Does Urbanization Reduce Rural Poverty? Evidence from Vietnam 1

Keywords: urbanization, per capita income, per capita expenditure, rural poverty, impact evaluation, household surveys, Vietnam, Asia. JEL Classification: O18, I30, R11

This paper contributes to the urbanization-poverty nexus by assessing the effect of urbanization on income, expenditure, and poverty in rural households in Vietnam, using data from household surveys. We find that the urbanization process stimulates the transition from farm to non-farm activities in rural areas. More specifically, urbanization tends to reduce farm income and increase wages and non-farm income in rural households. This suggests that total income and consumption expenditure of rural households are more likely to increase with urbanization. Finally, we find also that urbanization helps to decrease the expenditure poverty rate of rural households, albeit by a small magnitude.

Introduction

This paper contributes to the literature by investigating the urbanization-poverty nexus in rural developing economies. Previous research suggests urbanization is both a result and a cause of economic development [START_REF] Gallup | Geography and economic development[END_REF].The proportion of the world's urban population increased from 29.4 percent in 1950 to around 52.1 percent in 2011 (United Nations, 2012). While 77.7 percent of the populations of developed countries live in urban areas, urbanization levels are low in developing countries despite growing from 17.6 percent of the population in 1950 to 46.5 percent in 2011. 2 Moreover, according to UN projections, the world's urban population is expected to increase to72 percent by 2050, from 3.6 billion in 2011 to 6.3 billion in 2050, with 5.12 billion of this urban population living in a developing country.

In theory, the geographical agglomeration of people and firms can lead to lower production costs, and higher productivity and economic growth [START_REF] Krugman | Increasing returns and economic geography[END_REF][START_REF] Fujita | On the evolution of hierarchical urban systems[END_REF][START_REF] Quigley | Urbanization, Agglomeration, and Economic Development[END_REF]. Also, urbanization can help to reduce poverty through its impact on economic growth which is a prerequisite for poverty reduction [START_REF] Demery | Poverty in Africa: an Emerging Picture[END_REF][START_REF] Dollar | Growth Is Good for the Poor, Development Research Group[END_REF]. Urban areas tend to be less poor, and as a result, poverty levels tend to decrease as the share of urban population increases [START_REF] Ravallion | New evidence on the urbanization of global poverty[END_REF]. However, in practice the impact of urbanization on economic growth depends on the process and nature of urbanization [START_REF] Bloom | Urbanization and the Wealth of Nations[END_REF]Basuand Mallick, 2008;[START_REF] Kumar | Estimating consumption deprivation in India using survey data: a state-level rural-urban analysis before and during the reform period[END_REF]. In Asia urbanization has led to rapid economic growth but there has been no similar impact in Africa [START_REF] Ravallion | New evidence on the urbanization of global poverty[END_REF]. Despite the large literature on the relationship between urbanization and growth [START_REF] Bertinelli | Urbanization and growth[END_REF], little is known about the effect of urbanization on rural poverty, and the channels through which urbanization can influence rural poverty.

There are several channels through which urbanization can be expected to affect income expenditure and poverty among rural households [START_REF] Ravallion | New evidence on the urbanization of global poverty[END_REF][START_REF] Cali | Does Urbanisation Affect Rural Poverty? Evidence from Indian Districts[END_REF][START_REF] Martinez-Vazquez | Urbanization and the Poverty Level[END_REF][START_REF] Mallick | Disentangling the Poverty Effects of Sectoral Output, Prices, and Policies in India[END_REF][START_REF] Banerjee | The economic lives of the poor[END_REF].

First, urbanization often involves migration from a rural to an urban area. Workers tend to move from the agricultural sector and rural areas to industry sectors and urban areas [START_REF] Lewis | Economic development with unlimited supplies of labour[END_REF][START_REF] Harris | Migration, Unemployment and Development: A Two-Sector Analysis[END_REF]. Migration is expected to increase the incomes and consumption of both the migrants and the households they leave behind which benefit from remittances [START_REF] Stark | Migration Incentives, Migration Types: The Role of Relative Deprivation[END_REF][START_REF] Stark | The Migration of Labour[END_REF][START_REF] Mckenzie | Migration, remittances, poverty, and human capital: conceptual and empirical challenges[END_REF].Remittances can be used also to invest in human capital building or physical and social assets allowing rural households to increase agricultural productivity or start nonfarm businesses.However, the results from empirical studies on the impact of migration on the households left behind are rather mixed. Several studies show a positive impact of remittances on household income and poverty reduction (e.g., [START_REF] Adams | Do International Migration and Remittances Reduce Poverty in Developing Countries?[END_REF][START_REF] Acosta | What is the impact of international remittances on poverty and inequality in Latin America?[END_REF], Bouiyouret al., 2016) while others find no poverty reduction effects of migration [START_REF] Yang | International Migration, Remittances, and Household Investment: Evidence from Philippine Migrants' Exchange Rate Shocks[END_REF][START_REF] Azam | Migrants' Remittances and the Household in Africa: A Review of Evidence[END_REF][START_REF] Nguyen | The Impacts of International Remittances on Income, Work Efforts, Poverty and Inequality: New Evidence for Vietnam[END_REF]. Moreover, during times of economic crisis, rural to urban migration, and the remittances sent to rural areas, decrease due to higher unemployment in urban areas.

Second, urban development can have a positive impact on rural revenues by increasing demand for rural products [START_REF] Tacoli | Rural-Urban Interactions: A Guide to the Literature[END_REF][START_REF] Otsuka | The Rural Industrial Transition in East Asia: Influences and Implications[END_REF][START_REF] Cali | Does Urbanisation Affect Rural Poverty? Evidence from Indian Districts[END_REF][START_REF] Haggblade | The Rural Non-farm Economy: Prospects for Growth and Poverty Reduction[END_REF]. High levels of economic growth and population density in urban areas create higher demand for commodities from rural areas, especially agricultural and labor-intensive commodities. Transportation and infrastructure tend to improve overtime which reduces the cost of transporting commodities from rural households to urban markets. [START_REF] Otsuka | The Rural Industrial Transition in East Asia: Influences and Implications[END_REF] concludes that in developing Asian countries, urban-to-rural subcontracting for labor-intensive export manufactures increases due to reduced transport fees.

Third, urbanization can increase rural households' nonfarm income, and especially for households located close to a city [START_REF] Berdegue | Rural nonfarm employment and incomes in Chile[END_REF][START_REF] Fafchamps | Cities and Specialization: Evidence from South Asia[END_REF][START_REF] Deichmann | Urban Proximity, Agricultural Potential and Rural Non-Farm Employment: Evidence from Bangladesh[END_REF].Firms are agglomerated in cities, and attract both urban and also nearby rural workers. As a result, urbanization can increase the wages of rural workers. In addition, migration based on the wage differentials between urban and rural areas can reduce the rural labor supply, thereby increasing rural wages.

Finally, rural households' living standards can rise as a result of urbanization spillover effects [START_REF] Bairoch | Cities and Economic Development: From the Dawn of History to the Present[END_REF][START_REF] Williamson | Coping with City Growth During the British Industrial Revolution[END_REF][START_REF] Allen | The British Industrial Revolution in Global Perspective[END_REF]. As well as migration, other interactions between urban and rural areas can have positive effects on human capital formation in rural areas through transfers of information and advanced knowledge about production-related skills and technology [START_REF] Mckenzie | Migration, remittances, poverty, and human capital: conceptual and empirical challenges[END_REF]. Also, urbanization plays a vital role in the economic and social fabric of both urban and nearby rural areas by offering opportunities for education, health services and environmental facilities.Education capital determines the ability of rural inhabitants to adopt technologies; health capital can influence economic activity and poverty reduction directly, through the impact on labor productivity.

However, there are reasons to think that urbanization does not lead necessarily to higher incomes for rural households. For instance, a direct consequence of rural to urban migration is the reduction in the labor supply of rural households, especially in the laborintensive sector. In the short-run, migrants are unable to send remittances and their family members can suffer a decrease in income. In the long-run, rural to urban migration can prevent these households from engagement in high-return, labor intensive activities.

Moreover, remittances can create disincentives to work resulting in a moral hazard problem [START_REF] Farrington | Introduction: Cash Transfers: Panacea for Poverty Reduction or Money Down the Drain?[END_REF]. Several studies show that migration is likely to affect the labor decisions of other members of the migrant's rural household, or can increase their reservation wage; receiving remittances from migrants can have a negative effect on labor market participation for non-migrants in rural areas [START_REF] Kim | The impact of remittances on labor supply: The case of Jamaica[END_REF][START_REF] Grigorian | Destined to receive: The impact of remittances on household decisions in Armenia[END_REF]. Thus, through rural to urban migration, remittances, labor supply, the impact on the demand for agricultural products, and technology transfer, urbanization can affect production activities including the farm and non-farm activities of rural households, and can affect the incomes and poverty of rural households. Depending on the relative magnitude of the different channels of the effect of urbanization, its impact on rural households' poverty is theoretically uncertain and may be negative or positive, especially in the context of rapid urbanization in developing economies.

Despite the importance of the urbanization-poverty nexus for developing countries, very few empirical studies investigate the effect of urbanization on poverty reduction, and in particular, on rural poverty reduction. [START_REF] Ravallion | New evidence on the urbanization of global poverty[END_REF] find that urbanization has a positive effect on poverty reduction but that the effect varies across regions. [START_REF] Martinez-Vazquez | Urbanization and the Poverty Level[END_REF] also using cross-country datafind a U-shaped relation between urbanization levels and poverty indexes. This implies that the effect of urbanization on poverty is not necessarily linear and positive for all countries. To our knowledge, only [START_REF] Cali | Does Urbanisation Affect Rural Poverty? Evidence from Indian Districts[END_REF] explicitly examine the effect of urbanization on rural poverty.

These authors investigate thecase of rural poverty in India and find that urbanization helps to reduce rural poverty thanks mostly to positive spillovers from urbanization rather than migration of the rural poor to urban areas.A related study is [START_REF] Mallick | Disentangling the Poverty Effects of Sectoral Output, Prices, and Policies in India[END_REF], which shows that during the shrinking process of the agricultural sector, poor laborers move from rural to urban areas, and it helps to reduce poverty in rural areas in India.

In this study, we contribute to this research area by examining the effect of urbanization on the income, expenditure and poverty of rural households in Vietnam.

Vietnam is an interesting case for at least three main reasons. Firstly, Vietnam is a postcommunist country which has achieved high economic growth and remarkable poverty reduction following economic reforms in the 1980s. The poverty rate dropped dramatically from 58 percent in 1993 to 37 percent in 1998, and continued to decrease to 20 percent and 15 percent in 2004 and 2008respectively. 3 Secondly, Vietnam remains a rural country with 70 percent of its population living in a rural area, and poverty is a rural phenomenon in Vietnam with around 97 percent of the country's poor living in a rural area. 4 Vietnam's urbanization level is very similar to that of other developing countries.

However, in the first decade of 2000 the urbanization process in Vietnam increased quite remarkably. The share of urban population increased from around 24 percent in 2001 to 30 percent in 2009. Thirdly, although there are several studies of urbanization and rural-urban migration in Vietnam (e.g., [START_REF] Gso | Migration and Urbanization in Vietnam: Patterns, Trends and Differentials, Monograph, General Statistics Office of Vietnam[END_REF]World Bank, 2011), there are no quantitative studies that look at the effect of urbanization on rural households' income and expenditure.

Whether the urbanization process has contributed to rural poverty reduction in Vietnam remains unknown.

Using panel data from the 2002[START_REF] Greene | Fixed effects and bias due to the incidental parameters problem in the Tobit model[END_REF], 2006, and 2008Vietnam Household Living Standard Surveys (VHLSS) we show that urbanization tends to increase landlessness among rural households, and reduces their farm income. However, households living in provinces with high levels of urbanization are more likely to have higher wage and nonfarm incomes. For these households, the increase in non-farm income is greater than the loss of farm income, and as a result, rural households' total income and expenditure on consumption tend to increase with urbanization. We propose a simple method to measure the marginal effect of urbanization on poverty rates; we find that in Vietnam urbanization has led to a decrease in the poverty rate. Although our empirical analysis focuses on Vietnam, we believe our results are significant for a wider group of emerging and developing economies with high urbanization rates but also high rural poverty rates. This paper is structured in six sections. Section 2 presents the data sets used in the study; section 3 provides an overview of the urbanization process and rural poverty in Vietnam. Sections 4 and 5 describe the method, and present the results of the effect of urbanization on income, expenditure,and poverty of rural households. Section 6 offers some conclusions.

Data set

This study relies on data from the 2002[START_REF] Greene | Fixed effects and bias due to the incidental parameters problem in the Tobit model[END_REF], 2006, and 2008VHLSSconducted , and 2008VHLSSconducted

Urbanization and rural households in Vietnam

Urbanization process in Vietnam

Topographically Vietnam is a very diverse country, with eight well-defined agroecological zones. These regions range from the remote and poorly endowed zones of the Northern Mountain area bordering China, and the North and South Central Coastal regions, through the Central Highlands, to the fertile, irrigated regions of the Red River Delta in the North, and the Mekong Delta in the South. The North West is the poorest region with a low level of urbanization, while the South East is most urbanized region with the lowest poverty (table 1). The process of urbanization in Vietnam has been increasing since the early 1900s

(figure 1). According to the definition of urban area in Vietnam, this urbanization has two possible origins. Firstly, rural-urban migration; around 16 percent of the urban population in Vietnam is composed of migrants who moved from rural to urban areas in 2004 to 2009 [START_REF] Gso | Migration and Urbanization in Vietnam: Patterns, Trends and Differentials, Monograph, General Statistics Office of Vietnam[END_REF]. The key motivation for rural people to move to urban areas is high wage employment in the urban area (e.g., [START_REF] Brauw | Seasonal migration and improving living standards in Vietnam[END_REF]. Industrialization and foreign direct investment in industrial zones in urban areas attract rural laborers [START_REF] Dang | Internal migration and development in Vietnam[END_REF]. Secondly, a rural area can become an urban area if its population and non-farm economic activities increase. 5 In developing countries where agricultural production is a comparative advantage, farm households can increase their income by exporting agricultural products. Increasing incomes in the agricultural sector can result in greater demand for services and manufactured goods [START_REF] Tacoli | Rural-Urban Interactions: A Guide to the Literature[END_REF]

Urbanization and rural households

Table 2 presents the association between household and provincial urbanization income patterns. Households in the most urbanized provinces are more likely to have a lower share of crop and livestock income in total income. This is expected since households in more urbanized provinces have smaller agricultural landholdings than households in less urbanized provinces. The share of wages and other non-farm income in total household income is higher for households in more urbanized provinces.

Estimation methods

Fixed-effects regressions

To estimate the effect of urbanization on rural households, we assume a rural household outcome indicator as a function of household characteristics and the urbanization level:

ikt ik ikt t kt ikt X T U Y             ) ln( ) ln( (1)
where ikt Y is anoutcome indicator of household iin province k at time t (years 2002, 2004, 2006, and 2008), and kt U is an indicator of urbanization. In this study, urbanization is measured as the percentage of urban population to total population in the province. kt U is the percentage of urban population in province k at the time t. We use the lagged urban population share, i.e., the urban population shares in 2001, 2003, and 2005, and 2007 so that the urbanization variables are determined before the outcome variables.7 Although the 2002[START_REF] Greene | Fixed effects and bias due to the incidental parameters problem in the Tobit model[END_REF], 2006, and 2008VHLSSs were conducted in 2002[START_REF] Greene | Fixed effects and bias due to the incidental parameters problem in the Tobit model[END_REF], 2006, and 2008, respectively, they were implementedmainly in June and September, and the data on householdsreferred to the previous 12 months.

In Vietnam, estimates of urban and rural populationsare based on the Vietnam Population and Housing Censuses which are conducted every ten years. For the years when there was no population census, GSO conductedwhat they call a Population Change and Family Planning Survey to collect data on basic demographics and fertility since 2001. The surveys are representative of urban and rural provinces. Around 6,000 households were sampled in each province [START_REF] Gso | án điều tra biến động dân số và kế hoạch hoá gia đình 1/4/2010 (The plan to conduct Population Change and Family Planning Survey 1/4/2010[END_REF]. In this study, the share of urban population in the provinces is computed based on these surveys.8 t T is the dummy variable for year t. ikt X is a vector of household characteristics. Estimating the impact of a factor is always challenging. There are two difficulties involved in estimating the effect in a country of urbanization on rural households . Firstly, the urbanization process involves the country's total population. If urbanization is considered as the treatment, there are no clean treatment and control groups. In this study, we assume that urbanization at the provincial level affects only those people living in the province. It is possible that rural households close to the boundary dividing two provinces might be affected by the respective urbanization processesin those two provinces.

However,since the proportion of households living near a provincial boundary is small, the spill-over effects are expected to be small compared to the main effect of urbanization.Appendix Figure A.1 shows that most urban areas lie completely within provinces.Testing the spill-over effect of the urbanization process is beyond the scope of this study due to data limitations but would be an important are for further studies.

Secondly, urbanization is not a random process, and the urbanization process cannot befully observed.We use a fixed-effects regression to eliminate unobserved timeinvariant variables (variable ik  in the equation ( 1)) whichcan cause endogeneity bias. We would expect endogeneity bias to be negligible afterthe elimination of unobserved timeinvariant variables andafter controlling for theobserved variables.Also, to achieve a robust analysis we ran the fixed-effects with instrumental variable regressions where the instrumental variable for the urbanization variable (one-year lagged share of urban population) was the two-year lagged share of urban population. Lagged endogenous variables are often used as instruments for current endogenous variables. This type of instrument has the advantage that it is strongly corrected with the endogenous variables, and as a result, can reduce bias due to weak instruments.However, the assumption of theexclusion condition of the instruments might not hold. Thus, in this study, we rely mainly on the results of the fixed-effects regressions. In addition, the results for the causal effect of urbanization on rural households should be interpreted with caution.

Two part fixed-effects models

Our study uses different dependent variables for the income and expenditure subcomponents. For total income and consumption expenditure, we employ a fixed-effects regression. However, several dependent variables such asthe sub-components of income and landholding,have zero values for a large number of households. Dependent variableswith zero values suggest use of a Tobit model. However, in our case there are two problems with a Tobit model. Firstly, there are no available fixed-effects Tobit estimators due to the so-called incidental parameter problem in maximum likelihood methods [START_REF] Greene | Fixed effects and bias due to the incidental parameters problem in the Tobit model[END_REF] 9 .Secondly, Tobit estimators are not consistent if the assumption related to the normality and homoskedasticity of the error terms is violated [START_REF] Cameron | Microeconometric using Stata[END_REF]. This assumption is very strong and often does not hold. In health economics, a two-part model is often used to model a variable with a large number of zero values [START_REF] Duan | A comparison of alternative models for the demand for medical care[END_REF][START_REF] Manning | Monte Carlo evidence on the choice between sample selection and two-part models[END_REF]. In this study, we apply the two-part model in the context of fixed-effects panel data, as follows:

ikt D ik D D ikt D t D kt D ikt X T U D             ) ln( , ( 2
) ikt Y ik Y Y ikt Y t Y kt Y Y ikt X T U Y ikt              ) ln( ) ln( 0 | , ( 3
)
where ikt D is a binary variable which is equal to 1 for (3) are estimated using the fixed-effects regressions.

Although equation (2) (with the binary dependent variable) is often estimated using a logit or probit model, we estimate equation (2) using a linear probability regressionsince the aim is to estimate equation ( 2) using a fixed-effects estimator (there are no available fixed-effects probit estimators). Although we could use a fixed-effects logit regression this is not efficient since it drops observations with fixed values for the dependent variable. Linear probability models generally are used to estimate the marginal effect of independent variables when there is no non-linear probability modelavailable (e.g., [START_REF] Angrist | Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors: Simple Strategies for Empirical Practice[END_REF][START_REF] Angrist | Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments[END_REF].

The effect of urbanization on the outcome indicator is measured by

    ikt ikt Y ikt ikt Y D Y lm D n Y n E P A 1 ) ln( 1 ) (   , (4)

The effect on poverty rate

While urbanization has an effect on consumption expenditure, it also can affect poverty. In this study, we measure poverty by the expenditure poverty rate. A household is classified as the poor if its per capita expenditure is below the expenditure poverty line. We use a simple method to estimate the effect of urbanization on the poverty rate of rural households. The APE of the urbanization variable on the poverty rate can be estimated as follows (see Appendix 2 for proof):  is the probability density function of the standard normal distribution. The standard errors of the APE estimators (in equations ( 4) and ( 5)) are calculated using non-parametric bootstrap with 500 replications.

                     ikt ikt ikt ikt ikt ikt P Y z U H M E P A     ˆ) ln( ln 1 ˆ (5)

Empirical results

Effects of urbanization on household income

As discussed in section 3, urbanization combined with the process of industrialization can create more non-farm employment and promote the economic transition of rural households.In this section, we first regress rural household income variables on the share of urban population and other control variables. Earning variables depend on a set of household characteristics which can be grouped into five categories [START_REF] Glewwe | Investigating the Determinants of Household Welfare in Cote d'Ivoire[END_REF]

: (i)
Household composition, (ii) Regional variables, (iii) Human assets, (iv) Physical assets, and (v) Commune characteristics. Thus, the explanatory variables include household demographics, level of education of the household head, and availability of an automobile road in the village. Variables such as regional dummies which are time-invariant, are excluded from the fixed-effects regressions. Note that the explanatory variables should not be affected by the urbanization variable [START_REF] Heckman | The economics and econometrics of active labor market programs[END_REF]. Thus, we limit our estimation to the most exogenous explanatory variables. The summary statistics of the explanatory variables are presented in Appendix3 table A.1.

We estimate both the fixed-effects regressions and the fixed-effects using instrumental variable regressions in which the instrumental variable for the urbanization variable (1-year lagged share of urban population) is the two-year lagged share of urban population. The first-stage regression shows a strongly positive correlation between this instrument andthe urbanization variable. The results of thefixed-effects estimates with instrumental variable regressions are very similar to the fixed-effects regressions (presented in Appendix 3). We use the results from the fixed-effects regressions for interpretation.

Table 4 presents the fixed-effects regressions for crop and livestock income on urbanization, and estimation of the APE using fixed-effects two-part models. Tables 4 to 9report only the estimated coefficients of the variable urbanization. The full regression results are provided in Appendix 3tables A.2 to A.7.Table 4 shows that a1 percent increase in the urban population share of provinces reduces the probability of income from crops and livestock by 0.064 percent and 0.102 percent respectively. However, the effect of urbanization on crop and livestock incomes conditional on households having such income is not statistically significant. This is consistent with the effect of urbanization on landholding. Urbanization decreases the proportion of rural households with arable land but not the area of arable land owned by rural households with crop land.

Overall, the APE of urbanization on crop and livestock incomeremains negative.

A1 percent increase in the urban population share of provinces decreases crop and livestock incomes by 0.1 percent and 0.5 percent respectively. Urbanization also has a negative effect on other farm income (table 5). Other farm income includes income from agriculture, forestry, and other agricultural activities.

Urbanization can decrease the probability of having other farm income by 0.055 percent,

and can reduce the level of rural households' other farm income by 0.14 percent. Shortage of agricultural land in Vietnam can push farmersinto non-farm employment [START_REF] Dang | Internal migration and development in Vietnam[END_REF][START_REF] Cu | Rural to urban migration in Vietnam. Chapter 5 in "Impact of Socioeconomic Changes on the Livelihoods of People Living in Poverty in Vietnam[END_REF]. Urbanization can lead to an increase in land prices in rural areas near to cities, allowing rural households to sell their land at higher prices. Land sales can enable the householdto invest in capital-intensive, nonfarm production (Cali and Menon, 2009). The urbanization and industrialization process also creates more non-farm employment opportunities for rural dwellers.

Table 6shows that urbanization increases both the wages and income of rural households from non-farm business and production (excluding wages). A 1 percent increase in the urban population share of provinces increases wages and non-farm income by 0.37 percent and 0.27 percent, respectively. During the urbanization process, agricultural land may be converted to non-agricultural uses such as infrastructure and nonfarm businesses. Farmers subject to enforced acquisition of farmland can be liable for compensation which will increase their income and reduce their level of poverty -at least in the short-run [START_REF] Nguyen | The effect of crop land on poverty reduction: Evidence from Vietnam[END_REF]. Urbanization does not have a significant effect on the private transfers received by households,or income from other sources (table 7). However, urbanization increases the probability of receiving a transfer.Migration is likely to increaseduring the urbanization process which leads to a higher proportion of rural households in receipt of remittances. [START_REF] Nguyen | The impact of work and non-work migration on household welfare, poverty and inequality[END_REF] show for Vietnam that migration leads to an increase in the remittances received by home households.However, in periods of economic crisis, the effect of urbanization on private transfers may be smallerwith both migration and remittances decreasing. [START_REF] Actionaid | The impacts of the global economic crisis on migration patterns in Viet Nam[END_REF] found that in some provinces, remittances from migrants have decreased as a result of therecent global economic crisis. Previous analysesshows that urbanization reduces farm income but increases nonfarm income. An important question is whether urbanization affects the aggregate income of rural households.Table 8 presents the effect of urbanization on per capita income, and the ratio of subcomponent incomes to total income. Urbanization has a positive effect on the per capita income of rural households. A 1 percent increase in the urban population share of provinces increases the per capita income of rural households by 0.09 percent.

The effect of urbanization on the shares of different incomes is small,and is consistent with the findings on the effect of urbanization ontotal income10 .Specifically, a 1 percent increase in the urban population share of provinces reduces the share of crop income and other farm income in total household income by 0.04 percent and 0.03 percent, respectively. Also, a 1 percent increase in the share of urban population in the province increases the share of wages and non-farm income in total household income by 0.03 percent and 0.02 percent, respectively.

Effect of urbanization on household asset, expenditure and poverty

We are interested also in whether the increased income due to urbanization increases the living standards of rural households and contributes to reducing rural households' poverty.

We measure poverty as expenditure poverty. It has been suggested that monetary poverty does not provide a comprehensive evaluation of human well-being, andthat poverty should be examined from a multidimensional perspective [START_REF] Bourguignon | The Measurement of Multidimensional Poverty[END_REF][START_REF] Alkire | Counting and Multidimensional Poverty Measurement[END_REF]. To investigate whether urbanization improvesthe non-monetary welfare of rural households, we regress several outcomes including sanitation, housing, electricity, and durables on the urbanization variables. The upper panel in Table 9 presents the fixed-effects linear probability regressions without controlling for per capita income;

the lower panel presents the fixed-effects linear probability regressions controlling for per capita income. We see that urbanization increases access to piped water, septic tank latrines, and electricity. Controlling for per capita income does not change the effect of urbanization on these outcome variables which implies that householdincome is not the main channel through which urbanization increases access to infrastructure.

We find that theurbanization process results in a decrease in households' living area. A 1 percent increase in the urban population share reduces the per capita living areas (measured in square meters) of rural households by 0.0489 percent. If we control for per capita income, the effect is higher, at 0.0677 percent. This might be because residential land becomes more expensive during the process of urbanization, and households tend to live in smaller houses.We regress two popular durables in Vietnam, television and refrigerator, on urbanization. We find that urbanization has a positive and significant effect on refrigerator but not televisionownership.

Rural households in provinces with high proportions of urban population tend to have higher consumption expenditure. A 1percent increase in the urban population share increases the per capita expenditure of rural households by 0.096 percent (table 10).If we control for income, the effect of urbanization on expenditure remains significant but is smaller.A part of increased income due to urbanization translates into increased consumption. Finally, we estimate the effect of urbanization on rural poverty using equation ( 5) (table 10). Since urbanization increases household expenditure, it reduces the expenditure poverty rate of rural households. Similar to the case of India (Cali and Menon, 2009), we find that in Vietnam urbanization reduces expenditure poverty although only slightly.

The effect of urbanization on the poverty rate tends to be smaller overtime since the poverty rate decreases overtime. In 2002, a1 percentage point increase in the proportion of urban population in the provinces results in a 0.12 percentage point reduction in the expenditure poverty rate. In 2008, the povertyreducing effect of urbanization was 0.05 percentage points.

Conclusions

This paper examined the effect of urbanization on income, expenditure, and poverty among rural households in Vietnam using 2002[START_REF] Greene | Fixed effects and bias due to the incidental parameters problem in the Tobit model[END_REF], 2006, and 2008 VHLSS panel VHLSS panel data.Our main findings are as follows. Urbanization stimulates the transition from farm to non-farm activities in rural areas. Rural households in highly urbanized provinces have lower crop income and lower livestock income but higher wages and higher non-farm income.Urbanization increases the probability of receivingprivate transfers.This implies that urbanization increases rural-urban migration, and migrant-sending households are likely to receive remittances from their migrant members.

The increased income due to higher wages and higher non-farm income outweighs the income decreases due to lower farm income. Thus, urbanization contributes to increasing rural households' per capita income and per capita expenditure. More specifically, a 1 percent increase in the share of urban population at the provincial level increasesthe per capita income and per capita expenditure of rural households by around 0.09 percent. We also found a positive effect of urbanization on households' access to sanitation, piped water, and electricity. However, urbanization leads to a reduction in the living areas of rural households, possibly because urbanization makes residential land more expensive. Note that the positive effect of urbanization on access to sanitation, piped water, and electricity is not due to income. It is possible that urbanization increases rural households' demand for and knowledge about sanitation, or alternatively, that urbanization leads to improved infrastructure.

Overall, our analysis suggests that urbanization increases income and consumption expenditure and reducespoverty among rural households in Vietnam. Urbanization also allows rural households increased access to sanitation, piped water, and electricity. These findings have important implications for poverty reduction policies, especially since thepace of poverty reduction has been slow in recent years. In addition to poverty reduction programs targeted towardsthe poor, policies and programs to stimulate urbanization, and policies linking urban and rural development might be effective for reducing both overall poverty and rural poverty.Similarly, urbanization might playan important role inreducing rural poverty in developing countries with similar economic and geographical conditions to Vietnam such as the Philippines, Indonesia, Laos, and Cambodia.

Finally, the effect of urbanization on the urban-rural gap and inequality is interesting. Several studies suggest that inequality reduces happiness (e.g., [START_REF] Alesina | Inequality and happiness: are Europeans and Americans different[END_REF][START_REF] Verme | Life satisfaction and income inequality[END_REF][START_REF] Schröder | How income inequality influences life satisfaction: hybrid effects evidence from the German SOEP[END_REF]. Urbanization can affect not only income and expenditure of rural households but also the urban-rural gap in income and consumption.

With an increasing urban-rural gap, relative welfare and happiness among rural households might decrease despite their absolute income and consumption increasing.

Testing this hypothesis would require data on urban-rural gaps in income and consumption at the geographical level is small areas such as districts. This is beyond the scope of the present study but would be worth investigating.

           X T U Y P X T U Y Y E X T U Y P U X T U Y Y E X T U Y Y E U X T U Y P U X T U Y Y E X T U Y P U X T U Y E Y D                      (A.1)
The partial effect varies across the value of U, T, and X. Note that we can differentiate ) ln(Y with respect to ) ln(U since the fixed-effects model assumes that the time-invariant error term ( ) is fixed, and the time-invariant error term (  ) is uncorrelated with ) ln(U .

Based on (A.1), the estimator of the APE of ) ln(U on ) ln(Y can be expressed as follows:

    ikt ikt Y ikt ikt Y D Y lm D n Y n E P A 1 ) ln( 1 ) (   , (A.2)
where D  ˆ and Y  ˆare estimates from the fixed-effects regressions of equations ( 2

The effect on poverty rate

Based on the expenditure model (1) the probability that household i is poor can be expressed as follows [START_REF] Hentschel | Combining census and survey data to trace the spatial dimensions of poverty: a case study of Ecuador[END_REF]:

                      X T U z X T U P E ) ln( ln ] , , , | [ (A.3)
We can rewrite (A.3) more simply as:

               ) ln( ln ] , , , | [ Y z X T U P E (A.4)
where P is a variable taking the value 1 if the household is poor and 0 otherwise, z is the poverty line, and Φ is the cumulative standard normal function.

Y isthe household's per capita expenditure (for simplicity we drop the subscriptsi, k, and t).  is the standard deviation of the error term  in equation ( 1). It should be noted that in the fixed-effects model,  is assumed to be fixed, while  is assumed to be normally distributed with zero- mean and variance 2  ). Unlike [START_REF] Hentschel | Combining census and survey data to trace the spatial dimensions of poverty: a case study of Ecuador[END_REF], we allow  to vary across observations. Since expenditure is positive for all the households, we estimate equation (1) using a fixed-effects regression rather than a fixed-effects two-part model. The partial effect of urbanization on the poverty probability is as follows:

                                      ) ln( ln ) ln( ) ln( ln ] , , , | [ Y z U Y Y z U X T U P E , (A.5)
where is the probability density function of the standard normal distribution.The APE of the urbanization variable on poverty rate can be estimated:

                     ikt ikt ikt ikt ikt ikt P Y z U H M E P A     ˆ) ln( ln 1 ˆ (A.6) whereH i is the size of household i, M

  by the General Statistics Office (GSO) of Vietnam. The surveys provide data on households and communes. The household data include basic demographics, employment and labor force participation, education, health, income, expenditure, housing, fixed assets, durable goods, and household participation in poverty alleviation programs. Commune data includethe demographics and general situation of communes, general economic conditions and aid programs, non-farm employment, agriculture production, local infrastructure and transportation, education, health, and social issues. Commune data can be merged with household data. However, commune data are collected only for the rural areas (2,181 rural communes); there are no data on urban communes. The 2002VHLSS covered 29,530 households, while the 2004, 2006, and 2008 VHLSSs each covered 9,189 households. The larger sample size of the 2002 VHLSS was because GSO wanted to obtain income and consumption estimates representative of the provincial level. The other VHLSSs are representative of the regional level. In Vietnam, there are 64 provinces and cities grouped into 8 geographic regions (see figure 2). The VHLSSs collect information on commune characteristicsfrom 2,181 rural communes. According to the 2009 Population and Housing Census, there are 10,894 communes in Vietnam with an average of some 7,900 people per commune. The existence of random panel data in these surveys is helpful. From each VHLSS, GSO randomly selects a number of households to be included in the next VHLSS; the 2002 and 2004 VHLSSs refer to a panel of 4,008 households,and the 2004 and 2006 VHLSSs refer to a different panel of 4,219 households. However, among these households, 1,873 were covered by the 2002, 2004, and 2006 VHLSSs. The 2006 and 2008 VHLSSs selecteda panel of 4,090 households. There are 1,873 households that were sampled by the 2004, 2006, and 2008 VHLSSs. Only 30 households were sampled by all four VHLSSs. The four VHLSSs provide unbalanced panel data for 20,950 households. In this study, we focus on the impact of urbanization on rural households;the number of rural households in our panel data set is 15,886.

Figure 1 .

 1 Figure 1. Percentage of urban population in 1931-2009

Figure2.

  Figure2. Provincial urbanization and rural poverty



  are respectively time-invariant and time-variant unobserved variables. The effect of urbanization on the outcome indicator is measured by  which is interpreted as the elasticity of the rural householdoutcome indicator to the proportion of the urban populationin the province. We estimate the effect of urbanization for a number of household outcome indicators including per capita income, per capita income from different sources, per capita consumption expenditure, and housing and asset variables. For all outcome indicators we use the same model specification as equation (1). In other words, we regress different dependent variables of household outcomes on the same set of explanatory variables.

  Y in the parameters of equations (2) and (3) denote parameters in the models of . Equation (2) is a linear probability model. Equation (3) is a linear model of ) ln( ikt Y for households with positive values of ikt Y . Both equations (2) and

  each parameters can indicate something interesting. We are interested also in the averagepartial effect (APE)of ) ln(U on the unconditional dependent variable ) ln(Y which is estimated as follows (see Appendix 2 for the proof):



  ˆare estimates based on the fixed-effects regressions of equations (2) and (3), Y n is the number of observations with positive values of Y, and n is the total number of 20 observations in the panel data sample.

  whereH i is the size of household i, and M is the total number of people in the data sample which is equal to  ikt i H .The summation includes the households in the two periods.  ˆ, ikt ˆand ikt ˆare estimated from the fixed-effects regression of log of per capita expenditure.

  change in the poverty rate as a result of a 1 percentage point change in the share of urban population in the provinces. We can estimate P E P A ˆfor each year to see how the effect of urbanization changes overtime.

  ) and (3), Y n is the number of observations with positive values of Y, and n is the total number of observations in the panel data sample.

  is the total number of people in the data sample, which is equal to  ikt i H . The summation is taken over households in the two periods.  ˆ, ikt ˆand ikt ˆ are estimated based on the fixed-effects regression of the log of per capita expenditure.

Table 1 :

 1 Urbanization and rural poverty in 2002-2008

		Proportion of urban people
	Regions	(%)		Rural poverty rate (%)
		2002	2008	2002	2008
	Red River Delta	19.7	25.6	27.1	10.4
	North East	18.4	20.2	45.8	29.3
	North West	13.1	12.9	77.9	52.0
	North Central Coast	12.6	14.5	49.1	25.9
	South Central Coast	28.0	29.8	31.3	18.2
	Central Highlands	26.1	28.7	61.0	31.4
	South East	48.9	54.1	17.7	5.7
	Mekong River Delta	17.3	21.4	26.6	13.6
	Total	23.2	27.6	35.6	18.7
	Source: Authors' estimation based on VHLSSs.
	Notes: In this study, a household is classified as poor if its per capita expenditure is below
	the expenditure poverty line. The expenditure poverty lines are VND1,917,000, 2,077,000,
	2,560,000, and 3,358,000 for the years 2002, 2004, 2006, and 2008, respectively. These
	poverty lines are constructed by the World Bank and GSO. The poverty lines are equivalent
	to the expenditure level that allows for nutritional needs, and some essential non-food
	consumption such as clothing and housing.
	Before 2008, Vietnam was split into 59 provinces and 5 centrally controlled cities:
	Hanoi (the capital), Ho Chi Minh City, HaiPhong, Da Nang, and Can Tho. In this study,
	provinces include both provinces and centrally controlled cities. In 2008, Ha Tay province
	was merged with Hanoi, reducing the number of Vietnam's provinces to 63. Each province
	is split into districts, and each district is split further into communes. Communes are the
	smallest administrative divisions in Vietnam. In 2009, there were 684 districts and 11,112
	communes (2009 Population Census). Communes are classified into three types: rural
	communes, commune-level towns, wards of urban districts. Urban areas consist of
	commune-level towns andwards. An area is classified as urban if it has a minimum
	population of 4,000, and a minimum population density of 2,000 inhabitants/km 2 . The
	proportion of non-farm workers is required to be at least 65 percent (see Government of

Table 2 .

 2 Provincial urbanization and income share of rural households in 2008

	Share of urban		2002			2008
	population of	Share of	Share of	Share of	Share of	Share of	Share of
	provinces	wage	non-farm	private	wage	non-farm	private
		income in	income in	transfers in	income in	income in	transfers in
		total income	total income	total income	total income	total income	total income
		(%)	(%)	(%)	(%)	(%)	(%)
	0-10%	20.6	13.1	9.8	25.9	11.6	6.8
	10% -20%	22.0	12.2	9.1	25.3	12.4	6.3
	20% -30%	24.7	14.4	6.8	26.0	12.9	4.6
	30% -40%	30.5	16.1	11.7	29.8	13.5	7.3
	40% +	35.8	20.7	11.1	40.7	17.7	5.5
	Total	23.7	13.6	9.2	27.1	12.9	6.1

Source: Authors' estimations based on from 2002-2008 VHLSS panel data.

Table 3presents

 3presents

the association between income, expenditure, and poverty among rural households, and urbanization. Rural households in more urbanized provinces have higher per capita income and expenditure than rural households in less urbanized provinces. Table

3

also shows the large difference in the expenditure poverty rate between rural households in low and high urbanized areas.

Table 3 .

 3 Provincial urbanization and rural households during 2002-2008All variables are 'per capita', i.e.. equal to total annual household income (expenditure) divided by household size.Income and expenditure variables are based on Jan 2002 prices.

	Share of urban		2002			2008
	population of	Per capita	Per capita	Poverty rate	Per capita	Per capita	Poverty rate
	provinces	income	expenditure	(%)	income	expenditure	(%)

Source: Authors' estimation based on

2002 -2008 VHLSS panel data. VHLSS panel data.

Table 4 .

 4 Fixed-effects regression of crop and livestock income

				Dependent variables
		Having crop	Log of crop	APE on Log	Having	Log of	APE on Log
	Explanatory	income	income	of crop	livestock	livestock	of livestock
	variables	(yes=1,		income	income	income	income
		no=0)			(yes=1,
					no=0)
	Log of urbanization	-0.0638***	-0.0517	-0.4140***	-0.1016***	0.0205	-0.4910***
	rate	(0.0121)	(0.0433)	(0.0848)	(0.0164)	(0.0672)	(0.0962)
	Control variables	Yes	Yes		Yes	Yes
	Observations	15,886	13,247		15,886	11,111
	R-squared	0.033	0.047		0.035	0.047
	Number of	5,605	5,073		5,605	4,724

Table 5 .

 5 Fixed-effects regression of other farm income

			Dependent variables
	Explanatory variables	Having other farm income	Log of other farm income	APE on Log of other farm
		(yes=1, no=0)		income
	Log of urbanization rate	-0.0550*** (0.0166)	0.1367** (0.0673)	-0.3334*** (0.0968)
	Control variables	Yes	Yes
	Observations	15,886	9,656
	R-squared	0.185	0.496
	Number of households	5,605	4,506
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling
	and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimation based on 2002-2008 VHLSS panel data.

Table 6 .

 6 Fixed-effects wages and non-farm income regressions

				Dependent variables
		Having wage	Log of per	APE on log	Having non-	Log of non-	APE oflog of
	Explanatory variables	(yes=1, no=0)	capita wage for wage>0	of wage	farm income no=0) (yes=1,	farm households incomeof	non-farm income
						having non-
						farm income
	Log of urbanization	0.0373**	0.1556***	0.3657***	0.0283	0.2469**	0.2731**
	rate	(0.0187)	(0.0512)	(0.1316)	(0.0175)	(0.1040)	(0.1216)
	Control variables	Yes	Yes		Yes	Yes
	Observations	15,886	9,040		15,886	5,391
	R-squared	0.073	0.110		0.023	0.091
	Number of households	5,605	4,328		5,605	2,904
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling and cluster correlation).
	*** p<0.01, ** p<0.05, * p<0.1

[START_REF] Tuyen | Farmland Loss and Poverty in Hanoi's Peri-Urban Areas, Vietnam: Evidence from Household Survey Data[END_REF]

, and Ravallion and van de Walle (

2008

) find that in Vietnam, landlessness does not necessarily lead to poverty.

Source: Authors' estimation based on

2002 -2008 VHLSS panel data. VHLSS panel data.

Table 7 .

 7 Fixed-effects regressions of transfers and other non-farm income

				Dependent variables
		Receiving	Log of per	APE of log of	Having other	Log of other	APE on log
	Explanatory	private	capita private	per capita	income	income for	of other
	variables	transfers	transfers for	private	(yes=1,	other	nonfarm
		(yes=1,	transfer > 0	transfers	no=0)	nonfarm	income
		no=0)				income
	Log of urbanization rate	0.0259*	0.0653	0.1659*	0.0130	0.1679	0.0909
		(0.0144)	(0.0823)	(0.0944)	(0.0194)	(0.2495)	(0.1038)
	Control variables	Yes	Yes		Yes	Yes
	Observations	15,886	13,731	15,886	15,886	9,376	15,886
	R-squared	0.020	0.096	0.072	0.307	0.053	0.237
	Number of households	5,605	5,368	5,605	5,605	4,875	5,605
	Heteroskedastic robust standard errors in parentheses (also corrected for sampling and cluster correlation).
	*** p<0.01, ** p<0.05, * p<0.1.
	The APE is computed using the formula in equation (5).
	Source: Authors' estimation based on 2002-2008 VHLSS panel data.

Table 8 .

 8 Fixed-effects regression of income and income share

					Dependent variables
	Explanatory variables	Log of per capita income	Share of crop income	Share of livestock income	Share of other farm income	Share of wage income	Share of non-farm income	Share of private transfers	Share of other non-farm
									income
	Log of	0.0948***	-0.0425***	-0.0050	-0.0278***	0.0328***	0.0164*	0.0071	-0.0010
	urbanization rate	(0.0303)	(0.0086)	(0.0046)	(0.0081)	(0.0102)	(0.0084)	(0.0067)	(0.0054)
	Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Observations	15,886	15,886	15,886	15,886	15,886	15,886	15,886	15,886
	R-squared	0.227	0.034	0.010	0.582	0.061	0.022	0.106	0.142
	Number of households	5,605	5,605	5,605	5,605	5,605	5,605	5,605	5,605
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling and cluster correlation).
	*** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimations based on VHLSSs 2002-2008 panel data.

Table 9 .

 9 Fixed-effects regression of per capita expenditure and household assets

				Dependent variables
	Explanatory variables	Having piped water	Having septic tank latrine	Having electricity (yes=1,	Log of per capita living area	Having a television (yes=1,	Having a refrigerator (yes=1,	Log of per capita expenditure
		(yes=1,	(yes=1,	no=0)		no=0)	no=0)
		no=0)	no=0)
	Models without explanatory variable of log of per capita income
	Log of urbanization	0.0320***	0.1134***	0.0187*	-0.0489***	0.0145	0.0341***	0.0964***
	rate	(0.0094)	(0.0134)	(0.0104)	(0.0175)	(0.0172)	(0.0109)	(0.0165)
	Control variables	Yes	Yes	Yes	Yes No	No	No	No
	Observations	15,886	15,886	15,886	15,886	15,886	15,886	15,886
	R-squared	0.019	0.069	0.080	0.323	0.168	0.084	0.267
	No. households	5,605	5,605	5,605	5,605	5,605	5,605	5,605
	Modelswith explanatory variable of log of per capita income
	Log of urbanization	0.0311***	0.1042***	0.0144	-0.0677***	-0.0029	0.0266**	0.0502***
	rate	(0.0094)	(0.0133)	(0.0103)	(0.0171)	(0.0168)	(0.0108)	(0.0131)
	Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Log of per capita	0.0099**	0.0970***	0.0439***	0.1972***	0.1824***	0.0789***	0.4872***
	income	(0.0044)	(0.0063)	(0.0049)	(0.0081)	(0.0079)	(0.0051)	(0.0062)
	Observations	15,886	15,886	15,886	15,886	15,886	15,886	15,886
	R-squared	0.019	0.090	0.087	0.361	0.209	0.105	0.544
	No. households	5,605	5,605	5,605	5,605	5,605	5,605	5,605
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling and cluster correlation).
	*** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimations based on VHLSSs 2002-2008 panel data.

Table 10 :

 10 Impact of urbanization on rural poverty rate (percentage points)

	Year 2002	Year 2004	Year 2006	Year 2008
	-0.119***	-0.076***	-0.056***	-0.051***
	(0.032)	(0.022)	(0.016)	(0.015)
	Note: Both the poverty rate and the urbanization level are measured as percentages.
	Robust standard errors in parentheses. Standard errors calculated using non-parametric
	bootstrap with 500 replications. *** p<0.01, ** p<0.05, * p<0.1.

Source: Authors' estimation based on

2002 -2008 VHLSS panel data. VHLSS panel data.

Table A .

 A 2. Fixed-effect regressions of farm income

		Having crop	Log of crop	Having	Log of	Having other	Log of other
	Explanatory variables	(yes=1, no=0) income	income	income livestock	income livestock	(yes=1, no=0) farm income	farm income
				(yes=1, no=0)
	Log of urbanization rate	-0.0638***	-0.0517	-0.1016***	0.0205	-0.0550***	0.1367**
		(0.0121)	(0.0433)	(0.0164)	(0.0672)	(0.0166)	(0.0673)
	Household size	0.0185***	-0.0676***	0.0237***	-0.0879***	0.0229***	-0.0564***
		(0.0022)	(0.0071)	(0.0030)	(0.0104)	(0.0030)	(0.0115)
	Proportion of children below 15	-0.0526***	-0.4668***	-0.1064***	-0.5296***	-0.0796***	-0.2537**
		(0.0192)	(0.0626)	(0.0261)	(0.0916)	(0.0264)	(0.1027)
	Proportion of elderly above 60	-0.1235***	-0.3602***	-0.1252***	-0.2446***	-0.0958***	-0.4269***
		(0.0189)	(0.0632)	(0.0256)	(0.0947)	(0.0259)	(0.1114)
	Proportion of female member	-0.0544***	-0.1152*	-0.0864***	-0.0223	-0.1023***	-0.2753***
		(0.0183)	(0.0615)	(0.0248)	(0.0919)	(0.0251)	(0.1034)
	Age of household head	0.0013***	0.0023*	0.0007	-0.0030*	-0.0010**	-0.0021
		(0.0004)	(0.0012)	(0.0005)	(0.0018)	(0.0005)	(0.0020)
	Head less than primary school	Reference
	Head primary school	0.0186**	0.1080***	0.0432***	0.1429***	-0.0255**	0.0502
		(0.0087)	(0.0285)	(0.0117)	(0.0429)	(0.0119)	(0.0446)
	Head lower secondary school	-0.0018	0.1721***	0.0370***	0.3363***	-0.0589***	0.0288
		(0.0100)	(0.0324)	(0.0136)	(0.0472)	(0.0138)	(0.0522)
	Head upper secondary school	-0.0427***	0.1482***	-0.0162	0.3424***	-0.1206***	0.0896
		(0.0154)	(0.0504)	(0.0209)	(0.0728)	(0.0211)	(0.0881)
	Head technical degree	-0.0218	-0.0012	-0.0164	0.2285***	-0.1276***	0.0138
		(0.0142)	(0.0456)	(0.0192)	(0.0653)	(0.0194)	(0.0829)
	Head post secondary school	-0.1048***	-0.3606***	-0.0638	0.0862	-0.2640***	-0.2911
		(0.0292)	(0.1017)	(0.0396)	(0.1486)	(0.0401)	(0.2067)
	Village having a car road	-0.0049	0.0026	-0.0081	0.0541	-0.0657***	0.0075
		(0.0077)	(0.0242)	(0.0105)	(0.0349)	(0.0106)	(0.0393)
	Village having a market	-0.0659***	-0.0619***	-0.0643***	-0.0244	-0.0630***	0.0061
		(0.0068)	(0.0227)	(0.0093)	(0.0340)	(0.0094)	(0.0390)
	Dummy year 2008	0.0102	0.1846***	-0.0671***	0.1672***	-0.3213***	-1.9460***
		(0.0099)	(0.0319)	(0.0135)	(0.0466)	(0.0137)	(0.0506)
	Dummy year 2006	0.0151	0.0952***	-0.0346***	0.1312***	-0.3096***	-1.9810***
		(0.0092)	(0.0293)	(0.0124)	(0.0424)	(0.0126)	(0.0453)
	Dummy year 2004	0.0085	0.0420	-0.0070	0.0188	-0.2794***	-2.1104***
		(0.0086)	(0.0273)	(0.0117)	(0.0395)	(0.0119)	(0.0418)
	Constant	0.9428***	7.2252***	0.9681***	5.9861***	1.1404***	7.5883***
		(0.0403)	(0.1398)	(0.0547)	(0.2114)	(0.0554)	(0.2180)
	Observations	15,886	13,247	15,886	11,111	15,886	9,656
	R-squared	0.033	0.047	0.035	0.047	0.185	0.496
	Number of households	5,605	5,073	5,605	4,724	5,605	4,506
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimation based on 2002-2008 VHLSS panel data.

Table A .

 A 3. Fixed-effect regressions of non-farm income

		Having wage	Log of wage	Having non-	Log of non-	Having private	Log of private	Having other	Log of other
	Explanatory variables	income (yes=1, no=0)	income	farm income (yes=1, no=0)	farm income	transfers (yes=1, no=0)	transfers	non-farm income	non-farm income
								(yes=1, no=0)
	Log of urbanization rate	0.0373**	0.1556***	0.0283	0.2469**	0.0259*	0.0653	0.0130	0.1679
		(0.0187)	(0.0512)	(0.0175)	(0.1040)	(0.0144)	(0.0823)	(0.0194)	(0.2495)
	Household size	0.0452***	-0.0594***	0.0264***	-0.1063***	0.0013	-0.2841***	0.0061*	-0.1076***
		(0.0034)	(0.0099)	(0.0032)	(0.0159)	(0.0026)	(0.0147)	(0.0035)	(0.0404)
	Proportion of children below 15	-0.1341***	-0.7741***	-0.0825***	-0.5278***	0.0455**	0.0860	0.0200	-0.6672*
		(0.0298)	(0.0850)	(0.0278)	(0.1394)	(0.0228)	(0.1282)	(0.0308)	(0.3418)
	Proportion of elderly above 60	-0.3602***	-0.5731***	-0.0704**	-0.3538**	0.0769***	0.4197***	0.1645***	1.3675***
		(0.0292)	(0.1232)	(0.0273)	(0.1671)	(0.0224)	(0.1221)	(0.0302)	(0.3124)
	Proportion of female member	-0.1282***	-0.1374	0.0471*	0.2890*	0.0469**	0.5321***	0.0635**	0.1621
		(0.0283)	(0.0894)	(0.0265)	(0.1556)	(0.0217)	(0.1213)	(0.0293)	(0.3347)
	Age of household head	-0.0024***	0.0028*	-0.0027***	-0.0047*	0.0010**	0.0182***	0.0007	0.0424***
		(0.0006)	(0.0017)	(0.0005)	(0.0028)	(0.0004)	(0.0024)	(0.0006)	(0.0063)
	Head less than primary school	Reference
	Head primary school	0.0011	0.1027***	0.0170	0.1247*	0.0053	0.1469**	0.0057	0.1413
		(0.0134)	(0.0371)	(0.0125)	(0.0645)	(0.0103)	(0.0577)	(0.0139)	(0.1528)
	Head lower secondary school	-0.0210	0.1510***	0.0525***	0.4194***	0.0231*	0.2305***	0.0058	0.8451***
		(0.0156)	(0.0439)	(0.0145)	(0.0771)	(0.0119)	(0.0668)	(0.0161)	(0.1808)
	Head upper secondary school	0.0374	0.3985***	0.0984***	0.4841***	0.0184	0.3928***	0.0491**	1.1138***
		(0.0238)	(0.0639)	(0.0223)	(0.1047)	(0.0183)	(0.1033)	(0.0246)	(0.2627)
	Head technical degree	0.0440**	0.4195***	0.0548***	0.5838***	0.0476***	0.5274***	0.0617***	1.1788***
		(0.0219)	(0.0589)	(0.0205)	(0.0979)	(0.0168)	(0.0937)	(0.0227)	(0.2298)
	Head post secondary school	0.1667***	0.9503***	0.0019	0.2965	-0.0099	0.6463***	0.1437***	1.2337***
		(0.0452)	(0.1012)	(0.0423)	(0.2146)	(0.0347)	(0.1988)	(0.0468)	(0.4542)
	Village having a car road	0.0386***	0.0320	-0.0182	0.0130	0.0140	-0.0683	-0.0209*	0.0890
		(0.0120)	(0.0326)	(0.0112)	(0.0526)	(0.0092)	(0.0517)	(0.0124)	(0.1207)
	Village having a market	0.0042	0.0408	0.0402***	0.1438***	-0.0162**	0.0996**	0.0336***	-0.2564**
		(0.0106)	(0.0289)	(0.0099)	(0.0450)	(0.0081)	(0.0455)	(0.0109)	(0.1208)
	Dummy year 2008	0.0512***	0.2655***	0.0283**	0.2555***	0.0636***	-0.0523	0.5392***	0.1114
		(0.0154)	(0.0429)	(0.0144)	(0.0720)	(0.0118)	(0.0668)	(0.0159)	(0.2343)
	Dummy year 2006	0.0432***	0.2138***	0.0303**	0.2367***	0.0765***	0.2739***	0.7195***	-0.0005
		(0.0142)	(0.0400)	(0.0133)	(0.0667)	(0.0109)	(0.0619)	(0.0147)	(0.2262)
	Dummy year 2004	0.0303**	0.0009	0.0339***	0.0465	0.0486***	0.2032***	0.5406***	-0.0546
		(0.0134)	(0.0372)	(0.0125)	(0.0603)	(0.0103)	(0.0588)	(0.0138)	(0.2235)
	Constant	0.4652***	6.8322***	0.2271***	6.6096***	0.6234***	4.3478***	-0.0648	1.3163
		(0.0624)	(0.1732)	(0.0584)	(0.3326)	(0.0479)	(0.2729)	(0.0645)	(0.8322)
	Observations	15,886	9,040	15,886	5,391	15,886	13,731	15,886	9,376
	R-squared	0.073	0.110	0.023	0.091	0.020	0.096	0.307	0.053
	Number of households	5,605	4,328	5,605	2,904	5,605	5,368	5,605	4,875
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimation based on 2002-2008 VHLSS panel data.

Table A .

 A 4. Fixed-effect regressions of log of per capita income and income share

		Log of per	Share of crop	Share of	Share of other	Share of	Share of non-	Share of	Share of other
	Explanatory variables	capita income	income	livestock	farm income	wage income	farm income	private	non-farm
				income				transfers	income
	Log of urbanization rate	0.0948***	-0.0425***	-0.0050	-0.0278***	0.0328***	0.0164*	0.0071	-0.0010
		(0.0303)	(0.0086)	(0.0046)	(0.0081)	(0.0102)	(0.0084)	(0.0067)	(0.0054)
	Household size	-0.0693***	0.0025	0.0003	0.0061***	0.0179***	0.0034**	-0.0199***	-0.0075***
		(0.0048)	(0.0016)	(0.0008)	(0.0015)	(0.0019)	(0.0015)	(0.0012)	(0.0010)
	Proportion of children below 15	-0.5785***	0.0105	-0.0117	0.0352***	-0.0750***	-0.0085	0.0662***	0.0023
		(0.0426)	(0.0137)	(0.0074)	(0.0129)	(0.0163)	(0.0134)	(0.0107)	(0.0086)
	Proportion of elderly above 60	-0.3820***	-0.0490***	-0.0069	-0.0349***	-0.1905***	-0.0324**	0.1574***	0.1389***
		(0.0437)	(0.0134)	(0.0072)	(0.0127)	(0.0160)	(0.0131)	(0.0105)	(0.0085)
	Proportion of female member	-0.0873**	-0.0176	-0.0056	-0.0297**	-0.0695***	0.0357***	0.0687***	0.0109
		(0.0431)	(0.0130)	(0.0070)	(0.0123)	(0.0155)	(0.0127)	(0.0102)	(0.0082)
	Age of household head	0.0027***	0.0003	-0.0004***	-0.0004	-0.0011***	-0.0012***	0.0012***	0.0015***
		(0.0008)	(0.0003)	(0.0001)	(0.0002)	(0.0003)	(0.0003)	(0.0002)	(0.0002)
	Head less than primary school	References
	Head primary school	0.1414***	0.0007	0.0047	-0.0095	-0.0057	0.0122**	0.0003	0.0071*
		(0.0178)	(0.0062)	(0.0033)	(0.0058)	(0.0073)	(0.0060)	(0.0048)	(0.0039)
	Head lower secondary school	0.2813***	-0.0224***	0.0120***	-0.0273***	-0.0207**	0.0410***	0.0002	0.0222***
		(0.0214)	(0.0072)	(0.0038)	(0.0068)	(0.0085)	(0.0070)	(0.0056)	(0.0045)
	Head upper secondary school	0.4048***	-0.0634***	0.0013	-0.0504***	0.0043	0.0541***	0.0023	0.0378***
		(0.0324)	(0.0110)	(0.0059)	(0.0103)	(0.0130)	(0.0107)	(0.0085)	(0.0069)
	Head technical degree	0.4677***	-0.0892***	-0.0136**	-0.0502***	0.0217*	0.0456***	0.0094	0.0649***
		(0.0285)	(0.0101)	(0.0054)	(0.0095)	(0.0120)	(0.0098)	(0.0079)	(0.0063)
	Head post secondary school	0.6462***	-0.1423***	-0.0255**	-0.1050***	0.1925***	-0.0082	-0.0260	0.0729***
		(0.0509)	(0.0208)	(0.0112)	(0.0196)	(0.0248)	(0.0203)	(0.0162)	(0.0131)
	Village having a car road	0.0351**	-0.0203***	0.0040	0.0180***	0.0169***	-0.0018	-0.0034	0.0006
		(0.0139)	(0.0055)	(0.0030)	(0.0052)	(0.0065)	(0.0054)	(0.0043)	(0.0035)
	Village having a market	0.0326**	-0.0344***	-0.0123***	-0.0225***	0.0086	0.0343***	0.0062	0.0003
		(0.0133)	(0.0049)	(0.0026)	(0.0046)	(0.0058)	(0.0048)	(0.0038)	(0.0031)
	Dummy year 2008	0.2872***	-0.0080	-0.0084**	-0.4780***	0.0265***	-0.0035	-0.0009	0.0495***
		(0.0201)	(0.0071)	(0.0038)	(0.0067)	(0.0084)	(0.0069)	(0.0055)	(0.0045)
	Dummy year 2006	0.2723***	-0.0253***	-0.0094***	-0.4776***	0.0181**	-0.0019	0.0126**	0.0572***
		(0.0180)	(0.0065)	(0.0035)	(0.0062)	(0.0078)	(0.0064)	(0.0051)	(0.0041)
	Dummy year 2004	0.1113***	-0.0124**	-0.0080**	-0.4720***	0.0013	-0.0011	0.0200***	0.0481***
		(0.0155)	(0.0062)	(0.0033)	(0.0058)	(0.0073)	(0.0060)	(0.0048)	(0.0039)
	Constant	8.0148***	0.4578***	0.1314***	0.6435***	0.1839***	0.0840***	0.0258	-0.0499***
		(0.0969)	(0.0287)	(0.0154)	(0.0271)	(0.0342)	(0.0280)	(0.0224)	(0.0181)
	Observations	15,886	15,886	15,886	15,886	15,886	15,886	15,886	15,886
	R-squared	0.227	0.034	0.010	0.582	0.061	0.022	0.106	0.142
	Number of households	5,605	5,605	5,605	5,605	5,605	5,605	5,605	5,605
	Heteroskedastic robust standard errors in parentheses (corrected also for sampling and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimation based on 2002-2008 VHLSS panel data.

Table A .

 A 5. IV Fixed-effect regressions of farm income

		Having crop	Log of crop	Having	Log of	Having other	Log of other
	Explanatory variables	(yes=1, no=0) income	income	income livestock	income livestock	(yes=1, no=0) farm income	farm income
				(yes=1, no=0)
	Log of urbanization rate	-0.0695***	-0.0503	-0.1021***	0.0246	-0.0623***	0.1482*
		(0.0180)	(0.0660)	(0.0216)	(0.0873)	(0.0200)	(0.0877)
	Household size	0.0185***	-0.0676***	0.0237***	-0.0879***	0.0229***	-0.0564***
		(0.0025)	(0.0087)	(0.0032)	(0.0115)	(0.0033)	(0.0122)
	Proportion of children below 15	-0.0526**	-0.4668***	-0.1064***	-0.5296***	-0.0795***	-0.2542**
		(0.0229)	(0.0755)	(0.0288)	(0.1003)	(0.0290)	(0.1111)
	Proportion of elderly above 60	-0.1233***	-0.3602***	-0.1252***	-0.2447**	-0.0956***	-0.4271***
		(0.0245)	(0.0754)	(0.0306)	(0.0996)	(0.0304)	(0.1290)
	Proportion of female member	-0.0544**	-0.1153	-0.0864***	-0.0224	-0.1022***	-0.2760**
		(0.0236)	(0.0779)	(0.0290)	(0.1070)	(0.0290)	(0.1172)
	Age of household head	0.0013***	0.0023	0.0007	-0.0030	-0.0010*	-0.0021
		(0.0004)	(0.0015)	(0.0006)	(0.0020)	(0.0006)	(0.0022)
	Head less than primary school	Reference
	Head primary school	0.0186*	0.1080***	0.0432***	0.1429***	-0.0255**	0.0504
		(0.0104)	(0.0331)	(0.0134)	(0.0479)	(0.0129)	(0.0498)
	Head lower secondary school	-0.0017	0.1721***	0.0370**	0.3362***	-0.0588***	0.0289
		(0.0115)	(0.0372)	(0.0150)	(0.0522)	(0.0152)	(0.0587)
	Head upper secondary school	-0.0427**	0.1481**	-0.0162	0.3423***	-0.1205***	0.0895
		(0.0184)	(0.0622)	(0.0228)	(0.0824)	(0.0234)	(0.1046)
	Head technical degree	-0.0217	-0.0013	-0.0164	0.2284***	-0.1274***	0.0137
		(0.0156)	(0.0520)	(0.0201)	(0.0715)	(0.0211)	(0.0886)
	Head post secondary school	-0.1046***	-0.3607**	-0.0638	0.0861	-0.2638***	-0.2916
		(0.0391)	(0.1583)	(0.0446)	(0.1631)	(0.0449)	(0.2304)
	Village having a car road	-0.0049	0.0025	-0.0081	0.0541	-0.0658***	0.0075
		(0.0075)	(0.0262)	(0.0105)	(0.0355)	(0.0110)	(0.0394)
	Village having a market	-0.0658***	-0.0619**	-0.0643***	-0.0244	-0.0630***	0.0060
		(0.0083)	(0.0263)	(0.0106)	(0.0370)	(0.0102)	(0.0460)
	Dummy year 2008	0.0110	0.1844***	-0.0671***	0.1667***	-0.3203***	-1.9475***
		(0.0109)	(0.0377)	(0.0145)	(0.0504)	(0.0142)	(0.0536)
	Dummy year 2006	0.0157	0.0951***	-0.0345***	0.1308***	-0.3089***	-1.9820***
		(0.0099)	(0.0346)	(0.0132)	(0.0451)	(0.0130)	(0.0471)
	Dummy year 2004	0.0089	0.0419	-0.0069	0.0186	-0.2789***	-2.1113***
		(0.0092)	(0.0317)	(0.0124)	(0.0418)	(0.0123)	(0.0434)
	Observations	15,547	12,641	15,547	10,079	15,547	8,234
	R-squared	0.033	0.047	0.035	0.047	0.185	0.496
	Number of households	5,266	4,467	5,266	3,692	5,266	3,084
	Heteroskedastic robust standard errors in parentheses (also corrected for sampling and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1
	Source: Authors' estimation based on 2002-2008 VHLSS panel data.

Table A .

 A 6. IV Fixed-effect regressions of non-farm income Heteroskedastic robust standard errors in parentheses (also corrected for sampling and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1 Source: Authors' estimation based on 2002-2008 VHLSS panel data.

		Having wage	Log of wage	Having non-	Log of non-	Having private	Log of private	Having other	Log of other
	Explanatory variables	income (yes=1, no=0)	income	farm income (yes=1, no=0)	farm income	transfers (yes=1, no=0)	transfers	non-farm income	non-farm income
								(yes=1, no=0)
	Log of urbanization rate	0.0422*	0.1661**	0.0294	0.2369*	0.0249	0.0403	0.0085	0.1526
		(0.0220)	(0.0655)	(0.0219)	(0.1338)	(0.0173)	(0.0976)	(0.0209)	(0.3569)
	Household size	0.0451***	-0.0594***	0.0264***	-0.1062***	0.0013	-0.2840***	0.0061*	-0.1074**
		(0.0040)	(0.0116)	(0.0038)	(0.0223)	(0.0029)	(0.0165)	(0.0036)	(0.0457)
	Proportion of children below 15	-0.1341***	-0.7743***	-0.0825***	-0.5272***	0.0455*	0.0861	0.0201	-0.6668*
		(0.0336)	(0.0984)	(0.0316)	(0.1590)	(0.0247)	(0.1377)	(0.0316)	(0.3883)
	Proportion of elderly above 60	-0.3604***	-0.5728***	-0.0704**	-0.3533*	0.0770***	0.4205***	0.1646***	1.3686***
		(0.0309)	(0.1650)	(0.0309)	(0.2147)	(0.0217)	(0.1253)	(0.0303)	(0.3242)
	Proportion of female member	-0.1282***	-0.1380	0.0471	0.2893	0.0469**	0.5323***	0.0636**	0.1623
		(0.0327)	(0.1095)	(0.0310)	(0.2117)	(0.0232)	(0.1337)	(0.0313)	(0.3985)
	Age of household head	-0.0024***	0.0028	-0.0027***	-0.0047	0.0010**	0.0182***	0.0007	0.0424***
		(0.0006)	(0.0021)	(0.0006)	(0.0036)	(0.0004)	(0.0026)	(0.0006)	(0.0068)
	Head less than primary school	Reference
	Head primary school	0.0011	0.1024**	0.0170	0.1246	0.0053	0.1470**	0.0057	0.1411
		(0.0146)	(0.0424)	(0.0143)	(0.0768)	(0.0106)	(0.0607)	(0.0141)	(0.1629)
	Head lower secondary school	-0.0210	0.1508***	0.0525***	0.4196***	0.0231*	0.2307***	0.0058	0.8449***
		(0.0174)	(0.0519)	(0.0169)	(0.0935)	(0.0127)	(0.0703)	(0.0162)	(0.2111)
	Head upper secondary school	0.0373	0.3984***	0.0984***	0.4842***	0.0184	0.3933***	0.0491*	1.1138***
		(0.0260)	(0.0735)	(0.0257)	(0.1131)	(0.0202)	(0.1163)	(0.0253)	(0.3091)
	Head technical degree	0.0439*	0.4195***	0.0548**	0.5836***	0.0476***	0.5276***	0.0617***	1.1787***
		(0.0236)	(0.0700)	(0.0232)	(0.1129)	(0.0175)	(0.1018)	(0.0232)	(0.2557)
	Head post secondary school	0.1666***	0.9503***	0.0018	0.2956	-0.0098	0.6478***	0.1438***	1.2345***
		(0.0383)	(0.1249)	(0.0439)	(0.2158)	(0.0378)	(0.2202)	(0.0463)	(0.4467)
	Village having a car road	0.0386***	0.0321	-0.0182	0.0128	0.0140	-0.0686	-0.0209*	0.0891
		(0.0125)	(0.0346)	(0.0117)	(0.0584)	(0.0096)	(0.0542)	(0.0126)	(0.1243)
	Village having a market	0.0042	0.0407	0.0402***	0.1440***	-0.0162*	0.0999**	0.0336***	-0.2563*
		(0.0111)	(0.0316)	(0.0110)	(0.0481)	(0.0086)	(0.0470)	(0.0112)	(0.1420)
	Dummy year 2008	0.0505***	0.2638***	0.0281*	0.2569***	0.0638***	-0.0486	0.5398***	0.1122
		(0.0169)	(0.0496)	(0.0159)	(0.0869)	(0.0127)	(0.0721)	(0.0160)	(0.2895)
	Dummy year 2006	0.0427***	0.2126***	0.0302**	0.2378***	0.0766***	0.2767***	0.7200***	-0.0002
		(0.0157)	(0.0456)	(0.0149)	(0.0788)	(0.0119)	(0.0660)	(0.0142)	(0.2814)
	Dummy year 2004	0.0299**	0.0000	0.0338**	0.0472	0.0486***	0.2051***	0.5409***	-0.0548
		(0.0147)	(0.0410)	(0.0139)	(0.0684)	(0.0116)	(0.0627)	(0.0135)	(0.2744)
	Observations	15,547	7,748	15,547	4,247	15,547	12,955	15,547	7,752
	R-squared	0.073	0.110	0.023	0.091	0.020	0.096	0.307	0.053
	Number of households	5,266	3,036	5,266	1,760	5,266	4,592	5,266	3,251

Table A .

 A 7. IV Fixed-effect regressions of log of per capita income and income share Heteroskedastic robust standard errors in parentheses (also corrected for sampling and cluster correlation). *** p<0.01, ** p<0.05, * p<0.1 Source: Authors' estimation based on 2002-2008 VHLSS panel data.

		Log of per	Share of crop	Share of	Share of other	Share of	Share of non-	Share of	Share of other
	Explanatory variables	capita income	income	livestock	farm income	wage income	farm income	private	non-farm
				income				transfers	income
	Log of urbanization rate	0.0934***	-0.0441***	-0.0061	-0.0254**	0.0367***	0.0128	0.0055	-0.0010
		(0.0257)	(0.0116)	(0.0056)	(0.0115)	(0.0140)	(0.0114)	(0.0082)	(0.0061)
	Household size	-0.0693***	0.0025	0.0003	0.0061***	0.0178***	0.0034**	-0.0199***	-0.0075***
		(0.0042)	(0.0019)	(0.0009)	(0.0018)	(0.0023)	(0.0017)	(0.0014)	(0.0011)
	Proportion of children below 15	-0.5785***	0.0106	-0.0117	0.0352**	-0.0750***	-0.0084	0.0662***	0.0023
		(0.0379)	(0.0165)	(0.0085)	(0.0153)	(0.0192)	(0.0155)	(0.0122)	(0.0097)
	Proportion of elderly above 60	-0.3819***	-0.0490***	-0.0069	-0.0350**	-0.1906***	-0.0323**	0.1575***	0.1389***
		(0.0388)	(0.0161)	(0.0078)	(0.0152)	(0.0174)	(0.0153)	(0.0152)	(0.0136)
	Proportion of female member	-0.0873**	-0.0176	-0.0056	-0.0297*	-0.0695***	0.0358**	0.0688***	0.0109
		(0.0380)	(0.0160)	(0.0077)	(0.0158)	(0.0192)	(0.0156)	(0.0138)	(0.0106)
	Age of household head	0.0027***	0.0003	-0.0004***	-0.0004	-0.0011***	-0.0012***	0.0012***	0.0015***
		(0.0007)	(0.0003)	(0.0002)	(0.0003)	(0.0004)	(0.0003)	(0.0002)	(0.0002)
	Head less than primary school	Reference
	Head primary school	0.1414***	0.0008	0.0047	-0.0095	-0.0057	0.0122*	0.0003	0.0071*
		(0.0164)	(0.0074)	(0.0037)	(0.0071)	(0.0085)	(0.0070)	(0.0052)	(0.0042)
	Head lower secondary school	0.2813***	-0.0223***	0.0121***	-0.0273***	-0.0207**	0.0410***	0.0002	0.0222***
		(0.0191)	(0.0085)	(0.0043)	(0.0083)	(0.0097)	(0.0082)	(0.0058)	(0.0050)
	Head upper secondary school	0.4048***	-0.0634***	0.0014	-0.0504***	0.0042	0.0541***	0.0023	0.0378***
		(0.0288)	(0.0132)	(0.0067)	(0.0127)	(0.0152)	(0.0128)	(0.0091)	(0.0078)
	Head technical degree	0.4678***	-0.0891***	-0.0135**	-0.0503***	0.0216	0.0457***	0.0094	0.0649***
		(0.0269)	(0.0106)	(0.0058)	(0.0105)	(0.0140)	(0.0111)	(0.0085)	(0.0080)
	Head post secondary school	0.6462***	-0.1422***	-0.0255***	-0.1051***	0.1924***	-0.0081	-0.0259*	0.0729***
		(0.0479)	(0.0199)	(0.0099)	(0.0217)	(0.0295)	(0.0186)	(0.0136)	(0.0148)
	Village having a car road	0.0351***	-0.0203***	0.0040	0.0181***	0.0170**	-0.0018	-0.0034	0.0006
		(0.0136)	(0.0059)	(0.0031)	(0.0051)	(0.0067)	(0.0053)	(0.0042)	(0.0035)
	Village having a market	0.0326**	-0.0344***	-0.0123***	-0.0225***	0.0085	0.0343***	0.0062	0.0003
		(0.0127)	(0.0055)	(0.0029)	(0.0055)	(0.0066)	(0.0056)	(0.0041)	(0.0033)
	Dummy year 2008	0.2873***	-0.0078	-0.0082**	-0.4783***	0.0259***	-0.0030	-0.0007	0.0495***
		(0.0190)	(0.0082)	(0.0042)	(0.0081)	(0.0094)	(0.0075)	(0.0057)	(0.0045)
	Dummy year 2006	0.2724***	-0.0251***	-0.0093**	-0.4779***	0.0177**	-0.0015	0.0128**	0.0572***
		(0.0171)	(0.0076)	(0.0039)	(0.0077)	(0.0086)	(0.0069)	(0.0053)	(0.0041)
	Dummy year 2004	0.1114***	-0.0123*	-0.0079**	-0.4722***	0.0010	-0.0009	0.0202***	0.0481***
		(0.0160)	(0.0071)	(0.0036)	(0.0074)	(0.0080)	(0.0065)	(0.0050)	(0.0040)
	Observations	15,544	15,547	15,547	15,547	15,547	15,547	15,547	15,547
	R-squared	0.227	0.034	0.010	0.582	0.061	0.022	0.106	0.142
	Number of households	5,266	5,266	5,266	5,266	5,266	5,266	5,266	5,266

There are economic theories and empirical studies supporting an inverted U-shaped relationship where urbanization first increases to a peak, then decreases with economic development (see[START_REF] Henderson | Urbanization and Economic Development[END_REF] for a review).

According to the 1993[START_REF] Tacoli | Rural-Urban Interactions: A Guide to the Literature[END_REF], 2004, and 2008Vietnam Household Living Standard Surveys.

Rural households tend to have lower education levels, larger household size, and a larger share of farm income compared to urban households. In 2008 the poverty rates in urban and rural areas were 3.3% and 18.7% respectively. Also in 2008, the average per capita expenditure of urban households was nearly twice that of rural households.

Vietnam's population increased by around 1 million annually between 1999 and 2009.

Authors' estimation based on the VHLSS 2002 and 2008.

There are no data on urban or district level population for 2005-2008. The urban population share is available for 2009 when there wasa population census.

Data are available from the GSO website at: www.gso.gov.vn

Instead of a fixed-effects Tobit model, it is possible to use a random-effects Tobitmodel with the available explanatory variables and group means of these explanatory variables to remove time-invariant unobserved variables[START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF].

Note that all the fraction variables are measured as percentages. In this case, a 1% increase in urbanization will increase or decrease the dependent variables by a percentage point that is equal approximately to the coefficient divided by 100.

 2) and (3) we can compute the marginal partial effect of the log of urbanization on the dependent variable as follows (for simplicity, subscripts i, k, and t are dropped):