Seasonal migration and improving living standards in Vietnam.

Seasonal migrants in Vietnam share characteristics with migrants from other countries (table 2, panel A). Migrants are typically young, relatively well-educated men when compared with the rest of the rural population (rows 1 through 3). The average migrant in the sample has 6.8 years of schooling, while nonmigrants have an average of 5.9 years of schooling. However, the difference in schooling levels can wholly be attributed to the difference in the average age of the two groups. Migrants in 1998 are also twice as likely as others to have some vocational training. In general, migrants tend to be younger members of households with a relatively large endowment of human capital.

When we label households as either migrant households, defined as households that have increased participation in migration between 1993 and 1998, or nonmigrant households, we also find differences in descriptive statistics (table 2, panel B). In general, migrant households have lower per capita expenditure levels than the sample mean (1,740 thousand dong in 1993). Thus, the typical migrant household can be characterized as a relatively poor household residing in a lower lying areas, which may have more developed social networks through which to migrate. However, expenditures among migrant households grew faster than among nonmigrant households (6.3% versus 5.7%). Although this difference is small, it might be important for poor households; more migrant households were below the World Bank's poverty line for Vietnam in 1993. Furthermore, these figures do not account for other observable differences between migrant and non migrant households. We control for such differences in our econometric section.

Theoretical Model

To illustrate how seasonal migration can affect household expenditure growth, consider a household composed of N laborers that produces a farm good using technology f(L; K), where f(*) is a strictly increasing function, L is the labor input on the farm, and K is the household capital stock, including land. For now, we ignore the household's demographic composition as well as its human capital endowment, and assume that the farm good is the only product of the household. We further assume that f(*) is concave in labor and in the short run the capital stock does not change. If the household consumes all of its income and is credit constrained, then its base consumption is f(N; K). For illustrative purposes, we assume that f(L) = [alpha]n(L), where the effect of capital is absorbed in the constant [alpha].

As migrant labor markets begin to develop, the household can dedicate a share of its labor endowment m to migration. When households decide whether or not to send out migrants, they consider wages w in distant markets, migration costs c, and the information Z they possessed that shapes expectations about the expected net returns to migration, including knowledge about jobs, the probability of employment, and the ease of transition to the urban environment. Informational factors only affect household consumption through their influence on migration. When deciding whether or not to participate in migration, the household considers the net return to migration [phi](w, c, Z), where [phi](*) is increasing in w and Z and decreasing in c.

The model implies straightforward expressions for both participating in migration and for consumption. Since households maximize consumption, they choose a positive value of m if [alpha]ln(N(1 - m)) + Nm[phi](w, c, Z) > [alpha]ln(N) for some m > 0. If so, total household consumption will be C = [alpha]ln(N(1 - m)) + Nm[phi](w, c, Z). In this case, it is straightforward to show that the household will choose a migration level m = 1 - [alpha]/N[phi](w, c, Z). If farm productivity [alpha] is high or the household small, the household will send out a smaller share of migrants; if net returns to migration are high, then the household will send out a larger share of migrants. On the other hand, if the marginal product of labor on the farm exceeds the net return to migration when all N laborers work on the farm, the household will not participate in migration and consumption will be [alpha]ln(N).

Abstracting from the functional form assumption for farm production and assuming that net returns to migration are linear, the two possible equilibria can be illustrated (figure 2). In the extreme case, assume that the expected net returns to migration are zero (e.g., [phi](w, c, Z) = 0). Then the household will not send out migrants ([m.sup.*] = 0), as the marginal product of labor in farming always be higher than in migration. If the expected return to migration is positive and higher than the marginal product of labor when the entire household works on the farm, it will send out N[m.sup.*] migrants, and the equilibrium farm production is f (N(1 - [m.sup.*])). The latter equilibrium is the point of tangency between the line with slope [phi](w, c, Z) = K and the farm production function.

[FIGURE 2 OMITTED]

Since our interest is in understanding the relationship between migration and consumption growth, we consider a household that did not perceive net returns to migration in the first period, but did in the second period. For this case, first period consumption is [C.sub.1] = [alpha]ln(N), and second period consumption is [C.sub.2] = [alpha]ln(N(1 - [m.sup.*])) + Nm[phi](w, c, Z). The change in consumption between periods can be written:

(1) [DELTA]C = [alpha]ln(1 -- [m.sup.*]) + N[m.sup.*][phi](w, c, Z).

The first term in equation (1) represents the loss of farm production due to migration, whereas the second term represents the increase in consumption due to migration, which could come either as remittances or money that migrants bring home.

Our theoretical model suggests that household consumption may depend upon household participation in migration, the number of household members, wage rates for migrants, the cost of migration, and the household's capital stock. Therefore, in estimating the relationship between migration and consumption growth, we must account for as many of these factors as we can observe. As migration is a choice variable, it is influenced by net returns to migration as well as the informational factors that affect participation in migration. The model implies that we can exclude variables that proxy for informational factors from the consumption equation, as they only affect consumption through migration.

Empirical Model and Estimation Strategy

In order to explain the effect of migration on household expenditure growth in the spirit of equation (1), we first abstract somewhat from the model. Workers in the household with different human capital attributes may have different marginal products on the farm or in migration. Therefore, instead of including household size in our empirical model, we control for the demographic composition of the household, X. We then specify the relationship between per capita consumption, migration, and other variables affecting consumption and decisions about migration as log-linear:

(2) ln [(C/N).sub.hvt] = [[alpha].sub.h] + [[beta].sub.1][M.sub.hvt] + [[beta].sub.2][X.sub.hvt] + [[gamma].sub.1][W.sub.hvt] + [[gamma].sub.2][C.sub.hvt] + [[gamma].sub.3][K.sub.hvt] + [[epsilon].sub.hvt]

where h, v, and t index households, communes, and time, respectively, M represents the number of migrants sent out by the household. Since we have data for two periods, we include a household dummy variable which accounts for any household or supra-household variables ([[alpha].sub.h]) that do not vary over time. We include an error term, [[epsilon].sub.hvt], which is assumed to be correlated within communes but independent between communes. The error term represents both the random component of per capita consumption as well as any unobservable factors that might affect per capita consumption and vary over time. Since M is endogenous variable, we must account for its endogeneity in estimation.

Before estimating equation (2), we consolidate some of its terms because unobservables regarding household decision making may be correlated with some of the variables. Any unobservable factors about the household that will affect its expenditures may also be correlated with its propensity to send out migrants. So, if we were to estimate equation (2), even if we used instrumental variables techniques to limit bias in the estimated coefficient of interest, [[beta].sub.1], we might introduce bias by including other endogenous variables. Such variables, some of which are unobservable in the VLSS, include measures of migrant wages, migration costs, and the physical capital of the household.

To avoid this concern, rather than attempting to measure the [gamma] coefficients in equation (2), we drop those variables from the model. We, therefore, assume that these variables can be absorbed into either the household dummy variable or a regional growth rate. To remove the household dummy variable, we difference the two time periods and estimate the effect of the difference in migration behavior on the annualized expenditure growth rate. We write our initial estimator as:

(3) [r.sub.hvp] = [[tau].sub.p] + [[beta].sub.1][DELTA][M.sub.hvp] + [[beta].sub.2][DELTA][X.sub.hvp] + [DELTA][epsilon.sub.hvp]

where r represents the annualized per capita expenditure growth rate, p indexes regions, and [DELTA]X represents the household demographic profile, which includes the number of elderly men and women, the number of working age men and women, and the number of school age children. (5) To ensure we account for regional differences in economic conditions, we include regional dummies [[tau].sub.p] in all specifications.