Ethnicity and poverty in Vietnam: A new decomposition approach

Transcription

1 Ethnicity and poverty in Vietnam: A new decomposition approach Tomoki Fujii School of Economics, Singapore Management University Phone: Fax: tfujii@smu.edu.sg I gratefully acknowledge the receipt of SMU Research Grant (C244/MSS13E004) funded under the Singapore Ministry of Education s Academic Research Fund Tier 1 Programme. Yang Tao has provided research assistance.

2 Ethnicity and poverty in Vietnam: A new decomposition approach Abstract Using a new regression-based decomposition method that works without a panel data set, we investigate the difference in poverty between the ethnic minority and majority groups in Vietnam. The decomposition analysis shows that the rate of poverty reduction would have been faster by several percent between 2002 and 2006 had it not for the minority disadvantage. The decomposition analysis is also applied to the difference in poverty change between the ethnic minority and majority groups. for the period between 1993 and Age and marital status of the household head are among the important factors contributing to this difference. JEL classification codes: I32, O10 Keywords: FGT measure, difference in difference, repeated cross section data, ethnic minority, poverty reduction 1 Introduction Since the beginning of doi moi in 1986, Vietnam has achieved a truly remarkable economic development. The Gross Domestic Product per capita has soared from $301 in 1990 to $900 in 2010 in constant 2005 US dollars. Various other development indicators have also improved significantly. Indeed, Vietnam is often regarded as a success story of economic development in recent decades together with its neighbor, China. Yet, such a success story seldom conveys the whole picture. Vietnam is home to 54 officially recognized ethnic groups. These groups differ in culture, language, religion, and historical backgrounds, though some groups are closer to each other than others. Therefore, it would not be surprising that the fruits of economic development may not have been equally shared across different ethnic groups. Indeed, as we elaborate later, there is evidence that ethnic minority groups lag behind ethnic majority groups in economic development and the gap has widened between 1993 and 2006 in some indicators. There are at least two important reasons the development gap between the ethnic minority and majority groups should be taken seriously. First, the development gap may undermine the social cohesion. Second, if the gap stems from the discrimination against the minority group, eliminating the gap is an important objective on its own from the perspective of fairness. To deepen the understanding of ethnicity and poverty in Vietnam, this study documents in detail the changes in the difference in poverty, arguably most important indicator of (the lack of) development between 1993 and 2006, and decomposes them into a variety 1

3 of observable factors such as demographic characteristics education, sector of employment, occupational classification, and geographic location using a new decomposition method. This study makes at least two main contributions. The first contribution is methodological. This study develops a new regression-based poverty decomposition. Unlike the existing regression-based poverty decomposition method, the new method works with a repeated cross-section data set and requires no panel data set. While this comes at a cost of approximation errors and stronger assumptions, comparisons between the existing panel method and new method using the same panel data indicate that our method produces credible results. The second contribution is empirical. While there have been several studies describing the development gap between ethnic minority and majority groups in Vietnam, this is the first study to systematically analyze the evolution of the difference in poverty between the majority and minority groups in a consistent manner over more than a decade in Vietnam. This long term analysis allows us to investigate whether there have been any factors or structural elements that have prevented the ethnic minorities from catching up the majority. This paper is organised as follows. In Section 2, we provide a brief review of the related literature. Section 3 develops the new regression-based decomposition method and discuss its applicaton to the change of the difference in poverty measures between two subgroups such as the ethnic minority and majority groups. Section 4 describes the data and discusses some measurement issues. Section 5 presents the decomposition results. Section 6 offers some discussion. 2 Review of related literature Because this paper makes contribution both to the methodology of poverty decomposition and to the empirical analysis of the development gap between ethnic minority and majority in Vietnam, this paper is closely related to these two strands of literature. Below, we discuss them separately. Poverty decomposition literature The body of literature on poverty decomposition has grown steadily over the last two and a half decades. The popular general approach in this literature has been to let only one factor change at a time with all the other factors fixed. For example, this approach was adopted by Kakwani and Subbarao (1990), Jain and Tendulkar (1990) and Datt and Ravallion (1992). These studies differ in the way the reference period is determined. Datt and Ravallion 2

4 (1992) chooses to set the reference period at the initial period such that the growth [redistribution] component is defined as the part of observed poverty change that can be attributed to the change in the mean consumption [consumption distribution] when the consumption distribution [mean consumption] is held fixed in the initial period. Their method has been applied to a number of studies including Ravallion and Huppi (1991), Grootaert (1995), and Sahn and Stifel (2000). The decomposition proposed by Datt and Ravallion (1992) has the benefit of being consistent in the choice of the reference period unlike Kakwani and Subbarao (1990) and Jain and Tendulkar (1990), who essentially arbitrarily impose the sequence in which the growth or consumption distribution changes. A slightly different approach is the Shapley decomposition, in which the contribution of each component is calculated as the average of the marginal contribution over all the possible sequences of change in which only one factor is allowed to change at a time. This decomposition is called the Shapley decomposition because it resembles the Shapley solution in cooperative games (Kolenikov and Shorrocks, 2005; Shorrocks, 2013). None of these decomposition methods satisfy the sub-period additivity. Therefore, when there are three periods of data for t 0, t 1 (> t 0 ), and t 2 (> t 1 ), the growth (or redistribution) component for the period between t 0 and t 1 cannot be calculated as the sum of the growth components between t 0 and t 1 and between t 1 and t 2. Furthermore, with an exception of the Shapley decomposition, none of these decompositions satisfy the time-reversion consistency. That is, the growth component for the change from time t 0 to time t 1 is not the same as the growth component for the change from time t 1 to time t 0 with the opposite sign. As Fujii (2014) shows, these issues can be resolved by using an integration-based decomposition, which in effect internalizing the reference period. All of the decomposition methods mentioned above are based on the components defined by population-level characteristics. Therefore, their results are not helpful when one wants to understand the roles that household- or community-level variables have played in the poverty change over time. Given this limitation of the existing methods, Fujii (2015) proposed a poverty decomposition regression that is derived from a regression model and satisfies subperiod additivity and time-reversion consistency and applied to two panel data sets in Tanzania. He finds that both absolute and relative importance of various factors in explaining the observed poverty varies over time. This study also uses a regression-based poverty decomposition but it differs from Fujii (2015) in several respects. First, we propose a new regression-based poverty decomposition methodology that works with a repeated cross-section data set. This is a significant improvement because panel data sets are often not available in places where the results of poverty decomposition analysis are relevant and desired. Second, we apply our method to the change of the difference in poverty between two 3

5 demographic groups (i.e., ethnic minority and majority groups in Vietnam), rather than to the change in poverty. While this difference may appear trivial and it is indeed trivial for our methodology, this is not the case for the traditional population-based poverty decomposition. For example, we cannot decompose the difference in poverty between two demographic groups into growth and redistribution components by simply taking the difference in growth and redistribution components for each of the two groups. Our decomposition is also somewhat similar to the Oaxaca-Blinder decomposition (Oaxaca, 1973; Blinder, 1973) 1. This decomposition method is typically applied to a wage regression to analyze the wage gap across different demographic groups (e.g., male vs. female and white vs. black). While it can be used for a model of consumption per capita (van de Walle and Gunewardena, 2001), the Oaxaca-Blinder decomposition is not directly applicable to poverty, which is a non-linear transformation of consumption per capita. More importantly, because we only look at the change rather then the crosssectional difference, our analysis does not require the (arbitrary) choice of reference group unlike typical application of Oaxaca-Blinder decomposition. Ethnicity and development in Vietnam This study relates to and provides a more up-to-date analysis of the development gap between ethnic minority and majority groups in Vietnam. Hereafter, we regard the Kinh and Hoa (Chinese) ethnic groups as the ethnic majority group and the remaining ethnic groups as the ethnic minority group. Even though the Hoa (Chinese) ethnic group only accounts for less than 1 percent of the total population in Vietnam, it is usually regarded as a part of the ethnic majority group because the Hoa ethnic group has a high cultural assimilation with the Kinh ethnic group and enjoys a high standards of living. This minority/majority classification has been used by a number of existing studies including Baulch et al. (2007), Dang (2014), Nguyen et al. (2007), and van de Walle and Gunewardena (2001). The development gap between the ethnic minority and majority groups is well recognized in the literature. For example, Haughton and Haughton (1997) show that the children from the ethnic minority groups tend to be more malnourished than the ethnic majority groups in 1993 using the Vietnam Living Standards Survey (VLSS) for Using VLSS 1993 and VLSS 1998, Litchfield and Justino (2004) show that the ethnic minority groups are poorer than the ethnic majority groups and the gap in the poverty 1 See Fortin et al. (2011) for a relatively recent review of the Oaxaca-Blinder other related decomposition methods. 2 To be exact, the data collection for their data set started in October 1992 and ended in October However, because the majority of households were surveyed in 1993, we shall simply take the reference year to be 1993 and call this data set as VLSS Likewise, the second round of VLSS undertaken between December 1997 and December 1998 will be referred to as VLSS 1998 below. 4

6 rate between the ethnic minority and majority groups has widened between 1993 and Widening gap between the minority and majority groups in the 1990s was also reported in Nguyen et al. (2007), who explore the urban-rural disparity in Vietnam using VLSS 1993 and VLSS They find that there is a penalty for the ethnic minority status in their regression of the real per capita household consumption expenditure after controlling for other covariates. Further, they find that this penalty increased between 1993 and Because the ethnic minority is geographically segregated from the ethnic majority. Because the low welfare of ethnic minority households may be as a result of remoteness rather than their ethnicity per se, the it is not clear whether the location of the household is more or less important for household welfare than whether a household belongs to an ethnic minority group. Using spatially disaggregated estimates of poverty based on VLSS 1998 and the Vietnam Population Census for 1999, Epprecht et al. (2011) attempt to answer this question. Their results indicate that ethnicity is considerably more important than remoteness in determining household welfare, even though the latter also matters. This study also closely relates to van de Walle and Gunewardena (2001), who apply the Oaxaca-Blinder decomposition to consumption per capita using the northern rural subsample of the VLSS They find, among others, that there are significant differences between the ethnic minority and majority groups in the returns to education. Their results also suggest that it may be appropriate to use a separate regression model for ethnic minority and majority groups. Baulch et al. (2007) document the development gap between ethnic minority and majority groups in Vietnam. Using the VLSS 1998, they find that the ethnic minority group has a lower standards of living than the ethnic majority group. Their analysis of the population census data shows that the ethnic minority groups tend to have a lower enrollment rates at the lower secondary level than the ethnic majority group. They also conduct a decomposition analysis similar to van de Walle and Gunewardena (2001) using VLSS 1998 and find that the structural difference is more important than the difference in the distribution of covariates. Using more recent data, Dang (2014) describes the development gap between ethnic majority and ethnic minority in Vietnam. Based mainly on the Vietnam Household Living Standards Survey (VHLSS) for 2006, he finds that the ethnic minority lags behind ethnic majority in a variety of indicators such as income, education, health, prevalence of child labor, and access to community services despite a number of efforts made by the government to close the gap. His study finds no indication that the gap between ethnic minority and majority groups in average consumption has narrowed between 1998 and This study complements the above-mentioned studies by providing a detailed descrip- 5

7 tion of the change in the development gap between ethnic minority and majority groups over the 1993 and 2006 period using a series of VLSS and VHLSS data. Furthermore, we apply a new decomposition method consistently to see in which time period the difference in various poverty measures between ethnic groups has most widened and what factors have played most important roles. 3 Methodology In this section, we first extend the existing regression-based decomposition method by Fujii (2015), which relies on a panel data set, to allow for time-varying weights. We then discuss a special case where a theoretically equivalent decomposition can be carried out without a panel data set. This provides the motivation for the new decomposition method that can be implemented only with a repeated cross-sectional data set. Finally, we extend these decomposition methods to explain the changes in the difference in poverty measures between two subpopulations. Panel method Suppose that individual i s consumption at time t is given by y it (> 0). We start with the following individual-level poverty measure p it : p it = g ( yit z ) 1(y i z) = g (ỹ it ) 1(ỹ it 1), (1) where z, ỹ it y it /z, and 1( ) are respectively the poverty line, consumption per capita normalized by the poverty line, and an indicator function, which takes one if the argument is true and zero otherwise. The function g(ỹ ht ) is once-differentiable for all ỹ > 0 and g(1) = 0. The latter requirement is satisfied by most commonly used poverty measures. One important exception, though, is the head count index which has g(ỹ) = 1 for all ỹ. Therefore, the head count index of poverty is not used in the current study. We shall further discuss the poverty measures for our empirical application later. We consider the following additively decomposable measure of poverty P t that can be calculated from p it in sample S: P t = i S w it p it, where w it is the weight attached to individual i at time t and satisfy i w it = 1 for all t [0, 1]. In the case where there is no attrition and the sample is self-weighted, w it is constant over time and equal to the reciprocal of the sample size. The goal of the decomposition exercise is to ascribe the observed change in poverty D P 1 P 0 into various components of interest. 6

8 Now, suppose that y it is related to its covariates x it by the following linear model: ln ỹ ht = x T itβ t + ɛ it, (2) where β t is a (potentially) time-varying parameter and ɛ it is an idiosyncratic error term. We will write the jth component of x it and ω t ase x it,j and ω t,j, respectively. Without loss of generality, we assume that t = 0 is the initial time and t = 1 the terminal time for decomposition. The change in poverty, therefore, can be written as follows: D i S w i1 p i1 w i0 p i0 = i S = i S = i S [ ] d dt w itp it dt (w it ṗ it + ẇ it p it )dt [w it g (ỹ it )ỹ it (ẋ Titβ t + x Tit β t + ɛ it ) ] + ẇ it g(ỹ it ) 1(ỹ it 1)dt, (3) where the dot notation is used to denote the time derivative (e.g., β dβ/dt). Eq. (3) shows that the change in poverty at the individual level can be decomposed into the following three components: (i) covariate component, or the change in poverty due to the change in covariates, (ii) structural component, or the change in poverty due to the change in the parameter β, and (iii) the residual component, or the change in poverty due to the change in the error term. Because the poverty measure is additively decomposable, each of these components are also additively decomposable. One major practical issue that one faces when implementing this decomposition is that we would need a continuous observation of y it and x it. Therefore, we will need to make some assumptions about the underlying path along which x, w, β, and ɛ changes. A reasonable assumption would be that these values change linearly over time between t = 0 and t = 1. That is, we can make the following assumptions: x it = (1 t)x i0 + tx i1 (4) w it = (1 t)w i0 + tw i1 (5) β t = (1 t)β 0 + tβ 1 (6) ɛ it = (1 t)ɛ i0 + tɛ i1 (7) 7

9 The coefficients β 0 and β 1 can be estimated from the sample for the initial and terminal time, respectively. Using these, the idiosyncratic error terms can be also estimated. Therefore, under these assumptions, we can obtain the following decomposition: D = j (X j p + S j p) + W p + R p, (8) where Xp, j Sp, j and R p are, respectively, the jth covariate component, jth structural component, and residual component with the following definitions: Xp j = Sp j = W p = 1 R p = 1 n w it g (ỹ it )ỹ it (x T i1,j x T i0,j)β t,j 1(ỹ it 1)dt (9) 0 i S 1 w it g (ỹ it )ỹ it x T ij(β i1,j β i0,j )1(ỹ it 1)dt (10) 0 i S 1 (w i1 w i0 )g(ỹ it )1(ỹ it 1)dt (11) 0 i S 1 ( w it g (ỹ it )ỹ it ln ỹi1 x T i1β 1 (ln ỹ i0 x T i0β 0 ) ) 1(ỹ it 1)dt. (12) 0 i S The subscript p is used above to emphasize that this decomposition requires a panel data set. We evaluate these integrals by the fourth-order Runge-Kutta method. Notice that the presence of a panel data set is crucial for this decomposition because we need to know (x i0, w i0, ɛ i0 ) and (x i1, w i1, ɛ i1 ) for the same individual i. A special case applicable to repeated cross-section data Despite its increasing availability in recent years, panel data sets remain relatively scarce. Therefore, it would be useful to have a method that can operate without a panel data set, even if it comes at a cost of stronger assumptions. To see how and why it may be possible to conduct a decomposition analysis without a panel data set, let us begin with the analytically simplest case. We assume that the weight is simply the reciprocal of the sample size such that w it = n 1 for all i and t, where n = #{S}. Because W p = 0 is obvious in this case, we will not consider this component here. Suppose further that everyone is always poor (i.e., 1(ỹ i t 1) = 1) during the time period for poverty decomposition and the poverty measure of interest is the Watts poverty measure which is the average logarithmic shortfall from the poverty line with g WTS ( ) = ln(ỹ), where the superscript WTS is used to distinguish between different functional forms of g( ) for different poverty measures. It is clear that g WTS (1) = 0 is satisfied in 8

10 this case. Under the assumption of eqs. (4)-(7), the components given in eq. (9)-(12) can be written as: X j p = 1 n (x T i1,j x T i0,j)(β 0 + β 1 ) i S 2 = 1 x T i0,j(β 0 + β 1 ) 1 x T i1,j(β 0 + β 1 ) (13) n 2 n 2 i S i S Sp j = 1 (x T i0,j + x T i1,j)(β 1 β 0 ) n 2 i S = 1 x T i1,j(β 1 β 0 ) 1 x T i0,j(β 1 β 0 ) (14) n 2 n 2 i S i S R p = 1 (ln ỹ i0 x T n i0β 0 ) 1 (ln ỹ i1 x T n i1β 1 ) (15) i S When S is a nationally representative sample, the two terms in each of eqs. (13), (14), and (15) do not necessarily have to be calculated from the same sample. Therefore, when we have two samples S 0 and S 1 collected at t = 0 and t = 1, respectively, we can still derive the repeated cross section analogues of these equations in the following manner: i S X p j = 1 i0,j(β 0 + β 1 ) 1 n 0 2 n 1 i S 0 xt i S 1 xt S p j = 1 xt i1,j(β 1 β 0 ) 1 n 0 2 n 1 i S 0 R = 1 n 0 i1,j(β 0 + β 1 ) 2 i S 1 xt i0,j(β 1 β 0 ) 2 (16) (17) (ln ỹ i0 x T i0β 0 ) 1 (ln ỹ i1 x T n i1β 1 ), (18) 1 i S 0 i S 1 where n t #{S t } for t {0, 1} is the sample size at time t. Notice that the residual component given in eqs. (12) and (15) is equal to zero when β is estimated by the Ordinary Least Squares regression run separately for t = 0 and t = 1. Repeated cross section method The reason it is possible to carry out the decomposition described above is because there is no summation term simultaneously containing x i0 and x ij. Similarly, to operationalize the decomposition with a repeated cross-section data set, it is necessary to keep the summations of the terms involving x i0 and x i1 separately. To this end, we plug eq. (2) in 9

11 eq. (1) and consider the following transformation: p it = g(ỹ it )1(ỹ it 1) = f(x it, θ t ) + ω it, (19) where θ is a vector of parameters to estimate and f and ω it are defined as follows: f(x it, θ t ) = E ɛ [g(ỹ it )1(ỹ it 1) x it ] ω it = g(ỹ it )1(ỹ it 1) E ɛ [g(ỹ it )1(ỹ it 1) x it ] One can think of f and ω it as the expected poverty given covariates and the poverty due to idiosyncratic errors. Because the changes in poverty due to the changes in idiosyncratic errors are not informative, we will focus on the poverty change due to the changes in x it and θ t through f(x it, θ t ). In the empirical application, we consider the poverty gap and poverty severity measures or, respectively, the Foster-Greer-Thorbecke (FGT) poverty measures (Foster et al., 1984) with the parameter value of one and two in addition to the Watts poverty measure discussed already. Because poverty gap (FGT 1 ) and poverty severity (FGT 2 ) have g( ) = (1 ỹ) and g( ) = (1 ỹ) 2, respectively, g(1) = 0 is satisfied as with the Watts poverty measure. With the normality assumption, we can write f(x it, θ) in the following manner. Lemma 1 Assume that eq. (2) is satisfied and that ɛ N (0, σ 2 ) and θ = (β T, σ 2 ) hold for σ 0. Then, we have the following results: f FGT 1 (x it, θ) E[(1 ỹ) 1(ỹ it 0) x it ] ( ) = Φ xt itβ exp σ (x Titβ + σ2 2 ) ( Φ xt itβ σ f FGT 2 (x it, θ) E[(1 ỹ) 2 1(ỹ it 0) x it ] ( ) = Φ xt itβ 2 exp (x Titβ ) ( + σ2 Φ σ 2 + exp ( 2x T itβ + 2σ 2) ( ) Φ xt itβ σ 2σ xt itβ σ ) σ ) σ (20) (21) f W T S (x it, θ) E[ ln ỹ 1(ỹ it 0) x it ] ( ) ( ) = x T itβφ xt itβ x T + σφ it β, (22) σ σ where φ( ) and Φ( ) are, respectively, the probability density and cumulative distribution functions for a standard normal distribution. To operationalize the decomposition, we assume that the following analogue of eq. (6) 10

12 holds: θ t = (1 t)θ 0 + tθ 1 (23) Further, instead of eq. (4), we assume that the w it f(x it, θ t ) can be approximated as follows: w it f(x it, θ t ) = (1 t)w i0 f(x 0t, θ t ) + tw i1 f(x 1t, θ t ) + a it, (24) where a it is the approximation error. This equation means that the contribution of a particular individual i to the total expected poverty changes linearly between t = 0 and t = 1. Alternatively, the right hand side of the equation above can be alternatively interpreted as the expected value of w it f(x it, θ t ), where (w it, x it ) takes (w 0t, x 0t ) with probability 1 t and (w 1t, x 1t ) with probability t at time t [0, 1]. This alternative interpretation has an advantage that eq. (24) arise from the same underlying data generating process when multiple poverty measures are considered. Applying eq. (24), we have the following relationship P t = i S w it p it = (1 t) i S w it f(x i0, θ t ) + t i S w it f(x i1, θ t ) + i S (w it ω it + a it ). Taking the time derivative and rearranging the terms, we have the following result: P t = w i1 f(x i1, θ t ) w i0 f(x i0, θ t ) i S i S + θ t,j [ (1 t) f(x i0, θ t ) w i0 θ j t,j i S + i S + t i S ] f(x i1, θ t ) w i1 θ t,j [ẇ it ω it + w it ω it + ȧ it ] (25) Integrating P t over the unit time interval and using sample S 0 and S 1 for the terms involving x i0 and x i1, respectively, we have the following decomposition result. D = X c + j [S j c] + R c, (26) where the cross-sectional versions of covariate component X c, structural component S j c 11

13 for jth covariate, and residual component R c are defined as follows: [ ] 1 X c w i1 f(x i1, θ t ) w i0 f(x i0, θ t ) dt (27) 0 i S 1 i S 0 [ 1 Sc j (θ 1,j θ 0,j ) (1 t) f(x i0, θ t ) w i0 + t ] f(x i1, θ t ) w i1 dt (28) θ t,j θ t,j i S 0 i S 1 0 R c = D X c j [S j c], (29) The subscript c is used to denote the decomposition based on repeated cross-sectional data. This decomposition can be implemented for poverty gap, poverty severity, and the Watts poverty measure using the results of Lemma 1 and numerical integration. The decomposition of other poverty measures can be also carried out in a similar manner. There are a few notable differences between the panel-based decomposition in eq. (8) and the one based on repeated cross section data in eq. (26). First, unlike eq. (9), the covariate component in eq. (27) does not have the superscript j. That is, the covariate component generally cannot be further disaggregated in the repeated cross section version of decomposition. This is because we cannot tell the direction of change of x it for each i from a repeated cross-section data set. One exception is the case where f is linear in x it. In this case, regardless of the values of other covariates, the contribution of a given covariate to the poverty change is determined. This is essentially the reason why the covariate component can be computed for the jth covariate in eq. (13). Second, the role of the idiosyncratic error is different between eqs. (8) and (26). In the former, the change in the error term ɛ is explicitly modelled. As a result, the parameter σ plays no (explicit) role in the decomposition. On the other hand, the change in σ also affects X c and Sc j in the latter, because X c and Sc j capture the changes in the expected poverty as opposed to the observed poverty. Further, R c captures the approximation error. Third, it is possible to explicitly evaluate the computational error in the panel method, because we are able to compute D directly from the data and each of X j, S j, W, and R separately. Therefore, taking the difference between D and the sum of all these components, we are able to evaluate the magnitude of computational errors. However, a similar evaluation is not possible for the repeated cross section method. The decomposition methods given in eqs. (8) and (26) allow us to attributing the observed poverty change to both structural and covariate components in Vietnam. In our application, we use these methods to attribute some of the observed poverty changes to the ethnicity of the household head. Because the ethnicity of household head is time invariant, this means that the structural component fully provides the observed poverty 12

14 change that can be attributed to ethnicity. Difference-in-difference decomposition While the preceding analysis is useful, we would also be interested in what could be called the difference in difference decomposition. In this decomposition, we decompose the change in the difference in poverty between ethnic minority and majority groups rather than the change in poverty at the national level. Formally, we consider the decomposition of the following measure: D MIN D MAJ = (P MIN 1 P MAJ 1 ) (P MIN 0 P MAJ 0 ) = G 1 G 0, where the superscripts MIN and MAJ refer to the ethnic minority and majority groups and G t Pt MIN Pt MAJ is the difference in the poverty measure between the ethnic minority and majority groups at time t {0, 1}. Given the linear form, it is fairly straightforward to decompose : By applying the decomposition in eq. (8) or eq. (26) to the ethnic minority and separately, we are able to decompose the difference in difference of poverty into covariate and structural components among others. For example, applying eq. (26) to ethnic minority and majority groups and taking the difference, we obtain the following decomposition: = Xc + j [ Sj c ] + Rc, (30) where Xc Xc MIN Xc MAJ, Sj c Sc j,min Sc j,maj, and Rc Rc MIN Rc MAJ. The difference-in-difference decomposition with the panel data can be obtained using eq. (8) in a similar manner. 4 Data and Summary Statistics In this study, we use the two rounds of VLSS conducted in 1993 and 1998 and the first three rounds of VHLSS conducted in 2002, 2004, and 2006, both of which were implemented by the General Statistical Office (GSO) of Vietnam with funding and technical assistance from donors. 3 VLSS 1993 and 1998 cover about 4,800 and 6,000 households. With close to 30,000 households, VHLSS 2002 has a largest sample among the data sets we use. Both VHLSS 2004 and 2006 cover about 9,200 households. 4 We exclude a small number 3 Data collection for VHLSS 2002 data sets occurred between January and December of year VHLSS 2004 and 2006 were e collect between May and November in 2004 and 2006, respectively. See also Footnotes 2. 4 Further information on VLSS 1993 and 1998 can be obtained from 2XZVST3681 and respectively. Information on VHLSS 2002, 13

15 of households for which we do not observe the consumption or covariates used in the consumption model. The exact set of questions covered in the survey varies both across rounds and between the VLSS and VHLSS series. However, these household surveys all cover a wide range of topics including demographic characteristics of the household, the education, health and employment of household members, the household assets, incomes and consumption, and housing conditions. For the consumption measure, we use the consumption aggregates in real terms included in the dataset received from the GSO, which are adjusted for spatial price differences and expressed in the prices for 1998 for VLSS and 2002 for VHLSS. In addition, there are a few important differences in the design of the consumption module between VLSS and VHLSS series. The number of food (and drink) items included in VLSS 1998 is 45 as opposed to 58 in VHLSS Further, the recollection period for non-food items has changed from the last four weeks in VLSS 1998 to the last 12 months. Because of these and other issues, the consumption measures are not directly comparable between VLSS and VHLSS series. Both VLSS and VHLSS data sets are partial panel data. households recorded in VLSS 1993 also appear in VLSS Therefore, some of the Similarly, some of the households in VHLSS 2002 [VHLSS 2004] appear in VHLSS 2004 [VHLSS 2006]. However, there are no households that are surveyed both in VLSS 1998 and VHLSS While some households appear in three periods of VHLSS 2002 and VHLSS 2006, we only use the two adjacent periods because the number of such households are relatively small. Therefore, we have the following three panel data sets: VLSS , VHLSS , and VHLSS We only keep in our data those households whose household head did not change. We also eliminate about 1-3 percent of the households whose reported ethnicity or sex has changed between the survey rounds. The panel component is useful in particular for comparing the panel and repeated cross section methods. While we do not expect the results to be identical, the structural components obtained from the panel and repeated cross section methods should be consistent with each other both in terms of the sign and magnitude. This is because both are derived from the same theoretical model even though the latter rests on stronger assumptions. As will be shown in Section 5, the decomposition results obtained from the two methods are reasonably close. Table 1 describes the means of the characteristics of the cross-sectional data we use. These variables will be used as covariates for consumption regressions. All the figures are calculated with the population expansion factor because the unit of primary interest in our analysis is individual. Therefore, Table 1 shows that there are about 13.1 percent of 2004, and 2006 can be obtained form org/4lewrisgu0, and 14

16 Table 1: Means of key variables of the VLSS and VHLSS data. Year Household s demographic characteristics Minority head Female head Head s age Married head Divorced/separated/widowed head Household size Head s literacy and education a Literate head No education Primary Lower secondary Upper secondary/vocational University or higher Head s employment b No work Agricultural work Manual non-agricultural work Skilled work Primary sector Secondary sector Tertiary sector Location of residence Urban Sample size a In 1998, the literate heads are those heads who are able to read a simple given sentence. In all other years, the literacy heads are those who self-reported to be literate. Education level is based on the highest grade completed and upper secondary/vocational include those who have completed upper secondary school and those who have finished vocational training after the lower secondary level. b Each working head belongs to one of the three occupational categories (agricultural/manual non-agricultural/skilled) and one of the three sectors (primary/secondary/tertiary). Skilled work includes professional, technical, administrative, managerial and various service workers that are neither agricultural work or manual non-agricultural work. Primary sector includes agriculture, fishing, hunting, and mining. Secondary sector includes manufacturing sector. Tertiary sector includes services. 15

17 individuals residing in a household headed belonging to the ethnic minority group (i.e., belonging to an ethnic group other than the Kinh-Hoa majority group). This proportion did not change much over time. Table 1 shows a typical pattern that is exhibited in a country that is developing rapidly. The average household size is decreasing. Educational attainment is clearly improving. Table 1 also shows that manual non-agricultural and skilled work have gained importance over time and both the secondary (manufacturing) and tertiary (service) sectors have gained importance relative to the primary sector. Because Table 1 only provides the population average, it does not show whether the development gap between ethnic minority and majority groups is changing. To motivate the importance of this development gap, we present a graph of several development indicators for the minority and majority ethnic groups in Figure 1 for the period between 1993 and Figure 1(a) plots the proportion of individuals living in a household headed by a person with secondary education or higher for the ethnic minority and majority groups. This proportion has increased substantially for the ethnic majority group whereas it has not changed much for the ethnic minority group. Figure 1(b) is the graph of the proportion of individuals whose main water source for cooking and drinking is private water tap. While both the ethnic minority and majority groups have witnesses an increase in this indicator the gap has actually widened because the improvement for the minority group has been slow. Figure 1(c) shows the proportion of individuals that are living in a permanent house. Permanent houses are those made with strong materials such as concrete and bricks and include villas and multi-story houses and apartments but exclude semi-permanent and make-shift houses. Therefore, it can be thought of as the proportion of people who are living in a (relatively) high-quality house. This indicator, too, shows a widening gap over time. Of course, the development gap between the ethnic minority and majority groups are not widening. For example, the gap in the proportion of people living in a house without a toilet, a basic amenity in the household, has narrowed as Figure 1(d) shows. Another indicator that has exhibited a substantial narrowing is the share of individuals whose main lighting source is electricity. In 1993, this share was 54 percent for the ethnic majority group and 8 percent for the ethnic minority group. In 2006, this share has reached 98 percent for the ethnic majority group and 81 percent for the minority group. Let us now look at the gap in the individual welfare as measured by the real consumption per capita. In Figure 2, we plot the difference in the logarithmic consumption per capita at different consumption quantiles between the ethnic minority and majority groups. The narrow dotted and solid lines are drawn using VLSS 1993 and 1998, re- 16

18 (a) Secondary education or higher (b) Water primarily from private tap Ethnic majority Ethnic minority Year Ethnic majority Ethnic minority Year % people living in a household headed by lower secondary education or higher % people who obtain water for cooking and driking primarily from a private water tap (c) Permanent house (d) No toilet in the house 50 Ethnic majority Ethnic minority Year Ethnic majority Ethnic minority Year % people living in a permanent house % people living in a house wtih no toilet 17

19 1.3 Difference in log pc consumption Quantile Figure 2: Logarithmic difference in household consumption between ethnic majority and minority groups at different consumption quantiles. 18

20 spectively. The bold dashed, solid, and dot-dash lines are, respectively, drawn with the VHLSS 2002, 2004, and Therefore, Figure 2 shows that the gap between the ethnic minority and majority groups have widened between 1993 and 1998, except for the lower tail. The gaps also widened between 2002 and Between 2004 and 2006, the gap has widened in the middle quantiles, but it has dropped in the top quantiles. As mentioned above, the consumption expenditure is not directly comparable between VLSS and VHLSS because of the difference in the consumption module. Therefore, even though we see a substantial drop in the logarithmic gap between the ethnic minority and majority groups in Figure 2 between 1998 and 2002, it may be spurious. To see whether the difference in consumption module has any impact on the logarithmic difference in consumption per capita, we also used an alternative aggregate consumption measure included in VHLSS The alternative measure is designed to be as comparable to the consumption aggregates in VLSS 1998 as possible. While the two consumption aggregates are highly correlated, the gap tends to be higher except for the top 30 percent of the consumption distribution when the alternative measure is used. Overall, Figure 2 shows no evidence that the gap between the ethnic minority and majority groups is narrowing. To define poverty, we use the $1.25 and $2 international poverty lines expressed in 2005 US dollars per day per capita. We convert these poverty lines for the local currency for years 1998 and The former is used for the VLSS series and the latter for the VHLSS series. 5 We first convert them into the Vietnamese Dong using the purchasing power parity conversion factor for 2005 in the World Development Indicators (USD 1=VND 5920). We then deflate them by the consumer price index in 1998 and 2002 as a ratio to that in

21 Table 2: Poverty statistics for the ethnic minority and majority groups, Year MAJ MIN TOT MAJ MIN TOT MAJ MIN TOT MAJ MIN TOT MAJ MIN TOT Poverty line: $1.25 FGT (0.017) (0.012) (0.016) (0.017) (0.024) (0.017) (0.007) (0.013) (0.007) (0.006) (0.018) (0.006) (0.004) (0.020) (0.005) FGT (0.008) (0.021) (0.009) (0.006) (0.020) (0.007) (0.002) (0.008) (0.002) (0.001) (0.009) (0.002) (0.001) (0.008) (0.002) FGT (0.005) (0.020) (0.006) (0.003) (0.013) (0.004) (0.001) (0.005) (0.001) (0.001) (0.005) (0.001) (0.000) (0.004) (0.001) WTS (0.012) (0.049) (0.014) (0.008) (0.032) (0.010) (0.002) (0.013) (0.003) (0.002) (0.014) (0.003) (0.001) (0.012) (0.002) Poverty line: $2 FGTo (0.010) (0.003) (0.009) (0.016) (0.009) (0.014) (0.009) (0.007) (0.008) (0.008) (0.010) (0.008) (0.007) (0.014) (0.007) FGT (0.010) (0.014) (0.009) (0.009) (0.016) (0.010) (0.004) (0.008) (0.004) (0.003) (0.009) (0.004) (0.002) (0.010) (0.003) FGT (0.007) (0.018) (0.008) (0.006) (0.016) (0.007) (0.002) (0.007) (0.002) (0.002) (0.008) (0.002) (0.001) (0.007) (0.002) WTS (0.018) (0.050) (0.019) (0.015) (0.036) (0.016) (0.006) (0.015) (0.006) (0.005) (0.018) (0.006) (0.003) (0.018) (0.005) Note: MAJ, MIN, and TOT refer to the ethnic majority group, ethnic minority group and total in Vietnam. The standard errors are reported in the parentheses, which are calculated allowing for clustering at the commune level. 20

22 Table 2 reports the poverty rate (FGT 0 ), poverty gap (FGT 1 ), poverty severity (FGT 2 ), and the Watts measure (WTS) for the two poverty lines. Even though the comparability between VLSS-based figures and VHLSS-based figures are not strictly comparable, a large drop in poverty over time indicates that poverty has indeed declined substantially over time however it is measured. Table 2 also shows that the difference in poverty rate between the ethnic minority and majority group has widened between 1993 and 2006 regardless of the choice of poverty line, though this is not true for other poverty measures. In the next section, we apply the decomposition method developed in Section 3 to find what factors have contributed to both the change in overall poverty and the difference in poverty measures between the ethnic minority and majority groups. 5 Results Regression results Assuming that we have a reasonably good consumption model, our decomposition results are driven by two main ingredients. The first ingredient is the distribution of the covariates. Suppose that the value of a particular covariate increases for everyone and its coefficient remains positive, this covariate tends to be positively associated with the logarithmic consumption per capita and therefore its covariate component is non-positive both in the panel and cross-section methods. However, it is unlikely in practice that the direction of change for a covariate is the same for everyone in the data. Our decomposition result essentially gives the weighted average of the marginal effect of a covariates on poverty across people and over the period. It should be noted that a summary table like Table 1 does not allow us to determine whether and in what direction the distributional change of that covariate has contributed to the observed poverty change, even when we know the sign of the coefficient of a covariate of interest. This is because our decomposition focuses on the poor, the change in covariates only matters for those below the poverty line. Therefore, even though summary tables are informative, we cannot draw any conclusions about poverty. The second ingredient of our decomposition is the structural component. When the sign of a covariate remains unchanged, which is often the case in practice, we are able to tell in what direction the structural component for a particular covariate contributes to the observed poverty change. For example, suppose that the coefficient on a particular covariate is positive in both periods and the terminal coefficient is larger than the initial coefficient in absolute value. In this case, if the covariate is always positive, the structural component for this covariate is non-positive. With this in mind, let us look at the regression results for eq. (2) reported in Table 3 21

23 Table 3: OLS Regression results: panel samples Data VLSS Panel, VHLSS Panel, VHLSS Panel, Year Minority HH (0.027) (0.025) (0.028) (0.028) (0.028) (0.027) Female HH (0.023) (0.022) (0.023) (0.025) (0.024) (0.024) HH s age (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) HH married (0.056) (0.060) (0.055) (0.066) (0.055) (0.061) HH div/sep/wid (0.058) (0.061) (0.058) (0.067) (0.057) (0.062) HH literate (0.027) (0.026) (0.031) (0.032) (0.032) (0.032) HH educ: primary (0.022) (0.020) (0.021) (0.023) (0.023) (0.023) HH educ: Lower sec (0.024) (0.021) (0.023) (0.025) (0.024) (0.025) HH educ: Upper sec/voc (0.027) (0.027) (0.028) (0.029) (0.028) (0.028) HH educ: Univ/college (0.056) (0.049) (0.047) (0.047) (0.047) (0.046) HH occ: Agricultural (0.031) (0.026) (0.027) (0.027) (0.028) (0.028) HH occ: Manual non-ag (0.055) (0.043) (0.045) (0.049) (0.050) (0.049) HH occ: Skilled (0.056) (0.052) (0.039) (0.044) (0.045) (0.042) HH sect: Secondary (0.052) (0.043) (0.043) (0.045) (0.046) (0.045) HH sect: Tertiary (0.049) (0.046) (0.036) (0.041) (0.042) (0.040) Urban (0.022) (0.020) (0.020) (0.020) (0.020) (0.115) North East Reg (0.026) (0.025) (0.027) (0.028) (0.028) (0.028) North West Reg (0.050) (0.048) (0.044) (0.045) (0.043) (0.044) North Central Coast Reg (0.026) (0.023) (0.027) (0.028) (0.027) (0.027) South Central Coast Reg (0.029) (0.028) (0.028) (0.029) (0.030) (0.030) Central Highlands Reg (0.060) (0.057) (0.034) (0.035) (0.035) (0.035) Southeast Reg (0.026) (0.025) (0.027) (0.028) (0.028) (0.028) Mekong River Delta Reg (0.024) (0.023) (0.024) (0.025) (0.025) (0.025) Elec available in commune (0.027) (0.030) (0.036) (0.072) (0.066) (0.113) Constant (0.081) (0.087) (0.087) (0.116) (0.104) (0.141) σ R Obs 3,755 3,755 3,507 3,507 3,800 3,800 Note: Standard errors in parenthesis.,, and denote the statistical significance at 10 percent, 5 percent, and 1 percent levels, respectively. 22