agriculture compared to households in other in Lao Cai, Phu Tho, Dien Bien, Quang Nam and Dak Lak in 2010 and Dien Bien, Nghe An, Dak Lak and Dak Nong in 2012, where transition is much less likely. A number of common household characteristics emerge as determinants of the transition out of agriculture in both years. For example, larger households are more likely to diversify. This is not surprising given that in larger households there is more scope for individual members to engage in different types of activities. This effect could partially be because in larger households with limited arable land holdings, more household members will be forced to seek non-agricultural employment. While we indirectly control for land holdings by including number of plots, area owned, and irrigated area owned in our wealth index measure, we do not directly control for these factors in our vector of household-level information. There is strong evidence that more educated households are more likely to move from specialized agriculture, particularly in 2010 but also to a certain extent in 2012. It is also the case that households classified as poor by the authorities are more likely to move from specialized agricultural production. Overall, this analysis suggests that there is significant heterogeneity in the types of households that are diversifying within and across the years of our dataset. For the most part it appears that it is poorer, lower income households that are choosing to diversify and those that are more vulnerable (e.g. those exposed to shocks and female-headed households). There is some evidence, however, that more educated households are more likely to diversify suggesting that, as has been found in case studies of other developing countries, there may also be different types of diversification taking place in the Vietnamese case: i) households that are pushed into diversification; and ii) those that are availing of more productive opportunities.

                                                            8 It is important to note that provincial differences are absorbed by the time invariant household fixed effect in the previous models and so are implicitly controlled for in estimating the impact of diversification on welfare.

14

Table 9: Determinants of transition out of specialized agriculture (marginal effects) (1) (2) (3) (4) (5) (6) (7) (8) Dep Variable: Trans Trans Lab Ent Lab + Ent Lab Ent Lab + Ent 2010 2012 2010 2012 Lag Log income

0.007 (0.026)

-0.141*** (0.043)

0.047* (0.027)

-0.002 (0.019)

-0.045*** (0.018)

-0.027 (0.027)

0.027 (0.027)

0.000 (0.000)

Lag wealth 0.040 (0.025)

0.074* (0.042)

-0.021 (0.024)

-0.014 (0.017)

0.034** (0.016)

-0.108*** (0.036)

0.108*** (0.036)

-0.0001 (0.0004)

Lag Size 0.129*** (0.034)

0.162*** (0.059)

-0.013 (0.036)

0.006 (0.026)

0.007 (0.025)

0.168*** (0.047)

-0.169*** (0.047)

0.001 (0.001)

Lag Size squared

-0.007** (0.003)

-0.012** (0.005)

0.000 (0.003)

-0.001 (0.002)

0.001 (0.002)

-0.010*** (0.004)

0.011*** (0.004)

0.000 (0.000)

Lag Female Head

0.179*** (0.035)

0.123 (0.077)

0.065* (0.036)

-0.048* (0.026)

-0.016 (0.027)

0.045* (0.026)

-0.044* (0.026)

-0.001 (0.001)

Lag Age Head -0.009 (0.008)

-0.017 (0.013)

-0.001 (0.007)

0.001 (0.005)

0.000 (0.005)

0.013** (0.007)

-0.013** (0.007)

0.0001 (0.0001)

Lag Age Head squared

0.00005 (0.0001)

0.0001 (0.0001)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

-0.0001** (0.0005)

0.0001** (0.00005)

0.000 (0.000)

Lag Ed Head cat 2

0.106** (0.047)

0.083 (0.096)

-0.065 (0.070)

0.004 (0.035)

0.061 (0.061)

0.067* (0.030)

-0.068** (0.030)

0.001 (0.003)

Lag Ed Head cat 3

0.215*** (0.047)

0.043 (0.090)

-0.011 (0.059)

-0.031 (0.031)

0.042 (0.045)

0.047 (0.035)

-0.048 (0.035)

0.001 (0.002)

Lag Ed Head cat 4

0.174*** (0.049)

0.222** (0.097)

-0.022 (0.065)

-0.005 (0.037)

0.027 (0.052)

0.067 (0.043)

-0.067 (0.043)

0.001 (0.001)

Lag Ed Head cat 5

0.186*** (0.051)

0.172 (0.126)

-0.068 (0.115)

-0.066*** (0.025)

0.134 (0.113)

0.068** (0.030)

-0.076*** (0.027)

0.008 (0.011)

Lag Ed Head cat 6

0.273*** (0.043)

-0.045 (0.273)

0.039 (0.111)

-0.042 (0.050)

0.003 (0.098)

0.079*** (0.024)

-0.079*** (0.024)

0.000 (0.002)

Lag Number of children

-0.013 (0.018)

0.001 (0.035)

-0.008 (0.015)

0.019* (0.011)

-0.011 (0.010)

-0.073*** (0.022)

0.073*** (0.022)

0.0004 (0.001)

Lag Kinh Head -0.093** (0.048)

-0.125 (0.101)

-0.056** (0.040)

0.045 (0.030)

0.041 (0.027)

0.045 (0.066)

-0.045 (0.066)

0.0001 (0.001)

Lag Head born in commune

0.060 (0.049)

-0.023 (0.079)

-0.048 (0.044)

0.039 (0.027)

0.009 (0.037)

0.031 (0.052)

-0.032 (0.052)

0.001 (0.0010

Lag Poor 0.078** (0.037)

0.134* (0.075)

0.058* (0.033)

-0.047** (0.021)

-0.011 (0.025)

0.043 (0.040)

-0.043 (0.040)

0.0002 (0.001)

Natural shock 0.086*** (0.033)

-0.015 (0.063)

0.020 (0.032)

-0.002 (0.022)

-0.017 (0.022)

0.097** (0.045)

-0.097** (0.045)

0.0004 (0.001)

Lao Cai -0.414*** (0.090)

-0.067 (0.148)

-0.413* (0.176)

0.436** (0.195)

-0.022 (0.054)

-0.071 (0.118)

0.071 (0.118)

-0.0005 (0.001)

Phu Tho -0.189*** (0.065)

-0.104 (0.102)

-0.161* (0.100)

0.170* (0.103)

-0.009 (0.034)

0.092*** (0.028)

-0.092*** (0.028)

-0.0003 (0.001)

Lai Chau -0.128 (0.094)

-0.056 (0.164)

-0.760** (0.070)

0.692*** (0.133)

0.068 (0.090)

-0.248 (0.206)

0.243 (0.207)

0.005 (0.007)

Dien Bien -0.429*** (0.079)

-0.341*** (0.108)

-0.401** (0.178)

0.391** (0.202)

0.010 (0.067)

-0.022 (0.086)

0.025 (0.086)

-0.003 (0.004)

Nghe An -0.062 (0.069)

-0.195* (0.110)

-0.200** (0.103)

0.179* (0.107)

0.020 (0.042)

0.057* (0.033)

-0.058* (0.033)

0.000 (0.001)

Quang Nam -0.142** (0.066)

-0.081 (0.105)

0.057 (0.065)

0.018 (0.063)

-0.075*** (0.021)

0.054 (0.035)

-0.054 (0.035)

-0.001* (0.0004)

Khanh Hoa 0.096 (0.118)

… -0.165 (0.160)

0.090 (0.161)

0.075 (0.095)

…

Dak Lak -0.222*** (0.084)

-0.290*** (0.109)

-0.014 (0.129)

0.094 (0.129)

-0.080*** (0.024)

0.071*** (0.024)

-0.073*** (0.023)

0.001 (0.003)

Dak Nong -0.153 (0.097)

-0.319** (0.132)

-0.155 (0.148)

0.152 (0.154)

0.003 (0.062)

0.020 (0.062)

-0.018 (0.062)

-0.001 (0.0020

Lam dong -0.120 (0.107)

-0.235 (0.147)

0.023 (0.136)

0.033 (0.136)

-0.059* (0.033)

0.060** (0.030)

-0.059** (0.030)

-0.001 (0.002)

Long An … -0.181 (0.111)

….. 0.067** (0.028)

-0.064** (0.027)

-0.003 (0.003)

Households 1,029 415 693 207 Note: The category ‘lab’ includes all waged employment with and without agricultural production. This is also the case for the category ‘ent’ and ‘lab+ent’. Robust standard errors are presented in parentheses. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level.

15

To explore this further we consider whether there are certain household characteristics associated with moving into waged employment, enterprise activities or a combination of the two. Table 9 also presents the results of a multinomial logit model which we estimate separately for each year and estimate conditional on transitioning out of specialized agriculture. We consider three different categories of households: those that move into waged activities, Lab, combining households that continue agricultural production with those that completely switch out of agriculture; those that move into enterprise activities, Ent, again with or without continuing agriculture; and those that move into both waged and enterprise activities, Lab+Ent, with and without agriculture. The multinomial logit specification allows us to determine the factors that make households more or less likely to move into each of these respective categories. The marginal effects presented in columns (3) to (5) relate to 2010 while those presented in columns (6) to (8) relate to 2012. They can be interpreted as the probability that a household is in a particular occupational grouping relative to the other two groupings and so the marginal effects for each variable will sum to zero. In other words if a variable has a positive impact on the probability that a household is in one category it must have a corresponding negative impact in one or both of the other categories. In 2010, higher income transition households are more likely to move into waged activities while lower income households are more likely to diversify into multiple activities, i.e. both labor and enterprise activities. While those that do diversify into multiple activities have lower incomes they score better on the wealth index suggesting that these households tend to have an abundance of illiquid assets that may motivate them to earn additional income from other activities. Female headed households are more likely to transition into waged activities as compared with enterprise activities as are households classified as poor by the authorities. Households of Kinh ethnicity are less likely to move into waged employment. Provincial variations in the choice of activities are also evident. Households in the base category, Ha Tay and Long An, are more likely to move into waged employment as compared with households in Lao Cai, Phu Tho, Lai Chau, Dien Bien and Nghe An, while in these provinces transition into enterprise activities is much more likely. This may reflect a lack of labor market opportunities in these provinces compared to Ha Tay and Long An requiring that firms wishing to transition out of agriculture must engage in their own non-farm non-wage household enterprise activities. The first point of note in relation to the 2012 results is that the group of households that transition from specialized agriculture into both waged employment and enterprise activities (Lab+Ent) is very small and as a result there are no statistically significant differences between these households and those that transition into waged employment or enterprise activities only. Also of note is that there are distinct differences in the factors that determine the choice of activities as compared with 2010. Wealth appears to be an important predictor of whether households diversify by starting an enterprise and has a negative effect on moving into waged employment. Larger households are more likely to choose waged employment while smaller households diversify into enterprise activities. As in 2010, female headed households are more likely to diversify into waged employment and less likely to start household enterprises. We find a strong education effect in 2012 that is not present in 2010. Educated heads of household are much more likely to diversify into waged employment while less educated households engage in enterprise activities. This can be explained by the fact that a higher level of education may be required to join the labor market while no education is needed to start an enterprise. In 2012 we also find that households that diversify in response to natural shocks are more likely to enter waged employment. A very different

16

pattern of provincial variation is also evident in 2012 compared with 2010. In 2012 households in Phu Tho, Nghe An, Dak Lak, Lam Dong and Long An were significantly more likely to enter waged employment compared with households in Ha Tay and Khanh Hoa (base categories). Correspondingly, households in these provinces are much less likely to start enterprises. This section has highlighted the significant differences in the types of households that transition from specialized agriculture and those that do not. It also sheds some light on the pattern of income diversification and how it is changing over time, both in terms of household characteristics and provincial variation. The final part of our analysis investigates whether the welfare effects of the transition out of agriculture depend on these characteristics. 4.3 Heterogeneous welfare effects of diversification In this section we revisit the welfare analysis presented in Section 4.1 and consider whether the welfare effects of transitioning out of agriculture depend on household or regional characteristics. As highlighted in Section 2, much of the literature examining the impact of diversification on livelihoods emphasizes the role of heterogeneity across households in constraints, opportunities and incentives to diversify income sources which in turn impacts on welfare outcomes. In this section, we address the question of who is benefiting from the transition out of specialized agriculture in Vietnam and so attempt to establish the extent to which there are pre-requisites for welfare gains. We estimate a similar model to that specified in equation (1) above and presented in Table 8 but simplify the transition variables to the categories used in the previous section (i.e. Lab, Ent and Lab+Ent). We add interaction terms between these transition variables and various household characteristics. The results are presented in Table 10. We only present the results for the variables of interest. Each model includes the same set of control variables as included in Tables 6 and 8 the results of which are presented in Table A5 of the Appendix. Table 10: Heterogeneous impacts of diversification out of agriculture on household welfare (1) (2) (3) (4) (5) (6) Interaction variable: Wealth Female Education Ethnicity Poor Shock Transition into Lab 0.044

(0.041) 0.026

(0.046) 0.048

(0.043) 0.033

(0.063) 0.056

(0.044) 0.090* (0.050)

Transition into Ent 0.172** (0.072)

0.128* (0.073)

0.185*** (0.072)

0.073 (0.080)

0.189** (0.080)

0.214** (0.098)

Transition into Lab+Ent 0.121 (0.079)

0.109 (0.081)

0.179** (0.079)

0.066 (0.091)

0.113 (0.083)

0.168 (0.109)

Interaction effects Level Effect 0.032

(0.032) -0.186 (0.121)

0.081 (0.076)

-0.135 (0.140)

-0.116** (0.060)

0.004 (0.034)

Var x Transition into Lab -0.052 (0.038)

0.130 (0.095)

-0.007 (0.125)

0.025 (0.080)

-0.030 (0.083)

-0.081 (0.056)

Var x Transition into Ent -0.009 (0.058)

0.454** (0.196)

-0.142 (0.180)

0.188 (0.136)

-0.063 (0.149)

-0.074 (0.116)

Var x Transition into Lab+Ent -0.055 (0.075)

0.098 (0.240)

-0.524** (0.220)

0.087 (0.137)

0.037 (0.144)

-0.089 (0.121)

R-squared 0.369 0.368 0.374 0.370 0.369 0.369 Number of households 1,835 1,835 1,835 1,835 1,835 1,835 Number of observations 3,586 3,586 3,586 3,586 3,586 3,586 Note: Household fixed effects model estimated in each case. Each model includes controls for the activities of non-transition households, household characteristics, controls for the current level of income, controls for selection and time dummies. Robust standard errors clustered at the household level presented in parentheses. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level.

17

In almost all specifications we find that households that transition into household enterprises do better in welfare terms than other households. We do not find much evidence, however, of heterogeneous effects. Of particular note is the fact that the welfare benefits of diversification do not seem to be related to wealth which is in contrast with much of the literature as discussed in Section 2. Also in contrast to previous literature, in the Vietnamese case it appears that female headed households that transition into household enterprises do much better than households that remain specialized, and all other transitioning households. This is again in contrast with the literature examining the gender differences in the welfare effects of diversification (Owusu et al., 2010). We also find that less educated households that diversify by moving into both labor and enterprise activities do much better in welfare terms than educated households that diversify by engaging in both activities. This suggests that educated households that diversify are likely to be better off engaging in either waged employment or enterprise activities but not both. Finally, we find that households that transition into waged employment and have not suffered a shock (natural disaster) are better off than those that transition into waged employment and experience a shock. This suggests that if diversification is in response to a shock it is less likely to have an impact on welfare. This is consistent with much of the literature which highlights that being pushed into diversification as a result of shocks is less likely to yield the same welfare effects as diversification for the sake of increasing productivity (see discussion in Section 2). In Section 4.2 we also uncovered significant provincial variation in diversification patterns. We consider here whether the welfare effects of the transition out of agriculture impact on households differently depending on the province they are in. To capture this we interact the transition variables with the province indicators to disaggregate the impact by province. For this specification we do not exclude any category (in other words there is no base category) so the coefficients are not interpreted relative to any particular province as was the case in the previous analysis. Instead, each coefficient can be interpreted as the average impact of transition on the welfare of households in that province. The results are presented in Table 11.9 Overall, the welfare impact of the transition from specialized agriculture is confined to households in Lao Cai, Nghe An and Quang Nam. Households in other provinces do not appear to experience significant welfare gains from diversifying their activities. When we disaggregate by the types of activities that households transition into we find positive welfare effects associated with transition into waged employment for households in Lao Cai and Quang Nam. Some negative effects are observed in Dak Lak suggesting that households that remain specialized in agriculture are better off compared with those that diversify or move into waged employment in this province. The significant positive welfare effects associated with transition into enterprise activities primarily relate to households in Dien Bien, Khanh Hoa and Long An, while combining enterprise and wage activities is welfare enhancing for households in Quang Nam and Khanh Hoa.

                                                            9 Some of the provincial variables are omitted because of too little variation in transition within the province leading to collinearity.

18

Table 11: Provincial variation in welfare effects of diversification (1) (2) Transition out of agriculture Hay Tay 0.127 Lao Cai 0.372*** Phu Tho -0.058 Lai Chau -0.048 Dien Bien 0.073 Nghe An 0.340** Quang Nam 0.346*** Khanh Hoa … Dak Lak -0.076 Dak Nong 0.031 Lam dong -0.259 Long An 0.095 Transition into Lab Hay Tay 0.135 Lao Cai 0.315** Phu Tho -0.076 Lai Chau 0.129 Dien Bien 0.032 Nghe An 0.047 Quang Nam 0.318*** Khanh Hoa Dak Lak -0.261** Dak Nong 0.047 Lam dong -0.222 Long An -0.010 Transition into Ent Hay Tay 0.149 Lao Cai -0.067 Phu Tho 0.072 Lai Chau 0.029 Dien Bien 0.292* Nghe An 0.020 Quang Nam 0.303 Khanh Hoa 1.043*** Dak Lak 0.557* Dak Nong 0.129 Lam dong -0.239 Long An 0.734** Transition into Lab+Ent Hay Tay -0.141 Lao Cai 0.181 Phu Tho -0.130 Lai Chau 0.101 Dien Bien 0.001 Nghe An 0.128 Quang Nam 0.690** Khanh Hoa 0.985*** Dak Lak -0.009 Dak Nong -0.188 Lam dong … Long An 0.225 R-squared 0.335 0.347 Number of households 1,835 1,835 Number of observations 3,586 3,586 Note: Household fixed effects model estimated in each case. Each model includes controls for the activities of non-transition households, household characteristics, controls for the current level of income, controls for selection and time dummies. Robust standard errors clustered at the household level not presented for ease of illustration but available on request. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level.

19

Finally, given the positive welfare effects associated with transitioning from agriculture to enterprise activity the final part of our analysis considers whether there are heterogeneous effects associated with different enterprise characteristics. To do so we condition on households that move from specialized agriculture to engage in some form of enterprise activity and examine whether the characteristics of the enterprise matter for the observed welfare effects. The model is estimated separately for 2010 and 2012 as there are insufficient observations to estimate a panel data regression. As such, some caution should be exercised in interpreting the results. The results are presented in Table 12. Table 12: Enterprise characteristics and welfare effects of diversification (1)

2010 (2)

2012 Enterprise characteristics Paid labor 0.479**

(0.197) 0.134

(0.269) Formal enterprise 0.079

(0.177) -0.018 (0.178)

Female manager 0.054 (0.118)

0.218 (0.164)

Education of manager 0.157 (0.171)

0.119 (0.179)

Log initial investment -0.021 (0.032)

0.066* (0.038)

HH characteristics Yes Yes Current income controls Yes Yes Selection control Yes Yes R-squared 0.599 0.368 Number of households 160 83 Note: Robust standard errors presented in parentheses. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level.

The results suggest that the characteristics of the enterprise do not matter too much for welfare. There are two exceptions. First, in 2010 we find that households that start an enterprise that employs paid workers do much better than other households. Second, in 2012 we find that the actual initial levels of investment matter for welfare outcomes. This suggests that there may be some barriers to realizing welfare gains from enterprise activity. Enterprises with paid employees are more formal than other enterprises and are more likely to survive and grow, but also require higher start-up capital. The fact that the biggest welfare effects are observed for households that engage in more large scale entrepreneurial activity suggests that resource constraints may influence the extent of the gains that can be realized from household enterprise activities.

20

5. Conclusion and Policy Recommendations This paper used detailed panel data from a sample of Vietnamese households to evaluate the extent to which structural transformation is observed at the microeconomic level through household-level income diversification. Our results confirm and supplement extensive existing empirical work and anecdotal evidence about diversification away from agriculture: the data show a large-scale shift in the allocation of labor from agriculture towards operating a household enterprise and, to a lesser extent, waged labor outside the home. This paper extends existing work in Vietnam by focusing on the welfare impact of diversification along these margins, where we follow the standard practice of evaluating welfare using per-capita household (real) consumption. Our full specification includes time-varying household-level observable information with proxies for contemporaneous income and household fixed effects, which also absorb province- and commune-level time-invariant information. The core finding from estimating this model is that diversification into owning and operating a household enterprise is welfare-enhancing. Compared to purely agricultural households, per-capita consumption is over 20% higher for fully diversified households, nearly 17% higher amongst households participating in both agriculture and operating a household enterprise, and 8% greater for households that combine agricultural production with working for a wage outside the home. Using information about individual households enables us to extend this analysis and evaluate the role that observable characteristics play in determining the welfare impact of moving out of agriculture. A surprising result is that wealth, as measured, does not affect the benefits of diversification; this is in direct contrast to much of the existing evidence from other countries that suggests that households’ wealth is a significant determinant of the extent to which they can benefit from participating in new income-earning activities. Encouragingly (and also contradicting evidence from other countries), female-headed households that transition into operating an enterprise have better welfare outcomes than households that remain specialized and other transitioning households. Finally, we find that education has a complex relationship with diversification and welfare: better-educated households that specialize in waged labor or operating a business do better than better-educated households that do both. The data also show clear cross-province differences in both the degree of diversification and the welfare-impacts of diversification. Including household fixed effects also absorbs and fixed province- or commune-level information, but we do not explicitly control for provincial characteristics in our main specification. For example, other data sources report significant differences in basic infrastructure that could mediate the households’ ability to diversify and the benefits of diversification. The most recent Rural, Agricultural, and Fishery Census (General Statistics Office, 2012) shows significant variation in the share of households with electricity (ranging from 78% in Lai Chau to nearly 100% in Ha Tay), access to asphalt roads (just 45% in Lao Cai to 98% in Ha Tay), and share of communes with telecommunications access (71% in Lai Chau but 100% in Lam Dong). Similarly, the annual enterprise census of all registered businesses shows dramatic cross-province differences in the number of registered firms, total employment and the average wages paid: average monthly compensation was 2.9 million VND in Quang Nam and 6.3 million VND in Ha Tay, for example. These large disparities in the price of labor may indicate significant differences in productivity due to differences in human, physical, and financial capital. However, while some share of the observed variation in the welfare effects of diversification is likely due to

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these and related differences across provinces, this variation is likely slow-moving: including a vector of province-level information would not materially affect our key results. Nevertheless, this remains an interesting area for future research and suggests that policies should be province- or region-specific: implementing sub-national policies is particularly feasible in Vietnam in light of the capacity of provincial and local governments. While we are cautious about suggesting large-scale policy prescriptions on the basis of partial equilibrium microeconomic analysis, our robust results do clearly indicate some areas for policy innovation. Our core finding is that diversification is welfare-improving on average, albeit not equally and not everywhere. While policymakers should work to enable more rural households to diversify their income sources, either as a stepping stone to wholesale specialization (the objective of the national Socio-Economic Development Plan) or simply to enable these households to increase welfare, policies should be tailored to address province- and household-specific constraints. Our findings also show policymakers should be cautious about making specialization the focus of national policy, since many households benefit from some degree of diversification rather than wholesale specialization.10 Moreover, on average the most beneficial form of diversification is into a household enterprise rather than waged labor suggesting that from a welfare perspective rural labor markets remain underdeveloped and rural labor remains relatively unproductive. Two policies likely to address a large number of constraints include investment in infrastructure and investment in human capital. The large provincial differences in the impact of diversification reflect significant dispersion in households’ access to markets and high-quality infrastructure. Region-specific policies focused on improving transport and communications infrastructure could help to improve returns from diversification. Similarly, increasing the quality and quantity of education is likely to have a large effect on households’ ability to diversify and their success in non-traditional activities. While agriculture remains the main source of income and employment for the vast majority of rural Vietnamese, results strongly confirm that diversification is happening on a large scale in Vietnam and some households are benefiting from this transformation. This process will continue and likely accelerate; monitoring its effect on welfare through high-quality panel data on rural households remains essential to the policymaking process.

                                                            10 This is not true for more-educated households, indicating as average effective years of education increase, specialization will become more desirable.

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References Abdulai, A. and Crole-Rees, A. (2001) “Determinants of income diversification among rural households in Southern Mali”, Food Policy, 26, 437-452 Angrist, J. D. and J.-S. Pischke (2008). Chapter 8, Mostly harmless econometrics: An empiricist's companion. Princeton University Press. Arellano, M., and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297. Barrett, C., Reardon, T. and Webb, P. (2001) “Nonfarm Income Diversification and Household Livelihood Strategies in Rural Africa: Concepts, Dynamics and Policy Implications”, Food Policy, 26, 315-331 Barrett, C., Bezuneh, M., Clay, D., and Reardon, T. (2005) “Heterogeneous Constraints, Incentives and Income Diversification Strategies in Rural Africa”, Quarterly Journal of International Agriculture, 44, 37-60 General Statistics Office of Vietnam (2012) “The Viet Nam Rural, Agriculture and Fishery Census 2012” Hanoi: Department of Agriculture, Forestry and Fishery Statistics of General Statistics Office of Vietnam. Giesbert, L. and Schindler, K. (2012) “Assets, Shocks and Poverty Traps in Rural Mozambique”, World Development, 40, 1594-1609 Kijima, Y., Matsumoto, T. and Yamano, T. (2006) Nonfarm employment, agricultural shocks and poverty dynamics: evidence from rural Uganda. Agricultural Economics. 35, 459-467 Van Den Berg, M. and Kumbi, G. (2006) “Poverty and the rural nonfarm economy in Oromia, Ethiopia”, Agricultural Economics. 35, 469-475 Bezu, S., Barrett, C. and Holden, S. (2012) “Does the Nonfarm Economy Offer Pathways for Upward Mobility? Evidence from a Panel Data Study in Ethiopia”, World Development, 40, 1634-1646 Block, S. and Webb, P. (2001) “The dynamics of livelihood diversification in post-famine Ethiopia”, Food Policy. 26, 333-350 Owusu, V., Abdulai, A. and Abdul-Rahman, S. (2010) “Non-farm work and food security among farm households in Northern Ghana”, Food Policy 36, 108-118 Lay, J., Mahmoud, T. and Mukaria, G. (2008) “Few Opportunities, Much Desperation: The Dichotomy of Non-Agricultural Activities and Inequality in Western Kenya”, World Development, 36, 2713-2732 McKay, Andy and Tarp, F. (2011) “Welfare Dynamics in Rural Vietnam, 2006 to 2010”, Mimeo.

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Wainwright, F., Tarp, F. and Newman, C. (2012) “Risk and Household Investment Decisions: Evidence from Rural Vietnam”, Mimeo.

World Development Indicators (WDI) 2012, The World Bank.

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Appendix Table A1: Summary statistics 2008 2010 2011 Mean Std. Dev Mean Std. Dev Mean Std. Dev. Per capita consumption 5.346 0.830 5.509 0.699 5.778 0.669 Real income 10.618 0.812 10.890 0.803 10.912 0.815 Wealth index 0.004 0.823 0.223 0.897 0.450 0.890 Size 4.535 1.761 4.290 1.716 4.194 1.794 Female Head 0.205 0.404 0.208 0.406 0.216 0.411 Age Head 51.564 13.738 53.03 13.40 54.519 13.257 Ed Head cat 2 0.178 0.382 0.154 0.361 0.146 0.353 Ed Head cat 3 0.358 0.479 0.290 0.454 0.303 0.460 Ed Head cat 4 0.242 0.428 0.317 0.465 0.317 0.465 Ed Head cat 5 0.084 0.278 0.098 0.298 0.093 0.290 Ed Head cat 6 0.017 0.130 0.022 0.148 0.023 0.150 Number of children 1.493 1.249 1.358 1.231 1.279 1.167 Kinh Head 0.785 0.411 0.789 0.408 0.791 0.406 Head born in commune 0.817 0.387 0.809 0.393 0.844 0.363 Household classified as poor 0.199 0.399 0.140 0.347 0.173 0.378

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Table A2: Impact of diversification on household welfare – full results (1) (2) (3) (4) Diversification Ag + Lab 0.036 0.084** 0.083** 0.083** Ag + Ent 0.162*** 0.173*** 0.168*** 0.168*** Ag + Lab + Ent 0.182** 0.227*** 0.227*** 0.227*** Lab Only -0.034 0.018 0.036 0.037 Ent Only -0.026 -0.005 0.006 0.006 Lab + Ent -0.032 -0.017 -0.015 -0.014 HH characteristics Wealth 0.188*** 0.186*** 0.192*** Size -0.243*** -0.236*** -0.237*** Size squared 0.011** 0.010** 0.010** Female Head -0.061 -0.061 -0.060 Age Head 0.037** 0.035** 0.034* Age Head squared -0.0003** -0.0003* -0.0003* Ed Head cat 2 0.126** 0.141** 0.141** Ed Head cat 3 0.201*** 0.226*** 0.225*** Ed Head cat 4 0.286*** 0.340*** 0.339*** Ed Head cat 5 0.255*** 0.315*** 0.314*** Ed Head cat 6 0.094 0.187 0.187 Number of children 0.016 0.008 0.009 Kinh Head 0.404** 0.338 0.337 Head born in commune 0.116* 0.098 0.096 HH classified as poor -0.072 -0.119** -0.117** Current income controls Lag log income -0.052** -0.053** Lag Size 0.033 0.030 Lag Size squared -0.0003 -0.0001 Lag Female Head -0.091 -0.089 Lag Age Head -0.023 -0.023 Lag Age Head squared 0.0002 0.0002 Lag Ed Head cat 2 0.048 0.047 Lag Ed Head cat 3 0.059 0.058 Lag Ed Head cat 4 0.132* 0.130* Lag Ed Head cat 5 0.154 0.154 Lag Ed Head cat 6 0.178 0.176 Lag Number of children -0.019 -0.019 Lag Kinh Head -0.103 -0.104 Lag Head born in commune -0.030 -0.030 Lag HH classified as poor -0.129*** -0.127*** Selection control Lag wealth 0.015 Time dummies Year 2010 -0.271*** -0.205*** -0.218*** -0.213*** R-squared 0.037 0.359 0.360 0.365 Number of households 1,835 1,835 1,835 1,835 Number of observations 3,588 3,588 3,588 3,588 Note: Household fixed effects model estimated in each case. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level. Standard errors not presented for ease of illustration.

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Table A3: Lagged dependent variable specification, full results (1) (2) (3)

Original model LDV FE

Ag + Lab 0.083** 0.054** 0.084** Ag + Ent 0.168*** 0.115*** 0.173*** Ag + Lab + Ent 0.227*** 0.163*** 0.227*** Lab Only 0.037 0.186*** 0.018 Ent Only 0.006 0.173*** -0.005 Lab + Ent -0.014 0.064 -0.017 Lagged consumption 0.135*** HH characteristics

Wealth 0.192*** 0.264*** 0.188*** Size -0.237*** -0.182*** -0.243*** Size squared 0.011* 0.007*** 0.011** Female Head -0.060 -0.026 -0.061 Age Head 0.034* -0.002 0.036** Age Head squared -0.000* 0.000 -0.000** Ed Head cat 2 0.141** 0.058 0.126** Ed Head cat 3 0.225*** 0.055 0.202** Ed Head cat 4 0.339*** 0.122*** 0.285*** Ed Head cat 5 0.314*** 0.170*** 0.255** Ed Head cat 6 0.094 0.287*** 0.093 Number of children 0.009 -0.031*** 0.017 Kinh Head 0.337 0.091*** 0.403** Head born in commune 0.096 0.039* 0.116* HH classified as poor -0.117** -0.193*** -0.072 Current income controls

Lag log income 0.030 Lag Size -0.000 Lag Size squared -0.089 Lag Female Head -0.023 Lag Age Head 0.000 Lag Age Head squared 0.047 Lag Ed Head cat 2 0.058 Lag Ed Head cat 3 0.130* Lag Ed Head cat 4 0.154 Lag Ed Head cat 5 0.176 Lag Ed Head cat 6 -0.019 Lag Number of children -0.104 Lag Kinh Head -0.030 Lag Head born in commune -0.127** Lag HH classified as poor -0.053** Selection control

Lag wealth 0.015 Time dummies

Year 2010 -0.213*** -0.181*** -0.205*** R-squared 0.36 0.44 0.36 Number of households 1,835 1,835 1,835 Number of observations 3,587 3,588 3,593

Note: LDV indicates lagged dependent variable. FE indicates the household fixed effects model. The model estimated in column (3) excludes current income and the selection control so that the set of control variables are comparable to those included in column (2). Standard errors clustered at the household level are presented in parentheses. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level.

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Table A4: Impact of diversification out of agriculture on household welfare – full results (1) (2) Transition out of Ag 0.101*** Of which: Into Ag+Lab 0.067 Into Ag+ Ent 0.204*** Into Ag+Lab+Ent 0.180** Into Other 0.079 Activities of non-transition hhs: Ag + Lab 0.115 0.121 Ag + Ent 0.193* 0.200* Ag + Lab + Ent 0.313** 0.319*** Lab Only 0.092 0.096 Ent Only 0.069 0.075 Lab + Ent 0.053 0.060 HH characteristics Wealth 0.194*** 0.193*** Size -0.243*** -0.239*** Size squared 0.011** 0.011** Female Head -0.055 -0.063 Age Head 0.031* 0.033* Age Head squared -0.0003* -0.0003* Ed Head cat 2 0.144** 0.143** Ed Head cat 3 0.230*** 0.230*** Ed Head cat 4 0.345*** 0.345*** Ed Head cat 5 0.324*** 0.322*** Ed Head cat 6 0.199 0.193 Number of children 0.010 0.009 Kinh Head 0.333 0.341 Head born in commune 0.104 0.103 HH classified as poor -0.115** -0.115** Current income controls Lag log income -0.058** -0.054** Lag Size 0.028 0.030 Lag Size squared 0.0002 -0.0001 Lag Female Head -0.086 -0.092 Lag Age Head -0.022 -0.024 Lag Age Head squared 0.0002 0.0002 Lag Ed Head cat 2 0.051 0.048 Lag Ed Head cat 3 0.061 0.057 Lag Ed Head cat 4 0.133* 0.130* Lag Ed Head cat 5 0.155 0.154 Lag Ed Head cat 6 0.189 0.176 Lag Number of children -0.019 -0.018 Lag Kinh Head -0.110 -0.110 Lag Head born in commune -0.028 -0.027 Lag HH classified as poor -0.129*** -0.129*** Selection control Lag wealth 0.016 0.015 Time dummies Year 2010 -0.211*** -0.217*** R-squared 0.369 0.368 Number of households 1,835 1,835 Number of observations 3,586 3,586 Note: Household fixed effects model estimated in each case. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level. Standard errors not presented for ease of illustration.

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Table A5: Heterogeneous impacts of diversification out of agriculture on household welfare – full results (1) (2) (3) (4) (5) (6) Interaction variable: Wealth Female Education Ethnicity Poor Shock Transition into Lab 0.044 0.026 0.048 0.033 0.056 0.090* Transition into Ent 0.172** 0.128* 0.185*** 0.073 0.189** 0.214** Transition into Lab+Ent 0.121 0.109 0.179** 0.066 0.113 0.168 Interaction effects Level Effect 0.032 -0.186 0.081 -0.135 -0.116** 0.004 Var x Transition into Lab -0.052 0.130 -0.007 0.025 -0.030 -0.081 Var x Transition into Ent -0.009 0.454** -0.142 0.188 -0.063 -0.074 Var x Transition into Lab+Ent -0.055 0.098 -0.524** 0.087 0.037 -0.089 Activities of non-trans hhs: Ag + Lab 0.128 0.123 0.113 0.123 0.122 0.119 Ag + Ent 0.208* 0.201* 0.195* 0.203* 0.202* 0.198* Ag + Lab + Ent 0.324*** 0.318*** 0.307*** 0.320*** 0.319*** 0.316*** Lab Only 0.101 0.097 0.086 0.098 0.096 0.095 Ent Only 0.078 0.077 0.068 0.077 0.076 0.073 Lab + Ent 0.068 0.061 0.048 0.062 0.061 0.059 HH characteristics Wealth 0.196*** 0.196** 0.197*** 0.192*** 0.194*** 0.194*** Size -0.237*** -0.238** -0.227*** -0.236*** -0.237*** -0.236*** Size squared 0.010** 0.011** 0.010* 0.010** 0.010** 0.010** Female Head -0.059 -0.048 -0.063 -0.061 -0.064 -0.068 Age Head 0.033* 0.034* 0.034* 0.033* 0.033* 0.034* Age Head squared -0.0003* -0.0003* -0.0003* -0.0003* -0.0003 -0.0003* Ed Head cat 2 0.146** 0.144** 0.129** 0.148** 0.145** 0.149** Ed Head cat 3 0.233*** 0.233*** 0.214*** 0.233*** 0.231*** 0.239*** Ed Head cat 4 0.352*** 0.350*** 0.295*** 0.349*** 0.346*** 0.353*** Ed Head cat 5 0.332*** 0.327*** 0.300*** 0.327*** 0.322*** 0.323*** Ed Head cat 6 0.200 0.211 0.135 0.205 0.198 0.187 Number of children 0.007 0.009 0.006 0.008 0.007 0.009 Kinh Head 0.344 0.357 0.357 0.337 0.344 0.337 Head born in commune 0.102 0.099 0.102 0.106 0.104 0.106 HH classified as poor -0.113** -0.116** -0.116** -0.116** -0.114** -0.110** Current income controls Lag log income -0.054** -0.054** -0.056** -0.052** -0.052** -0.054** Lag Size 0.027 0.031 0.031 0.029 0.030 0.032 Lag Size squared 0.0001 -0.00004 -0.002 0.0001 -0.0001 -0.0004 Lag Female Head -0.092 Level effect -0.03 -0.095 -0.094 -0.101 Lag Age Head -0.023 -0.024 -0.025 -0.025 -0.024 -0.025 Lag Age Head squared 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 Lag Ed Head cat 2 0.053 0.042

Level Effect

0.052 0.049 0.050 Lag Ed Head cat 3 0.062 0.052 0.060 0.056 0.058 Lag Ed Head cat 4 0.135* 0.124* 0.133* 0.129* 0.128* Lag Ed Head cat 5 0.161 0.148 0.159 0.153 0.146 Lag Ed Head cat 6 0.181 0.175 0.182 0.175 0.172 Lag Number of children -0.019 -0.019 -0.017 -0.018 -0.018 -0.017 Lag Kinh Head -0.122 -0.115 -0.119 Level effect -0.114 -0.116 Lag Head born in commune -0.028 -0.035 -0.030 -0.026 -0.027 -0.027 Lag HH classified as poor -0.132*** -0.131*** -0.131*** -0.129*** Level effect -0.128*** Selection control Lag wealth Level effect 0.013 0.016 0.013 0.014 0.018 Time dummies Year 2010 -0.218*** -0.220*** -0.229*** -0.221*** -0.221*** -0.216*** R-squared 0.369 0.368 0.379 0.370 0.369 0.369 Number of households 1,835 1,835 1,835 1,835 1,835 1,835 Number of observations 3,586 3,586 3,586 3,586 3,586 3,586 Note: Household fixed effects model estimated in each case. *** indicates significance at the 1% level, ** indicates significance at the 5% level, * indicates significance at the 10% level. Standard errors not presented for ease of illustration.