### 3.2.2. Détection de la présence d'autocorrélation spatiale dans les résidus de la régression

Nous cherchons à présent à vérifier la présence d'autocorrélation spatiale dans notre modèle. Dans cette optique nous utilisons le test I de Moran adapté aux résidus de la régression (Cliff et Ord, 1981). Ce test est effectué sur les résidus de l'estimation par les Moindres Carrés Ordinaires (MCO) des quatre modèles de croissance, pour les différentes matrices de pondérations spatiales retenues. Les résultats du test I de Moran sont présentés dans le tableau 2.1. Ils révèlent que, quelle que soit la matrice de poids spatial utilisée, l'hypothèse nulle d'absence d'autocorrélation spatiale peut être rejetée au seuil de 1%.

Par ailleurs, les résultats de ce test peuvent également être utilisés afin de choisir la matrice de pondérations spatiales la mieux adaptée au modèle. Ainsi, si l'on compare la valeur des statistiques I de Moran pour les différentes matrices de poids considérées, on se rend compte qu'elle est maximale pour la matrice $W_3$ (correspondant à une distance seuil $d_t = 3850$), et ce, quel que soit le modèle de croissance pris en considération. La suite des estimations est donc réalisée à partir de la matrice de pondérations spatiales $W_3$ qui maximise les interactions entre provinces. Le choix de cette matrice implique par conséquent que les provinces chinoises peuvent toutes interagir entre elles quelle que soit la distance qui les sépare.

<table><caption>Tableau 2.1 : Résultats du test I de Moran adapté aux résidus de la régression des quatre modèles de croissance par MCO selon les différentes matrices pondérations spatiales</caption><thead><tr><th>Matrices de pondérations spatiales W</th><th>I de Moran Modèle 1</th><th>I de Moran Modèle 2</th><th>I de Moran Modèle 3</th><th>I de Moran Modèle 4</th></tr></thead><tbody><tr><td>W<sub>1</sub> : d<sub>t</sub> =1300</td><td>9,353<br>(0,000)</td><td>6,529<br>(0,000)</td><td>6,741<br>(0,000)</td><td>6,833<br>(0,000)</td></tr><tr><td>W<sub>2</sub> : d<sub>t</sub> =1840</td><td>11,016<br>(0,000)</td><td>7,776<br>(0,000)</td><td>8,091<br>(0,000)</td><td>8,191<br>(0,000)</td></tr><tr><td>W<sub>3</sub> : d<sub>t</sub> =3850</td><td>12,274<br>(0,000)</td><td>8,600<br>(0,000)</td><td>9,071<br>(0,000)</td><td>9,120<br>(0,000)</td></tr></tbody></table>

Source : Calculs de l'auteur, à partir de *China Statistical Yearbook* (1996-2010).
Probabilités critiques entre parenthèses.
### 3.2.3. Spécification du modèle économique intégrant la dépendance spatiale

Le test I de Moran ne permet pas de déterminer laquelle des deux spécifications du modèle économique, *spatial autoregressive model (SAR)*<fnref n="10" /> ou *spatial error model (SEM)*<fnref n="11" />, est la plus adaptée pour la régression. Il est donc nécessaire de déterminer la forme prise par l'autocorrélation spatiale grâce aux deux tests du multiplicateur de Lagrange, ainsi que leurs versions robustes (Anselin et Florax, 1995) : LMLAG et R-LMLAG pour une variable autorégressive, et LMERR et R-LMERR pour une autocorrélation spatiale des erreurs.

<table><caption>Tableau 2.2 : Résultats des tests du multiplicateur de Lagrange sur chaque modèle économique à partir de la matrice de pondérations spatiales $W_3$</caption><thead><tr><th>Tests</th><th>Modèle 1</th><th>Modèle 2</th><th>Modèle 3</th><th>Modèle 4</th></tr></thead><tbody><tr><td>LMLAG</td><td>202,318<br>(0,000)</td><td>115,695<br>(0,000)</td><td>124,789<br>(0,000)</td><td>127,406<br>(0,000)</td></tr><tr><td>LMERR</td><td>101,264<br>(0,000)</td><td>39,099<br>(0,000)</td><td>35,362<br>(0,000)</td><td>35,062<br>(0,000)</td></tr><tr><td>R-LMLAG</td><td>308,543<br>(0,000)</td><td>371,704<br>(0,000)</td><td>443,257<br>(0,000)</td><td>675,549<br>(0,000)</td></tr><tr><td>R-LMERR</td><td>207,490<br>(0,000)</td><td>295,108<br>(0,000)</td><td>353,830<br>(0,000)</td><td>583,205<br>(0,000)</td></tr></tbody></table>

Source : Calculs de l'auteur, à partir de China Statistical Yearbook (1996-2010).

Probabilités critiques entre parenthèses. LMLAG est le test du multiplicateur de Lagrange pour une variable endogène décalée, et R-LMLAG est sa version robuste. LMERR est le test du multiplicateur de Lagrange pour une autocorrélation des erreurs, et R-LMERR est sa version robuste.

Les résultats des tests du multiplicateur de Lagrange appliqués aux quatre modèles de croissance pour la matrice de pondérations spatiales $W_3$ sont reportés dans le tableau 2.2. A

<footnote n="10">¹⁰ Si l'existence d'une corrélation spatiale entre les différentes observations de la variable dépendante n'est pas prise en compte, l'estimateur des MCO ne sera pas convergent et produira des résultats biaisés. La solution consistera alors à ajouter un retard spatial à l'équation à estimer (*spatial autoregressive model*).</footnote>

<footnote n="11">¹¹ En présence d'une corrélation entre les termes d'erreur d'observations adjacentes, l'hypothèse d'indépendance des termes d'erreur est violée, et les estimations produites par les MCO sont par conséquent inefficaces. La solution consiste à introduire la dépendance spatiale dans l'équation à estimer au moyen d'un terme d'erreur autorégressif (*spatial error model*).</footnote>
partir de la règle de décision décrite par Anselin et Florax (1995)<fnref n="12" />, nous constatons que le modèle avec variable endogène décalée (SAR) est, pour tous les modèles, plus approprié que le modèle avec autocorrélation des erreurs (SEM).

Nous estimons donc l'équation de croissance (2.1) en y incluant la variable dépendante spatialement décalée. L'équation à estimer devient ainsi :

$$y_{i,t} = \beta \ln Y_{i0} + \psi X_{i,t} + \pi Z_{i,t} + \rho Wy_{i,t} + \varepsilon_{i,t} \quad (2.3)$$

où Wy$_{i,t}$ est la variable endogène décalée pour la matrice de poids W, et ρ est le paramètre spatial autoregressif mesurant l'ampleur des interactions spatiales. Ainsi, en plus d'être expliqué par l'ensemble des variables de contrôle énoncées dans la section 2.1., le taux de croissance de la province i est également potentiellement expliqué par le taux de croissance dans les provinces environnantes.

## 4. Effets de diffusion de la croissance et convergence des provinces chinoises : résultats de l'estimation économétrique

Le modèle SAR à variable endogène décalée est estimé par la méthode du maximum de vraisemblance (Anselin, 1988). Les résultats des estimations pour les différents modèles de croissance à partir de la matrice W₃ sont reportés dans le tableau 2.3. Ils se révèlent particulièrement stables quel que soit le modèle de croissance considéré. Il est intéressant de noter que les quatre modèles possèdent un pouvoir explicatif satisfaisant puisque les R² ajustés qui leur sont associés sont compris entre 0,533 et 0,551. En d'autres termes, près de 55% de la variance totale du taux de croissance du PIB réel par habitant est expliquée par les variables incluses dans chaque modèle.

Afin d'évaluer la qualité de l'ajustement des différents modèles, nous nous basons sur la comparaison des critères d'information d'Akaiké (AIC) et de Bayès (BIC). Le modèle présentant la meilleure qualité d'ajustement est le modèle 3, puisqu'il dispose des critères

<footnote n="12">¹² Si LMLAG (respectivement LMERR) est plus significatif que LMERR (respectivement LMLAG), et si R-LMLAG (respectivement R-LMERR) est significatif alors que R-LMERR (respectivement R-LMLAG) ne l'est pas, alors le modèle le plus approprié est le modèle SAR avec variable autorégressive (respectivement le modèle SEM avec autocorrélation des erreurs).</footnote>
d'informations les plus faibles. Par conséquent, dans cette section nous nous focalisons essentiellement sur les résultats obtenus à partir du modèle 3 et de la matrice de pondérations $W_3^{13}$.

<table><caption>Tableau 2.3 : Résultats des estimations des différentes spécifications du modèle de croissance régionale à partir de la matrice de pondérations spatiales $W_3$</caption><thead><tr><th>Variables</th><th>Modèle 1</th><th>Modèle 2</th><th>Modèle 3</th><th>Modèle 4</th></tr></thead><tbody><tr><td>lnY<sub>i,0</sub></td><td>-0,0121**<br>(-2,38)</td><td>-0,0109*<br>(-1,71)</td><td>-0,0187***<br>(-2,70)</td><td>-0,0179**<br>(-2,50)</td></tr><tr><td>ln POP</td><td>-0,1859***<br>(-3,59)</td><td>-0,1949***<br>(-3,31)</td><td>-0,1527***<br>(-2,56)</td><td>-0,1538***<br>(-2,58)</td></tr><tr><td>ln INV</td><td>0,0422***<br>-3,960</td><td>0,0442***<br>-3,520</td><td>0,0599***<br>-4,420</td><td>0,0592***<br>-4,330</td></tr><tr><td>ln EDU</td><td>--</td><td>0,002<br>-0,320</td><td>0,005<br>-0,720</td><td>0,005<br>-0,650</td></tr><tr><td>ln DP</td><td>--</td><td>--</td><td>-0,0215***<br>(-2,68)</td><td>-0,0223***<br>(-2,69)</td></tr><tr><td>ln INFRAST</td><td>--</td><td>--</td><td>--</td><td>0,002<br>-0,390</td></tr><tr><td>constante</td><td>-0,3637***<br>(-2,96)</td><td>-0,4096**<br>(-2,16)</td><td>-0,215<br>(-1,08)</td><td>-0,214<br>(-1,07)</td></tr><tr><td>Spat.aut (ρ)</td><td>0,7299***<br>-9,650</td><td>0,7369***<br>-9,540</td><td>0,7539***<br>-10,260</td><td>0,7519***<br>-10,150</td></tr><tr><td>R<sup>2</sup></td><td>0,546</td><td>0,547</td><td>0,570</td><td>0,570</td></tr><tr><td>Adj. R<sup>2</sup></td><td>0,533</td><td>0,530</td><td>0,551</td><td>0,548</td></tr><tr><td>Log-lik.</td><td>301,831</td><td>303,881</td><td>307,376</td><td>307,453</td></tr><tr><td>AIC</td><td>-593,662</td><td>-595,762</td><td>-600,752</td><td>-598,907</td></tr><tr><td>BIC</td><td>-578,779</td><td>-577,902</td><td>-579,915</td><td>-575,093</td></tr></tbody></table>

Source : Calculs de l'auteur, à partir de China Statistical Yearbook (1996-2010).

Estimations par la méthode du maximum de vraisemblance du modèle à variable dépendante spatialement décalée. Niveau de significativité statistique : 1% ***, 5% **, et 10% *. z-statistiques entre parenthèses.

<footnote n="13">¹³ A titre comparatif, les résultats de l'estimation de ce modèle à partir des autres matrices de poids spatiales sont présentés en Annexe 2.1. Ce tableau présente aussi les résultats de l'estimation du modèle de croissance 3 par la méthode des effets fixes (qui prend en compte uniquement la dimension panel des données, mais plus la dimension spatiale).</footnote>
4.1. Les déterminants de la croissance régionale en Chine de 1995 à
2009

La seule variable influençant positivement et significativement la croissance régionale en Chine sur la période 1995-2009 est l'accumulation du capital physique. L'impact positif du taux d'investissement sur la croissance du PIB par habitant régional est également mis en évidence dans les estimations empiriques de Cai et al. (2002) pour la période 1978-1998, et Li et Xu (2008) entre 1991 et 2005. Si à court et/ou moyen terme, le rôle fondamental de l'accumulation du capital physique dans la dynamique de croissance chinoise est largement reconnu (Wu, 2004), certains auteurs émettent cependant des doutes sur la soutenabilité de ce modèle à long terme (Gaulard, 2009).

A l'opposé, nous pouvons observer qu'au cours de la période considérée, le taux d'accroissement naturel de la population a un impact significatif et négatif sur la croissance régionale. Ce résultat concorde avec celui obtenu par les estimations de Brun et al. (2002). Il peut par ailleurs être mis en perspective avec l'un des objectifs clés du 12ème Plan Quinquennal (2011-2015) qui prévoit de limiter la croissance de la population, en cherchant à maintenir le nombre d'habitants en dessous de 1,39 milliards.

En outre, nos estimations révèlent que le niveau des dépenses publiques exerce
également une influence négative et significative sur la variable dépendante. On peut penser
que dans le cas de la Chine, l'action publique exerce un effet d'éviction et désincite
l'initiative privée, ce qui réduit les opportunités de croissance. A ce sujet, Gipouloux (1998) et
Catin et Van Huffel (2004) expliquent qu'une trop forte intervention de l'Etat, notamment à
travers le régime de propriété des entreprises, peut avoir un effet répulsif sur les
investissements directs étrangers. Il pourrait être intéressant d'avoir la possibilité d'exclure
certains postes des dépenses publiques, comme les dépenses militaires, afin de voir si les
dépenses publiques en capital humain (éducation et santé notamment) et en infrastructures
exercent pour leur part un effet positif sur le taux de croissance.
## 4.2. Existence et intensité des effets de débordement géographique entre provinces chinoises

Les estimations révèlent également l'existence d'une autocorrélation spatiale positive puisque le coefficient associé à la variable endogène spatialement décalée est positif et significatif. Ainsi, le taux de croissance du revenu par tête d'une province est positivement lié à celui des provinces environnantes. Plus précisément, la hausse (respectivement la baisse) du taux de croissance du PIB par habitant dans les provinces voisines à la province i va entraîner une hausse (respectivement une baisse) du taux de croissance dans cette même province. Il existe donc bien des effets de débordement géographique de la croissance entre les différentes régions chinoises. Il est intéressant de souligner que non seulement le coefficient d'autocorrélation spatiale est significatif à 1% quelle que soit la matrice de poids considérée (voir Annexe 2.1), mais surtout qu'il est relativement élevé et augmente lorsque le seuil de distance géographique retenu $d_t$ s'élève (de 0,679 pour $d_t=1300$ à 0,754 pour $dt = 3850$). Ceci semble indiquer que, comme le soulignent Madariaga et Poncet (2007) sur une étude des performances économiques des villes chinoises, la prospérité d'une province peut non seulement bénéficier aux provinces géographiquement proches, mais aussi aux provinces plus éloignées. Ce résultat diffère de ceux de Guillaumont et Boyreau-Debray (1996), Démurger (2000) et Brun et al. (2002), qui montrent que, jusqu'à la fin des années 1990, la dynamique de croissance se propage essentiellement vers les provinces limitrophes de la zone côtière, et que les effets de diffusion diminuent (voire s'annulent) au fur et à mesure que l'on s'éloigne vers l'Ouest du pays. Ceci laisse à penser que l'existence d'effets d'entrainement entre l'ensemble des provinces chinoises est un phénomène récent, qui a pu notamment être encouragé par la mise en place de politiques spécifiques visant à rééquilibrer le développement régional.

## 4.3. Impact de la prise en compte des interactions spatiales sur le processus de convergence

Enfin, alors que l'estimation du modèle à effets fixes ne tenant pas compte des effets d'entrainement interprovinciaux (Annexe 2.1) ne permet pas de conclure à la convergence conditionnelle des PIB par tête provinciaux sur la période considérée, la prise en compte de l'autocorrélation spatiale dans l'estimation des déterminants de la croissance régionale permet
de vérifier cette hypothèse, puisque le coefficient associé au niveau initial du PIB par habitant est négatif et significatif quel que soit le modèle considéré. Ceci indique que les provinces initialement les plus riches ont enregistré sur la période considérée des taux de croissance moins élevés que les provinces initialement les plus pauvres, compte tenu de l'ensemble des variables de contrôle. Chaque province converge ainsi vers son propre état régulier de croissance. Par ailleurs, à partir du coefficient estimé $\hat{\beta}$ du modèle 3 présentant la meilleure qualité d'ajustement, nous détectons une vitesse de convergence de 1,96% par an<fnref n="14" />. Cette vitesse de convergence est plus élevée que celle mise en évidence par Zou et Zhou (2007), qui l'estiment à 1,64% au niveau national sur la période 1981-2004. Cette différence peut s'expliquer par le fait que les effets de débordement géographique interprovinciaux, en impactant positivement sur les performances économiques des provinces, leur permettent de converger plus rapidement. De plus, la demi-vie observée est de 36,72 années, ce qui signifie que les provinces chinoises comblent la moitié de l'écart qui les sépare de leur état stationnaire en près de 37 ans.

Ce résultat souligne l'importance de la prise en compte des interactions régionales dans le processus de convergence en Chine. En effet, il tend à vérifier l'existence d'effets d'entraînement qui peuvent permettre aux provinces les moins prospères de bénéficier de la dynamique de croissance des provinces environnantes et de réaliser un processus de rattrapage. Ceci pourrait en partie expliquer la récente réduction des inégalités régionales, et semble indiquer qu'un processus de rééquilibrage de la croissance est en train de se mettre en place progressivement sur le territoire chinois.

## Conclusion

L'objectif de ce chapitre était de contribuer à l'analyse des trajectoires régionales de croissance en Chine en mettant l'accent sur l'importance de la prise en compte des effets d'entrainement entre provinces. Le récent développement des outils de l'économétrie spatiale a conduit à un renouveau des travaux empiriques cherchant à étudier le rôle de la localisation

<footnote n="14">¹⁴ Cette vitesse de convergence est très proche de celle de 2% habituellement détectée dans la littérature empirique sur la convergence conditionnelle (e.g. Barro et Sala-i-Martin (1991, 1992) pour les Etats-Unis et les régions d'Europe et du Japon).</footnote>
dans la compréhension des phénomènes économiques. Baumont et al. (2002) et Dall'erba et Le Gallo (2005) ont par exemple souligné l'intérêt d'intégrer la dépendance spatiale dans l'estimation d'un modèle de convergence conditionnelle. Dans la lignée de ces travaux, nous avons ainsi cherché à vérifier l'existence d'interactions entre les différentes provinces chinoises, et à analyser leur impact sur la dynamique de croissance régionale et le processus de convergence. Dans cette optique, nous mettons en œuvre une analyse empirique portant sur les déterminants du taux de croissance du PIB réel par habitant de chaque province sur la période 1995-2009. A partir des techniques de l'économétrie spatiale, nous estimons un modèle de croissance avec variable endogène spatialement décalée qui intègre les effets d'entrainement interprovinciaux.

Trois résultats principaux sont à souligner. Premièrement, l'investissement en capital physique joue un rôle positif dans la dynamique de croissance régionale, alors que le taux de croissance naturel de la population et les dépenses publiques exercent pour leur part un effet négatif. Deuxièmement, il existe bien des effets de débordement géographique positifs entre les provinces chinoises. Ainsi, le taux de croissance du PIB par habitant d'une province s'améliore lorsque la moyenne pondérée des taux de croissance du PIB par habitant des provinces environnantes s'élève. De plus, leur intensité ne diminue pas lorsque la distance seuil considérée augmente. Mis en perspective avec des travaux portant sur une période plus ancienne (Démurger, 2000 ; Brun et al., 2002), ceci suggère que la mise en place d'effets de diffusion de la croissance entre l'ensemble des provinces chinoises est un phénomène récent. Troisièmement, la prise en compte des interactions spatiales permet de vérifier l'hypothèse de convergence conditionnelle des provinces chinoises. Dans une optique de réduction des disparités régionales, ces résultats suggèrent que pour encourager le rééquilibrage spatial de la croissance sur le territoire, le gouvernement chinois doit chercher à renforcer les complémentarités et les interactions entre les différentes provinces.

Si un processus de convergence et de réduction des disparités de revenu par habitant semble s'être enclenché au niveau régional, il serait également intéressant de voir ce qu'il en est à un niveau plus désagrégé. En effet, certains travaux utilisant des données microéconomiques issues d'enquêtes-ménages ont souligné que les inégalités intra-provinciales sont en hausse, notamment entre les habitants des zones rurales et ceux des zones urbaines (Sicular et al., 2007). Jusque dans les années 2000, le maintien des restrictions à la migration, notamment à travers le système du *hukou*, a par exemple freiné le processus de
convergence urbain/rural (Benjamin et al., 2005a ; Khan et Riskin, 2005). Depuis le début des années 1980, la Chine a connu une urbanisation sans précédent, passant de 19% en 1980 à plus de 52% en 2012. Le système du *hukou* a permis au gouvernement de contrôler l'urbanisation du pays et de limiter l'exode rural, mais il a en contrepartie fait exploser le nombre de travailleurs migrants (*mingong*). Ces travailleurs illégaux, car dépourvus de *hukou* urbain, n'ont pas accès aux services de base réservés aux citadins : santé, éducation, etc. Le système du *hukou* contribue ainsi à engendrer des inégalités entre les résidents urbains et ruraux. Cependant, dans le 12ème Plan Quinquennal (2012-2017), les autorités chinoises ont fait part de leur volonté de relâcher les restrictions à la mobilité de la main d'œuvre, plus particulièrement vers les villes de tailles moyennes. Par ailleurs, la forte baisse du soutien de l'Etat dans les villes, notamment avec la fin des subventions et les vagues de privatisation, pourrait conduire à une réduction du fossé urbain/rural. Ke et Feser (2010), Chen et Partridge (2011) et Duvivier (2013) ont récemment montré l'existence d'effets d'entrainement entre les villes et les campagnes. Il pourrait par conséquent être intéressant d'analyser les effets potentiels du renforcement des interactions entre zones urbaines et rurales (notamment grâce au relâchement du système du *hukou*) sur le processus de convergence.
Chapter 3

An analysis of income polarization in rural
and urban China

Ce chapitre est la reproduction d'un article co-écrit avec Matthieu Clément, publié en mars 2012 dans la revue *Post-Communist Economies*, 24(1): 15-37.

Introduction

While there is an extensive literature on income disparities in China using provincial panel data,<fnref n="1" /> studies based on household level data are less common. As explained by Benjamin *et al.* (2005a), these studies tend to examine two main issues: (i) the estimation of the level and evolution of inequality in rural and urban areas and (ii) the identification of the socioeconomic factors governing these trends. First, although microeconomic studies on income inequality are based on a range of household level data sources and produce results that are not fully comparable, there is a rough consensus concerning the continuous increase in inequality since the late 1970s. Furthermore, this increase has concerned both rural areas (Bramall, 2001; Benjamin *et al.* 2005a, 2005b; Wan and Zhou, 2005) and urban areas (Bramall, 2001; Benjamin *et al.* 2005a; Démurger *et al.* 2006). However, depending on the data used, some studies have qualified this trend when examining the 1990s (Khan and Riskin, 2005; Démurger *et al.*, 2006; Ravallion and Chen, 2007; Gao and Riskin, 2009). For instance, Khan and Riskin (2005) showed that between 1995 and 2002, income inequality declined significantly in rural areas and only slightly in cities. However, when examining the most recent period (from the late 1990s to the mid 2000s), the increase in income inequality is clearly more pronounced (Chen and Zhang, 2009). The second point of interest of the literature examining income inequality in China is the identification of socioeconomic factors

<footnote n="1">¹ For a synthesis, see Li and Xu (2008) and Hao and Wei (2010).</footnote>
that account for the increase in inequality. In this perspective, a number of microeconomic
studies have analyzed the contribution of various income sources to overall inequality. Studies
focusing on rural areas have highlighted the crucial contribution of non agricultural income to
the increase in inequality (Hare, 1994; Khan and Riskin, 1998; Tsui, 1998; Benjamin et al.,
2005b; Gao and Riskin, 2009; Zhou, 2009). More precisely, the decline in agricultural income
(an equalizing component) and the increase in non agricultural income, i.e. wages or business
income (a more disequalizing component), account for the largest part of the increase in rural
inequality. In urban areas, the explosion of income disparities is related to pronounced
changes in household income structures. Gao and Riskin (2009) showed that the relative
increase in market income (a disequalizing component of income) compared to social benefits
(an equalizing component) is the first contributor to urban inequality. Furthermore, among
market income, wages became more disequalizing between the beginning of the reforms and
the early 2000s (Benjamin et al., 2005a). Overall, changes in the labor market and the
determination of wages are crucial factors for explaining the increase in urban inequality.

The issue of income inequality in China is closely linked to the sustainability of
China's economic development. The explosion of inequalities may pose a threat to social
cohesion and political stability (Renard, 2002; Knight et al., 2006). Turzy (2008) explained
that the explosion of inequality and spatial disparities have resulted in increased social unrest.
Every year, the Chinese authorities release information concerning mass incidents such as
strikes and demonstrations. The statistics provided by the Chinese authorities show a clear
increase in the number of incidents (from 10,000 in 1994 to 87,000 in 2005). This evolution
may possibly constitute evidence of rising social tensions and may in turn threaten the
stability and durability of economic growth. The Chinese authorities are aware of the
potentially harmful impact of inequality. The adoption of the 'Harmonious Society' political
doctrine in 2006 aims to shift the focus from economic growth to more societal objectives,
such as the reduction of inequality and the emergence of the middle class.

The effect of changes in income distribution on social cohesion and political stability
can be better understood by considering income polarization rather than income inequality.
The phenomenon of polarization can be defined as “the extent to which a population is
clustered around a small number of distant poles” (Esteban, 2002: 10) and is “closely linked to
the generation of tensions, to the possibilities of articulated rebellion and revolt, and to the
existence of social unrest in general” (Esteban and Ray, 1994: 820). In the Chinese context,
there have been very few empirical analyses of income polarization. Zhang and Kanbur (2001) showed that income polarization indices do not provide significantly different evidence compared to inequality indices. However, their study highlighted a strong spatial polarization between rural and urban areas and also between coastal and inland provinces. More recently, Araar (2008) provided evidence of income polarization in China. However, the chief focus of Araar’s paper is methodological rather than contextual. As argued by Chen and Zhang (2009), the analysis of income polarization should become a new focus of interest in the literature on inequality in China. The purpose of this chapter is to investigate income polarization in rural and urban China by analyzing China Health and Nutrition Survey (CHNS) data over seven years between 1989 and 2006. To address China’s polarization issues, the most recent methods are used – mainly kernel densities and the DER (Duclos-Esteban-Ray) index.

The chapter is divided into four sections. Section one presents the methodological choices made in the chapter. Section two presents the CHNS data. Section three discusses the extent and evolution of income polarization in rural and urban areas. Section four identifies the main sources of income polarization by identifying the contribution to overall polarization of the various components of income.

# 1. Concepts and methods

## 1.1. Kernel density

The Kernel density estimator is the continuous alternative to histograms and a non-parametric tool aimed at estimating the probability density function of a random variable. Suppose that $(x_1, x_2, ..., x_N)$ is a sample from X, where X has an unknown density function $f(x)$. The expression of kernel density estimation at point $x$ is given by:

$$ \hat{f}(x) = \frac{1}{n} \sum_{i=1}^{N} K\left(\frac{x - x_i}{h}\right) \quad (3.1) $$
Where *K* denotes the kernel function<fnref n="2" /> and *h* is a bandwidth parameter. The main advantage of a kernel density approach compared to inequality indices is that it provides a picture of total income distribution.

As explained by Jenkins (1995: 408), “*the basic idea [of the kernel density approach] is that one slides a viewing window along the income scale, and the estimate of the density depends on the number of observations that happen to fall within the window as it passes*”.

The degree of smoothing of the kernel density function depends on bandwidth *h*. A high level of *h* can produce an over-smoothed distribution, whereas a low level may result in an under-smoothed distribution. Many criteria have been proposed to determine optimal bandwidths.<fnref n="3" />

Most procedures approximate the optimal bandwidth by minimizing an error measurement component under specific conditions. Among the available methods, this chapter is based on the method developed by Sheather and Jones (1991).<fnref n="4" /> The method involves an iterative approach which, as noted by Salgado-Ugarte (2003), has many excellent properties. In order to take into account the variability of densities between urban and rural areas and across years, a pooled bandwidth is also used, as suggested by Marron and Schmitz (1992). The pooled bandwidth is simply defined as the weighted mean of the initial bandwidths.

## 1.2. The concept of polarization

The concept of polarization was initially developed in sociology and political science in the 19th century. While Alexis de Tocqueville wrote about the rising equality of social conditions and the emergence of a middle class in industrial society, Karl Marx raised concerns about the growing disparities between the bourgeoisie and the proletariat. According to Marx, the polarization (or bipolarization) of society would inevitably lead to social conflict.

In economics, it was not until the early 1990s that the notion of polarization was formally analyzed, in Foster and Wolfson (1992), Wolfson (1994) and Esteban and Ray (1994). The specific objectives of these pioneer studies were to provide analytical tools for measuring the degree of income polarization and for testing the hypothesis of a declining middle class in industrialized countries. More recently, Duclos *et al.* (2004) developed a new

<footnote n="2">² In this chapter, Gaussian kernel densities are used.</footnote>

<footnote n="3">³ For a synthesis, see Jones *et al.* (1996).</footnote>

<footnote n="4">⁴ This procedure was implemented using Xplore software: http://fedc.wiwi.hu-berlin.de/xplore.php.</footnote>
methodology for improving the measurement of polarization. As explained by Gradin (2000: 457), the concept of polarization “is closely related to inequality; however its development emerged as a result of some dissatisfaction in the use of standard inequality measurement to deal with the formation of groups in a society”.

Esteban and Ray (1994: 824) note that a polarized society is characterized by three features: (i) a small number of groups; (ii) a high degree of homogeneity within each group; and (iii) a strong degree of heterogeneity between groups. On this basis, they provide an analysis of polarization founded on an alienation/identification framework. Given a specific attribute (e.g. income), the identification component refers to homogeneity within groups, whereas the alienation component captures the extent of disparities between groups in terms of the specific attribute. Both elements, combined with the size of groups, produce significant antagonism among the population that may be harmful to social cohesion and lead to conflict.

**1.3. Polarization indices**

Gasparini *et al.* (2006) explain that income polarization can be classified into two distinct categories. In the first category, i.e. polarization by characteristics, groups are identified on the basis of a discrete characteristic (e.g. region, ethnic group, religion). In the second category, i.e. pure income polarization, groups are determined solely on the basis of income. The idea is that group members feel identification only with individuals of similar income levels. In this chapter, we will refer to the second category and use three distinct indices.

**1.3.1. Wolfson index**

The first pure polarization index was proposed by Foster and Wolfson (1992) and Wolfson (1994) and focuses on bipolarization. More precisely, the Wolfson index (W) is derived from the Lorenz curve and measures twice the area between the Lorenz curve and the tangent line at the median point. Where *f* represents income distribution and *L* the Lorenz curve, the Wolfson index is expressed as follows:
$$W(f) = 2 \frac{\mu}{m} [2[0.5 - L(0.5)] - G(f)] \quad (3.2)$$

Where $L(0.5)$ is the income share of the bottom half of the population, $\mu$ and $m$ are the median and mean incomes respectively, and $G$ is the Gini index. The main drawback of the Wolfson index is that it captures only bipolarization around the median point, and therefore focuses on two groups of equal size.

### 1.3.2. Esteban-Gradin-Ray index

Unlike the Wolfson index, the ER (Esteban-Ray) index allows $K$ groups of potentially different sizes and is based on the identification / alienation framework (Esteban and Ray, 1994). It is given by:

$$ER = \sum_{i=1}^{K} \sum_{j=1}^{K} \pi_{i}^{1+\alpha} \pi_{j} |y_{i} - y_{j}| \quad (3.3)$$

With $y_i$ the income of group $i$, $\pi_i$ the share of group $i$ in total population and $\alpha$ a parameter of sensitivity to polarization. When $\alpha = 0$, the ER index is equivalent to the Gini index. The main drawback of the ER index is that it results in a loss of information about intra-group income distribution. This explains why Esteban et al. (2007) opted to extend the ER index by incorporating information concerning intra-group dispersion. Given a continuous income distribution $f$ and a discrete income distribution $\rho^*$ resulting from the partition, the EGR (Esteban-Gradin-Ray) index is expressed as follows:

$$EGR(f, \alpha, \rho^*, \beta) = ER(\alpha, \rho^*) - \beta[G(f) - G(\rho^*)] \quad (3.4)$$

It is equal to the ER index minus the gap between the Gini index measured for the initial continuous income distribution $f$ and the Gini index calculated for the discrete distribution induced by the $K$-group partition ($\rho^*$). The main idea behind the correction is that there is a gap between the true Lorenz curve and the discrete Lorenz curve. In this chapter, the EGR index is calculated by considering a two-group partition. The EGR methodology implies some drawbacks, mainly because of the arbitrary choice of the number of groups.
1.3.3. Duclos-Esteban-Ray index

Duclos et al. (2004) proposed a new measure of polarization. The advantages of the DER (Duclos-Esteban-Ray) index compared to the EGR index are that it is defined in a continuous form of income and does not require an arbitrary definition of the number of groups. Following Esteban and Ray (1994), the DER index analyzes polarization by referring to the identification / alienation framework. For an individual with income x, identification depends on the density function f(x). When considering two individuals with income x and y respectively, alienation is measured by the distance |y - x|. The interaction between the two components characterizes the antagonism from x to y, which can be represented by the following function:

$$
T(I, A) \quad (3.5)
$$

Where $I = f(x)$, $A = |y - x|$ and $T$ is a continuous and non decreasing function such as
$T(I,0) = T(0,A) = 0$. A general polarization index can thus be defined as the sum of all
antagonisms characterizing income distribution:

$$
P(f) = \iint T(I, A) f(x) f(y) dy dx \quad (3.6)
$$

Taking into account several axioms to determine the functional form of T, Duclos et
al. (2004) proposed the following polarization index:

$$
DER(f, \alpha) = \iint f(x)^{1+\alpha} f(y) |y-x| dy dx \quad (3.7)
$$

The parameter α captures the degree of aversion to polarization and belongs to the interval [0.25; 1]. If α = 0, the polarization index equals the Gini coefficient and ignores population clusters. For increasing values of α, the DER index emphasizes the growing importance of the formation of income groups in society.

Using this methodology, changes in polarization can be analyzed based on the
contribution of alienation and identification and their correlation (joint co-movements). On
the one hand, an increase in alienation will be associated with a rise in income distances
(inequality). On the other hand, an increase in identification will imply a sharper definition of
income groups, e.g. if the biggest clusters become even more populated. These two elements interact, and may reinforce or counterbalance each other.

For empirical purposes, Duclos et al. (2004) define a natural estimator for DER as follows:

$$DER(\hat{f}, \alpha) = \frac{1}{N} \sum_{i=1}^{N} \hat{f}(y_i)^{\alpha} \hat{a}(y_i) \quad (3.8)$$

Where $\hat{f}(y_i)^{\alpha}$ is the non-parametric estimation of $f(y_i)^{\alpha}$ derived from a Gaussian kernel density approach and where $\hat{a}(y_i)$ is given by:

$$\hat{a}(y_i) = \hat{\mu} + y_i \left( \frac{2i-1}{N} - 1 \right) - \frac{1}{N} \left( 2 \sum_{j=1}^{i-1} y_j + y_i \right) \quad (3.9)$$

$\hat{\mu}$ is the mean sample.

## 1.4. Decomposing the DER index

### 1.4.1. Decomposition by groups

Suppose that we are able to identify distinct groups (e.g. urban / rural). The objective of a group-based decomposition is to assess the proportion of overall polarization linked to within-group polarization and between-group polarization respectively. Let us denote by $\phi_m$ the population share of group m and by $\psi_m$ the income share of group m. Araar (2008) shows that the DER index can be decomposed as follows:

$$DER = \sum_{m} \phi_{m}^{1+\alpha} \psi_{m}^{1-\alpha} R_{m} DER_{m} + DER * \quad (3.10)$$
The first term is the within-group component. If $a(x)$ is the alienation component and $\pi(x)$ is the local proportion of households belonging to group $m$ and earning income $x$, we have:<fnref n="5" />

$$R_m = \frac{\int a_m(x) \pi_m(x) f(x)^{1+\alpha} dx}{\phi_m \int a_m(x) f_g(x)^{1+\alpha} dx} \quad (3.11)$$

The second term (DER*) measures the between-group component. It is equivalent to the DER index when the within-group polarization is ignored, that is to say when we assume that every household earns the mean income of its group:

$$DER^* = \sum_m \phi_m^{1+\alpha} a(\dot{\mu}_m) \quad (3.12)$$

With $\dot{\mu}_m = \mu_m / \mu$.

### 1.4.2. Decomposition by income components

The decomposition of the DER index by income components provided by Araar (2008) is derived from the Gini decomposition proposed by Rao (1969). Let us suppose that total income can be decomposed by several income sources and that $\psi_k$ is the share of source k in total income. Following Araar (2008), the DER index is given by:

$$DER = \sum_k \psi_k CP_k \quad (3.13)$$

Where $CP_k$ is the pseudo-polarization index of income source $k$. Araar (2008: 14) explains that this pseudo-polarization index “is similar to the concentration index except for its sensitivity to income identification component, $f(x)^\alpha$”.

<footnote n="5">⁵ Araar (2008) explains that if income groups do not overlap, $\pi_m(x) = 1$ and consequently $R_m = 1$.</footnote>
## 2. Data

Chen and Zhang (2009) emphasize the great variety of household level data in China. While the wide range of household data may represent a significant advantage, it may also cause problems of comparability between various empirical studies on income inequality. The most popular household data are those provided by the *National Bureau of Statistics* (NBS), the *Research Centre on the Rural Economy* (RCRE), the *China Household Income Project* (CHIP) and the *China Health and Nutrition Survey* (CHNS). Although NBS surveys constitute official Chinese data, they present many drawbacks (Bramall, 2001). Firstly, they are based on a narrow definition of household income, since NBS data leave out crucial elements of income, such as various kinds of subsidies (Gao and Riskin, 2009). Secondly, the sampling scheme is based on the household registration system (i.e. the *hukou* system)6 rather than a population census. Rural migrants in cities are therefore not included in the sample (Bramall, 2001; Benjamin *et al.*, 2005a; Ravallion and Chen, 2007). Thirdly, as explained by Ravallion and Chen (2007), there is an underrepresentation of poor households living in remote rural areas. In view of these weaknesses (largely addressed in the literature), this research uses alternative data sources. RCRE data are specific to rural areas, whereas CHIP and CHNS data cover both urban and rural areas. Many studies on income inequality are based on CHIP data. However, the most recent year for which CHIP are available is 2002, which is not recent enough for providing relevant information on the recent evolution of Chinese income distribution. This explains why CHNS data were used, since data are available for 2006.

The CHNS is a longitudinal survey conducted by an international collaborative project involving the Carolina Population Centre at the University of North Carolina, the National Institute of Nutrition and Food Safety, and the Chinese Centre for Disease Control and Prevention. CHNS data cover a wide range of topics. Although the main aim of the survey is to analyze health, nutrition and family planning in rural and urban China, other information concerning income, the labor market and education is also collected. The survey includes seven waves (1989, 1991, 1993, 1997, 2000, 2004 and 2006). The period covered is therefore long enough to provide a long-term view of the impact of Chinese reforms on income distribution. The population size for 1989, 1991, 1993, 1997, 2000, 2004 and 2006 is

<footnote n="6">⁶ The *hukou* system involves a household registration system that categorizes individuals as urban or rural residents.</footnote>
respectively 3,743, 3,586, 3,410, 3,805, 4,300, 4,318, and 4,359 households. This means that approximately 14,000 individuals were surveyed during each wave. CHNS data cover nine provinces, including Guangxi, Guizhou, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Liaoning and Shandong.<fnref n="7" /> Although the survey is not nationally representative, provinces are selected to provide a highly diversified picture of geographic and socioeconomic characteristics. For instance, the survey covers both coastal and mainland provinces, but also rich provinces (Jiangsu and Shandong) and poorer provinces (Guizhou). CHNS data are representative of rural and urban areas and of each province. The main drawback of CHNS data is that major Chinese cities such as Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin or Hong Kong are not covered.

CHNS household income includes wages, retirement income, business income, agricultural income, subsidies and other income (private transfers and rents). In this chapter, annual per capita household income expressed at 2006 prices was used. One advantage of CHNS data is that they provide a consumer price index that is specific to rural and urban areas but also to the different provinces included in the sample. Therefore, the income measure provided in this chapter incorporates both temporal and spatial prices differences.

<table><caption>Table 3.1 : Key figures on annual per capita household income (Yuan, 2006 prices)</caption><thead><tr><th rowspan="2"></th><th colspan="3">Total</th><th colspan="3">Rural</th><th colspan="3">Urban</th><th rowspan="2">Urban to rural ratio</th></tr><tr><th>N</th><th>Mean</th><th>Std. Dev.</th><th>N</th><th>Mean</th><th>Std. Dev.</th><th>N</th><th>Mean</th><th>Std. Dev.</th></tr></thead><tbody><tr><td>1989</td><td>3743</td><td>2622.48</td><td>2628.83</td><td>2493</td><td>2339.09</td><td>2513.62</td><td>1250</td><td>3187.65</td><td>2760.15</td><td>1.36</td></tr><tr><td>1991</td><td>3586</td><td>2559.60</td><td>1913.57</td><td>2420</td><td>2347.71</td><td>1957.51</td><td>1166</td><td>2999.37</td><td>1739.22</td><td>1.28</td></tr><tr><td>1993</td><td>3410</td><td>2955.11</td><td>2552.89</td><td>2368</td><td>2723.62</td><td>2378.51</td><td>1042</td><td>3481.18</td><td>2842.52</td><td>1.28</td></tr><tr><td>1997</td><td>3805</td><td>3702.15</td><td>3100.10</td><td>2548</td><td>3479.72</td><td>3026.37</td><td>1257</td><td>4153.04</td><td>3198.52</td><td>1.19</td></tr><tr><td>2000</td><td>4300</td><td>4785.22</td><td>4989.58</td><td>2915</td><td>4298.59</td><td>4601.16</td><td>1385</td><td>5809.43</td><td>5586.48</td><td>1.35</td></tr><tr><td>2004</td><td>4318</td><td>6371.11</td><td>6691.68</td><td>2940</td><td>5570.92</td><td>5984.01</td><td>1378</td><td>8078.32</td><td>7724.82</td><td>1.45</td></tr><tr><td>2006</td><td>4359</td><td>7371.00</td><td>9791.70</td><td>2959</td><td>6417.23</td><td>8084.17</td><td>1400</td><td>9386.84</td><td>12429.53</td><td>1.46</td></tr></tbody></table>

Source: CHNS.

Table 3.1 summarizes descriptive statistics for global, rural and urban per capita household income. Over the period 1989-2006, the mean income increased by 181 %,

<footnote n="7">⁷ A number of changes concerning the provinces covered by the survey were made between 1989 and 2006. For instance, in 1989, only eight provinces (Heilongjiang excluded) were covered.</footnote>
reaching 7,371 Yuan in 2006. All in all, the mean urban household income is higher than the mean rural household income, but the distribution of income appears to be more heterogeneous in urban areas. For instance, in 2006, the coefficient of variation was 1.32 for urban incomes and only 1.26 in the case of rural incomes. Furthermore, the mean urban household income increased faster than the mean rural household income (+194% against +147%). The ratio of urban to rural mean income has increased, especially since the beginning of the 2000s. The discrepancy of income growth rates may have the effect of increasing the gap between urban and rural areas.

# 3. Extent and evolution of income polarization

## 3.1. Overall inequality and polarization

Table 3.2 presents trends in inequality and polarization from 1989 to 2006. There are two distinct phases in the evolution of inequality and polarization indices. Inequality decreased in the early 1990s before stagnating until the end of the decade. In 1989 and 1997, the inequality level remained almost the same irrespective of the index considered. The late 1990s represent a turning point in income disparity trends. From 1997 to 2006, household income inequality soared, with the Gini and Theil indices increasing from 0.401 to 0.513 (+27.9%) and from 0.273 to 0.480 (+75.8%) respectively.

<table><caption>Table 3.2 : Inequality and polarization indices, all households</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Gini</th><th rowspan="2">Theil</th><th rowspan="2">W</th><th rowspan="2">EGR<sup>1</sup></th><th colspan="4">DER<sup>2</sup></th></tr><tr><th>Total</th><th>Alienation</th><th>Identification</th><th>Correlation</th></tr></thead><tbody><tr><td>1989</td><td>0.391</td><td>0.286</td><td>0.170</td><td>0.137</td><td>0.232</td><td>0.391</td><td>0.687</td><td>-0.137</td></tr><tr><td>1991</td><td>0.372</td><td>0.234</td><td>0.169</td><td>0.132</td><td>0.225</td><td>0.372</td><td>0.689</td><td>-0.121</td></tr><tr><td>1993</td><td>0.413</td><td>0.291</td><td>0.199</td><td>0.148</td><td>0.241</td><td>0.413</td><td>0.679</td><td>-0.139</td></tr><tr><td>1997</td><td>0.401</td><td>0.273</td><td>0.185</td><td>0.143</td><td>0.235</td><td>0.401</td><td>0.679</td><td>-0.137</td></tr><tr><td>2000</td><td>0.448</td><td>0.356</td><td>0.202</td><td>0.159</td><td>0.252</td><td>0.448</td><td>0.669</td><td>-0.158</td></tr><tr><td>2004</td><td>0.483</td><td>0.395</td><td>0.239</td><td>0.175</td><td>0.269</td><td>0.483</td><td>0.666</td><td>-0.165</td></tr><tr><td>2006</td><td>0.513</td><td>0.480</td><td>0.253</td><td>0.185</td><td>0.283</td><td>0.513</td><td>0.679</td><td>-0.187</td></tr></tbody></table>

Notes: (1) Based on a two-group partition; (2) For $\alpha$ =0.5.

Source: CHNS.
Polarization indices do not highlight significantly different trends, although the overall
trend is significantly more attenuated.<fnref n="8" /> The W and EGR indices – both hypothesizing two
income groups in the present case – suggest that income bipolarization increased by 48.8 %
and 35 % respectively between 1989 and 2006. However, the majority of the increase
occurred between 1997 and 2006. The DER polarization index followed a similar evolution.
For α = 0.5,<fnref n="9" /> the DER polarization index remained relatively stable between 1989 and 1997
(at approximately 0.23). Between 1997 and 2006, the DER index increased continuously
(+20.4 %), reaching 0.283 in 2006. This indicates that household income is relatively
polarized in China. As a comparison, Duclos *et al.* (2004) found that Russia and Mexico
recorded high income polarization levels in the mid-1990s, with a DER index of 0.257 and
0.283 respectively.

<figure><img src="image_7.png" /><figcaption>Figure 3.1 : Kernel density for income distribution, all households</figcaption></figure>

Note: Optimal bandwidth = 307.881 Yuan.

Source: CHNS.

<footnote n="8">⁸ Zhang and Kanbur (2001) also found that inequality and polarization measures increased at a substantially different rate over the period 1983-1995.</footnote>

<footnote n="9">⁹ Only the trends in DER index for α = 0.5 are reported since values close to 0.25 and 1 may conflict with some axioms (Duclos et al., 2004: 1744 and 1758). The estimation of DER indices with α = 0.25 and α = 0.75 are reported in Appendix 3.1.</footnote>
Based on kernel densities (Figure 3.1), the increase of polarization implies a
significant shift of household income distribution between 1989 and 2006. In 1989, household
income distribution has a unimodal shape, with a modal value at around 2,000 Yuan. In 2006,
income distribution differed significantly and became clearly multimodal. The highest peak is
still at around 2,000 Yuan, but three local modes appear in the right tail, at approximately
4,000, 6,500 and 9,000 Yuan. A bump close to 15,500 Yuan is also found. In fact, the
emergence of local modes reveals the constitution of population clusters, especially in the
middle and upper income classes. There is also a reduction in the thickness of the tail relative
to the lowest income ranges. This can be explained by the significant decline in poverty since
the beginning of economic reforms.<fnref n="10" /> Overall, between 1989 and 2006, the dramatic shift in
density curves confirms the view that household income in China is becoming ever more
polarized.

The distinction between alienation and identification components can be used to extend the analysis. Table 3.2 shows that the increase in polarization is mainly due to the hugely significant rise in the alienation component (i.e. the rise of inequality). However, this increase is counterbalanced and attenuated by the relative stability (or even slight decrease) of the identification component. Therefore, we may conclude that social tensions have increased in Chinese society as a result of exacerbated differences between income groups (i.e. increasing alienation). Nevertheless, the risk of social unrest is probably still limited since individuals do not identify more than before with their income group.

**3.2. Decomposition by group**

To complete the analysis, this subsection focuses on rural-urban income disparities.
Decomposition procedures are used to assess the contribution of between-group and within-
group disparities to overall inequality and polarization. The between-group component
measures the contribution of the urban-rural income gap to inequality and polarization,
whereas the within-group component accounts for income disparities within rural and urban
areas. The results are presented in Table 3.3.

<footnote n="10">¹⁰ As an illustration, Ravallion and Chen (2007) showed that the proportion of Chinese people living in poverty dropped from 53 % to 8 % between 1981 and 2001.</footnote>
<table><caption>Table 3.3 : Inequality and polarization indices decomposition by groups (rural-urban). Relative contributions of within-group and between-group components.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Gini</th><th colspan="2">Theil</th><th colspan="2">DER<sup>1</sup></th></tr><tr><th>Within</th><th>Between</th><th>Overlap</th><th>Within</th><th>Between</th><th>Intra-group</th><th>Inter-group</th></tr></thead><tbody><tr><td><strong>1989</strong></td><td>0.536</td><td>0.184</td><td>0.280</td><td>0.960</td><td>0.039</td><td>0.538</td><td>0.462</td></tr><tr><td><strong>1991</strong></td><td>0.549</td><td>0.150</td><td>0.301</td><td>0.971</td><td>0.030</td><td>0.549</td><td>0.451</td></tr><tr><td><strong>1993</strong></td><td>0.557</td><td>0.132</td><td>0.311</td><td>0.977</td><td>0.023</td><td>0.562</td><td>0.438</td></tr><tr><td><strong>1997</strong></td><td>0.547</td><td>0.100</td><td>0.353</td><td>0.987</td><td>0.013</td><td>0.550</td><td>0.450</td></tr><tr><td><strong>2000</strong></td><td>0.538</td><td>0.154</td><td>0.308</td><td>0.971</td><td>0.030</td><td>0.545</td><td>0.455</td></tr><tr><td><strong>2004</strong></td><td>0.528</td><td>0.177</td><td>0.295</td><td>0.958</td><td>0.041</td><td>0.537</td><td>0.463</td></tr><tr><td><strong>2006</strong></td><td>0.527</td><td>0.171</td><td>0.302</td><td>0.962</td><td>0.035</td><td>0.535</td><td>0.465</td></tr></tbody></table>

Note: (1) For $\alpha$ =0.5.

Source: CHNS.

The rural-urban decomposition of Theil and Gini indices indicates that the between component decreased from 1989 to 1997,<fnref n="11" /> before increasing slightly between 2000 and 2006. But the contribution of the between-group component to overall inequality is very limited (on average less than 4 % for the Theil index and 16 % for the Gini index). This may suggest that, based on CHNS data, the urban-rural gap is not as considerable as previous studies had found (e.g. Gustafsson and Li, 2001; Sicular et al., 2007). However, this finding is consistent with the study by Benjamin et al. (2005a), which also reports a similar counter-result. Benjamin et al. found that the urban-rural gap only accounts for a small part of the overall inequality and that rural income follows the urban income trend. In fact, the low contribution of the rural-urban gap to overall inequality is likely compensated by a significant contribution of regional disparities, especially between coastal and inland provinces.<fnref n="12" /> In the rural-urban decomposition used in this chapter, the coastal-inland gap is included in the within component and therefore explains the significant contribution of intra-group inequality.

Strikingly, the decomposition of the DER index is far more balanced. Income polarization is slightly more influenced by the within component, the relative contribution of which (53.5% in 2006) suggests the existence of income divergences in rural and urban areas.

<footnote n="11">¹¹ Using household data from 1978 to 1997, Yang and Fang (2003) calculated income and consumption ratios and also reported a decline in the rural-urban gap since 1993.</footnote>

<footnote n="12">¹² In this respect, Kanbur and Zhang (1999), and Zhang and Yao (2001) used provincial data and found that the inland-coastal gap increased sharply throughout the 1990s and became the major component of overall inequality.</footnote>
Nevertheless, the non-negligible contribution of between-group variations (46.5% in 2006) implies that there are significant differences between rural and urban income polarization.

### 3.3. Rural and urban income polarization

In view of the significant contribution of within-group inequality and polarization, the level and evolution of disparities within rural and urban areas will now be examined. Tables 3.4 and 3.5 report inequality and polarization indices for both areas.

<table><caption>Table 3.4 : Inequality and polarization indices, rural households</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Gini</th><th rowspan="2">Theil</th><th rowspan="2">W</th><th rowspan="2">EGR<sup>1</sup></th><th colspan="4">DER<sup>2</sup></th></tr><tr><th>Total</th><th>Alienation</th><th>Identification</th><th>Correlation</th></tr></thead><tbody><tr><td>1989</td><td>0.424</td><td>0.325</td><td>0.201</td><td>0.152</td><td>0.247</td><td>0.424</td><td>0.651</td><td>-0.103</td></tr><tr><td>1991</td><td>0.401</td><td>0.274</td><td>0.188</td><td>0.143</td><td>0.237</td><td>0.401</td><td>0.663</td><td>-0.106</td></tr><tr><td>1993</td><td>0.426</td><td>0.305</td><td>0.207</td><td>0.154</td><td>0.248</td><td>0.426</td><td>0.658</td><td>-0.114</td></tr><tr><td>1997</td><td>0.414</td><td>0.289</td><td>0.200</td><td>0.149</td><td>0.242</td><td>0.414</td><td>0.663</td><td>-0.117</td></tr><tr><td>2000</td><td>0.457</td><td>0.368</td><td>0.217</td><td>0.163</td><td>0.257</td><td>0.457</td><td>0.643</td><td>-0.123</td></tr><tr><td>2004</td><td>0.485</td><td>0.402</td><td>0.238</td><td>0.175</td><td>0.269</td><td>0.485</td><td>0.637</td><td>-0.125</td></tr><tr><td>2006</td><td>0.516</td><td>0.477</td><td>0.253</td><td>0.187</td><td>0.283</td><td>0.516</td><td>0.646</td><td>-0.147</td></tr></tbody></table>

Notes: (1) Based on a two-group partition; (2) For $\alpha$ =0.5.

Source: CHNS.

<table><caption>Table 3.5 : Inequality and polarization indices, urban households.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Gini</th><th rowspan="2">Theil</th><th rowspan="2">W</th><th rowspan="2">EGR<sup>1</sup></th><th colspan="4">DER<sup>2</sup></th></tr><tr><th>Total</th><th>Alienation</th><th>Identification</th><th>Correlation</th></tr></thead><tbody><tr><td>1989</td><td>0.310</td><td>0.202</td><td>0.115</td><td>0.105</td><td>0.201</td><td>0.310</td><td>0.722</td><td>-0.097</td></tr><tr><td>1991</td><td>0.297</td><td>0.152</td><td>0.118</td><td>0.102</td><td>0.194</td><td>0.297</td><td>0.715</td><td>-0.080</td></tr><tr><td>1993</td><td>0.373</td><td>0.249</td><td>0.161</td><td>0.131</td><td>0.223</td><td>0.373</td><td>0.707</td><td>-0.145</td></tr><tr><td>1997</td><td>0.370</td><td>0.238</td><td>0.149</td><td>0.128</td><td>0.220</td><td>0.370</td><td>0.700</td><td>-0.142</td></tr><tr><td>2000</td><td>0.413</td><td>0.311</td><td>0.168</td><td>0.143</td><td>0.236</td><td>0.413</td><td>0.704</td><td>-0.179</td></tr><tr><td>2004</td><td>0.455</td><td>0.345</td><td>0.214</td><td>0.162</td><td>0.253</td><td>0.455</td><td>0.701</td><td>-0.197</td></tr><tr><td>2006</td><td>0.484</td><td>0.441</td><td>0.214</td><td>0.170</td><td>0.266</td><td>0.484</td><td>0.713</td><td>-0.215</td></tr></tbody></table>

Notes: (1) Based on a two-group partition; (2) For $\alpha$ =0.5.

Source: CHNS.

Rural inequality remained relatively stable until 1997, whereas urban inequality began to increase from 1993. Furthermore, the rise in inequality was more substantial in urban areas, where the Gini coefficient increased by 56.1% between 1989 and 2006, compared to just
21.7% in rural areas. As a result, income inequality was still higher in rural areas than in urban areas in 2006, but the gap has narrowed since urban disparities increased faster than rural disparities. The DER index suggests that income polarization is also higher for rural households, as noted previously by Zhang and Kanbur (2001). However, in addition to inequality, the increase in polarization is far more striking in urban areas, where the DER index increased from 0.201 to 0.266 (+32.3% compared to just +14.6% in rural areas).

The analysis of density curves sheds light on the underlying phenomena associated with the surge in rural and urban polarization. Figure 3.2 shows that in 1989, the kernel density curve for rural areas is unimodal, with a modal value of approximately 1,000 Yuan.

<figure><img src="image_8.png" /><figcaption>Figure 3.2 : Kernel density for income distribution, rural households</figcaption></figure>

Note: Optimal bandwidth = 288.693 Yuan.

Source: CHNS.

In 2006, this mode rises to 1,600 Yuan, but the income distribution also becomes multimodal. Several local modes emerge at around 4,500, 6,000 and 9,000 Yuan. The left tail of the distribution thins down considerably, indicating a decline in the concentration at the lowest income levels. This shift can be related to the significant decline in poverty in rural
areas. As an illustration, Ravallion and Chen (2007) found that approximately 75-80% of the
decline in national poverty incidence is due to poverty reduction in rural sectors. There is also
evidence of a change in density toward middle and upper income levels, which become more
concentrated. This suggests that some individuals are becoming clustered around local modes
in the middle-upper income ranges.

<figure><img src="image_9.png" /><figcaption>**Figure 3.3 : Kernel density for income distribution, urban households**</figcaption></figure>

Note: Optimal bandwidth = 577.49 Yuan.

Source: CHNS.

The shift in density curves is even more striking in urban areas, as shown in Figure 3.3. Whereas the shape of the distribution is leptokurtic and unimodal in 1989, it becomes nearly platykurtik in 2006. The former modal value (at around 2,500 Yuan) turns into a slight bump, and two modal values appear at approximately 3,500 and 6,500 Yuan. Note also the emergence of two other local modes, at around 9,000 and 15,000 Yuan. These local modes provide evidence of a considerable shift of density toward middle and upper income ranges
and support the idea of an emerging urban middle class.<fnref n="13" /> Furthermore, and contrary to rural areas, we may also note that the very left tail of the distribution thickens significantly. Following Wang (2004), this may lend support to the idea of a “new urban poverty” in China. The new urban poverty and the emergence of middle and upper income classes are the main phenomena associated with the surge in urban polarization. From our point of view, these factors suggest that the risk of social unrest is more prevalent in Chinese cities than in rural areas.

To provide a more detailed analysis, rural-urban differences can be further clarified and explored in the light of the decomposition of DER indices into alienation and identification components, as shown in Tables 3.4 and 3.5. Despite the rise in the alienation component for rural households, the increase in rural income polarization is moderate since the identification component has stagnated and even slightly decreased since 2000. In other words, income disparities between rural households are accentuated but rural households become a little more scattered around the mean income of their group. The decrease in the identification component in rural areas suggests that households in the same range of income are less and less similar and so are less aware of sharing common interests. In urban areas, the trend of alienation and identification components is relatively similar, but the increase in polarization was more significant since the rise in the alienation component is associated with a relative stability of the identification component. Furthermore, the level of identification is significantly higher in cities than in rural areas, thus confirming the idea that social tensions are more likely to occur in urban areas than in rural areas. The rural-urban gap concerning the identification component also serves to explain the non-negligible share of the between-group component in the DER index (compared to the inequality indices).

## 4. The sources of income polarization

Based on CHNS data, household income can be broken down into six components: wages, business income, agricultural income, subsidies, retirement income and other income. Agricultural income includes farming, fishing, gardening and livestock incomes. Other

<footnote n="13">¹³ By identifying the middle classes in developing countries as people belonging to households with a per capita consumption above the median poverty line and below the US poverty line, Ravallion (2010) showed that the middle class increased by 1.2 billion of people between 1990 and 2005, and that half of this surge can be attributed to China.</footnote>
income includes a range of components such as rents from leased land and non-land assets, money from family, friends or relatives (including remittances from rural migrants in Chinese cities), the value of in-kind gifts from family, friends or relatives and enterprises. Following Gao and Riskin (2009), these components are also aggregated to distinguish market income (wages, business income and agricultural income), social benefits (retirement income and subsidies) and other income.

Tables 3.6 and 3.7 present the results of the DER decomposition by income components, for rural and urban households respectively in 1989, 1997 and 2006.<fnref n="14" /> Three elements are reported: the share of different components in total income, the concentration index associated with each component (pseudo-polarization), and the contribution of each component to overall polarization. On this basis, an income source may be considered to be depolarizing (polarizing) if its contribution to polarization is lower (greater) than its share in total income or if the concentration index of the component is lower (greater) than the overall DER index.

## 4.1. Rural areas

As previously noted, Table 3.6 indicates a stability of income polarization between 1989 and 1997 and a significant increase from 1997 to 2006. While the share of social benefits is relatively constant between 1989 and 2006, a decrease in the proportion of market income in total income and an increase in other income is observed. Nevertheless, market income is still predominant in 2006, representing roughly 75% of total income.<fnref n="15" /> In 2006, other income accounts for 16% of total income (compared to just 5% in 1989).<fnref n="16" /> This trend provides evidence of the increasing role of remittances from rural migrants to cities. The loosening of the constraints imposed by the *hukou* system<fnref n="17" /> on rural-urban migration facilitated mass migration flows during the 1990s and the 2000s. According to Li (2008), the

<footnote n="14">¹⁴ The results of the decomposition for the whole sample are reported in Appendix 3.2.</footnote>

<footnote n="15">¹⁵ Using CHIP data, Gao and Riskin (2009) also highlight the predominance of market income in rural areas.</footnote>

<footnote n="16">¹⁶ The impact of other income on polarization is neutral insofar as its contribution to polarization is equal to its share in total income.</footnote>

<footnote n="17">¹⁷ The *hukou* system was established as a permanent system in 1958 to control migration from rural to urban areas. Under this system, rural migrants to cities had at best a temporary permit of residency and were deemed more often to be clandestine. The system has created strong barriers between rural areas and cities but was relaxed from the mid 1990s. For further information about the *hukou* system, see Chan and Buckingham (2008).</footnote>
number of rural out-migrant workers increased from 2 million in 1978 to 30 million in 1989
and to over 130 million in 2006.

For market income, the results indicate a decline of agricultural income compared to non agricultural income (wages and business income). The comparison of the share of both components in total income with their contribution to polarization indicates that a disequalizing income component (i.e. non agricultural income) replaced an equalizing income component (i.e. agricultural income). The change in the structure of market income explains why market income was a polarizing component in 2006, whereas it was relatively depolarizing in 1989. The role of non-farm income in the increase of rural income inequality has already been largely documented (Hare, 1994; Benjamin et al., 2005a, 2005b; Chen and Zhang, 2009; Gao and Riskin, 2009).

The depolarizing effect of agricultural income is linked to a relatively egalitarian land
distribution. For instance, Wan and Zhou (2005) show that among capital endowments and
geographic factors, the only determinant that contributes negatively to inequality is land. In
1989, agricultural income accounted for almost 50% of the total income of rural households.
As suggested by Benjamin et al. (2005a), this is a reflection of the fact that nearly all rural
households in China had been allocated land. However, the structure of rural market income
had changed drastically by the end of the period. In 2006, agricultural income accounted for
just 26% of total income, whereas the proportion of non agricultural income (wages and
business income) reached 47%. According to Benjamin et al. (2005a), the expansion of
Township and Village Enterprises (TVEs) in the 1980s and 1990s contributed significantly to
the development of off-farm opportunities in some rural areas. TVEs are economic units
located in rural areas, which are not involved in agriculture but in industry or services (Fu and
Balasubramanyam, 2003). They consist of the former commune and brigade enterprises,
which were renamed township enterprises and village enterprises respectively in 1984.

In a broad sense, TVEs also include farmers’ cooperatives and individual or family
businesses. Based on this broader definition, official statistics show that between 1978 and
2003, the number of TVEs was multiplied by 4.8, increasing from 28.3 to 135.7 million
(Yang, 2005). The main point is that non-farm income is clearly polarizing whereas
agricultural income is still depolarizing. The 2006 values of the concentration indices provide
clear evidence of this gap. The concentration or pseudo-polarization index for agricultural
income is under 0.16, whereas it is above 0.3 for wages and business income. We suggest that
the capacity of some rural households to benefit from non-farm opportunities probably accounts for the constitution of identified clusters in the middle (middle class?) of the 2006 rural income distribution, as highlighted by kernel densities. In other words, the increase in income polarization in rural areas is closely linked to the inequality of opportunities for non-farm activities.

The proportion of social benefits in total income remained relatively stable between 1989 and 2006 and was extremely low (around 10%). Nonetheless, a rise in the share of retirement income from 1% in 1989 to 9% is observed in 2006. However, retirement income in rural China is limited compared to urban areas, where it reaches 33% of total income (Table 3.7). Moreover, retirement income is a polarizing component since its contribution to the DER index is higher than its share in total income. Several studies examining the Chinese pension system have emphasized the marginal role of the social benefits system (particularly pensions) in rural areas (Hu, 2006; Gao, 2008). As explained by Gao (2008: 345), “*only a very small privileged group of rural residents has had access to pensions due to their prior employment in state-owned or collective enterprises*”. The ‘five guarantees’ program was designed to assist the most vulnerable elderly people in rural areas by providing food, clothing, housing, medical assistance and burial expenses. Eligible individuals include those with no family members to support them, no ability to work and no source of income. These restrictive conditions explain why only a minority of Chinese rural elders have been able to benefit from this system (Lowry, 2009; Shen and Williamson, 2010).<fnref n="18" /> The assistance provided to beneficiaries is not adequate in many rural areas (Gao, 2008). The introduction of a pension scheme in rural areas is a major task for the objective of poverty reduction in rural areas and may contribute to the reduction of the degree of income polarization in rural China.<fnref n="19" />

Overall, the results concerning income polarization tend to confirm the findings highlighted in the literature dealing with inequality: the rise of rural polarization and inequality is closely linked to non agricultural opportunities in rural areas.

<footnote n="18">¹⁸ In 2007, 5.3 million rural elderly people benefited from the five guarantees system, while 3.9 million people benefited from the old pension scheme. Given that there are 100 million elders in rural areas, the coverage rate is less than 10% (Shen and Williamson, 2010).</footnote>

<footnote n="19">¹⁹ In 2009, the Chinese authorities began to implement a new rural pension reform articulated around four principles: (i) a guaranteed basic benefit; (ii) wide coverage; (iii) regional flexibility; and (iv) sustainability (Shen and Williamson, 2010).</footnote>
<table><caption>Table 3.6: DER index decomposition by income components (α = 0.5), rural households</caption><thead><tr><th rowspan="2"></th><th colspan="3">1989</th><th colspan="3">1997</th><th colspan="3">2006</th></tr><tr><th>Income share</th><th>Concentration index</th><th>Contribution to polarization</th><th>Income share</th><th>Concentration index</th><th>Contribution to polarization</th><th>Income share</th><th>Concentration index</th><th>Contribution to polarization</th></tr></thead><tbody><tr><td><strong>Market income</strong></td><td><strong>0.84</strong></td><td><strong>0.233</strong></td><td><strong>0.80</strong></td><td><strong>0.85</strong></td><td><strong>0.240</strong></td><td><strong>0.85</strong></td><td><strong>0.74</strong></td><td><strong>0.273</strong></td><td><strong>0.71</strong></td></tr><tr><td>Agricultural income</td><td>0.47</td><td>0.156</td><td>0.30</td><td>0.39</td><td>0.135</td><td>0.22</td><td>0.26</td><td>0.157</td><td>0.15</td></tr><tr><td>Business income</td><td>0.07</td><td>0.289</td><td>0.08</td><td>0.15</td><td>0.305</td><td>0.19</td><td>0.12</td><td>0.315</td><td>0.13</td></tr><tr><td>Wages</td><td>0.30</td><td>0.340</td><td>0.42</td><td>0.31</td><td>0.342</td><td>0.44</td><td>0.35</td><td>0.345</td><td>0.43</td></tr><tr><td><strong>Social benefits</strong></td><td><strong>0.11</strong></td><td><strong>0.340</strong></td><td><strong>0.16</strong></td><td><strong>0.05</strong></td><td><strong>0.345</strong></td><td><strong>0.07</strong></td><td><strong>0.10</strong></td><td><strong>0.366</strong></td><td><strong>0.13</strong></td></tr><tr><td>Subsidies</td><td>0.10</td><td>0.339</td><td>0.14</td><td>0.02</td><td>0.344</td><td>0.02</td><td>0.01</td><td>0.359</td><td>0.02</td></tr><tr><td>Retirement</td><td>0.01</td><td>0.354</td><td>0.02</td><td>0.03</td><td>0.345</td><td>0.05</td><td>0.09</td><td>0.367</td><td>0.11</td></tr><tr><td><strong>Other income</strong></td><td><strong>0.05</strong></td><td><strong>0.242</strong></td><td><strong>0.04</strong></td><td><strong>0.10</strong></td><td><strong>0.198</strong></td><td><strong>0.08</strong></td><td><strong>0.16</strong></td><td><strong>0.275</strong></td><td><strong>0.16</strong></td></tr><tr><td><strong>DER index</strong></td><td></td><td><strong>0.247</strong></td><td></td><td></td><td><strong>0.242</strong></td><td></td><td></td><td><strong>0.283</strong></td><td></td></tr></tbody></table>

Source: CHNS.
## 4.2. Urban areas

The results of the decomposition of the DER index by income components point to a relative stability of the three main components of income (market income, social benefits and other income) in urban areas, if we compare 1989 and 2006. While a predominance of market income (74% in 2006) was found in rural areas, the income structure appears to be more balanced in cities: 56% for market income, 34% for social benefits and 10% for other income.

Despite this relative inertia, the share of other income increased significantly over the period, from 4% in 1989 to 10% in 2006. This component is relatively depolarizing since its contribution to polarization is lower than its share in total income. This is indicative of the increasing role of private transfers and social networks in mitigating inequality and polarization.

When considering market income, the main result concerns the polarizing effect of wages, irrespective of the year considered. For instance, in 2006, the share of wages in total income was 47%, whereas its contribution to the DER index reached 52% and the gap was far more pronounced in 1989 (49% compared to 58%). The disequalizing effect of wages has already been emphasized in the literature and is closely linked to the gradual transition to market mechanisms (Gustafsson and Li, 2001; Khan and Riskin, 2005; Benjamin et al., 2005a; Knight and Song, 2006; Gustafsson et al., 2008). First, the liberalization of the urban labor market, even within the state sector, has resulted in increased wage inequality since the method of wage determination takes better account of differences in productivity. Second, the restructuring of State enterprises (particularly their increased freedom to hire or lay off workers) has resulted in significant unemployment and has consequently increased inequalities in access to employment.

The main significant evolution of the urban income structure concerns social benefits. A significant decrease in the share of social benefits is observed, particularly between 1989 and 1997.<fnref n="20" /> This decline is explained primarily by the decrease in the share of subsidies from 30% to 8%. The declining role of subsidies has already been addressed in previous studies (Khan et al. 1999; Benjamin et al., 2005a). During the pre-reform period, subsidies on food, housing and transport services were publicly funded and relatively equally allocated by work

<footnote n="20">²⁰ The decrease in subsidies resulted in a relative increase of market income, particularly wages.</footnote>
units. Subsidies were slashed in the 1990s, thereby removing the main inequality-attenuating component of income.

The decline in subsidies was compensated by a significant increase in the proportion of retirement income between 1997 and 2006. The expansion of retirement income is linked to the urban pension reform introduced in 1997. In 1997, the State Council promulgated a Decision on establishing a uniform basic old age insurance system for enterprise employees aimed at establishing a multi-pillar system combining a public mandatory pension and a private voluntary pension.<fnref n="21" /> Contrary to subsidies, retirement income exerts a polarizing effect since its contribution to polarization is stronger than its share in total income in 2006. Wage differentials created by the liberalization of the urban labor market are now reproduced at retirement age. This explains why the substitution of retirement income for subsidies resulted in a polarizing effect of social benefits in 2006, whereas they were relatively depolarizing in 1989.

The study by Benjamin *et al*. (2005a) draws a distinction between transition process and development process to explain the rise of inequality in China. The development process describes a pure scale effect by which economic growth improves the standard of living. The transition process captures the effect of institutional change and in particular the shift toward market mechanisms for the valuation of productive endowments. Although it is impossible to clearly identify both effects, the distinction helps to explain the rise in polarization in urban China. In the previous section, the analysis of kernel densities served to highlight a shift from a leptokurtic and unimodal income distribution in 1989 to a platykurtic and multimodal distribution in 2006. In other words, two underlying phenomena are associated with increased polarization: (i) a decrease in the density of low income levels resulting in the quasi-disappearance of the mode in the bottom of distribution and (ii) the emergence of middle and upper income classes characterized by the appearance of several local modes. The decrease in the proportion of households with low incomes is likely related to the development effect and to the continuous increase in mean income induced by economic growth. The emergence of local modes in the 2006 distribution is more strongly related to the transition process.

<footnote n="21">²¹ This program has been followed by other decisions. For a discussion, see Hu (2006).</footnote>
<table><caption>Table 3.7 : DER index decomposition by income components (α = 0.5), urban households</caption><thead><tr><th rowspan="2"></th><th colspan="3">1989</th><th colspan="3">1997</th><th colspan="3">2006</th></tr><tr><th>Income share</th><th>Concentration index</th><th>Contribution to polarization</th><th>Income share</th><th>Concentration index</th><th>Contribution to polarization</th><th>Income share</th><th>Concentration index</th><th>Contribution to polarization</th></tr></thead><tbody><tr><td><strong>Market income</strong></td><td><strong>0.59</strong></td><td><strong>0.235</strong></td><td><strong>0.69</strong></td><td><strong>0.70</strong></td><td><strong>0.232</strong></td><td><strong>0.74</strong></td><td><strong>0.56</strong></td><td><strong>0.272</strong></td><td><strong>0.57</strong></td></tr><tr><td>Agricultural income</td><td>0.04</td><td>0.145</td><td>0.03</td><td>0.02</td><td>0.169</td><td>0.02</td><td>0.01</td><td>0.080</td><td>0.00</td></tr><tr><td>Business income</td><td>0.06</td><td>0.263</td><td>0.08</td><td>0.13</td><td>0.212</td><td>0.13</td><td>0.08</td><td>0.173</td><td>0.05</td></tr><tr><td>Wages</td><td>0.49</td><td>0.238</td><td>0.58</td><td>0.55</td><td>0.239</td><td>0.59</td><td>0.47</td><td>0.292</td><td>0.52</td></tr><tr><td><strong>Social benefits</strong></td><td><strong>0.37</strong></td><td><strong>0.146</strong></td><td><strong>0.27</strong></td><td><strong>0.21</strong></td><td><strong>0.206</strong></td><td><strong>0.20</strong></td><td><strong>0.34</strong></td><td><strong>0.288</strong></td><td><strong>0.37</strong></td></tr><tr><td>Subsidies</td><td>0.30</td><td>0.129</td><td>0.19</td><td>0.08</td><td>0.255</td><td>0.09</td><td>0.03</td><td>0.322</td><td>0.04</td></tr><tr><td>Retirement</td><td>0.07</td><td>0.219</td><td>0.08</td><td>0.13</td><td>0.177</td><td>0.11</td><td>0.31</td><td>0.284</td><td>0.33</td></tr><tr><td><strong>Other income</strong></td><td><strong>0.04</strong></td><td><strong>0.216</strong></td><td><strong>0.04</strong></td><td><strong>0.09</strong></td><td><strong>0.158</strong></td><td><strong>0.06</strong></td><td><strong>0.10</strong></td><td><strong>0.159</strong></td><td><strong>0.06</strong></td></tr><tr><td><strong>DER index</strong></td><td></td><td><strong>0.201</strong></td><td></td><td></td><td><strong>0.220</strong></td><td></td><td></td><td><strong>0.266</strong></td><td></td></tr></tbody></table>

Source: CHNS.
The liberalization of the urban labor market and state enterprises has resulted in inequalities of access to employment and wage disparities. This chapter suggests that these inequalities account for the clustering of population around intermediate and high income levels.

Conclusion

The purpose of this chapter was to contribute to the analysis of Chinese income inequality by focusing more specifically on income polarization. As suggested by Esteban and Ray (1994), the risk of social tensions is better addressed by polarization than inequality since polarization focuses on the formation of groups and captures both alienation (i.e. the degree of heterogeneity between groups) and identification (i.e. the degree of homogeneity within groups). The analysis of income polarization conducted in this chapter was based on CHNS household level data from 1989 to 2006 and relied primarily on the DER (Duclos-Esteban-Ray) index, its decompositions (by groups and by income sources) and kernel densities.

Six main results can be drawn from the various investigations conducted as part of this study. First, two phases can be identified in the evolution of income inequality and polarization: (i) a period of stagnation between 1989 and 1997 and (ii) a period of increase from 1997 to 2006. It is important to note that the rise in polarization is more moderate than the increase of inequality. In fact, the increase in the alienation component is attenuated by the relative stability of the identification component. Nonetheless, the level of polarization in 2006 is high and comparable to the level prevailing in highly-polarized countries such as Russia and Mexico (Duclos *et al.*, 2004). Second, the increase in polarization conceals dramatic shifts in the income distribution, as indicated by kernel densities. More precisely, the income distribution moved from a leptokurtic and unimodal shape in 1989 to a more platykurtik and multimodal shape in 2006. This provides evidence of the emergence of middle and upper income classes in both rural and urban areas. Third, the share of the between-group (rural-urban) component in overall polarization is higher than the share of the between-group component in overall inequality. This indicates a significant degree of rural-urban polarization, as emphasized by Zhang and Kanbur (2001). Fourth, even if the level of income polarization is higher in rural areas, the increase in polarization is far more conspicuous in urban areas, suggesting that the risk of social tensions is more pregnant in Chinese cities.
Fifth, the analysis of the sources of income polarization in rural areas shows that the increase
in polarization is closely linked to non agricultural opportunities issued from Township and
Village Enterprises (TVEs). Sixth, in urban areas, the emergence of identified groups in
middle and upper income classes, characterizing the rise in polarization, can be explained by
two phenomena: (i) the sharp decline in subsidies, which were a depolarizing income
component, and (ii) the liberalization of the urban labor market and state enterprises, resulting
in inequalities in access to employment and wage disparities.

To a great extent, the increase in inequality and polarization in China is linked to the
development strategy implemented since the 1980s. Based on Deng Xiaoping's decision to
'let some people get rich first', the first stage of economic reforms has clearly favored coastal
provinces and cities. The implemented policies have enabled China to grow rapidly, but have
also resulted in increased social and regional disparities. The risk of social unrest associated
with an increasingly divided society has recently raised concerns among Chinese authorities.
Contrary to previous governments, Hu Jintao's administration recognizes that China needs to
move toward a new stage of development based on 'common prosperity'. In this respect, the
Chinese Communist Party adopted the concept of 'harmonious society' in 2006 aimed at
achieving a fairer and more balanced development. The 11th Five-Year Plan (2006-2010),
which emphasizes the issues of distribution and sustainability, shows that the promotion of a
'harmonious society' has become a new priority (Fan, 2006). The shift toward societal
objectives appears to be even more obvious following the recent adoption of the 12th Five-
Year Plan (2011-2015) in March 2011. The Chinese authorities have identified several threats
to ongoing development and have proposed guidelines aimed at addressing these challenges.
Among these objectives, some directly concern issues related to inequality and polarization.
Improving people's livelihoods to ensure the transition toward a 'happier society' is at the
heart of the latest Five-Year Plan.<fnref n="22" /> The government plans to increase per capita income and
the minimum wage at an annual rate of 7% and 13% respectively. A comprehensive public
social welfare system is also due to be introduced to provide government benefit programs.
For instance, within the next five years, pension schemes should cover all rural residents and
357 million people in urban areas. The criteria for acquiring urban *hukou* will also be relaxed
to accelerate urbanization, particularly in medium-sized and small cities.

<footnote n="22">²² See: “The 12th Five-Year Plan: Making China a Better Place”, speech delivered by Ambassador Liu Xiaoming on 4th April 2011 at the Monday Luncheon Club, The Reform Club, London. Available on: http://www.chinese-embassy.org.uk/eng/ambassador/dsjhjcf/t812461.htm.</footnote>
It now seems essential for China to tackle the issues of income inequality and
polarization to ensure the transformation of its economic development model and to reduce
the risk of social unrest. Since the global economic crisis in 2007-2008, China has attempted
to refocus on its internal market to move from export-led growth to domestic-led growth.
From this perspective, narrowing the income gap is considered to be necessary for stimulating
the consumption of the many Chinese households with low and middle incomes.
Chapter 4

In search of the elusive Chinese urban
middle class(es): an exploratory
investigation

Ce chapitre est une version enrichie d'un article co-écrit avec Matthieu Clément et François Combarnous, et soumis à la revue *Development and Change* (septembre 2013).

Introduction

One of the effects of increasing wealth and the drastic reduction in poverty rates in China since the beginning of economic reforms is the emergence of a new social group known as the “new rich”. However, this new socio-economic category has many of the characteristics of a new middle class. As noted by Tomba (2004), the emergence of a new middle class has been one of the most important determinants of social change over the past decade. In an influential study of the *global middle class*, Ravallion (2010) showed that the share of the Chinese middle class (i.e. people with an income of $2 to $13 per day) in total population increased from 15.3% in 1990 to 61.8% in 2005. Many other studies have provided evidence of the widening income distribution and the growth of the middle class (Wilson and Dragusanu, 2008; ADB, 2010; Yuan *et al.*, 2011).

The rise of the Chinese middle class is explained first by China's economic boom since the beginning of the reforms. The increasing openness and liberalization of the Chinese economy has resulted in a significant increase in mean per capita income. However, the expansion of the Chinese middle class is also related to socio-economic factors such as urbanization and urban housing reforms, the development of higher education, property rights
reforms, and the westernization of lifestyles under the influence of globalization and the mass
media, among other things (Chunling, 2010; Li, 2010). Following Li (2010), we may argue
that two categories of actors have contributed to the rise of the Chinese middle class. The first
is the business community, which has played a crucial role in promoting the idea of the
middle class since the rise of the middle class is associated with the expansion of the domestic
market and is a driving force for private consumption. The second actor is the Chinese state, a
key factor in the rise of the middle class (Tomba, 2004; Li, 2010; Elfick, 2011). While the
Maoist regime led to the near disappearance of social classes, the adoption of a strategy of
inclusive growth from the 2000s has come to be seen as a major turning point in China's
development path. In 2002, the sixteenth national party congress called for an increase in the
size of the middle income group (zhongdeng shouru qunti) and the development of an affluent
society. These objectives were institutionalized in the 11th Five-Year Plan (2006-2010), which
emphasized the promotion of a 'harmonious society', and the 12th Five-Year Plan (2011-
2015), which aims to improve livelihoods and to promote the transition to a "happier society".
The shift to a strategy of inclusive growth has led the Chinese authorities to re-appropriate the
Confucian concept of xiaokang, which refers to the idea of a moderately well-off society or a
middle class society (Hanlong, 2010).

Why is the analysis of the Chinese middle class an important issue for economists? In
Western countries, the emergence of the middle class contributed significantly to the
acceleration of economic development at the end of the nineteenth century (Landes, 1998)
and to the perpetuation of high growth rates during the post-World War II period. The
significant expansion of the middle income classes in emerging countries (mainly in Asia, and
in China in particular) may ensure the sustainability of development, just as it did in the West.
Broadly speaking, the emerging middle class may be seen as a catalyst, i.e. as a class driving
socio-economic and political change. The middle class is assumed to be a catalyst through
two main channels. First, the emergence of a large middle class is considered to be a driver of
consumption (Banerjee and Duflo, 2008). Research suggests that the expansion of the middle
class and the associated reduction in poverty tend to result in increased household
expenditure, while also resulting in a change in consumption patterns. Compared to poor
people, middle class members are assumed to spend a lower percentage of their income on
necessity goods and to spend more on discretionary and luxury goods and household
appliances and equipment. Therefore, the emergence of a middle class is associated with a
higher demand for high-quality goods (Murphy *et al.*, 1989; Matsuyama, 2002; Foellmi and Zweimuller, 2006). Second, there is evidence to suggest that the emergence of a large middle class may help to strengthen social cohesion and political stability and even promote democratization (Lipset, 1959; Muller, 1997; Easterly, 2001; Loayza *et al.*, 2012). The underlying idea is that societies with a growing middle class are less polarized and are therefore more inclined to implement policies in favor of economic and social development. The middle class acts as a buffer zone between the poor and the rich (Jing, 2010) and, as explained by Easterly (2001), the establishment of a “middle class consensus” is a driving force in promoting good institutions, good policies and democratic governance.

In China, the public image of the middle class is closely related to the first channel and is associated with the westernization of lifestyles. What is this public image? Typical members of China's middle class are urban, buy modern cars, own modern flats or houses, purchase high-technology products such as flat screen TVs and mobile phones, travel around the world, and are able to obtain credit. These developments suggest that the Chinese middle class is the driving force of a real “consumer revolution” (Elfick, 2011). However, the Chinese middle class is an ambivalent entity since it is at the vanguard of consumption but at the rearguard of politics (Xiaohong and Chen, 2010). This is specific to China (consider the role of the middle class in the promotion of democracy in other emerging countries such as South Korea, Indonesia or Brazil). These distinctive features are primarily relayed by the mass media, but are also found in reports published by institutions providing a picture of Asian or Chinese middle classes (McKinsey Global Institute, 2006; ADB, 2010; Deutsche Bank, 2009; GoldmanSachs<fnref n="1" />). The literature devoted to the analysis of the Chinese middle class is mainly sociological and managerial, and economists are currently lagging behind on these issues, probably because the main focus of their work over the past two decades has been the analysis of income inequality.

The middle class is found in all major Chinese cities. However, there is no consensus on the method of identifying (i.e. characterizing) the middle class, for two main reasons. First, the concept of middle class is a fuzzy, controversial and cross-disciplinary concept. As noted by Pressman (2007) and Chunling (2010), there are many approaches to the definition and identification of the middle class. We can distinguish four disciplinary approaches: (i) an

<footnote n="1">¹ See Wilson and Dragusanu (2008).</footnote>
economic approach (based on income), (ii) a sociological approach (based on occupation and education), (iii) a subjective approach (based on class consciousness), and (iv) a managerial approach (based on consumption habits). In each approach, there is a wide range of definitions and methods of identification. Second, the Chinese middle class is highly heterogeneous. As argued by Li (2010: 4), “China’s emerging middle class is, of course, a complex mosaic of groups and individuals” that differ on many social, economic and demographic characteristics.

This chapter has two main objectives. First, using factor analysis and clustering methods, we propose to improve the identification and characterization of China's middle class using an original approach combining an economic approach (income) and a sociological approach (education and occupation). Second, based on a wide range of socio-economic variables, a statistical analysis is performed to demonstrate the heterogeneity of the Chinese middle class. The chapter focuses on urban China and uses household-level data from the *China Health and Nutrition Survey*. The chapter is structured as follows. Section 1 reviews the literature on the identification and definition of the middle class and describes the statistical methodology. Section 2 describes the data, while section 3 presents and discusses the results.

# 1. Identifying and characterizing the middle class: an innovative statistical approach

## 1.1. Identifying the middle income class

Since the 1980s, an extensive economic literature has developed on the decline of the middle class in industrialized countries (Blackburn and Bloom, 1985; Horrigan and Haugen, 1988; Pressman, 2007). These studies, but also more recent studies devoted to the emergence of the middle class in developing countries, use an income-based definition of the middle class where people who fall within a specific income range (or other monetary indicator) are considered to belong to the middle income class. In this view, the important question is the definition of the lower and upper boundaries of the income range. There is no consensus in the empirical literature, and the definitions and descriptions of the middle class that result
from different ranges are often contradictory. However, four ways of defining the middle
income class can be identified in the literature.

The first approach is relative and defines the middle class as the population located in
the middle of the distribution of a monetary indicator, such as income, consumption or wage.
In this approach, one of the most commonly used criteria identifies the middle class as people
earning between 75% and 125% of the median income (Thurow, 1987; Birdsall et al.,
2000; Pressman, 2007).<fnref n="2" /> Other relative criteria are based on quintiles. For example, Easterly
(2001) considers that the middle class includes households belonging to the second, third and
fourth expenditure quintiles, while Alesina and Perotti (1996) and Castellani and Parent
(2011) consider the third and fourth income quintiles. Second, the absolute approach is often
used in current research since the boundaries (PPP adjusted) are time-constant and common to
all countries, thus allowing for international comparisons. However, in the same way as for
the relative approach, there is no consensus on how to determine an appropriate income range.
According to Milanovic and Yitzhaki (2002), the middle class is composed of households
with a daily income ranging between the mean per capita income of Brazil and Italy, i.e.
roughly between $10 and $20 in PPP 2005. Kharas (2010) considers a larger interval, from
$10 to $100. Recent studies have viewed people belonging to the middle class as poorer than
previous absolute criteria by referring to the $2 international poverty line (PPP 2005).<fnref n="3" /> The
underlying assumption is that the middle class begins where poverty ends. The upper limit is
more arbitrary. Ravallion (2010) uses the US poverty line, i.e. $13 a day (PPP 2005). The
upper limit is sometimes set at $10 (Banerjee and Duflo, 2008) or $20 (ADB, 2010; Castellani
and Parent, 2011). A third category of empirical studies uses a mixed approach combining an
absolute lower boundary and a relative upper boundary (Birdsall, 2010; Sosa Escudero and
Petralia, 2010). In particular, Birdsall (2010) proposes a lower limit set at $10 a day (PPP
2005) that is high enough (compared to the $2 threshold) to ensure people whose income is
higher have reached a certain level of economic security. The upper boundary is fixed at the
95th income percentile. According to Birdsall (2010), this relative and therefore country-
specific limit excludes "that portion of the population within a country whose income is most
likely to be from inherited wealth, or based on prior or current economic rents (...) and thus

<footnote n="2">² Other studies use wider ranges around the median income. See Blackburn and Bloom (1985) and Castellani and Parent (2011).</footnote>

<footnote n="3">³ The international poverty line is defined as the median national poverty line for 70 developing countries (Ravallion, 2010).</footnote>
less associated with productive and primarily labor activity than for the non rich” (Birdsall,
2010: 7). The fourth approach involves adopting a poverty line based definition of the middle
class. The underlying idea is to construct an income range based on the national poverty line.
For Burkhauser et al. (1996), the middle class includes households with an income between
two and five times the country-specific poverty line. Peichl et al. (2010) adopt a relative
approach and set the middle class between the poverty threshold defined as 60% of the
median income and a wealth threshold equal to 200% of the median income (i.e. 333% of the
poverty threshold).

In China, the identification of the middle income group is often based on an absolute
approach, although there is no consensus on the lower and upper boundaries. Wilson and
Dragusanu (2008), for Goldman Sachs, define a lower limit of $6,000 and an upper limit of
$60,000 (i.e. $16 and $82 a day respectively), whereas ADB (2010) uses an income range of
$2 to $20 a day. Because the two criteria only overlap for the $16-$20 bracket, they give a
different picture of the Chinese middle class. It is worth noting that the ADB’s estimation has
been widely criticized for overestimating the size of China’s middle class.<fnref n="4" /> The lowest limit
of $2 (PPP adjusted) adopted by ADB (2010), but also by Ravallion (2010), is highly
debatable since it suggests that it is enough to be non-poor (i.e. to earn more than $2 a day) to
be considered a member of the middle class. As ADB (2010) acknowledges, “the majority of
the Asian middle class still falls in the $2-$4 range, leaving them highly vulnerable to slipping
back into poverty due to economic shocks” (ADB, 2010: 3). This idea was already present in
Horrigan and Haugen (1988: 5), who argued that we have “to ensure that the lower endpoint
of the middle class represents an income significantly above the poverty level”. Empirical
studies on the middle class devoted specifically to China (and not devoted to international
comparisons) adopt higher income ranges than those used by ADB (2010). However, upper
and lower boundaries are often applied to household income rather than per capita household
income, which can cloud comparisons. For instance, McKinsey Global Institute (2006)
defines the Chinese urban middle class as households earning between 25,000 and 100,000
yuan a year (between $13,500 and $53,900, PPP adjusted), while the National Bureau of
Statistics uses a range of 60,000 to 500,000 yuan for a three-member household. When we

<footnote n="4">⁴ See the note by Rose Scobie in the *Economic Observer*, available on:
http://www.eeo.com.cn/ens/observations/2010/09/15/180857.shtml</footnote>
consider a per capita basis, Wang and Davis (2010) suggest that 10,000 yuan is a minimum threshold marking entry into a lifestyle corresponding to the middle class in urban China.

How should we interpret the wide range of criteria? Although the relative approach seems intuitive and produces country-specific boundaries, it is not well-suited to the analysis of trends and changes over time since the share of the middle class is not or weakly sensitive to changes in income distribution. As we discussed above, the absolute approach is useful for international comparisons but seems too arbitrary for a country-specific analysis. The poverty-line based approach aims to reduce arbitrariness in the definition of boundaries. However, it raises issues in the Chinese context since the official poverty line is very low, which could result in a biased identification of the middle class.<fnref n="5" /> These objections suggest that we need to adopt the mixed approach combining an absolute lower boundary and a relative upper boundary. Following Wang and Davis (2010), the lower boundary is set at 10,000 yuan. This minimum threshold enables us to focus on households that escape poverty on a permanent basis and have reached a reasonable level of socio-economic security. In line with Birdsall (2010), the relative upper limit is the 95th percentile of the income distribution. The urban Chinese middle class is thus defined as all households with an annual per capita income between 10,000 yuan and the 95th income percentile.

**1.2. Multidimensional characterization of the middle class**

Although the most commonly used method for identifying the middle class in the economic literature involves a strictly monetary (financial) approach, we propose to improve and expand the purely income-based approach by considering occupation and education, the chief focus of the sociological literature on class structure.

In a study of the Chinese middle class, Wang and Davis (2010) argued that occupation determines class homogeneity. According to Jing (2010), the changing class structure of Chinese society has followed a similar pattern to industrialized countries, particularly in terms of occupational structure. However, in terms of the income-based definition of the middle

<footnote n="5">⁵ In 2009, the official Chinese poverty line was set at 1,196 yuan a year. On this basis, only 8% of the population was considered to be poor in 2009 (CIA World Factbook). As a comparison, according to international standards (World Bank, World Development Indicators), in 2008, 13.06% of the Chinese population were living on less than 1.25$ a day (in 2005 PPP) and 29.79% on less than 2$.</footnote>
class, the sociological literature on occupation and education varies widely. Goodman (2008) took a narrow view of the Chinese middle class, focusing solely on ownership of the means of production and entrepreneurship from a Marxist perspective (here, classes are assumed to be defined by their relationship to the means of production). This definition is often referred to as the old middle class and can be associated with the petty bourgeoisie. Other studies that recognize the specificity of the Chinese middle class use a broader spectrum of occupations and include high-wage earners in the middle class. One of the most influential studies of social stratification in China was by sociologists from the Chinese Academy of Social Sciences (Lu, 2002; 2012). Using occupation as the main criterion, Chinese society is divided into ten occupation-based categories: top executives and government officials, managers, private entrepreneurs, professional and technical workers, clerical workers, self-employed workers, sales and service workers, manual workers, agricultural workers and the unemployed. From these ten categories, five socio-economic groups are defined: the upper, the upper middle, the middle, the lower middle and the lower groups. It is worth noting that each member of a specific occupation-based stratum can potentially belong to several socio-economic groups. For instance, a manager may belong either to the upper group or to the upper middle group. Following the seminal work of Mills (1951) on the American class structure, Chunling (2010) also proposed an innovative approach to China's class structure. She considers four groups in the Chinese middle class: (i) the "capitalist class", composed of private entrepreneurs;<fnref n="6" /> (ii) the "old middle class", composed of small employers, small business owners and self-employed workers; (iii) the "new middle class", composed of wage earners such as officials, professionals and managers in the public sector and professionals and managers in the private sector; and (iv) the "marginal middle class", composed of low-wage white collar workers and other workers. Li (2010) distinguishes three components in the Chinese middle class: an economic component (private entrepreneurs, small urban businesses, rural industrialists, etc.), a political component (government officials, public sector managers, office clerks, etc.) and a cultural and educational component (academics and educators, intellectuals, media personalities, etc.).

<footnote n="6">⁶ The capitalist class is generally included in the upper class and is not considered to be a part of the middle class. However, Chunling (2010) argues that because it is a new group in China, the upper class symbolizes the expansion of the middle class.</footnote>
How can we reconcile the economic and sociological approaches to the middle class?
We propose to use a multidimensional approach combining an income-based definition of the
middle class and an occupation/education-based definition. In this view, there are two
possible methodologies.

The first method involves constructing a multidimensional index of social status and
defining upper and lower boundaries to apply to the composite index. In the sociological field,
there are a number of well-known indices. The first is Warner's index of status characteristics
(Warner et al., 1949) based on four dimensions (occupation, source of income, house type and
dwelling). The Hollingshead two-factor index of social position (ISP), probably one of the
most influential indices, includes two dimensions (occupation and education; Hollingshead
and Redlich, 1958).<fnref n="7" /> We may also refer to the Computerized Status Index (CSI) developed by
Coleman (1983), which takes into account education (of an individual and his/her spouse),
occupational prestige, area of residence and income. These indices are calculated as the
weighted sum of the scores that individuals attain in each selected dimension. Social classes
are identified by applying lower and upper boundaries. Composite indices recognize the
multidimensionality of social status and provide a more detailed description of social classes
than a single variable. However, they raise two issues. First, the weighting of dimensions is
relatively subjective. As an illustration, the Hollingshead ISP assigns a weight of 7 to
occupation and 4 to education, while the CSI assigns a weight of 2 to occupational prestige
and a weight of 1 to the other dimensions. In other words, there is no objective criterion for
defining the relative importance of different dimensions. Second, the identification of social
classes relies on ad hoc boundaries. For instance, the CSI is a global score ranging from 4 to
53. Coleman (1983) arbitrarily defines the middle class as including all individuals with a
score ranging from 24 to 36. The same comment applies to Warner's and Hollingshead's
indices.

Given the limitations of composite indices of social status, we prefer to use a more
innovative approach involving two stages. In the first stage, we use the above-mentioned
income-based definition to describe the Chinese urban middle class, thus prioritizing a
financial criterion. In the second stage, we use data on occupation and education to analyze

<footnote n="7">⁷ Hollingshead (1976) proposed the ISP index, which includes four factors (occupation, education, sex and marital status).</footnote>
the structure of the previously identified middle income class. Although income is closely
correlated with occupation and education, these characteristics help to improve the
identification and characterization of the middle class and to demonstrate its heterogeneity. To
implement the second stage, we implement a classification procedure to establish
homogeneous and meaningful clusters of households in terms of their multidimensional
"middle class status".

## 2. Data

The data used in this study come from the *China Health and Nutrition Survey* (CHNS), a collaborative project between the Carolina Population Center (University of North Carolina) and the Chinese Center for Disease Control and Prevention. The CHNS involves a multi-wave longitudinal study providing detailed information on income, labor market, education, health, nutrition and migration (among other things). The first survey was conducted in 1989. Seven other waves (1991, 1993, 1997, 2000, 2004, 2006 and 2009) are now available. The survey covers nine provinces, including coastal provinces (Shandong and Jiangsu), northeastern provinces (Heilongjiang and Liaoning) and inland provinces (Henan, Hubei, Hunan, Guangxi and Guizhou). Although the survey is not nationally representative, provinces were selected to provide a diverse picture of Chinese provinces in terms of geography, economic development and health and nutritional outcomes. The sample was selected by multistage random cluster sampling<fnref n="8" /> and covered approximately 4,400 households in 2009.

This study focuses primarily on the 2009 wave. The other waves are used to
demonstrate the extent to which the middle class has changed. We only consider the sub-
sample of urban households composed of 1,320 households after checking for missing values.
The sample does not include major Chinese cities such as Beijing, Shanghai, Guangzhou,
Shenzhen, Tianjin, Chongqing or Hong Kong. However, other major cities are covered since
for each province the provincial capital and a lower-income city are included. It is worth
noting that the urbanization profile of the nine provinces is relatively heterogeneous,

<footnote n="8">⁸ For a description of the sampling scheme, see Popkin *et al*. (2009).</footnote>
including both highly urbanized provinces (Liaoning and Jiangsu) and less urbanized provinces (Henan and Guizhou).

Although CHNS data are primarily designed for the analysis of health and nutrition issues, they also include basic information on income, occupation and education – i.e. the three dimensions used to identify the middle class. The first variable is household income expressed in per capita annual income and composed of wages, retirement income, business income, subsidies, agricultural income and other income (private transfers and rent). At an individual level, CHNS data provide detailed information on employment and education. Because our analysis is at a household level, we use information on occupation and education for household heads. Four classification variables are considered: primary occupation (professional or technical worker, administrator or executive, office staff, skilled or unskilled worker, service worker, retired, inactive, etc.), employment position (self-employed, salaried employee, etc.), the type of work unit (government department, state service, state-owned, collective or private enterprise, etc.) and level of education (highest level of education completed).

Our analysis also requires additional variables to describe the profile of China's middle class. These variables relate to household head characteristics (age and marital status), household characteristics (size, activity ratio, province of residence) and housing (homeowner or tenant, surface, number of rooms). CHNS data include rich information on household appliances and equipment. We use data on computer and car ownership. We also created a composite index of household appliances and equipment that takes into account ten types of goods: color TV, washing machine, refrigerator, air conditioner, computer, camera, microwave oven, cell phone, VCD/DVD, and automobile. The household appliances and equipment index has the advantage of comparing household appliances and equipment with an ad hoc equipment standard, rather than simply adding up a wide range of household appliances and equipment. Instead of measuring an inevitably heterogeneous stock of household appliances and equipment, we consider the degree of over or under-equipment of households compared with a local standard.<fnref n="9" /> The index is computed by adding up, for each of the ten household appliances and equipment, a + 1 score if the household is over-equipped

<footnote n="9">⁹ In our sample, “normal” equipment is refers to a household that owns a television, a refrigerator, a washing machine and one or two cell phones, but no VCD/DVD, camera, computer, microwave, air conditioner or car.</footnote>
(for example, because the household has more than one television) and a -1 score if the
household is under-equipped (for example, because the household does not have a
refrigerator). The index is brought within the [0;1] interval using linear interpolation. The last
characterization variable is the urbanization index developed by Jones-Smith and Popkin
(2010) and included in CHNS data.<fnref n="10" />

3. Results

3.1. The middle income class: identification, evolution and characterization

Table 4.1 compares the middle class income boundaries derived from four different criteria and their implications for the size of the middle class. We consider one criterion for each of the four approaches identified above: an absolute criterion (between $2 and $20), a relative criterion (75%-125% of 2009 median income), a poverty line based criterion (between two and five times the national poverty line) and a mixed criterion (between 10,000 yuan and the 95th percentile). The main finding is that the lower and upper limits of different criteria do not converge, resulting in significant differences in estimates of the size of China's urban middle class.

The share of the middle income class ranges from 15.8% based on the poverty line
based approach to approximately 80% based on the absolute approach. Both criteria give an
inaccurate picture of China's income distribution. The absolute approach gives a wide income
range (between 3,257 and 32,573 yuan). Although the figure of 80% of urban households
belonging to the middle class is consistent with ADB estimates (ADB, 2010),<fnref n="11" /> the lower
extremity of the income range is debatable. As explained above, considering households
whose daily per capita income is between $2 and $4 is not satisfactory since these households
are probably still vulnerable to poverty. The poverty line based approach defining the middle

<footnote n="10">¹⁰ Based on community-level data, this index is a composite measure synthesizing the information on twelve dimensions of urbanicity: population density, economic activity, traditional markets, modern markets, transportation infrastructure, sanitation, communications, housing, education, diversity (in education and income levels), health infrastructure and social services. For each component, a score ranging from 0 to 10 is calculated. The global index is the sum of the twelve component scores. A higher urbanization score indicates higher urbanicity. See Jones-Smith and Popkin (2010) for more details.</footnote>

<footnote n="11">¹¹ Using the CHIP (Chinese Household Income Project) household survey for 2007 and the $2-$20 criterion, ADB's estimates show that the share of the urban middle class in total population is over 91% (ADB, 2010).</footnote>
class as households with per capita income between two and five times the national poverty line gives an income range in the lower tail of the income distribution. It is worth noting that this criterion only intersects with the lower end of the income range associated with the absolute approach. In other words, the wealthiest households of the middle income group have a per capita income of $2 to $4 a day. This criterion is misleading since it clearly underestimates the size of the middle class and only identifies poor and vulnerable households as belonging to the middle class. The main problem with this approach lies in the underestimation of the Chinese poverty line, as noted above. The last two criteria seem more realistic despite producing different estimates of the size of the middle class. With the mixed approach, 50% of urban households are found to belong to the middle class, i.e. more than twice the size of the middle class identified using the relative approach (75%–125% of 2009 median income). One advantage of a mixed criterion compared to a relative criterion is its ability to adequately capture trends and changes over time.

<table><caption>Table 4.1: Comparison of different criteria for defining the middle income class (urban households, 2009)</caption><thead><tr><th rowspan="2">Approach</th><th rowspan="2">Criterion</th><th colspan="2">Middle class income range¹</th><th rowspan="2">Middle Class</th><th rowspan="2">Poor</th><th rowspan="2">Rich</th></tr><tr><th>Annual per capita yuan (2009 prices)</th><th>Annual per capita $ (PPP, 2009 prices)</th></tr></thead><tbody><tr><td>Mixed</td><td>[10,000 yuan; P95]</td><td>[10,000; 36,285]</td><td>[2,487; 9,026]</td><td>49.9%</td><td>45.1%</td><td>5%</td></tr><tr><td>Absolute</td><td>[$2 PPP; $20 PPP]</td><td>[3,257; 32,573]</td><td>[810; 8,102]</td><td>79.5%</td><td>13.9%</td><td>6.6%</td></tr><tr><td>Relative</td><td>[75%-125% of median income]</td><td>[8473; 14122]</td><td>[2,107; 3,513]</td><td>21.8%</td><td>39.3%</td><td>38.9%</td></tr><tr><td>Poverty line Based</td><td>[Between 2 and 5 times national poverty line]²</td><td>[2392; 5980]</td><td>[595; 1487]</td><td>15.8%</td><td>10.4%</td><td>73.8%</td></tr></tbody></table>

Source: CHNS (2009)

PPP = purchasing power parity.

Notes: (1) The PPP conversion factor is 4.462 for the year 2009. It is calculated using the urban PPP conversion rate for 2005 derived from PovcalNet data (World Bank) and consumer price indices stem from China Statistical Yearbooks (National Bureau of Statistics of China). (2) The national official poverty line is set at 1,196 annual yuan.

Figure 4.1 shows the changing size of the Chinese urban middle income class between 1989 and 2009 based on the four criteria described above. The poverty line based approach indicates that the size of the Chinese middle class decreased from over 60% in 1989 to around 15% in 2009. This counter-intuitive trend confirms the limitations of the poverty line
approach. Given the underestimation of the Chinese poverty line, this criterion is more an
indication of the reduction in poverty that has occurred since the beginning of the reforms
(Ravallion and Chen, 2007) than an indication of the expansion of middle and higher groups.
The absolute approach gives a very different picture. With the $2-$20 criterion, the size of the
Chinese middle class appears to have increased significantly over the study period. However,
there were already 44.7% of people belonging to the middle class in 1989, which seems
unlikely given that respectively 60.2% and 84.6% of the Chinese population were living on
less than 1.25$ and 2$ a day in 1990 (Ravallion and Chen, 2010).

<figure><img src="image_10.png" /><figcaption>**Figure 4.1 :** Evolution of the size of the Chinese urban middle class between 1989 and 2009 regarding different identification criteria (urban households, 1989-2009).</figcaption></figure>

Source: CHNS (1989, 1991, 1993, 1997, 200, 2004, 2006, 2009)

PPP = purchasing power parity.

Notes: (1) The PPP conversion factor is 4.462 for the year 2009. It is calculated using the urban PPP conversion rate for 2005 derived from *PovcalNet* data (World Bank) and consumer price indices stem from China Statistical Yearbooks (National Bureau of Statistics of China). (2) For the relative approach, the boundaries are calculated from the 2009 median income and remain the same for the preceding years (adjusted for inflation). (3) The national official poverty line is set at 1,196 annual yuan (2009 prices).
Once again, the relative and the mixed criteria seem more appropriate since they confirm that the middle class was almost non-existent until the mid 1990s. However, they highlight different expansion patterns in China's urban middle class. Based on the relative criterion, the middle class may be said to have emerged in the mid-1990s and to have grown rapidly until the mid-2000s. Since then, the share of the middle class has remained stable and even decreased slightly between 2006 and 2009. The relative approach tends to smooth out the temporal evolution of the middle class and is therefore not well-suited to demonstrating the extent of the recent increase in the size of the middle class. The mixed approach shows that the rise of China's urban middle class is a more recent phenomenon dating back to the early 2000s. Not surprisingly, the increase is more pronounced and uninterrupted and probably better reflects the growth of the middle class.

In short, the comparison of the four approaches confirms that the choice of income boundaries is critical and may lead to a misidentification of the middle income group or an underestimation of its increase. Our findings indicate that the mixed criterion must be privileged in this respect.

Before analyzing the heterogeneity of the Chinese urban middle class, we propose to discuss its similarities with poor and rich households and its distinctive features. The distributions or means of classification and characterization variables for each of the three groups are reported in Table 4.2. Not surprisingly, households belonging to middle and high income categories are headed by people with higher levels of education. In 2009, 17.9% of household heads in the middle class had completed a technical or vocational degree, while 18.2% had a master's degree or higher. Household heads with no education or primary education are significantly more numerous in the poor category.

The Chinese middle class is clearly distinct from both the poor and the rich in terms of employment category, employment status and work unit. The findings indicate that the middle class is composed of a significantly higher proportion of households whose head belongs to the professional and technical worker category (15.7%), the administrator and executive category (8.1%), and the office staff category (5.6%). It also includes a significant proportion of pensioners (43.2%). Clearly, the diverse occupational profile of the Chinese urban middle class suggests that the middle income group is heterogeneous and validates the multidimensional approach. Another distinctive feature of China's urban middle class is its
over-representation in the salaried employee category (41.4%), particularly among people working in government departments or state services (25.6%) and in state-owned/collective enterprises (7%).

<table><caption>Table 4.2: Characteristics (classification variables) of “poor”, “middle-class” [10,000 yuan; 95th percentile] and “rich” (urban households, 2009)¹</caption><thead><tr><th></th><th>Poor<br>N=573</th><th>Middle-class<br>N=681</th><th>Rich<br>N=66</th><th>All<br>N=1320</th></tr></thead><tbody><tr><td><strong>Education</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>No school</td><td><strong>19.7%</strong></td><td>10.0%</td><td>10.6%</td><td>14.2%</td></tr><tr><td>Primary school</td><td><strong>19.7%</strong></td><td>8.5%</td><td>6.1%</td><td>13.3%</td></tr><tr><td>Secondary school</td><td>48.0%</td><td>45.4%</td><td>37.9%</td><td>46.1%</td></tr><tr><td>Technical/Vocational degree</td><td>7.5%</td><td><strong>17.9%</strong></td><td><strong>21.2%</strong></td><td>13.6%</td></tr><tr><td>Superior</td><td>5.1%</td><td><strong>18.2%</strong></td><td><strong>24.2%</strong></td><td>12.8%</td></tr><tr><td><strong>Occupation</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Inactive / Seeking work</td><td><strong>36.3%</strong></td><td>6.5%</td><td>1.5%</td><td>19.2%</td></tr><tr><td>Professional / Technical worker</td><td>5.1%</td><td><strong>15.7%</strong></td><td><strong>24.2%</strong></td><td>11.5%</td></tr><tr><td>Administrator / Executive</td><td>2.8%</td><td><strong>8.1%</strong></td><td>7.6%</td><td>5.8%</td></tr><tr><td>Office staff</td><td>2.4%</td><td><strong>5.6%</strong></td><td>3.0%</td><td>4.1%</td></tr><tr><td>Skilled worker</td><td>4.7%</td><td>5.4%</td><td>3.0%</td><td>5.0%</td></tr><tr><td>Non-skilled worker</td><td><strong>5.4%</strong></td><td>3.2%</td><td>3.0%</td><td>4.2%</td></tr><tr><td>Service worker</td><td><strong>12.0%</strong></td><td>7.5%</td><td><strong>18.2%</strong></td><td>10.0%</td></tr><tr><td>Other</td><td><strong>8.0%</strong></td><td>4.8%</td><td>3.0%</td><td>6.1%</td></tr><tr><td>Retired</td><td>23.2%</td><td><strong>43.2%</strong></td><td>36.4%</td><td>34.2%</td></tr><tr><td><strong>Employment status</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>No job</td><td><strong>59.5%</strong></td><td>49.6%</td><td>37.9%</td><td>53.3%</td></tr><tr><td>Self-employed with employees</td><td>2.3%</td><td>2.1%</td><td><strong>9.1%</strong></td><td>2.5%</td></tr><tr><td>Self-employed with no employees</td><td><strong>12.4%</strong></td><td>6.5%</td><td>9.1%</td><td>9.2%</td></tr><tr><td>Paid employee</td><td>24.8%</td><td><strong>41.4%</strong></td><td>43.9%</td><td>34.3%</td></tr><tr><td>Other status</td><td>1.0%</td><td>0.4%</td><td>0%</td><td>0.7%</td></tr><tr><td><strong>Work unit</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>No job</td><td><strong>59.5%</strong></td><td>49.6%</td><td>37.9%</td><td>53.3%</td></tr><tr><td>Government / State service</td><td>8.9%</td><td><strong>25.6%</strong></td><td>25.8%</td><td>18.3%</td></tr><tr><td>State-owned enterprise</td><td>3.8%</td><td><strong>7.0%</strong></td><td>6.1%</td><td>5.6%</td></tr><tr><td>Collective / Private / Individual enterprise</td><td><strong>27.7%</strong></td><td>17.8%</td><td>30.3%</td><td>22.7%</td></tr></tbody></table>

Notes: (1) Bold characters denote the fact that the value is significantly higher in the cluster than in the rest of the population (adjusted standardized residuals of $khi^2$ for categorical variables, $p < 0.05$ and independent samples t-test for continuous variables, $p < 0.10$); italic characters do the same for values significantly lower in the cluster than in the rest of the population.

Source: CHNS (2009).
<table><caption>Table 4.3 : Characteristics (characterization variables) of “poor”, “middle-class” [10,000 yuan ; 95th percentile] and “rich” (urban households, 2009)¹</caption><thead><tr><th></th><th>Poor<br>N=573</th><th>Middle-class<br>N=681</th><th>Rich<br>N=66</th><th>All<br>N=1320</th></tr></thead><tbody><tr><td><strong>Province</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Liaoning</td><td>7.2%</td><td><strong>11.7%</strong></td><td><strong>12.1%</strong></td><td>9.8%</td></tr><tr><td>Heilongjiang</td><td>5.1%</td><td><strong>16.9%</strong></td><td><strong>16.7%</strong></td><td>11.7%</td></tr><tr><td>Jiangsu</td><td>7.7%</td><td><strong>13.8%</strong></td><td><strong>18.2%</strong></td><td>11.4%</td></tr><tr><td>Shandong</td><td>11.0%</td><td>10.6%</td><td><strong>18.2%</strong></td><td>11.1%</td></tr><tr><td>Henan</td><td>12.4%</td><td>10.7%</td><td>6.1%</td><td>11.2%</td></tr><tr><td>Hubei</td><td>12.4%</td><td>9.5%</td><td>6.1%</td><td>10.6%</td></tr><tr><td>Hunan</td><td><strong>14.1%</strong></td><td>10.3%</td><td>9.1%</td><td>11.9%</td></tr><tr><td>Guangxi</td><td><strong>18.0%</strong></td><td>6.3%</td><td>1.5%</td><td>11.1%</td></tr><tr><td>Guizhou</td><td>12.2%</td><td>10.1%</td><td>12.1%</td><td>11.1%</td></tr><tr><td><strong>Head's characteristics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Age (mean)</td><td>57.5</td><td>57.6</td><td>57.7</td><td>57.6</td></tr><tr><td>Married</td><td>77.5%</td><td><strong>84.4%</strong></td><td><strong>90.9%</strong></td><td>81.7%</td></tr><tr><td>Female</td><td><strong>27.9%</strong></td><td>22.5%</td><td>10.6%</td><td>24.2%</td></tr><tr><td>Rural hukou</td><td><strong>12.7%</strong></td><td>4.9%</td><td>9.2%</td><td>8.4%</td></tr><tr><td><strong>Housing</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Tenant</td><td><strong>16.5%</strong></td><td>8.7%</td><td>7.7%</td><td>12.1%</td></tr><tr><td>Homeowner</td><td><strong>33.3%</strong></td><td>16.4%</td><td>18.5%</td><td>24.0%</td></tr><tr><td>Homeowner (purchased)</td><td>50.2%</td><td><strong>74.9%</strong></td><td>73.8%</td><td>63.8%</td></tr><tr><td>Surface per capita (mean)</td><td>39.5</td><td><strong>43.7</strong></td><td>43.6</td><td>41.9</td></tr><tr><td>Rooms per capita (mean)</td><td>1.5</td><td><strong>1.8</strong></td><td>1.9</td><td>1.7</td></tr><tr><td><strong>Household size and activity</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Household size (mean)</td><td><strong>3.2</strong></td><td>2.6</td><td>2.5</td><td>2.9</td></tr><tr><td>Activity ratio (mean)</td><td>0.28</td><td><strong>0.40</strong></td><td><strong>0.47</strong></td><td>0.35</td></tr><tr><td><strong>Household income</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Annual per capita income (mean)</td><td>5,063</td><td><strong>18,171</strong></td><td><strong>66,299</strong></td><td>14,887</td></tr><tr><td><em>Household income structure</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Business income</td><td><strong>11.1%</strong></td><td>7.9%</td><td>13.6%</td><td>9.5%</td></tr><tr><td>Agricultural income</td><td><strong>3.9%</strong></td><td>0.9%</td><td>0.1%</td><td>2.1%</td></tr><tr><td>Wage</td><td>39.2%</td><td><strong>46.1%</strong></td><td>48.0%</td><td>43.3%</td></tr><tr><td>Retirement income</td><td>23.1%</td><td><strong>39.3%</strong></td><td>31.0%</td><td>32.0%</td></tr><tr><td>Other income</td><td><strong>22.8%</strong></td><td>5.8%</td><td>7.2%</td><td>13.1%</td></tr><tr><td><strong>Household appliances and equipment</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Own a computer</td><td>28.1%</td><td><strong>47.1%</strong></td><td><strong>59.1%</strong></td><td>39.4%</td></tr><tr><td>Own a car</td><td>4.0%</td><td><strong>6.9%</strong></td><td><strong>15.1%</strong></td><td>6.1%</td></tr><tr><td>Equipment goods index (mean)</td><td>0.42</td><td><strong>0.55</strong></td><td><strong>0.62</strong></td><td>0.50</td></tr><tr><td><strong>Urbanicity</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Urbanization index (mean)</td><td>83.9</td><td><strong>88.1</strong></td><td><strong>88.4</strong></td><td>86.4</td></tr></tbody></table>

Notes: (1) Bold characters denote the fact that the value is significantly higher in the cluster than in the rest of the population (adjusted standardized residuals of $khi^2$ for categorical variables, $p < 0.05$ and independent samples t-test for continuous variables, $p < 0.10$); italic characters do the same for values significantly lower in the cluster than in the rest of the population.

Source: CHNS (2009).
Household heads in rich and poor categories are significantly more likely to be employed in collective and private enterprises (respectively 30.3% and 27.7%) and are also more likely to be self-employed (with employees in the case of the rich group and without employees in the case of the poorest group). These results show that entrepreneurship is not specific to the middle class and, therefore, that the Chinese urban middle class cannot be limited to the “old middle class”. The strong representation of the middle class in the salaried employee category and in the public sector confirms that there is a middle class of wage earners in China, the expansion of which has been promoted by the Chinese authorities (Chunling, 2010; Li, 2010).

The description of the three income groups based on characterization variables (Table 4.3) calls for several additional comments. The provincial location of different income groups reflects the traditional pattern of the distribution of wealth between Chinese provinces. The richest category tends to be more concentrated in the coastal province of Shandong, while the poorest group is overrepresented in poorer inland provinces (Guangxi, Hunan, Hubei). Members of the middle class are significantly more likely to live in Jiangsu, the richest province in our dataset, and in the northeastern provinces of Heilongjiang and Liaoning.<fnref n="12" /> Furthermore, the Chinese urban middle class has specific characteristics in terms of housing. A higher percentage of its members are homeowners (75%) and have significantly more spacious homes (44m² on average). This may be evidence of the role of housing in the rise of middle class values, as documented by Tomba (2004, 2010). In terms of household appliances and equipment, the middle class is in an intermediate position between the poor and the rich. For example, 47% of middle-class households own a computer, while 7% own a car. The household appliances and equipment index is also significantly higher in the middle class group than among poor households but significantly lower than for rich households. Finally, the urbanicity index for the middle class is close to the index for the rich category. This indicates that households belonging to the middle class are significantly more likely to live in highly urbanized areas characterized by high population density, significant economic activity and coexisting traditional and modern markets, but also also a high level of sanitation, housing, education, health infrastructures, transport and communication infrastructures, etc.

<footnote n="12">¹² In 2011, Jiangsu had the second highest regional GDP after Guangdong. Jiangsu has benefited greatly from the open door policy since the late 1970s and has become one of the most important locations for exports and foreign direct investment. Heilongjiang and Liaoning are industrialized provinces and have benefited from a policy to revitalize the old industrial bases of Northeast China implemented in 2003.</footnote>
## 3.2. The heterogeneous composition of China's middle class

To analyze the composition of the urban middle class, we apply a mixed classification approach to the four variables describing the educational and occupational profile of the households included in the sample (level of education, primary occupation, employment status and work unit) of households heads belonging to the middle income class. Mixed classification involves hierarchical cluster analysis and consolidates the appropriate partition through k-means iterations aimed at maximizing between-cluster variance while minimizing within-cluster variance. The appropriate partition, i.e. the appropriate number of clusters, is based on the analysis of the dendrogram and the analysis of two indicators that respectively (i) maximize the marginal improvement of the between to within-cluster variance ratio from one partition to another and (ii) minimize the effect of k-means clustering on that ratio. Based on these criteria, we adopt a four-group classification. Finally, we compare the distributions of each active (classification) variable to give a precise description of each cluster (Table 4.4). We also provide a detailed examination of clusters by comparing the distributions (or means) of a set of additional informative (characterization) variables (Table 4.5).

### 3.2.1. First group (A): Elderly and inactive middle class

The first cluster includes households headed by someone with a low level of education and who is retired, inactive or unemployed. Elderly households represent 87% of the membership of this group. From our point of view, the identification of an elderly and inactive middle class in urban China is a new finding, particularly since the share of this group in the middle class as a whole is approximately 50%. The chief focus of sociological research on the Chinese middle class is the working-age population. In other words, sociological studies do not consider the possibility of an elderly middle class.

Conducting an investigation at a household level helps to broaden the scope of the analysis. Another interesting result is that, with a mean income significantly higher than the three other groups put together, elderly households are located at the top of the income distribution in the middle class. This result is difficult to interpret. The role of the urban pension system is a crucial factor in explaining the relatively favourable position of elderly people since almost 70% of the mean income of this group is composed of retirement income
(Table 4.5). Despite the constraints related to population aging that threaten the financial stability of the pension system, there have been improvements in the provision of support to the elderly at two levels. First, the coverage of the urban pension system has improved as a result of various institutional reforms (Wang, 2006).<fnref n="13" /> Second, the Chinese authorities recently introduced a series of reforms aimed at increasing pensions. For example, for eight consecutive years, the pension paid to enterprise retirees has increased significantly: in 2012, the average basic pension was 1,721 yuan, compared to 700 yuan in 2005.<fnref n="14" />

When looking at income structure (Table 4.5), the significant share of wages and other income (whose main component is private transfers from other households) indicates that elderly urban households also depend, at least partly, on their children and relatives, whether they belong to the same household or not. This form of financial support to the elderly is deeply ingrained in Chinese culture through the tradition of filial piety (Cheung and Kwan, 2009). However, the level of support seems relatively limited since wages and other income only account for 25% of the income of elderly households. The limited dependency of the elderly on family support is probably the result of the erosion of support from children, a trend related to modernization (Cheung and Kwan, 2009) and the decline of cohabitation with children or other relatives (Meng and Luo, 2008), but also the expansion of the pension system (Mu and Du, 2012).

Finally, the elderly and the inactive middle class seem largely insensitive to modernism and are unlikely to drive socio-economic change. The household appliances and equipment index and the computer and car ownership rates are significantly lower than in other middle class groups. Elderly households are also more likely to be tenants. We may argue that the values of the elderly and the inactive middle class are unrelated to modern, westernized consumption patterns.

<footnote n="13">¹³ One of the most emblematic reforms is the 1997 reform, which established a three-pillar system for old-age pensions for enterprise employees (in line with World Bank recommendations). The system combines (i) a pay-as-you-go basic pension; (ii) a supplementary pension based on individual accounts and (iii) a voluntary pension scheme.</footnote>

<footnote n="14">¹⁴ http://www.cs.com.cn/english/finance/201301/t20130115_3820292.html.</footnote>
<table><caption>Table 4.4: Characteristics (classification variables) of clusters derived from mixed classification procedure (urban households, 2009)¹</caption><thead><tr><th>Classification variables</th><th>A<br>N=338</th><th>B<br>N=83</th><th>C<br>N=79</th><th>D<br>N=181</th><th>All MC<br>N=681</th><th>All<br>N=1320</th></tr></thead><tbody><tr><td><strong>Education</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No school</td><td><strong>17.2%</strong></td><td>10.8%</td><td>0%</td><td>0.6%</td><td>10.0%</td><td>14.2%</td></tr><tr><td>Primary school</td><td><strong>14.2%</strong></td><td>7.2%</td><td>3.8%</td><td>0.6%</td><td>8.5%</td><td>13.3%</td></tr><tr><td>Secondary school</td><td>42.6%</td><td><strong>69.9%</strong></td><td><strong>73.4%</strong></td><td>27.1%</td><td>45.4%</td><td>46.1%</td></tr><tr><td>Technical /Vocational degree</td><td>13.0%</td><td>9.6%</td><td>20.3%</td><td><strong>29.8%</strong></td><td>17.9%</td><td>13.6%</td></tr><tr><td>Superior</td><td>13.0%</td><td>2.4%</td><td>2.5%</td><td><strong>42.0%</strong></td><td>18.2%</td><td>12.8%</td></tr><tr><td><strong>Occupation</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Inactive / Seeking work</td><td><strong>13.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>6.5%</td><td>19.2%</td></tr><tr><td>Professional or technical worker</td><td>0%</td><td>8.4%</td><td>11.4%</td><td><strong>50.3%</strong></td><td>15.7%</td><td>11.5%</td></tr><tr><td>Administrator / Executive</td><td>0%</td><td>4.8%</td><td>8.3%</td><td><strong>24.3%</strong></td><td>8.1%</td><td>5.8%</td></tr><tr><td>Office staff</td><td>0%</td><td>1.2%</td><td>2.5%</td><td><strong>19.3%</strong></td><td>5.6%</td><td>4.1%</td></tr><tr><td>Skilled worker</td><td>0%</td><td>2.4%</td><td><strong>44.3%</strong></td><td>0%</td><td>5.4%</td><td>5.0%</td></tr><tr><td>Non-skilled worker</td><td>0%</td><td>1.2%</td><td><strong>26.6%</strong></td><td>0%</td><td>3.2%</td><td>4.2%</td></tr><tr><td>Service worker</td><td>0%</td><td><strong>51.8%</strong></td><td>5.1%</td><td>2.2%</td><td>7.5%</td><td>10.0%</td></tr><tr><td>Other</td><td>0%</td><td><strong>30.1%</strong></td><td>1.3%</td><td>3.9%</td><td>4.8%</td><td>6.1%</td></tr><tr><td>Retired</td><td><strong>87.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>43.2%</td><td>34.2%</td></tr><tr><td><strong>Employment status</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No job</td><td><strong>100.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>49.6%</td><td>53.3%</td></tr><tr><td>Self-employed with employees</td><td>0%</td><td><strong>16.9%</strong></td><td>0%</td><td>0%</td><td>2.1%</td><td>2.5%</td></tr><tr><td>Self-employed with no employees</td><td>0%</td><td><strong>53.0%</strong></td><td>0%</td><td>0%</td><td>6.5%</td><td>9.2%</td></tr><tr><td>Paid employee</td><td>0%</td><td>26.5%</td><td><strong>100.0%</strong></td><td><strong>100.0%</strong></td><td>41.4%</td><td>34.3%</td></tr><tr><td>Other status</td><td>0%</td><td><strong>3.6%</strong></td><td>0%</td><td>0%</td><td>0.4%</td><td>0.7%</td></tr><tr><td><strong>Work unit</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No job</td><td><strong>100.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>49.6%</td><td>53.3%</td></tr><tr><td>Government / State service</td><td>0%</td><td>1.2%</td><td>12.7%</td><td><strong>90.1%</strong></td><td>25.6%</td><td>18.3%</td></tr><tr><td>State-owned enterprise</td><td>0%</td><td>0%</td><td><strong>58.2%</strong></td><td>1.1%</td><td>7.0%</td><td>5.6%</td></tr><tr><td>Collective / Private / Individual enterprise</td><td>0%</td><td><strong>98.8%</strong></td><td><strong>29.1%</strong></td><td>8.8%</td><td>17.8%</td><td>22.7%</td></tr></tbody></table>

Notes: (1) Bold characters denote the fact that the value is significantly higher in the cluster than in the rest of the middle class (adjusted standardized residuals of $khi^2$ for categorical variables, $p < 0.05$ and independent samples t-test for continuous variables, $p < 0.10$); italic characters do the same for values significantly lower in the cluster than in the rest of the middle class.

Source: CHNS (2009).

### 3.2.2. Second group (B): “Old” middle class

The second cluster is mainly composed of households headed by self-employed workers, either with no employee (53%) or with employees (17%). However, it is important to note that the second group also includes wage earners since more than 25% of household heads are salaried employees. Another distinctive feature of this group is the fact that household heads are not involved in the public sector but are almost exclusively employed in
collective and private enterprises. They are also more likely to work as service workers, whatever their employment status or work unit. The occupational profile of this group suggests a small business community often referred as the ‘old’ middle class in the literature on social stratification (Chunling, 2010). This class (not to be confused with the capitalist class composed of managers and owners of large export-oriented firms) is composed of moderately well-off households. First, as shown in Table 4.5, the mean per capita income is lower than the rest of the middle class. Second, approximately 20% of household heads in the old middle class are rural *hukou* holders. As noted by Lu (2002), a significant proportion of this small business community consists of rural-to-urban migrants, many of whom were originally peasants. Once they have migrated to cities, their rural hukou status means that they are often subject to wage and employment discrimination and have limited access to education, housing and social benefits (Liu, 2005). Because of barriers to entry into the formal urban sector, they often opt to open small informal businesses to improve their socio-economic status (Meng, 2001).<fnref n="15" /> It also should be noted that the old middle class is relatively small since it represents just 12% of the middle class as a whole (which includes a non-negligible proportion of wage earners, as noted above). Therefore, in the Chinese context, entrepreneurship is not the main determinant of middle class membership.

Characterization variables provide useful additional information. Households belonging to the small business class are more likely to own a car (over 15%, compared to 7% for the middle class as a whole). One possible interpretation is that their entrepreneurial activity requires a high degree of mobility. Households belonging to this group also tend to live in urban areas with a low degree of urbanicity and are overrepresented among long-standing homeowners. This suggests that the old middle class tends not to live in modern urban areas.

### 3.2.3. Third group (C): “Marginal” middle class

The third group identified in this study describes households whose heads are low-wage earners employed by enterprises (whether private, collective or state-owned) and skilled

<footnote n="15">¹⁵ Meng (2001) explains that migrants in the urban informal sector have two employment options: salaried employment or self-employment. Meng shows that being employed as a wage earner is a default strategy, while self-employment is a deliberate choice.</footnote>
or unskilled workers. Their level of education is mostly intermediate (middle school and technical or vocational degree). This cluster corresponds to a working class whose members have benefited from economic reforms aimed at promoting industrialization (many of them have secured a permanent contract in the expanding manufacturing sector). Job stability partly explains why they have reached a standard of living high enough to ensure a certain level of socio-economic security and to avoid poverty.

However, on average, households in this group have a significantly lower annual per capita income than other middle class groups. The existence of a lower component among the Chinese urban middle class is an interesting result consistent with the findings of Chunling (2010). Using the same terminology as Chunling (2010), this lower category may be defined as the marginal middle class. However, the share of the lower middle class in the middle class as a whole is relatively limited (no more than 12%). In terms of characterization variables, it is impossible to establish a profile in terms of place of residence (province, urbanicity) or equipment (household appliances). However, as we might have anticipated, households in the lower middle class tend to live in significantly smaller dwellings than other groups.

### 3.2.4 Fourth group (D): “New” middle class

The last group comprises households whose heads are well-educated higher-wage earners (mainly professional and technical workers, administrators, executives and office workers). Members of this group are almost exclusively employed in the public sector as state employees (over 90%). This suggests that the Chinese authorities have played a crucial role in promoting the expansion of the middle class. Tomba (2004) argued that the development of a category of salaried professionals in the public sector “has been as much the outcome of the social engineering project of the contemporary reformist state and its agencies as it has been a consequence of the opening up of the economy and society” (Tomba, 2004: 3). To support this idea, Tomba (2004) explains that, in the mid-1990s, the Chinese state significantly increased the wages of employees working in government departments and state services, particularly in sectors such as health care, education and science and academia. Based on Chunling (2010), the fourth component may be described as the ‘new’ middle class. It is the richest component of the Chinese urban middle class, with a mean annual per capita household income of around 19,000 yuan, which is significantly higher than other middle
class groups (particularly the old and lower middle class groups). The new middle class accounts for approximately 26% of the middle class as a whole and is likely the most visible component in terms of consumption behaviors.

From our point of view, the characterization variables show that the new middle class is attracted to modern lifestyles. Three arguments support this view. First, households belonging to this group have the highest household appliances and equipment index and the highest computer ownership rate (approximately 67% compared to 47% for the middle class as a whole), suggesting that they have a significant interest in high-technology and high-quality products. Second, households belonging to the new middle class tend to have purchased their dwelling recently and to live in high-urbanicity areas.<fnref n="16" /> This indicates that they have a strong preference for modern housing in areas offering high-quality services (transportation, communication, education, health care, etc.) and confirms the role of housing and place of residence as social markers in urban China (Li and Niu, 2003; Tomba, 2004, 2010). Tomba (2004) provides evidence of the construction of “new cities” designed as residential compounds for the professional middle class. Tomba (2004) also emphasizes the active role of the Chinese authorities in promoting residential segregation through its involvement in housing distribution and through the provision of homeownership subsidies, mainly for public sector employees. Three, although the proportion of married household heads is not statistically different between the old middle class, the marginal middle class and the new middle class, the share of female-headed households is significantly greater in the new middle class. This may be evidence of the increasing access of women to high-paying jobs in the public sector, but also of their increasing role in urban Chinese families with a high socio-economic status. In line with modernization, their increasing role could be interpreted as a specific dimension of a more general transformation of traditional family values that also includes the transformation of intergenerational relations and the decline of filial piety (Whyte, 2005).

<footnote n="16">¹⁶ Table 4.5 also shows that members of the new middle class tend to live in smaller dwellings than other middle-class groups, perhaps because they tend to be recent homeowners and have been constrained by the growth of the real estate market.</footnote>
<table><caption>Table 4.5: Characteristics (characterization variables) of clusters derived from mixed classification procedure (urban households, 2009)</caption><thead><tr><th>Characterization variables</th><th>A<br>N=338</th><th>B<br>N=83</th><th>C<br>N=79</th><th>D<br>N=181</th><th>All MC<br>N=681</th><th>All<br>N=1320</th></tr></thead><tbody><tr><td><strong>Province</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Liaoning</td><td>13.0%</td><td>9.6%</td><td>13.9%</td><td>9.4%</td><td>11.7%</td><td>9.8%</td></tr><tr><td>Heilongjiang</td><td>13.3%</td><td>2.4%</td><td>21.5%</td><td><strong>28.2%</strong></td><td>16.9%</td><td>11.7%</td></tr><tr><td>Jiangsu</td><td>15.7%</td><td>20.5%</td><td>11.4%</td><td>8.3%</td><td>13.8%</td><td>11.4%</td></tr><tr><td>Shandong</td><td>12.1%</td><td>8.4%</td><td>8.9%</td><td>9.4%</td><td>10.6%</td><td>11.1%</td></tr><tr><td>Henan</td><td>10.4%</td><td>16.9%</td><td>10.1%</td><td>8.8%</td><td>10.7%</td><td>11.2%</td></tr><tr><td>Hubei</td><td>7.4%</td><td>8.4%</td><td><strong>17.7%</strong></td><td>10.5%</td><td>9.5%</td><td>10.6%</td></tr><tr><td>Hunan</td><td>9.5%</td><td>13.3%</td><td>3.8%</td><td>13.3%</td><td>10.3%</td><td>11.9%</td></tr><tr><td>Guangxi</td><td>7.4%</td><td><strong>13.3%</strong></td><td>6.3%</td><td>1.1%</td><td>6.3%</td><td>11.1%</td></tr><tr><td>Guizhou</td><td>11.2%</td><td>7.2%</td><td>6.3%</td><td>11.0%</td><td>10.1%</td><td>11.1%</td></tr><tr><td><strong>Head's characteristics</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age (mean)<sup>2</sup></td><td><strong>67.3</strong></td><td><strong>49.7</strong></td><td>48.4</td><td>47.1</td><td>57.6</td><td>57.6</td></tr><tr><td>Married<sup>2</sup></td><td>77.2%</td><td>94.0%</td><td>94.9%</td><td>89.0%</td><td>84.4%</td><td>81.7%</td></tr><tr><td>Female<sup>2</sup></td><td><strong>32.0%</strong></td><td>8.4%</td><td>6.3%</td><td><strong>18.2%</strong></td><td>22.5%</td><td>24.0%</td></tr><tr><td>Rural hukou</td><td>3.8%</td><td><strong>19.7%</strong></td><td>4.0%</td><td>1.1%</td><td>4.9%</td><td>8.4%</td></tr><tr><td><strong>Housing</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tenant</td><td><strong>13.6%</strong></td><td>11.0%</td><td>0%</td><td>2.2%</td><td>8.7%</td><td>12.0%</td></tr><tr><td>Homeowner</td><td>13.6%</td><td><strong>43.9%</strong></td><td>20.5%</td><td>7.2%</td><td>16.4%</td><td>23.8%</td></tr><tr><td>Homeowner (purchased)</td><td>72.8%</td><td>45.1%</td><td>79.5%</td><td><strong>90.6%</strong></td><td>74.9%</td><td>64.2%</td></tr><tr><td>Surface per capita (mean)</td><td>46.7</td><td>51.5</td><td>34.8</td><td>38.6</td><td>43.7</td><td>41.9</td></tr><tr><td>Rooms per capita (mean)</td><td><strong>2.0</strong></td><td>1.9</td><td>1.4</td><td>1.6</td><td>1.8</td><td>1.7</td></tr><tr><td><strong>Household size and activity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Household size (mean)<sup>2</sup></td><td>2.5</td><td><strong>3.0</strong></td><td>2.8</td><td>2.6</td><td>2.7</td><td>2.9</td></tr><tr><td>Activity ratio (mean)<sup>2</sup></td><td>0.13</td><td>0.65</td><td>0.68</td><td>0.68</td><td>0.40</td><td>0.35</td></tr><tr><td><strong>Household income</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Annual per capita income (mean)</td><td><strong>18,571</strong></td><td>16,818</td><td>16,266</td><td><strong>18,874</strong></td><td>18,171</td><td>14,887</td></tr><tr><td><em>Household income structure</em></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Business income</td><td>5.9%</td><td><strong>35.7%</strong></td><td>0.9%</td><td>2.4%</td><td>7.9%</td><td>9.5%</td></tr><tr><td>Agricultural income</td><td>0.6%</td><td><strong>4.4%</strong></td><td>0.6%</td><td>0%</td><td>0.9%</td><td>2.1%</td></tr><tr><td>Wage</td><td>16.8%</td><td>42.4%</td><td><strong>82.6%</strong></td><td><strong>86.8%</strong></td><td>46.1%</td><td>43.3%</td></tr><tr><td>Retirement income</td><td><strong>68.9%</strong></td><td>12.2%</td><td>12.3%</td><td>7.5%</td><td>39.3%</td><td>32.0%</td></tr><tr><td>Other income</td><td><strong>7.8%</strong></td><td>5.3%</td><td>3.7%</td><td>3.2%</td><td>5.8%</td><td>13.1%</td></tr><tr><td><strong>Household appliances and equipment</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Own a computer</td><td>36.3%</td><td>41.5%</td><td>54.4%</td><td><strong>66.7%</strong></td><td>47.1%</td><td>39.4%</td></tr><tr><td>Own a car</td><td>5.3%</td><td><strong>15.7%</strong></td><td>3.8%</td><td>7.2%</td><td>6.9%</td><td>6.1%</td></tr><tr><td>Equipment goods index (mean)</td><td>0.51</td><td>0.52</td><td>0.57</td><td><strong>0.62</strong></td><td>0.55</td><td>0.50</td></tr><tr><td><strong>Urbanicity</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urbanization index (mean)</td><td>88.0</td><td>85.2</td><td>88.1</td><td><strong>89.6</strong></td><td>88.1</td><td>86.4</td></tr></tbody></table>

Notes: (1) Bold characters denote the fact that the value is significantly higher in the cluster than in the rest of the population (adjusted standardized residuals of $khi^2$ for categorical variables, $p < 0.05$ and independent samples t-test for continuous variables, $p < 0.10$); italic characters do the same for values significantly lower in the cluster than in the rest of the middle class. (2) For these variables, the significance of values is tested in clusters B, C and D by comparison with the rest of the middle class in these same clusters, in order to avoid the bias due to the strong proportion of elderly in group A.
Source: CHNS (2009).
Conclusion

This chapter does not claim to provide an accurate estimate of the size of the Chinese urban middle class since CHNS data are not nationally representative and do not include a number of major cities, but also because the identification of the middle class implies making difficult and debatable methodological choices. The study had two main objectives: (i) to analyze the structure of the Chinese urban middle class and to demonstrate its heterogeneity; (ii) to propose an innovative statistical approach combining an economic definition and a sociological definition of the middle class.

First, we defined an income criterion to delimit the boundaries of the Chinese middle class. After comparing several income criteria, we chose to define the middle income class as households with an annual per capita income of between 10,000 yuan and the 95th percentile of the income distribution. We showed that the growth of the middle class is a recent phenomenon that began in the early 2000s. For 2009, we estimated the size of the urban middle class in the nine provinces covered by CHNS data at nearly 50% of the total population. We also showed that the urban middle class has specific occupational and educational characteristics compared to the poorest group and the rich.

Second, we used a classification procedure using information on occupation and education to examine the composition of the middle income class. The cluster analysis showed the significant heterogeneity of the urban middle class by identifying four distinct clusters: (i) the “elderly and the inactive middle class”, mainly composed of pensioners; (ii) the “old middle class”, composed of self-employed workers; (iii) the “marginal middle class”, composed of skilled and unskilled workers; and (iv) the “new middle class”, composed of highly educated and highly-paid employees in the public sector. The core of the Chinese urban middle class was found to be composed of elderly households and high-earning households accounting for approximately 80% of the urban middle class as a whole.

It is important to emphasize that the four groups have distinctive features when it comes to housing, household appliances and equipment and province of residence. More specifically, our results suggest that the four components of the middle class have different consumption patterns. The Chinese middle class is often seen as driving the consumer revolution by adopting modern, westernized consumption patterns. Our study shows that these behaviors are mainly characteristic of the “new middle class”. The “new middle class” is
clearly rooted in modernity, with relatively high computer ownership rates and a taste for modern housing. This result has two implications. First, of the four components of the Chinese urban middle class, the new middle class appears to be the main driver of socio-economic change through consumption. The role of the upper middle class in the diffusion of new consumption patterns is probably explained by its greater exposure to globalization and westernization, but also by the active role of the Chinese state in promoting high-wage earners in the public sector (Tomba, 2004). However, it is important to keep in mind that the “new middle class” accounts for less than 14% of urban households in our sample. Second, consistent with Elfick (2011) and Tomba (2004), we suggest that the development of new consumption patterns is indicative of the emergence of common values among the upper middle class, a key factor in the formation of class identity.
Chapter 5

Social class and body weight among
Chinese urban adults: the role of the middle
classes in the nutrition transition

Ce chapitre est une version enrichie d'un article coécrit avec Matthieu Clément et soumis à la revue *Social Science and Medicine* (octobre 2013).

Introduction

In 2008, about 1.5 billion adults were overweight in the world, of whom 500 million were considered obese by international standards (WHO, 2012). As evidenced by the recently coined term “globesity”, the global epidemic of obesity has become a worldwide problem, not only in rich countries but also in poorer ones (Zhang *et al.*, 2008). Currently, an estimated 115 million people affected by obesity-related problems live in the developing world. As a result, a growing number of developing countries, especially emerging ones, are now shouldering the double burden of malnutrition, i.e. the persistence of under-nutrition and people who are underweight, along with an upsurge in the number of people who are overweight or obese (FAO, 2006). The concept of nutrition transition is often invoked to explain these recent and rapid changes. It describes the near-universal changes in dietary patterns, nutrient intakes and physical activity when populations adopt modern lifestyles during economic development and urbanization (Popkin, 1993).

China is a relevant example illustrating these recent shifts in the developing world.
The country is undergoing a rapid nutrition transition, and has gone through several stages
over roughly half a century (Popkin, 1993; Du *et al.*, 2002). Such rapid changes have been
fostered by the improvement in food supply and the increasing diversity of food products since the mid-1980s (Popkin *et al.*, 2001, Wang *et al.*, 2007b).<fnref n="1" /> Concomitantly, a consumer revolution has emerged, resulting in striking changes as regards food consumption. Chinese people, especially in coastal cities, are experiencing a “tendency to shift from eat-to-live to live-to-eat” (Zhang *et al.*, 2008: 39). According to Popkin, “the shift in the Chinese diet follows a classic Westernisation pattern” (2003: 587), as exemplified by the proliferation of fast food outlets since the late 1980s. The traditional Chinese diet, with its high carbohydrate content composed of rice, wheat and cooked vegetables, is being replaced by diets higher in fat (Popkin *et al.*, 2001; Wang *et al.*, 2007a). Over the past twenty years, the consumption of animal food and edible oil has increased dramatically, while the intake of cereals and starchy roots has declined (Zhai *et al.*, 2009b). This obviously reflects the improvement in quality of the average Chinese diet through an increase in energy and protein intakes. However, the downside of this shift is that a high intake of dietary energy and dietary fat, associated with a decrease in physical activity, are partially responsible for the rise in the number of overweight and obese Chinese people, especially for urban inhabitants (Zhai *et al.*, 2009b).

A growing number of scholars and experts have documented the rapid upsurge of such nutrition-related health problems in the country since the late 1980s (Du *et al.*, 2004; Wang *et al.*, 2007b; Zhang *et al.*, 2008). Even if the prevalence of overweight and obesity in China is relatively moderate compared to other developing countries (below 30% according to Zhang *et al.*, 2008),<fnref n="2" /> the increasing trend is alarming. Nowadays, nearly one in five people suffering from obesity in the world are Chinese (Wu, 2006) and it is estimated that the number of obese people in China will exceed that of the United States by 2020 (Levine, 2008). Not surprisingly, the prevalence of overnutrition is higher in urban areas, although the rise is particularly pronounced in rural areas (Wu *et al.*, 2009). Also of note is that this upward trend is more conspicuous among urban men, while urban women seem to demonstrate a relatively greater resistance (Wang *et al.*, 2007a; Wu *et al.*, 2009).<fnref n="3" /> In fact, the rapid modernization and westernization of urban areas expose Chinese people to an increasingly “obesogenic environment”, characterised by the “abundance of stimuli to encourage consumers to eat and

<footnote n="1">¹ During the beginning of economic reforms, Chinese authorities have implemented a set of food policies. For instance, government food procurement was relaxed in 1985, and the food grain rationing system was abolished in 1993.</footnote>

<footnote n="2">² As a comparison nearly 50% of the population is overweight in Brazil and 70% in Mexico (Zhang et al., 2008).</footnote>

<footnote n="3">³ Scholars have also focused on the increase in childhood obesity, linked to the one-child policy and the development of the “little emperor syndrome” (Jing, 2000; Ma, 2012).</footnote>
*to consume more on the one hand, and to exercise less on the other'* (Zhang *et al.*, 2008: 41). Chinese authorities have recently recognized obesity and the associated chronic diseases (such as diabetes or cardiovascular diseases) as an important public health concern (Wu, 2006). The government has started to provide some guidelines to combat the adverse effects of the nutrition transition and has taken steps to prevent obesity and to control the number of overweight and obese people in the country (Zhai *et al.*, 2009a).<fnref n="4" />

Analysing the shifts in dietary patterns associated with the nutrition transition is of particular interest in order to understand the rise in obesity and eventually tackle this health issue. Indeed, diet and physical activity, as well as food likes, constitute risk factors identified as proximal causes of disease by most epidemiologic studies. However, the seminal work of Link and Phelan (1995) suggested that social factors, such as socio-economic status, should be considered as *"fundamental causes of disease"*. In this regard, China is an interesting case insofar as its socio-economic structure was largely shaped after the beginning of economic reforms in the early 1980s. The sustained economic development over the past three decades has been followed by a spectacular increase in mean per capita income, which has recently promoted the expansion of the Chinese urban middle class. The Chinese middle class is considered to be at the vanguard of consumption (Xiaohong and Chen, 2010), and is often seen as a catalyst class that drives changes in attitudes, behaviours and values (Elfick, 2011). As a consequence, the middle class may have played an important role in the adoption of modern food consumption patterns and may have a particular relationship with body weight compared to the rich or the poor. However, the Chinese middle class is heterogeneous and consists of a complex mosaic of groups and individuals that differ in terms of many social, economic and demographic characteristics (Li, 2010). We suggest that the different components of the middle class do not necessarily have the same values regarding food consumption and body perception.

This chapter has two main purposes. First, we propose to strengthen the analysis of the social determinants of body weight by creating an indicator of social class which accounts for a wide range of socioeconomic variables. This indicator is expected to emphasize the

<footnote n="4">⁴ For instance, the Chinese Nutrition Society defines food-based dietary guidelines which provide principles related to food behaviours in order to tackle the issues of under- and over nutrition. The first guidelines were drawn up in 1989 and then revised in 1997. The last version was compiled by the Chinese Nutrition Society in 2007 and proclaimed by the Ministry of Health in 2008. For more details, see Ge (2011).</footnote>
heterogeneity of the Chinese middle class. Second, we perform an econometric analysis to identify the position of each social class in relation to being overweight or obese. To deal with the endogeneity of social class, we use multinomial treatment regressions. We have chosen to focus on Chinese urban adults and use both individual and household-level data from the China Health and Nutrition Survey. The chapter is organized as follows. Section 1 establishes the potential links between SES/social class and obesity, and gives some pieces of evidence for China. Section 2 develops the conceptual framework and describes the data and the variables of interest. Section 3 focuses on the identification of social classes and analyses the heterogeneity of the Chinese urban middle class. Finally, results from the econometric analysis are presented and discussed in section 4.

# 1. Social dimensions of obesity in China

## 1.1. SES and obesity: association and mechanisms

A seminal review of empirical studies examining the relationship between socioeconomic status (SES) and obesity was first published by Sobal and Stunkard (1989) for the period from the 1960s to the mid-1980s, and then updated by McLaren (2007) through to 2004. Two main findings can be drawn from these studies. First, in developed societies a consistent inverse relationship is observed between SES and obesity for women, although the association remains unclear for men and children. Second and conversely, a positive association between SES and obesity is largely confirmed in developing countries for women, men and children. Nevertheless, Monteiro et al. (2004) qualify the nature of the relationship between SES and obesity in developing countries. They note a shift in the burden of obesity towards the lower income group as a country grows and develops. These observations lead them to allude to a transition in the social standards of body weight perception, which would follow the process of economic development. Popkin and Gordon-Larsen also underline that “belonging to the lower-SES group confers strong protection against obesity in low-income economies, can reduce or increase obesity in lower-middle income economies and is a systematic risk factor for obesity in upper-middle income developing countries” (2004: S6). All in all, the burden of obesity in developing countries seems to shift to the poor as a country’s annual gross national product increases.
Generally speaking, most SES dimensions are positively related to better health. For instance, a higher level of education, which provides health literacy, is positively associated with a healthier and longer life (Sobal, 1991; Mirowsky and Ross, 2003). Moreover, individuals with higher incomes (which is the most commonly investigated dimension of SES) have access to better nutrition and housing, allowing them to have better health (Adler and Newman, 2002). Consequently, individuals with higher SES are supposed to have better access to resources for protecting them from diseases, but also more knowledge about health status (Fu and George, 2011). However, even if the relationship between occupational status and health is recognized, the pathways between these two dimensions are not well established (Zhang, 2012).

These mechanisms contribute to the explanation of the inverse relationship between SES and obesity which is perceived as a sign of poor health. A possible explanation for the divergence of results between developing and developed countries may be related to differences in the set of norms and values. Obesity is likely to constitute a sign of wealth in developing countries, because “where food is less ubiquitous, the ability to afford food is an important factor in the socioeconomic patterning of weight” (McLaren, 2007: 37). The influence of SES on health also varies at different life stages (Bulatao and Anderson, 2004). As regards adulthood, the mechanisms through which SES may affect health are diverse. They primarily include individual health behaviours and psychological characteristics. The role of social networks and social support is also underlined, as well as the influence of the social and economic residential context and environmental conditions.

## 1.2. SES and obesity in China

Studies focusing on the association between SES and obesity in China fall into two main categories: those analysing the relationship in children and those interested in the
association among adults. Since our analysis covers only urban adults, we do not report
results on the SES-obesity gradient in childhood.<fnref n="5" />

Du *et al*. (2004) emphasize that being overweight and obese are positively associated
with income insofar as their prevalence among high-income people is approximately twice
that of low-income people. Zhang *et al*. (2008) confirm that Chinese urban residents with a
higher income and a higher level of education are more likely to be overweight or obese. As
mentioned by Wu *et al.*, “in Chinese culture, there is still a widespread belief that excess body
fat represents health and prosperity” (2009: 23). Nevertheless, Du *et al*. (2004) show that the
features of the Chinese nutrition transition suggest a shift in the burden of obesity towards the
poor. Recent findings also indicate that the relationship is not so clear and that significant
gender disparities exist in the SES-obesity gradient. For instance, Jones-Smith *et al*. (2012)
and Ma (2012) show that current SES (proxied by the level of education) is positively
associated with being overweight or obese for men while the relationship is negative for
women. In the same way, using data for Zhejiang province, Xiao *et al*. (2013) find a positive
association with income for men, and a negative association with education for women. In a
study on the elderly (aged 45 and over) in Gansu and Zhejiang provinces, Strauss *et al*. (2010)
also report a positive correlation of male body weight with income and education, and an
inverted-U relationship between education and female body weight. Finally, by bringing
together data from nine Chinese provinces as regards their development level (low-middle-
high), Tafreschi (2011) emphasizes that the shift in the income-body weight gradient occurs
in the later stages of economic development. To sum up, the relationship between SES and
obesity in China seems not to be linear, and the transition from the “developing country
pattern” towards the “developed country pattern” appears to be faster for women than for
men.

<footnote n="5">⁵ When using primarily income and education to define SES, a significant positive association between parents’ SES and obesity appears to emerge, in accordance with the observed trend for developing countries (Xie *et al.*, 2007). However, this relationship varies with age (Ma, 2012) and when more comprehensive measures of SES are used the association is not always clear-cut (Fu and George, 2011).</footnote>
## 2. Conceptual framework and data

Body weight in general, and in particular the fact of being overweight or obese, are explained by the energy balance in a given period which is the difference between calories consumed and calories expended (Chou et al., 2004). As a consequence, a behavioural model for people who are overweight or obese should include all the factors that affect that energy balance directly. Such proximate determinants may be diet, eating and cooking practices and physical activities as well as individual and genetic factors. If we consider the influence of SES we have to shift the analysis to more indirect determinants of body weight. In this perspective and in line with Pieroni and Salmasi (2010), we adopt a reduced form model which can be expressed as follows:

$$BW = F(IND, ENV, SES) \quad (5.1)$$

Where BW denotes body weight, IND consists of individual characteristics that are key determinants of body weight (age, gender, matrimonial status, smoking behaviour, alcohol consumption, etc.), ENV comprises variables describing the living environment (access to media, community environment, etc.). SES is composed of variables that account for the multiple dimensions of socioeconomic status (education, occupation, work status, etc.). One contribution of this paper lies in the way in which we take SES into account. While many studies include a limited number of dimensions (mainly income and education) and examine their impact on body weight separately, we adopt a more complete and comprehensive approach. Given the rapid increase in the size of the urban middle class in China and its contribution to the diffusion of new consumption patterns, we assume that this expanding group has specific food and physical activity behaviours compared to the poor and the rich groups. We also consider that this middle class is heterogeneous which means that its different components could have distinct attitudes to food consumption and physical exercise, resulting in differences in body weight. To take these considerations into account, we propose to combine SES dimensions to construct a categorical variable of social class that underlines the heterogeneity of the Chinese middle class.

Data used in this paper come from the China Health and Nutrition Survey (CHNS), a collaborative project between the Carolina Population Center (University of North Carolina) and the Chinese Center for Disease Control and Prevention. The CHNS survey consists of a
multi-wave longitudinal survey. The first round was conducted in 1989 and seven other waves
(1991, 1993, 1997, 2000, 2004, 2006 and 2009) are now available. The survey covers nine
provinces including coastal provinces (Shandong and Jiangsu), North-Eastern provinces
(Heilongjiang and Liaoning) and inland provinces (Henan, Hubei, Hunan, Guangxi and
Guizhou). Even if the survey is not nationally representative, the provinces have been selected
to provide a highly-diversified picture of Chinese regions in terms of geography, economic
development and health and nutritional outcomes.<fnref n="6" /> The sample was selected through a
multistage random cluster procedure and CHNS data are representative of rural and urban
areas.<fnref n="7" /> For the purpose of our study, we consider the urban sub-sample in 2009 that covers
1,320 households and 2,841 adults after checking for missing values. Although CHNS data
are primarily designed to analyze health and nutrition issues (with detailed anthropometric
data), they also include variables describing socio-economic status such as income,
occupation and education.

We consider three distinct variables to measure adult body weight. The first variable is the Body Mass Index (BMI) which is the most commonly used measure of body weight. The BMI is defined as the weight (in kilograms) divided by the square of the height (in meters). It should be noted that we use data on height and weight that have been collected through physical examination (and not self-reported data). The other two variables are derived from the BMI and consist of dummies indicating respectively if an adult is overweight or strictly obese. If we follow the WHO's standards, a person is considered to be overweight if 25 ≤ BMI < 30 and obese if BMI ≥ 30 (WHO, 2000). However, as explained by Wu (2006) and Wang et al. (2007b), these cut-off points are derived from Caucasians, and therefore may not be suitable for other populations. In particular, specific studies show that Asian populations, including Chinese people, record higher risks for obesity-related diseases at a lower BMI than white populations (Zhou, 2002; WHO, 2004). This is the reason why we use the cut-off points set by the Working Group on Obesity in China, and consider a person to be overweight if 24 ≤ BMI < 28 and obese if BMI ≥ 28.

<footnote n="6">⁶ The sample does not cover major Chinese cities such as Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin, Chongqing or Hong Kong. However, other big cities are included insofar as in each of the nine provinces the provincial capital and a lower income city have been selected. It is worth noting that the urbanization profile of the nine provinces is quite heterogeneous with highly-urbanized provinces (Liaoning or Jiangsu) as well as less-urbanized provinces (Henan or Guizhou).</footnote>

<footnote n="7">⁷ For a description of the sampling scheme, see Popkin *et al.* (2009).</footnote>
One important contribution of this paper is to combine SES dimensions to create an indicator of social class. Five variables are taken into account. The first one is household income which is expressed as annual income per capita and composed of wages, retirement income, business income, subsidies, agricultural income and other income (private transfers and rent). The other four variables relate to the education and employment of the head of the household and are: (i) education level (highest level of education completed); (ii) primary occupation (professional or technical worker, administrator or executive, office staff, skilled or non-skilled worker, service worker, retired, inactive, etc.); (iii) employment position in the occupation (self-employed, paid-employee, etc.); (iv) the type of work unit (government department, state service, state-owned or collective enterprise, etc.). We also consider the *hukou* status of the head of the household as an additional variable to describe and characterize the identified social classes.

In line with our framework, we take into account control variables that are key indirect determinants of body weight. As explained above, two categories of variables are taken into account. The first category consists of individual characteristics: age, squared-age, gender, matrimonial status (married or not), smoking and alcohol consumption (at least once a week or not). The second category of variables describes the living environment with three variables. Two variables describe access to media: the daily time spent watching TV (in minutes) and a dummy indicating if a person is active in surfing the Internet. To describe the community environment, we use an urbanization index.<fnref n="8" />

Descriptive statistics for body weight, SES and control variables are summarized in Appendix 5.1, respectively for the whole sample, women and men.

<footnote n="8">⁸ Based on community-level data, Jones-Smith and Popkin (2010) have developed an index which is a composite measure synthesizing the information on 12 dimensions of urbanicity: population density, economic activity, traditional markets, modern markets, transportation infrastructure, sanitation, communications, housing, education, diversity (in education and income levels), health infrastructure, and social services. For each component, a score is calculated varying from 0 to 10. The global index is the sum of the twelve component scores. A higher urbanization score indicates higher urbanicity.</footnote>
# 3. The identification of social class

## 3.1. Methodology

While the preferred method for identifying the middle class in the economic literature is a strictly monetary approach, we propose to strengthen this purely income-based definition by considering occupation and education, which are the chief focus of sociological literature on class structure. To do this, we adopt a two-step statistical methodology.

In the first step, we have chosen to prioritize a monetary criterion and thus refer to an income-based definition of the middle class. In the empirical literature, there is absolutely no consensus on the lower and upper boundaries for defining the middle class. Following Birdsall (2010), we propose to identify the Chinese urban middle income class by combining an absolute lower boundary and a relative upper boundary. The lower boundary is set at 10,000 Yuan in order to ensure that people whose income is above have reached a certain level of economic security (Wang and Davis, 2010). As for the upper limit, we use the 95th percentile of the income distribution (Birdsall, 2010). This procedure leads us to distinguish three main income classes: (i) the poor, whose annual per capita household income falls below 10,000 Yuan; (ii) the middle class, earning between 10,000 and 36,285 Yuan; and (iii) the rich with an income above 36,285 Yuan.

In the second step, we use information on employment (primary occupation, employment position and work unit) and education (highest level completed) to analyze the structure of the middle income class previously identified. Although income is closely correlated to occupation and education, these last characteristics allow us to strengthen the identification of the middle class and to highlight its potential heterogeneity. To implement this second step, we carry out a mixed classification procedure in order to establish homogeneous and meaningful clusters of households with respect to their multidimensional "middle-class status". A mixed classification procedure involves conducting hierarchical cluster analysis and consolidating the relevant partition through some k-means-like iterations aiming at increasing inter-cluster variance while minimizing intra-cluster variance.<fnref n="9" /> We

<footnote n="9">⁹ The so-called relevant partition, i.e. the relevant number of clusters, is derived from the analysis of the provided dendrogram and the analysis of two indicators that respectively (i) maximize the marginal improvement of the inter to intra-cluster variance ratio from a given partition to another and (ii) minimize the impact of k-means consolidation on that ratio.</footnote>
finally compare the distributions of each active (classification) variable in order to give a
precise description of each cluster. We also consider additional variables (the *hukou* status of
the head of the household and the annual per capita household income) that can help to
improve our description of social classes.

3.2. The heterogeneity of the Chinese urban middle class

Results from the mixed classification procedure are presented in Table 5.1. Generally speaking, this cluster analysis confirms the strong heterogeneity of the urban middle class by identifying four distinct clusters within it. Our results also indicate that those four middle class components display specific features compared to the poor and the rich.

The first cluster, referred to as the elderly and inactive middle class, is mainly composed of households whose head is poorly-educated and retired. This group accounts for approximately 50% of the whole Chinese middle class. The chief focus of sociological research on the Chinese middle class is the working-age population. In other words, sociological studies do not consider the possibility of an elderly middle class. Conducting an investigation at a household level helps to broaden the scope of the analysis.

The second cluster is composed of self-employed people and wage-earners, who are quasi-exclusively employed in collective and private enterprises. This small business community is often referred to as the old middle class in the literature on social stratification, and represents only 12% of the whole middle class. This class (not to be confused with the capitalist class composed of managers and owners of large export-oriented firms) is composed of moderately well-off households. Moreover, approximately 20% of household heads in the old middle class are rural-to-urban migrants with rural *hukou*.

The third cluster describes a working class which consists of skilled and non-skilled
workers. On average, households in this group have a significantly lower annual per capita
income than in the rest of the middle class. The share of this lower middle class in the whole
middle class is quite limited with no more than 12%.
<table><caption>Table 5.1 : Socioeconomic characteristics of middle-classes (four clusters derived from mixed classification procedure), poor and rich (urban households, 2009).</caption><thead><tr><th></th><th>Poor<br>N=573</th><th>Elderly and<br>inactive Middle<br>Class<br>N=338</th><th>Old<br>Middle<br>Class<br>N=83</th><th>Lower<br>Middle<br>Class<br>N=79</th><th>New<br>Middle<br>Class<br>N=181</th><th>Rich<br>N=66</th><th>All<br>N=1320</th></tr></thead><tbody><tr><td><strong>Education</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No school</td><td><strong>19.7%</strong></td><td><strong>17.2%</strong></td><td>10.8%</td><td>0%</td><td>0.6%</td><td>10.6%</td><td>14.2%</td></tr><tr><td>Primary school</td><td><strong>19.7%</strong></td><td><strong>14.2%</strong></td><td>7.2%</td><td>3.8%</td><td>0.6%</td><td>6.1%</td><td>13.3%</td></tr><tr><td>Secondary school</td><td>48.0%</td><td>42.6%</td><td><strong>69.9%</strong></td><td><strong>73.4%</strong></td><td>27.1%</td><td>37.9%</td><td>46.1%</td></tr><tr><td>Technical /Vocational<br>degree</td><td>7.5%</td><td>13.0%</td><td>9.6%</td><td>20.3%</td><td><strong>29.8%</strong></td><td><strong>21.2%</strong></td><td>13.6%</td></tr><tr><td>Superior</td><td>5.1%</td><td>13.0%</td><td>2.4%</td><td>2.5%</td><td><strong>42.0%</strong></td><td><strong>24.2%</strong></td><td>12.8%</td></tr><tr><td><strong>Occupation</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Inactive / Seeking work</td><td><strong>36.3%</strong></td><td><strong>13.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>1.5%</td><td>19.2%</td></tr><tr><td>Professional / Technical<br>worker</td><td>5.1%</td><td>0%</td><td>8.4%</td><td>11.4%</td><td><strong>50.3%</strong></td><td><strong>24.2%</strong></td><td>11.5%</td></tr><tr><td>Administrator / Executive</td><td>2.8%</td><td>0%</td><td>4.8%</td><td>8.3%</td><td><strong>24.3%</strong></td><td>7.6%</td><td>5.8%</td></tr><tr><td>Office staff</td><td>2.4%</td><td>0%</td><td>1.2%</td><td>2.5%</td><td><strong>19.3%</strong></td><td>3.0%</td><td>4.1%</td></tr><tr><td>Skilled worker</td><td>4.7%</td><td>0%</td><td>2.4%</td><td><strong>44.3%</strong></td><td>0%</td><td>3.0%</td><td>5.0%</td></tr><tr><td>Non-skilled worker</td><td><strong>5.4%</strong></td><td>0%</td><td>1.2%</td><td><strong>26.6%</strong></td><td>0%</td><td>3.0%</td><td>4.2%</td></tr><tr><td>Service worker</td><td><strong>12.0%</strong></td><td>0%</td><td><strong>51.8%</strong></td><td>5.1%</td><td>2.2%</td><td><strong>18.2%</strong></td><td>10.0%</td></tr><tr><td>Other</td><td><strong>8.0%</strong></td><td>0%</td><td><strong>30.1%</strong></td><td>1.3%</td><td>3.9%</td><td>3.0%</td><td>6.1%</td></tr><tr><td>Retired</td><td>23.2%</td><td><strong>87.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>36.4%</td><td>34.2%</td></tr><tr><td><strong>Employment status</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No job</td><td><strong>59.5%</strong></td><td><strong>100.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>37.9%</td><td>53.3%</td></tr><tr><td>Self-employed with<br>employees</td><td>2.3%</td><td>0%</td><td><strong>16.9%</strong></td><td>0%</td><td>0%</td><td><strong>9.1%</strong></td><td>2.5%</td></tr><tr><td>Self-employed with no<br>employees</td><td><strong>12.4%</strong></td><td>0%</td><td><strong>53.0%</strong></td><td>0%</td><td>0%</td><td>9.1%</td><td>9.2%</td></tr><tr><td>Paid employee</td><td>24.8%</td><td>0%</td><td>26.5%</td><td><strong>100.0%</strong></td><td><strong>100.0%</strong></td><td>43.9%</td><td>34.3%</td></tr><tr><td>Other status</td><td>1.0%</td><td>0%</td><td><strong>3.6%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>0.7%</td></tr><tr><td><strong>Work unit</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No job</td><td><strong>59.5%</strong></td><td><strong>100.0%</strong></td><td>0%</td><td>0%</td><td>0%</td><td>37.9%</td><td>53.3%</td></tr><tr><td>Government / State<br>service</td><td>8.9%</td><td>0%</td><td>1.2%</td><td>12.7%</td><td><strong>90.1%</strong></td><td>25.8%</td><td>18.3%</td></tr><tr><td>State-owned enterprise</td><td>3.8%</td><td>0%</td><td>0%</td><td><strong>58.2%</strong></td><td>1.1%</td><td>6.1%</td><td>5.6%</td></tr><tr><td>Collective / Private /<br>Individual enterprise</td><td><strong>27.7%</strong></td><td>0%</td><td><strong>98.8%</strong></td><td><strong>29.1%</strong></td><td>8.8%</td><td>30.3%</td><td>22.7%</td></tr><tr><td><strong>Characterization variables</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Annual per capita income<br>(mean)</td><td>5,063</td><td><strong>18,571</strong></td><td>16,818</td><td>16,266</td><td><strong>18,874</strong></td><td><strong>66,299</strong></td><td>14,887</td></tr><tr><td>Rural hukou</td><td><strong>12.7%</strong></td><td>3.8%</td><td><strong>19.7%</strong></td><td>4.0%</td><td>1.1%</td><td>9.2%</td><td>8.4%</td></tr></tbody></table>

Notes: Bold characters denote the fact that the value is significantly higher in the group than in the rest of the population (or in the rest of the middle class for the four middle class groups). Italic characters do the same for values significantly lower in the group than in the rest of the population (or than in the rest of the middle class for the four middle class groups). Adjusted standardized residuals of $khi^2$ for categorical variables ($p < 0.05$) and independent samples t-test for continuous variables ($p < 0.10$).
Source: CHNS (2009).