EXPLORING VIETNAMESE INEQUALITY - · PDF fileEXPLORING VIETNAMESE INEQUALITY ... inequality...

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EXPLORING VIETNAMESE INEQUALITY USING A MICROSIMULATION FRAMEWORK This Draft: February, 10, 2007 (Do not quote without permission) Rosaria Vega Pansini* ABSTRACT: Even tough Vietnam has experienced very good performance in terms of sustained growth rates driven by the reforms introduced by the Doi Moi and an impressive reduction in the poverty rate over the past fifteen years, the rising inequality gives reason for concern. Using a microsimulation technique to derive a counterfactual distribution of income, this research studies the evolution of Vietnamese inequality and its decomposition into four different effects: the change in rates of return to variables (price effect); the effect of a change in the socio-demographic structure of the population (population effect), a change in the residual dispersion of unobservables of wages (change in the unobservables effect) and the effect of a change in the occupational behaviour (occupational effect). The price effect and population effect appear to be the main unequalising forces though the effects differ when applied to urban and rural areas Keywords: microsimulation technique, inequality decomposition, Vietnam *Rosaria Vega Pansini, Catholic and Bocconi University, Milan, [email protected] , [email protected] . The author would like to thank all the people whose help and contribution have been precious for the compilation of the paper. First, I thank Professor Sherman Robinson for his useful comments and suggestions. Second, my thank goes to Professor Francesco Timpano and Professor Renata Targetti Lenti for their constant and useful comments and supervision. Usual disclaimers apply. All remain errors are to be attributable to the author. 1

Transcript of EXPLORING VIETNAMESE INEQUALITY - · PDF fileEXPLORING VIETNAMESE INEQUALITY ... inequality...

EXPLORING VIETNAMESE INEQUALITY

USING A

MICROSIMULATION FRAMEWORK

This Draft: February, 10, 2007

(Do not quote without permission)

Rosaria Vega Pansini*

ABSTRACT: Even tough Vietnam has experienced very good performance in terms of

sustained growth rates driven by the reforms introduced by the Doi Moi and an impressive

reduction in the poverty rate over the past fifteen years, the rising inequality gives reason for

concern. Using a microsimulation technique to derive a counterfactual distribution of income,

this research studies the evolution of Vietnamese inequality and its decomposition into four

different effects: the change in rates of return to variables (price effect); the effect of a change

in the socio-demographic structure of the population (population effect), a change in the

residual dispersion of unobservables of wages (change in the unobservables effect) and the

effect of a change in the occupational behaviour (occupational effect). The price effect and

population effect appear to be the main unequalising forces though the effects differ when

applied to urban and rural areas

Keywords: microsimulation technique, inequality decomposition, Vietnam

*Rosaria Vega Pansini, Catholic and Bocconi University, Milan, [email protected], [email protected]. The author would like to thank all the people whose help and contribution have been precious for the compilation of the paper. First, I thank Professor Sherman Robinson for his useful comments and suggestions. Second, my thank goes to Professor Francesco Timpano and Professor Renata Targetti Lenti for their constant and useful comments and supervision. Usual disclaimers apply. All remain errors are to be attributable to the author.

1

1. INTRODUCTION

Of transition economies, Vietnam is surely one of the most successful examples. The

economic reforms approved under the Doi Moi have put the country on a path of sustained

growth and significant improvements in the standard of living of its population. Besides the

more general objective of boosting the aggregate growth of the country, one of main concerns

of Vietnamese authorities in trying to manage the consequences of the transition to a more

market oriented economy, has been keeping the level of inequality as low as possible. Even

though Vietnam has experienced very good performance in terms of proportion of people

brought out of poverty1 and continues to have a lower level of inequality than most other

developing countries, the fact that inequality rose during the first period of reforms is

worrying. Reasons for concern vary. First, the reduction in the rate of poverty brought about

by economic growth could be diminished by the increase in inequality; second, the rate of

future economic growth could be lowered by an increased disparity; and third, the rise in

inequality may negatively affect the general evaluation of the impact of future economic

policies.

Accordingly to data published by the World Bank, the Gini coefficient calculated using per

capita expenditures rose from 0.329 in 1993 to 0.352 in 1998. A more accurate analysis of the

change in the inequality indexes reveals that the majority of the Vietnamese inequality can be

explained by the gap between urban and rural areas and that this disparity has widened from

1993 to 1998. Per capita urban expenditure has, in fact, increased by 61% while that in rural

areas only by half of that, i.e. by 30%. There is also a regional trend in the distribution of

resources: 83% of the overall rise in inequality is attributable to widening difference between

regions while the remaining 17% is due to rising inequality within each region.

A first look at the dispersion in the distribution of expenditure between different areas of the

country gives only a partial sight of the level of inequality. Even though in some cases data on

per capita expenditures are preferable to capture the level of welfare especially in the context

of developing countries (Deaton, 1997), it is useful to analyse which are the other main

sources of household income and how accounting for them changes the analysis about the

level of inequality. In the context of Vietnamese economy, it seems reasonable to account for 1 Accordingly to the WB 1999, the percentage of people felt from 58% in 1993 to 37% in 1998 using the national total poverty line.

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differences in the distribution of three sources of earned income: wages, agricultural profits

and self employment profits.2

The first aim of this research will be to derive the level of household income, taking into

account all its components and to explore which factors can influence its level and its

distribution. The second objective will be the study of the evolution of household income over

the period 1993-1998 and the decomposition of inequality indexes into different components

that may have affected the distribution of income. As mentioned before, understanding more

deeply the sources of inequality helps to formulate a better judgement of the impact of policy

measures adopted by a country and to indicate more efficient ways to reach a higher level of

welfare with a more equalised distribution of resources.

Among the different techniques available to understand the nature and components of

inequality, microsimulation techniques allow for the construction of counterfactual

distributions by changing the behaviour of individuals and markets, holding all other aspects

constant. This methodology, applied to various case studies in Bourguignon et al (2001),

allows for the distinction of four different forces that may influence the dynamics in the

inequality of income: the effect of change in rates of return to variables at the individual and

household level (treatment effect or price effect); the effect of a change in the socio-

demographic structure of the population (endowment effect or population effect), a change in

the residual dispersion of unobservable components of wages (change in the unobservables

effect) and the effect of a change in the occupational behaviour (occupational effect).

Both these objectives are reached based on the following structure. Section two presents some

of the main approaches used in literature to address the problem of evaluating the impact of

economic policy on the distribution of resources using microsimulation. In the same section,

an overview on how to use microsimulation to derive disaggregated inequality indexes is

presented. Section three contains a brief description of the data used in this research. The

referred model and the richness in the availability of information about economic behaviour

of Vietnamese households justify the choice of variables at different levels: individual,

household and commune. In the same section, the methodology used for this study is

2 An accurate look at Vietnamese questionnaire, which the two datasets used in this research are based on, helped in selecting those indicated in the text as the main sources of household income for which data were available not only on profits but also on the relative proportion of people employed in all these three categories.

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presented. In section four, some of the main findings emerging from a first look at the data in

terms of summary statistics are presented. Tables are reported presenting the descriptive

distribution of the three components of household income and how they vary in terms of

selected criteria (sex, gender, level of education, region and area of location). Section five

presents the results of the empirical analysis applied to Vietnamese data. First, the basic

estimation of the various components of the income generating model is presented in order to

underline the determining factors. Second, the evolution of household income is sketched to

evaluate the determination of income inequality between 1993 and 1998. Finally, a

counterfactual distribution is derived and the adopted decomposition technique is applied to

obtain the magnitude of the four different effects influencing the change in the distribution of

income. Section six contains the main conclusions of the analysis highlighting some pros and

cons and indicating directions of future research.

2. USING MICROSIMULATION TO DECOMPOSE DISTRIBUTIONAL CHANGES

Understanding the different factors behind the change in the distribution of income has

always been a key instrument to inform policy making about which direction interventions

should take. This task is made difficult also by the fact that these forces are not independent

but they tend to offset one another.

Traditionally, the way in which these factors have been explored in the literature has been by

using summary measures while ignoring the changes in the entire distribution. This

methodology consists in deriving the change in the overall mean or change in the inequality

measures as the aggregation of means of the socio demographic characteristics of various

subgroups in which the population could have been divided. The need to take into account the

full distribution of income leads to exploring new techniques of decomposition able to exceed

the analysis based on the use at the mean of the distribution. These techniques belong to the

use of the parametric representation of the household income and its relationship with some

individual or household characteristics.

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The remains of this section will be devoted to briefly present some of the approaches to

decomposition based on summary statistics measures. After that, some example of the

parametric approach to the study of distributional change will be presented.

2.1 SCALAR METHODS

2.2.1 THE OAXACA-BLINDER DECOMPOSITION

One of the most famous decomposition in distributional changes in the means of two

populations is the one found independently by Oaxaca and Blinder in 1973. Assuming that the

individual incomes of two subgroups A and B can be estimated using the following linear

model:

ˆ'AA Ay X uβ= + A (2.1)

ˆ'B BBy X uβ= + B (2.2)

where the individual income depends on some observable mean characteristics iX and on an

error term . If the components of the matrix X are some individual endowments, the

coefficients

iu

β can be considered as returns to these characteristics, or ‘price effects’.

Estimating the previous relations using the ordinary least squared estimator and considering

the difference in the mean income between the two groups, the results is represented by:

ˆ ˆ'( ) ( ) 'A BA AA By y y X X Xβ β∆ = − = − + − ˆ BBβ (2.3)

The change in the mean income could be considered as the sum of two different effects: i) the

endowment effect, i.e. the change in the mean characteristics at constant prices and ii) the

treatment effect, i.e. change in the prices at constant mean of characteristics. This

methodology can be applied to decompose the different in the mean income between two

groups or the different in the mean income of the same group between two periods. Some

points must be noticed about the Oaxaca- Blinder decomposition: first, it is path dependent, in

the sense that there is no reason to think that the decomposition contained in (2.2) brings at

the same results of a decomposition using as a staring point the mean income of the group B.

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Second, the Oaxaca decomposition is a static methodology, as it analyzes the endowment and

treatment effects at a given time. Juhn, Murphy and Pierce (1991) introduced a dynamic

dimension of the decomposition of the first moment, as stated in Paci and Reilly (2004).

2.2.2 DECOMPOSITION IN INEQUALITY MEASURES

An alternative way of deriving an analysis about the decomposition of changes in the

distribution of income is to use the decomposition not just for the first moment of the

distribution but for higher moments. This in particular can be done using the General Entropy

inequality measures that are characterised with very convenient properties of decomposition.

Supposing that the population can be split into different subgroups and denoting with gI the

inequality index of the group g and with I the overall inequality index, these measures satisfy

the general property:

( ) (1 11

, ;....; , ,G

G

b w G g g gg

)I I I I n y n y I w n m=

= + = +∑ (2.4)

Where gn and gm represent respectively the population and the income shares of group g and

WI and bI stand for within and between components of the inequality measure. Due to the

possibility of decomposing these indexes in the two components, also the change in the

distribution can be easily decomposed in the change of the component between WI∆ and

within BI∆ . In turn, both changes can be expressed as linear combinations of changes in the

within group inequality measure gI∆ and a change in the population and income shares, gn∆

and gm∆ . Here are many application of this methodology to the analysis of the evolution of

inequality.

One of the most famous is the one contained in the study f the evolution of distribution of

income in the UK in Shorrocks and Mookherjee (1982). This kind of procedure is more

appealing than the one contained in the Oaxaca- Blinder formula for two reasons: change in

the socio demographic characteristics of the population are equivalent to change in the

group’s population weights and change in the group relative incomes play a similar role to

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change in the price coefficients. Two differences with respect to the Oaxaca decomposition

must be also noticed: first, this kind of decomposition is a non parametric one while the

Oaxaca is based on the mean of the distribution; second, the decomposition contains a

residual that sometimes is difficult to interpret especially when it is big.

2.3 PARAMETRIC METHODS FOR THE ENTIRE DISTRIBUTION

The methodologies presented in the previous paragraph have been extensively used to study

the change in the distribution of income. As noticed before, these methods are based on the

analysis of one synthetic measure of the distribution without taking into account changes that

may occur in all the parameters that characterized the distribution of income.

The approach followed in this research is different principally because it studies the

determinants of changes in the distribution of income when some or all the parameters are

taken into account. In the specific case of the income generating model presented in the

following section, the analysis is conducted simulating the change in all the parameters that

characterised household income. In order to disentangle the effect of some of the

characteristics contained in the matrix X, the analysis could also be conducted changing only

the coefficient corresponding to these variables3. This decomposition methodology based on

the derivation of counterfactual distribution for one year using the parameters for the other

years has been adapted to the specification of the country studied and to the availability of the

data on the various components of household income, (Bourguignon et al 2001).

Bourguignon, Fournier and Gurgand (2001) apply this methodology to explore the factors

behind the stable household income distribution in Taiwan during a period, 1979-1994, of

high growth rates. They found that far from being the result of an almost unchanged

distribution of income, the stability in the inequality indexes is the results of numerous

offsetting forces. Change in the population structure and changes in the female participation

to the labour market appear to have been the most important unequalising forces behind the

change in the distribution of household income.

Grimm (2001) applies the same methodology to the decomposition of inequality and poverty

changes in Cote d’ Ivoire. The referred context is interesting for our analysis of the

3 If we want to explore, for example, the effect on income inequality derived from a change in the rates of return to schooling, we should substitute only the parameters relative to the variables related to the level of schooling.

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Vietnamese case because it is applied to a period of rapid macroeconomic adjustment. Grimm

shows that the increase in income inequality was due principally to changes in the population

structure and to increased dispersion in the unobservables determining the wage function.

Alatas et al (2000) apply this kind of methodology to the study of evolution of inequality in

Indonesia in the period 1980-1996. They run the microsimulation exercise not only over the

entire distribution of household income but also over its single components, like per example

individual wages. They discover that while in the decomposition of inequality of individual

earning the price effect represents an unequalizing force the overall price effect for the

household income was negative, thus implying a decrease in the level of inequality. These

results show how a particular factor can contribute differently to the sign and dynamic of

individual ages and household income.

Finally, the methodology based on the parametric derivation of counterfactual distribution has

been applied not only to just a microeconomic context but also to models that link the micro

level to the macro level. An example of this approach is contained in Bourguignon, Robilliard

and Robinson (2003) that study the change in the inequality and poverty indexes linking the

evolution of the labour market supply, the change in the level of earned wages and the level of

self employment profits to their variation emerging at the macroeconomic level. This study is

applied to the case of Indonesia before the Asian crisis and the findings show that taking into

account also effects at the micro level and link them to the transformations emerging at the

aggregate level contributes to a better understanding of the factors influencing the distribution

of resources among the household and how they react to changes intervened at the macro

level.

This short view at the alternative approaches used to capture different effects on the evolution

of the household income distribution has shown how the methodology chosen for this study

on Vietnamese data has its own advantages in trying to capture different effects operating on

the income inequality using the entire parameterisation of the household income The fact that

it has been widely applied to different contexts shows that it is quite flexible to the

specification that can be done in order to adapt the general model to the specific features of

the country analysed.

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3. DATA AND METHODOLOGY

3.1 VLSS 1993 AND 1998

The present research uses data from two different household surveys representing the first two

rounds of the Vietnam Living Standard Surveys, the VLSS 1992/93 and the VLSS 1997/98.

Both surveys have been conducted by the General Statistical Office (GSO) under the

supervision and technical support of the World Bank, UNDP and the Swedish International

Development Authority (SIDA). Following the more general framework of the Living

Standard Measurement Study (LSMS), a World Bank project about the measurement of living

standards in the developing countries, both of the VLSS are based on a methodology of

collecting data on households interviewed twice in the same round at a distance of two weeks.

The first round of the VLSS was completed in October 1993 and is comprised of a sample of

4,800 self-weighted4 households selected from the two stratification groups, urban and rural,

according to the proportions of household in both groups emerged from the 1989 Population

Census.5 The aim of the second round of the VLSS undertaken in 1998 was to increase the

number of households in the sample in order to be representative at the national level. In order

to reach the target of 6,000 households, the 4,800 units of the first round were re-interviewed

and 1,200 new households were selected from the sample of the Multi Purpose Household

Survey (MPHS) conducted in the 1995.6

3.2 SELECTED VARIABLES

Both the two rounds of household surveys contain two different kinds of questionnaire: the

household and the commune questionnaire. The first contains several sections with

information about the household demographic structure, education, health, employment,

migration, characteristics of the household dwelling and fertility, agricultural and non-

4 Self–weighting characteristic of the first round of the VLSS refers to the fact that each household in the sample had the same probability to be selected than other household. 5 According to the 1989 Population Census, the 20% of the total number of households lived in the urban areas. The design of the sample for the VLSS was then chosen in order to respect these proportions between the two stratifications: at the end, out of total 4,800 households, 3839 were selected from rural areas and 960 from urban areas. 6 The final composition of sample in the VLSS 1997/98 was 6,000 households, of which 1730 from urban areas and 4269 from rural areas.

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agricultural activities and food and non- food expenses, remittances, saving and loans. The

commune questionnaire contains information about socio-demographic characteristics of the

commune, economic activities, infrastructures, information about educational and health

facilities and finally, information about agricultural production at the community level7.

3.2.1 INDIVIDUAL AND HOUSEHOLD LEVEL

In order to perform the analysis about the evolution of household income inequality between

1993 and 1998, different variables have been selected both at the individual and household

level (see Table A1). At the individual level, different variables have been selected indicating

socio -demographic characteristics of the respondent like the age and sex, ethnic group, some

measures of the quality of health8, the level of schooling9, the relative position in the

household, the marital status and variables related to the labour market, like the sector of

employment, the wage received, a dummy indicating whether or not the person receives a

retirement pension and another dummy indicating whether the person works or the public

sector.

At the household level, variables indicating the demographic structure of the household have

been selected like the size, the proportion of children and adult people and the dependency

ratio.10 Characteristics of the household dwelling like the type of house and of the water used

are also included, like the area of the land owned by the household. In addition, regional

dummies and four dummies indicating the quarter of the interview have been created to

capture regional and seasonal effects, respectively. Variables indicating revenues and

expenses in the agricultural sector and in the self employment sector have also been selected

in order to calculate agricultural and self employment profit at the household level.

3.2.3 COMMUNE LEVEL

7 For a more detailed description of the characteristics of both questionnaires and of data, see World Bank (2000a, 2000b). 8 A dummy indicating to have suffered from an illness and a dummy to have been to the hospital in the last four months are variables used as proxies for the level of health. 9 The dummy variables indicate the level of schooling refer to the highest degree obtained. In both round of the VLSS analysed, there was no question indicating the total years of schooling. This is why the highest degree obtained was used as a proxy for the level of human capital. 10 People with less than 15 years old over the whole sample are considered children. The dependency ratio is constructed calculating the proportion of people with more than 60 years old relative to the household size.

10

The two rounds of the VLSS used in this analysis allow for the selection of variables also at

the commune level. In order to explore which are the characteristics of the context in which

the household operates and how they influence the analysis, variables indicating the different

types and extension of land of the commune and dummies indicating the level of

infrastructure within and accessing the commune have been taken into account..

3.3 METHODOLOGICAL FRAMEWORK

3.3.1 DECOMPOSITION OF DISTRIBUTIONAL CHANGES USING MICROSIMULATION

The methodology adopted in this research was first introduced by Juhn, Murphy and Pierce

(1993) and further developed by Bourguignon, Fournier and Gurgand (1999, 2000) and

Alatas, Bourguignon (2000). Different examples of the same methodology applied to study

the evolution of income inequality can be found in Bourguignon, Ferreira and Lustig (2004).

Let Y be a simple household income function, where the income of the household i at the

time depends on a set of five parameters: some observable socio-demographic

characteristics of its members

t

( )itx ; unobservable characteristics ( )itε , a vector of

remuneration rates observed ( )tβ and unobserved earning determinants ( )tσ and a set of

parameters defining the participation and occupational choice behaviour of its members ( )tλ :

( , , , , )it it it t t ty Y x ε β σ λ= (3.1)

The overall distribution of household income at time t is then obtained summing up all

and some demographic characteristics possibly included ( )ity ( )itx , in one vector ( ) .

can be written as a function H of the former parameters and the distribution of the observable

and unobservable household characteristics at date :

tD tD

t

{ },( , , ,t it it t tD H x )tε β σ λ= (3.2)

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Where {} refers to the distribution of the corresponding variables in the population. The

difference between two distributions and observed at two points in time can be

decomposed into four different effects: i) a change in the remuneration rates of the observed

earnings determinants

tD 'tD

(price effect), ii) a change in the remuneration rates of the unobserved

earnings determinants (effect of unobservable), iii) a change in the occupational choice

behaviour (occupational effect), and iv) a change in the distribution of observed and

unobserved earnings determinants (population effect). The decomposition of the change in the

distribution of the household income can formally be written as:

{ } { }' , ' ,( ) : ( , , , ) ( , , , )tt it it t t t it it t t ti B H x H xε β σ λ ε β σ λ= −

{ } { }' , ' , '( ) : ( , , , ) ( , , , )tt it it t t t it it t t tii S H x H xε β σ λ ε β σ λ= − (3.3)

{ } { }' , ' , '( ) : ( , , , ) ( , , , )tt it it t t t it it t t tiii L H x H xε β σ λ ε β σ λ= −

{ } { }' ', ' ,( ) : ( , , , ) ( , , , )tt it it t t t it it t t tiv X H x H xε β σ λ ε β σ λ= −

The microsimulation exercise consists in evaluating the impact of a change in the

remuneration rates of the observed earnings determinants (price effect) by comparing the

observed distribution at time with the hypothetical distribution obtained by imposing the

remuneration structure of observed earnings determinants at date on the population at time

. The change in the remuneration rates of the unobservable earnings determinants is

measured by the change in the residual variance in the earnings functions, using a rank

preserving transformation

t

't

t

11. In the same way, the population and the occupational effect can

be calculated imposing the population and the parameters for occupational choice observed at

time on the observed distribution at the time t. After computing the price, occupational and

effect of a change in the unobservable, the population effect can be computed as the residual

of the other three effects.

't

The overall change in the distribution between t and can be expressed by the following

identity:

't

11 The residual variance at time t is changed in order to make it identical to the its distribution observed at time t’ using the following rank-preserving transformation:

'( )tit it

t

σε εσ

=

12

' ' ' ' 'tt tt tt tt ttC B S L X= + + +

=

=

(3.4)

In the case of the present research, the estimation and derivation of the household income will

be performed considering two cross-sectional data sets, for 1993 and 1998. The

decomposition of the overall change in the distribution of income will be calculated using

1993 as base year and applying to the distribution of observed earnings determinants in 1993,

the remuneration rates of the unobservable earnings determinants and the population from

1998.

3.3.2 HOUSEHOLD INCOME GENERATING MODEL

In order to simulate the counterfactual distribution of household income, it is necessary to

define how to calculate the household income according to (1).

The household income generating model can be summarized by the following set of

equations:

, 1 , , , 1 , , , 1 , ,( . . . . . . , , . . . . . . , , . . . . . . , ,j t t t t t t th i h i j h i j J h i j h i j J h i j h i j JL x x z z υ υ= = = = ==

. (3.5) , 1, , , 1, ,......, , ......, )t t t thi j zi j J zi j zi j Jλ λ λ λ= = =

i=1 to hk h∀ and j=W, A, SE i∀

( , ,t j W t t th i h i h iw w x u )β= = , i=1 to (3.6) hk h∀

, ( , , , , )tj A SE t t t t thi hi hi hi x zx z s β β=Π =Π

, i=1 to (3.7) hk h∀

, , ,0

1 1 1

h h hk k kt t t j W t t j A t t j S E th h i h i h i h i h i h i

i i iy L w L L= = =

= = =

= + Π + Π∑ ∑ ∑ hy+ (3.8)

Equation (3.5) describes the labour supply of each household member i as a wage worker

outside the family business (W), as a manager in an agricultural business (A) and as a

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manager in a self employment business (SE). These functions express the labour supply of the

household member i as a function of his/her characteristics hix , some characteristics of the

household and its environment and some characteristics at the community level, . Equation

(3.6) describes the wage equation as a function of proxies of human capital and other personal

characteristics; equation (3.7) represents the profit function depending on a set of variables

describing the individual characteristics of the person running the business and the

characteristics concerning the environment in which the business is run. Both of the profit

functions, agricultural and self employment, have the same specification but are estimated on

a slightly different set of variables that could influence specifically the type of business to

which they refer. Finally, equation (3.8) calculates the household income by aggregating the

different sources of income over the household members. The term indicates income

from other sources like transfers

hiz

0thy

12. Wage income is observed at the individual level, while

profits, both agricultural and self employment, are observed at the household level according

to both the rounds of the VLSS used in this study13. All the coefficients of the model (

, ,, , ,t t tx j z j x z

tλ λ β β ) and the standard deviations of the residual terms ( ) are estimated

over the two cross sections of data available for 1992/93 and 1997/98. Once that they have

been estimated, they are used to simulate the effect on household income of a change in the

parameters of the model valid for the year t with the ones estimated for the year 14

, ,t t thi hi hiu sυ

't . This

kind of model enables to evaluate what would have been the income of household m at the

time t if it had adopted the labour supply behaviour observed at time or earning have been

those observed at time .

't

't

4. THE EVOLUTION OF VIETNAMESE HOUSEHOLD

INCOME FROM 1993 TO 1998

4.1 CHANGES IN THE OCCUPATIONAL CHOICE

12 As in Bourguignon et al. (2001), income from transfers and benefits is supposed exogenous. 13 Data on revenues and expenditures referred to agricultural and self employment activities are provided as mean values at the household level and are used to compute data on profits. 14 In the specific case of this research, the parameters will be estimated for both years but the microsimulation exercise will be performed apply only the parameters for 1998 to the population of 1993.

14

Accordingly to the model presented in section 3.3.2, one of the components used in the

estimation of the household income is the occupational choice. Before presenting the results

of the estimation it will help looking at the evolution of Vietnamese employment over the

period considered by the two used surveys. Table 4.1 presents summary results about the

structure of employment at the national level and distinguished using different criteria like

gender and sectoral differentiation.

The first feature to be noticed is the high rate of employment. Participation remains

essentially constant over the years at the level of 1993 showing that the transformation of the

Vietnamese economy has resulted more in a reorganization within the labour market instead

of a real contraction of it. Employment in the rural sector is dominant, even in 1998 and

varies with gender. A gender effect can be identified when the overall rate of employment is

examined in a dynamic prospective. Male participation was 83% in 1993 decreasing by

almost three percentage points after six years. In contrast, the overall female employment rate

remains constant trough the years decreasing in the urban area and increasing by roughly the

same proportion in the rural areas. This finding suggests that women are still dependent on

employment opportunities in the rural sector while the urban labour market is relatively

dominated by male workers.

The differentiation of gender-specific participation rates between urban and rural areas could

be brought about by the organisation of the labour market in the agriculture, self employment

and wage labour. Female participation is still very high in the agricultural sector even if

decreasing over time while female participation has remained low in the wage labour. These

proportions show how female labour is still highly dependent on opportunities offered by the

agricultural sector or by being involved in small, self-run businesses, mainly related to the

manufacturing sector, though sometimes run together with labour supplied on the farm. From

1993 to 1998, economic growth influenced the sectoral changes in the labour market drawing

people from the primary sector mainly to the service sector as it developed due to better

economic performance. As shown in the following table 4.1, the percentage change in the

number of people employed in the agricultural sector is more or less compensated by the

increasing of the employment in the wage labour, mainly driven by the expansion of the

service sector (ADB, 2000 and UNDP, 2001).

15

Table4.1: The Evolution of Employment Structure, 1993-1998 1993 1998 Labour force Participation (%) 81.16 80.10 Urban 71.60 69.80 Rural 83.93 84.51 Male 83.05 81.10 - urban 72.60 72.65 - rural 86.05 84.65 Female 79.45 79.17 - urban 70.71 67.23 -rural 82.01 84.38 Unemployment rate (%) 4.02 3.87 Male 5.21 5.79 - urban 8.73 7.89 - rural 3.01 4.14 Female 2.89 2.54 - urban 3.79 3.69 - rural 3.26 2.78 Structure of employment Agriculture 65.46 55.39 - male 63.94 52.94 - female 66.87 57.71 Self employment 21.92 20.47 - male 16.98 24.10 - female 18.05 16.62 Wage employment 12.62 24.14 - male 19.08 30.43 - female 15.08 18.18 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: as suggested in Pham and Reilly (2006), labour force has been calculated as those aged between 15 and 60 years old over the all sample; employment has been defined having a job over the past 7 days before the surveys while unemployment as being n the labour force, not working in the 7 days before the survey and not looking or a job; finally, employment sectors have been identified accordingly to the main occupation. Figures are calculated as percentage over the total of labour force.

4.2 DISTRIBUTION OF INDIVIDUAL WAGES

Besides looking at the changes in the structure of employment, a deeper analysis of the

distribution of individual wages is needed. Table A.2 in the appendixes presents results from

the two surveys used in this research. The data on hourly wages seem to indicate that the

economic growth experienced by Vietnam during nineties, principally driven by the reforms

of the Doi Moi, brought about a general increase in the level of individual wages. This means

that Vietnam’s transition towards greater economic liberalization, as well as the structural

16

changes induced by these reforms, has been accompanied by a stable level of employment

and by a higher level of wages.

It is interesting to see how the figure changes moving from a national to regional analysis.

Higher incomes are earned by people employed by the public sector instead of those that

work for a private company. This is related to the fact that remuneration in the public sector is

comprised of higher benefits, sometimes not earned by workers in the private sector.

There is a clear gender pay gap in the Vietnamese labour market but what is interesting, is

that it is narrowing with the time. The greater participation of women in wage employment

also emerged from table 4.1 suggests that women have increasing access to better paid jobs in

1998 compared to 1993. Pham and Reilly (2006) show a general improvement in the relative

position of women in the Vietnamese labour market during the transition both in terms of

level of payment and in their participation.

Not surprisingly, there is a positive correlation between the level of wages and the level of

education. In 1993, only a person with a primary education earned less than the mean wage;

having a university degree assured a level of earnings almost 70% higher than having a

primary school diploma. From a dynamic perspective, the positive effect of education and

wages has been maintained and increased in 1998. The interesting thing is that after five years

also people with a lower secondary diploma do not reach the mean level of wages; otherwise

people with a higher level of schooling have a level of earnings that is more than double the

mean value. This means that the returns to higher level of education have increased while the

returns to primary and lower secondary education have decreased. This picture could be

related with the fact that the economic growth and the structural changes (the progressive shift

of employment from the low paid agricultural sector to he higher paid wage labour) incurred

in the Vietnamese economy have increased the opportunities available to better educated

people and has awarded them with a higher level of payment.

Another important factor determining15 the level of individual wages is location. The urban-

rural paid gap already wide in 1993, increased substantially by 1998, contributing to the

observed widening of overall income inequality, as noted in Molini (2005). This unsurprising

15 As it also emerges from the results of the estimation of the Mincerian function contained in table 6.3.

17

gap could be related to the fact that the rural labour market is composed mainly of people

occupied in the agricultural sector that has, on average, a lower level of payment than the

wage sector dominant in the urban areas (UNDP, 2001).

Moving from the north to the south of the country, wages go from below to above the national

mean, even if this is just part of the story. As shown in table A.2, wages are higher on the

North Central Coast and in the Southeast region in 1993. In 1998, the Southeast becomes the

region with the highest level of wages16.

Finally, wages increases with the age of the worker. Not surprisingly, as one considers the age

as a proxy of the experience, higher wages are registered as the individual becomes older but

until a certain level of age. In 1993 the highest level of income is observed for people aged

between 50 and 59; while in 1998 the highest remuneration is registered for younger people,

between 40 and 49 years old. The fact than people with more than 60 years received the

lowest payment could be related to the fact that after that age an individual receive a state

pension and so potentially are out of the labour market17.

4.3 DISTRIBUTION OF AGRICULTURAL AD SELF EMPLOYMENT PROFITS

Tables A.3 and A.4 present summary results about the evolution of agricultural and self

employment household profits18.

Mean agricultural household profits increased between 1993 to 1998. This could be related to

the fact that economic expansion helped the agricultural sector in terms of higher revenues

especially in the export oriented cultivation, like coffee and rice (Minot et al 2001). Not

surprisingly, there is a gender gap also in the distribution of earnings from agriculture. 16 The region of Southeast is also the richest in the country, where there has been the greater improvements in the economic conditions of the population bringing the highest proportion of people out of poverty in the country, WB(1999). In addition, the Southeast region counts the highest proportion of people living in urban areas, another factor that contributes increase the average level of ages in the region. 17 In the preceding paragraphs about the evolution of the employment, people more than 60 years old are not considered in the labour force. The fact that they are considered as recipients of earned income is related to the fact that could continue to work in the agricultural sector. 18 It should be noticed that in both the used surveys data about agricultural and self employment revenues and expenditures are reported as mean values at the household level instead of individual level as in the case of wages. Moreover, there is a high number of missing values in the reported data and this could a reason why they are not considered reliable as measures of household welfare. In the case of the present research they have been used in conjunction with more accurate data on occupational choice and individual wages in order to give an indicative picture of the different sources of income at the household level and how they are distributive among the Vietnamese population.

18

Households with a male head appear to earn on average a profit higher than those whose head

is a female. Not surprisingly, there does not appear to be a strong correlation between the

level of education of the household head and agricultural profits. Returns to schooling are not

strong in the case of agricultural employment and the figure for 1998 confirms that profits for

households with a primary school educated head earned more than those with a head with a

secondary school diploma. Looking also to the evolution of employment and the relative

increase in wage labour participation against the agricultural sector, this effect could be

related in general to the presence of more households’ heads with a primary level of education

in the agricultural sector with respect to those with better educated household heads.

Agricultural profits are also differentiated between different regions. The highest level is

registered going to the south of the country and this trend is maintained also in 1998. This

could be explained with the fact that southern regions are those with the highest productivity

and dedicated to the production of rice, for with Vietnam is one of the main exporter.

With regard to the profits derived from a self employment activity, there is still an increasing

trend that confirms the positive economic performance of Vietnam during the nineties. The

gender gap in the case of self employment profits is the reverse of that identified in the

agricultural sector. Households with a female head have, on average, higher income. This will

be confirmed also by the results of the estimation of the determinants of occupational choice

in the self employment sector and it could be explained by the fact that women are more often

involved in some small businesses devoted to manufacturing. Male head households are more

successful in agricultural activities or in the wage labour, as noted above. Not surprisingly,

and in contrast with agriculture, both the level of education and the age of the household head

are positively correlated to the level of profits in the self employment sector. More experience

and a higher level of education allow the household’s head to be involved in more profitable

activities. Finally, as expected, the southern regions of the country remain the most profitable.

5. EMPIRICAL RESULTS USING MICROSIMULATION

5.1 ESTIMATION OF HOUSEHOLD INCOME

5.1.1 OCCUPATIONAL CHOICE

As stated in the model described in chapter 4, in order to derive the household income, we

first need to estimate the occupational choice made by each member of the household. The

19

aim will be to investigate factors influencing the probability of attachment to one of three

main employment categories: 1) paid labour; 2) agricultural sector and 3) self employment

labour. Given that multiple occupational choices have been selected, a multinomial logit

model (MNL) (McFadden, 1973, 1984) will be used to estimate the relationship between the

probability of attachment to one of the three sectors and some characteristics at the individual,

household and commune level19. The model has been estimated for both surveys 1992/93 and

1997/98 and separately for different members of the household. Accordingly to Grimm

(2001), it is reasonable to assume that occupational choices within the household are

interdependent. This effect is taken into account estimating the MNL model in a sequential

decision process, i.e. estimating first the decision of the household head and afterwards that of

the household’s head spouse and of the other members of the household conditional to the

decision taken by the head20.

Before presenting the results of the estimation of the model, some observations need to be

done about the specification of the model. First, the Theil normalization21 for the MNL model

has been applied to the category with the highest frequency of employed people, in this case

the agricultural sector. Following this specification, all the coefficients would be interpreted

as the contribution to the probability to be involved in one occupational category relative to

the one chosen as base reference. Second, consistency with the hypothesis of Independence of

Irrelevant Alternatives (IIA), which states that coefficients remain unchanged if there are

changes in the set of alternative outcomes, has to be verified. Results for the Hausman, the

Small-Hsiao and the Wald test have been reported in table A2 in the appendixes. There are

19 Letting if the individual chooses the employment outcome and otherwise (where j=1,2,3), the probability that an individual i chooses the alternative j can be expressed (following the suggested specification for Vietnam contained in Hung Pham (2006:10) as:

1ijY = thi thj 0ijY =

3

1

e x pP r ( )

e x p

i n d h h c o mj i j i j i

ii n d h h c o m

j i j i j ij

j X X Xo b Y j

j X X X

α β λ γ

α β λ γ=

⎡ ⎤+ + +⎣ ⎦= =⎡ ⎤+ + +⎣ ⎦∑

Where , , Pr ( 1)ob Y = Pr ( 2)ob Y = Pr ( 3)ob Y = represent respectively the probability that the individual

is employed as a wage worker, n the agricultural sector or is a self employed person, and where indiX , hh

iX and comiX are matrices of individual, household and commune characteristics, and jβ , jλ and jγ are vectors of

coefficients. 20 The sequential feature of the occupational choices of the different household members is capture inserting in the estimation of the model for the spouse and or other household members the predicted probability to find the household head as a wage worker, as working in the agricultural sector and as a self employed worker, respectively. 21 The Theil normalization implies that one category is chosen as a base and is coefficients are then set to zero.

20

some cases in which the IIA hypothesis tested using the Small-Hsiao test22 is not respected

and this can be due to the fact that there are some observations for people reported in more

than one occupational category. This phenomenon can be attributed, for example, to the fact

that people can report to work as a wage workers and also for the household in some

agricultural business. This may further suggest the presence of an additional occupational

category that needs some more investigation, the one of ‘occupational multiplicity’23 for of

those people working or the household.

Results from the estimation of the multinomial logit models for different household members

are reported in the appendixes. The derived marginal effects for infinitesimal changes in the

continuous variables or the impact effects for dummy variables (indicated with a d in

brackets) on the probability to choose one of three occupational outcomes, evaluated at the

sample mean of the dependent variable and ceteris paribus, are reported bellow in tables 5.1,

5.2, 5.324.

Analysing the factors influencing the occupational choice of the household head at the

individual level over the two surveyed years, while the ethnicity doesn’t seem to be have a

strongly determined25 impact on occupational choice, the first significant effect at the

individual level is represented by the sex of the respondent capturing the gender effect on

occupational choice. Men are more likely to be wage workers and less likely to be self-

employed with respect o being

22 Results are reported for the three tests performed on the model specification. As one of the drawbacks of the Hausman test is that it could produced negative values for the statistics, like in the case of our model as showed in the table A2, the results of the Small-Hsiao test are preferred, as noted also in Small and Hsiao (1985). 23 The phenomenon of occupational multiplicity, i.e. of people working in more than one sector, or of people with more than one job in the Vietnamese labour market is reported also in Haughton et al.(2001). 24 The estimated coefficients for the multinomial logit are exported in the appendixes. 25 It should be noted that the marginal effect associated to the coefficient of the dummy for belonging or not to the major Vietnamese ethnic group (Kihn) is significant only at the 5% level only for the category of wage labour for 1998: on average and ceteris paribus, belonging to the Kihn majority increases by 7.2 percentage points the probability to be self employed with respect to be employed in the agricultural sector in 1998. This ethnic effect can be due to the fact that other ethnic minorities are concentrated in the remote rural areas of Vietnam making then difficult for them to be a self employed person.

21

Table 5.1: Occupational choice- household head, marginal effects, 1993-1998.

Wage Labour Self Employment Agriculture (ref.) Variables 1993 1998 1993 1998 1993 1998 Individual level variables Kinh (d) .0255178 .0720364** .0090412 -.0238527 -.0345589 -.0481837 sex (d) .0400161* .1496397*** -.0334686*** -.1380923*** -.0065475 -.0115474 married (d) -.0208084 -.0908576** -.0018971 -.0072017 .0227055 .0980593 agesq -.0000389*** -.0000696*** -4.32e-06** -1.69e-06* .0000433 .0000713 illness (d) -.0118693 .0162026 -.0025378 .0265456* .0144071 -.0427482 lsecondary (d) .0617486* .0267263* .0381154** .0603008*** -.099864 -.0870271 usecondary (d) .0378007 -.0039902 .0289021* .1742524*** -.0667028 -.1702622 higher (d) -.0020525 -.0598132 .0073102 .3031581*** -.0052578 -.2433449 Household level variables lnhhsize .0737185** .0901143*** .0101677 .0680366** -.0838862 -.1581509 children -.0054789 .0099227 -.0018552 .0048289 .0073341 -.0147516 female .0039778 -.0172753* .000147 -.0035183 -.0041248 .0207936 depend -.0048904 -.0069395 .0116842* .0394279* -.0067937 -.0324884 lrlpcex2 .0480087*** .0126828** .0269672*** .1593689*** -.0749759 -.1720517 lnarea -.0715169*** -.0810403*** -.0198285*** -.0622313*** .0913454 .1432716 itoilet1 (d) .055208 .0062191 .0110754 .1824788*** -.0662834 -.1886979 itoilet2 (d) -.0479348** -.0775089*** -.0083222 -.0135008 .0562569 .0910097 electricity (d) .0597598*** .0890388*** .0210928*** .0620504*** -.0808526 -.1510892 iwater1 (d) -.0010845 -.0270226 -.0008638 .0220428 .0019482 .0049798 iwater2 (d) -.0195919 -.0299672 -.0038861 .0256902 .023478 .004277 ihouse1 (d) .0573242* .1024106*** .0262038** .0301671 -.083528 -.1325777 ihouse2 (d) .012742 .0070367 .0052451 .0114728 -.0179871 -.0185095 urban (d) .1570913*** .1457831*** .075425*** .062424*** -.2325163 -.2082071 regio1 (d) -.0201254 -.097036*** -.0187176* -.12431*** .038843 .221346 regio2 (d) -.0241592 -.0028961 -.0165191* -.0803071** .0406783 .0832032 regio3 (d) -.0650254** -.0376541* -.0166205* -.1153038*** .0816459 .1529579 regio4 (d) -.037017 -.0869073*** -.0063228 -.0630736** .0433398 .1499809 regio5 (d) -.0284865 -.1733116*** -.0102914 -.1128785*** .0387779 .28619 regio6 (d) .1045555** -.0233038 .0164812* -.0883698*** -.1210367 .1116736 quart2 (d) -.0315344 -.0219038 .0000804 -.0387346* .031454 .0606384 quart3 (d) -.0177934 -.0033572 .0105178 -.0085583 .0072756 .0119155 quart4 (d) .0249855 .021714 .0132396 -.0005515 -.0382251 -.0211625 Commune level variables pubtransp (d) -.0077338 -.028325 .0190629** .0258505 -.0113291 .0024746 irland -.0087574* -.0161872*** -.0032971** -.0157751*** .0120546 .0319623 laclandarcom .0121161* .0252257*** -.0020527 .0095045* -.0100633 -.0347302 pcrlandcom (d) .0624822* .1763221*** .003025 -.0419733 -.0655072 -.1343488 watersur (d) -.0158304 .0190303 -.0069114 .0172793 .0227418 -.0363096 forest (d) -.0361346 -.099255*** -.0057262 -.0158348 .0418608 .1150898 other (d) -.0700827 -.2562636*** .0145867*** -.0621387* .120186 .3184023 Average Prob .10218726 .20472959 .02759226 .61536368 .87022048 .17990673 Observations 3085 4587 3085 4587 3085 4587 Pseudo R-squared 0.28 0.29 0.28 0.29 0.28 0.29 WALD Test 43176.72 1645.02 Log Likelihood -1573.44 -3169.81 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: ***, **, * indicates variables whose coefficients are statistically significant at the 10%, 5% and 1% level, respectively; marginal effects are calculated at the sample mean; (d) indicates that the independent variable is a dummy and the calculated effect is an impact effect.

employed in the agricultural sector. On average and ceteris paribus, being a male increases the

probability to be a wage worker of 4 in 1993 to 15 percentage points in 1998; the probability

to be involved in a self employment activity with respect to agriculture decreases for males by

3.34 percentage points in 1993 and 13.80 in 1998. As noted in Pham and Reilly (2006), men

are more likely to look for a wage employment and women are more likely to be employed in

22

small business activities. This effect has surely been reinforced by the time, as can be seen

also in our estimation exercise, by the export driven increase in the manufacturing sector in

the last fifteen years. Covariates indicating the level of experience and the level of schooling

are in some cases poorly determined. As a proxy of experience, the square of the age of the

respondent is used. Its effect on the probability of attachment to the other two occupational

categories with respect to agriculture appears to be very small. The associated marginal

effects are negative, highly significant for wage work but less for self employment especially

for 1998. The level of schooling is significant only in 1998 and for self employment activities

and that effect increases with the level achieved: having completed the lower secondary

school increases the probability to be self employed by 6.08 percentage points in 1998 on

average and ceteris paribus with respect to agriculture and the primary level chosen as base

reference, while having a higher education increases the probability by 30.31 percentage

points. Not surprisingly, a household head with a higher level of schooling is more likely to

be involved in small business activity instead of being employed in the agricultural sector.

Household level variables for the demographic composition of the household are poorly

determined, the log of the household size significantly influences the probability of

attachment to one of the two occupational outcome relative to the agricultural one: increasing

the family size by 10% increase the probability of employed as a wage worker by 7.37

percentage points in 1993. The effect increases to 9.01 percentage points in 1998. The same

positive and increasing effect through the years emerge from the analysis of the relationship

between family size and the probability of self employment. This positive effect of the

household size is intuitively straightforward. As the number of household members increases,

the head is increasingly likely to look for a job outside the agricultural sector in the

expectation of greater earnings needed to satisfy the increased family needs. At the household

level, other variables important in influencing the probability of attachment to a particular

sector are those related to the household welfare. Real household expenditure and the total

land area owned by the household have been selected as continuous variables for household

welfare in addition to some dummies indicating the type and features of the dwelling

belonging to the household. Real expenditure is positively correlated with the probability of

the household head being employed outside the agricultural sector26. As the household

26 It must be noted that there could be clear problem of endogeneity between variables indicating the level of household welfare and the probability of being employed outside the agricultural sector. Further investigation

23

becomes wealthier, the head is more likely to find a job as a wage worker or in a self

employed activity.

The size of the land belonging to the household has a negative effect on the probability of the

head to find a job outside the agricultural sector. A 10% increase in the land area is associated

with a decrease in the probability of attachment to the wage labour category by 0.71

percentage points in 1998 and by 0.19 in 1993 on average and ceteris paribus. This negative

effects increases with time and is more pronounced for the wage labour category. The sign

indicates that a head of a household with more land is more likely to remain in the agricultural

sector.

Other significant variables at the household level are those related to the regional location of

the household. Not surprisingly, head of household that live in urban areas are more likely to

be employed as wage workers or to be involved in a business activity on their own. The effect

of regional dummies is more pronounced for 1998 than for 199327. In general, there is a

negative effect of being the household head, regardless of location, on the probability to

working outside the farm. The constant negative sign of this effect is consistent with the fact

that the majority of the households surveyed are rural and so concentrated in the agricultural

sector.

Finally, the commune level covariates appear to be statistically significant only in the case of

wage labour and for 1998: for example, a 10% increase in the irrigated land of the commune,

decreases the probability that the head of the household finds a job as a wage worker or as a

self employed by 0.16 percentage points.

Table 5.2 presents results about the estimation of marginal effects of the occupational choice

for the household head’s spouse conditional on the decision taken by the head. Even though

the relationship between the covariates and the probability of attachment to one of the three

sectors might appear similar in terms of the sign and significance of the coefficients and the

corresponding marginal effects, some relevant differences should be noticed. First, the effect

is highly significant only in the case of wage labour. As expected being a male spouse

needs to be done in order this kind of problem, using for example IV approach to solve for endogeneity problems. 27 The base is the region of Mekong River Delta.

24

Table 5.2: Occupational choice- household head’s spouse, marginal effects, 1993-1998.

Marginal effects for occupational choice_spouse

Wage Labour Self Employment Agriculture (ref.) Variables 1993 1998 1993 1998 1993 1998

Individual level variables Kinh (d) .0190656 .0573374* .0348653* .0097851 -.0539309 -.0671225 sex (d) .1745546*** .2280812*** -.015928 -.0716352 -.1586266 -.156446 agesq -.0000157* -.000077*** 5.21e-06 3.03e-07 .0000105 .0000767 illness (d) -.0095047 .0427016** .0018228 .0037655 .0076819 -.0464671 lsecondary (d) -.0121018 .0258196 .0093567 .013923 .0027451 -.0397426 usecondary (d) -.0196341 .0211263 .0640496* .1488912** -.0444155 -.1700175 higher (d) .0204103 -.0088769 .1249993*** .3813489*** -.1454097 -.3724721

Household level variables lnhhsize .0366767* .078211** .0257029 .1001932** -.0623795 -.1784041 children -.003731 -.0129727 -.0026912 -.0016916 .0064222 .0146643 depend -.0022776 .0222783 .0030488 -.0188393 -.0007711 -.003439 p2_head93 .1100636 .2709572 .0017459 -.0359801 -.1118095 -.2349771 p3_head93 .2013343** -.0229072 .0606588 -.2128807 -.2619932 .2357879 lrlpcex2 .0313127** .06735*** .053194*** .2296418*** -.0845066 -.2969918 lnarea -.0276184* -.058348*** -.037877* -.0964936*** .0654954 .1548416 itoilet1 (d) .0108868 .0732196* .0406805 .1071424* -.0515673 -.180362 itoilet2 (d) -.0159383 -.0939128*** -.0278775* -.0730836*** .0438158 .1669965 electricity (d) .0274203* .055121** .0483648** .1140287*** -.0757851 -.1691498 iwater1 (d) -.0017747 -.0136373 -.0001436 .0044678 .0019183 .0091695 iwater2 (d) -.0146751 .0084438 .0007221 -.0536549 .0139529 .0452111 ihouse1 (d) .0449728* .0240866 .0228241 .0603916 -.0677968 -.0844782 ihouse2 (d) .017118 .0031916 -.004864 -.0000456 -.012254 -.003146 urban (d) .0332712 .1677614*** .0749963* .1695371*** -.1082675 -.3372985 regio1 (d) -.0080341 -.1274005*** -.0389129* -.1555462*** .0469469 .2829466 regio2 (d) .0150103 -.0708791** -.0209515 -.1161255*** .0059412 .1870046 regio3 (d) -.0067283 -.089726*** -.0349017 -.1414501*** .04163 .231176 regio4 (d) -.0114439 -.0991997*** -.0039574 -.069999* .0154013 .1691988 regio5 (d) -.0132686 -.1485228*** -.020042 -.1013525* .0333106 .2498753 regio6 (d) .0714776** -.0572085** .0522411* -.1203799*** -.1237187 .1775884 quart2 (d) -.0299392** -.0081417 -.0011591 -.0408171 .0310983 .0489587 quart3 (d) -.0090928 .0283975 .0215675 -.0083778 -.0124747 -.0200198 quart4 (d) .0040141 .0175693 .0160638 .0469521 -.0200779 -.0645214

Commune level variables pubtransp (d) -.0127818 -.0486638* .0093103 .0493478 .0034716 -.000684 irland -.0031226 -.0182073*** -.0049475 -.0217708*** .0080702 .0399781 laclandarcom .0002845 .0327534*** -.0103868* .0233343**** .0101024 -.0560876 pcrlandcom (d) .0555359** .0453476 .0256347 -.0506706 -.0811705 .005323 watersur (d) .0094535 -.039588 -.0089335 .0777526 -.0005201 -.0381646 forest (d) -.0079719 -.0974534*** .0169928 -.0550532* -.0090209 .1525066 other (d) -.0661393*** -.152053** -.0903463*** -.1430581* .1564856 .2951111 Average Prob .04268811 .05959465 .89771724 .1459236 .20414221 .6499342 Observations 2382 3565 Pseudo R-squared 0.31 0.3 Wald Test 29829.5 1131.33 Log Likelihood -1176.28 -2323.93 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: see note for table 5.1.

increases the probability of employment as a wage worker by 17.45 percentage points in

1993. This effect becomes even larger in 1998. Being a male spouse has not a significant

effect on the probability to be employed in the self employment sector. Second, there is a

25

strong and significant effect of having a higher education on the probability of self

employment28. While for the household head the level of education was not such a strong

determinant of the self employment, a certain level of expertise is required for the spouse.

Finally, the distinguishing factor included in the estimated model for both the spouse and the

other household members is that their occupational choice is estimated conditional to the

choice of the household head, captured by the predicted probability of employment in one of

the three identified categories. In the case of the spouse, this effect is poorly determined. For

1993, having the household head involved in a self employment activity raises the probability

that the spouse has a wage work by 20.13 percentage points. The absence of significance of

the other marginal effects suggests that the occupational choice of the spouse is not heavily

conditioned by the one of the household head29.

Marginal effects and estimated coefficients for the multinomial logit model for other

household members are reported in the appendixes. Relevant differences with the model

estimated for the household head are connected with the effect of ethnicity and the effect of

variables indicating the occupational choice of the household head. Even though ethnicity was

not a robust determinant of the probability of employment in the three occupational categories

neither for the household head nor for the spouse, it influences the likelihood of an household

member to find a job as a wage worker or a self employed relative to be employed in the

agricultural sector. The effect is positive for both categories and remains positive over the

years. This ethnicity effect could be related to the fact that in Vietnam minority groups are

located to the remote rural areas of the country and they are mainly employed in the

agricultural sector. On the contrary, people belonging to the Kihn majority are more likely to

be employed in sectors outside agriculture.

5.1.2 INDIVIDUAL WAGES

The model of occupational choice varied depending on the relative position of the individual

within the household. The evaluation of household income requires the estimation of the 28 The marginal effects of having a higher level of education on the probability to be in the self employment more than double passing from 12.49 percentage points on average and ceteris paribus to 38.13 in 1998. 29 The very low rate of employment in Vietnam witnesses that it is not difficult to find a job in whatever sector even though the household head is not employed in the same sector.

26

determinants of the individual wages. This has been done using as specification an augmented

semi-logarithm Mincerian wage equation used in literature to test the relationship with the log

of wages and variables indicating the level of human capital of the respondent (Mincer, 1974).

Following the suggestion contained in Pham-Reilly (2006), the dependent variable is

represented by hourly real wages rates obtained deflating the nominal wage rates by a

monthly consumer price index.30 . Instead of using the years of education as an indicator, the

highest level of education completed has been used31. Other variables considered are

ethnicity, marital status, an indicator for health, a dummy that takes value one if the

respondent works for the public sector, regional dummies and dummies indicating the time of

the interview in term of quarters to capture any seasonal effect.

Estimating the wage equation there is a clear problem of bias in selecting people observed

only in the category of wage workers. Because occupational choice is not binary, the two

steps procedure based on an estimation of a probit in the first stage as set out in Heckman

(1979), is inappropriate. Instead, the methodology of Lee (1983) is used to correct for

possible selection biases32. Estimating first a multinomial logit for occupational choice similar

to the one presented in the previous section, we estimate the predicted probabilities for

each individual i to be in the occupational category j. These predicted probabilities are then

used to construct the correction terms

Pij

ijλ which are finally used in the estimation of the wage

equation using OLS33 that represent the second stage of the procedure34.

30 The nominal wages comprise also all the payments in cash as annual bonus, for lunch, for on-the-job training, for honorarium and for other forms of compensation. 31 This choice has been driven by the absence of information about the number of years of education in both the surveys used. 32 This methodology was applied to the case of Vietnam in Liu (2004) 33 Because the methodology contained in Lee (1983) requires correcting for the presence of heteroscedasticity, the Huber (1967) variance-covariance matrix was used in the estimation of the multinomial logit. 34 Estimation results using Heckman two-step procedure have also been computed and they are presented in the appendixes.

27

Table 5.3: Wage equation, selection bias correction, pooled model, 1993-1998.

Dependent Variable Log of monthly wage 1993 1998 Kinh -0.21197* 0.10562 [0.12148] [0.11709] Married 0.00885 -0.09717 [0.08410] [0.06649] Sex 0.29953*** 0.09963* [0.06431] [0.05488] Age 0.00974** 0.00119 [0.00413] [0.00398] Lsecondary 0.35830*** 0.85983*** [0.11877] [0.08420] Usecondary 0.12313 1.30712*** [0.13965] [0.08955] Higher 0.45069*** 1.34264*** [0.09253] [0.09917] Illness -0.12252 -0.37010*** [0.08265] [0.05837] Public 1.17351*** 1.02461*** [0.07202] [0.05744] regio1 0.12714 0.49645*** [0.13082] [0.11639] regio2 0.24837** 0.32197*** [0.10578] [0.08925] regio3 0.72176*** 0.54347*** [0.15976] [0.10892] regio4 -0.05012 0.31132*** [0.11613] [0.09960] regio5 -0.39330 0.11589 [0.34934] [0.16726] regio6 1.12034*** 0.99064*** [0.10389] [0.08676] urban93 0.60144*** 0.80744*** [0.08673] [0.05990] quart2 0.07052 0.07534 [0.11422] [0.06561] quart3 0.37933*** -0.09090 [0.12200] [0.07072] quart4 -0.25926** -0.22897*** [0.10728] [0.08412] Imr1_93 0.00676 0.16447 [0.17647] [0.15594] Constant 3.39847*** 3.49856*** [0.30773] [0.19805] Observations 2146 3009 Wald Test 1573.76 2433.12 R squared 0.29 0.41 adjusted R squared 0.28 0.40 N° of observations 2146.00 3009.00 Log Likelihood -3942.55 -5327.22 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: ***, **, * indicates variables whose coefficients are statistically significant at the 10%, 5% and 1% level, respectively; standard errors in brackets. Robust standard errors are obtained bootstrapping with 200 replications. Selection biases in the wage equation have been corrected using the procedure indicated in Lee (1983). The explanatory variables in the selection model are: log of the household size, proportion of children, proportion of women and proportion of old people in the household, age, age squared, dummy to have a lower secondary level of education, dummy to have a upper secondary level of education, dummy to have a higher level of education, dependency ratio, log of total area of the household, log of the irrigated land of the commune, a dummy if the commune has perennial crop land and a dummy indicating if the commune as public transports.

28

The previous table 5.3 presents results for the estimation of the wage equation over the pooled

sample. The model has been estimated over different sub-samples: urban male, urban female,

rural male and rural female: results are reported in the appendixes.

Looking at the results for the estimated coefficients provided by the estimation of the wage

equation using ordinary least squared methodology after controlling for possible selection

biases, neither ethnicity nor marital status exert a strong relationship over the level of wages35.

Other variables included in the estimation present the expected effects. There is a sensible

gender effect that losses part of its significance with the time. On average and ceteris paribus,

the male hourly age is 34.92% higher than the female hourly wage in 1993; in 1998 the effect

is smaller36: a man is paid 10.48% more than a woman37. The fact that the gender effect

becomes weaker with the time could be related to the fact that as reforms brought by the Doi

Moi started to have positive effects on the economic performance of the country, the level of

wages increases both for male and for female workers. As expected and also hypnotised in the

formulation of the Mincerian function, dummies indicating the level of education all exert a

positive and highly significant effect on the hourly wage. On average and ceteris paribus, the

effect of having progressed to a higher level of education raises the level of individual wages

of 136.27% for lower secondary school to 282.91% for higher education in 1998. Having

suffered from any kind of illness in the last four month has a negative impact effect on the

level of wages though is significant only in 1998. An interesting aspect is the related to the

effect of the sector of employment on the level of hourly wages. Being employed in the public

sector significantly increases the level of payment both in 1993 and in the 1998. This

difference can lead to two conclusions. First, it’s clear that, on average and ceteris paribus,

working for the public sector means receiving a higher level of payment. Second, this

differential effect becomes weaker with the time indicating that as a result of economic

growth driven by reforms, the payment gap between public and private sector in Vietnam is

narrowing. In order to capture geographical differences, seven regional dummies have been

35 The dummy indicating the belonging to the Kinh majority is negative, significant at the 10% level only in 1993: 36 The wage equation has been estimated over the pooled sample. The presence of a gender effect suggest that a more deep investigation is needed in order to test if there is a systematic and significant gender pay gap in Vietnam. 37 The effect on the level of hourly wage has been derived using the formula: 1 100sexeβ⎡ ⎤− ×⎣ ⎦

29

added to the estimation. The coefficients are significant and positive in almost every case38.

The highest impact on the level of wages is represented by people living in the region of the

Southeast that earn 206 % in 1993, and 169% in 1998 more than people living in the Mekong

River Delta on average and ceteris paribus. This effect is also expected considering that the

Southeast is the richest region of the country (World Bank, 1999) and the one with the highest

concentration of people living in urban areas, another actor that has a positive effect on the

level of earned income , as showed by significant coefficient for both years.

The last coefficient to be analysed is the one related to the correction term. Several variables

related to the socio-demographic structure of the household were included in the selection

regression together with those already used in the wage equation. There appears not to be any

systematic correlation between the unobservables that determine the participation in the

labour market as wage workers with those influencing the level of earned income. Therefore

there is no selection bias. As suggested in Pham and Reilly (2006), the absence of a selection

bias could be related to the weakness of instruments used in explaining the participation

equation. Further research into this issue with particular attention paid to the identification of

stronger instruments is warranted.

5.1.3 AGRICULTURAL PROFIT

The agricultural profit function has a specification similar to the one used for the wage

equation. The dependent variable is the logarithm of agricultural profit calculated as the

difference between total earnings from the sale of agricultural products (food crops, industrial

crops, fruit crops and livestock) and all the farm expenditures (for fertilizers, insecticides and

hired labour outside the household) over the past 12 months and it is adjusted for a monthly

CPI, as in the case of wages, in order to derive a monthly real agricultural profit39. The main

difference with the wage equation is that dummies indicating the level of education are

excluded from the estimation leaving only age as a proxy for experience and four dummies

indicating the extension of irrigated land belonging to the household are taken into account as

an important determinant of agricultural profits. Selection bias was corrected in the same way

as the one used for wage equation adopting the Lee (1983) methodology.

38 The base is constituted by the region of Mekong River Delta in the south of the country. 39 While the wage equation was estimated at the individual level, agricultural and self employment functions are estimated at the household level.

30

Having a household head that is married has a positive effect on the level of agricultural profit

and this effect remain roughly constant between the two surveys. This could be related to the

fact that the spouse can be employed in the agricultural activity contributing positively to the

business. The effect of inserting regional dummies turns out to be highly significant as in the

case of the wage equation even though negative: this was expected considering that the base

is the region of the Mekong River Delta with a high agricultural productivity; thereore, having

the business in one of the other regions of the country has lower the level of profit that can be

earned.

Table 5.4: Agricultural Profit function, selection bias correction, 1993-1998.

Dependent variable Log agricultural monthly profit 1993 1998 Kinh 0.27968*** 0.25318*** [0.08175] [0.06576] Married 0.39443*** 0.40869*** [0.07829] [0.06985] Sex 0.04518 0.15726*** [0.07754] [0.05963] Age 0.00286* -0.00307* [0.00171] [0.00160] regio1 -0.67284*** -0.55865*** [0.08867] [0.08410] regio2 -0.72969*** -0.64697*** [0.07205] [0.07636] regio3 -1.00710*** -0.79007*** [0.08072] [0.07665] regio4 -0.85629*** -0.35560*** [0.10817] [0.07667] regio5 -0.13768 0.44743*** [0.17265] [0.09581] regio6 -0.21263* 0.12292 [0.12448] [0.09277] irland1 0.03833 0.11649* [0.05768] [0.06930] irland2 0.40791** 0.61447*** [0.16577] [0.07987] irland3 1.05445*** 1.25770*** [0.19032] [0.08886] irland4 1.95648** 2.60375*** [0.90565] [0.35425] quart2 0.24676*** 0.09453* [0.06900] [0.05393] quart3 0.18645*** 0.12034** [0.07082] [0.05253] quart4 0.00822 -0.19207*** [0.06883] [0.05862] imr2agrp98 -0.17919 0.25475** [0.17932] [0.10620] Constant 7.01044*** 0.09453* [0.18773] [0.05393] Observations 3089 3751 Wald Test 527.06 1551.15 R squared 0.12 0.21 adjusted R squared 0.12 0.21 N° of observations 3089.00 3751.00 Log Likelihood -4878.01 -5810.91

Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: see notes for table 5.3.

31

Inserted as important determinants of the agricultural function, dummies in indicating the

extension of the irrigated area exert a positive and well determined effect on the level of

profits: as it can be noticed from the previous table, the magnitude of the coefficients

increases either passing from 1993 to 1998 or passing to bigger extension of the irrigated area

available to the household related to the base40. For example, having an area between 5000

and 10000 squared meters increases the agricultural profit by 50.36% in 1993 and by 90.54%

in 1998 relative to the biggest extension considered as a base. If the extension increases till

100000 squared meters the effect on profit is even bigger: this suggest that the availability of

a more extensive area has a positive effect on the profit but at the same time there should be a

turning point in the extension of ‘productive’ irrigated area: this suggested the fact that in

Vietnam there is an intensive use of the area by households involved in an agricultural

business rather than extensive, so when the available area reaches big extension, the lack of

resources makes the profit increase at a decreasing rate.

Differently from the wage equation, in the estimation of the agricultural profit function, there

is some selection bias that turns out to be significant and positive only in 1998. This suggests

the presence of some kind of correlation between unobservables determining the selection

into agricultural profit function and unobservables determining the level of profit. A

household with sample average characteristics selected into the agricultural sector has 29%

higher profit in 1998 than a household randomly drawn from the population with the average

set of characteristics. The coefficient on the selection term can be interpreted with the fact if

household with better socio demographic characteristics (proportions of female, adults, old

people and better educated) participate in having an agricultural business, their expertise

(unobservable) could be positively correlated with unobservables determining the level of

profit: this correlation secures that households that participate get higher level of profits.

.

5.1.4 SELF EMPLOYMENT PROFIT

The last component of the household income is represented by self employment profit. The

dependent variables has been constructed by the logarithm of the difference between revenues

and expenditures for households recorded as involved in a non-farm self employment activity.

Results of estimated coefficients are reported in the following table 5.5.

40 Here the base is represented by an irrigated area of more than 100000 squared meters.

32

Table 5.5: Self Employment Profit function, selection bias correction, 1993-1998. Dependent Variable Log of self employment profit 1993 1998 Kinh 0.43108*** 0.35600*** [0.14577] [0.11729] Married 0.38521*** 0.23253* [0.14151] [0.12040] Sex -0.37115 0.62041** [0.24211] [0.27580] Age 0.00447 -0.01238*** [0.00335] [0.00415] Lsecondary 0.42527* -0.16256 [0.22161] [0.12684] Usecondary 0.44941** -0.38341 [0.19862] [0.29253] Higher 0.27915 -0.75820* [0.20838] [0.43183] Urban 1.31742*** 0.84908*** [0.10432] [0.07921] regio1 -0.80002*** -0.73814*** [0.13644] [0.11761] regio2 -0.40872*** -0.37343*** [0.11926] [0.10917] regio3 -0.32333** -0.66035*** [0.14073] [0.11837] regio4 -0.23615 0.18915* [0.14673] [0.10035] regio5 0.57450* 0.26217 [0.31922] [0.17335] regio6 0.55765*** 0.53327*** [0.14314] [0.10329] quart2 0.38343*** -0.10587 [0.11960] [0.08557] quart3 0.41577*** 0.04891 [0.13079] [0.08674] quart4 0.27506** -0.11171 [0.11473] [0.09987] imr3selfp98 0.31385 -1.20852*** [0.33546] [0.42343] Constant 7.40391*** 11.09160*** [0.58116] [0.74250] Observations 1951 2493 Wald Test 615.50 735.68 R squared 0.18 0.19 adjusted R squared 0.17 0.18 N° of observations 1951.00 2493.00 Log Likelihood -3798.36 -4619.24 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: see notes for table 5.3. Surprisingly, the age of the head of the household is negatively and significantly related to the

level of profit in the self employment sector in 1998 and her/his level of education is not a

strong determinant of it. In the case of self employment profit, the basic hypothesis contained

in the semi-logarithmic Mincerian function doesn’t seem to be respected. Estimated

coefficients related to the location of the activity have the expected signs: living in an urban

area has a well determined positive impact on the level of profit for both years, on average

and ceteris paribus; dummies corresponding to the different regions of the country indicate

that living in a region like the Southeast with the highest level of economic performance and

33

where presumably there is a high concentration of business activities increases the level of

self employment profit by 74.66% in 1993 with respect of being in the region of the Mekong

River Delta; otherwise living in the northern part of the country doesn’t help to increase the

level of profit and this negative effects last trough the years, as negative and significant

coefficient show.

Analogously to the estimation of the agricultural one, also the self employment function

exerts a significant selection bias only for 1998 this time with a negative effect. This means

that there is some negative correlation between the unobservable characteristics governing the

participation of household in the sector of self employment activities and unobservable

determining the level of self employment earnings. With respect to a household with mean

sample characteristic randomly drawn, one that is selected in the sample of those involved in

a self employment business has a lower profit: this bias can be due to the fact that selected

households do not develop the necessary kind of ability required to run a business on their

own, thus showing a lower level of capacity to earn a higher profit from it

5.2 MICROSIMULATION AND DECOMPOSITION OF HOUSEHOLD INCOME

5.2.1 THE STRUCTURE AND DISTRIBUTION OF HOUSEHOLD INCOME

Before looking at the evolution of household income inequality, it is necessary to analyse its

structure over one year as well as dynamically. Household income has been derived using the

formula contained in section 3.3, summing up the three sources of income considered:

individual wages, agricultural and self employment profits. Results are reported in table 5.6.

As already seen in the previous chapter, all three components of household income

experienced a significant increase as economic growth started to bring more opportunities for

households to increase their level of income. The increase has been more pronounced for

female headed households. As we have already seen they benefit from an improvement in

their relative position in the labour market and especially in the self employment sector.

Household income appears to be increasing with the level of education of the household head

and this is true also in a dynamic perspective even if in 1998 it seems that returns to upper

secondary education are higher than those to university level education. Those results could

have been driven by the increasing impact of this level of education on the level of earnings

34

from wage labour, as only in that case there appears to be a positive well determined

relationship41. Studying the average household income in a geographical perspective, it turns

out to be higher in the urban than in the rural areas and also the rate of growth over time is

biased toward the richer urban areas of the country. The region with the highest level of

income per household in both the surveyed years is the Southeast confirming that is the area

with the highest level of all the three sources of income considered.

Table 5.6: The structure of Household Income, 1993-1998 1993 1998 National 21850.19 39071.2 Household Income by gender Male 25302.88 39716.42 Female 20580.89 37333.27 Household Income by level of education

Primary 21923.03 27224.12 Lower Secondary 23508.47 35678.73 Upper Secondary 28388.39 81273.27 Higher 29495.26 64035.37 Household Income by location Urban 52499.93 74056.65 Rural 14189.45 24920.9 Household Income by region Northern Mountains & Midlands 10112.53 14636.07 Red River Delta 16594.61 41149.28 North Central Coast 9697.15 19351.17 South Central Coast 25973.95 38470.63 Central Highlands 17600.73 24517.61 Southeast 47950.81 72377.67 Mekong River Delta 28812.94 42994.82 Household Income by age structure Age less than 20 2225.73 1597.402 Age between 20 and 29 12160.42 28136.73 Age between 30 and 39 24266.93 50882.48 Age between 40 and 49 27090.62 46909.76 Age between 50 and 59 22308.65 36582.04 Age more than 60 19718.52 21200.76 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: Figures are in thousand Dongs. Household income has been calculated adding up different components represented by individual wages, household agricultural profits and household self employment profits, following the formula (3.8). The monthly consumer price index is used to adjust for regional differences. Variable for gender, level of education and age refer to the household head.

41 See table 5.3 in the previous section.

35

All seven regions in which the country has been divided gained from the expansion of

economic opportunities offered by the reforms and by the progressive transformation of the

Vietnamese economy.

A sustained higher level of economic growth reached by the country in the fifteen years after

the approval of the Doi Moi has not prevented the distribution of resources from being

unequal both between urban and rural areas and between regions. This spatial pattern has been

maintained and even reinforced over time. As shown in table 6.7, all the inequality measures

calculated using the derived household per capita income increased form 1993 to 1998

indicating an increasing disparity in the distribution of resources. The Gini coefficient rose

considerably from 1993 to 1998. The level of income inequality appears to be quite high in

both years and its increasing trend is for sure worrying. Other measures of dispersion confirm

the same picture. Even though the good economic performance of the country brought a

considerably high proportion of people out of poverty (WB, 2000a) it also produced a more

unequal distribution of resources. An interesting picture emerges if we look at inequality

measures distinguished for urban and rural areas. Even though, as observed earlier, the

dispersion of incomes rose in both urban and rural areas, the major contribution to the rise in

the overall inequality seems to come from the inequality within the urban areas. The Gini

coefficient rose in the urbanised part of the country from 0.688 to 0.721 registering an

increase of roughly the same magnitude as the overall inequality, while the increase in

inequality in the rural areas is more conservative, from 0.664 to 0.684.

Table5.7 Evolution of household income inequality, 1993-1998

National Urban Rural 1993 1998 1993 1998 1993 1998 Summary Inequality Measures Standard deviation of logs 1.165 1.195 1.241 1.294 1.0629 1.064 Gini coefficient 0.685 0.728 0.688 0.721 0.664 0.684 Theil entropy measure 1.024 1.343 0.916 1.203 0.991 1.313 Theil mean log deviation measure 0.893 1.038 0.879 1.056 0.801 0.894 Atkinson(0.5) 0.389 0.452 0.368 0.438 0.367 0.419 Atkinson(1) 0.590 0.646 0.585 0.652 0.551 0.591 Atkinson(2) 0.746 0.785 0.772 0.814 0.696 0.725 Source: author’s calculation based on VLSS 1993 and VLSS 1998.

36

This differentiated picture of the evolution of household income suggests that the factors

influencing its distribution have operated differently in urban and in rural areas. This is why it

is interesting to decompose the overall change in inequality measures in different factors that

might have affected this increasing trend and how they operate differently for different areas

of the country.

5.2.2 DECOMPOSITION USING MICROSIMULATION

Using the theoretical framework presented in paragraph 3.3.1, the microsimulation exercise

has been conducted using as base year the 1993 and then applying the parameters estimated

for occupational choice, individual wages and household agriculture and self employment

profits for 1998. The overall new distribution using new parameters have been then used to

calculate Gini coefficients that could capture each single factor of the decomposition. Results

from the microsimulation and decomposition analysis are presented in the following table 5.8.

Table 5.8:Decomposition of Inequality using Microsimulation, 1993-1998 National Urban Rural Gini Var Gini Gini Var Gini Gini Var Gini Initial population 1992/1993 Initial value 1998 0.685 0.688 0.664 Changes Price Effect 0.691 +0.006 0.753 +0.065 0.630 -0.034 Change in Unobservables 0.668 -0.017 0.694 +0.006 0.671 +0.007 Total Price Change -0.011 +0.071 -0.027 Occupational Choice 0.677 -0.008 0.634 -0.054 0.682 +0.018 Price and Occupational Choice -0.019 -0.017 -0.009 Population effect +0.062 +0.036 +0.029 Final value 1998 0.728 +0.043 0.741 +0.053 0.684 +0.020 Source: author’s calculation based on VLSS 1993 and VLSS 1998.

We begin the analysis by evaluating what has been called the price effect. It has been obtained

by replacing at the same time in the wage equation of people observed as wage earners in

1993, in the household agriculture profit function and in the self employment profit function

respectively, all the β coefficients estimated for 1998 using the same set of variables at the

37

individual, household and community levels, while keeping the residual terms constant. Both

at the national level and in urban areas the price effect appears to have positively affected the

level of inequality, while in rural areas it contributes to a decreasing of the Gini coefficient.

The overall positive effect may be related to a general increase in the return to schooling that

affects principally the level of individual wages especially at the upper secondary and higher

level of education, as emerged from the results of the estimation of the Mincerian function. At

the national level, a wider wage differential between urban and rural areas could have been

another unequalising factor affecting the dispersion of household incomes. Besides returns to

schooling, other factors that may have affected the increase in the Gini coefficient can be the

increase in the returns to experience for wage workers and an increasing ethnicity effect on

wages from 1993 to 1998. For agricultural profits, factors that may have affected the increase

of inequality at the national level could be a profit gender differential, a change in the returns

for married household head in the agricultural sector and an increasing return over the

quantity of land belonging to the household. For self employment profits, the only factor that

may have affected the positive price effect, is the gender differential, as already noticed. All

these factors seem to have had an unequalising effect in urban areas, while in rural areas the

decreasing returns to schooling seem to have most strongly affected the dispersion of

incomes.

Second, we study the effect of how a change in the variance of unobservables in the wage

equation may affect the level of inequality. The overall effect is a drop in the income

dispersion. The negative trend is confirmed for urban inequality while appearing positive in

rural areas. The equalizing effect in the urban areas that drives the overall positive effect at

the national level could be related to a decrease in the unobservable heterogeneity of wages in

the urban sector that can be connected to a narrowing gap between private and public wages

occurring with the progressive development of the private sector after the reforms. The

unobservable heterogeneity seems to have acted in an unequalising way in rural areas.

Third, changes in the parameters reflecting the occupational choice from 1993 to 1998 help to

capture the effect on inequality of a change in the employment structure. The overall effect of

the occupational choice is equalising even though there are differences between urban and

rural areas. In the urban areas, the occupational choice contributes to the general decrease in

income inequality. Even though Vietnam has always been characterised by a high level of

employment, the reorganization of the labour market between agricultural, wage labour and

38

self employment activity contributes to a general decrease in the inequality. The major factor

responsible for his effects can be attributable to a progressive increase in the proportion of

people in the wage labour and self employment sectors and a decrease in the proportion of

agricultural employment, as observed in the analysis of summary statistics on employment.

Another important factor that can be taken into account is a progressive migration of labour

force from rural to urban areas that create at the same time pressure in urban area to absorb

new workers producing slight increases in the rural wage and causing a increase in the level

of rural Gini coefficient.

The last effect to be explored, the population effect, has been derived as a residual from the

total change in the inequality index between the two years and the change due to other factors.

It explains the effect on the inequality caused by a change in the socio- demographic

characteristics of the population and a change in the characteristics analyzed for Vietnam at

the community level, observed from 1993 to 1998. At the national level and for urban and

rural areas separately, the effect is positive, leading to an increase of overall inequality. One

reason related to this effect could be the progressive modification of educational level in

Vietnam. From 1993 to 1998 it becomes increasingly likely to find people with higher levels

of education and with higher earning potential, increasing the level of inequality in both urban

and rural areas. The change in the demographic structure of the population could be another

factor connected to the positive impact of population effect. The decrease in the number of

household members, especially in the urban area could be responsible to a higher level of per

capita income that increases the level of dispersion.

Finally, two other factors can be taken into account. At the farm level, the higher availability

of land produces a higher level of agricultural profit. At the national level, improved

infrastructure at the community level leads to the more efficient use of the available economic

resources in order to retain a higher level of profit.

6. CONCLUSIONS

The principal aim of this research was to study the evolution of Vietnamese inequality during

the transition in order to understand some of its determinants. The objective was mainly

driven by the consideration that Vietnam underwent a massive transformation of its economy

39

by the end of the eighties and all these reforms heavily affected the availability and the

distribution of resources within the country. In order to reach this object, a methodology

based on microsimulation and the calculation of counterfactual distribution has been applied.

The methodological framework consisted first of the estimation of an income generating

model by which it has been possible to estimate the determinants of occupational choice and

factors influencing the three main sources of household income: individual wages,

agricultural profits and self-employment profits. The estimation of the different components

of the household income has been b functional to the derivation of the parameters that should

be then applied from one year to the other in order to derive the counterfactual distribution of

income.

The microsimulation exercise has been conducted using 1993 as base year and then applying

the parameters estimated for occupational choice, individual wages and household agriculture

and self employment profits to 1998. The overall new distribution using new parameters has

been then used to calculate Gini coefficients that could capture each single effect of the

decomposition of overall inequality: the price effect, the effect of change in the variance of

unobservables, occupational choice effect and the population effect.

The price effect appears to be an unequalising factor, both nationally and in urban areas, while

it contributes to a decrease in the inequality index in rural areas. The overall positive effect

may be related to a general increase in the return to schooling that principally affected the

level of individual wages especially at the upper secondary and higher level of education.

Other factors influencing the overall price effect at the national level could have been the

widening wage gap between urban and rural areas and an increase in the return to experience

for wage workers. For agricultural profit, an unequalising effect may have been an increasing

return to the land belonging to the household while for self employment profit, gender

differential contribute positively to the unequalising price effect. As for the effect of a change

in the variance of the unobservables of wage equation, the equalizing effect in the urban areas

that drives the overall positive effect at the national level could be related to a decrease in the

unobservable heterogeneity of wages in the urban sector that can be connected to a narrowing

gap between private and public sector wages occurring with the progressive development of

the private sector after the reforms. The unobservable heterogeneity seems to have acted in an

unequalising way in rural areas. The third analysed effect is the one related to the change in

the parameters determining the occupational choice. The overall effect of the occupational

choice is to decrease the level of inequality. This effect can be related to a general

reorganization within the labour market of people progressively moving out of the agricultural

40

sector and in the wage labour. Finally, the residual positive population effect explains how the

change in the socio-demographic structure of the population and some changes at the

community level have positively affected the dispersion of household earnings. Reasons for

the sign of this effect could be the progressive modification of the educational level with more

educated people entering the labour market, to a progressive decrease in the household size

increasing household per capita income, to a higher availability of land that increases

agricultural profit and, finally to a better quality in the infrastructure available at the

community level that helps to better use the available economic resources.

The construction of a counterfactual distribution of household income using data from 1993

applying parameters estimated to data for 1998 helped to disaggregate various elements

contributing differently to the overall increased level of inequality. Different effects had

different impacts on the Gini coefficient. In urban areas, the most unequalising forces have

been the change in the structure of the population and the price effect driven by an increase in

the returns to schooling and to the land. On the contrary, in rural areas, price effect has

contributed to a decrease in the Gini coefficient, mainly due to a decrease return to the level

of education in those areas.

This microsimulation exercise has been an attempt to more deeply investigate the

determinants of the evolution of Vietnamese inequality during the nineties. Some points must

be raised in order to judge this kind of exercise. First, the mirosimulation has been

constructed using 1993 as the base year. The methodology suffers from an important

shortcoming represented by path dependency. Nothing ensures that the evolution and

decomposition of inequality will be the same if we start considering the population of 1998

applying to that parameters estimated for 1993. Second, further research is needed at different

points of the model. The selection of more accurate instruments with which to test the

presence of selection biases in the wage equation are needed for the further decomposition of

the four effects substituting different types of coefficients at one time. Attempts must also be

made to link changes emerging at the micro level that may influenced the dispersion of

household income with changes in the macroeconomic structure incurred by the economy in

order to also capture feedback effects.

41

APPENDIXES Table A1: Description of Variables and Summary Statistics

Variables Brief description 1993 1998

Pooled Sample

Pooled Sample

wage Monthly wage adjusted by CPI 462.0697 1156.463 pagr Monthly agric profit adjusted by CPI 2755.503 7089.239 pself Monthly self employment profit adjusted by CPI 49403.8 83181.54 Head = 1 being the head of the household .19941 .209611 Married = 1 if married, 0 otherwise . 5555357 .5259096 Spouse = 1 being the spouse of the household head . 1565279 .1645945 hhsize Household size 5.961273 5.579556 Age Age (years) 25.37892 28.14319 Sex =1 being male, being female .4824233 .4865544 Age squared Age squared (years) 1036.439 1203.437

Illness =1 if having suffered for any disease during the past four months

.2762093 .4136045

Primary education = 1 having primary education, 0 otherwise .190819 .3964783 Lower secondary = 1 having lower secondary education, 0 otherwise .0533752 .3910891 Upper secondary = 1 having upper secondary education, 0 otherwise .0227359 .1361699 Higher education = 1 having higher education, 0 otherwise .0401126 .0750721

Health = 1 if having a treatment at hospital over the past 4 week

.0080547 .0264727

Kinh = 1 if belonging in the Kinh majority . 8450511 .8422155 Public = 1 if being employed in the public sector .3500428 .3835026 Urban = 1 if living in urban areas . 2196551 .2710313 iwater1 =1 dummy for private or public tap .6709466 .744744 iwater2 =1 dummy for rainwater and well .1103632 .0875882 iwater3 =1 dummy for rivers and lakes .2006565 .1092408 electricity =1 dummy for having electricity .4781434 .7705874 ihouse1 =1 dummy for permanent house .1662096 . 1529301

ihouse2 =1 dummy for dummy for semi permanent or wooden frame house

.4704562 . 5918489

ihouse3 =1 dummy for simple house .3633342 . 2552211 pubtransp 1 dummy for having a public transport in the commune .4129062 . 2225676 irland1 total irrigated land of the HH less than 5000 msq .2355104 . 8105966 irland2 total irrigated land of the HH less than 10000 msq . .1654753 . 0919846 irland3 total irrigated land of the HH less than 50000 msq . .0152405 . 0697951 irland4 total irrigated land of the HH more than 50000 msq . .0002796 . 0018114 North Mountains & Midland = 1 if residing in North Mountains & Midland

.1554955 .1449134

Red River Delta = 1 if residing in Red River Delta .2146686 .1661193 North Central Coast = 1 if residing in North Central Coast .1275296 .1158119 South Central Coast = 1 if residing in South Central Coast .1195512 .1300307 Central Highlands = 1 if residing in Central Highlands .0317474 .0738541 Southeast = 1 if residing in Southeast .1258674 .1764254 Mekong River Delta = 1 if residing in Mekong River Delta . 2251402 .1928452 Quarter 1 = 1 if interviewed in 1st quarter . 1518388 .2116406 Quarter 2 = 1 if interviewed in 2nd quarter . 3147725 .3154695 Quarter 3 = 1 if interviewed in 3rd quarter . 2517764 .3084824 Quarter 4 = 1 if interviewed in 4th quarter . 2816123 .1644075 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: Mean values for each variable have been reported for both years considered.

42

Table A2: The structure of Individual Earnings, 1993-1998 1993 1998 National 462.0697 1156.463 Wage by sector Private 304.1709 706.5043 Public 755.0618 1882.894 Wage by gender Male 492.3749 1219.436 Female 416.2693 1059.989 Wage by level of education Primary 379.37 399.4887 Lower Secondary 509.5758 877.8878 Upper Secondary 626.2014 1484.34 Higher 790.7863 2434.059 Wages by location Urban 687.3721 1818.62 Rural 311.9217 474.4291 Wage by region Northern Mountains & Midlands 245.4705 792.2922 Red River Delta 460.3494 1069.993 North Central Coast 733.9802 1240.088 South Central Coast 453.2808 712.2049 Central Highlands 160.8947 417.5461 Southeast 681.8692 1953.826 Mekong River Delta 292.0252 523.4174 Wage by age structure Age less than 20 298.878 354.0403 Age between 20 and 29 423.0182 936.0486 Age between 30 and 39 551.3693 1306.193 Age between 40 and 49 557.3861 1749.311 Age between 50 and 59 567.141 1696.986 Age more than 60 291.9757 584.9662 Source: author’s calculation based on VLSS 1993 and VLSS 1998.

43

Table A3: The structure of Agricultural Profits, 1993-1998 1993 1998 National 2755.503 6033.509 Agricultural Profits by gender Male 2604.117 6656.233 Female 1897.426 3885.964 Agricultural Profits by level of education

Primary 2331.494 6259.667 Lower Secondary 2406.739 5832.585 Upper Secondary 2088.144 8339.647 Higher 2816.884 5186.342 Agricultural Profits by region Northern Mountains & Midlands 1705.491 3524.602 Red River Delta 1694.718 2936.579 North Central Coast 1309.873 2898.063 South Central Coast 1638.148 4242.232 Central Highlands 4186.886 13141.14 Southeast 3904.229 10719.93 Mekong River Delta 4552.176 9690.288 Agricultural Profits by age structure Age less than 20 1071.974 1577.434 Age between 20 and 29 1591.854 5188.119 Age between 30 and 39 2434.005 5875.562 Age between 40 and 49 3126.841 6846.659 Age between 50 and 59 2563.646 7012.979 Age more than 60 2314.724 4732.199 Source: author’s calculation based on VLSS 1993 and VLSS 1998.

44

Table A4: The structure of Self Employment Profits, 1993-1998 1993 1998 National 49403.8 76171.05 Self Employment Profits by gender Male 40237.27 75989.94 Female 52443.45 76659.25 Self Employment Profits by level of education

Primary 43378.76 52085.71 Lower Secondary 43261.63 64102.87 Upper Secondary 48483.69 139731.8 Higher 53390.63 123252.8 Self Employment Profits by location Urban 78934.51 114853.9 Rural 29305.51 50470.45 Self Employment Profits by region Northern Mountains & Midlands 22244.39 30369.09 Red River Delta 32604.72 82404.12 North Central Coast 20392.08 32737.3 South Central Coast 53613.14 79650.59 Central Highlands 55661.75 46408.05 Southeast 82966.38 130141.5 Mekong River Delta 50316.85 76273.48 Self Employment Profits by age structure

Age less than 20 1903.608 - Age between 20 and 29 25696.62 56403.1 Age between 30 and 39 47427.9 93812.23 Age between 40 and 49 46988.29 86557.96 Age between 50 and 59 42666.49 64387.5 Age more than 60 46450.26 49765.86 Source: author’s calculation based on VLSS 1993 and VLSS 1998.

45

Table A5: Tests for specification of the MNL model for occupational choice VLSS 1993 VLSS1998 Pooled Sample Small-Hsiao Test

Chi-squared Df P>chi-squared

Chi-squared Df P>chi-squared

Prob=1 omitt

96.847 44 0.0000 64.268 44 0.025

Prob=2 omitt

96.244 44 0.0000 60.954 44 0.046

Prob=3 omitt

41.691 44 0.571 61.104 44 0.045

Hausman Test

Prob=1 omitt

-28.238 44 1.0000 68.793 44 0.010

Prob=2 omitt

4.053 44 1.0000 35.194 44 0.826

Prob=3 omitt

10.454 44 1.0000 -5.241 44 1.0000

Wald Test Out 1-out3 514.345 43 0.0000 1255.796 43 0.0000 Out 1-out2 1650.154 43 0.0000 2593.095 43 0.0000 Out 3-out2 1922.128 43 0.0000 3300.345 43 0.0000 Household Head sample Small-Hsiao Test

Chi-squared Df P>chi-squared

Chi-squared Df P>chi-squared

Prob=1 omitt

96.847 44 0.0000 37.404 39 0.543

Prob=2 omitt

96.244 44 0.0000 60.328 39 0.016

Prob=3 omitt

41.691 44 0.571 69.382 39 0.002

Hausman Test

Prob=1 omitt

-28.238 1 1.0000 3.944 38 1.0000

Prob=2 omitt

4.053 38 1.0000 -23.004 38 1.0000

Prob=3 omitt

10.454 1 1.0000 -0.569 38 1.0000

Wald Test Out 1-out3 2098.284 38 0.0000 460.322 38 0.0000 Out 1-out2 429.657 38 0.0000 784.610 38 0.0000 Out 3-out2 41571.271 38 0.0000 1176.769 38 0.0000 Head’s spouse sample Small- Chi-squared Df P>chi- Chi-squared Df P>chi-

46

Hsiao Test squared squared Prob=1

omitt 140.763 39 0.0000 49.616 39 0.119

Prob=2 omitt

74.082 39 0.0000 311.968 39 0.0000

Prob=3 omitt

29.631 39 0.861 262.878 39 0.0000

Hausman Test

Prob=1 omitt

0.0000 1 1.0000 12.875 38 1.0000

Prob=2 omitt

18.016 37 0.996 -5.305 38 1.0000

Prob=3 omitt

0.0000 1 1.0000 -24.311 38 1.0000

Wald Test Out 1-out3 98.772 38 0.0000 223.412 38 0.0000 Out 1-out2 26839.599 38 0.0000 584.591 38 0.0000 Out 3-out2 27828.216 38 0.0000 862.329 38 0.0000 Household members sample Small-Hsiao Test

Chi-squared Df P>chi-squared

Chi-squared Df P>chi-squared

Prob=1 omitt

43.033 36 0.229 53.634 37 0.038

Prob=2 omitt

71.085 36 0.001 36.653 37 0.927

Prob=3 omitt

44.018 36 0.199 25.305 37 0.485

Hausman Test

Prob=1 omitt

9.818 37 1.0000 15.308 36 0.999

Prob=2 omitt

23.559 37 0.945 31.217 36 1.0000

Prob=3 omitt

0.353 37 1.0000 -37.339 36 0.695

Wald Test Out 1-out3 226.340 36 0.0000 471.488 36 0.0000 Out 1-out2 946.042 36 0.0000 1147.325 36 0.0000 Out 3-out2 803.844 36 0.0000 1226.463 36 0.0000 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: ***Small-Hsiao tests of IIA assumption: Ho: Odds(Outcome-J vs Outcome-K) are independent of other alternatives.*** Hausman tests of IIA assumption: Ho: Odds(Outcome-J vs Outcome-K) are independent of other alternatives. **** Wald tests for combining outcome categories: Ho: All coefficients except intercepts associated with given pair of outcomes are 0 (i.e., categories can be collapsed).

47

Table A6: Occupational choice- household members, marginal effects, 1993-1998.

Marginal effects for occupational choice_member Wage Labour Self Employment Agriculture Variables 1993 1998 1993 1998 1993 1998 Individual level variables Kinh (d) .0654115*** .1497483*** .0180663* .0246339** -.0834778 -.1743822 Sex (d) -.0035076 .1868074*** -.0514802*** -.1502574*** .0549878 -.03655 agesq .0000102 -.0000693** .0000119* .0000341* -.0000221 .0000353 illness (d) .0072256 -.0725973*** -.0005402 .0026421 -.0066854 .0699552 lsecondary (d) .1072751*** -.0103438 .0488237** -.0076438 -.1560988 .0179876 usecondary (d) .2164557* -.0404029 .02868 .0912157** -.2451357 -.0508128 higher (d) .0171746 -.1560986 .0870896* .4069748*** -.1042642 -.2508762 Household level variables lnhhsize .0402317* .0078459 .0229153* .0024964 -.0631471 -.0103423 depend -.0148516 -.0569622** .0016481 .0164731 .0132035 .040489 p1_head93 .1351579 -.579276*** -.1399351* .0470317 .0047773 .5322443 p3_head93 .0587284 -.4687583** -.0327765 -.1446398* -.0259519 .6133981 lnarea -.1127602*** -.1157476*** -.0626475*** -.0259965*** .1754077 .1417441 lrlpcex2 .0379717* -.0662279 .0585739*** .096658*** -.0965455 -.0304301 itoilet1 (d) .1028994* .062065 .0633904*** .0379352* -.1662898 -.1000001 itoilet2 (d) -.0457007** -.0299841 -.0299034** -.018096 .075604 .0480801 electricity (d) .0327132 .1050617*** .0521925*** .0746369*** -.0849057 -.1796986 iwater1 (d) -.0021176 -.0236523 .0070491 .0269384 -.0049315 -.0032861 iwater2 (d) -.0392895 -.0269028 -.0192882 .0190449 .0585777 .0078579 ihouse1 (d) .1731343*** .1109896* .0565859*** -.030004 -.2297203 -.0809856 ihouse2 (d) .0548486** .0600468 .0145832* -.0249106 -.0694317 -.0351362 urban (d) .2097924*** .2129861*** .1466193*** .0355647*** -.3564116 -.2485507 regio1 (d) -.1148145*** -.214083*** -.0534232*** -.0846684*** .1682376 .2987514 regio2 (d) -.119769*** -.1751273*** -.0623028*** -.045741*** .1820718 .2208683 regio3 (d) -.106278*** -.1379508*** -.069119*** -.0743558*** .175397 .2123066 regio4 (d) -.0542458* -.1370693*** -.0212335 -.0456648*** .0754794 .182734 regio5 (d) -.152357*** -.3261229*** -.0504921** -.0745064*** .2028491 .4006294 regio6 (d) .028002 -.0177906 .0028053 -.0497393** -.0308073 .0675299 quart2 (d) -.0469217* .0631074 -.0201485 -.0263261 .0670702 -.0367813 quart3 (d) -.0185267 .0105267 .0153038 -.0071715 .0032229 -.0033551 quart4 (d) -.0006871 .0370526 .0092559 -.0269123 -.0085688 -.0101404 Commune level variables pubtransp (d) .0181081 .0208729 .0232581 .0346351* -.0413663 -.055508 irland -.0067442* -.0097755*** -.0069297*** -.0089354*** .0136739 .0187109 laclandarcom .0094746 .0174331*** -.0006609 .0111706*** -.0088137 -.0286037 pcrlandcom (d) .0255469 -.0446747* .011626 -.0783475*** -.0371729 .1230222 watersur (d) .0908372*** -.0678463 .002841 .0093201 -.0936782 .0585262 forest (d) -.0960642*** -.0931433** -.0294114* -.001546 .1254756 .0946893 Average Prob .16328924 .42664709 .06004459 .12547892 .77666616 .44787399 Observations 4524 5040 Pseudo R-squared 0.3 0.28 Wald Test 1367.18 1883.23 Log Likelihood -2546.22 -3744.35 Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: ***, **, * indicates variables whose coefficients are statistically significant at the 10%, 5% and 1% level, respectively; marginal effects are calculated at the sample mean; (d) indicates that the independent variable is a dummy and the calculated effect is an impact effect.

48

Table A7: Wage equation estimated by sex and location, 1993-1998.

Male urban Female urban Male rural Female rural 1993 1998 1993 1998 1993 1998 1993 1998

Kinh -0.62665*** -0.24888 0.19318 0.03637 0.22306 0.68695*** 0.24338 0.03024 [0.20307] [0.21821] [0.28755] [0.22631] [0.19642] [0.17191] [0.30305] [0.27378]

married 0.46794*** 0.14023 -0.02696 0.06489 -0.03514 -0.2272 -0.18253 -0.50551*** [0.18094] [0.13473] [0.17442] [0.12221] [0.16214] [0.14008] [0.18223] [0.16814]

Age 0.00026 -0.01602* 0.01632** 0.01963 0.00928 0.01412** 0.00055 0.00004 [0.00808] [0.00887] [0.00829] [0.01219] [0.00684] [0.00642] [0.00913] [0.00908]

lsecondary 0.26534 0.83839*** 0.08218 0.98550*** 0.37400* 0.89921*** 0.91390*** 0.70830*** [0.22701] [0.18012] [0.23762] [0.21927] [0.22618] [0.13415] [0.32279] [0.17974]

usecondary 0.31976 1.33338*** -0.34728 1.49960*** 0.42169 1.19407*** 0.20356 1.30251*** [0.23404] [0.18595] [0.22051] [0.21775] [0.25935] [0.17145] [0.41209] [0.22288]

higher 0.33842** 1.38434*** 0.19954 1.60062*** 0.63880*** 1.10958*** 0.84539*** 1.14900*** [0.16309] [0.23098] [0.14730] [0.28210] [0.21434] [0.20047] [0.27420] [0.21528]

illness -0.00617 -0.12556 -0.04965 -0.24006** -0.04594 -0.58207*** -0.44537*** -0.47564*** [0.17883] [0.11645] [0.15456] [0.10336] [0.13504] [0.10464] [0.17272] [0.13447]

public 1.03468*** 1.03212*** 1.03967*** 0.83978*** 1.25770*** 0.92985*** 1.44822*** 1.58442*** [0.16318] [0.11050] [0.16392] [0.12634] [0.13752] [0.11772] [0.22076] [0.14559]

regio1 0 -0.74891* 0.09672 0 0.68911* 0.88422*** 0.21566 0.59243* [0.28852] [0.40859] [0.25745] [0.31215] [0.41623] [0.29384] [0.95169] [0.31348]

regio2 0.08712 -0.48928 0.08488 0.29482 0.63457* 0.14927 0.67051 0.1082 [0.32628] [0.41697] [0.29764] [0.34510] [0.38010] [0.26250] [0.95643] [0.33183]

regio3 0.16787 0 0 0.52480* 1.18058*** 0.17929 1.65273* 0.28583 [0.27288] [0.42673] [0.28400] [0.31705] [0.43248] [0.26421] [0.97769] [0.28527]

regio4 0.00502 -0.47907 0.25284 0.03147 0.23467 0.24751 -0.04806 0.07129 [0.33317] [0.41625] [0.30771] [0.37363] [0.37267] [0.26193] [0.97378] [0.27439]

regio6 1.33791*** 0.1653 1.47456*** 0.99631*** 1.21412*** 0.47180* 0.88519 0.98833*** [0.28960] [0.41333] [0.26029] [0.34575] [0.39111] [0.26324] [0.95233] [0.28708]

regio7 -0.00241 -0.97753** 0.24775 0.09289 0.38987 -0.18142 0.18133 -0.14793 [0.34856] [0.43583] [0.33482] [0.35610] [0.36029] [0.24104] [0.97470] [0.25325]

quart2 0.13443 0.15118 0.05388 0.14951 -0.08008 0.04892 0.35302 -0.25758 [0.25139] [0.11071] [0.23141] [0.13093] [0.22011] [0.14764] [0.33518] [0.18847]

quart3 0.64502** -0.19873 0.52768* -0.04464 0.05676 -0.21201 0.60128* -0.297 [0.28253] [0.14542] [0.27353] [0.14169] [0.18947] [0.15134] [0.31378] [0.18827]

quart4 -0.64528*** 0.10573 -0.41821** -0.13031 -0.37654* -0.64853*** 0.21995 -0.22188 [0.20329] [0.13223] [0.17519] [0.13842] [0.19528] [0.17073] [0.31311] [0.19005]

Imr1_93 -0.15308 0.5076 -0.25324 -0.54052 0.50864** 0.10437 0.14531 0.0699 [0.34401] [0.46609] [0.37883] [0.58197] [0.20332] [0.24071] [0.36185] [0.36634]

Constant 5.04976*** 5.50743*** 3.94411*** 4.40739*** 2.29921*** 3.28809*** 2.45088** 4.11401*** [0.54776] [0.56388] [0.60337] [0.51836] [0.49757] [0.34498] [1.08745] [0.51114]

Observations 498 891 407 666 801 937 440 515

Wald Test 216.71 372.07 207.2 313.01 323.58 700.56 294.08 638.41

R squared 0.3 0.3 0.28 0.31 0.2 0.3 0.34 0.43

adjusted R squared 0.28 0.28 0.25 0.29 0.19 0.29 0.31 0.41

N° of observations 498 891 407 666 801 937 440 515

Log Likelihood -884.28 -1580.32 -681.64 -1115.88 -1497.36 -1660.44 -808.31 -902.86

49

Source: author’s calculation based on VLSS 1993 and VLSS 1998. Note: ***, **, * indicates variables whose coefficients are statistically significant at the 10%, 5% and 1% level, respectively; standard errors in brackets. Robust standard errors are obtained bootstrapping with 200 replications. Selection biases in the wage equation have been corrected using the procedure indicated in Lee (1983). The explanatory variables in the selection model are: log of the household size, proportion of children, proportion of women and proportion of old people in the household, age, age squared, dummy to have a lower secondary level of education, dummy to have a upper secondary level of education, dummy to have a higher level of education, dependency ratio, log of total area of the household, log of the irrigated land of the commune, a dummy if the commune has perennial crop land and a dummy indicating if the commune as public transports.

50

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